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Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). Tensile behavior of humid aged advanced
composites for helicopter external fuel tank
development Mihaela Condruz1*, Alexa... |
https://openalex.org/W3172459663 | https://wrap.warwick.ac.uk/157454/1/WRAP-grey-white-matter-volume-changes-preterm-birth-Wolke-2021.pdf | English | null | Grey and white matter volume changes after preterm birth: A meta-analytic approach | medRxiv (Cold Spring Harbor Laboratory) | 2,021 | cc-by | 9,016 |
10
Department of Psychiatry, School of Medicine, Technical University of Munich, Ismaninger Str. 22,
81675 Munich, Germany Citation: Schmitz-Koep, B.; Haller,
B.; Coupé, P.; Menegaux, A.; Gaser, C.;
Zimmer, C.; Wolke, D.; Bartmann, P.;
Sorg, C.; Hedderich, D.M. Grey and
White Matter Volume Changes af... |
https://openalex.org/W4389524588 | https://aclanthology.org/2023.findings-emnlp.816.pdf | English | null | Dimensions of Online Conflict: Towards Modeling Agonism | null | 2,023 | cc-by | 11,028 | Abstract Not all conflicts are equal, and the confusion
over the political and social value of conflict partly
stems from its diverse nature. Conflict exists on a
sliding scale ranging from antagonistic conflict be-
tween enemies (which is often silencing, undemo-
cratic, and hateful because it is focused on delegit-
i... |
https://openalex.org/W3111837814 | http://umu.diva-portal.org/smash/get/diva2:1525853/FULLTEXT03 | English | null | Argumentation-Based Health Information Systems: A Design Methodology | IEEE intelligent systems | 2,021 | cc-by | 6,000 | Argumentation-Based Health Information
Systems: A Design Methodology Helena Lindgren, Timotheus Kampik
, Esteban Guerrero Rosero
, Madeleine Blusi
, and Juan Carlos Nieve
Umea
University, 90187 Umea
, Sweden In this article, we present a design methodology for argumentation-based health
information systems. With a f... |
https://openalex.org/W2291357105 | https://europepmc.org/articles/pmc4766945?pdf=render | English | null | ERICA: prevalence of healthy eating habits among Brazilian adolescents | Revista de saúde pública/Revista de Saúde Pública | 2,016 | cc-by | 5,048 | Correspondence:
Laura Augusta Barufaldi
Edifício Premium Térreo, sala 15
SAF Sul, Trecho 02 Lotes 05/06
Bloco “F” Torre 1
70070-600 Brasília, DF, Brasil
E-mail: laurabarufaldi@yahoo.com.br http://www.rsp.fsp.usp.br/ Supplement ERICA
Original Article Supplement ERICA
Original Article Rev Saúde Pública 2016;50... |
https://openalex.org/W2410783462 | https://europepmc.org/articles/pmc4926619?pdf=render | English | null | Barcoding Chrysomelidae: a resource for taxonomy and biodiversity conservation in the Mediterranean Region | ZooKeys | 2,016 | cc-by | 5,202 | http://zoobank.org/4D7CCA18-26C4-47B0-9239-42C5F75E5F42 Citation: Magoga G, Sassi D, Daccordi M, Leonardi C, Mirzaei M, Regalin R, Lozzia G, Montagna M (2016)
Barcoding Chrysomelidae: a resource for taxonomy and biodiversity conservation in the Mediterranean Region. In: Jolivet P, Santiago-Blay J, Schmitt M (Eds) Rese... |
https://openalex.org/W2087711953 | https://europepmc.org/articles/pmc3621509?pdf=render | English | null | Cultural background modulates how we look at other persons’ gaze | International journal of behavioral development | 2,013 | cc-by | 5,184 | Introduction can we study the effect of postnatal environment on the develop-
ment of face gaze in humans? One of the most promising ways is
a cross-cultural comparison, because different cultural norms
would systematically modulate how the people in each culture
would learn to process and interact with others in face-... |
https://openalex.org/W3162709001 | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.652673/pdf | English | null | Perception of Nigerian Dùndún Talking Drum Performances as Speech-Like vs. Music-Like: The Role of Familiarity and Acoustic Cues | Frontiers in psychology | 2,021 | cc-by | 10,508 | ORIGINAL RESEARCH
published: 20 May 2021
doi: 10.3389/fpsyg.2021.652673 ORIGINAL RESEARCH
published: 20 May 2021
doi: 10.3389/fpsyg.2021.652673 ORIGINAL RESEARCH
published: 20 May 2021
doi: 10.3389/fpsyg.2021.652673 Perception of Nigerian Dùndún
Talking Drum Performances as
Speech-Like vs. Music-Like: The
Role of Famil... |
https://openalex.org/W2966115183 | https://www.biorxiv.org/content/biorxiv/early/2019/04/11/606590.full.pdf | English | null | Knockout of Babesia bovis rad51 ortholog and its complementation by expression from the BbACc3 artificial chromosome platform | PloS one | 2,019 | cc-by | 23,315 | .
CC-BY 4.0 International license
a
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was not
this version posted April 11, 2019.
;
https://doi.org/10.1101/606590
doi:
bioR... |
https://openalex.org/W2125691952 | https://pure.mpg.de/pubman/item/item_2151777_8/component/file_2152104/2151777.pdf | English | null | Directional properties of polar paramagnetic molecules subject to congruent electric, magnetic and optical fields | New journal of physics | 2,015 | cc-by | 10,792 | This content has been downloaded from IOPscience. Please scroll down to see the full text.
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Directional properties of polar paramagnetic molecules subject to congruent electric
and ... |
https://openalex.org/W2803448435 | http://old.scielo.br/pdf/urbe/v10n3/2175-3369-urbe-2175-3369010003AO01.pdf | Portuguese | null | Financeirização da moradia e segregação socioespacial: Minha Casa, Minha Vida em São José dos Campos, Taubaté e Jacareí/SP | Urbe. Revista Brasileira de Gestão Urbana | 2,018 | cc-by | 7,496 | Financeirização da moradia e segregação
socioespacial: Minha Casa, Minha Vida em São
José dos Campos, Taubaté e Jacareí/SP Housing financing and socio-spatial segregation: “Minha Casa Minha Vida” in
São José dos Campos, Taubaté and Jacareí/SP Daniela das Neves Alvarenga[a], Paulo Romano Reschilian[a] [a] Universidad... |
https://openalex.org/W2954678766 | https://europepmc.org/articles/pmc6599201?pdf=render | English | null | Building a DNA barcode library for the freshwater fishes of Bangladesh | Scientific reports | 2,019 | cc-by | 8,860 | Md. Mizanur Rahman1, Michael Norén2, Abdur Rob Mollah1 & Sven O. Kullander We sequenced the standard DNA barcode gene fragment in 694 newly collected specimens,
representing 243 species level Operational Barcode Units (OBUs) of freshwater fishes from Bangladesh. We produced coi sequences for 149 out of the 237 species... |
https://openalex.org/W2286515682 | http://tao.cgu.org.tw/index.php/articles/archive/geophysics/item/download/2794_1814b6237bd201af30408480c4fa872b | English | null | Traveltime Analysis of VSP Seismograms in a Horizontal Transversely Isotropic Medium: A Physical Modeling Result | Terrestrial, atmospheric and oceanic sciences/Terrestrial, atmospheric, and oceanic sciences | 2,001 | cc-by | 4,246 | TAO, Vol. 12, No. 4, 685-693, December 2001 TAO, Vol. 12, No. 4, 685-693, December 2001 1 Institute of Applied Geophysics, Institute of Seismology, National Chung Cheng University, Min-hsiung
Chia-yi, Taiwan, ROG
2Department of Applied Physics, National Chiayi University, Min-hsiung, Chia-yi, Taiwan, ROG
*Correspondi... |
https://openalex.org/W2070752776 | https://www.biodiversitylibrary.org/partpdf/72470 | English | null | On the Existence of Rudimentary Antlers in the Okapi | Proceedings of the Zoological Society of London | 1,907 | public-domain | 3,134 | * For explanation of the Plates, see p. 134. 26
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| (Plates
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W282090071.txt | http://www.bioinformation.net/010/97320630010555.pdf | en | Erratum in Jayapalan & Natarajan, The role of CDK5 and GSK3B kinases in hyperphosphorylation of microtubule associated protein tau (MAPT) in Alzheimer’s disease | Bioinformation | 2,014 | cc-by | 120 | open access
www.bioinformation.net
Erratum
Volume 10(8)
Erratum: A numerical error in methodology section under Sequence
retrieval and modeling side heading
Bioinformation - 2013 Dec 9(20): 1023-1030
The role of CDK5 and GSK3B kinases in
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tau (MAPT) in Alzheimer... | |
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Springer
Received: September 26, 2018
Accepted: November 8, 2018
Published: November 14, 2018
Roji Piusa and Ashoke Senb
a
Perimeter Institute for Theoretical Physics,
Waterloo, ON N2L 2Y5, Canada
b
Harish-Chandra Research Institute, HBNI,
Chhatnag Road, Jhusi, Allahabad 211019, India
E-mail... | |
https://openalex.org/W2962950012 | https://link.springer.com/content/pdf/10.1007%2FJHEP03%282018%29079.pdf | English | null | Chiral algebras in Landau-Ginzburg models | The Journal of high energy physics/The journal of high energy physics | 2,018 | cc-by | 22,242 | Published for SISSA by
Springer Published for SISSA by
Springer Published for SISSA by
Springer Received: January 22, 2018
Accepted: March 2, 2018
Published: March 13, 2018 Received: January 22, 2018
Accepted: March 2, 2018
Published: March 13, 2018 Received: January 22, 2018
Accepted: March 2, 2018
Published: March 13... |
https://openalex.org/W2083606760 | https://researchonline.lshtm.ac.uk/id/eprint/7839/1/1475-2875-7-6.pdf | English | null | Feasibility and acceptability of artemisinin-based combination therapy for the home management of malaria in four African sites | Malaria journal | 2,008 | cc-by | 7,396 | Received: 22 October 2007
Accepted: 8 January 2008 Received: 22 October 2007
Accepted: 8 January 2008 Published: 8 January 2008 Malaria Journal 2008, 7:6
doi:10.1186/1475-2875-7-6 Malaria Journal 2008, 7:6
doi:10.1186/1475-2875-7-6 This article is available from: http://www.malariajournal.com/content/7/1/6 © 2008 Ajayi... |
https://openalex.org/W2081730131 | https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-015-1670-0 | English | null | Malaria policies versus practices, a reality check from Kinshasa, the capital of the Democratic Republic of Congo | BMC public health | 2,015 | cc-by | 5,194 | * Correspondence: mavoko@yahoo.com
1Département de Médecine Tropicale, Université de Kinshasa, B.P. 747, Kin XI,
Kinshasa, République Démocratique du Congo
2International Health Unit, Department of Epidemiology, University of
Antwerp, Campus Drie Eiken, Universiteitsplein 1, 2610 Kinshasa, République
Démocratique du Co... |
https://openalex.org/W3102565266 | https://repository.ubn.ru.nl/bitstream/handle/2066/227649/1/227649.pdf | English | null | Key role for lipids in cognitive symptoms of schizophrenia | Translational psychiatry | 2,020 | cc-by | 9,766 | 2020, Article / Letter to editor (Translational Psychiatry, 10, 1, (2020), pp. 1-12, article 399)
Doi link to publisher: https://doi.org/10.1038/s41398-020-01084-x Version of the following full text: Publisher’s version
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https://openalex.org/W3216264425 | https://www.investigacionesgeograficas.com/article/view/18723/pdf, https://rua.ua.es/dspace/bitstream/10045/119750/6/Investigaciones_Geograficas_77_12.pdf | es | Susceptibilidad de afección por enjambres de medusas (Pelagia noctiluca) en las playas del litoral occidental de la provincia de Málaga | Investigaciones geográficas/Investigaciones geográficas | 2,022 | cc-by | 10,539 | Investigaciones Geográficas
Instituto Interuniversitario de Geografía
Universidad de Alicante
Nº 77, enero-junio de 2022, pp. 239-258.
ISSN: 0213 - 4691. eISSN: 1989 - 9890.
DOI: 10.14198/INGEO.18723
Cita bibliográfica: de la Fuente Roselló, A., Sortino Barrionuevo, J.F., Reyes Corredera, S. J., & Perles Roselló, M.J... | |
https://openalex.org/W4296538822 | https://www.utupub.fi/bitstream/10024/173335/1/The%20use%20of%20scenarios%20in%20climate%20policy%20planning%20an%20assessment%20of%20actors%20experiences%20and%20lessons%20learned%20in%20Finland.pdf | English | null | The use of scenarios in climate policy planning: an assessment of actors’ experiences and lessons learned in Finland | Climate policy | 2,022 | cc-by | 9,523 | Climate Policy ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tcpo20 Full Terms & Conditions of access and use can be found at
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Rikkonen To cite t... |
https://openalex.org/W2605803363 | https://www.nature.com/articles/srep46576.pdf | English | null | Neurodevelopmental Changes in Excitatory Synaptic Structure and Function in the Cerebral Cortex of Sanfilippo Syndrome IIIA Mice | Scientific reports | 2,017 | cc-by | 10,255 | Neurodevelopmental Changes in
Excitatory Synaptic Structure and
Function in the Cerebral Cortex of
Sanfilippo Syndrome IIIA Mice
Chrissa A. Dwyer1, Samantha L. Scudder2, Ying Lin1, Lara E. Dozier2, Dustin Phan1,
Nicola J Allen3 Gentry N Patrick2 & Jeffrey D Esko1 received: 06 December 2016
accepted: 17 March 2017
P... |
https://openalex.org/W2749666098 | https://link.springer.com/content/pdf/10.1007%2Fs11406-017-9866-4.pdf | English | null | Analytic Pragmatism and Universal LX Vocabulary | Philosophia | 2,017 | cc-by | 12,523 | 1
Department of Philosophy, The Ohio State University, Columbus, OH, USA
2
Department of Philosophy; Arché Philosophical Research Centre; Centre for Exoplanet Science,
University of Saint Andrews, St Andrews, Fife, UK Philosophia (2017) 45:1803–1827
DOI 10.1007/s11406-017-9866-4 Analytic Pragmatism and Universal LX Voc... |
https://openalex.org/W2053948868 | https://europepmc.org/articles/pmc3929584?pdf=render | English | null | The Novel Long Noncoding RNA linc00467 Promotes Cell Survival but Is Down-Regulated by N-Myc | PloS one | 2,014 | cc-by | 8,950 | Abstract Competing Interests: The authors have declared that no competing interests exist. Funding: The authors were supported by National Health and Medical Research Council Australia and Cancer Council New South Wa
recipient of an ARC Future Fellowship. The funders had no role in study design, data collection and ana... |
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on human and social capital in Isfahan University of
technology
Abedi, Mohammad Reza; Nilforoushan, Parisa; Charsoughi, Batoul Tadayon
Veröffentlichungsversion / Published Version
Zeitschriftenartikel / journal article
Empfohlene Zitierun... |
https://openalex.org/W2904861441 | https://europepmc.org/articles/pmc6496756?pdf=render | English | null | Zerumbone, a cyclic sesquiterpene, exerts antimitotic activity in HeLa cells through tubulin binding and exhibits synergistic activity with vinblastine and paclitaxel | Cell proliferation | 2,018 | cc-by | 13,204 | O R I G I N A L A R T I C L E O R I G I N A L A R T I C L E Funding information g
This work is partly supported by the grant
from the DST/SERB, Government of India
(SR/SO/BB‐0013/2010), to Dr Rathinasamy
K and partly by TEQIP‐II, NIT Calicut, for the
Student Project to SMA (Ref. NITC/TEQIP‐
II/R&D/2014). Results:... |
https://openalex.org/W2578351455 | https://hrcak.srce.hr/file/254552 | Latin | null | Wood Waste Turned Into Value Added Products: Thermal Plasticization by Benzylation Process | Drvna industrija | 2,017 | cc-by | 6,108 | ner, Köse, Yürümez, Ümit Yalçın, Akgül: Wood Waste Turned Into Value Added Products ner, Köse, Yürümez, Ümit Yalçın, Akgül: Wood Waste Turned Into Value Added Products Birol Üner1, Gökhan Köse1, Yeşim Yürümez1, Ömer Ümit Yalçın2, Mehmet Akgül3 1 Autori su izvanredni profesor, dodiplomski student i doktorand Sveučilišta... |
https://openalex.org/W2891902755 | https://revistas.pucsp.br/index.php/ReCaPe/article/download/37285/26434 | Portuguese | null | Gamificação e gestão de pessoas: um estudo de caso sobre treinamento e ambiente de diversidade cultural | Revista de Carreiras e Pessoas | 2,018 | cc-by | 6,494 | RESUMO Este artigo tem como objetivo discutir a aplicação de gamificação
na gestão de pessoas, mais especificamente no treinamento
e desenvolvimento de competências de colaboradores de
diferentes níveis culturais dentro de uma grande organização. Para tal estudo, iremos trabalhar, principalmente, com as
teorias de ... |
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verah.bonilha@gmail.com
3 English version: Viviane Ramos - vivianeramos@gmail.com 1 Responsible editor: Carmen Lúcia Soares - https://orcid.org/0000-0002-4347-1924 (ii) Universidade Federal do Espírito Santo – UFES, Ce... |
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edge-illumination Peter Modregger1, Tiziana P. Cremona2, Charaf Benarafa3, Johannes C. Schittny2,
Alessandro Olivo1 & Marco Endrizzi1 received: 14 April 2016
accepted: 11 July 2016
Published: 05 August 2016 Sensitivity to sub-pixel sample features has been demonstrated as a valuable ... |
https://openalex.org/W4322501456 | https://arthritis-research.biomedcentral.com/counter/pdf/10.1186/s13075-023-03001-1 | English | null | TGF-β is elevated in hyperuricemic individuals and mediates urate-induced hyperinflammatory phenotype in human mononuclear cells | Arthritis research & therapy | 2,023 | cc-by | 8,572 | Arthritis Research & Therapy Arthritis Research & Therapy Klück et al. Arthritis Research & Therapy (2023) 25:30
https://doi.org/10.1186/s13075-023-03001-1 Open Access Abstract Background Soluble urate leads to a pro-inflammatory phenotype in human monocytes characterized by increased
production of IL-1β ... |
https://openalex.org/W2124593363 | https://europepmc.org/articles/pmc4999641?pdf=render | English | null | Extensive Mucocutaneous Verruca Vulgaris in a Nonimmunocompromised Patient | Jaypee's international journal of clinical pediatric dentistry | 2,011 | cc-by | 1,736 | IJCPD CASE REPORT
10.5005/jp-journals-10005-1084 Extensive Mucocutaneous Verruca Vulgaris
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provoke, the uncontrolled reactive power flow in the superor... |
https://openalex.org/W2145363311 | https://digital.library.adelaide.edu.au/dspace/bitstream/2440/59375/1/hdl_59375.pdf | English | null | Reference genes for normalising gene expression data in collagenase-induced rat intracerebral haemorrhage | BMC molecular biology | 2,010 | cc-by | 8,721 | PUBLISHED VERSION Cook, Naomi Louise; Kleinig, Timothy John; Van Den Heuvel, Corinna; Vink, Robert
Reference genes for normalising gene expression data in collagenase-induced rat intracerebral
haemorrhage
BMC Molecular Biology, 2010; 11:7 Cook, Naomi Louise; Kleinig, Timothy John; Van Den Heuvel, Corinna; Vink, Ro... |
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https://doi.org/10.1186/s40425-019-0508-1 (2019) 7:30 Chung et al. Journal for ImmunoTherapy of Cancer ... |
https://openalex.org/W2904715359 | https://www.frontiersin.org/articles/10.3389/fendo.2018.00753/pdf | English | null | Metformin-Induced Mitochondrial Complex I Inhibition: Facts, Uncertainties, and Consequences | Frontiers in endocrinology | 2,018 | cc-by | 8,055 | Metformin-Induced Mitochondrial
Complex I Inhibition: Facts,
Uncertainties, and Consequences INSERM, LBFA, Université Grenoble Alpes, Grenoble, France Metformin is the most widely prescribed drug to treat patients with type II diabetes, for
whom retrospective studies suggest that metformin may have anticancer propertie... |
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CC-BY 4.0 International license
a
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was not
this version posted July 20, 2019.
;
https://doi.org/10.1101/708719
doi:
bioRx... |
https://openalex.org/W2130952443 | https://www.cdc.gov/pcd/issues/2015/pdf/15_0290.pdf | English | null | Consumption of Alcoholic Beverages and Liquor Consumption by Michigan High School Students, 2011 | Preventing chronic disease | 2,015 | public-domain | 7,895 | Katherine R. Gonzales, MPH; Thomas W. Largo, MPH; Corinne Miller, PhD, DDS;
Dafna Kanny, PhD; Robert D. Brewer, MD, MSPH Katherine R. Gonzales, MPH; Thomas W. Largo, MPH; Corinne Miller, PhD, DDS;
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https://openalex.org/W3091487379 | https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-020-09558-9 | English | null | Propensity for COVID-19 severe epidemic among the populations of the neighborhoods of Fortaleza, Brazil, in 2020 | BMC public health | 2,020 | cc-by | 5,729 | Open Access Propensity for COVID-19 severe epidemic
among the populations of the
neighborhoods of Fortaleza, Brazil, in 2020 Jose Ueleres Braga1,2*
, Alberto Novaes Ramos Jr3,4, Anderson Fuentes Ferreira4, Victor Macêdo Lacerda5,
Renan Monteiro Carioca Freire5 and Bruno Vieira Bertoncini6,7 Abstract Background: The sta... |
https://openalex.org/W2904013152 | https://jhiphalexu.journals.ekb.eg/article_19913_8e65a695adb6e8ffcd23e74a55fd75ad.pdf | English | null | Evaluation of Leading Safety Performance of Primary School Buildings in Alexandria, Egypt: Cross-Sectional Study | Journal of High Institute of Public Health | 2,018 | cc-by-sa | 5,869 | Evaluation of Leading Safety Performance of Primary
School Buildings in Alexandria, Egypt: Cross-
Sectional Study Gehan R. Zaki1 ¥, Kholoud Y. Tayel2, Mayada M. Reda3, 4, Aleya H. Mahmoud3, Engy I. Labib
1 Department of Occupational Health and Air Pollution, High Institute of Public Health, Alexandria University, Egy... |
https://openalex.org/W4320073862 | https://www.gp-mgimo.ru/jour/article/download/7/7 | English | null | Russia’s Sovereignty and Emergence of Pragmatic Polycentrism | Upravlenie i politika | 2,022 | cc-by | 16,687 | 2022, Vol. 1, No. 1
Received: February 15, 2022 / Accepted: March 10, 2022
Political Science / Country in Focus 2022, Vol. 1, No. 1
Received: February 15, 2022 / Accepted: March 10, 2022
Political Science / Country in Focus 2022, Vol. 1, No. 1
Received: February 15, 2022 / Accepted: March 10, 2022
Political Science / C... |
https://openalex.org/W2810630497 | https://cyberleninka.ru/article/n/microbiological-oropharyngeal-patterns-in-patients-with-different-phenotypes-of-chronic-obstructive-pulmonary-disease/pdf | English | null | Microbiological Oropharyngeal Patterns in Patients with Different Phenotypes of Chronic Obstructive Pulmonary Disease | Sovremennye tehnologii v medicine | 2,018 | cc-by | 5,726 | Microbiological Oropharyngeal Patterns
in Patients with Different Phenotypes
of Chronic Obstructive Pulmonary Disease DOI: 10.17691/stm2018.10.2.11
Received April 27, 2017 DOI: 10.17691/stm2018.10.2.11
Received April 27, 2017 M.A. Karnaushkina, MD, PhD, Associate Professor, Department of Hospital Therapy No.21; M... |
https://openalex.org/W3003401706 | https://europepmc.org/articles/pmc7072309?pdf=render | English | null | How Quickly Do Proteins Fold and Unfold, and What Structural Parameters Correlate with These Values? | Biomolecules | 2,020 | cc-by | 12,319 | Received: 27 December 2019; Accepted: 26 January 2020; Published: 29 January 2020 Abstract: The correlations between the logarithm of the unfolding rate of 108 proteins and their
structural parameters were calculated. We showed that there is a good correlation between the
logarithm of folding and unfolding rates (0.79)... |
https://openalex.org/W2581537342 | https://www.frontiersin.org/articles/10.3389/fnhum.2017.00390/pdf | English | null | When null hypothesis significance testing is unsuitable for research: a reassessment | bioRxiv (Cold Spring Harbor Laboratory) | 2,016 | cc-by | 21,862 | Edited by:
Satrajit S. Ghosh,
Massachusetts Institute of
Technology, United States Edited by:
Satrajit S. Ghosh,
Massachusetts Institute of
Technology, United States
Reviewed by:
Bertrand Thirion,
Institut National de Recherche en
Informatique et en Automatique
(INRIA), France
Cyril R. Pernet,
University of Edinburgh,
... |
https://openalex.org/W2460871468 | https://europepmc.org/articles/pmc4991230?pdf=render | English | null | Video observations of treatment administration to children on antiretroviral therapy in rural KwaZulu-Natal | AIDS care | 2,016 | cc-by | 5,802 | AIDS CARE, 2016
VOL. 28, NO. S2, 34–41
http://dx.doi.org/10.1080/09540121.2016.1176674 AIDS CARE, 2016
VOL. 28, NO. S2, 34–41
http://dx.doi.org/10.1080/09540121.2016.1176674 AIDS CARE, 2016
VOL. 28, NO. S2, 34–41
http://dx.doi.org/10.1080/09540121.2016.1176674 AIDS CARE, 2016
VOL. 28, NO. S2, 34–41
http://dx.doi.org/10... |
https://openalex.org/W4280581650 | https://orca.cardiff.ac.uk/id/eprint/152778/4/netn_a_00279.pdf | English | null | Increased structural connectivity in high schizotypy | bioRxiv (Cold Spring Harbor Laboratory) | 2,022 | cc-by | 11,733 | a n o p e n a c c e s s
j o u r n a l Corresponding Author:
Eirini Messaritaki
messaritakie2@cardiff.ac.uk
Handling Editor:
Alex Fornito
Copyright: © 2022
Massachusetts Institute of Technology
Published under a Creative Commons
Attribution 4.0 International
(CC BY 4.0) license
The MIT Press Keywords: Schizophrenia, Sch... |
https://openalex.org/W4292748779 | https://backend.orbit.dtu.dk/ws/files/289959238/JGR_Atmospheres_2022_Skeie_The_Temporal_Relationship_Between_Terrestrial_Gamma_Ray_Flashes_and_Associated_Optical.pdf | English | null | The Temporal Relationship Between Terrestrial Gamma‐Ray Flashes and Associated Optical Pulses From Lightning | Journal of geophysical research. Atmospheres | 2,022 | cc-by | 12,295 | Citation (APA):
Skeie, C. A., Østgaard, N., Mezentsev, A., Bjørge‐Engeland, I., Marisaldi, M., Lehtinen, N., Reglero, V., &
Neubert, T. (2022). The temporal relationship between Terrestrial Gamma‐ray flashes and associated optical
pulses from lightning. Journal of Geophysical Research: Atmospheres, 127(17), Article e20... |
https://openalex.org/W2071568738 | https://zenodo.org/records/2012261/files/article.pdf | Dutch | null | Het phytopathologisch laboratorium willie commelin scholten van 1895 TOT 1906 | Tijdschrift over planteziekten/Tijdschrift over plantenziekten | 1,906 | public-domain | 9,652 | 28 -- 28 -- HET PHITOPATHOLOGISCH LABORATORIUIII WILLIE COiilHELIH
SCHOLTEli VAH 1895 TOF 1906, Met ingang van Januari 1906 vrangde ik ontslag als
Directeur van her phytopathologizch laboratorium Willie
Commelin Schelten te Amsterdam, om op to fxeden als
Directeur van een Instituut veer phytopat.hologie, 't wclk
d... |
https://openalex.org/W2585119302 | https://cronfa.swan.ac.uk/Record/cronfa36103/Download/0036103-17102017100114.pdf | English | null | Bioremediation efficacy—comparison of nutrient removal from an anaerobic digest waste-based medium by an algal consortium before and after cryopreservation | Journal of applied phycology | 2,017 | cc-by | 9,413 | Cronfa - Swansea University Open Access Repository This is an author produced version of a paper published in:
Journal of Applied Phycology Cronfa URL for this paper:
http://cronfa.swan.ac.uk/Record/cronfa36103 p
Silkina, A., Nelson, G., Bayliss, C., Pooley, C. & Day, J. (2017). Bioremediation efficacy—comparison of ... |
https://openalex.org/W4361812727 | https://figshare.com/articles/journal_contribution/Supplementary_Figure_from_Upfront_Biology-Guided_Therapy_in_Diffuse_Intrinsic_Pontine_Glioma_Therapeutic_Molecular_and_Biomarker_Outcomes_from_PNOC003/22486337/1/files/39937916.pdf | English | null | Supplementary Figure from Upfront Biology-Guided Therapy in Diffuse Intrinsic Pontine Glioma: Therapeutic, Molecular, and Biomarker Outcomes from PNOC003 | null | 2,023 | cc-by | 988 | A
p = 9.6e-1
0.00
0.25
0.50
0.75
1.00
0
10
20
30
40
50
Time
Overall Survival
CBTN (H3K27-altered DMG)
PNOC003 (H3K27-altered DIPG)
22
11
4
1
1
1
28
19
4
0
0
0
PNOC003
CBTN
0
10
20
30
40
50
Time
Number at risk
PNOC003+CBTN A
p = 9.6e-1
0.00
0.25
0.50
0.75
1.00
0
10
20
30
40
50
Time
Overall Survival
CBTN (H3K27-altered D... |
W4367300121.txt | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1161145/pdf?isPublishedV2=False | en | Inactivated ostreid herpesvirus-1 induces an innate immune response in the Pacific oyster, Crassostrea gigas, hemocytes | Frontiers in immunology | 2,023 | cc-by | 7,713 | Brief Research Report
28 April 2023
DOI 10.3389/fimmu.2023.1161145
TYPE
PUBLISHED
OPEN ACCESS
EDITED BY
Daniela Melillo,
National Research Council (CNR), Italy
REVIEWED BY
Alejandro Romero,
Spanish National Research Council (CSIC),
Spain
Junfa Yuan,
Huazhong Agricultural University, China
*CORRESPONDENCE
Lizenn De... | |
https://openalex.org/W4393047257 | https://ateroskleroz.elpub.ru/jour/article/download/1026/919 | Russian | null | Assessment of the association of the rs12329760 polymorphism of the <i>TMPRSS2</i> gene with acute coronary syndrome in patients with new coronavirus infection | Ateroskleroz | 2,024 | cc-by | 5,297 | Оценка ассоциации полиморфизма rs12329760 гена TMPRSS2 с острым
коронарным синдромом у пациентов, перенесших новую коронавирусную
инфекцию В.А. Козик1, Л.А. Шпагина1, И.С. Шпагин1, С.В. Максимова1, 2, Н.Г. Ложкина3,
В.Н. Максимов1, 2 1 Новосибирский государственный медицинский университет
Министерства здравоохранен... |
https://openalex.org/W2894688931 | https://revistas.ucm.es/index.php/CIYC/article/download/60692/4564456547542 | es | "Teoría e historia de la propaganda" (2017), Adrián Huici Módenes, Madrid, Editorial Síntesis, 2017, pp. 278. | Cuadernos de información y comunicación | 2,018 | cc-by | 1,786 | RESEÑAS
CIC. Cuadernos de Información y Comunicación
ISSN: 1135-7791
http://dx.doi.org/10.5209/CIYC.60692
Teoría e historia de la propaganda (2017), Adrián Huici Módenes, Madrid, Editorial
Síntesis, 2017, pp. 278.
