Philippa Kaur

DEFRA. Strategy for Achieving Officially Bovine Tuberculosis Free Status for England: The ‘edge area’ strategy. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/300447/pb14088-bovine-tb-strategy-140328.pdf (2014).Campbell, E. L. et al. Interspecific visitation of cattle and badgers to fomites: A transmission risk for bovine tuberculosis?. Ecol. Evol. 9(15), 8479–8489 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Roberts, T., O’Connor, C., Nuñez-Garcia, J., De La Rua-Domenech, R. & Smith, N. H. Unusual cluster of Mycobacterium bovis infection in cats. Vet. Rec. 174(13), 326–326 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Phipps, E. et al. Bovine tuberculosis in working foxhounds: Lessons learned from a complex public health investigation. Epidemiol. Infect. 147, 1–6 (2019).Article 

Google Scholar 
Delahay, R. J., De Leeuw, A. N. S., Barlow, A. M., Clifton-Hadley, R. S. & Cheeseman, C. L. The status of Mycobacterium bovis infection in UK wild mammals: A review. Vet. J. 164(2), 90–105 (2002).Article 
CAS 
PubMed 

Google Scholar 
Fitzgerald, S. D. & Kaneene, J. B. Wildlife reservoirs of bovine tuberculosis worldwide: Hosts, pathology, surveillance, and control. Vet. Pathol. 50(3), 488–499 (2013).Article 
CAS 
PubMed 

Google Scholar 
Skuce, R. A., Allen, A. R. & McDowell, S. W. J. Herd-level risk factors for bovine tuberculosis: A literature review. Vet Med Int 2012, 621210 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Ayele, W. Y., Neill, S. D., Zinsstag, J., Weiss, M. G. & Pavlik, I. Bovine tuberculosis: An old disease but a new threat to Africa. Int. J. Tuberc. Lung Dis. 8(8), 924–937 (2004).CAS 
PubMed 

Google Scholar 
Gallagher, J. & Clifton-Hadley, R. S. Tuberculosis in badgers; a review of the disease and its significance for other animals. Res. Vet. 69(3), 203–217 (2000).Article 
CAS 

Google Scholar 
Allen, A. et al. Genome epidemiology of Mycobacterium bovis infection in contemporaneous, sympatric badger and cattle populations in Northern Ireland. Access Microbiol. 1(1A), 385 (2019).Article 

Google Scholar 
APHA. Bovine Tuberculosis in England in 2020—Epidemiological analysis of the 2020 data and historical trends. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1027591/tb-epidemiological-report-2020.pdf (2021).DAERA. Tuberculosis disease statistics in Northern Ireland 2022. https://www.daera-ni.gov.uk/publications/tuberculosis-disease-statistics-northern-ireland-2022 (2022).Woodroffe, R. et al. Effects of culling on badger Meles meles spatial organization: Implications for the control of bovine tuberculosis. J. Appl. Ecol. 43(1), 1–10 (2006).Article 

Google Scholar 
Byrne, A. W., Paddy Sleeman, D., O’Keeffe, J. & Davenport, J. The ecology of the European badger (Meles meles) in Ireland: A review. Biol. Environ. 112, 105–132 (2012).Article 

Google Scholar 
McDonald, J., Robertson, A. & Silk, M. Wildlife disease ecology from the individual to the population: Insights from a long-term study of a naturally infected European badger population. J. Anim. Ecol. 87(1), 101–112 (2017).Article 
PubMed 

Google Scholar 
Macdonald, D. W., Newman, C. & Buesching, C. D. Badgers in the rural landscape—conservation paragon or farmland pariah? Lessons from the Wytham Badger Project. Wildlife conservation on farmland 2, 65–95 (2015).
Google Scholar 
Judge, J., Wilson, G. J., Macarthur, R., McDonald, R. A. & Delahay, R. J. Abundance of badgers (Meles meles) in England and Wales. Sci. Rep. 7(1), 1–8 (2017).Article 
CAS 

Google Scholar 
Feore, S. & Montgomery, W. I. Habitat effects on the spatial ecology of the European badger (Meles meles). J. Zool. 247(4), 537–549 (1999).Article 

Google Scholar 
Reid, N., Etherington, T. R., Wilson, G. J., Montgomery, W. I. & McDonald, R. A. Monitoring and population estimation of the European badger Meles meles in Northern Ireland. Wildlife Biol. 18(1), 46–57 (2012).Article 

Google Scholar 
DAERA. Farm animal populations: Cattle populations in Northern Ireland from 1981 to 2019. https://www.daera-ni.gov.uk/publications/farm-animal-population-data (2019).DEFRA. Livestock numbers in the UK (data to December 2019). https://www.gov.uk/government/statistical-data-sets/structure-of-the-livestock-industry-in-england-at-december.39 (2020).DEFRA. Setting the minimum and maximum numbers in badger cull areas in 2021—Advice to Natural England. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1015421/tb-min-max-numbers-2021.pdf (2021).Griffin, J. M. et al. The impact of badger removal on the control of tuberculosis in cattle herds in Ireland. Prev. Vet. Med. 67(4), 237–266 (2005).Article 
CAS 
PubMed 