Adrián Huici Módenes es profesor titular en la Facultad de Comunicación de la Universidad de Sevilla. D... | |
https://openalex.org/W4311505148 | https://www.frontiersin.org/articles/10.3389/fragi.2022.1063760/pdf | English | null | Living alone reduces the decline of calf circumference among Chinese older adults: A 4-year longitudinal study | Frontiers in aging | 2,022 | cc-by | 5,880 | TYPE Original Research
PUBLISHED 15 December 2022
DOI 10.3389/fragi.2022.1063760 TYPE Original Research
PUBLISHED 15 December 2022
DOI 10.3389/fragi.2022.1063760 TYPE Original Research
PUBLISHED 15 December 2022
DOI 10.3389/fragi.2022.1063760 OPEN ACCESS OPEN ACCESS
EDITED BY
Alan Bruno Silva Vasconcelos,
University Ce... |
https://openalex.org/W4315864599 | https://www.mdpi.com/2075-4450/14/1/80/pdf?version=1673680064 | English | null | Presence of Spodoptera frugiperda Multiple Nucleopolyhedrovirus (SfMNPV) Occlusion Bodies in Maize Field Soils of Mesoamerica | Insects | 2,023 | cc-by | 11,387 | Presence of Spodoptera frugiperda Multiple
Nucleopolyhedrovirus (SfMNPV) Occlusion Bodies in Maize
Field Soils of Mesoamerica Trevor Williams 1,*
, Guadalupe del Carmen Melo-Molina 2, Jaime A. Jiménez-Fernández 2,
Holger Weissenberger 3, Juan S. Gómez-Díaz 1, Laura Navarro-de-la-Fuente 1 and Andrew R. R 1
Instituto de ... |
https://openalex.org/W3174449198 | https://zenodo.org/record/7092294/files/45%2025262.pdf | English | null | Unsupervised feature selection with least-squares quadratic mutual information | Indonesian journal of electrical engineering and computer science | 2,021 | cc-by | 6,625 | Corresponding Author: Janya Sainui
Division of Computational Science, Faculty of Science
Prince of Songkla University
Songkhla, Thailand
Email: janya.s@psu.ac.th Indonesian Journal of Electrical Engineering and Computer Science
Vol. 22, No. 3, June 2021, pp. 1619∼1628
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v22i3.pp1619-... |
https://openalex.org/W4392892860 | https://www.qeios.com/read/O0X1UB/pdf | English | null | Review of: "Ancient Houses in Ben Tre City: A Multifaceted Approach to Preserve Artistic Architectural Heritage and Boost Local Tourism" | null | 2,024 | cc-by | 311 | Qeios, CC-BY 4.0 · Review, March 17, 2024 Review of: "Ancient Houses in Ben Tre City: A Multifaceted
Approach to Preserve Artistic Architectural Heritage and
Boost Local Tourism" Rute Matos1 Rute Matos1 Rute Matos1 1 Universidade de Evora 1 Universidade de Evora 1 Universidade de Evora Potential competing interests... |
https://openalex.org/W4225727893 | https://bmjopen.bmj.com/content/bmjopen/12/1/e053327.full.pdf | English | null | Adoption and continued use of mobile contact tracing technology: multilevel explanations from a three-wave panel survey and linked data | BMJ open | 2,022 | cc-by | 6,938 | Strengths and limitations of this study Objective To identify the key individual-level
(demographics, attitudes, mobility) and contextual
(COVID-19 case numbers, tiers of mobility restrictions,
urban districts) determinants of adopting the NHS
COVID-19 contact tracing app and continued use overtime. Design and ... |
https://openalex.org/W2093810977 | https://figshare.com/articles/journal_contribution/PDC_Mediated_Tandem_Oxidative_8211_Wittig_Olefination/1009039/1/files/1476662.pdf | English | null | PDC-Mediated Tandem Oxidative–Wittig Olefination | Synthetic communications | 2,014 | cc-by | 2,583 | 2. General Procedure To a magnetically stired suspension of pyridinium dichromate (1.85 mmol), Wittig reagent
(1.85 mmol) in anhyd CH2Cl2 (10 mL), alcohol (1.85 mmol) in anhyd CH2Cl2 (10 mL) was added in
one portion. The reaction mixture was stirred at r.t. for 24h. Et2O (30 mL) was added to
the reaction mixture and... |
https://openalex.org/W4299631625 | https://hal.science/hal-01602210/document | English | null | Classifying simulated wheat yield responses to changes in temperature and precipitation across a european transect | HAL (Le Centre pour la Communication Scientifique Directe) | 2,016 | cc-by-sa | 1,239 | International Crop Modelling Symposium
15-17 March 2016, Berlin 15-17 March 2016, Berlin Introduction A wide variety of dynamic crop growth simulation models have been developed over
the past few decades that can differ greatly in their treatment of key processes and
hence in their response to environmental conditi... |
https://openalex.org/W2171243607 | https://europepmc.org/articles/pmc4635008?pdf=render | English | null | Interactions Increase Forager Availability and Activity in Harvester Ants | PloS one | 2,015 | cc-by | 10,083 | RESEARCH ARTICLE Data Availability Statement: All relevant data are
within the paper and its Supporting Information files. Data Availability Statement: All relevant data are
within the paper and its Supporting Information files. Funding: Field work for EP in 2012 was funded by a
Stanford UAR Student Small Grant, and fi... |
W4225157612.txt | https://journals.wlb-stuttgart.de/ojs/index.php/zwlg/article/download/1938/2010 | de | Rezension von: Schmidt, Ernst, Heimatgeschichtliches Lesebuch | Zeitschrift für württembergische Landesgeschichte | 2,022 | cc-by | 1,016 | Städte und Orte
555
Linke, Juden, „Zigeuner“, Zwangsarbeiter, Homosexuelle und andere so genannte „Volksschädlinge“ geprägt; Folterungen und willkürliche Exekutionen in großer Zahl waren an
der Tagesordnung. Betroffene haben ihn beschrieben als „fauchenden Zwerg und zappelnden Sadisten mit kreischender Stimme“. Bei ... | |
https://openalex.org/W2989903755 | http://www.scielo.br/pdf/pat/v49/1983-4063-pat-49-e55628.pdf | English | null | Behavioral aspects of Helicoverpa armigera in the cotton vegetative phase1 | Pesquisa Agropecuária Tropical | 2,019 | cc-by | 5,786 | RESUMO Aspectos comportamentais de Helicoverpa
armigera na fase vegetativa do algodoeiro Helicoverpa armigera (Hübner) is part of an important
complex of insects-pests that attack the cotton crop. This
study aimed to identify the preferential plant parts for the
oviposition of moths, as well as the movement and fee... |
https://openalex.org/W2582221738 | https://repub.eur.nl/pub/97988/journal.pntd.0005310.pdf | English | null | Diagnosing Polyparasitism in a High-Prevalence Setting in Beira, Mozambique: Detection of Intestinal Parasites in Fecal Samples by Microscopy and Real-Time PCR | PLoS neglected tropical diseases | 2,017 | cc-by | 11,926 | RESEARCH ARTICLE Background Many different intestinal parasite species can co-occur in the same population. However,
classic diagnostic tools can only frame a particular group of intestinal parasite species. Hence, one or two tests do not suffice to provide a complete picture of infecting parasite spe-
cies in a given ... |
https://openalex.org/W2121310997 | https://www.scielo.br/j/rbh/a/5t8SPLNx6sxhLRNNyXQfSkr/?lang=pt&format=pdf | Portuguese | null | As cidades da juventude em Fortaleza | Revista brasileira de história | 2,007 | cc-by | 12,926 | ABSTRACT A cidade de Fortaleza é apresentada a
partir da experiência juvenil, de suas
organizações, de seus deslocamentos e
de suas formas específicas de apropria-
ção da cidade.Desta observação delineio
a idéia de Experiência Musical e de Des-
locamentos Geo-Estéticos na cidade. As
organizações e experiências juvenis ... |
W1986052297.txt | https://www.frontiersin.org/articles/10.3389/fnins.2014.00186/pdf | en | Pairmate-dependent pup retrieval as parental behavior in male mice | Frontiers in neuroscience | 2,014 | cc-by | 8,092 | ORIGINAL RESEARCH ARTICLE
published: 11 July 2014
doi: 10.3389/fnins.2014.00186
Pairmate-dependent pup retrieval as parental behavior in
male mice
Mingkun Liang 1,2 , Jing Zhong 1,2 , Hong-Xiang Liu 1 , Olga Lopatina 1 , Ryusuke Nakada 2 ,
Agnes-Mikiko Yamauchi 2 and Haruhiro Higashida 1*
1
2
Department of Basic Rese... | |
https://openalex.org/W3016593255 | https://www.journals.vu.lt/slavistica-vilnensis/article/download/16834/15961 | Russian | null | Immigration Discourse and the Prospects of Russian Language in Lithuania | Slavistica Vilnensis | 2,019 | cc-by | 6,918 | Received: 24/11/2019. Accepted: 15/12/2019
Copyright © 2019 Алла Борисовна Лихачева. Published by Vilnius University Press. This is an Open Access article distributed
under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in
any medium, provided the... |
https://openalex.org/W4244562454 | https://scielosp.org/article/ssm/content/raw/?resource_ssm_path=/media/assets/csp/v29n7/05.pdf | Portuguese | null | Qualidade de vida dos indivíduos expostos ao césio-137, em Goiânia, Goiás, Brasil | Cadernos de Saúde Pública | 2,013 | cc-by | 6,641 | 1 Programa de Pós-graduação
Ciências da Saúde,
Universidade Federal de
Goiás, Goiânia, Brasil.
2 Instituto de Patologia
Tropical e Saúde Pública,
Universidade Federal de
Goiás, Goiânia, Brasil.
3 Faculdade de Medicina,
Universidade Federal de
Goiás, Goiânia, Brasil.
4 Faculdade de Farmácia,
Universidade Federa... |
https://openalex.org/W2028130894 | https://curis.ku.dk/ws/files/127547653/Control_region_sequences_indicate_that_multiple_externae_represent_multiple_infections_by_Sacculina_carcini.pdf | English | null | Control region sequences indicate that multiple externae represent multiple infections by Sacculina carcini (Cirripedia: Rhizocephala) | Ecology and evolution | 2,014 | cc-by | 6,410 | university of copenhagen university of copenhagen
Control region sequences indicate that multiple externae represent multiple infections
by Sacculina carcini (Cirripedia: Rhizocephala) university of copenhagen Control region sequences indicate that multiple externae represent multiple infections
by Sacculina carcini (... |
https://openalex.org/W4321458814 | https://www.frontiersin.org/articles/10.3389/fgene.2023.1137017/pdf | English | null | Regulatory mechanisms of microRNAs in endocrine disorders and their therapeutic potential | Frontiers in genetics | 2,023 | cc-by | 12,865 | Abbreviations: miRNA, microRNA; DM, Diabetes mellitus; T1D, Type 1 diabetes; T2D, Type 2 diabetes;
Treg Regulatory T cells AITDs, Autoimmune thyroid disorders; GD, Graves’ disease; HT, Hashimoto’s
thyroiditis; BMD, Bone mineral density; MSC, Mesenchymal stem cells; APA, Aggressive pituitary
adenoma; CAB, Cabergoline. K... |
https://openalex.org/W3037036092 | https://journals.iucr.org/e/issues/2020/07/00/wm5569/wm5569.pdf | English | null | Selective synthesis and crystal structures of manganese(I) complexes with a bi- or tridentate terpyridine ligand | Acta crystallographica. Section E, Crystallographic communications | 2,020 | cc-by | 7,688 | research communications Selective synthesis and crystal structures of
manganese(I) complexes with a bi- or tridentate
terpyridine ligand Selective synthesis and crystal structures of
manganese(I) complexes with a bi- or tridentate
terpyridine ligand ISSN 2056-9890 Kosei Wadayama,a Tsugiko Takaseb and Dai Oyamab* aGradu... |
https://openalex.org/W4312964994 | https://revistas.ucm.es/index.php/ANHA/article/download/83058/4564456561480 | es | Memoria oral del pueblo Mapuche en la reforma agraria y la dictadura. Aplicación del archivo en el proyecto Dungun, instalación sonora interactiva | Anales de historia del arte | 2,022 | cc-by | 10,668 | FORO
Anales de Historia del Arte
ISSN: 0214-6452
https://dx.doi.org/10.5209/anha.83058
Memoria oral del pueblo Mapuche en la reforma agraria y la dictadura.
Aplicación del archivo en el proyecto Dungun, instalación sonora
interactiva
Luis Urquieta Robles1
Recibido: 1 de febrero de 2022 / Aceptado: 6 de junio de 2022... | |
https://openalex.org/W4280605643 | https://repositorioinstitucional.ceu.es/bitstream/10637/14376/1/News_Pou_CCAC_2022.pdf | English | null | News coverage of the Church dealing with the pandemic: Spanish and Italian newspapers | Church, communication and culture | 2,022 | cc-by | 11,678 | Church, Communication and Culture Church, Communication and Culture ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rchu20 ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rchu20 Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action... |
https://openalex.org/W4304807314 | https://www.aging-us.com/article/204334/pdf | English | null | Palmitic acid inhibits vascular smooth muscle cell switch to synthetic phenotype via upregulation of miR-22 expression | Aging | 2,022 | cc-by | 7,293 | ABSTRACT Synthetic phenotype switch of vascular smooth muscle cells (VSMCs) has been shown to play key roles in
vascular diseases. Mounting evidence has shown that fatty acid metabolism is highly associated with vascular
diseases. However, how fatty acids regulate VSMC phenotype is poorly understood. Hence, the effec... |
https://openalex.org/W4229988195 | https://zenodo.org/records/2231079/files/article.pdf | English | null | A Note-Book on Experimental Mathematics. C. Godfrey and G. M. Bell. (Arnold.) | The mathematical gazette/Mathematical gazette | 1,906 | public-domain | 2,585 | Review
Source:
The Mathematical Gazette, Vol. 3, No. 56 (Mar., 1906), pp. 297-298
Published by:
Mathematical Association
Stable URL:
http://www.jstor.org/stable/3603481
Accessed: 02-12-2015 05:46 UTC Review Review Source:
The Mathematical Gazette, Vol. 3, No. 56 (Mar., 1906), pp. 297-298
Published by: ... |
https://openalex.org/W4231506190 | http://e-journal.metrouniv.ac.id/index.php/istinbath/article/download/951/783 | Indonesian | null | FAKTOR PENGHAMBAT DALAM PENEGAKAN QANUN JINAYAT DI ACEH | null | 2,019 | cc-by-sa | 8,010 | Abstrak Tulisan ini membuktikan bahwa pelaksanaan Qanun Aceh No. 14 Tahun 2003 tentang
khalwat di Kota Subulussalam belum sepenuhnya berjalan dengan baik. Banyak kendala
yang dihadapi baik dari pelaksananya (pemerintah) maupun masyarakat sebagai objek
hukum penerapan syariat Islam itu sendiri. Faktor hukum yang meru... |
https://openalex.org/W2031764972 | https://europepmc.org/articles/pmc2805710?pdf=render | English | null | Microphthalmia in Texel Sheep Is Associated with a Missense Mutation in the Paired-Like Homeodomain 3 (PITX3) Gene | PloS one | 2,010 | cc-by | 8,280 | Abstract The funders had no role in study design, data collection and analysis, decision to publish, or preparation of
the manuscript. Competing Interests: The authors have declared that no competing interests exist. Competing Interests: The authors have declared that no competing interests exist. * E-mail: Cord.Droege... |
https://openalex.org/W2108456140 | https://link.springer.com/content/pdf/10.1186%2F2052-336X-12-87.pdf | English | null | Optimizing photo-Fenton like process for the removal of diesel fuel from the aqueous phase | Journal of environmental health science & engineering | 2,014 | cc-by | 5,275 | Abstract Background: In recent years, pollution of soil and groundwater caused by fuel leakage from old underground
storage tanks, oil extraction process, refineries, fuel distribution terminals, improper disposal and also spills during
transferring has been reported. Diesel fuel has created many problems for water res... |
https://openalex.org/W2464335014 | https://zenodo.org/records/56298/files/NB_article_7113.pdf | English | null | Biological control of weeds in the 22 Pacific island countries and territories: current status and future prospects | NeoBiota | 2,016 | cc-by | 10,824 | Abstract Biological control of introduced weeds in the 22 Pacific island countries and territories (PICTs) began in
1911, with the lantana seed-feeding fly introduced into Fiji and New Caledonia from Hawaii. To date, a to
tal of 62 agents have been deliberately introduced into the PICTs to control 21 weed species in ... |
https://openalex.org/W2944092791 | https://europepmc.org/articles/pmc6572060?pdf=render | English | null | Prevalence and Risk Factors for Positive Nasal Methicillin-Resistant Staphylococcus aureus Carriage among Orthopedic Patients in Korea | Journal of clinical medicine | 2,019 | cc-by | 6,607 | Received: 14 April 2019; Accepted: 7 May 2019; Published: 8 May 2019 Abstract: Methicillin-resistant Staphylococcus aureus (MRSA) causes purulent skin and soft tissue
infections as well as other life-threatening diseases. Recent guidelines recommend screening for MRSA
at the time of admission. However, few studies have... |
https://openalex.org/W4293661304 | https://journal2.unusa.ac.id/index.php/MTPHJ/article/download/3187/1749 | Indonesian | null | The Relationship between Snack Consumption Patterns and Nutritional Status in Adolescents in Malang City Indonesia | Medical technology and public health journal | 2,022 | cc-by-sa | 2,785 | ABSTRACT Background : The consumption of snacks is often done between meals. About 20% of the
daily intake of adolescents comes from snacks. Snacks contribute ‘empty’ energy but provide
an excess intake of substances that are not beneficial to the teenager’s body. Objectives; this
study aims to determine the pattern... |
https://openalex.org/W2951066048 | https://europepmc.org/articles/pmc6572740?pdf=render | English | null | Public health application of predictive modeling: an example from farm vehicle crashes | Injury epidemiology. | 2,019 | cc-by | 8,629 | © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original au... |
https://openalex.org/W832180393 | https://journals.agh.edu.pl/geotour/article/download/687/512 | English | null | Middle Jurassic black shales (Skrzypny Shale Formation) – palaeoenvironmental significance of one of the oldest deposits of the Pieniny Klippen Belt. | Geotourism/Geoturystyka | 2,008 | cc-by | 5,450 | Środkowojurajskie czarne łupki (formacja łupków ze Skrzypnego) – znaczenie
paleośrodowiskowe jednych z najstarszych osadów pienińskiego pasa skałkowego Katarzyna Górniak1, Krzysztof Bahranowski1, Adam Gaweł1,
Leszek Marynowski2 & Tadeusz Szydłak1
1 AGH University of Science and Technology, Department of Mineralogy, ... |
https://openalex.org/W3042632995 | https://revistas.ucm.es/index.php/ANQE/article/download/66701/4564456554017 | es | Traducción comentada de un acta notarial de celebración de matrimonio civil del español al árabe | Anaquel de estudios árabes | 2,020 | cc-by | 14,414 | ARTÍCULOS
Anaquel de Estudios Árabes
ISSN: 1130-3964
https://dx.doi.org/10.5209/anqe.66701
Traducción comentada de un acta notarial de celebración de matrimonio
civil del español al árabe
Beatriz Soto Aranda1; Rabab Kabbour2
Recibido: 2 de diciembre de 2019 / Aceptado: 29 de febrero de 2020
Resumen. Las actas notar... | |
https://openalex.org/W2920243230 | https://europepmc.org/articles/pmc6473468?pdf=render | English | null | Role of Nitrate Reductase in NO Production in Photosynthetic Eukaryotes | Plants | 2,019 | cc-by | 9,306 | Received: 16 January 2019; Accepted: 8 February 2019; Published: 6 March 2019 Abstract: Nitric oxide is a gaseous secondary messenger that is critical for proper cell signaling
and plant survival when exposed to stress. Nitric oxide (NO) synthesis in plants, under standard
phototrophic oxygenic conditions, has long bee... |
https://openalex.org/W4212939849 | https://discovery.ucl.ac.uk/10144625/1/s13023-022-02191-2.pdf | English | null | Challenges and improvement needs in the care of patients with central diabetes insipidus | Orphanet journal of rare diseases | 2,022 | cc-by | 10,727 | Abstract Central diabetes insipidus (CDI) is a rare condition, with significant impact on patient health and well-being. It is a
chronic condition which usually requires meticulous long-term care. It can affect both children and adults. There is
limited literature considering the needs and challenges inherent in prov... |
W4394815528.txt | https://www.emerald.com/insight/content/doi/10.1108/IJDI-12-2023-0287/full/pdf?title=efficiency-of-brics-countries-in-sustainable-development-a-comparative-data-envelopment-analysis | en | Efficiency of BRICS countries in sustainable development: a comparative data envelopment analysis | International journal of development issues | 2,024 | cc-by | 8,611 | The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1446-8956.htm
Efficiency of BRICS countries in
sustainable development: a
comparative data envelopment
analysis
Isabella Melissa Gebert
Iscte-Instituto Universitário de Lisboa, Lisbon, Portugal, an... | |
https://openalex.org/W4313417200 | https://sensors.myu-group.co.jp/sm_pdf/SM3144.pdf | English | null | Deep-learning-based Automatic Detection and Classification of Traffic Signs Using Images Collected by Mobile Mapping Systems | Sensors and materials | 2,022 | cc-by | 4,563 | *Corresponding author: e-mail: kemyoung@nsu.ac.kr
https://doi.org/10.18494/SAM3956 Keywords: high-definition maps, traffic sign, mask R-CNN, Inception-v3, autonomous driving Keywords: high-definition maps, traffic sign, mask R-CNN, Inception-v3, autonomous driving As interest in autonomous driving has increased in re... |
https://openalex.org/W4313589007 | https://www.researchsquare.com/article/rs-2178907/latest.pdf | English | null | Study on the mechanical properties of clayey slip zone soil considering montmorillonite content | Research Square (Research Square) | 2,023 | cc-by | 7,680 | Research Article Keywords: cohesive slipband soils, expansion characteristics, hydrophilic minerals, montmorillonite,
shear strength parameters
Posted Date: January 4th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-2178907/v1
License:
This work is licensed under a Creative Commons Attribution 4.0 International
Licens... |
https://openalex.org/W2152263162 | https://europepmc.org/articles/pmc3201606?pdf=render | English | null | Vertebral cryptococcosis in an immunocompetent patient - a case report | The Pan African medical journal | 2,011 | cc-by | 1,351 | Abstract We report an unusual case of 70 years old, immunocompetent woman who was diagnosed with vertebral cryptococcosis. The diagnosis was made
on the basis of radiological and histological findings. The outcome was favorable under antifungal treatment. &Corresponding author: Ammouri Wafa, Department of Internal med... |
https://openalex.org/W4391329061 | https://egusphere.copernicus.org/preprints/2023/egusphere-2023-3000/egusphere-2023-3000.pdf | English | null | Comment on egusphere-2023-3000 | null | 2,024 | cc-by | 34,210 | ERROR: type should be string, got "https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0.\nl\n.\n0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Impact of Hurricane Irma on Coral Reef Sediment Redistribution at \n5 \nLooe Key Reef, Florida, USA \nKimberly K. Yates1, Zachery Fehr2, Selena Johnson1, David Zawada1 \n1U.S. Geological Survey, St. Petersburg, FL, 33701, United States \n2Cherokee Nation System Solutions, Tulsa, OK, 74166, United States, Contractor to the U.S. Geological Survey \nCorrespondence to: Kimberly K. Yates (kyates@usgs.gov) \n10 Rates of elevation \nchange during this post-storm period were one to two orders of magnitude greater than decadal and multi-decadal rates of \nchange in the same location, and changes showed erosion of approximately 50% of sediment deposited during the storm event \nas seafloor sediment distribution began to re-equilibrate to non-storm sea state conditions. Our results suggest higher resolution \n30 \nelevation-change data collected over seasonal and annual time periods could enhance characterization and understanding of \nshort-term and long-term rates and processes of seafloor change and help guide post-storm recovery and restoration of benthic \nhabitats in topographically complex coral reef systems. Abstract. Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts to reef \nspecies, habitats, restoration, and mitigation, yet there remains a global knowledge gap due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h-1 winds and greater than 8 m waves on 10 \nSeptember 2017, directly impacting the Florida Reef Tract (FRT), and providing an opportunity to perform a unique comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and \n15 \nmultibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 \narea along the Lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to \nstorm impacts. Elevation change was calculated at over 4-million point-locations across 10 habitat types within this study area \nfor two time periods using data collected from 1) approximately one year before the passing of Irma and three to six months 15 comprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and \n15 \nmultibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 \narea along the Lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to \nstorm impacts. Elevation change was calculated at over 4-million point-locations across 10 habitat types within this study area \nfor two time periods using data collected from 1) approximately one year before the passing of Irma and three to six months following the storm’s impact, and 2) from three to six months after, and up to 16.5 months after, the storm. Impact of Hurricane Irma on Coral Reef Sediment Redistribution at \n5 \nLooe Key Reef, Florida, USA \nKimberly K. Yates1, Zachery Fehr2, Selena Johnson1, David Zawada1 \n1U.S. Geological Survey, St. Petersburg, FL, 33701, United States \n2Cherokee Nation System Solutions, Tulsa, OK, 74166, United States, Contractor to the U.S. Geological Survey \nCorrespondence to: Kimberly K. Yates (kyates@usgs.gov) \n10 Impact of Hurricane Irma on Coral Reef Sediment Redistribution at \n5 \nLooe Key Reef, Florida, USA \nKimberly K. Yates1, Zachery Fehr2, Selena Johnson1, David Zawada1 \n1U.S. Geological Survey, St. Petersburg, FL, 33701, United States \n2Cherokee Nation System Solutions, Tulsa, OK, 74166, United States, Contractor to the U.S. Geological Survey \nCorrespondence to: Kimberly K. Yates (kyates@usgs.gov) \n10 rly K. Yates1, Zachery Fehr2, Selena Johnson1, David Zawada1 Abstract. Understanding event-driven sediment transport in coral reef environments is essential to assessing impacts to reef \nspecies, habitats, restoration, and mitigation, yet there remains a global knowledge gap due to limited quantitative studies. Hurricane Irma made landfall in the Lower Florida Keys with sustained 209 km h-1 winds and greater than 8 m waves on 10 \nSeptember 2017, directly impacting the Florida Reef Tract (FRT), and providing an opportunity to perform a unique \ncomprehensive, quantitative assessment of its impact on coral reef structure and sediment redistribution. We used lidar and \n15 \nmultibeam derived digital elevation models (DEMs) collected before and after the passing of Hurricane Irma over a 15.98 km2 \narea along the Lower FRT including Looe Key Reef to quantify changes in seafloor elevation, volume, and structure due to \nstorm impacts. Elevation change was calculated at over 4-million point-locations across 10 habitat types within this study area \nfor two time periods using data collected from 1) approximately one year before the passing of Irma and three to six months \nfollowing the storm’s impact, and 2) from three to six months after, and up to 16.5 months after, the storm. Elevation-change \n20 \ndata were then used to generate Triangulated Irregular Network (TIN) models in ArcMap to calculate changes in seafloor \nvolume during each time-period. Our results indicate that Hurricane Irma was primarily a depositional event that increased \nmean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported \nprimarily west-southwest (WSW) and downslope modifying geomorphic seafloor features including the migration of sand \nwaves and rubble fields, formation of scour marks in shallow seagrass habitat, and burial of seagrass and coral-dominated \n25 \nhabitat. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was \nobserved across all habitats with mean elevation-change of -0.15 m and net volume change up to -2.46 Mm3. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 1 Introduction Coral coverage \nhas been reported at less than 7% along the Florida Keys Reef Tract and less than 3% along the northern FRT in recent years \n50 \n(Jackson et al., 2014; Walton et al, 2018; Knowlton, 2020); and many of Florida’s reefs are in a net erosional state (Yates et \nal., 2017; Morris et al, 2022). Additionally, seagrass has been decreasing in coverage since early Thalassia testudinum die-\noffs in 1987 and more contemporary die-offs in 2015 following storm events and water quality variations (Hall et al., 2016). al., 2009; Gorstein et al., 2016, Towle et al., 2020). Benthic communities of the Florida Reef Tract (FRT) have been degrading \n40 \nfor the past several decades. Coral coverage has declined across the Caribbean and Florida reefs by more than 50% since the \n1970’s due to coral disease and bleaching (Porter et al., 2001; Patterson et al., 2002; Williams and Miller, 2012; Joyner et al., \n2015; Walker et al., 2018), pollution and overfishing (Littler et al., 1986; Lapointe & Clark, 1992; and Hughes 1994), and \nmass-mortality of macroalgal grazers (e.g., Lessios et al. 1983). Progression of climate change has increased thermal stress, al., 2009; Gorstein et al., 2016, Towle et al., 2020). Benthic communities of the Florida Reef Tract (FRT) have been degrading \n40 \nfor the past several decades. Coral coverage has declined across the Caribbean and Florida reefs by more than 50% since the \n1970’s due to coral disease and bleaching (Porter et al., 2001; Patterson et al., 2002; Williams and Miller, 2012; Joyner et al., \n2015; Walker et al., 2018), pollution and overfishing (Littler et al., 1986; Lapointe & Clark, 1992; and Hughes 1994), and \nmass-mortality of macroalgal grazers (e.g., Lessios et al. 1983). Progression of climate change has increased thermal stress, coral bleaching and disease, ocean acidification, and corallivory (predation of corals) (Wilkinson 1996; Mumby et al., 2006; \n45 \nBrandt and McManus, 2009; Soto et al., 2011; Kuffner et al., 2015; Randall and van Woesik, 2015; Muehllehner et al., 2016; \nHughes et al., 2017; Rice et al., 2019). These multiple stressors and increased storm occurrences have caused a shift from \nstony-coral-dominated reefs to macroalgae and octocoral dominated reefs (Bohsnack 1983; Hughs, 1994; Knowlton, 1992; \nMiller et al., 2002; Norstrom et al., 2009; Bruno et al., 2009; Ruzicka et al., 2013 and Jackson et al., 2014). Impact of Hurricane Irma on Coral Reef Sediment Redistribution at \n5 \nLooe Key Reef, Florida, USA \nKimberly K. Yates1, Zachery Fehr2, Selena Johnson1, David Zawada1 \n1U.S. Geological Survey, St. Petersburg, FL, 33701, United States \n2Cherokee Nation System Solutions, Tulsa, OK, 74166, United States, Contractor to the U.S. Geological Survey \nCorrespondence to: Kimberly K. Yates (kyates@usgs.gov) \n10 Elevation-change \n20 \ndata were then used to generate Triangulated Irregular Network (TIN) models in ArcMap to calculate changes in seafloor \nvolume during each time-period. Our results indicate that Hurricane Irma was primarily a depositional event that increased \nmean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported \nprimarily west-southwest (WSW) and downslope modifying geomorphic seafloor features including the migration of sand following the storm’s impact, and 2) from three to six months after, and up to 16.5 months after, the storm. Elevation-change \n20 \ndata were then used to generate Triangulated Irregular Network (TIN) models in ArcMap to calculate changes in seafloor \nvolume during each time-period. Our results indicate that Hurricane Irma was primarily a depositional event that increased \nmean seafloor elevation and volume at this study site by 0.34 m and up to 5.4 Mm3, respectively. Sediment was transported \nprimarily west-southwest (WSW) and downslope modifying geomorphic seafloor features including the migration of sand waves and rubble fields, formation of scour marks in shallow seagrass habitat, and burial of seagrass and coral-dominated \n25 \nhabitat. Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was \nobserved across all habitats with mean elevation-change of -0.15 m and net volume change up to -2.46 Mm3. Rates of elevation \nchange during this post-storm period were one to two orders of magnitude greater than decadal and multi-decadal rates of \nchange in the same location, and changes showed erosion of approximately 50% of sediment deposited during the storm event as seafloor sediment distribution began to re-equilibrate to non-storm sea state conditions. Our results suggest higher resolution \n30 \nelevation-change data collected over seasonal and annual time periods could enhance characterization and understanding of \nshort-term and long-term rates and processes of seafloor change and help guide post-storm recovery and restoration of benthic \nhabitats in topographically complex coral reef systems. as seafloor sediment distribution began to re-equilibrate to non-storm sea state conditions. Our results suggest higher resolution \n30 \nelevation-change data collected over seasonal and annual time periods could enhance characterization and understanding of \nshort-term and long-term rates and processes of seafloor change and help guide post-storm recovery and restoration of benthic \nhabitats in topographically complex coral reef systems. 1 1 Introduction Coral reefs provide a variety of services to coastal communities including protection from coastal hazards such as storms, \n35 \nwaves, and erosion (Ferrario et al., 2014; Storlazzi et al., 2021); socioeconomic benefits such as fisheries, recreation, and \ntourism (Moberg and Folk, 1999; Hall et al. 2020); and they support numerous habitats and diverse marine species (Knowlton, \n2020). Socioeconomic benefits of Florida reefs have an estimated value of over 8 billion dollars a year, supporting 39,000 \nSouth Florida jobs and 70,400 total jobs, with at least 2.9 billion dollars contributing directly to the local economy (Krediet et Coral reefs provide a variety of services to coastal communities including protection from coastal hazards such as storms, \n35 \nwaves, and erosion (Ferrario et al., 2014; Storlazzi et al., 2021); socioeconomic benefits such as fisheries, recreation, and \ntourism (Moberg and Folk, 1999; Hall et al. 2020); and they support numerous habitats and diverse marine species (Knowlton, \n2020). Socioeconomic benefits of Florida reefs have an estimated value of over 8 billion dollars a year, supporting 39,000 \nSouth Florida jobs and 70,400 total jobs, with at least 2.9 billion dollars contributing directly to the local economy (Krediet et \nal., 2009; Gorstein et al., 2016, Towle et al., 2020). Benthic communities of the Florida Reef Tract (FRT) have been degrading \n40 \nfor the past several decades. Coral coverage has declined across the Caribbean and Florida reefs by more than 50% since the \n1970’s due to coral disease and bleaching (Porter et al., 2001; Patterson et al., 2002; Williams and Miller, 2012; Joyner et al., \n2015; Walker et al., 2018), pollution and overfishing (Littler et al., 1986; Lapointe & Clark, 1992; and Hughes 1994), and \nmass-mortality of macroalgal grazers (e.g., Lessios et al. 1983). Progression of climate change has increased thermal stress, \ncoral bleaching and disease, ocean acidification, and corallivory (predation of corals) (Wilkinson 1996; Mumby et al., 2006; \n45 \nBrandt and McManus, 2009; Soto et al., 2011; Kuffner et al., 2015; Randall and van Woesik, 2015; Muehllehner et al., 2016; \nHughes et al., 2017; Rice et al., 2019). These multiple stressors and increased storm occurrences have caused a shift from \nstony-coral-dominated reefs to macroalgae and octocoral dominated reefs (Bohsnack 1983; Hughs, 1994; Knowlton, 1992; \nMiller et al., 2002; Norstrom et al., 2009; Bruno et al., 2009; Ruzicka et al., 2013 and Jackson et al., 2014). 1 Introduction Coral coverage has been reported at less than 7% along the Florida Keys Reef Tract and less than 3% along the northern FRT in recent years \n50 \n(Jackson et al., 2014; Walton et al, 2018; Knowlton, 2020); and many of Florida’s reefs are in a net erosional state (Yates et \nal., 2017; Morris et al, 2022). Additionally, seagrass has been decreasing in coverage since early Thalassia testudinum die-\noffs in 1987 and more contemporary die-offs in 2015 following storm events and water quality variations (Hall et al., 2016). Multi-decadal seafloor elevation-change analyses along the FRT indicate that degradation of coral reefs and surrounding \n55 \nseafloor habitats has led to substantial erosion and loss of elevation from the 1930’s to 2002 and increased water depths to \nlevels not expected until near the year 2100 (Yates et al., 2017). Projected socioeconomic impacts due to continued FRT coral \nreef degradation and loss of seafloor elevation estimate increases of flooding risk from storms and coastal inundation to more \nthan 7,300 people and $823.6 million (2010 U.S. dollars, USD) in direct and indirect damage to housing, buildings, and Multi-decadal seafloor elevation-change analyses along the FRT indicate that degradation of coral reefs and surrounding \n55 \nseafloor habitats has led to substantial erosion and loss of elevation from the 1930’s to 2002 and increased water depths to \nlevels not expected until near the year 2100 (Yates et al., 2017). Projected socioeconomic impacts due to continued FRT coral \nreef degradation and loss of seafloor elevation estimate increases of flooding risk from storms and coastal inundation to more \nthan 7,300 people and $823.6 million (2010 U.S. dollars, USD) in direct and indirect damage to housing, buildings, and businesses, annually (Storlazzi et al., 2021). Storm frequency and strength are projected to increase as sea-surface temperatures \n60 \nand atmospheric energy increase due to climate change and global warming (Elsner et al., 2008, Bhatia et al., 2019; Knutson \net al., 2020). While advances have been made in understanding long-term change in seafloor elevation and structure and its \npotential socioeconomic consequences, understanding the effects of event-driven changes to seafloor geomorphology due to \nstorms remains a major knowledge gap. businesses, annually (Storlazzi et al., 2021). Storm frequency and strength are projected to increase as sea-surface temperatures \n60 \nand atmospheric energy increase due to climate change and global warming (Elsner et al., 2008, Bhatia et al., 2019; Knutson \net al., 2020). https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Major tropical storms persistently impact the state of Florida with historical hurricane impacts estimated to have caused more \nthan $450 billion dollars of damage across the state from the early 1900’s to 2007 (Malmstadt et al., 2009). The Middle to \nLower Florida Keys (from Islamorada to Key West) has been impacted by 15 major hurricane landfall events (Category 3 \nthrough 5) and numerous tropical storm and Category 1 and 2 hurricanes from the early 1900’s to 2022 (NOAA, 2022a). Hurricane Irma made landfall at Cudjoe Key in the Lower Florida Keys after passing directly over Looe Key Reef on 10 \n70 \nSeptember 2017 as a category 4 hurricane with maximum wind speeds of 213 km h-1 (115 kts) (Cangialosi et al., 2021) and \nsignificant wave heights of approximately 14 m a few kilometers offshore of the Florida Keys (Xian et al., 2018, Fig. 1a, b). Satellite imagery showed extensive sediment plumes throughout South Florida and the FRT caused by sediment resuspension \nand transport during the storm (Fig. 1c, d). The storm damaged up to 75% of buildings near its landfall point and caused Hurricane Irma made landfall at Cudjoe Key in the Lower Florida Keys after passing directly over Looe Key Reef on 10 \n70 \nSeptember 2017 as a category 4 hurricane with maximum wind speeds of 213 km h-1 (115 kts) (Cangialosi et al., 2021) and \nsignificant wave heights of approximately 14 m a few kilometers offshore of the Florida Keys (Xian et al., 2018, Fig. 1a, b). Satellite imagery showed extensive sediment plumes throughout South Florida and the FRT caused by sediment resuspension \nand transport during the storm (Fig. 1c, d). The storm damaged up to 75% of buildings near its landfall point and caused \napproximately 50 billion USD of wind and water damage across the state of Florida (Xian et al., 2018; Cangialosi et al., 2021; \n75 \nNOAA, 2022b). Prior to Hurricane Irma, the most recent, direct impact to Looe Key Reef from a tropical storm was in 2008 \nduring Tropical Storm Fay (NOAA, 2022a). Diver\nbased surveys of coral reefs at 57 locations along the FRT by the National Oceanic and Atmospheric Administration showed \n80 \nhighest levels of damage in the Middle to Lower Keys including dislodged and fractured corals, clogged and damaged sponges, \nheavy sedimentation, burial of corals, displaced rubble and sand, reef erosion, fractured substrate, and marine debris; 14% of \nsites showed severe impact, 33% showed moderate impact, and 53% showed minimal impact (Viehman et al., 2018). Looe \nKey Reef, located near the hurricane landfall location, showed more than 26% prevalence of hurricane-impacted corals (Florida based surveys of coral reefs at 57 locations along the FRT by the National Oceanic and Atmospheric Administration showed \n80 \nhighest levels of damage in the Middle to Lower Keys including dislodged and fractured corals, clogged and damaged sponges, \nheavy sedimentation, burial of corals, displaced rubble and sand, reef erosion, fractured substrate, and marine debris; 14% of \nsites showed severe impact, 33% showed moderate impact, and 53% showed minimal impact (Viehman et al., 2018). Looe \nKey Reef, located near the hurricane landfall location, showed more than 26% prevalence of hurricane-impacted corals (Florida Resilience Program, 2017). Similar surveys along the northern FRT from Key Biscayne north showed from approximately 5% \n85 \nto 17% of 62 sites with impacts to corals including dislodged and buried colonies, and at least one site with slabs of hardbottom \n2 to 5 m in size fractured and displaced several meters (Walker, 2018). Analyses of long-term monitoring-transect data at 40 \nsites throughout the Florida Keys National Marine Sanctuary (FKNMS) showed instantaneous losses in seagrass and \ncalcareous green macroalgae density after the storm passed, particularly in the Lower Florida Keys near where Hurricane Irma Resilience Program, 2017). Similar surveys along the northern FRT from Key Biscayne north showed from approximately 5% \n85 \nto 17% of 62 sites with impacts to corals including dislodged and buried colonies, and at least one site with slabs of hardbottom \n2 to 5 m in size fractured and displaced several meters (Walker, 2018). Analyses of long-term monitoring-transect data at 40 \nsites throughout the Florida Keys National Marine Sanctuary (FKNMS) showed instantaneous losses in seagrass and \ncalcareous green macroalgae density after the storm passed, particularly in the Lower Florida Keys near where Hurricane Irma made landfall (Wilson et al., 2020). 1 Introduction While advances have been made in understanding long-term change in seafloor elevation and structure and its \npotential socioeconomic consequences, understanding the effects of event-driven changes to seafloor geomorphology due to \nstorms remains a major knowledge gap. 65 2 2 Hurricane Irma made landfall at Cudjoe Key in the Lower Florida Keys after passing directly over Looe Key Reef on 10 \n70 \nSeptember 2017 as a category 4 hurricane with maximum wind speeds of 213 km h-1 (115 kts) (Cangialosi et al., 2021) and \nsignificant wave heights of approximately 14 m a few kilometers offshore of the Florida Keys (Xian et al., 2018, Fig. 1a, b). Satellite imagery showed extensive sediment plumes throughout South Florida and the FRT caused by sediment resuspension \nand transport during the storm (Fig. 1c, d). The storm damaged up to 75% of buildings near its landfall point and caused \napproximately 50 billion USD of wind and water damage across the state of Florida (Xian et al., 2018; Cangialosi et al., 2021; \n75 approximately 50 billion USD of wind and water damage across the state of Florida (Xian et al., 2018; Cangialosi et al., 2021; \n75 \nNOAA, 2022b). Prior to Hurricane Irma, the most recent, direct impact to Looe Key Reef from a tropical storm was in 2008 \nduring Tropical Storm Fay (NOAA, 2022a). Numerous rapid assessments of seafloor habitats were conducted along the FRT in the weeks following Hurricane Irma. Diver-\nbased surveys of coral reefs at 57 locations along the FRT by the National Oceanic and Atmospheric Administration showed \n80 \nhighest levels of damage in the Middle to Lower Keys including dislodged and fractured corals, clogged and damaged sponges, \nheavy sedimentation, burial of corals, displaced rubble and sand, reef erosion, fractured substrate, and marine debris; 14% of \nsites showed severe impact, 33% showed moderate impact, and 53% showed minimal impact (Viehman et al., 2018). Looe \nKey Reef, located near the hurricane landfall location, showed more than 26% prevalence of hurricane-impacted corals (Florida \nResilience Program, 2017). Similar surveys along the northern FRT from Key Biscayne north showed from approximately 5% \n85 \nto 17% of 62 sites with impacts to corals including dislodged and buried colonies, and at least one site with slabs of hardbottom \n2 to 5 m in size fractured and displaced several meters (Walker, 2018). Analyses of long-term monitoring-transect data at 40 \nsites throughout the Florida Keys National Marine Sanctuary (FKNMS) showed instantaneous losses in seagrass and \ncalcareous green macroalgae density after the storm passed, particularly in the Lower Florida Keys near where Hurricane Irma \nmade landfall (Wilson et al., 2020). Additionally, several locations showed moderate burial of seagrass with up to 5 to 10 cm \n90 \nof sand, while other locations showed heavy erosion or moderate seagrass canopy thinning (Wilson et al., 2020). Reef Visual \nCensus (RVC) surveys including structure from motion (SfM) habitat photogrammetry at sites in the Lower Florida Keys from \nFebruary 2017 to December 2018 showed a 30% decrease in macroalgae at the Looe Key Sanctuary Preservation Area (SPA) \nand a 30% increase at the Looe Key Special Use Area (SPU) post Irma; while both Looe Key locations showed a 10% decrease \nin live coral cover and a 20% increase in octocoral cover (Simmons et al., 2022). Comparison of restored (outplant) coral \n95 \nsurvival rates at two fore reef and two patch reef sites near Tavernier Key in the Upper Florida Keys showed approximately \n85% outplant survival at all locations prior to the passage of Hurricane Irma; however, no outplants survived at the fore reef \nsites and only 51% of outplants survived at the patch reef sites post-Irma, the difference likely due to protection of the patch \nreefs from dissipation of wave energy by the reef crest (Lohr et al., 2020). Examination of Diadema antillarum sea urchins (a Numerous rapid assessments of seafloor habitats were conducted along the FRT in the weeks following Hurricane Irma. Diver-\nbased surveys of coral reefs at 57 locations along the FRT by the National Oceanic and Atmospheric Administration showed \n80 \nhighest levels of damage in the Middle to Lower Keys including dislodged and fractured corals, clogged and damaged sponges, \nheavy sedimentation, burial of corals, displaced rubble and sand, reef erosion, fractured substrate, and marine debris; 14% of \nsites showed severe impact, 33% showed moderate impact, and 53% showed minimal impact (Viehman et al., 2018). Looe \nKey Reef, located near the hurricane landfall location, showed more than 26% prevalence of hurricane-impacted corals (Florida Numerous rapid assessments of seafloor habitats were conducted along the FRT in the weeks following Hurricane Irma. Additionally, several locations showed moderate burial of seagrass with up to 5 to 10 cm \n90 \nof sand, while other locations showed heavy erosion or moderate seagrass canopy thinning (Wilson et al., 2020). Reef Visual \nCensus (RVC) surveys including structure from motion (SfM) habitat photogrammetry at sites in the Lower Florida Keys from \nFebruary 2017 to December 2018 showed a 30% decrease in macroalgae at the Looe Key Sanctuary Preservation Area (SPA) \nand a 30% increase at the Looe Key Special Use Area (SPU) post Irma; while both Looe Key locations showed a 10% decrease in live coral cover and a 20% increase in octocoral cover (Simmons et al., 2022). Comparison of restored (outplant) coral \n95 \nsurvival rates at two fore reef and two patch reef sites near Tavernier Key in the Upper Florida Keys showed approximately \n85% outplant survival at all locations prior to the passage of Hurricane Irma; however, no outplants survived at the fore reef \nsites and only 51% of outplants survived at the patch reef sites post-Irma, the difference likely due to protection of the patch \nreefs from dissipation of wave energy by the reef crest (Lohr et al., 2020). Examination of Diadema antillarum sea urchins (a 3 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 100 re 1. Location of the Florida Keys Reef Tract, Hurricane Irma trackline and impact. (a) NOAA, National Weather Service WSR-\nradar image (decibels, DBZ) from south Florida on 10 September 2017 at 5:22 am Eastern Daylight Time (EDT) showing approach of\nicane Irma (inset black line = hurricane trackline). (b) Significant wave height (m) from the U.S. Geological Survey (USGS) Coupled\nan, Atmosphere, Wave, Sediment Transport (COAWST) model on 10 September 2017 at 5:00 am EDT (Warner et al., 2010, image\nit: Patricia Dalyander, USGS). (c) Satellite imagery from 30 August 2017, 11 days prior to landfall of Hurricane Irma in the Florida\ns (NASA, 2023, EOSDIS Worldview Imagery). (d) Satellite imagery from 13 September 2017, 3 days after Hurricane Irma landfall in\nFlorida Keys showing extensive resuspended sediment plume (NASA, 2023, EOSDIS Worldview Imagery). Red boxes show the location\nooe Key Reef relative to other reefs along the reef tract Figure 1. Location of the Florida Keys Reef Tract, Hurricane Irma trackline and impact. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 key reef grazer) density, size structure, and coral reef community structure before and 2.5 months after Irma at 10 locations in \n110 \nthe Middle and Upper Florida Keys showed a significant decrease in D. antillarum density with increased sedimentation, \nsuggesting sediment transport caused mortality through abrasion and burial (Kobelt et al. 2019). key reef grazer) density, size structure, and coral reef community structure before and 2.5 months after Irma at 10 locations in \n110 \nthe Middle and Upper Florida Keys showed a significant decrease in D. antillarum density with increased sedimentation, \nsuggesting sediment transport caused mortality through abrasion and burial (Kobelt et al. 2019). While observational data from several locations indicate seafloor sediments were transporte While observational data from several locations indicate seafloor sediments were transported and likely caused damage to \nbenthic habitats, the direct impact of Hurricane Irma or other tropical storms on seafloor elevation and geomorphologic \n115 \nstructures has not previously been quantified along the FRT. In this study, we used high-resolution light-detection-and-ranging \n(lidar) and multibeam bathymetry data collected before and after the passage of Hurricane Irma to quantify seafloor elevation \nand volume change of benthic habitats and geomorphological structures resulting from the storm’s impact and post-storm re-\nequilibration of seafloor sediments at more than 4-million point-locations at the Looe Key Reef system in the Lower FRT. benthic habitats, the direct impact of Hurricane Irma or other tropical storms on seafloor elevation and geomorphologic \n115 \nstructures has not previously been quantified along the FRT. In this study, we used high-resolution light-detection-and-ranging \n(lidar) and multibeam bathymetry data collected before and after the passage of Hurricane Irma to quantify seafloor elevation \nand volume change of benthic habitats and geomorphological structures resulting from the storm’s impact and post-storm re-\nequilibration of seafloor sediments at more than 4-million point-locations at the Looe Key Reef system in the Lower FRT. 2.1 Looe Key Reef Study Site The FRT is the only living coral barrier reef in the continental United States, and it spans more than 580 km along the east \ncoast of Florida from St. Lucie Inlet to the Dry Tortugas, with total reef area of approximately 1,179 km2 (Finkl and Andrews, \n2008; Jackson et al., 2014; Florida Department of Environmental Protection, 2022). Water depth along the FRT is up to approximately 20 m with discontinuous spur and groove formations and patch reefs separated by tidal passes, and it is \n125 \ncharacterized by both coral-dominated and non-coral dominated seafloor habitat as characterized and mapped by the Florida \nFish and Wildlife Conservation Commission-Fish and Wildlife Research Institute (FWC, 2015). Much of the FRT is protected \nby the FKNMS, Biscayne National Park, and Dry Tortugas National Park, and includes several sanctuary preservation areas \n(SPAs) and special use areas (SPUs) within FKNMS, including the Looe Key SPA and SPU, that together protect over 6000 approximately 20 m with discontinuous spur and groove formations and patch reefs separated by tidal passes, and it is \n125 \ncharacterized by both coral-dominated and non-coral dominated seafloor habitat as characterized and mapped by the Florida \nFish and Wildlife Conservation Commission-Fish and Wildlife Research Institute (FWC, 2015). Much of the FRT is protected \nby the FKNMS, Biscayne National Park, and Dry Tortugas National Park, and includes several sanctuary preservation areas \n(SPAs) and special use areas (SPUs) within FKNMS, including the Looe Key SPA and SPU, that together protect over 6000 marine species (Keller and Donahue, 2006). Looe Key Reef is a barrier bank reef located approximately 10 km offshore in the \n130 \nLower Florida Keys, south of Cudjoe Key, and it is characterized by a prominent, shallow reef crest with two extensive coral \nrubble fields, a fore reef with a spur-and-groove formation, a forereef terrace and deep reef zone, and a back reef area with \nseagrass communities, patch reefs, and individual coral heads (Fig. 2a-d). Seagrass beds and sandflats with intermittent patch \nreefs extend shoreward from Looe Key Reef proper to Hawk Channel, approximately 2 km to the north. Looe Key SPA, marine species (Keller and Donahue, 2006). (a) NOAA, National Weather Service WSR-\n88D radar image (decibels, DBZ) from south Florida on 10 September 2017 at 5:22 am Eastern Daylight Time (EDT) showing approach of \nHurricane Irma (inset black line = hurricane trackline). (b) Significant wave height (m) from the U.S. Geological Survey (USGS) Coupled \nOcean, Atmosphere, Wave, Sediment Transport (COAWST) model on 10 September 2017 at 5:00 am EDT (Warner et al., 2010, image \ncredit: Patricia Dalyander, USGS). (c) Satellite imagery from 30 August 2017, 11 days prior to landfall of Hurricane Irma in the Florida \nKeys (NASA, 2023, EOSDIS Worldview Imagery). (d) Satellite imagery from 13 September 2017, 3 days after Hurricane Irma landfall in \nthe Florida Keys showing extensive resuspended sediment plume (NASA, 2023, EOSDIS Worldview Imagery). Red boxes show the location \nof Looe Key Reef relative to other reefs along the reef tract. 105 4 2.1 Looe Key Reef Study Site Looe Key Reef is a barrier bank reef located approximately 10 km offshore in the \n130 \nLower Florida Keys, south of Cudjoe Key, and it is characterized by a prominent, shallow reef crest with two extensive coral \nrubble fields, a fore reef with a spur-and-groove formation, a forereef terrace and deep reef zone, and a back reef area with \nseagrass communities, patch reefs, and individual coral heads (Fig. 2a-d). Seagrass beds and sandflats with intermittent patch \nreefs extend shoreward from Looe Key Reef proper to Hawk Channel, approximately 2 km to the north. Looe Key SPA, located at approximately 24° 32' N, 81° 24' W, is just over 18 km2 and surrounds LKR proper which is less than 1.7 km2. Looe \n135 \nKey Reef contains a coral nursery and several restoration sites for coral outplants; it is one of seven FKNMS iconic reefs, and \nthe focus of a major collaborative habitat restoration effort known as Mission: Iconic reefs (NOAA Fisheries, 2022). located at approximately 24° 32' N, 81° 24' W, is just over 18 km2 and surrounds LKR proper which is less than 1.7 km2. Looe \n135 \nKey Reef contains a coral nursery and several restoration sites for coral outplants; it is one of seven FKNMS iconic reefs, and \nthe focus of a major collaborative habitat restoration effort known as Mission: Iconic reefs (NOAA Fisheries, 2022). 5\nThe northeastern eyewall of Hurricane Irma passed directly over LKR with the storm’s center passing approximately 9 km \nwest of LKR. However, the storm was approximately 684 km in diameter and covered the entire FRT and much of South \n140 \nFlorida. The National Weather Service’s technical summary of the storm reported tropical storm force winds more than 640 The northeastern eyewall of Hurricane Irma passed directly over LKR with the storm’s center passing approximately 9 km 5\nwest of LKR. However, the storm was approximately 684 km in diameter and covered the entire FRT and much of South \n140 \nFlorida. The National Weather Service’s technical summary of the storm reported tropical storm force winds more than 640 5\nwest of LKR. However, the storm was approximately 684 km in diameter and covered the entire FRT and much of South \n140 \nFlorida. The National Weather Service’s technical summary of the storm reported tropical storm force winds more than 640 5 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 km away from the storm’s center, and hurricane force winds more than 125 km from the storm’s center (NWS, 2022b). Gale \nforce winds (sustained winds above 63 km h-1) were detected by the evening of 9 September 2017 at the National Oceanic and \nAtmospheric Administration - National Ocean Service (NOAA-NOS), Tides and Currents Station at Vaca Key (number \n8723970), 35 km to the northeast of LKR; maximum sustained winds of 213 km h-1 were reported as the storm made landfall, 155 and latent gale force winds were detected after the storm passed on the evening of 10 September 2017 (NOAA-NOS, 2023). 155 \nThe average wind direction for this period was 67.01 degrees indicating winds moved from ENE toward WSW. Wind speeds \nfell sharply below gale force after the storm, shifting north eastward. Wind conditions were relatively quiescent from July \n2016 through January 2019 (except during Hurricane Irma) with wind speeds occasionally ranging up to approximately 56 km \nh-1 during winter storms (NOAA-NOS, 2023). and latent gale force winds were detected after the storm passed on the evening of 10 September 2017 (NOAA-NOS, 2023). 