Google Scholar 
Ham, C., Donnelly, C. A., Astley, K. L., Jackson, S. Y. B. & Woodroffe, R. Effect of culling on individual badger Meles meles behaviour: Potential implications for bovine tuberculosis transmission. J. Appl. Ecol. 56(11), 2390–2399 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Olea-Popelka, F. J. et al. Targeted badger removal and the subsequent risk of bovine tuberculosis in cattle herds in county Laois, Ireland. Prev. Vet. Med. 88(3), 178–184 (2009).Article 
CAS 
PubMed 

Google Scholar 
Donnelly, C. A. et al. Positive and negative effects of widespread badger culling on tuberculosis in cattle. Nature 439(7078), 843–846 (2006).Article 
CAS 
PubMed 

Google Scholar 
Byrne, A. W., White, P. W., McGrath, G., O’Keeffe, J. & Martin, S. W. Risk of tuberculosis cattle herd breakdowns in Ireland: Effects of badger culling effort, density and historic large-scale interventions. Vet. Res. 45(1), 1–10 (2014).Article 

Google Scholar 
Wright, D. M. et al. Herd-level bovine tuberculosis risk factors: Assessing the role of low-level badger population disturbance. Sci. Rep. 5, 1–11 (2015).Article 

Google Scholar 
Jenkins, H. E., Woodroffe, R. & Donnelly, C. A. The duration of the effects of repeated widespread badger culling on cattle tuberculosis following the cessation of culling. PLoS ONE 5(2), e9090 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Tuyttens, F. A. M. et al. Spatial perturbation caused by a badger (Meles meles) culling operation: Implications for the function of territoriality and the control of bovine tuberculosis (Mycobacterium bovis). J. Anim. Ecol. 69(5), 815–828 (2000).Article 
CAS 
PubMed 

Google Scholar 
Carter, S. P. et al. Culling-induced social perturbation in Eurasian badgers Meles meles and the management of TB in cattle: An analysis of a critical problem in applied ecology. Proc. R. Soc. B. 274(1626), 2769–2777 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Donnelly, C. A. et al. Impact of localized badger culling on tuberculosis incidence in British cattle. Nature 426(6968), 834–837 (2003).Article 
CAS 
PubMed 

Google Scholar 
Vicente, J., Delahay, R. J., Walker, N. J. & Cheeseman, C. L. Social organization and movement influence the incidence of bovine tuberculosis in an undisturbed high-density badger Meles meles population. J Anim Ecol. 76(2), 348–360 (2007).Article 
CAS 
PubMed 

Google Scholar 
Riordan, P., Delahay, R. J., Cheeseman, C., Johnson, P. J. & Macdonald, D. W. Culling-induced changes in badger (Meles meles) behaviour, social organisation and the epidemiology of bovine tuberculosis. PLoS ONE 6(12), e28904 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kowalczyk, R., Jȩdrzejewska, B. & Zalewski, A. Annual and circadian activity patterns of badgers (Meles meles) in Białowieża Primeval Forest (eastern Poland) compared with other palaearctic populations. J. Biogeogr. 30(3), 463–472 (2003).Article 

Google Scholar 
Smith, G. C., Delahay, R. J., McDonald, R. A. & Budgey, R. Model of selective and non-selective management of badgers (Meles meles) to control bovine tuberculosis in badgers and cattle. PLoS ONE 11(11), e0167206 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Garnett, B. T., Delahay, R. J. & Roper, T. J. Ranging behaviour of European badgers (Meles meles) in relation to bovine tuberculosis (Mycobacterium bovis) infection. Appl. Anim. Behav. Sci. 94(3–4), 331–340 (2005).Article 

Google Scholar 
Weber, N. et al. Badger social networks correlate with tuberculosis infection. Curr. 23(20), 915–916 (2013).Article 

Google Scholar 
Ellwood, S. A. et al. An active-radio-frequency-identification system capable of identifying co-locations and social-structure: Validation with a wild free-ranging animal. Methods Ecol. Evol. 8(12), 1822–1831 (2017).Article 

Google Scholar 
Noonan, M. et al. A new Magneto-Inductive tracking technique to uncover subterranean activity: what do animals do underground?. Methods Ecol. Evol. 6(5), 510–520 (2015).Article 

Google Scholar 
Schütz, K. et al. Behavioral and physiological responses of trap-induced stress in European badgers. J. Wildl. Manag. 70(3), 884–891 (2006).Article 