155 \nThe average wind direction for this period was 67.01 degrees indicating winds moved from ENE toward WSW. Wind speeds \nfell sharply below gale force after the storm, shifting north eastward. Wind conditions were relatively quiescent from July \n2016 through January 2019 (except during Hurricane Irma) with wind speeds occasionally ranging up to approximately 56 km \nh-1 during winter storms (NOAA-NOS, 2023). https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 gure 2. Looe Key Reef location, bathymetry, and seafloor habitats. (a) Location of the Florida Keys along the southern coast of Florida, \nckline of Hurricane Irma and the location of its landfall (red box). (b) Proximity of Hurricane Irma’s trackline to the Looe Key Reef study \ne (purple box), location of landfall at Cudjoe Key, and location of Vaca Key where the nearest NOAA-NOS (2023) Tides and Currents \ntion was located. (c) 2016 lidar bathymetric map of the Looe Key Reef study site showing location of Florida Keys National Marine \nnctuary Special Preservation Area (SPA), Special Use Area (SPU), and geomorphic features of focused investigation for this study. (d) \nbitat distribution at the Looe Key Study site from Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research \ntitute (2015). Figure 2. Looe Key Reef location, bathymetry, and seafloor habitats. (a) Location of the Florida Keys along the southern coast of Florida, \ntrackline of Hurricane Irma and the location of its landfall (red box). (b) Proximity of Hurricane Irma’s trackline to the Looe Key Reef study \nsite (purple box), location of landfall at Cudjoe Key, and location of Vaca Key where the nearest NOAA-NOS (2023) Tides and Currents \n145 \nstation was located. (c) 2016 lidar bathymetric map of the Looe Key Reef study site showing location of Florida Keys National Marine \nSanctuary Special Preservation Area (SPA), Special Use Area (SPU), and geomorphic features of focused investigation for this study. (d) \nHabitat distribution at the Looe Key Study site from Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research \nInstitute (2015). 150 150 6 The 2016 lidar DEM refers to data that were collected on 23 July 2016 (13.5 months before the passage of Hurricane Irma) by \nthe NOAA Office for Coastal Management, National Geodetic Survey, Topobathy Lidar Dem Block 1 dataset (Office for \nCoastal Management, 2017). The 2017 multibeam DEM refers to multibeam bathymetry data collected by the U.S. Geological \nSurvey in December 2017, and February–March 2018 at Looe Key Reef (between three and six months after the passage of \nHurricane Irma), specifically to examine impacts from the storm (Fredericks et al., 2019). The 2019 lidar DEM refers to data \n170 \ncollected January 8–31, 2019 by NOAA NGS Topobathy Lidar DEM Hurricane Irma: Miami to Marquesas Keys, FL dataset \n(National Geodetic Survey, 2022). The Florida Fish and Wildlife Conservation Commission (FWC) Unified Florida Reef Tract \n(UFRT) Map version 2.0, Level 2 habitat categories (FWC, 2015) were used to delineate geographic boundaries for 10 habitat 2.2 Elevation and Habitat Data \n160 Three Digital Elevation Models (DEMs) derived from lidar or multibeam bathymetric surveys were used for seafloor elevation- \nand volume-change analyses and are referenced in this study as 2016 lidar, 2017 multibeam, and 2019 lidar (Table 1). Table 1: Elevation datasets used in this analysis; collection dates are specific to the geographic extent of this study. Digital Elevation Model \nSource \nCollection Dates \nHorizontal Resolution/ \nVertical RMSE \n(meters/meters) \n2016 NOAA NGS Topobathy \nLidar DEM:Florida Keys \nOuter Reef Block 01 \nOffice for \nCoastal \nManagement, \n2017 \n23 July 2016 \n \n1.0/0.15 \nMultibeam bathymetry data \ncollected in December 2017, \nFebruary and March 2018 at \nLooe Key, the Florida Keys \nFredericks et al, \n2019 \nLeg 1: 12 December 2017 – 16 December 2017 \nLeg 2: 2 February 2018 – 9 February 2018 \nLeg 3: 9 March 2018 –11 March 2018 \n \n1.0/0.14 \n2018-2019 NOAA NGS \nTopobathy Lidar Hurricane \nIrma: Miami to Marquesas \nKeys, FL \nNational \nGeodetic Survey, \n2022 \n8 January 2019– 31 January 2019 \n \n1.0/0.11 \nRMSE = root mean square error. 165 165 The 2016 lidar DEM refers to data that were collected on 23 July 2016 (13.5 months before the passage of Hurricane Irma) by \nthe NOAA Office for Coastal Management, National Geodetic Survey, Topobathy Lidar Dem Block 1 dataset (Office for \nCoastal Management, 2017). The 2017 multibeam DEM refers to multibeam bathymetry data collected by the U.S. Geological \nSurvey in December 2017, and February–March 2018 at Looe Key Reef (between three and six months after the passage of \nHurricane Irma), specifically to examine impacts from the storm (Fredericks et al., 2019). The 2019 lidar DEM refers to data \n170 \ncollected January 8–31, 2019 by NOAA NGS Topobathy Lidar DEM Hurricane Irma: Miami to Marquesas Keys, FL dataset \n(National Geodetic Survey, 2022). The Florida Fish and Wildlife Conservation Commission (FWC) Unified Florida Reef Tract \n(UFRT) Map version 2.0, Level 2 habitat categories (FWC, 2015) were used to delineate geographic boundaries for 10 habitat The 2016 lidar DEM refers to data that were collected on 23 July 2016 (13.5 months before the passage of Hurricane Irma) by \nthe NOAA Office for Coastal Management, National Geodetic Survey, Topobathy Lidar Dem Block 1 dataset (Office for \nCoastal Management, 2017). The 2017 multibeam DEM refers to multibeam bathymetry data collected by the U.S. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 types within the LKR study site (Fig. 2d). Habitat labelled as ‘not classified’ was indistinguishable during mapping due to \nturbidity, cloud cover, water depth, or other interferences with obtaining an optical signature of the seafloor (Zitello et al., \n175 \n2009). turbidity, cloud cover, water depth, or other interferences with obtaining an optical signature of the seafloor (Zitello et al., \n175 \n2009). turbidity, cloud cover, water depth, or other interferences with obtaining an optical signature of the seafloor (Zitello et al., \n175 \n2009). 2.2 Elevation and Habitat Data \n160 Geological \nSurvey in December 2017, and February–March 2018 at Looe Key Reef (between three and six months after the passage of Hurricane Irma), specifically to examine impacts from the storm (Fredericks et al., 2019). The 2019 lidar DEM refers to data \n170 \ncollected January 8–31, 2019 by NOAA NGS Topobathy Lidar DEM Hurricane Irma: Miami to Marquesas Keys, FL dataset \n(National Geodetic Survey, 2022). The Florida Fish and Wildlife Conservation Commission (FWC) Unified Florida Reef Tract \n(UFRT) Map version 2.0, Level 2 habitat categories (FWC, 2015) were used to delineate geographic boundaries for 10 habitat Hurricane Irma), specifically to examine impacts from the storm (Fredericks et al., 2019). The 2019 lidar DEM refers to data \n170 \ncollected January 8–31, 2019 by NOAA NGS Topobathy Lidar DEM Hurricane Irma: Miami to Marquesas Keys, FL dataset \n(National Geodetic Survey, 2022). The Florida Fish and Wildlife Conservation Commission (FWC) Unified Florida Reef Tract \n(UFRT) Map version 2.0, Level 2 habitat categories (FWC, 2015) were used to delineate geographic boundaries for 10 habitat 7 7 2.3 Elevation- and Volume-Change Analyses A 2-m XY grid was created in Global Mapper and clipped to the same footprint. Elevation values were then extracted \nfrom each of the three DEMs at the center points of co-aligned 2-m grid boxes. Elevation change between time periods was \ncalculated for each of 4,007,961 paired elevation values (e.g., 2017 elevation – 2016 elevation, and 2019 elevation – 2017 extent of each DEM was then clipped to the areal extent of the common overlapping footprint prior to elevation change analysis \n185 \nusing the ‘Clip’ tool in ArcMap. The following steps were performed in Global Mapper 22.1 due to file size limitations in \nArcMap. A 2-m XY grid was created in Global Mapper and clipped to the same footprint. Elevation values were then extracted \nfrom each of the three DEMs at the center points of co-aligned 2-m grid boxes. Elevation change between time periods was \ncalculated for each of 4,007,961 paired elevation values (e.g., 2017 elevation – 2016 elevation, and 2019 elevation – 2017 elevation). Elevation-change (XYZ) point maps were generated as shapefiles for each time-period of change for the total study \n190 \nsite; positive values indicate an increase in elevation and negative values indicate a decrease in elevation. Data are available \nfrom Fehr et al. (2021). Vertical uncertainty of elevation change analyses were estimated using methods of Yates et al. 2017 \nand the reported vertical accuracy of the lidar and multibeam data sets (typically reported as the 95% root-mean-square error, \nRMSE, Table 1) to calculate a composite RMSE (RMSETotal) for each elevation change analysis (Fig. 3b). The RMSE of lidar and multibeam data sets used for elevation-change analyses in our study ranged from 0.11 to 0.15 m (Table 1). These values \n195 \nare consistent with RMSEs determined in performance evaluations of lidar sensors that ranged from 0.08 to 0.52 m (Fernandez-\nDiaz et al., 2014; Legleiter et al., 2016; Kinzel et al., 2013; Tonina et al., 2019; Yoshida et al., 2022). Composite RMSE values \nfor elevation-change analyses based on comparison of lidar to multibeam DEMs ranged from 0.19 to 0.21m in our study. These \nvalues are consistent with RMSEs determined in performance evaluations of lidar sensors against multibeam echosounders and multibeam data sets used for elevation-change analyses in our study ranged from 0.11 to 0.15 m (Table 1). 2.3 Elevation- and Volume-Change Analyses The RMSE of lidar \nand multibeam data sets used for elevation-change analyses in our study ranged from 0.11 to 0.15 m (Table 1). These values \n195 \nare consistent with RMSEs determined in performance evaluations of lidar sensors that ranged from 0.08 to 0.52 m (Fernandez-\nDiaz et al., 2014; Legleiter et al., 2016; Kinzel et al., 2013; Tonina et al., 2019; Yoshida et al., 2022). Composite RMSE values \nfor elevation-change analyses based on comparison of lidar to multibeam DEMs ranged from 0.19 to 0.21m in our study. These \nvalues are consistent with RMSEs determined in performance evaluations of lidar sensors against multibeam echosounders \nthat ranged from 0.02 to 0.23 m (Awadallah et al., 2023). The FWC UFRT habitat map was clipped to the intersect footprint \n200 \nfor each elevation-change analysis using ArcMap 10.7. Each total-study-site elevation-change data set was then clipped to \nindividual habitat polygons to create individual elevation-change shapefiles for each habitat type. of the three 1-m resolution digital elevation models (DEMs) in ArcMap 10.7 and were used to create a common footprint \n180 \npolygon shapefile for the total LKR study site encompassing the overlapping area among the three datasets. The original (full \nareal extent, or unclipped) 2016–2017 elevation-change data set was 19.71 km2 and included 4,934,364 data points. The \noverlapping areal extent for the 2016, 2017, and 2019 DEMs was 15.98 km2 and excluded areas where water depths were too \nshallow for boat access to collect multibeam data in 2017 and areas of coarse interpolation within the 2017 DEM. The areal of the three 1-m resolution digital elevation models (DEMs) in ArcMap 10.7 and were used to create a common footprint \n180 \npolygon shapefile for the total LKR study site encompassing the overlapping area among the three datasets. The original (full \nareal extent, or unclipped) 2016–2017 elevation-change data set was 19.71 km2 and included 4,934,364 data points. The \noverlapping areal extent for the 2016, 2017, and 2019 DEMs was 15.98 km2 and excluded areas where water depths were too \nshallow for boat access to collect multibeam data in 2017 and areas of coarse interpolation within the 2017 DEM. The areal 180 extent of each DEM was then clipped to the areal extent of the common overlapping footprint prior to elevation change analysis \n185 \nusing the ‘Clip’ tool in ArcMap. The following steps were performed in Global Mapper 22.1 due to file size limitations in \nArcMap. 2.3 Elevation- and Volume-Change Analyses Seafloor elevation- and volume-change analyses were conducted using the methods of Yates et al. techniques of Murphy et al. (2022) (Fig. 3a). Briefly, individual geographic footprint areas (poly Seafloor elevation- and volume-change analyses were conducted using the methods of Yates et al. (2017) and 2-m grid spacing \ntechniques of Murphy et al. (2022) (Fig. 3a). Briefly, individual geographic footprint areas (polygons) were created for each Seafloor elevation- and volume-change analyses were conducted using the methods of Yates et al. (2017) and 2-m grid spacing \ntechniques of Murphy et al. (2022) (Fig. 3a). Briefly, individual geographic footprint areas (polygons) were created for each \nof the three 1-m resolution digital elevation models (DEMs) in ArcMap 10.7 and were used to create a common footprint \n180 \npolygon shapefile for the total LKR study site encompassing the overlapping area among the three datasets. The original (full \nareal extent, or unclipped) 2016–2017 elevation-change data set was 19.71 km2 and included 4,934,364 data points. The \noverlapping areal extent for the 2016, 2017, and 2019 DEMs was 15.98 km2 and excluded areas where water depths were too \nshallow for boat access to collect multibeam data in 2017 and areas of coarse interpolation within the 2017 DEM. The areal \nextent of each DEM was then clipped to the areal extent of the common overlapping footprint prior to elevation change analysis \n185 \nusing the ‘Clip’ tool in ArcMap. The following steps were performed in Global Mapper 22.1 due to file size limitations in \nArcMap. A 2-m XY grid was created in Global Mapper and clipped to the same footprint. Elevation values were then extracted \nfrom each of the three DEMs at the center points of co-aligned 2-m grid boxes. Elevation change between time periods was \ncalculated for each of 4,007,961 paired elevation values (e.g., 2017 elevation – 2016 elevation, and 2019 elevation – 2017 \nelevation). Elevation-change (XYZ) point maps were generated as shapefiles for each time-period of change for the total study \n190 \nsite; positive values indicate an increase in elevation and negative values indicate a decrease in elevation. Data are available \nfrom Fehr et al. (2021). Vertical uncertainty of elevation change analyses were estimated using methods of Yates et al. 2017 \nand the reported vertical accuracy of the lidar and multibeam data sets (typically reported as the 95% root-mean-square error, \nRMSE, Table 1) to calculate a composite RMSE (RMSETotal) for each elevation change analysis (Fig. 3b). 2.3 Elevation- and Volume-Change Analyses These values \n195 \nare consistent with RMSEs determined in performance evaluations of lidar sensors that ranged from 0.08 to 0.52 m (Fernandez-\nDiaz et al., 2014; Legleiter et al., 2016; Kinzel et al., 2013; Tonina et al., 2019; Yoshida et al., 2022). Composite RMSE values \nfor elevation-change analyses based on comparison of lidar to multibeam DEMs ranged from 0.19 to 0.21m in our study. These \nvalues are consistent with RMSEs determined in performance evaluations of lidar sensors against multibeam echosounders that ranged from 0.02 to 0.23 m (Awadallah et al., 2023). The FWC UFRT habitat map was clipped to the intersect footprint \n200 \nfor each elevation-change analysis using ArcMap 10.7. Each total-study-site elevation-change data set was then clipped to \nindividual habitat polygons to create individual elevation-change shapefiles for each habitat type. that ranged from 0.02 to 0.23 m (Awadallah et al., 2023). The FWC UFRT habitat map was clipped to the intersect footprint \n200 \nfor each elevation-change analysis using ArcMap 10.7. Each total-study-site elevation-change data set was then clipped to \nindividual habitat polygons to create individual elevation-change shapefiles for each habitat type. Elevation-change data from each time-period were then used to generate TIN (Triangulated Irregular Network) surface mode Elevation-change data from each time-period were then used to generate TIN (Triangulated Irregular Network) surface models \nin ArcMap for calculation of volume change. TIN models were clipped to the original overall study site intersect footprint to \n205 \nremove interpolation across areas where no data were collected. Lower bound (conservative) volume-change was calculated Elevation-change data from each time-period were then used to generate TIN (Triangulated Irregular Network) surface models in ArcMap for calculation of volume change. TIN models were clipped to the original overall study site intersect footprint to \n205 \nremove interpolation across areas where no data were collected. Lower bound (conservative) volume-change was calculated in ArcMap for calculation of volume change. TIN models were clipped to the original overall study site intersect footprint to \n205 \nremove interpolation across areas where no data were collected. Lower bound (conservative) volume-change was calculated 8 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 𝑅𝑀𝑆𝐸𝑡𝑜𝑡𝑎𝑙= √(𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 1\n2\n+ 𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 2\n2\n) 𝑅𝑀𝑆𝐸𝑡𝑜𝑡𝑎𝑙= √(𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 1\n2\n+ 𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 2\n2\n) \nation and volume change methods. 2.3 Elevation- and Volume-Change Analyses (a) Flowchart outlining generalized geoprocessing steps in ArcMap and Global \nh 3), and in the Seafloor Elevation Change Analysis Tool (SECAT, step 4) for seafloor elevation and volume change \ns et al. (2017), Murphy et al. (2022), and Zieg and Zawada (2021). (b) Composite RMSE (RMSETotal) for each \nis (2016 to 2017 and 2017 to 2019) calculated using reported RMSE for lidar and multibeam source data and methods \nBlack boxes indicate source data files. Blue boxes indicate steps using geoprocessing tools from ArcMap or Global \ndicate data analysis conducted using SECAT. 𝑅𝑀𝑆𝐸𝑡𝑜𝑡𝑎𝑙= √(𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 1\n2\n+ 𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 2\n2\n) \nand volume change methods. (a) Flowchart outlining generalized geoprocessin\nnd in the Seafloor Elevation Change Analysis Tool (SECAT, step 4) for seafloor\nl. (2017), Murphy et al. (2022), and Zieg and Zawada (2021). (b) Composite\n6 to 2017 and 2017 to 2019) calculated using reported RMSE for lidar and multib\nboxes indicate source data files. Blue boxes indicate steps using geoprocessing \ndata analysis conducted using SECAT. 𝑅𝑀𝑆𝐸𝑡𝑜𝑡𝑎𝑙= √(𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 1\n2\n+ 𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 2\n2\n) 𝑅𝑀𝑆𝐸𝑡𝑜𝑡𝑎𝑙= √(𝑅𝑀𝑆𝐸𝑆𝑜𝑢\n2 𝑅𝑀𝑆𝐸𝑡𝑜𝑡𝑎𝑙= √(𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 1\n2\n+ 𝑅𝑀𝑆𝐸𝑆𝑜𝑢𝑟𝑐𝑒 𝐷𝑎𝑡𝑎𝑠𝑒𝑡 2\n2\n) Figure 3. Seafloor elevation and volume change methods. (a) Flowchart outlining generalized geop\n0 Figure 3. Seafloor elevation and volume change methods. (a) Flowchart outlining generalized geoprocessing steps in ArcMap and Global \nMapper (steps 1 through 3), and in the Seafloor Elevation Change Analysis Tool (SECAT, step 4) for seafloor elevation and volume change \n210 \nanalyses based on Yates et al. (2017), Murphy et al. (2022), and Zieg and Zawada (2021). (b) Composite RMSE (RMSETotal) for each \nelevation change analysis (2016 to 2017 and 2017 to 2019) calculated using reported RMSE for lidar and multibeam source data and methods \nof Yates et al. (2017). Black boxes indicate source data files. Blue boxes indicate steps using geoprocessing tools from ArcMap or Global \nMapper. Green boxes indicate data analysis conducted using SECAT. Figure 3. Seafloor elevation and volume change methods. (a) Flowchart outlining generalized geoprocessing steps in ArcMap and Global \nMapper (steps 1 through 3), and in the Seafloor Elevation Change Analysis Tool (SECAT, step 4) for seafloor elevation and volume change \n210 \nanalyses based on Yates et al. (2017), Murphy et al. (2022), and Zieg and Zawada (2021). 2.4 Geomorphic Feature Analyses Sub-areas or geomorphic features of high-magnitude elevation change (greater than approximately ±0.5 m) were delineated \non each total-study-site elevation-change point map by manually drawing polygons in ArcMap 10.7 and creating elevation-\nchange shapefiles for each sub-area. Each sub-area was clipped to individual habitat polygons to create individual shapefiles \n230 \nfor each habitat type within a given sub-area. Elevation- volume-change statistics were computed for each geomorphic feature \nof interest, and each habitat within sub-areas of interest using SECAT and methods described in section 2.3. Sub-areas or geomorphic features of high-magnitude elevation change (greater than approximately ±0.5 m) were delineated \non each total-study-site elevation-change point map by manually drawing polygons in ArcMap 10.7 and creating elevation-\nchange shapefiles for each sub-area. Each sub-area was clipped to individual habitat polygons to create individual shapefiles \n230 \nfor each habitat type within a given sub area Elevation volume change statistics were computed for each geomorphic feature Sub-areas or geomorphic features of high-magnitude elevation change (greater than approximately ±0.5 m) were delineated \non each total-study-site elevation-change point map by manually drawing polygons in ArcMap 10.7 and creating elevation- on each total study site elevation change point map by manually drawing polygons in ArcMap 10.7 and creating elevation\nchange shapefiles for each sub-area. Each sub-area was clipped to individual habitat polygons to create individual shapefiles \n230 \nfor each habitat type within a given sub-area. Elevation- volume-change statistics were computed for each geomorphic feature \nof interest, and each habitat within sub-areas of interest using SECAT and methods described in section 2.3. We examined elevation and elevation-change along four 200 to 300 m transects across exam We examined elevation and elevation-change along four 200 to 300 m transects across examples of high-elevation change \ngeomorphic features. Elevation profiles for 2016, 2017, and 2019 were created for each feature of interest by extracting \n235 \nelevation values from each DEM along transect lines across the areas of greatest elevation change for each feature using \nArcMap. Points were selected using the Select Feature by Line tool in ArcMap, and the selected features were then exported \nas a new shapefile. Positions and types of geomorphic features of interest were verified through in-situ observation by SCUBA \ndivers using methods of Fehr and Yates (2020) at 30 diver reconnaissance sites throughout the total study site. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 based on areal volume above and below surface plane heights corresponding to plus and minus the RMSETotal of the elevation \nchange analysis (RMSETotal = 21 cm for 2016 to 2017, and 19 cm for 2017 to 2019 change analyses). Upper bound volume \nchanges were calculated based on area volume above and below a plane height of 0 m. The attribute values stored within the \nelevation-change and TIN surface shapefiles were then used to compute elevation and volume change statistics for the total LKR study site and each habitat type using the Seafloor Elevation Change Analysis Tool (SECAT) custom Python script of \n220 \nZieg and Zawada (2021). Pearson correlation and linear regression analyses were performed using Microsoft Excel Version \n2302 (build 16.0.16130.20690) to assess relationships between mean habitat water depth, elevation change, and area-\nnormalized volume change for each habitat type including: 1) 2016 mean water depth (estimated from mean elevation) and \nmean elevation change; 2) 2016 mean water depth and area-normalized volume change; 3) 2017 mean water depth and mean LKR study site and each habitat type using the Seafloor Elevation Change Analysis Tool (SECAT) custom Python script of \n220 \nZieg and Zawada (2021). Pearson correlation and linear regression analyses were performed using Microsoft Excel Version \n2302 (build 16.0.16130.20690) to assess relationships between mean habitat water depth, elevation change, and area-\nnormalized volume change for each habitat type including: 1) 2016 mean water depth (estimated from mean elevation) and \nmean elevation change; 2) 2016 mean water depth and area-normalized volume change; 3) 2017 mean water depth and mean elevation change; 4) 2017 mean water depth and area-normalized volume change; and 5) 2017 to 2019 mean elevation change \n225 \nand 2016 to 2017 mean elevation. 2.3 Elevation- and Volume-Change Analyses (b) Composite RMSE (RMSETotal) for each \nelevation change analysis (2016 to 2017 and 2017 to 2019) calculated using reported RMSE for lidar and multibeam source data and methods \nof Yates et al. (2017). Black boxes indicate source data files. Blue boxes indicate steps using geoprocessing tools from ArcMap or Global \nMapper. Green boxes indicate data analysis conducted using SECAT. 215 9 3.1 Elevation and Volume Change Analyses Elevation-change results for 4,007,961 point-locations at LKR between 2016–2017 (approximately 13.5 months before and 3 \nto 6 months after Hurricane Irma) and between 2017–2019 (from approximately 3 to 16.5 months after Hurricane Irma) are \nshown in Fig. 4a and b, respectively. Mean elevation-change for the total LKR study site from 2016–2017 was 0.34 m ± 0.21; \nand all ten habitat types (Fig. 4c) showed increases in mean elevation (accretion) ranging from 0.20 m to 0.54 m (Table 2). 245 \nLargest mean elevation changes were associated with ‘aggregate reef’ (mean 2016 elevation -13.41 m) and ‘not classified’ 10 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Table 2. Elevation change data by habitat type associated with each period and geomorphic feature subarea. Habitat type \nTotal points \nArea Mean elevation (m) \nMean elevation change (m) (SD) \n(no.) \n(km2) \n2016 \n2017 \n2019 \n2016 to 2017 2017 to 2019 \nOverall Looe Key Study Site \n Total Study Site \n4007961 \n15.98 \n-8.87 \n-8.53 \n-8.69 \n0.34 (0.21) \n-0.15 (0.11) \n Aggregate Reef \n76647 \n0.30 \n-13.41 -12.91 \n-13.16 \n0.51 (0.20) \n-0.25 (0.20) \n Colonized Pavement \n750 \n0.0028 \n-10.65 -10.33 \n-10.44 \n0.32 (0.12) \n-0.11 (0.08) \n Individual or Aggregate Patch Reef \n54414 \n0.22 \n-8.66 \n-8.33 \n-8.51 \n0.34 (0.15) \n-0.19 (0.10) \n Not Classified \n6932 \n0.026 \n-15.84 -15.30 \n-15.55 \n0.54 (0.25) \n-0.25 (0.17) \n Pavement \n645001 \n2.57 \n-10.00 \n-9.62 \n-9.79 \n0.37 (0.16) \n-0.16 (0.11) \n Reef Rubble \n80987 \n0.32 \n-6.19 \n-5.99 \n-6.17 \n0.20 (0.36) \n-0.18 (0.12) \n Seagrass Continuous \n402458 \n1.60 \n-7.69 \n-7.42 \n-7.54 \n0.27 (0.18) \n-0.12 (0.09) \n Seagrass Discontinuous \n1067504 \n4.26 \n-7.24 \n-6.96 \n-7.10 \n0.28 (0.21) \n-0.14 (0.10) \n Spur and Groove \n184875 \n0.74 \n-9.82 \n-9.45 \n-9.65 \n0.37 (0.25) \n-0.19 (0.19) \n Unconsolidated Sediment \n1488416 \n5.94 \n-9.63 \n-9.26 \n-9.42 \n0.37 (0.21) \n-0.16 (0.09) \nSand Wave \n Total Accretion Area \n15336 \n0.060 \n-6.32 \n-5.53 \n-5.68 \n0.79 (0.45) \n-0.15 (0.12) \n Seagrass Discontinuous \n7345 \n0.029 \n-5.98 \n-5.08 \n-5.23 \n0.90 (0.49) \n-0.15 (0.13) \n Unconsolidated Sediment \n7991 \n0.031 \n-6.63 \n-5.95 \n-6.09 \n0.68 (0.37) \n-0.14 (0.10) \n Total Erosion Area \n11265 \n0.043 \n-5.40 \n-5.75 \n-5.90 \n-0.36 (0.28) \n-0.15 (0.06) \n Seagrass Discontinuous \n580 \n0.002 \n-5.72 \n-5.87 \n-6.02 \n-0.15 (0.15) \n-0.15 (0.08) \n Unconsolidated Sediment \n10685 \n0.041 \n-5.38 \n-5.74 \n-5.90 \n-0.37 (0.29) \n-0.15 (0.05) \nScour Marks \n Scour Mark 1 \n202 \n0.00071 \n-7.03 \n-7.51 \n-7.41 \n-0.49 (0.26) \n0.10 (0.12) \n Seagrass Discontinuous \n197 \n0.00071 \n-7.02 \n-7.51 \n-7.41 \n-0.49 (0.26) \n0.11 (0.12) \n Unconsolidated Sediment \n5 <0.00001 \n-7.34 \n-7.47 \n-7.52 \n-0.12 (0.03) \n-0.05 (0.02) \n Scour Mark 2 \n388 \n0.0014 \n-5.41 \n-5.91 \n-5.71 \n-0.50 (0.27) \n0.20 (0.20) \n Seagrass Continuous \n338 \n0.00124 \n-5.41 \n-5.94 \n-5.70 \n-0.53 (0.27) \n0.24 (0.18) \n Unconsolidated Sediment \n50 \n0.00016 \n-5.42 \n-5.67 \n-5.72 \n-0.26 (0.16) \n-0.05 (0.13) \n Scour Mark 3 \n518 \n0.00188 \n-5.64 \n-6.14 \n-6.02 \n-0.50 (0.29) \n0.12 (0.19) \n Seagrass Continuous \n518 \n0.00188 \n-5.64 \n-6.14 \n-6.02 \n-0.50 (0.29) \n0.12 (0.19) \n Scour Mark 4 \n417 \n0.00152 \n-5.20 \n-5.74 \n-5.63 \n-0.54 (0.28) \n0.12 (0.21) \n Seagrass Continuous \n411 \n0.00151 \n-5.19 \n-5.74 \n-5.62 \n-0.55 (0.27) \n0.12 (0.21) \n Unconsolidated Sediment \n6 \n0.00001 \n-5.69 \n-5.74 \n-5.84 \n-0.06 (0.05) \n-0.10 (0.05) \nReef Rubble Field \n Total Accretion Area \n7216 \n0.028 \n-4.22 \n-3.32 \n-3.57 \n0.89 (0.45) \n-0.24 (0.30) \n Reef Rubble \n3102 \n0.012 \n-3.71 \n-2.84 \n-3.05 \n0.87 (0.44) \n-0.21 (0.36) \n Seagrass Discontinuous \n3489 \n0.014 \n-4.66 \n-3.67 \n-3.97 \n0.99 (0.42) \n-0.30 (0.24) \n Unconsolidated Sediment \n628 \n0.00237 \n-4.25 \n-3.82 \n-3.91 \n0.43 (0.26) \n-0.10 (0.12) \n Total Erosion Area \n6043 \n0.023 \n-3.00 \n-3.64 \n-3.74 \n-0.63 (0.48) \n-0.10 (0.19) \n Reef Rubble \n3409 \n0.013 \n-2.61 \n-3.39 \n-3.44 \n-0.77 (0.50) \n-0.06 (0.20) \n Seagrass Discontinuous \n1941 \n0.00708 \n-3.51 \n-4.05 \n-4.22 \n-0.54 (0.43) \n-0.17 (0.15) \n Unconsolidated Sediment \n694 \n0.00248 \n-3.50 \n-3.70 \n-3.82 \n-0.20 (0.15) \n-0.12 (0.13) \nSand Lobe \n Total Area \n67389 \n0.266 \n-12.41 -11.90 \n-12.10 \n0.51 (0.29) -0.20 (0.09) \n Unconsolidated Sediment \n67389 \n0.266 \n-12.41 -11.90 \n-12.10 \n0.51 (0.29) -0.20 (0.09) \n *58 data points fell on borders between habitats and were counted twice during habitat analysis. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Figure 4. Elevation-change results for 4,007,961 point-locations at Looe Key Reef. Elevation change between (a) 2016 \nmonths before and 3 to 6 months after Hurricane Irma) and (b) between 2017 to 2019 (from approximately 3 to 16.5 months af\nrma), and (c) corresponding seafloor habitats (FWC, 2015). The Hurricane Irma best track data in the panel b’s inset is fro\nNHC Irma Storm Track resource page (NHC, 2018, see also Figure 2b). Boundaries for the Looe Key Sanctuary Protection Ar\npecial Protection Unit (SPU) are shown as pink polygons. Geomorphic features of interest are indicated with black polygons\nreas indicate locations where water depth was too shallow for collection of multibeam bathymetric data. Figure 4. Elevation-change results for 4,007,961 point-locations at Looe Key Reef. Elevation change between (a) 2016 to 2017 (13.5 \nmonths before and 3 to 6 months after Hurricane Irma) and (b) between 2017 to 2019 (from approximately 3 to 16.5 months after Hurricane \nIrma), and (c) corresponding seafloor habitats (FWC, 2015). The Hurricane Irma best track data in the panel b’s inset is from the NOAA \n250 \nNHC Irma Storm Track resource page (NHC, 2018, see also Figure 2b). Boundaries for the Looe Key Sanctuary Protection Area (SPA) and \nSpecial Protection Unit (SPU) are shown as pink polygons. Geomorphic features of interest are indicated with black polygons. Gaps in map \nareas indicate locations where water depth was too shallow for collection of multibeam bathymetric data. 255 11 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 habitat types showed increases in net volume (accretion) with upper bound ranges from 0.001 to 2.19 Mm3 (Table 3). Largest \nnet volume changes were associated with habitats covering the largest areal extent of the study area including ‘pavement,’ \n‘discontinuous seagrass,’ and ‘unconsolidated sediment.’ Pearson correlation analysis also indicated a very strong positive \ncorrelation (r(8) = 0.99, p = 0.000) and linear relationship (r2 = 0.92, Fig. 5b)) between 2016 mean habitat water depth and habitat types showed increases in net volume (accretion) with upper bound ranges from 0.001 to 2.19 Mm3 (Table 3). Largest \nnet volume changes were associated with habitats covering the largest areal extent of the study area including ‘pavement,’ \n‘discontinuous seagrass,’ and ‘unconsolidated sediment.’ Pearson correlation analysis also indicated a very strong positive \ncorrelation (r(8) = 0.99, p = 0.000) and linear relationship (r2 = 0.92, Fig. 5b)) between 2016 mean habitat water depth and \narea-normalized volume change; area-normalized volume gains increased significantly with increasing water depth. Largest \n270 \narea-normalized volume changes of 0.51 mM3 and 0.54 mM3 were observed for ‘aggregate reef’ and ‘not classified’ habitats, \nrespectively; and smallest changes of 0.20 to 0.27 mM3 were observed for ‘reef rubble’ and ‘continuous seagrass’ habitats \n(Table 3), consistent with mean elevation changes for those habitats. Mean elevation-change values of the 2016–2017 elevation \nchange data set that was clipped to an area of 15.98 km2 and used for this analysis were within ±0.01 m, and area-normalized \nvolumes were within ±0.016 Mm3 km-2, of values calculated in the original 19.71 km2 published data set (unclipped) for the \n275 \noverall study site and all habitats (Yates et al., 2019). area-normalized volume change; area-normalized volume gains increased significantly with increasing water depth. Largest \n270 \narea-normalized volume changes of 0.51 mM3 and 0.54 mM3 were observed for ‘aggregate reef’ and ‘not classified’ habitats, \nrespectively; and smallest changes of 0.20 to 0.27 mM3 were observed for ‘reef rubble’ and ‘continuous seagrass’ habitats \n(Table 3), consistent with mean elevation changes for those habitats. SD = standard deviation. Elevation change data by habitat type associated with each period and geomorphic feature subarea. elevation -6.19 m) and ‘seagrass continuous’ (mean 2016 elevation -7.69 m) habitats (Table 2). Only 4% of all data points \n260 \nshowed losses in elevation (erosion) ranging from -0.01 m to -0.44 m, while 96% of all data points showed gains in elevation \nranging from 0.31 m to 0.55 m across all habitats. Pearson correlation analysis showed a very strong positive correlation (r(8) \n= 0.96, p = 0.000) and linear relationship (r2 = 0.92, Fig. 5a) between 2016 mean habitat water depth (estimated from mean \nelevation) and mean elevation change; mean elevation gains increased significantly with increasing water depth (i.e., \ndecreasing seafloor elevation). Net volume change was up to 5.36 mM3 over the total 15.98 km2 Looe Key study site; and all \n265 260 12 5c) between estimated to -0.25 m (Fig. 4b, Table 2). Largest mean elevation changes were associated with ‘aggregate reef’ and ‘not classified’ habitat \n280 \ntypes, and smallest changes were associated with ‘colonized pavement’ and ‘continuous seagrass’ habitats (Table 2). Only 5% \nof all data points showed gains in elevation with mean accretion ranging from 0.04 m to 0.19 m, while 95% of all data points \nshowed losses in elevation with mean erosion ranging from -0.13 m to -0.27 m across all habitat types. Pearson correlation \nanalysis indicated a moderate correlation (r(8) = -0.67, p = 0.035) and linear relationship (r2 = 0.45, Fig. 5c) between estimated 2017 mean habitat water depth and mean elevation change; mean elevation loss generally increased with increasing water \n285 \ndepth. Net volume change was up to -2.46 mM3 over the total 15.98 km2 Looe Key study site and area-normalized volume \nchange was -0.15 Mm3km-2. Losses in net volume up to -0.931 Mm3 (erosion) were observed across all habitat types (Table \n4). 2017 mean habitat water depth and mean elevation change; mean elevation loss generally increased with increasing water \n285 \ndepth. Net volume change was up to -2.46 mM3 over the total 15.98 km2 Looe Key study site and area-normalized volume \nchange was -0.15 Mm3km-2. Losses in net volume up to -0.931 Mm3 (erosion) were observed across all habitat types (Table \n4). Largest net volume changes were associated with habitats covering the largest areal extent of the study area including \n290 \n‘pavement,’ ‘discontinuous seagrass,’ and ‘unconsolidated sediment.’ Pearson correlation analysis indicated a moderate \ncorrelation (r(8) = -0.67, p = 0.035) and linear relationship (r2 = 0.45, Fig. 5d) between 2017 mean habitat water depth and \narea-normalized volume change; area-normalized volume losses generally increased with increasing water depth. Largest area-\nnormalized volume changes were observed for ‘aggregate reef’ and ‘not classified’ habitats, -0.254 and -0.247 Mm3 km-2, Largest net volume changes were associated with habitats covering the largest areal extent of the study area including \n290 \n‘pavement,’ ‘discontinuous seagrass,’ and ‘unconsolidated sediment.’ Pearson correlation analysis indicated a moderate \ncorrelation (r(8) = -0.67, p = 0.035) and linear relationship (r2 = 0.45, Fig. 5d) between 2017 mean habitat water depth and \narea-normalized volume change; area-normalized volume losses generally increased with increasing water depth. Mean elevation-change values of the 2016–2017 elevation \nchange data set that was clipped to an area of 15.98 km2 and used for this analysis were within ±0.01 m, and area-normalized area-normalized volume change; area-normalized volume gains increased significantly with increasing water depth. Largest \n270 \narea-normalized volume changes of 0.51 mM3 and 0.54 mM3 were observed for ‘aggregate reef’ and ‘not classified’ habitats, \nrespectively; and smallest changes of 0.20 to 0.27 mM3 were observed for ‘reef rubble’ and ‘continuous seagrass’ habitats \n(Table 3), consistent with mean elevation changes for those habitats. Mean elevation-change values of the 2016–2017 elevation \nchange data set that was clipped to an area of 15.98 km2 and used for this analysis were within ±0.01 m, and area-normalized \nvolumes were within ±0.016 Mm3 km-2, of values calculated in the original 19.71 km2 published data set (unclipped) for the \n275 \noverall study site and all habitats (Yates et al 2019) Mean elevation-change during a 13-month time-period between December 2017 to June 2019 (up to approximately 16.5 \nmonths after Hurricane Irma) was -0.15 ± 0.11 m, and all habitat types showed losses in mean elevation ranging from -0.11 m )\n,\nyp\ng g\nto -0.25 m (Fig. 4b, Table 2). Largest mean elevation changes were associated with ‘aggregate reef’ and ‘not classified’ habitat \n280 \ntypes, and smallest changes were associated with ‘colonized pavement’ and ‘continuous seagrass’ habitats (Table 2). Only 5% \nof all data points showed gains in elevation with mean accretion ranging from 0.04 m to 0.19 m, while 95% of all data points \nshowed losses in elevation with mean erosion ranging from -0.13 m to -0.27 m across all habitat types. Pearson correlation \nanalysis indicated a moderate correlation (r(8) = -0.67, p = 0.035) and linear relationship (r2 = 0.45, Fig. 5c) between estimated to -0.25 m (Fig. 4b, Table 2). Largest mean elevation changes were associated with ‘aggregate reef’ and ‘not classified’ habitat \n280 \ntypes, and smallest changes were associated with ‘colonized pavement’ and ‘continuous seagrass’ habitats (Table 2). Only 5% \nof all data points showed gains in elevation with mean accretion ranging from 0.04 m to 0.19 m, while 95% of all data points \nshowed losses in elevation with mean erosion ranging from -0.13 m to -0.27 m across all habitat types. Pearson correlation \nanalysis indicated a moderate correlation (r(8) = -0.67, p = 0.035) and linear relationship (r2 = 0.45, Fig. Largest area-\nnormalized volume changes were observed for ‘aggregate reef’ and ‘not classified’ habitats, -0.254 and -0.247 Mm3 km-2, Largest net volume changes were associated with habitats covering the largest areal extent of the study area including \n290 \n‘pavement,’ ‘discontinuous seagrass,’ and ‘unconsolidated sediment.’ Pearson correlation analysis indicated a moderate \ncorrelation (r(8) = -0.67, p = 0.035) and linear relationship (r2 = 0.45, Fig. 5d) between 2017 mean habitat water depth and \narea-normalized volume change; area-normalized volume losses generally increased with increasing water depth. Largest area-\nnormalized volume changes were observed for ‘aggregate reef’ and ‘not classified’ habitats, -0.254 and -0.247 Mm3 km-2, respectively; smallest changes were observed for ‘colonized pavement’ and ‘continuous seagrass’ habitats, -0.112 to -0.118 \n295 \nMm3 km-2 respectively (Table 4), consistent with mean elevation changes for those habitats. Pearson correlation analysis \nindicated a strong negative correlation (r(8) = -0.74, p = 0.014) and linear relationship (r2 = 0.55, Fig. 5e) between 2017 to \n2019 mean habitat elevation change and 2016 to 2017 mean habitat elevation change; mean elevation losses during 2017 to \n2019 were significantly greater in habitats with larger mean elevation gains during 2016 to 2017. Mean elevation change (loss) respectively; smallest changes were observed for ‘colonized pavement’ and ‘continuous seagrass’ habitats, -0.112 to -0.118 \n295 \nMm3 km-2 respectively (Table 4), consistent with mean elevation changes for those habitats. Pearson correlation analysis \nindicated a strong negative correlation (r(8) = -0.74, p = 0.014) and linear relationship (r2 = 0.55, Fig. 5e) between 2017 to \n2019 mean habitat elevation change and 2016 to 2017 mean habitat elevation change; mean elevation losses during 2017 to \n2019 were significantly greater in habitats with larger mean elevation gains during 2016 to 2017. Mean elevation change (loss) 13 during 2017 to 2019 was 35 to 55% of the mean elevation change (gain) during 2016 to 2017 for all habitats except for reef \n00 \nrubble which was 92% and had the shallowest mean depth (6.0 m) of all habitats. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Table 3. Compiled volume change data by habitat type for all study areas during the 2016 to 2017 study period (storm period). Habitat Type \nHabitat Area \n(km2) \nGross erosion \n(Mm3) \nGross accretion \n(Mm3) \nNet volume change \n(Mm3 study-area-1) \nArea-normalized volume \nchange (Mm3 km-2) \nLower \nUpper \nLower Upper \n Lower \nUpper \nLower \nUpper \nOverall Looe Key Study Site \n Total Study Site \n15.98 \n0.053 \n0.134 \n2.456 \n5.490 \n2.403 \n5.356 \n0.150 \n0.335 \n Aggregate Reef \n0.30 \n0 \n0 \n0.090 \n0.154 \n0.090 \n0.154 \n0.296 \n0.505 \n Colonized Pavement \n0.0028 \n0 \n0 \n0 \n0.001 \n0 \n0.001 \n0.124 \n0.323 \n Individual or Aggregate Patch Reef \n0.22 \n0 \n0 \n0.029 \n0.073 \n0.029 \n0.072 \n0.136 \n0.336 \n Not Classified \n0.026 \n0 \n0 \n0.009 \n0.014 \n0.009 \n0.014 \n0.337 \n0.540 \n Pavement \n2.57 \n0 \n0.003 \n0.445 \n0.962 \n0.445 \n0.959 \n0.173 \n0.373 \n Reef Rubble \n0.32 \n0.013 \n0.021 \n0.033 \n0.085 \n0.020 \n0.064 \n0.062 \n0.199 \n Seagrass Continuous \n1.60 \n0.008 \n0.021 \n0.152 \n0.449 \n0.144 \n0.428 \n0.090 \n0.267 \n Seagrass Discontinuous \n4.26 \n0.015 \n0.045 \n0.477 \n1.250 \n0.462 \n1.205 \n0.109 \n0.283 \n Spur and Groove \n0.74 \n0.003 \n0.007 \n0.136 \n0.278 \n0.133 \n0.271 \n0.181 \n0.367 \n Unconsolidated Sediment \n5.94 \n0.013 \n0.037 \n1.083 \n2.226 \n1.070 \n2.189 \n0.180 \n0.369 \nSand Wave \n Total Accretion Area \n0.060 \n0 \n0 \n0.036 \n0.048 \n0.036 \n0.048 \n0.598 \n0.800 \n Seagrass Discontinuous \n0.029 \n0 \n0 \n0.020 \n0.026 \n0.020 \n0.026 \n0.709 \n0.914 \n Unconsolidated Sediment \n0.031 \n0 \n0 \n0.015 \n0.021 \n0.015 \n0.021 \n0.494 \n0.694 \n Total Erosion Area \n0.043 \n0.009 \n0.016 \n0 \n0 \n-0.009 \n-0.016 \n-0.198 \n-0.370 \n Seagrass Discontinuous \n0.002 \n<0.001 \n<0.001 \n0 \n0 \n<-0.001 \n<-0.001 \n-0.045 \n-0.162 \n Unconsolidated Sediment \n0.041 \n0.008 \n0.016 \n0 \n0 \n-0.008 \n-0.016 \n-0.205 \n-0.380 \nScour Marks \n Scour Mark 1 \n0.00071 \n0.0002 \n0.0004 \n0 \n0 \n-0.0002 \n-0.0004 \n-0.3083 \n-0.5114 \n Seagrass Discontinuous \n0.00071 \n0.0002 \n0.0004 \n0 \n0 \n-0.0002 \n-0.0004 \n-0.3118 \n-0.5154 \n Unconsolidated Sediment \n<0.00001 \n0 \n0 \n0 \n0 \n0 <-0.0001 \n-0.0001 \n-0.1479 \n Scour Mark 2 \n0.0014 \n0.0005 \n0.0007 \n0 \n0 \n-0.0005 \n-0.0007 \n-0.3255 \n-0.5271 \n Seagrass Continuous \n0.00124 \n0.0004 \n0.0007 \n0 \n0 \n-0.0004 \n-0.0007 \n-0.3558 \n-0.5595 \n Unconsolidated Sediment \n0.00016 \n0 <-0.0001 \n0 \n0 \n0 <-0.0001 \n-0.0943 \n-0.2790 \n Scour Mark 3 \n0.00188 \n0.0006 \n0.0010 \n0 \n0 \n-0.0006 \n-0.0010 \n-0.3247 \n-0.5232 \n Seagrass Continuous \n0.00188 \n0.0006 \n0.0010 \n0 \n0 \n-0.0006 \n-0.0010 \n-0.3247 \n-0.5232 \n Scour Mark 4 \n0.00152 \n0.0006 \n0.0009 \n0 \n0 \n-0.0006 \n-0.0009 \n-0.3631 \n-0.5661 \n Seagrass Continuous \n0.00151 \n0.0006 \n0.0009 \n0 \n0 \n-0.0006 \n-0.0009 \n-0.3658 \n-0.5697 \n Unconsolidated Sediment \n0.00001 \n0 <-0.0001 \n0 \n0 \n0 <-0.0001 \n0.0000 \n-0.0748 \nReef Rubble Field \n Total Accretion Area \n0.028 \n0 \n0 \n0.020 \n0.025 \n0.020 \n0.025 \n0.707 \n0.914 \n Reef Rubble \n0.012 \n0 \n0 \n0.008 \n0.011 \n0.008 \n0.011 \n0.690 \n0.897 \n Seagrass Discontinuous \n0.014 \n0 \n0 \n0.011 \n0.014 \n0.011 \n0.014 \n0.802 \n1.011 \n Unconsolidated Sediment \n0.002 \n0 \n0 \n0.001 \n0.001 \n0.001 \n0.001 \n0.252 \n0.446 \n Total Erosion Area \n0.023 \n0.011 \n0.015 \n0 \n0 \n-0.011 \n-0.015 \n-0.464 \n-0.661 \n Reef Rubble \n0.013 \n0.008 \n0.010 \n0 \n0 \n-0.008 \n-0.010 \n-0.584 \n-0.788 \n Seagrass Discontinuous \n0.007 \n0.003 \n0.004 \n0 \n0 \n-0.003 \n-0.004 \n-0.382 \n-0.577 \n Unconsolidated Sediment \n0.002 \n0.000 \n0.001 \n0 \n0 \n0.000 \n-0.001 \n-0.064 \n-0.221 \nSand Lobe \n Total Area \n0.27 \n0 \n0.002 \n0.089 \n0.139 \n0.089 \n0.137 \n0.332 \n0.513 \n Unconsolidated Sediment \n0.27 \n0 \n0.002 \n0.089 \n0.139 \n0.089 \n0.137 \n0.332 \n0.513 \n‘Upper’ and ‘lower’ headings refer to the upper and lower bounds of volume change based on total RMSE root mean square error). https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 during 2017 to 2019 was 35 to 55% of the mean elevation change (gain) during 2016 to 2017 for all habitats except for reef \n300 \nrubble which was 92% and had the shallowest mean depth (6.0 m) of all habitats. during 2017 to 2019 was 35 to 55% of the mean elevation change (gain) during 2016 to 2017 for all habitats except for reef \n300 \nrubble which was 92% and had the shallowest mean depth (6.0 m) of all habitats. during 2017 to 2019 was 35 to 55% of the mean elevation change (gain) during 2016 to 2017 for all habitats except for reef \n300 \nrubble which was 92% and had the shallowest mean depth (6.0 m) of all habitats. (a) \n \n(b) \n \n(c) \n \n(d) \n \n (e) \n \nFigure 5. Linear relationships between elevation change, volume change, and water depth. Linear relationships and coeffici\ndetermination between (a) mean elevation change, (b) mean area-normalized volume change, and estimated 2016 mean water de\nseafloor habitats of the Looe Key study site between 2016 to 2017. Linear relationships and coefficients of determination between (c\n5 \nelevation change, (d) mean area-normalized volume change, and estimated 2017 mean water depth for seafloor habitats of the Loo\nstudy site between 2017 to 2019 (a, b). Linear relationship between 2017 to 2019 mean elevation change and 2016 to 2017 mean ele\nchange (e). (a) (b) (a) (d) (c) (d) (c) (e) (e) Figure 5. Linear relationships between elevation change, volume change, and water depth. Linear relationships and coefficients of \ndetermination between (a) mean elevation change, (b) mean area-normalized volume change, and estimated 2016 mean water depth for \nseafloor habitats of the Looe Key study site between 2016 to 2017. Linear relationships and coefficients of determination between (c) mean \nelevation change, (d) mean area-normalized volume change, and estimated 2017 mean water depth for seafloor habitats of the Looe Key \nstudy site between 2017 to 2019 (a, b). Linear relationship between 2017 to 2019 mean elevation change and 2016 to 2017 mean elevation \nchange (e). 14 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Table 4. Compiled volume change data by habitat type for all study areas during the 2017 to 2019 study period (post-storm re-\nequilibration period). Table 4. Compiled volume change data by habitat type for all study areas during the 2017 to 2019 study period (post-storm re-\nequilibration period). 4. Compiled volume change data by habitat type for all study areas during the 2017 to 2019 study \nbration period). hange data by habitat type for all study areas during the 2017 to 2019 study period (post-storm re- 320 Lower bounds use tota\nRMSE as a plane height in calculating volume. Table 3. Compiled volume change data by habitat type for all study areas during the 2016 to 2017 study period (storm period). 310 Table 3. Compiled volume change data by habitat type for all study areas during the 2016 to 2017 stud e change data by habitat type for all study areas during the 2016 to 2017 study period (storm period). 315 15 Habitat Type \nHabitat Area \n(km2) \nGross erosion \n(Mm3) \nGross accretion \n(Mm3) \nNet volume change \n(Mm3 study-area-1) \nArea-normalized volume \nchange (Mm3 km-2) \nLower \nUpper \nLower Upper \n Lower \nUpper \nLower \nUpper \nOverall Looe Key Study Site \n Total Study Site \n15.98 \n0.316 \n2.502 \n0.005 \n0.041 \n-0.311 \n-2.461 \n-0.019 \n-0.154 \n Aggregate Reef \n0.30 \n0.028 \n0.078 \n0 <0.001 \n-0.028 \n-0.077 \n-0.093 \n-0.254 \n Colonized Pavement \n0.0028 \n<0.001 \n<0.001 \n0 \n0 \n<-0.001 \n<-0.001 \n-0.004 \n-0.112 \n Individual or Aggregate Patch Reef \n0.22 \n0.006 \n0.040 \n0 \n0 \n-0.006 \n-0.040 \n-0.028 \n-0.186 \n Not Classified \n0.026 \n0.002 \n0.007 \n0 <0.001 \n-0.002 \n-0.006 \n-0.083 \n-0.247 \n Pavement \n2.57 \n0.059 \n0.424 \n0 \n0.004 \n-0.059 \n-0.420 \n-0.023 \n-0.163 \n Reef Rubble \n0.32 \n0.010 \n0.061 \n0.001 \n0.002 \n-0.009 \n-0.059 \n-0.029 \n-0.182 \n Seagrass Continuous \n1.60 \n0.012 \n0.197 \n0.001 \n0.008 \n-0.011 \n-0.189 \n-0.007 \n-0.118 \n Seagrass Discontinuous \n4.26 \n0.064 \n0.612 \n0.001 \n0.015 \n-0.063 \n-0.597 \n-0.015 \n-0.140 \n Spur and Groove \n0.74 \n0.032 \n0.145 \n0.001 \n0.003 \n-0.031 \n-0.141 \n-0.042 \n-0.192 \n Unconsolidated Sediment \n5.94 \n0.102 \n0.938 \n0.000 \n0.007 \n-0.102 \n-0.931 \n-0.017 \n-0.157 \nSand Wave \n Total Accretion Area \n0.060 \n0.0015 \n0.0093 \n0 0.0005 \n-0.0015 \n-0.0088 \n-0.0245 \n-0.1479 \n Seagrass Discontinuous \n0.029 \n0.0010 \n0.0048 \n0 0.0003 \n-0.0010 \n-0.0044 \n-0.0336 \n-0.1544 \n Unconsolidated Sediment \n0.031 \n0.0005 \n0.0045 \n0 0.0001 \n-0.0005 \n-0.0044 \n-0.0159 \n-0.1419 \n Total Erosion Area \n0.043 \n0.0003 \n0.0066 \n0 \n0 \n-0.0003 \n-0.0066 \n-0.0074 \n-0.1529 \n Seagrass Discontinuous \n0.002 \n<0.0001 \n0.0003 \n0 \n0 \n<-0.0001 \n-0.0003 \n-0.0158 \n-0.1521 \n Unconsolidated Sediment \n0.041 \n0.0003 \n0.0063 \n0 \n0 \n-0.0003 \n-0.0063 \n-0.0070 \n-0.1529 \nScour Marks \n Scour Mark 1 \n0.00071 \n0.0000 \n0.0000 \n<0.0001 0.0001 \n<0.0001 \n0.0001 \n0.0171 \n0.1201 \n Seagrass Discontinuous \n0.00071 \n0.0000 \n0.0000 \n<0.0001 0.0001 \n<0.0001 \n0.0001 \n0.0173 \n0.1219 \n Unconsolidated Sediment \n<0.00001 \n0.0000 \n<0.0001 \n0.0000 0.0000 \n0.0000 <-0.0001 \n0.0000 \n-0.0447 \n Scour Mark 2 \n0.0014 \n0 \n0 \n0.0001 0.0003 \n0.0001 \n0.0003 \n0.0880 \n0.2226 \n Seagrass Continuous \n0.00124 \n0 \n0 \n0.0001 0.0003 \n0.0001 \n0.0003 \n0.0996 \n0.2550 \n Unconsolidated Sediment \n0.00016 \n<0.0001 \n<0.0001 \n0 \n0 \n<-0.0001 <-0.0001 \n-0.0009 \n-0.0254 \n Scour Mark 3 \n0.00188 \n0 \n0 \n0.0001 0.0003 \n0.0001 \n0.0003 \n0.0524 \n0.1380 \n Seagrass Continuous \n0.00188 \n0 \n0 \n0.0001 0.0003 \n0.0001 \n0.0003 \n0.0524 \n0.1380 \n Scour Mark 4 \n0.00152 \n0 \n0 \n0.0001 0.0002 \n0.0001 \n0.0002 \n0.0615 \n0.1334 \n Seagrass Continuous \n0.00151 \n0 \n0 \n0.0001 0.0002 \n0.0001 \n0.0002 \n0.0620 \n0.1351 \n Unconsolidated Sediment \n0.00001 \n0.0000 \n<0.0001 \n0.0000 0.0000 \n0.0000 <-0.0001 \n0.0000 \n-0.1029 \nReef Rubble Field \n Total Accretion Area \n0.028 \n0.0040 \n0.0080 \n0.0010 0.0010 \n-0.0040 \n-0.0070 \n-0.1310 \n-0.2480 \n Reef Rubble \n0.012 \n0.0020 \n0.0040 \n0 0.0010 \n-0.0010 \n-0.0030 \n-0.1260 \n-0.2180 \n Seagrass Discontinuous \n0.014 \n0.0020 \n0.0040 \n0 \n0 \n-0.0020 \n-0.0040 \n-0.1560 \n-0.3020 \n Unconsolidated Sediment \n0.002 \n<0.0001 \n<0.0001 \n0 \n0 \n<-0.0001 <-0.0001 \n-0.0070 \n-0.0910 \n Total Erosion Area \n0.023 \n0.0005 \n0.0031 \n0.0003 0.0008 \n-0.0002 \n-0.0023 \n-0.0084 \n-0.1026 \n Reef Rubble \n0.013 \n0.0001 \n0.0015 \n0.0003 0.0007 \n0.0002 \n-0.0008 \n0.0113 \n-0.0612 \n Seagrass Discontinuous \n0.007 \n0.0003 \n0.0013 \n0 0.0001 \n-0.0003 \n-0.0012 \n-0.0429 \n-0.1741 \n Unconsolidated Sediment \n0.002 \n<0.0001 \n0.0003 \n0 \n0 \n<-0.0001 \n-0.0003 \n-0.0152 \n-0.1188 \nSand Lobe \n Total Area \n0.27 \n0.010 \n0.055 \n0 <0.001 \n-0.010 \n-0.054 \n-0.038 \n-0.204 \n Unconsolidated Sediment \n0.27 \n0.010 \n0.055 \n0 <0.001 \n-0.010 \n-0.054 \n-0.038 \n-0.204 \n‘Upper’ and ‘lower’ headings refer to the upper and lower bounds of volume change based on total RMSE (root mean square error). https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Several scour features developed in ‘discontinuous seagrass’ and ‘unconsolidated sediment’ habitats of the Looe Key back \nreef, indicated by areas of erosion that appear as pits. Rubble fields within and near the Looe Key SPA area were displaced, \nas indicated by adjacent areas of accretion and erosion. Substantial deposition of sediments occurred along a sand lobe at the \nbase of the Looe Key Reef ‘spur and groove’ habitat. 330 330 3.2.1 Sand Wave Total net volume change for the accretion area of the feature was 0.048 Mm3 and area-normalized \nvolume change was 0.800 Mm3 km-2 (Table 3). Mean elevation-change and area-normalized volume change was greatest within the discontinuous seagrass habitat (0.90 m and 0.914 Mm3 km-2, respectively), approximately 2.7 times greater than \n345 \nmean elevation change and area-normalized volume change for the overall Looe Key study site. It accounted for 55% of total \nnet volume gain, indicating burial of seagrass habitat during migration of the sand wave. Net volume change of the erosion \narea was approximately -0.016 Mm3 and area-normalized volume change -0.37 Mm3 km-2 with 98% of net volume change \nassociated with erosion of unconsolidated sediment habitat (Table 3). within the discontinuous seagrass habitat (0.90 m and 0.914 Mm3 km-2, respectively), approximately 2.7 times greater than \n345 \nmean elevation change and area-normalized volume change for the overall Looe Key study site. It accounted for 55% of total \nnet volume gain, indicating burial of seagrass habitat during migration of the sand wave. Net volume change of the erosion \narea was approximately -0.016 Mm3 and area-normalized volume change -0.37 Mm3 km-2 with 98% of net volume change \nassociated with erosion of unconsolidated sediment habitat (Table 3). 350 \nBetween 2017 and 2019, the sand wave (accretion area) showed mean elevation and net volume change of approximately -\n0.15 m and approximately -0.009 Mm3, respectively (Table 2, 4 and Fig. 6b). Similar mean elevation change values were \nobserved for discontinuous seagrass and unconsolidated sediment habitats associated with the feature, and net volume change \nfor each habitat was approximately 50% of the total net volume change (Table 4). Area-normalized volume change was similar 350 \nBetween 2017 and 2019, the sand wave (accretion area) showed mean elevation and net volume change of approximately -\n0.15 m and approximately -0.009 Mm3, respectively (Table 2, 4 and Fig. 6b). Similar mean elevation change values were \nobserved for discontinuous seagrass and unconsolidated sediment habitats associated with the feature, and net volume change \nfor each habitat was approximately 50% of the total net volume change (Table 4). Area-normalized volume change was similar 350 \nBetween 2017 and 2019, the sand wave (accretion area) showed mean elevation and net volume change of approximately -\n0.15 m and approximately -0.009 Mm3, respectively (Table 2, 4 and Fig. 6b). 3.2 Geomorphic Feature Analyses Large-scale geomorphic features that were 10s to 100s of m2 in areal extent and showed extensive erosion and/or accretion \nwith elevation-changes greater than 0.5 m were observed between 2016 and 2017 (Fig. 6 and 7). Examples of these features \n325 \nincluded migration of a sand wave in the back reef area of Looe Key reef indicated by adjacent areas of erosion and accretion. 16 3.2.1 Sand Wave An accretion of 0.060 km2 included \napproximately 50% discontinuous seagrass and 50% unconsolidated sediment habitat. of approximately 5.6 m. Transect elevation profiles showed the location of this feature in 2016, westward migration of \n335 \napproximately 78 m (crest to crest) in 2017, and minor erosion in 2019 (Fig. 7a). An accretion of 0.060 km2 included \napproximately 50% discontinuous seagrass and 50% unconsolidated sediment habitat. of approximately 5.6 m. Transect elevation profiles showed the location of this feature in 2016, westward migration of \n335 \napproximately 78 m (crest to crest) in 2017, and minor erosion in 2019 (Fig. 7a). An accretion of 0.060 km2 included \napproximately 50% discontinuous seagrass and 50% unconsolidated sediment habitat. Between 2016 and 2017, mean elevation change of the accretion area (2017 location of the sand wave) was 0.79 m (Table 2) \nwith a maximum elevation gain at the crest of 1.84m. An adjacent area of erosion was approximately 630 x 122 m in length \n340 \nand width (0.043 km2) and included approximately 5% discontinuous seagrass and 95% unconsolidated sediment. Mean \nelevation-change of the erosion area was -0.36 m (Table 2) with a maximum elevation loss of -1.23 m near the 2016 location \nof the sand wave crest. Total net volume change for the accretion area of the feature was 0.048 Mm3 and area-normalized \nvolume change was 0.800 Mm3 km-2 (Table 3). Mean elevation-change and area-normalized volume change was greatest g\nwith a maximum elevation gain at the crest of 1.84m. An adjacent area of erosion was approximately 630 x 122 m in length \n340 \nand width (0.043 km2) and included approximately 5% discontinuous seagrass and 95% unconsolidated sediment. Mean \nelevation-change of the erosion area was -0.36 m (Table 2) with a maximum elevation loss of -1.23 m near the 2016 location \nof the sand wave crest. Total net volume change for the accretion area of the feature was 0.048 Mm3 and area-normalized \nvolume change was 0.800 Mm3 km-2 (Table 3). Mean elevation-change and area-normalized volume change was greatest with a maximum elevation gain at the crest of 1.84m. An adjacent area of erosion was approximately 630 x 122 m in length \n340 \nand width (0.043 km2) and included approximately 5% discontinuous seagrass and 95% unconsolidated sediment. Mean \nelevation-change of the erosion area was -0.36 m (Table 2) with a maximum elevation loss of -1.23 m near the 2016 location \nof the sand wave crest. 