Google Scholar 
Clinchy, M. et al. Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore. Behav. Ecol. 27(6), 1826–1832 (2016).
Google Scholar 
Bidder, O. R. et al. Step by step: Reconstruction of terrestrial animal movement paths by dead-reckoning. Mov. Ecol. https://doi.org/10.1186/s40462-015-0055-4 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gunner, R. M. et al. Dead-reckoning animal movements in R: a reappraisal using Gundog. Tracks. Anim. Biotelem. 9(1), 1–37 (2021).
Google Scholar 
McClune, D. W., Marks, N. J., Delahay, R. J., Montgomery, W. I. & Scantlebury, D. M. Behaviour-time budget and functional habitat use of a free-ranging European badger (Meles meles). Anim. Biotelem. 3(7), 1–7 (2015).
Google Scholar 
McClune, D. et al. Tri-axial accelerometers quantify behaviour in the Eurasian badger (Meles meles): towards an automated interpretation of field data. Anim. Biotelem. 2(1), 1–6 (2014).Article 

Google Scholar 
Gaughran, A. et al. Dispersal patterns in a medium-density Irish badger population: Implications for understanding the dynamics of tuberculosis transmission. Ecol. Evol. 9(23), 13142–13152 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Kelly, D. J. et al. Extra Territorial Excursions by European badgers are not limited by age, sex or season. Sci. Rep. 10(1), 1–2 (2020).Article 

Google Scholar 
Macdonald, D. W., Newman, C., Buesching, C. D. & Johnson, P. J. Male-biased movement in a high-density population of the Eurasian badger (Meles meles). J. Mammal. 89(5), 1077–1086 (2008).Article 

Google Scholar 
Courcier, E. A. et al. Evaluating the application of the dual path platform VetTB test for badgers (Meles meles) in the test and vaccinate or remove (TVR) wildlife research intervention project in Northern Ireland. Res. Vet. Sci. 130, 170–178 (2020).Article 
CAS 
PubMed 

Google Scholar 
Menzies, F. D. et al. Test and vaccinate or remove: Methodology and preliminary results from a badger intervention research project. Vet. Rec. 189, e248 (2021).Article 
PubMed 

Google Scholar 
O’Hagan, M. J. H. et al. Effect of selective removal of badgers (Meles meles) on ranging behaviour during a “test and Vaccinate or Remove” intervention in Northern Ireland. Epidemiol. Infect. 149(1), e125 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Roper, T. J. The structure and function of badger setts. J. Zool. 227(4), 691–698 (1992).Article 

Google Scholar 
DAERA. The Test and Vaccinate or Remove (TVR) Wildlife Intervention Research Project. Year 1 Report—2014. https://www.daera-ni.gov.uk/sites/default/files/publications/dard/tvr-year-1-report.pdf (2014).Brown, E., Cooney, R. & Rogers, F. Veterinary guidance on the practical use of the BadgerBCG tuberculosis vaccine. In Pract. 35(3), 143–146 (2013).Article 

Google Scholar 
Magowan, E. A. et al. Dead-reckoning elucidates fine-scale habitat use by European badgers Meles meles. Anim. Biotelem. 10(1), 1–11 (2022).Article 

Google Scholar 
McGill, K. et al. Seroconversion against antigen MPB83 in badgers (Meles meles) vaccinated with multiple doses of BCG strain Sofia. Res. Vet. Sci. 149, 119–124. https://doi.org/10.1016/j.rvsc.2022.06.011 (2022).Article 
CAS 
PubMed 

Google Scholar 
Gaughran, A. et al. Super-ranging. A new ranging strategy in European badgers. PLoS ONE 13(2), e0191818 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Williams, H. J. et al. Identification of animal movement patterns using tri-axial magnetometry. Mov. Ecol. 5(1), 6 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Brendel C, Helder R, Chevallier D, Zaytoon J, Georges JY, and Handrich Y. Testing a global positioning system on free ranging badgers Meles meles. Mammal Notes, The Mammal Society, Southampton. https://www.mammal.org.uk/wp-content/uploads/2016/04/Note–Brendel-MN-2012-1.pdf (2012).Börger, L. et al. Effects of sampling regime on the mean and variance of home range size estimates. J. Anim. Ecol. 75(6), 1393–1405 (2006).Article 
PubMed 

Google Scholar 
Calenge, C. The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Modell. 197(3–4), 516–519 (2006).Article 

Google Scholar 
Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An r package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7(9), 1124–1132 (2016).Article 

Google Scholar 
QGIS.org. QGIS Geographic Information System. QGIS Association. https://qgis.org/en/site/ (2021).Fleming, C. H. et al. Rigorous home range estimation with movement data: A new autocorrelated kernel density estimator. Ecology 96(5), 1182–1188 (2015).Article 
CAS 
PubMed 

Google Scholar 
Fleming, C. H. et al. Estimating where and how animals travel: An optimal framework for path reconstruction from autocorrelated tracking data. Ecology 97(3), 576–582 (2016).CAS 
PubMed 

Google Scholar 
Fleming, C. H. et al. Correcting for missing and irregular data in home-range estimation. Ecol. Appl. 28(4), 1003–1010 (2018).Article 
CAS 
PubMed 

Google Scholar 
Gula, R. & Theuerkauf, J. The need for standardization in wildlife science: Home range estimators as an example. Eur. J. Wildl. Res. 59, 713–718 (2013).Article 