3.2.1 Sand Wave Migration of a sand wave was observed in the back reef area of Looe Key Reef between 2016 and 2017, with minor erosion \nof this feature occurring between 2017 and 2019 (Fig. 6a, b, and c). The sand wave was approximately 733 m long and 104 m \nwide at its widest point in 2017, 2 m in height from the crest to base on the deepest (western) edge, with average water depth \nof approximately 5.6 m. Transect elevation profiles showed the location of this feature in 2016, westward migration of \n335 \napproximately 78 m (crest to crest) in 2017, and minor erosion in 2019 (Fig. 7a). An accretion of 0.060 km2 included \napproximately 50% discontinuous seagrass and 50% unconsolidated sediment habitat. Migration of a sand wave was observed in the back reef area of Looe Key Reef between 2016 and 2017, with minor erosion \nof this feature occurring between 2017 and 2019 (Fig. 6a, b, and c). The sand wave was approximately 733 m long and 104 m \nwide at its widest point in 2017, 2 m in height from the crest to base on the deepest (western) edge, with average water depth \nof approximately 5.6 m. Transect elevation profiles showed the location of this feature in 2016, westward migration of \n335 \ni\nl\n78\n(\n) i\n2017\nd\ni\ni\ni\n2019 (Fi\n7 ) A\ni\nf 0 060 k\n2 i\nl d d Migration of a sand wave was observed in the back reef area of Looe Key Reef between 2016 and 2017, with minor erosion \nof this feature occurring between 2017 and 2019 (Fig. 6a, b, and c). The sand wave was approximately 733 m long and 104 m \nwide at its widest point in 2017, 2 m in height from the crest to base on the deepest (western) edge, with average water depth of this feature occurring between 2017 and 2019 (Fig. 6a, b, and c). The sand wave was approximately 733 m long and 104 m \nwide at its widest point in 2017, 2 m in height from the crest to base on the deepest (western) edge, with average water depth \nof approximately 5.6 m. Transect elevation profiles showed the location of this feature in 2016, westward migration of \n335 \napproximately 78 m (crest to crest) in 2017, and minor erosion in 2019 (Fig. 7a). 3.2.1 Sand Wave Similar mean elevation change values were \nobserved for discontinuous seagrass and unconsolidated sediment habitats associated with the feature, and net volume change \nfor each habitat was approximately 50% of the total net volume change (Table 4). Area-normalized volume change was similar for the total area of the sand wave and the sub-areas within it, including discontinuous seagrass and unconsolidated sediment \n355 \nhabitats, ranging from approximately -0.148 to -0.154 Mm3/km2. The adjacent erosion area (original 2016 location of the sand \nwave) also showed a mean elevation change of -0.15 m with similar values for the associated discontinuous seagrass and \nunconsolidated sediment habitats. Net volume change of the erosion area was approximately -0.007 Mm3 with approximately for the total area of the sand wave and the sub-areas within it, including discontinuous seagrass and unconsolidated sediment \n355 \nhabitats, ranging from approximately -0.148 to -0.154 Mm3/km2. The adjacent erosion area (original 2016 location of the sand \nwave) also showed a mean elevation change of -0.15 m with similar values for the associated discontinuous seagrass and \nunconsolidated sediment habitats. Net volume change of the erosion area was approximately -0.007 Mm3 with approximately 17 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 95% of this loss associated with unconsolidated sediment (Table 4). Area-normalized volume change was also consistent \nacross the total erosion feature area, discontinuous seagrass, and unconsolidated sediment habitats at -0.15 Mm3 km-2. 360 95% of this loss associated with unconsolidated sediment (Table 4). Area-normalized volume change was also consistent \nacross the total erosion feature area, discontinuous seagrass, and unconsolidated sediment habitats at -0.15 Mm3 km-2. 360 360 Figure 6. Elevation change data and transect positions for each geomorphic feature subarea. Geomorphic features included a s\nwave (a, b, c), scour marks (d, e, f), western rubble field (g, h, i), and sand lobe subareas (j, k, l). These feature locations and correspond\nhabitat are also shown in Fig. 4. Elevation-change from 2016 to 2017 (a, d, g, j), 2017 to 2019 (b, e, h, k), and corresponding reconnaissa\n5 \nimagery (c, f, i, l) Transect positions are indicated by black lines and lowercase letters in the elevation change panels (see also Fig. 7). Sc\nmarks in panels d and e are labelled SM1 through 4. Photo credit: Mitch Lemon, Cherokee Nations System Solutions for U.S. Geolog\nSurvey. Figure 6. Elevation change data and transect positions for each geomorphic feature subarea. Geomorphic features included a sand \nwave (a, b, c), scour marks (d, e, f), western rubble field (g, h, i), and sand lobe subareas (j, k, l). These feature locations and corresponding \nhabitat are also shown in Fig. 4. Elevation-change from 2016 to 2017 (a, d, g, j), 2017 to 2019 (b, e, h, k), and corresponding reconnaissance \n365 \nimagery (c, f, i, l) Transect positions are indicated by black lines and lowercase letters in the elevation change panels (see also Fig. 7). Scour \nmarks in panels d and e are labelled SM1 through 4. Photo credit: Mitch Lemon, Cherokee Nations System Solutions for U.S. Geological \nSurvey. 18 Figure 7. Elevation transects across geomorphic features in 2016, 2017, and 2019. Geomorphic features included a sand wave (a, e\nscour marks (b, f), western reef rubble field (c, g), and a sand lobe (d, h). Lowercase letters indicate direction of transects as shown in Figur\n6. Vertical red lines indicate areas of erosion and vertical blue lines indicate areas of accretion between (a-d) 2016 and 2017 (before an\nafter Hurricane Irma) and between (e-f) 2017 and 2019. SM = scour mark. Figure 7. Elevation transects across geomorphic features in 2016, 2017, and 2019. Geomorphic features included a sand wave (a, e), \n370 \nscour marks (b, f), western reef rubble field (c, g), and a sand lobe (d, h). Lowercase letters indicate direction of transects as shown in Figure \n6. Vertical red lines indicate areas of erosion and vertical blue lines indicate areas of accretion between (a-d) 2016 and 2017 (before and \nafter Hurricane Irma) and between (e-f) 2017 and 2019. SM = scour mark. 3.2.2 Scour Marks \n375 Development of scour marks was observed in seagrass and unconsolidated sediment habitats in the back reef area of Looe Key \nReef between 2016 and 2017 (Fig. 6d, e, and f). These features ranged from approximately 30 to 60 m in length and width \nwith average depths of approximately 5.7 to 7.5 m in 2017. Visual validation of select scour features indicated they developed 19 mean elevation change of 0.10 m and a net volume change of less than 0.001 Mm3 (Tables 2 and 4). Area-normalized volume \n390 \nchange was approximately 0.12 Mm3 km-2. Scour mark 2 was 1,400 m2 with 88% of the area consisting of continuous seagrass \nand 12% unconsolidated sediment. Between 2016 and 2017, mean elevation change was -0.50 m (Table 2) with maximum \nobserved change of -1.28 m. Net volume change was less than -0.001 Mm3 and area-normalized volume change was \napproximately -0.53 Mm3 km-2 (Table 3). Ninety-four percent of net volume change was associated with continuous seagrass mean elevation change of 0.10 m and a net volume change of less than 0.001 Mm3 (Tables 2 and 4). Area-normalized volume \n390 \nchange was approximately 0.12 Mm3 km-2. Scour mark 2 was 1,400 m2 with 88% of the area consisting of continuous seagrass \nand 12% unconsolidated sediment. Between 2016 and 2017, mean elevation change was -0.50 m (Table 2) with maximum \nobserved change of -1.28 m. Net volume change was less than -0.001 Mm3 and area-normalized volume change was \napproximately -0.53 Mm3 km-2 (Table 3). Ninety-four percent of net volume change was associated with continuous seagrass habitat, which also had the highest area-normalized volume change of -0.56 Mm3 km-2. Between 2017 and 2019, this feature \n395 \nshowed accretion with mean elevation change of 0.20 m and net volume change of 0.0003 Mm3 (Tables 2 and 4). Continuous \nseagrass showed an increase in mean elevation (0.24 m) and net volume (0.0003 Mm3) while unconsolidated sediment showed \na decrease in mean elevation (-0.05 m) and net volume (less than -0.0001 Mm3). Area-normalized volume change across the \nentire scour mark was approximately 0.22 Mm3 km-2. Scour mark 3 was 1,882 m2 with 100% of the area consisting of habitat, which also had the highest area-normalized volume change of -0.56 Mm3 km-2. Between 2017 and 2019, this feature \n395 \nshowed accretion with mean elevation change of 0.20 m and net volume change of 0.0003 Mm3 (Tables 2 and 4). Continuous \nseagrass showed an increase in mean elevation (0.24 m) and net volume (0.0003 Mm3) while unconsolidated sediment showed \na decrease in mean elevation (-0.05 m) and net volume (less than -0.0001 Mm3). Area-normalized volume change across the \nentire scour mark was approximately 0.22 Mm3 km-2. Scour mark 3 was 1,882 m2 with 100% of the area consisting of continuous seagrass. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 between 2016 and 2017 at the edges of seagrass beds where small (approximately 0.5 m) ledges marked the transition between \nthe slightly higher elevation of seagrass beds and lower elevation of adjacent unconsolidated sediment (Fig. 6f). Transect \n380 \nanalyses showed considerable erosion of the western boundaries of seagrass beds, development of pit-like features up to \napproximately 20 m in diameter and 1 m deep, transport of sediment westward, and burial of seagrass between scour features \n(Fig. 7b). Scour marks showed some infilling between 2017 and 2019. Validation imagery showed exposed rhizomatous \ngrowth at the western edges of seagrass beds (Fig. 8). 380 385 \nElevation- and volume-change analyses were performed on four examples of these features (Fig. 6d and e). Scour mark 1 was \n714 m2 with 99% of the area consisting of discontinuous seagrass. Between 2016 and 2017, mean elevation change was -0.49 \nm (Table 2) with a maximum observed change of -1.09 m. Net volume change was less than -0.001 Mm3, and area-normalized \nvolume change was approximately -0.51 Mm3 km-2 (Table 3). Between 2017 and 2019, this feature showed accretion with 385 \nElevation- and volume-change analyses were performed on four examples of these features (Fig. 6d and e). Scour mark 1 was \n714 m2 with 99% of the area consisting of discontinuous seagrass. Between 2016 and 2017, mean elevation change was -0.49 \nm (Table 2) with a maximum observed change of -1.09 m. Net volume change was less than -0.001 Mm3, and area-normalized \nvolume change was approximately -0.51 Mm3 km-2 (Table 3). Between 2017 and 2019, this feature showed accretion with Elevation- and volume-change analyses were performed on four examples of these features (Fig. 6d and e). Scour mark 1 was \n714 m2 with 99% of the area consisting of discontinuous seagrass. Between 2016 and 2017, mean elevation change was -0.49 \nm (Table 2) with a maximum observed change of -1.09 m. Net volume change was less than -0.001 Mm3, and area-normalized \nvolume change was approximately -0.51 Mm3 km-2 (Table 3). Between 2017 and 2019, this feature showed accretion with \nmean elevation change of 0.10 m and a net volume change of less than 0.001 Mm3 (Tables 2 and 4). Area-normalized volume \n390 \nchange was approximately 0.12 Mm3 km-2. Scour mark 2 was 1,400 m2 with 88% of the area consisting of continuous seagrass \nand 12% unconsolidated sediment. Between 2016 and 2017, mean elevation change was -0.50 m (Table 2) with maximum \nobserved change of -1.28 m. Net volume change was less than -0.001 Mm3 and area-normalized volume change was \napproximately -0.53 Mm3 km-2 (Table 3). Ninety-four percent of net volume change was associated with continuous seagrass \nhabitat, which also had the highest area-normalized volume change of -0.56 Mm3 km-2. Between 2017 and 2019, this feature \n395 \nshowed accretion with mean elevation change of 0.20 m and net volume change of 0.0003 Mm3 (Tables 2 and 4). Continuous \nseagrass showed an increase in mean elevation (0.24 m) and net volume (0.0003 Mm3) while unconsolidated sediment showed \na decrease in mean elevation (-0.05 m) and net volume (less than -0.0001 Mm3). Area-normalized volume change across the \nentire scour mark was approximately 0.22 Mm3 km-2. Scour mark 3 was 1,882 m2 with 100% of the area consisting of \ncontinuous seagrass. Between 2016 and 2017, mean elevation change was -0.50 m with a maximum observed change of -1.25 \n400 \nm (Table 2). Net volume change was -0.001 Mm3 and area-normalized volume change was approximately -0.52 Mm3 km-2 \n(Table 3). Between 2017 to 2019, this feature showed accretion with mean elevation change of 0.12 m and net volume change \nof 0.0003 Mm3 (Tables 2 and 4). Area-normalized volume change was approximately 0.14 Mm3 km-2. Scour mark 4 was 1,520 \nm2 with 99% of area consisting of continuous seagrass and 1% unconsolidated sediment. Between 2016 and 2017, mean \nelevation change was -0.54 m with a maximum observed change of -1.29 m (Table 2). Net volume change was -0.0009 Mm3 \n405 \nand area-normalized volume change was approximately -0.57 Mm3 km-2 (Table 3). Ninety-nine percent of net volume change \nwas associated with continuous seagrass habitat which also had the highest area-normalized volume change of -0.57 Mm3 km-\n2. Between 2017 to 2019, this feature showed accretion with mean elevation change of 0.12 m and net volume change of -\n0.0002 Mm3 (Tables 2 and 4). Area-normalized volume change was approximately 0.13 Mm3 km-2. More than 99% of net \nvolume change was associated with continuous seagrass habitat which also had the highest area-normalized volume change of \n410 \napproximately 0.14 Mm3 km-2. High erosion was noted between 2016 and 201\non the sand flat (western) side of the habitat transition and minimal accretion was noted on the seagrass bed (eastern) side of the habit Figure 8. Cardinal orientation imagery (a, b, d, and e represent north, west, east, and south, respectively) and elevation change (c) \nat a scour mark location used to validate benthic features observed in elevation change data. East and west arrows show the boundaries \n415 \nbetween seagrass beds and sand flats in the elevation change data (c) and imagery (b and d). High erosion was noted between 2016 and 2017 \non the sand flat (western) side of the habitat transition and minimal accretion was noted on the seagrass bed (eastern) side of the habitat \ntransition. Photo credit: Mitch Lemon, Cherokee Nations System Solutions for U.S. Geological Survey. 415 Between 2016 and 2017, mean elevation change was -0.50 m with a maximum observed change of -1.25 \n400 \nm (Table 2). Net volume change was -0.001 Mm3 and area-normalized volume change was approximately -0.52 Mm3 km-2 \n(Table 3). Between 2017 to 2019, this feature showed accretion with mean elevation change of 0.12 m and net volume change \nof 0.0003 Mm3 (Tables 2 and 4). Area-normalized volume change was approximately 0.14 Mm3 km-2. Scour mark 4 was 1,520 \nm2 with 99% of area consisting of continuous seagrass and 1% unconsolidated sediment. Between 2016 and 2017, mean elevation change was -0.54 m with a maximum observed change of -1.29 m (Table 2). Net volume change was -0.0009 Mm3 \n405 \nand area-normalized volume change was approximately -0.57 Mm3 km-2 (Table 3). Ninety-nine percent of net volume change \nwas associated with continuous seagrass habitat which also had the highest area-normalized volume change of -0.57 Mm3 km-\n2. Between 2017 to 2019, this feature showed accretion with mean elevation change of 0.12 m and net volume change of -\n0.0002 Mm3 (Tables 2 and 4). Area-normalized volume change was approximately 0.13 Mm3 km-2. More than 99% of net volume change was associated with continuous seagrass habitat which also had the highest area-normalized volume change of \n410 \napproximately 0.14 Mm3 km-2. 20 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Figure 8. Cardinal orientation imagery (a, b, d, and e represent north, west, east, and south, respectively) and elevation change (c) \nat a scour mark location used to validate benthic features observed in elevation change data. East and west arrows show the boundaries \n415 \nbetween seagrass beds and sand flats in the elevation change data (c) and imagery (b and d). High erosion was noted between 2016 and 2017 \non the sand flat (western) side of the habitat transition and minimal accretion was noted on the seagrass bed (eastern) side of the habitat \ntransition. Photo credit: Mitch Lemon, Cherokee Nations System Solutions for U.S. Geological Survey. Figure 8. Cardinal orientation imagery (a, b, d, and e represent north, west, east, and south, respectively) and elevation change (\nat a scour mark location used to validate benthic features observed in elevation change data. East and west arrows show the boundarie\n415 \nbetween seagrass beds and sand flats in the elevation change data (c) and imagery (b and d). The adjacent erosion \n440 \narea (original 2016 location of the rubble field) showed a mean elevation change of -0.10 m (Table 2) with a maximum \nelevation loss of -0.52 m. Total net volume change was approximately -0.002 Mm3 and area-normalized volume change was \n-0.103 Mm3 km-2 with 53% of net volume change associated with discontinuous seagrass (Table 4). Highest mean elevation \nand area-normalized volume changes were also associated with discontinuous seagrass. Mean elevation and volume losses \ngenerally decreased with increasing mean habitat depth in the erosion area (Tables 2 and 4). 445 loss in mean elevation and area-normalized volume change and accounted for 59% of net volume change. The adjacent erosion \n440 \narea (original 2016 location of the rubble field) showed a mean elevation change of -0.10 m (Table 2) with a maximum \nelevation loss of -0.52 m. Total net volume change was approximately -0.002 Mm3 and area-normalized volume change was \n-0.103 Mm3 km-2 with 53% of net volume change associated with discontinuous seagrass (Table 4). Highest mean elevation \nand area-normalized volume changes were also associated with discontinuous seagrass. Mean elevation and volume losses \ngenerally decreased with increasing mean habitat depth in the erosion area (Tables 2 and 4). 445 3.2.3 Rubble Fields \n420 Migration of reef rubble fields was observed in areas north and northeast of Looe Key Reef between 2016 and 2017. The \nlargest of these features was approximately 418 m long and x 122 m wide at its widest point in 2017, 3 m in height from the \ncrest to base on the deepest (western) edge, with average water depth of approximately 3.3 m (Fig 6g, h, and i). Transect \nelevation profiles showed the location of this feature in 2016, westward migration of approximately 80 m (crest to crest) in Migration of reef rubble fields was observed in areas north and northeast of Looe Key Reef between 2016 and 2017. The \nlargest of these features was approximately 418 m long and x 122 m wide at its widest point in 2017, 3 m in height from the \ncrest to base on the deepest (western) edge, with average water depth of approximately 3.3 m (Fig 6g, h, and i). Transect \nelevation profiles showed the location of this feature in 2016, westward migration of approximately 80 m (crest to crest) in 2017, and minor eastward migration of 8 m (crest to crest) in 2019 (Fig. 7c). The accretion area of this feature covered an area \n425 \nof about 0.03 km2 including approximately 43% reef rubble, 49% discontinuous seagrass, and 9% unconsolidated sediment. Between 2016 and 2017, mean elevation change of the accretion area (2017 location of the rubble field) was 0.89 m (Table 2) \nwith a maximum elevation gain of 2.21 m. Total net volume change was 0.025 Mm3 and area-normalized volume change was 21 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 0.914 Mm3 km-2 with discontinuous seagrass accounting for 54% of net volume change indicating burial of seagrass during \nmigration of the rubble field (Table 3). Highest mean elevation and area-normalized volume changes were also associated with \n430 \ndiscontinuous seagrass habitat. An area of erosion (0.023 km2) was observed in 2017 at the original 2016 location of the rubble \nfield that was approximately 428 m long and x 78 m wide including 58% reef rubble, 31% discontinuous seagrass, and 11% \nunconsolidated sediment. Mean elevation-change of the erosion area between 2016 and 2017 was -0.63 m (Table 2) with a \nmaximum elevation loss of -2.11 m. Total net volume change was approximately -0.015 Mm3 and area-normalized volume \nchange was -0.661 Mm3 km-2 with 69% of net volume change associated with reef rubble (Table 3). Highest mean elevation \n435 \nand area-normalized volume changes were also associated with reef rubble. 0.914 Mm3 km-2 with discontinuous seagrass accounting for 54% of net volume change indicating burial of seagrass during \nmigration of the rubble field (Table 3). Highest mean elevation and area-normalized volume changes were also associated with \n430 \ndiscontinuous seagrass habitat. An area of erosion (0.023 km2) was observed in 2017 at the original 2016 location of the rubble \nfield that was approximately 428 m long and x 78 m wide including 58% reef rubble, 31% discontinuous seagrass, and 11% \nunconsolidated sediment. Mean elevation-change of the erosion area between 2016 and 2017 was -0.63 m (Table 2) with a \nmaximum elevation loss of -2.11 m. Total net volume change was approximately -0.015 Mm3 and area-normalized volume change was -0.661 Mm3 km-2 with 69% of net volume change associated with reef rubble (Table 3). Highest mean elevation \n435 \nand area-normalized volume changes were also associated with reef rubble. Between 2017 and 2019, the rubble field (accretion area) showed mean elevation change of -0.24 m, net volume change of -\n0.007 Mm3, and area-normalized volume change of -0.248 Mm3 km-2 (Tables 2 and 4). Discontinuous seagrass showed greatest 0.007 Mm3, and area-normalized volume change of -0.248 Mm3 km-2 (Tables 2 and 4). Discontinuous seagrass showed greatest \nloss in mean elevation and area-normalized volume change and accounted for 59% of net volume change. 3.2.4 Sand Lobe Substantial accretion was observed along a sand lobe located near the base of the fore-reef slope of Looe Key Reef between \n2016 and 2017 (Fig. 6j, k, and l). This feature was approximately 1,383 m long and 344 m wide (approximately 0.27 km2) at \nthe widest point with an average water depth of approximately 11.9 m in 2017 and included only unconsolidated sediment \nhabitat. Between 2016 and 2017, mean elevation change was 0.51 m (Table 2) with maximum gains in elevation up to 1.5 m \n450 \nalong the southern (seaward) downslope section of this feature and maximum elevation losses of -0.58 m along the northern \nlandward section, nearest to the base of the of the fore-reef slope (Fig. 7d). Total net volume change was 0.14 Mm3 and area-\nnormalized volume change was 0.51 Mm3 km-2 (Table 3). Between 2017and 2019, mean elevation change was -0.20 m with \nmaximum elevation losses up to -1.12 m (Table 2, Fig. 7d). Only 852 of 67,389 elevation points analysed for this feature \nshowed gains in elevation after 2017, averaging 0.05 m. Transect elevation profiles showed relatively consistent losses in \n455 \nelevation (erosion) across the sand lobe north to south (landward to seaward) during this time-period. Total net volume change \nwas -0.05 Mm3 and area-normalized volume change was -0.20 Mm3 km-2 (Table 4). Substantial accretion was observed along a sand lobe located near the base of the fore-reef slope of Looe Key Reef between \n2016 and 2017 (Fig. 6j, k, and l). This feature was approximately 1,383 m long and 344 m wide (approximately 0.27 km2) at \nthe widest point with an average water depth of approximately 11.9 m in 2017 and included only unconsolidated sediment (\ng\nj, ,\n)\npp\ny ,\ng\n( pp\ny\n)\nthe widest point with an average water depth of approximately 11.9 m in 2017 and included only unconsolidated sediment \nhabitat. Between 2016 and 2017, mean elevation change was 0.51 m (Table 2) with maximum gains in elevation up to 1.5 m \n450 \nalong the southern (seaward) downslope section of this feature and maximum elevation losses of -0.58 m along the northern \nlandward section, nearest to the base of the of the fore-reef slope (Fig. 7d). Total net volume change was 0.14 Mm3 and area-\nnormalized volume change was 0.51 Mm3 km-2 (Table 3). https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 3.2.4 Sand Lobe Between 2017and 2019, mean elevation change was -0.20 m with \nmaximum elevation losses up to -1.12 m (Table 2, Fig. 7d). Only 852 of 67,389 elevation points analysed for this feature \nshowed gains in elevation after 2017, averaging 0.05 m. Transect elevation profiles showed relatively consistent losses in \n455 \nelevation (erosion) across the sand lobe north to south (landward to seaward) during this time-period. Total net volume change \nwas -0.05 Mm3 and area-normalized volume change was -0.20 Mm3 km-2 (Table 4). habitat. Between 2016 and 2017, mean elevation change was 0.51 m (Table 2) with maximum gains in elevation up to 1.5 m \n450 \nalong the southern (seaward) downslope section of this feature and maximum elevation losses of -0.58 m along the northern \nlandward section, nearest to the base of the of the fore-reef slope (Fig. 7d). Total net volume change was 0.14 Mm3 and area-\nnormalized volume change was 0.51 Mm3 km-2 (Table 3). Between 2017and 2019, mean elevation change was -0.20 m with \nmaximum elevation losses up to -1.12 m (Table 2, Fig. 7d). Only 852 of 67,389 elevation points analysed for this feature showed gains in elevation after 2017, averaging 0.05 m. Transect elevation profiles showed relatively consistent losses in\n455 \nelevation (erosion) across the sand lobe north to south (landward to seaward) during this time-period. Total net volume change\nwas -0.05 Mm3 and area-normalized volume change was -0.20 Mm3 km-2 (Table 4). 22 4 Discussion Our 2016 to 2017 elevation change results showed general movement of sediment \nand migration of major geomorphic features from ENE to WSW in shallow areas (ranging from approximately 2 to 5.5 m \nwater depth in 2016) of the reef proper and back reef area, consistent with the direction of sustained, high magnitude winds \nduring the passing of Hurricane Irma (Fig. 4; Fig. 6a, d, and g). For example, large sand waves and rubble fields (approximately \n475 \n0.02 to 0.06 km2 in area) migrated westward approximately 80 m (Fig. 6a and g) causing burial of seagrass habitat. Scour \nmarks developed due to erosion of the western edges of seagrass beds and westward transport of sediment, causing burial of \nadjacent seagrass beds between scour marks (Fig. 6d). Numerical modelling of the impact of hurricane-induced wave-current \ninteractions on the transport of material along the FRT during Hurricane Irma showed that wave radiation stress primarily \naffected particle transport trajectories during the passage of the hurricane (Dobbelaere et al., 2022). Additionally, wave energy \n480 \ndissipation occurred through depth-induced wave breaking and bottom dissipation at the shelf break and over the coral reefs. Furthermore, after the passage of the hurricane, suspended particles were transported northeastward by the Florida Current \n(Fig. 1d) and were advected (via Stokes drift) from the outer shelf to inshore for approximately 2 days (Dobbelaere et al., storm and, therefore, included any persistent change that occurred during quiescent sea state conditions before and after the \n465 \npassing of Irma. However, observations during several rapid reef assessments after the storm indicated broad-scale sediment \ndeposition as a direct result of Hurricane Irma (Viehman et al., 2018; Walker, 2018; Wilson et al., 2020; Kobelt et al. 2019), \nwhich corroborates our findings of increased mean elevation and sediment accretion resulting from this storm event. Furthermore, wind conditions were relatively quiescent from the 2016 lidar acquisition date up to the passing of Hurricane storm and, therefore, included any persistent change that occurred during quiescent sea state conditions before and after the \n465 \npassing of Irma. However, observations during several rapid reef assessments after the storm indicated broad-scale sediment \ndeposition as a direct result of Hurricane Irma (Viehman et al., 2018; Walker, 2018; Wilson et al., 2020; Kobelt et al. 2019), \nwhich corroborates our findings of increased mean elevation and sediment accretion resulting from this storm event. 