Google Scholar 
Schuler, K. L., Schroeder, G. M., Jenks, J. A. & Kie, J. G. Ad hoc smoothing parameter performance in kernel estimates of GPS-derived home ranges. Wildl. Biol. 20(5), 259–266 (2014).Article 

Google Scholar 
Huck, M., Davison, J. & Roper, T. J. Comparison of two sampling protocols and four home-range estimators using radio-tracking data from urban badgers Meles meles. Wildl. Biol. 14(4), 467–477 (2008).Article 

Google Scholar 
Scull, P., Palmer, M., Frey, F. & Kraly, E. A comparison of two home range modeling methods using Ugandan mountain gorilla data. Int. J. Geogr. Inf. Sci. 26(11), 2111–2121 (2012).Article 

Google Scholar 
Woodroffe, R. et al. Ranging behaviour of badgers Meles meles vaccinated with Bacillus Calmette Guerin. J. Appl. Ecol. 54(3), 718–725 (2017).Article 

Google Scholar 
Signer, J. & Fieberg, J. R. A fresh look at an old concept: Home-range estimation in a tidy world. PeerJ 9, e11031 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Woodroffe, R. et al. Badgers prefer cattle pasture but avoid cattle: implications for bovine tuberculosis control. Ecology 19(10), 1201–1208 (2016).
Google Scholar 
Hijmans RJ. Introduction to the geosphere package (version 1 .5–10). Cran (2019).Dewhirst, O. P. et al. Improving the accuracy of estimates of animal path and travel distance using GPS drift-corrected dead reckoning. Ecol. Evol. 6(17), 6210–6222 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
QGIS.org. Working with vector data. QGIS Desktop 3.16 User Guide. pp 304. https://docs.qgis.org/3.22/en/docs/user_manual/index.html (2022).Qasem, L. et al. Tri-axial acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector?. PLoS ONE 7(2), e31187 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies; a reappraisal. J anim Ecol. 89(1), 161–172 (2020).Article 
PubMed 

Google Scholar 
RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA http://www.rstudio.com/ (2021).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Barton K. Package “MuMin”. Cran (2018).Rogers, L. M., Cheeseman, C. L., Mallinson, P. J. & Clifton-Hadley, R. The demography of a high-density badger (Meles meles) population in the west of England. J. Zool. 242(4), 705–728 (1997).Article 

Google Scholar 
Macdonald, D. W. & Newman, C. Population dynamics of badgers (Meles meles) in Oxfordshire, UK: Numbers, density and cohort life histories, and a possible role of climate change in population growth. J. Zool. 256(1), 121–138 (2002).Article 

Google Scholar 
Kruuk, H., & MacDonald, D. Group territories of carnivores: empires and enclaves. In 25th Symposium of the British Ecological Society (1985).Roper, T. J., Shepherdson, D. J. & Davies, J. M. Scent marking with faeces and anal secretion in the European badger (Meles meles): seasonal and spatial characteristics of latrine use in relation to territoriality. Behaviour 97(1–2), 94–117 (1986).
Google Scholar 
Sleeman, D. P. et al. How many Eurasian badgers Meles meles L. are there in the republic of Ireland?. Eur. J. Wildl. Res. 55(4), 333–344 (2009).Article 

Google Scholar 
Carter, S. P. et al. BCG vaccination reduces risk of tuberculosis infection in vaccinated badgers and unvaccinated badger cubs. PLoS ONE 7(12), e49833 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Byrne, A., Parnell, A., O’Keeffe, J. & Madden, J. The challenge of estimating wildlife populations at scale: the case of the European badger (Meles meles) in Ireland. Eur. J. Wildl. Res. 67(5), 1–10 (2021).Article 

Google Scholar 
Minta, S. C. Sexual differences in spatio-temporal interaction among badgers. Oecologia 96(3), 402–409 (1993).Article 
PubMed 

Google Scholar 
Annavi, G. et al. Neighbouring-group composition and within-group relatedness drive extra-group paternity rate in the European badger (Meles meles). J. Evol. Biol. 27(10), 2191–2203 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
DEFRA. Monitoring regional changes in badger numbers. Research Project Final Report. http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&ProjectID=14237. Accessed 07 February 2023 (2009).Johnson, D. D., Jetz, W. & Macdonald, D. W. Environmental correlates of badger social spacing across Europe. J. Biogeogr. 29(3), 411–425 (2002).Article 

Google Scholar 
Kruuk, H. Spatial organization and territorial behaviour of the European badger Meles meles. J Zool. 184(1), 1–19 (1978).Article 

Google Scholar 
Macdonald, D., Newman, C., Dean, J., Buesching, C. & Johnson, P. The distribution of Eurasian badger, Meles meles, setts in a high-density area: field observations contradict the sett dispersion hypothesis. Oikos 106(2), 295–307 (2004).Article 

Google Scholar 
Sleeman, D. P. & Mulcahy, M. F. Loss of territoriality in a local badger Meles meles population at Kilmurry, Co Cork, Irealnd. Irish Nat. J. 28(1), 11–19 (2005).
Google Scholar 
Byrne, A. W., O’Keeffe, J., Buesching, C. D. & Newman, C. Push and pull factors driving movement in a social mammal: Context dependent behavioral plasticity at the landscape scale. Curr. Zool. 65(5), 517–525 (2019).Article 
PubMed 

Google Scholar 
Cheeseman, C. L., Cresswell, W. J., Harris, S. & Mallinson, P. J. Comparison of dispersal and other movements in two Badger (Meles meles) populations. Mamm. Rev. 18(1), 51–59 (1988).Article 

Google Scholar 
Seebacher, F. & Krause, J. Epigenetics of social behaviour. TREE 34(9), 818–830 (2019).PubMed 

Google Scholar 
Allen, A. et al. European badger (Meles meles) responses to low-intensity, selective culling: Using mark–recapture and relatedness data to assess social perturbation. Ecol. Solut. Evid. 3(3), e12165 (2022).Article 

Google Scholar 
Loureiro, F., Rosalino, L. M., Macdonald, D. W. & Santos-Reis, M. Path tortuosity of Eurasian badgers (Meles meles) in a heterogeneous Mediterranean landscape. Ecol. Res. 22(5), 837–844 (2007).Article 

Google Scholar 
Sun, Q., Stevens, C., Newman, C., Buesching, C. & Macdonald, D. Cumulative experience, age-class, sex and season affect the behavioural responses of European badgers (Meles meles) to handling and sedation. Anim Welf. 24(4), 373–385 (2015).Article 

Google Scholar 
Conlan, A. et al. Potential benefits of cattle vaccination as a supplementary control for bovine tuberculosis. PLoS Comput. Biol. 11(2), e1004038 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gormley, E. et al. Oral vaccination of free-living badgers (Meles meles) with Bacille Calmette Guérin (BCG) vaccine confers protection against tuberculosis. PLoS ONE 12(1), e0168851 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Benton, C. H. et al. Badger vaccination in England: Progress, operational effectiveness and participant motivations. People Nat. 2(3), 761–775 (2020).Article 

Google Scholar  More

Shaw, J., Weyrich, L. & Cooper, A. Using environmental (e)DNA sequencing for aquatic biodiversity surveys: A beginner’s guide. Mar. Freshw. Res. 68, 68 (2016).
Google Scholar 
Smith, K. J. et al. Stable isotope analysis of specimens of opportunity reveals ocean-scale site fidelity in an elusive whale species. Front. Conserv. Sci. 2, 1–11 (2021).Article 

Google Scholar 
Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS One 5, (2010).Cavanagh, R. D. & Gibson, C. Overview of the conservation status of cartilaginous fishes (Chondrichthyans) in the Mediterranean Sea. https://doi.org/10.2305/iucn.ch.2007.mra.3.en (2007).Pace, D. S., Tizzi, R. & Mussi, B. Cetaceans value and conservation in the Mediterranean Sea. Journal Biodivers. Endanger. Species S1:
S1.004 (2015).Carlucci, R. et al. Modeling the spatial distribution of the striped dolphin (Stenella coeruleoalba) and common bottlenose dolphin (Tursiops truncatus) in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). Ecol. Indic. 69, 707–721 (2016).Article 

Google Scholar 
Boldrocchi, G. et al. Distribution, ecology, and status of the white shark, Carcharodon carcharias, in the Mediterranean Sea. Rev. Fish Biol. Fish. 27, 515–534 (2017).Article 

Google Scholar 
Karamanlidis, A. A. et al. The Mediterranean monk seal Monachus monachus: Status, biology, threats, and conservation priorities. Mammal Review 46, 92–105. https://doi.org/10.1111/mam.12053 (2016).Article 

Google Scholar 
Johnson, W. M. The role of the Mediterranean monk seal (Monachus monachus) in European history and culture, from the fall of Rome to the 20th century Monk Seals in Post-Classical History. (2004).Johnson, W. M. & Lavigne, D. M. The Mediterranean Monk Seal (Monachus monachus) in Ancient History and Literature Monk Seals in Antiquity. (1999).Israëls, l. D. Thirty Years of Mediterranean Monk Seal Protection – A Review. Netherlands Com- Mission Int. Nat. Prot. Inst. voor Taxon. Zoölogie/Zoölogische Museum, Univ. van Amsterdam, Amsterdam, Netherlands. Meded. No. 281–65. (1992).Stringer, C. B. et al. Neanderthal exploitation of marine mammals in Gibraltar. Proc. Natl. Acad. Sci. U. S. A. 105, 14319–14324 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
La Mesa, G., Lauriano, G., Mo, G., Paglialonga, A. & Tunesi, L. Assessment of the conservation status of marine species of the Habitats Directive (92/43/EEC) in Italy: results, drawbacks and perspectives of the fourth national report (2013–2018). Biodivers Conserv (2021).Adamantopoulou, S., Karamanlidis, A. A., Dendrinos, P. & Gimenez, O. Citizen science indicates significant range recovery and defines new conservation priorities for Earth’s most endangered pinniped in Greece. Anim. Conserv. https://doi.org/10.1111/acv.12806 (2022).Article 