4 Discussion Furthermore, wind conditions were relatively quiescent from the 2016 lidar acquisition date up to the passing of Hurricane Irma and after the storm, and historical aerial imagery of LKR from 2014 and 18 March 2017 (3 years and 6 months prior to \n470 \nHurricane Irma, respectively, Fig. 9) show that patterns of major sedimentary features were mostly static (Finkl and Vollmer, \n2017) in the few years prior to the storm. Our 2016 to 2017 elevation change results showed general movement of sediment \nand migration of major geomorphic features from ENE to WSW in shallow areas (ranging from approximately 2 to 5.5 m \nwater depth in 2016) of the reef proper and back reef area, consistent with the direction of sustained, high magnitude winds Irma and after the storm, and historical aerial imagery of LKR from 2014 and 18 March 2017 (3 years and 6 months prior to \n470 \nHurricane Irma, respectively, Fig. 9) show that patterns of major sedimentary features were mostly static (Finkl and Vollmer, \n2017) in the few years prior to the storm. Our 2016 to 2017 elevation change results showed general movement of sediment \nand migration of major geomorphic features from ENE to WSW in shallow areas (ranging from approximately 2 to 5.5 m \nwater depth in 2016) of the reef proper and back reef area, consistent with the direction of sustained, high magnitude winds Irma and after the storm, and historical aerial imagery of LKR from 2014 and 18 March 2017 (3 years and 6 months prior to \n470 \nHurricane Irma, respectively, Fig. 9) show that patterns of major sedimentary features were mostly static (Finkl and Vollmer, \n2017) in the few years prior to the storm. Our 2016 to 2017 elevation change results showed general movement of sediment \nand migration of major geomorphic features from ENE to WSW in shallow areas (ranging from approximately 2 to 5.5 m \nwater depth in 2016) of the reef proper and back reef area, consistent with the direction of sustained, high magnitude winds during the passing of Hurricane Irma (Fig. 4; Fig. 6a, d, and g). For example, large sand waves and rubble fields (approximately \n475 \n0.02 to 0.06 km2 in area) migrated westward approximately 80 m (Fig. 6a and g) causing burial of seagrass habitat. 4 Discussion There are few comprehensive assessments of the effects of major hurricanes on seafloor elevation and geomorphology on coral \nreefs; and no quantitative studies of reef-scale seafloor elevation change resulting from tropical storm impacts have previously \n460 \nbeen conducted in the Florida Keys. Our results showed Hurricane Irma was primarily a depositional event that increased mean \nseafloor elevation and volume over a 15.98 km2 section of Looe Key Reef by 0.34 m (annualized elevation-change rate of up \nto 247 mm yr-1) and up to 5.4 Mm3, respectively, with area-normalized volume change of approximately 0.34 Mm3km-2. Our \nobservations were based on elevation measurements collected 13.5 months before the storm and three to six months after the There are few comprehensive assessments of the effects of major hurricanes on seafloor elevation and geomorphology on coral \nreefs; and no quantitative studies of reef-scale seafloor elevation change resulting from tropical storm impacts have previously \n460 \nbeen conducted in the Florida Keys. Our results showed Hurricane Irma was primarily a depositional event that increased mean \nseafloor elevation and volume over a 15.98 km2 section of Looe Key Reef by 0.34 m (annualized elevation-change rate of up \nto 247 mm yr-1) and up to 5.4 Mm3, respectively, with area-normalized volume change of approximately 0.34 Mm3km-2. Our \nobservations were based on elevation measurements collected 13.5 months before the storm and three to six months after the \nstorm and, therefore, included any persistent change that occurred during quiescent sea state conditions before and after the \n465 \npassing of Irma. However, observations during several rapid reef assessments after the storm indicated broad-scale sediment \ndeposition as a direct result of Hurricane Irma (Viehman et al., 2018; Walker, 2018; Wilson et al., 2020; Kobelt et al. 2019), \nwhich corroborates our findings of increased mean elevation and sediment accretion resulting from this storm event. Furthermore, wind conditions were relatively quiescent from the 2016 lidar acquisition date up to the passing of Hurricane \nIrma and after the storm, and historical aerial imagery of LKR from 2014 and 18 March 2017 (3 years and 6 months prior to \n470 \nHurricane Irma, respectively, Fig. 9) show that patterns of major sedimentary features were mostly static (Finkl and Vollmer, \n2017) in the few years prior to the storm. 4 Discussion Scour \nmarks developed due to erosion of the western edges of seagrass beds and westward transport of sediment, causing burial of \nadjacent seagrass beds between scour marks (Fig. 6d). Numerical modelling of the impact of hurricane-induced wave-current \ninteractions on the transport of material along the FRT during Hurricane Irma showed that wave radiation stress primarily during the passing of Hurricane Irma (Fig. 4; Fig. 6a, d, and g). For example, large sand waves and rubble fields (approximately \n475 \n0.02 to 0.06 km2 in area) migrated westward approximately 80 m (Fig. 6a and g) causing burial of seagrass habitat. Scour \nmarks developed due to erosion of the western edges of seagrass beds and westward transport of sediment, causing burial of \nadjacent seagrass beds between scour marks (Fig. 6d). Numerical modelling of the impact of hurricane-induced wave-current \ninteractions on the transport of material along the FRT during Hurricane Irma showed that wave radiation stress primarily affected particle transport trajectories during the passage of the hurricane (Dobbelaere et al., 2022). Additionally, wave energy \n480 \ndissipation occurred through depth-induced wave breaking and bottom dissipation at the shelf break and over the coral reefs. Furthermore, after the passage of the hurricane, suspended particles were transported northeastward by the Florida Current \n(Fig. 1d) and were advected (via Stokes drift) from the outer shelf to inshore for approximately 2 days (Dobbelaere et al., \n2022). affected particle transport trajectories during the passage of the hurricane (Dobbelaere et al., 2022). Additionally, wave energy \n480 \ndissipation occurred through depth-induced wave breaking and bottom dissipation at the shelf break and over the coral reefs. Furthermore, after the passage of the hurricane, suspended particles were transported northeastward by the Florida Current \n(Fig. 1d) and were advected (via Stokes drift) from the outer shelf to inshore for approximately 2 days (Dobbelaere et al., \n2022). 485 \nSimilar geomorphic seafloor changes have been documented for other category 4 hurricanes in the Florida Keys based on \nphotographic air and ground surveys, maps, sediment cores, and bottom markers. 4 Discussion In 1967, Hurricane Donna approached from \nthe southeast and passed over the central islands of the Florida Keys in September 1960 with sustained winds of 226 km h-1 \n(category 4) and with breaking waves and storm currents causing broken coral rubble up to a meter in diameter, shoreward 485 \nSimilar geomorphic seafloor changes have been documented for other category 4 hurricanes in the Florida Keys based on \nphotographic air and ground surveys, maps, sediment cores, and bottom markers. In 1967, Hurricane Donna approached from \nthe southeast and passed over the central islands of the Florida Keys in September 1960 with sustained winds of 226 km h-1 \n(category 4) and with breaking waves and storm currents causing broken coral rubble up to a meter in diameter, shoreward \ntransport of gravel to boulder sized rubble and sand approximately 60 to 150 m shoreward, and burial of seagrass with 15 cm \n490 485 \nSimilar geomorphic seafloor changes have been documented for other category 4 hurricanes in the Florida Keys based on \nphotographic air and ground surveys, maps, sediment cores, and bottom markers. In 1967, Hurricane Donna approached from \nthe southeast and passed over the central islands of the Florida Keys in September 1960 with sustained winds of 226 km h-1 \n(category 4) and with breaking waves and storm currents causing broken coral rubble up to a meter in diameter, shoreward \ntransport of gravel to boulder sized rubble and sand approximately 60 to 150 m shoreward, and burial of seagrass with 15 cm \n490 transport of gravel to boulder sized rubble and sand approximately 60 to 150 m shoreward, and burial of seagrass with 15 cm \n490 23 Figure 9. Historical satellite and aerial imagery of Looe Key Reef. Imagery from (a) 17 December 2014, before Hurricane Irma; (b) 18\nMarch 2017, before Hurricane Irma; (c) 30 December 2017, 3 months after Hurricane Irma; (d) 2019, 16.5 months after Hurricane Irma; \n2023. Panel d source: 2019 NOAA National Geodetic Survey via NOAA Digital Coast, downloaded 11 September 2023, \nhttps://www.fisheries.noaa.gov/inport/item/63292. and (e) from 1975 (Lidz et al., 2016). Panels a, b, and c source: Maxar 2023 via © Google Earth Pro 7.3.6.9345, downloaded 11 Septembe Figure 9. Historical satellite and aerial imagery of Looe Key Reef. 4 Discussion Imagery from (a) 17 December 2014, before Hurricane Irma; (b) 18 \nMarch 2017, before Hurricane Irma; (c) 30 December 2017, 3 months after Hurricane Irma; (d) 2019, 16.5 months after Hurricane Irma; \n2023. Panel d source: 2019 NOAA National Geodetic Survey via NOAA Digital Coast, downloaded 11 September 2023, \nhttps://www.fisheries.noaa.gov/inport/item/63292. 495 \nand (e) from 1975 (Lidz et al., 2016). Panels a, b, and c source: Maxar 2023 via © Google Earth Pro 7.3.6.9345, downloaded 11 September of sediment (Ball et al., 1967). Hurricane Betsy approached from the west and passed over the Florida Keys approximately 25 \nkm north of Hurricane Donna’s landfall in September 1965 with sustained winds of up to 226 km h-1. While both storms had \n500 \nsimilar destructive effects to corals on the outer reefs, Hurricane Betsy produced less rubble, showed an overall effect of \nerosion and recycling of sediment in the environment, and caused sediment plumes from the mainland to the edge of the Gulf km north of Hurricane Donna’s landfall in September 1965 with sustained winds of up to 226 km h-1. While both storms had \n500 \nsimilar destructive effects to corals on the outer reefs, Hurricane Betsy produced less rubble, showed an overall effect of \nerosion and recycling of sediment in the environment, and caused sediment plumes from the mainland to the edge of the Gulf 24 Fourqurean and Rutten (2004) showed that seagrass recovery was slowest at sites that were \neroded; losses by mechanical thinning and burial with only a few centimeters of sediment recovered quickly; and seagrass \nburied with 10s of centimeters of sediment hadn’t recovered by three years after the storm. Results from these studies show \n515 \nthe variability in storm impacts due complex interactions among factors such as location, fetch, wind speed, duration, storm \nhistory, and water depth (Fourqurean and Rutten, 2004), and demonstrate the value of comprehensive, quantitative post storm \nassessments of geological and ecological impacts. with maximum sustained winds of only 145 km h-1. However, data from 30 seagrass monitoring transects showed a 3% decline \n510 \nin density of Thalassia testudinum and 19% decline in density of Syringodium filiforme seagrasses, with complete loss of \nseagrass beds at 3 monitoring stations, burial of one station with 50cm of sediment, substantial erosion at two stations \n(Fourqurean and Rutten, 2004). Fourqurean and Rutten (2004) showed that seagrass recovery was slowest at sites that were \neroded; losses by mechanical thinning and burial with only a few centimeters of sediment recovered quickly; and seagrass with maximum sustained winds of only 145 km h-1. However, data from 30 seagrass monitoring transects showed a 3% decline \n510 \nin density of Thalassia testudinum and 19% decline in density of Syringodium filiforme seagrasses, with complete loss of \nseagrass beds at 3 monitoring stations, burial of one station with 50cm of sediment, substantial erosion at two stations \n(Fourqurean and Rutten, 2004). Fourqurean and Rutten (2004) showed that seagrass recovery was slowest at sites that were \neroded; losses by mechanical thinning and burial with only a few centimeters of sediment recovered quickly; and seagrass buried with 10s of centimeters of sediment hadn’t recovered by three years after the storm. Results from these studies show \n515 \nthe variability in storm impacts due complex interactions among factors such as location, fetch, wind speed, duration, storm \nhistory, and water depth (Fourqurean and Rutten, 2004), and demonstrate the value of comprehensive, quantitative post storm \nassessments of geological and ecological impacts. buried with 10s of centimeters of sediment hadn’t recovered by three years after the storm. Results from these studies show \n515 \nthe variability in storm impacts due complex interactions among factors such as location, fetch, wind speed, duration, storm \nhistory, and water depth (Fourqurean and Rutten, 2004), and demonstrate the value of comprehensive, quantitative post storm \nassessments of geological and ecological impacts. 520 \nA previous analysis of seafloor elevation change at LKR during the decade prior to Hurricane Irma (from 2004–2016, during \nwhich only one minor tropical storm impacted this location in 2008) indicated an increase in mean elevation of 0.39 m \n(annualized elevation-change rate of 32.5 mm yr-1), net volume gain of up to 6.4 Mm3 and area-normalized volume change of \n0.39 Mm3 km-2, with accretion observed across all habitat types and some WSW movement of sand waves (Yates et al., 2019). 520 \nA previous analysis of seafloor elevation change at LKR during the decade prior to Hurricane Irma (from 2004–2016, during \nwhich only one minor tropical storm impacted this location in 2008) indicated an increase in mean elevation of 0.39 m \n(annualized elevation-change rate of 32.5 mm yr-1), net volume gain of up to 6.4 Mm3 and area-normalized volume change of \n0.39 Mm3 km-2, with accretion observed across all habitat types and some WSW movement of sand waves (Yates et al., 2019). 520 \nA previous analysis of seafloor elevation change at LKR during the decade prior to Hurricane Irma (from 2004–2016, during \nwhich only one minor tropical storm impacted this location in 2008) indicated an increase in mean elevation of 0.39 m \n(annualized elevation-change rate of 32.5 mm yr-1), net volume gain of up to 6.4 Mm3 and area-normalized volume change of \n0.39 Mm3 km-2, with accretion observed across all habitat types and some WSW movement of sand waves (Yates et al., 2019). Our results showed that sediment deposited during the approximately 16.5 to 19.5-month time-period including impacts from \n525 \nHurricane Irma caused changes in seafloor elevation and volume across all habitat types similar in magnitude to net changes \nobserved over the past decade and at accumulation rates one order of magnitude greater. Previous studies on several coral reefs \naround St. Croix, U.S. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Stream for several days after the Hurricane passed (Perkins and Enos, 1968). Perkins and Enos (1968) noted the difference in \nwind directions for the two storms caused different effects, and that it is difficult to extrapolate quantitative sedimentation rates \nfrom the sedimentary record of one hurricane and frequency of recorded hurricanes. Hurricane Andrew made landfall along \n505 \nthe southeast coast of Florida just south of Miami also with sustained winds of 226 km h-1 with maximum wave heights of less \nthan 2 m. Branching corals were broken, massive coral heads were toppled, seafans and sponges were ripped loose, and shallow \nreefs sustained the most damage (Orr and Ogden, 1992); however, there was little damage to seagrass beds immediately \nseaward of coastal mangroves (Tilmant et al., 1994). Hurricane Georges was a category 2 storm that passed over Key West Stream for several days after the Hurricane passed (Perkins and Enos, 1968). Perkins and Enos (1968) noted the difference in \nwind directions for the two storms caused different effects, and that it is difficult to extrapolate quantitative sedimentation rates \nfrom the sedimentary record of one hurricane and frequency of recorded hurricanes. Hurricane Andrew made landfall along \n505 \nthe southeast coast of Florida just south of Miami also with sustained winds of 226 km h-1 with maximum wave heights of less \nthan 2 m. Branching corals were broken, massive coral heads were toppled, seafans and sponges were ripped loose, and shallow \nreefs sustained the most damage (Orr and Ogden, 1992); however, there was little damage to seagrass beds immediately \nseaward of coastal mangroves (Tilmant et al., 1994). Hurricane Georges was a category 2 storm that passed over Key West \nwith maximum sustained winds of only 145 km h-1. However, data from 30 seagrass monitoring transects showed a 3% decline \n510 \nin density of Thalassia testudinum and 19% decline in density of Syringodium filiforme seagrasses, with complete loss of \nseagrass beds at 3 monitoring stations, burial of one station with 50cm of sediment, substantial erosion at two stations \n(Fourqurean and Rutten, 2004). https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0.\nl\n.\n0 Virgin Islands showed that physical transport of sediment is primarily due to wave-induced oscillatory \nand unidirectional currents, and that storms can increase sediment transport by an order of magnitude higher than during non- Our results showed that sediment deposited during the approximately 16.5 to 19.5-month time-period including impacts from \n525 \nHurricane Irma caused changes in seafloor elevation and volume across all habitat types similar in magnitude to net changes \nobserved over the past decade and at accumulation rates one order of magnitude greater. Previous studies on several coral reefs \naround St. Croix, U.S. Virgin Islands showed that physical transport of sediment is primarily due to wave-induced oscillatory \nand unidirectional currents, and that storms can increase sediment transport by an order of magnitude higher than during non- storm conditions (Hubbard et al., 1981; Hubbard, 1986). Measurements from 15 locations around St. Croix showed sediment \n530 \ntransport rates ranging from 0.009 to 0.3 Mm3 km-2yr-1 during non-storm conditions, and 0.09 to 1.5 Mm3 km-2yr-1 during storm \nconditions (Hubbard et al., 1981; Yates et al., 2017). Sediment trap studies along the southwest coast of Puerto Rico showed \nmedian sediment accumulation rates increased by an order of magnitude (from approximately 6 to 68 mg m-2 d-1) after the \npassage of Hurricane Maria in September of 2017 (a category 4 storm) and a large October 2017 storm that caused resuspension storm conditions (Hubbard et al., 1981; Hubbard, 1986). Measurements from 15 locations around St. Croix showed sediment \n530 \ntransport rates ranging from 0.009 to 0.3 Mm3 km-2yr-1 during non-storm conditions, and 0.09 to 1.5 Mm3 km-2yr-1 during storm \nconditions (Hubbard et al., 1981; Yates et al., 2017). Sediment trap studies along the southwest coast of Puerto Rico showed \nmedian sediment accumulation rates increased by an order of magnitude (from approximately 6 to 68 mg m-2 d-1) after the \npassage of Hurricane Maria in September of 2017 (a category 4 storm) and a large October 2017 storm that caused resuspension 25 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 which is considered heavy sedimentation and has been associated with fewer coral species, less live coral, lower coral growth \nrates, reduced coral recruitment and calcification rates, and slower rates of reef accretion (Rogers, 1990). Mean elevation- and area-normalized volume-change from 2016–2017 for habitats examined in our study increased \nsignificantly with water depth suggesting that, in addition to broad-scale sediment deposition across the study site, sediment \n540 \nwas also transported from shallower to deeper habitats (Fig. 5a and b). Notably, greatest increases in elevation (accretion) were \nassociated with habitats in water depths exceeding 11 m including aggregate reef, a sand lobe consisting of unconsolidated \nsediment, and ‘not classified’ habitat located seaward and near the base of the reef’s spur-and-groove formation, suggesting \nsome movement of sediment offshore and downslope (Fig. 4a, Table 2). Additionally, erosion was observed in the shallower, upslope grooves of the spur-and-groove formation, and accretion was observed in the deeper, downslope areas of the grooves \n545 \nfrom 2016 to 2017 further suggesting downslope, offshore movement of sediments (Fig. 10). The sand lobe at the base of the \nspur and groove formation also showed upslope erosion and considerable downslope (seaward) accretion, further suggesting \noffshore transport of sediments (Fig. 7d). Our observations are consistent with previous bathymetric change analyses \nconducted along the northern FRT from 2001 to 2008 (approximately 3 years before Hurricane Ivan and 3 years after Hurricane Katrina) that showed movement of up to 1.8 Mm3 of sediment between these time periods and transport of sediment from the \n550 \ninner shelf to offshore and beyond the shelf edge through gaps in the barrier reef and diabathic (cross-shore) channels during \nhigh-energy events or when the back reef overfills with sand (Finkl, 2004; Finkl and Vollmer, 2017). These observations are \nalso consistent with results of Yates et al. (2017) that show a multi-decadal trend along the FRT of reef sediment transport \ndown the fore-reef-slope and export offshore. Field observations of currents, waves, and reef sediment grain-size analyses Katrina) that showed movement of up to 1.8 Mm3 of sediment between these time periods and transport of sediment from the \n550 \ninner shelf to offshore and beyond the shelf edge through gaps in the barrier reef and diabathic (cross-shore) channels during \nhigh-energy events or when the back reef overfills with sand (Finkl, 2004; Finkl and Vollmer, 2017). These observations are \nalso consistent with results of Yates et al. (2017) that show a multi-decadal trend along the FRT of reef sediment transport \ndown the fore-reef-slope and export offshore. Field observations of currents, waves, and reef sediment grain-size analyses Katrina) that showed movement of up to 1.8 Mm3 of sediment between these time periods and transport of sediment from the \n550 \ninner shelf to offshore and beyond the shelf edge through gaps in the barrier reef and diabathic (cross-shore) channels during \nhigh-energy events or when the back reef overfills with sand (Finkl, 2004; Finkl and Vollmer, 2017). These observations are \nalso consistent with results of Yates et al. (2017) that show a multi-decadal trend along the FRT of reef sediment transport \ndown the fore-reef-slope and export offshore. Field observations of currents, waves, and reef sediment grain-size analyses coupled with integrated ocean-atmosphere-wave-sediment transport modelling during a one-year study at Crocker reef in the \n555 \nUpper Florida Keys showed that sediment mobility was primarily driven by wave stress exceeding critical shear stress; current \nstress alone only exceeded the critical shear stress for sediment mobility 5% of the time usually due to Florida Current eddies \n(Torres-Garcia et al., 2018). Torres-Garcia (2018) showed that nonbreaking wave stress (characteristic of quiescent sea states) \nmobilizes sand approximately 23 to 59% of the time; and fine-grained material is winnowed from the shallow areas of the reef and deposited to the flanks and offshore, particularly to the southwest. Furthermore, the critical stress threshold of gravel-sized \n560 \nmaterial was exceeded only 1 to 13% of the time, particularly during near-field tropical storm conditions (similar to Hurricane \nWilma, a category 3 hurricane) that cause breaking waves, mobilize and transport gravel material, and can cause physical reef \ndegradation (Torres-Garcia et al., 2018). https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 570 \n \nFigure 10. Elevation-change along Looe Key Reef spur and groove formation. (a) Upslope to downslope transects along Looe Key\nReef spur and groove formation (green lines); image source: 2019 NOAA National Geodetic Survey via NOAA Digital Coast, downlo\n11 September 2023, https://www.fisheries.noaa.gov/inport/item/63292 with structure from motion overlay image of Hatcher et al. (202\nAreas of erosion (red circles) and accretion (blue circles) along transect 1 between (b) 2016 and 2017, and between (c) 2017 and 2019. 575 \nAreas of erosion (red circles) and accretion (blue circles) along transect 2 between (d) 2016 and 2017, and between (e) 2017 and 2019. Elevation profiles from 2016, 2017, and 2019 for (f) transect 1 and (g) transect 2. Vertical red lines indicate net erosion and vertical blu\nlines indicate net accretion between 2016 and 2017. 570 Figure 10. Elevation-change along Looe Key Reef spur and groove formation. (a) Upslope to downslope transects along Looe Key \nReef spur and groove formation (green lines); image source: 2019 NOAA National Geodetic Survey via NOAA Digital Coast, downloaded \n11 September 2023, https://www.fisheries.noaa.gov/inport/item/63292 with structure from motion overlay image of Hatcher et al. (2022). Areas of erosion (red circles) and accretion (blue circles) along transect 1 between (b) 2016 and 2017, and between (c) 2017 and 2019. 575 \nAreas of erosion (red circles) and accretion (blue circles) along transect 2 between (d) 2016 and 2017, and between (e) 2017 and 2019. Elevation profiles from 2016, 2017, and 2019 for (f) transect 1 and (g) transect 2. Vertical red lines indicate net erosion and vertical blue \nlines indicate net accretion between 2016 and 2017. Figure 10. Elevation-change along Looe Key Reef spur and groove formation. (a) Upslope to downslope transects along Looe Key \nReef spur and groove formation (green lines); image source: 2019 NOAA National Geodetic Survey via NOAA Digital Coast, downloaded \n11 September 2023, https://www.fisheries.noaa.gov/inport/item/63292 with structure from motion overlay image of Hatcher et al. (2022). Areas of erosion (red circles) and accretion (blue circles) along transect 1 between (b) 2016 and 2017, and between (c) 2017 and 2019. 575 \nAreas of erosion (red circles) and accretion (blue circles) along transect 2 between (d) 2016 and 2017, and between (e) 2017 and 2019. Elevation profiles from 2016, 2017, and 2019 for (f) transect 1 and (g) transect 2. Southwest counter currents due to the formation of Florida Current eddies (Lee and \nWilliams, 1999) and WSW movement of sand wave features over a decadal time-period (Yates et al., 2019b) have also been and deposited to the flanks and offshore, particularly to the southwest. Furthermore, the critical stress threshold of gravel-sized \n560 \nmaterial was exceeded only 1 to 13% of the time, particularly during near-field tropical storm conditions (similar to Hurricane \nWilma, a category 3 hurricane) that cause breaking waves, mobilize and transport gravel material, and can cause physical reef \ndegradation (Torres-Garcia et al., 2018). Southwest counter currents due to the formation of Florida Current eddies (Lee and \nWilliams, 1999) and WSW movement of sand wave features over a decadal time-period (Yates et al., 2019b) have also been observed near LKR. Results from these previous studies suggest that some sediment transport observed in our study could be \n565 \ndue to persistent transport of sand during quiescent sea state conditions; however, the large volume of material transported \n(including gravel-sized and larger reef rubble) during the short time-period of our study from 2016 to 2017 was likely due \nprimarily to storm conditions caused by Hurricane Irma. observed near LKR. Results from these previous studies suggest that some sediment transport observed in our study could be \n565 \ndue to persistent transport of sand during quiescent sea state conditions; however, the large volume of material transported \n(including gravel-sized and larger reef rubble) during the short time-period of our study from 2016 to 2017 was likely due \nprimarily to storm conditions caused by Hurricane Irma. observed near LKR. Results from these previous studies suggest that some sediment transport observed in our study could be \n565 \ndue to persistent transport of sand during quiescent sea state conditions; however, the large volume of material transported \n(including gravel-sized and larger reef rubble) during the short time-period of our study from 2016 to 2017 was likely due \nprimarily to storm conditions caused by Hurricane Irma. 26 Vertical red lines indicate net erosion and vertical blue \nlines indicate net accretion between 2016 and 2017. 