Google Scholar 
Nicolaou, H., Dendrinos, P., Marcou, M., Michaelides, S. & Karamanlidis, A. A. Re-establishment of the Mediterranean monk seal Monachus monachus in Cyprus: Priorities for conservation. Oryx 55, 526–528 (2021).Article 

Google Scholar 
Tenan, S. et al. Evaluating mortality rates with a novel integrated framework for nonmonogamous species. Conserv. Biol. 30, 1307–1319 (2016).Article 
PubMed 

Google Scholar 
Vanpe, C. et al. Estimating abundance of a recovering transboundary brown bear population with capture- recapture models. Peer Community Journal, 2, e71. (2022).Lecaudey, L. A., Schletterer, M., Kuzovlev, V. V., Hahn, C. & Weiss, S. J. Fish diversity assessment in the headwaters of the Volga River using environmental DNA metabarcoding. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 1785–1800 (2019).Article 

Google Scholar 
Itakura, H. et al. Environmental DNA analysis reveals the spatial distribution, abundance, and biomass of Japanese eels at the river-basin scale. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 361–373 (2019).Article 

Google Scholar 
Closek, C. J. et al. Marine vertebrate biodiversity and distribution within the central California current using environmental DNA (eDNA) metabarcoding and ecosystem surveys. Front. Mar. Sci. Vol. 6. (2019).Boldrocchi, G. & Storai, T. Data-mining social media platforms highlights conservation action for the Mediterranean Critically Endangered blue shark Prionace glauca. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 3087–3099 (2021).Article 

Google Scholar 
Thiel, M. et al. Citizen scientists and marine research: Volunteer participants, their contributions, and projection for the future. Oceanogr. Mar. Biol. An Annu. Rev. 52, 257–314 (2014).
Google Scholar 
Araujo, G. et al. Citizen science sheds light on the cryptic ornate eagle ray Aetomylaeus vespertilio. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 2012–2018 (2020).Article 

Google Scholar 
Silvertown, J. A new dawn for citizen science. Trends Ecol. Evol. 24, 467–471 (2009).Article 
PubMed 

Google Scholar 
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).Article 

Google Scholar 
Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, (2014).Strickler, K. M., Fremier, A. K. & Goldberg, C. S. Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol. Conserv. 183, 85–92 (2015).Article 

Google Scholar 
Eichmiller, J., Best, S. E. & Sorensen, P. W. Effects of temperature and trophic state on degradation of environmental DNA in lake water. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.5b05672 (2016).Article 
PubMed 

Google Scholar 
Mächler, E., Osathanunkul, M. & Altermatt, F. Shedding light on eDNA: neither natural levels of UV radiation nor the presence of a filter feeder affect eDNA-based detection of aquatic organisms. PLoS ONE 13, 1–15 (2018).Article 

Google Scholar 
Jo, T., Murakami, H., Yamamoto, S., Masuda, R. & Minamoto, T. Effect of water temperature and fish biomass on environmental DNA shedding, degradation, and size distribution. Ecol. Evol. 9, 1135–1146 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mauvisseau, Q. et al. The multiple states of environmental DNA and what is known about their persistence in aquatic environments. Environ. Sci. Technol. 56, 5322–5333 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Valsecchi, E. et al. A species – specific qPCR assay provides novel insight into range expansion of the Mediterranean monk seal (Monachus monachus ) by means of eDNA analysis. Biodivers. Conserv. 31, 1175–1196 (2022).Article 

Google Scholar 
Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. https://doi.org/10.1038/s42003-018-0192-6 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao, B., P.M., B. & Timbros, K. The particle size distribution of environmental DNA varies with species and degradation. Sci. Total Environ. 797, 149175 (2021).Würtz, M. Mediterranean submarine canyons. in Ecology and Governance (ed. IUCN) 192 (2012).Valsecchi, E. et al. Ferries and environmental DNA: Underway sampling from commercial vessels provides new opportunities for systematic genetic surveys of marine biodiversity. Front. Mar. Sci. 8, 1–17 (2021).Article 

Google Scholar 
Bustin, S. A. et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 622, 611–622 (2009).Article 

Google Scholar 
Klymus, K. E. et al. Reporting the limits of detection and quantification for environmental DNA assays. Environ. DNA 1–12. https://doi.org/10.1002/edn3.29 (2019).Goldberg, G. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 1299–1307. https://doi.org/10.1111/2041-210X.12595 (2016).Farrell, J. A. et al. Detection and population genomics of sea turtle species via noninvasive environmental DNA analysis of nesting beach sand tracks and oceanic water. Mol. Ecol. Resour. (2022).Shamblin, B. M. et al. Loggerhead turtle eggshells as a source of maternal nuclear genomic DNA for population genetic studies. Mol. Ecol. Resour. 11, 110–115 (2011).Article 
PubMed 

Google Scholar 
MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).Article 

Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 37–41 (1999).Akaike, H. Information theory and an extension of the maximum likelihood principle in Breakthroughs in Statistics, Vol.I, Foundations and Basic Theory, (eds. Kotz, S. and Johnson, N.L.) 610–624 (Springer-Verlag, New York, 1992).Adamantopoulou, S. et al. Movements of Mediterranean Monk Seals (Monachus monachus) in the Eastern Mediterranean Sea. Aquat. Mamm. 37, 256–261 (2011).Article 

Google Scholar  More

UNDESA. World urbanization prospects. Demographic Research 12, 1–103 (2018).Oke, C. et al. Cities should respond to the biodiversity extinction crisis. npj Urban Sustain. 1, 11 (2021).Article 

Google Scholar 
World Bank. A catalogue of nature-based solutions for urban resilience. www.worldbank.org (2021).Elmqvist, T. et al. Benefits of restoring ecosystem services in urban areas. Curr. Opin. Environ. Sustain. 14, 101–108 (2015).Article 

Google Scholar 
Aerts, R., Honnay, O. & Van Nieuwenhuyse, A. Biodiversity and human health: Mechanisms and evidence of the positive health effects of diversity in nature and green spaces. Br. Med. Bull. 127, 5–22 (2018).Article 

Google Scholar 
Reyes-Riveros, R. et al. Linking public urban green spaces and human well-being: A systematic review. Urban For. Urban Green 61, 127105 (2021).Article 

Google Scholar 
Bardgett, R. D. & Van Der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).Article 
CAS 

Google Scholar 
Mehring, A. S. & Levin, L. A. Potential roles of soil fauna in improving the efficiency of rain gardens used as natural stormwater treatment systems. J. Appl. Ecol. 52, 1445–1454 (2015).Article 

Google Scholar 
Brevik, E. C. et al. Soil and human health: current status and future needs. Air, Soil Water Res. 13, 1–23 (2020).Article 

Google Scholar 
Silver, W. L., Perez, T., Mayer, A. & Jones, A. R. The role of soil in the contribution of food and feed. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200181 (2021).Article 
CAS 

Google Scholar 
De Deyn, G. B. & Kooistra, L. The role of soils in habitat creation, maintenance and restoration. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200170 (2021).Article 

Google Scholar 
Samaddar, S. et al. Role of soil in the regulation of human and plant pathogens: Soils’ contributions to people. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200179 (2021).Article 

Google Scholar 
Thiele-Bruhn, S. The role of soils in provision of genetic, medicinal and biochemical resources. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200183 (2021).Article 
CAS 

Google Scholar 
O’Riordan, R., Davies, J., Stevens, C., Quinton, J. N. & Boyko, C. The ecosystem services of urban soils: A review. Geoderma 395, 115076 (2021).Article 

Google Scholar 
Banerjee, S. & Heijden, M. G. A. Soil microbiomes and one health. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-022-00779-w (2022).Schmidt, D. J. et al. Urbanization erodes ectomycorrhizal fungal diversity and may cause microbial communities to converge. Nat. Ecol. Evol. 1, 0123 (2017).Article 

Google Scholar 
Geisen, S., Wall, D. H. & van der Putten, W. H. Challenges and opportunities for soil biodiversity in the Anthropocene. Curr. Biol. 29, R1036–R1044 (2019).Article 
CAS 

Google Scholar 
Fenoglio, M. S., Rossetti, M. R. & Videla, M. Negative effects of urbanization on terrestrial arthropod communities: A meta-analysis. Glob. Ecol. Biogeogr. 29, 1412–1429 (2020).Article 

Google Scholar 
Guilland, C., Maron, P. A., Damas, O. & Ranjard, L. Biodiversity of urban soils for sustainable cities. Environ. Chem. Lett. 16, 1267–1282 (2018).Article 
CAS 

Google Scholar 
Milano, V. et al. The effect of urban park landscapes on soil Collembola diversity: A Mediterranean case study. Landsc. Urban Plan. 180, 135–147 (2018).Article 

Google Scholar 
Merckx, T. et al. Body-size shifts in aquatic and terrestrial urban communities. Nature 558, 113–116 (2018).Article 
CAS 

Google Scholar 
Zhu, Y. G. et al. Soil biota, antimicrobial resistance and planetary health. Environ. Int. 131, 105059 (2019).Article 

Google Scholar 
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity: A monitoring and indicator system can inform policy. Science 371, 239–241 (2021).Article 
CAS 

Google Scholar 
Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. R. Soc. B Biol. Sci. 281, 20141988 (2014).Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci. Adv. 7, eabg5809 (2021).Article 
CAS 

Google Scholar 
Braaker, S., Ghazoul, J., Obrist, M. K. & Moretti, M. Habitat connectivity shapes urban arthropod communities: the key role of green roofs. Ecology 95, 1010–1021 (2014).Article 
CAS 

Google Scholar 
Lin, B. B., Philpott, S. M. & Jha, S. The future of urban agriculture and biodiversity-ecosystem services: Challenges and next steps. Basic Appl. Ecol. 16, 189–201 (2015).Article 