580 27 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Approximately 16.5 months after Hurricane Irma (during a 13-month period between 2017 and 2019), net erosion was observed \nacross all habitats with mean elevation-change of -0.15 (annualized elevation change-rate of -139 mm yr-1), net volume change \nup to -2.46 Mm3, and area-normalized volume change of -0.15 Mm3km-2. Newly deposited carbonate sediments typically have \nporosities of 40 to 70% (Choquette and Pray, 1970) at shallow sediment depths of a few hundreds of meters (Schmoker and Halley, 1982). Porosity of carbonate sands on the FRT and in Hawk Channel ranges from 60 to 72% in the upper 22 cm of \n585 \ndeposited sediment (Walter et al. 2007). Schmoker and Halley (1982) showed that there is little or no sediment porosity loss \nat near-surface sediment depths. Application of their exponential function for porosity versus depth of sediment (porosity (%) \n= 41.73e-z/2498, where z = depth below sediment surface) indicates that the decrease in porosity of deposited carbonate sediments \nat 2 m below the sediment-surface is only 0.03%. Carbonate sands have settling velocities ranging from 0.025 to 0.364 m s-1 Halley, 1982). Porosity of carbonate sands on the FRT and in Hawk Channel ranges from 60 to 72% in the upper 22 cm of \n585 \ndeposited sediment (Walter et al. 2007). Schmoker and Halley (1982) showed that there is little or no sediment porosity loss \nat near-surface sediment depths. Application of their exponential function for porosity versus depth of sediment (porosity (%) \n= 41.73e-z/2498, where z = depth below sediment surface) indicates that the decrease in porosity of deposited carbonate sediments \nat 2 m below the sediment-surface is only 0.03%. Carbonate sands have settling velocities ranging from 0.025 to 0.364 m s-1 585 (Riazi et al., 2020). Satellite imagery shows the sediment plume caused by resuspension of sediment during Hurricane Irma \n590 \ncleared within approximately 5 days of the storm’s passing (NASA, 2023). Therefore, it is likely that resuspended sediment \nsettled quickly (within days) when storm conditions subsided; and it is unlikely that the decrease in elevation observed between \n2017 and 2019 was caused by compaction of sediment after the storm. This suggests that approximately 50% of sediment \ndeposited between 2016 and 2017 was eroded by 2019 due to physical transport away from the study site. The sand wave and (Riazi et al., 2020). Satellite imagery shows the sediment plume caused by resuspension of sediment during Hurricane Irma \n590 \ncleared within approximately 5 days of the storm’s passing (NASA, 2023). Therefore, it is likely that resuspended sediment \nsettled quickly (within days) when storm conditions subsided; and it is unlikely that the decrease in elevation observed between \n2017 and 2019 was caused by compaction of sediment after the storm. This suggests that approximately 50% of sediment \ndeposited between 2016 and 2017 was eroded by 2019 due to physical transport away from the study site. The sand wave and reef rubble field showed continued erosion between 2017 to 2019 with some evidence for migration of the crest of the rubble \n595 \nfield back toward its original 2016 position indicated in the elevation profile (Fig. 6 and 7). Shallow areas between the scour \nmarks showed erosion, while the scour mark pits showed infilling (Fig. 6 and 7). Spurs of the spur-and-groove formation \nprimarily showed erosion, while shallow (landward) sections of grooves showed some accretion, likely due to transport of \nsediments from spurs to grooves and downslope from the shallow reef (Fig. 10). Deeper (seaward) areas of grooves and the reef rubble field showed continued erosion between 2017 to 2019 with some evidence for migration of the crest of the rubble \n595 \nfield back toward its original 2016 position indicated in the elevation profile (Fig. 6 and 7). Shallow areas between the scour \nmarks showed erosion, while the scour mark pits showed infilling (Fig. 6 and 7). Spurs of the spur-and-groove formation \nprimarily showed erosion, while shallow (landward) sections of grooves showed some accretion, likely due to transport of \nsediments from spurs to grooves and downslope from the shallow reef (Fig. 10). Deeper (seaward) areas of grooves and the reef rubble field showed continued erosion between 2017 to 2019 with some evidence for migration of the crest of the rubble \n595 \nfield back toward its original 2016 position indicated in the elevation profile (Fig. 6 and 7). Shallow areas between the scour \nmarks showed erosion, while the scour mark pits showed infilling (Fig. 6 and 7). Spurs of the spur-and-groove formation \nprimarily showed erosion, while shallow (landward) sections of grooves showed some accretion, likely due to transport of \nsediments from spurs to grooves and downslope from the shallow reef (Fig. 10). Deeper (seaward) areas of grooves and the sand lobe located at the base of the spur and groove formation showed erosion (Fig. 4 and 6k) suggesting continued downslope, \n600 \noffshore transport of sediments. Historical aerial and satellite imagery from before and after the passing of Hurricane Irma \ncorroborates our elevation-change observations (Fig. 9). Imagery from 2014 and March 2017 shows that major geomorphic \nfeatures of Looe Key proper such as distribution of seagrass beds and the size and position of the sand lobe and rubble fields \nwere relatively static between these time periods leading up to Hurricane Irma (Fig. 9a and b). Imagery from December 2017, sand lobe located at the base of the spur and groove formation showed erosion (Fig. 4 and 6k) suggesting continued downslope, \n600 \noffshore transport of sediments. Historical aerial and satellite imagery from before and after the passing of Hurricane Irma \ncorroborates our elevation-change observations (Fig. 9). Imagery from 2014 and March 2017 shows that major geomorphic \nfeatures of Looe Key proper such as distribution of seagrass beds and the size and position of the sand lobe and rubble fields \nwere relatively static between these time periods leading up to Hurricane Irma (Fig. 9a and b). Imagery from December 2017, 3 months after Hurricane Irma passed, shows broad scale sediment deposition and burial of seagrass beds in the shallow areas \n605 \nof the reef proper, erosion and exposure of deeper, downslope spur-and-groove formation and downslope deposition on the \nsand lobe (Fig. 9c). Imagery from 2019 shows re-exposure of some shallow seagrass beds and deep spur-and-groove formation \nas sediments were eroded (Fig. 9d). Historical areal imagery from 1975 (Fig. 9e, Lidz et al., 2016) shows a distribution of \nseagrass, presence of rubble fields, and patterns of sediment along the sand lobe similar to 2014 and 2017 imagery (before 3 months after Hurricane Irma passed, shows broad scale sediment deposition and burial of seagrass beds in the shallow areas \n605 \nof the reef proper, erosion and exposure of deeper, downslope spur-and-groove formation and downslope deposition on the \nsand lobe (Fig. 9c). Imagery from 2019 shows re-exposure of some shallow seagrass beds and deep spur-and-groove formation \nas sediments were eroded (Fig. 9d). Historical areal imagery from 1975 (Fig. 9e, Lidz et al., 2016) shows a distribution of \nseagrass, presence of rubble fields, and patterns of sediment along the sand lobe similar to 2014 and 2017 imagery (before 3 months after Hurricane Irma passed, shows broad scale sediment deposition and burial of seagrass beds in the shallow areas \n605 \nof the reef proper, erosion and exposure of deeper, downslope spur-and-groove formation and downslope deposition on the \nsand lobe (Fig. 9c). Imagery from 2019 shows re-exposure of some shallow seagrass beds and deep spur-and-groove formation \nas sediments were eroded (Fig. 9d). Historical areal imagery from 1975 (Fig. 9e, Lidz et al., 2016) shows a distribution of \nseagrass, presence of rubble fields, and patterns of sediment along the sand lobe similar to 2014 and 2017 imagery (before Hurricane Irma) indicating these features have persisted over the past several decades despite repeated impact from tropical \n610 \nand seasonal storms. Lidz et al. (2016) suggested the formation of rubble fields in the shallow back reef area is mainly due to \nhistorical passage of hurricanes and winter storms, and our elevation change results suggest that these structures continue to \nmigrate in response to storm conditions. Lidz et al. (2016) also suggested that transport of sediment during hurricanes was Hurricane Irma) indicating these features have persisted over the past several decades despite repeated impact from tropical \n610 \nand seasonal storms. Lidz et al. (2016) suggested the formation of rubble fields in the shallow back reef area is mainly due to \nhistorical passage of hurricanes and winter storms, and our elevation change results suggest that these structures continue to \nmigrate in response to storm conditions. Lidz et al. (2016) also suggested that transport of sediment during hurricanes was Hurricane Irma) indicating these features have persisted over the past several decades despite repeated impact from tropical \n610 \nand seasonal storms. Lidz et al. (2016) suggested the formation of rubble fields in the shallow back reef area is mainly due to \nhistorical passage of hurricanes and winter storms, and our elevation change results suggest that these structures continue to \nmigrate in response to storm conditions. Lidz et al. (2016) also suggested that transport of sediment during hurricanes was 28 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 primarily to the north; however, our observations showed primary sediment movement during Hurricane Irma was WSW and \ndownslope from shallow to deep habitats with apparent seaward movement of the sand lobe after the storm passed. 615 615 elevation-change rate of -1.4 mm yr-1 between 1934 and 2004 (Yates et al., 2017). Furthermore, six of nine habitats at LKR \n620 \nshowed elevation loss over those periods, with greatest losses associated with shallow habitats, and mean elevation and \nvolume gains in deep-water habitats including at the base of the spur-and-groove habitat, indicating transport of reef \nsediments down the fore-reef-slope and export offshore (Yates et al., 2017). Our observed rate of mean elevation loss \nbetween 2017 and 2019 (-139 mm yr-1) was two orders of magnitude higher than the multi-decadal rates of Yates et al. (2017). Additionally, elevation loss (erosion) showed a moderate correlation with water depth, and mean elevation losses \n625 \nduring 2017 to 2019 were significantly greater in habitats with larger mean elevation gains during 2016 to 2017, suggesting \nthat sediment distribution was re-equilibrating or stabilizing to quiescent sea-state conditions up to 16.5 months after the \nstorm. The annualized mean rate of elevation-change for LKR from the 2.5-year period between July 2016 to January 2019 examined \nin our study, including sediment accretion from Hurricane Irma and the post-storm erosion and re-equilibration, was \n630 \napproximately 72 mm yr-1, which is almost double the rate of accretion observed in the previous decade of 32.5 mm yr-1 (Yates \net al. 2019b). Numerous field reconnaissance observations immediately after the passing of Hurricane Irma indicated \nbroadscale sediment deposition across the FRT due to the storm (e.g., Viehman et al., 2018; Walker, 2018; Wilson et al., 2020; \nKobelt et al. 2019). Our 2016 to 2019 elevation-change rate is consistent with annualized mean elevation-change rates from The annualized mean rate of elevation-change for LKR from the 2.5-year period between July 2016 to January 2019 examined \nin our study, including sediment accretion from Hurricane Irma and the post-storm erosion and re-equilibration, was \n630 \napproximately 72 mm yr-1, which is almost double the rate of accretion observed in the previous decade of 32.5 mm yr-1 (Yates \net al. 2019b). 5 Conclusion High-resolution lidar and multibeam bathymetric data were used to quantify seafloor elevation and volume change within the \nLooe Key Reef system of the Florida Keys Reef Tract over a 2.5-year period from 2016–2019 and to examine impacts from \ncategory-4 Hurricane Irma and post-storm re-equilibration of seafloor sediments. Analysis of seafloor elevation and volume \nchange over a 16.5-month period from July 2016 to December 2017 showed Hurricane Irma caused broadscale deposition of \n655 \nsediments across all benthic habitats of this reef system and burial of seagrass and coral dominated habitat. Rates of net \nelevation change were one order of magnitude greater during this short-term period that included storm impacts from Hurricane \nIrma than for the previous decade (Yates et al., 2019). Major seafloor geomorphic features such as sand waves and rubble \nfields migrated 10s of meters to the WSW in response to predominant wind conditions during the passing of Hurricane Irma, \nand sediment accretion was significantly greater in deep habitats than shallow habitats, suggesting downslope and offshore \n660 \ntransport of seafloor sediment. High-resolution lidar and multibeam bathymetric data were used to quantify seafloor elevation and volume change within the \nLooe Key Reef system of the Florida Keys Reef Tract over a 2.5-year period from 2016–2019 and to examine impacts from \ncategory-4 Hurricane Irma and post-storm re-equilibration of seafloor sediments. Analysis of seafloor elevation and volume change over a 16.5-month period from July 2016 to December 2017 showed Hurricane Irma caused broadscale deposition of \n655 \nsediments across all benthic habitats of this reef system and burial of seagrass and coral dominated habitat. Rates of net \nelevation change were one order of magnitude greater during this short-term period that included storm impacts from Hurricane \nIrma than for the previous decade (Yates et al., 2019). Major seafloor geomorphic features such as sand waves and rubble \nfields migrated 10s of meters to the WSW in response to predominant wind conditions during the passing of Hurricane Irma, \nand sediment accretion was significantly greater in deep habitats than shallow habitats, suggesting downslope and offshore \n660 \ntransport of seafloor sediment. 655 Loss of mean elevation and volume in all habitats in the period following the storm (from December 2017 to January 2019) \nindicated that 35% to 50% of sediment deposited during the storm had eroded by approximately 16.5 months after the storm \nand that erosion rates were two orders of magnitude greater than historical, multi-decadal rates of erosion. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Table 5. Annualized mean elevation-change rates (mm yr-1) for event-driven to multi-decadal time periods at the Florida Keys \nReef Tract. Annualized mean elevation change rate (mm yr-1) \nLocation \nEvent-drivena \n2016 to 2017 \nPost-stormb \n2017 to 2019 \nShort-termc \n2016 to 2019 \nDecadal \n2004 to 2016 \nMulti-decadal \n1930s to 2000s \nLooe Key Reef \n247 \n-139 \n72 \n32.5d \n-4.5e \nLower Florida Reef Tract \n(south of Big Pine) \nna \nna \n84f \nna \nna \nFlorida Reef Tract (Miami to \nKey West) \nna \nna \n76f \nna \nna \nUpper Florida Reef Tract \n(Elliott Key to Tavernier Key) \nna \nna \nna \nna \n-1.4e \na = calculated assuming a total time-period of 16.5 months (13.5 month pre- to 3 months post-storm); b = total time period 13.5 months (3 to 16.5 months \npost-storm); c = total time-period approximately 30 months (13.5 months pre- to 16.5 months post-storm); d = using data from Yates et al., 2019; e = using \ndata from Yates et al., 2017; f = using data from Fehr et al., 2021; na = no data available. 0 Table 5. Annualized mean elevation-change rates (mm yr-1) for event-driven to multi-decadal time periods at the Florida Keys \nReef Tract Table 5. Annualized mean elevation-change rates (mm yr-1) for event-driven to multi-decadal time per\nReef Tract. elevation-change rates (mm yr-1) for event-driven to multi-decadal time periods at the Florida Keys a = calculated assuming a total time-period of 16.5 months (13.5 month pre- to 3 months post-storm); b = total time period 13.5 months (3 to 16.5 months \npost-storm); c = total time-period approximately 30 months (13.5 months pre- to 16.5 months post-storm); d = using data from Yates et al., 2019; e = using \ndata from Yates et al., 2017; f = using data from Fehr et al., 2021; na = no data available. 650 Numerous field reconnaissance observations immediately after the passing of Hurricane Irma indicated \nbroadscale sediment deposition across the FRT due to the storm (e.g., Viehman et al., 2018; Walker, 2018; Wilson et al., 2020; \nKobelt et al. 2019). Our 2016 to 2019 elevation-change rate is consistent with annualized mean elevation-change rates from 2016 to 2019 for the Lower FRT from approximately Big Pine Key to Key West of 84 mm yr-1, and for the FRT from Miami \n635 \nto Key West of 76 mm yr-1 (Fehr et al., 2021), further suggesting that sediment distribution may have still been undergoing \npost-storm re-equilibration at our study site and along the broader FRT (Table 5). Collection and analysis of additional elevation-change data sets over shorter time-periods (e.g., seasonal to annual) could Collection and analysis of additional elevation-change data sets over shorter time-periods (e.g., seasonal to annual) could \nimprove characterization of post-storm elevation-change rates and duration of post-storm sediment re-equilibration periods \n640 \nrelative to persistent seasonal, interannual, decadal, and multi-decadal time periods. Our results also suggest that caution should \nbe used in selection of DEMs for use in elevation change and projection modelling to minimize bias that could result from \nselecting elevation surfaces that reflect periods of rapid elevation change due to storm impacts and periods of post-storm re-\nequilibration. improve characterization of post-storm elevation-change rates and duration of post-storm sediment re-equilibration periods \n640 \nrelative to persistent seasonal, interannual, decadal, and multi-decadal time periods. Our results also suggest that caution should \nbe used in selection of DEMs for use in elevation change and projection modelling to minimize bias that could result from \nselecting elevation surfaces that reflect periods of rapid elevation change due to storm impacts and periods of post-storm re-\nequilibration. 645 29 5 Conclusion Sediment erosion \n665 \nafter the storm (2017–2019) was moderately correlated with depth and was significantly greater in habitats that showed greater \naccumulation during the period including Hurricane Irma from 2016–2017, suggesting a period of rapid sediment re-\nequilibration after the storm. Historical satellite and aerial imagery show that major geomorphic features at this location \nincluding rubble fields, sand waves, and a sand lobe at the base of the spur-and-groove formation have persisted over the past several decades despite impacts from storms. However, our elevation-change results indicate these features are highly \n670 \nephemeral, migrating rapidly during storms, re-equilibrating to non-storm sea state conditions between storms, and periodically \nburying seafloor habitat such as seagrass. Such features and the area surrounding them likely represent localized areas of long- \nand short-term seafloor instability that could be less suitable for restoration of slow growing benthic species. Our observed several decades despite impacts from storms. However, our elevation-change results indicate these features are highly \n670 \nephemeral, migrating rapidly during storms, re-equilibrating to non-storm sea state conditions between storms, and periodically \nburying seafloor habitat such as seagrass. Such features and the area surrounding them likely represent localized areas of long- \nand short-term seafloor instability that could be less suitable for restoration of slow growing benthic species. Our observed 30 Data availability Elevation-change and multibeam bathymetric data are publicly available in U.S. Geological Survey Data Releases at \nhttps://doi.org/10.5066/P9CHC95D, \nhttps://doi.org/10.5066/P937LNZF, \nhttps://doi.org/10.5066/P9NXNX61, \nhttps://doi.org/10.5066/P9JTOOMB and https://doi.org/10.5066/P9P2V7L0. Lidar topobathymetric data are publicly available \n690 \nfrom \nthe \nNOAA \nOffice \nfor \nCoastal \nManagement \nat \nhttps://www.fisheries.noaa.gov/inport/item/63018 \nand \nhttps://www.fisheries.noaa.gov/inport/item/48373. Seafloor habitat data are publicly available from the Florida Fish and \nWildlife \nConservation \nCommission, \nFish \nand \nWildlife \nResearch \nInstitute \nat \nhttp://ocean.floridamarine.org/IntegratedReefMap/UnifiedReefTract.htm. p\ng\ng\np\n \n695 \nAuthor contribution \nKY and DZ conceptualized the research, data acquisition approach, and methodology for analysis of seafloor elevation data. KY, ZF, and DZ performed formal analysis of data. DZ developed the SECAT software for statistical analysis of elevation- \nand volume-change data. KY and ZF developed data interpretations and prepared the original manuscript draft with \ncontributions from SJ and DZ. All authors contributed to preparation of the final, published manuscript. 700 \n \nCompeting interests 695 \nAuthor contribution \nKY and DZ conceptualized the research, data acquisition approach, and methodology for analysis of seafloor elevation data. KY, ZF, and DZ performed formal analysis of data. DZ developed the SECAT software for statistical analysis of elevation- \nand volume-change data. KY and ZF developed data interpretations and prepared the original manuscript draft with \ncontributions from SJ and DZ. All authors contributed to preparation of the final, published manuscript. 700 Code availability Python script for the Seafloor Elevation Change Analysis Tool (SECAT), intended to be applied in ArcMap or ArcGIS Pro, is \npublicly \navailable \nas \na \nU. S. Geological \nSurvey \nsoftware \nrelease, \ndoi: \n10.5066/P9D5UUZ0, \nhttps://www.usgs.gov/software/seafloor-elevation-change-analysis-tool. Python script for the Seafloor Elevation Change Analysis Tool (SECAT), intended to be applied in ArcMap or ArcGIS Pro, is \npublicly \navailable \nas \na \nU. S. Geological \nSurvey \nsoftware \nrelease, \ndoi: \n10.5066/P9D5UUZ0, \nhttps://www.usgs.gov/software/seafloor-elevation-change-analysis-tool. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 rates of elevation change in the 16-month period after Hurricane Irma were one to two orders of magnitude greater than during \nthe past decade or multi-decadal period (Yates et al., 2017; 2019b) indicating seafloor sediments across all habitats may have \n675 \nstill been re-equilibrating to non-storm sea state conditions up January 2019. Higher resolution elevation-change data collected \nover seasonal and annual time periods could improve characterization and understanding of short-term (event-driven, seasonal, \ninterannual) and long-term (decadal to multi-decadal) rates and processes of seafloor change and help guide benthic habitat \npost-storm recovery and restoration efforts in topographically complex coral reef systems. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Acknowledgement Funding for this study was provided by the U.S. Geological Survey, Coastal and Marine Hazards and Resources Program and \n710 \nby 2018 Hurricane and Wildfire Supplemental Funding provided to the U.S. Geological Survey from the Additional \nSupplemental Appropriations for Disaster Relief Requirements Act of 2018 (P.L. 115-123). We would like to thank J.J. Fredericks, B.J. Reynolds, and A.S. Farmer for collection of the multibeam bathymetry data and J.J. Fredericks for \ndevelopment of the associated multibeam digital elevation model. We also thank J. Zieg for assistance with development of Funding for this study was provided by the U.S. Geological Survey, Coastal and Marine Hazards and Resources Program and \n710 \nby 2018 Hurricane and Wildfire Supplemental Funding provided to the U.S. Geological Survey from the Additional \nSupplemental Appropriations for Disaster Relief Requirements Act of 2018 (P.L. 115-123). We would like to thank J.J. Fredericks, B.J. Reynolds, and A.S. Farmer for collection of the multibeam bathymetry data and J.J. Fredericks for \ndevelopment of the associated multibeam digital elevation model. We also thank J. Zieg for assistance with development of the SECAT software and K. Murphy for assistance with development of methods for sub-sampling large-scale digital elevation \n715 \nmodels. We greatly appreciate reviews of the original draft manuscript and constructive comments from L. Toth and G. Hatcher. Competing interests The authors declare that they have no conflict of interest. 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Viehman, S., Gittings, S., Groves, S., Moore, J., Moore, T., and Stein, J.: NCCOS Assessment: Coral Disturbance Response \n1005 \nMonitoring (DRM) along the Florida Reef Tract Following Hurricane Irma from 2017-10-09 to 2017-10-18 (NCEI Accession \n0179071), NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/sscd-6h41, 2018. 40 Zitello, A. G., Bauer, L. J., Battista, T. A., Mueller, P. W., Kendall, M. S., and Monaco, M. E.: Shallow-water benthic \nhabitats of St. John, U.S. Virgin Islands, NOAA technical memorandum NOS NCCOS, 96, 53 pp. https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. 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II, 54, 1163–1200, https://doi.org/10.1016/j.dsr2.2007.04.014, 2007. 1015 \n \nWalton, C., Hayes, N. K., and Gilliam, D. S.: Impacts of a regional, multi-year, multi-species coral disease outbreak in \nsoutheast Florida, Frontiers in Marine Science, 5, 323, doi:10.3389/fmars.2018.00323, 2018. Walton, C., Hayes, N. K., and Gilliam, D. S.: Impacts of a regional, multi-year, multi-species coral disease outbreak in \nsoutheast Florida, Frontiers in Marine Science, 5, 323, doi:10.3389/fmars.2018.00323, 2018. Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a Coupled Ocean-Atmsophere-Wave-Sediment \n1020 \nTransport (COAWST) Modeling System, Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010. Williams, D., and Miller, M.: Attributing mortality among drivers of population decline in Acropora palmata in the Florida \nKeys (USA), Coral Reefs, 31, 369–382, doi: 10.1007/s00338-011-0847-y, 2012. Williams, D., and Miller, M.: Attributing mortality among drivers of population decline in Acropora palmata in the Florida \nKeys (USA), Coral Reefs, 31, 369–382, doi: 10.1007/s00338-011-0847-y, 2012. Wilkinson, C. R.: Global change and coral reefs: impacts on reefs, economies and human culture, Glob. Change Biol., 2, 547–\n558, doi:10.1111/j.1365-2486.1996.tb00066.x, 1996. 2045, https://doi.org/10.5194/nhess-18-2041-2018, 2018. 1035 \n \nYates, K. K., Arsenault, S. R., Fehr, Z. W., and Murphy, K. A.: Seafloor elevation change from the 1930s to 2016 along the \nFlorida Reef Tract, USA, U. S. Geological Survey data release, USGS [data set], https://doi.org/10.5066/P9NXNX61, 2021. Yates, K. K., Zawada, D. G., Smiley, N. A., and Tiling-Range, G.: Divergence of seafloor elevation and sea level rise in coral \n1040 \nreef ecosystems, Biogosciences, 14(6), 1739–1772, doi:10.5194/bg-14-1739-2017, 2017. 41 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 https://doi.org/10.5194/egusphere-2023-3000\nPreprint. Discussion started: 22 December 2023\nPub ic domain CC\n1.0. l\n. 0 Yates, K.K., Zawada, D. G., and Arsenault, S.R.: Seafloor elevation change from 2016 to 2017 at Looe Key, Florida Keys—\nImpacts from Hurricane Irma (ver. 2.0, November 2020), U.S. Geological Survey data release, USGS [data set], \nhttps://doi.org/10.5066/P937LNZF, 2019a. 1045 \n \nYates, K. K., Zawada, D. G., Murphy, K. A., and Arsenault, S. R.: Seafloor elevation change from 2004 to 2016 at Looe Key, \nFlorida Keys, U.S. Geological Survey data release, USGS [data set], https://doi.org/10.5066/P9JTOOMB, 2019b. Yates, K.K., Zawada, D. G., and Arsenault, S.R.: Seafloor elevation change from 2016 to 2017 at Looe Key, Florida Keys—\nImpacts from Hurricane Irma (ver. 2.0, November 2020), U.S. Geological Survey data release, USGS [data set], \nhttps://doi.org/10.5066/P937LNZF, 2019a. 5 Yoshida, K., Kajikawa, Y., Nishiyama, S., Islam, M.T., Adachi, S., and Sakai, K.: Three-dimensional numerical modelling \n1050 \nof floods in river corridor with complex vegetation quantified using airborne LiDAR imagery, J. Hydraul. Res., 1–21, \nhttps://www.tandfonline.com/doi/full/10.1080/00221686.2022.2106596, 2022. Zieg, J. A. and Zawada, D. G.: Seafloor Elevation Change Analysis Tool, U. S. Geological Survey software release, USGS \n[code], doi: 10.5066/P9D5UUZ0, 2021. 1055 Zitello, A. G., Bauer, L. J., Battista, T. A., Mueller, P. W., Kendall, M. S., and Monaco, M. E.: Shallow-water benthic \nhabitats of St. John, U.S. Virgin Islands, NOAA technical memorandum NOS NCCOS, 96, 53 pp. 42 42" |
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Targets Lei Wang, Jianbin Huang*, Yong Luo, Yao Yao, Zongci Zhao Lei Wang, Jianbin Huang*, Yong Luo, Yao Yao, Zongci Zhao
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tained the superfluity of the aliment. Aristotle
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ARTICLE
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