Google Scholar 
Baruch, Z. et al. Increased plant species richness associates with greater soil bacterial diversity in urban green spaces. Environ. Res. 196, 110425 (2021).Article 
CAS 

Google Scholar 
Robinson, J. M. et al. Vertical stratification in urban green space aerobiomes. Environ. Health Perspect. 128, 1–12 (2020).Article 

Google Scholar 
Robinson, J. M. et al. Exposure to airborne bacteria depends upon vertical stratification and vegetation complexity. Sci. Rep. 11, 9516 (2021).Article 
CAS 

Google Scholar 
Nugent, A. & Allison, S. D. A framework for soil microbial ecology in urban ecosystems. Ecosphere 13, 1–20 (2022).Article 

Google Scholar 
Knop, E. Biotic homogenization of three insect groups due to urbanization. Glob. Chang. Biol. 22, 228–236 (2016).Article 

Google Scholar 
Li, X. et al. Management effects on soil nematode abundance differ among functional groups and land-use types at a global scale. J. Anim. Ecol. 91, 1770–1780 (2022).Article 

Google Scholar 
McKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst. 11, 161–176 (2008).Article 

Google Scholar 
Piano, E. et al. Urbanization drives cross-taxon declines in abundance and diversity at multiple spatial scales. Glob. Chang. Biol. 26, 1196–1211 (2020).Article 

Google Scholar 
Joimel, S. et al. Contrasting homogenization patterns of plant and collembolan communities in urban vegetable gardens. Urban Ecosyst. 22, 553–566 (2019).Article 

Google Scholar 
Ge, B., Mehring, A. S. & Levin, L. A. Urbanization alters belowground invertebrate community structure in semi-arid regions: A comparison of lawns, biofilters and sage scrub. Landsc. Urban Plan. 192, 103664 (2019).Article 

Google Scholar 
Tóth, Z. & Hornung, E. Taxonomic and functional response of millipedes (Diplopoda) to urban soil disturbance in a metropolitan area. Insects 11, 25 (2020).Article 

Google Scholar 
Selhorst, A. & Lal, R. Net carbon sequestration potential and emissions in home lawn turfgrasses of the United States. Environ. Manage. 51, 198–208 (2013).Article 

Google Scholar 
Cividini, S. & Montesanto, G. Aggregative behavior and intraspecific communication mediated by substrate-borne vibrations in terrestrial arthropods: An exploratory study in two species of woodlice. Behav. Process. 157, 422–430 (2018).Article 

Google Scholar 
Bray, N., Thompson, G. L., Fahey, T., Kao-Kniffin, J. & Wickings, K. Soil macroinvertebrates alter the fate of root and rhizosphere carbon and nitrogen in a turfgrass lawn. Soil Biol. Biochem. 148, 107903 (2020).Article 
CAS 

Google Scholar 
Barthod, J., Dignac, M. F. & Rumpel, C. Effect of decomposition products produced in the presence or absence of epigeic earthworms and minerals on soil carbon stabilization. Soil Biol. Biochem. 160, 108308 (2021).Article 
CAS 

Google Scholar 
Aquino, R. S. S. et al. Filamentous fungi vectored by ants (Hymenoptera: Formicidae) in a public hospital in north-eastern Brazil. J. Hosp. Infect. 83, 200–204 (2013).Article 
CAS 

Google Scholar 
Hodges, M. N. & McKinney, M. L. Urbanization impacts on land snail community composition. Urban Ecosyst. 21, 721–735 (2018).Article 

Google Scholar 
Saeki, I., Niwa, S., Osada, N., Azuma, W. & Hiura, T. Contrasting effects of urbanization on arboreal and ground-dwelling land snails: role of trophic interactions and habitat fragmentation. Urban Ecosyst. 23, 603–614 (2020).Article 

Google Scholar 
Buczkowski, G. & Bertelsmeier, C. Invasive termites in a changing climate: A global perspective. Ecol. Evol. 7, 974–985 (2017).Article 

Google Scholar 
Ford, A. E. S., Graham, H. & White, P. C. L. Integrating human and ecosystem health through ecosystem services frameworks. Ecohealth 12, 660–671 (2015).Article 

Google Scholar 
Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528, 69–76 (2015).Article 
CAS 

Google Scholar 
Wei, Z. et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci. Adv. 5, 1–12 (2019).Article 

Google Scholar 
Song, C., Jin, K. & Raaijmakers, J. M. Designing a home for beneficial plant microbiomes. Curr. Opin. Plant Biol. 62, 102025 (2021).Article 
CAS 

Google Scholar 
Neiderud, C. J. How urbanization affects the epidemiology of emerging infectious diseases. African J. Disabil. 5, 27060 (2015).
Google Scholar 
Liddicoat, C. et al. Can bacterial indicators of a grassy woodland restoration inform ecosystem assessment and microbiota-mediated human health? Environ. Int. 129, 105–117 (2019).Article 

Google Scholar 
Baumgardner, D. J. Soil-related bacterial and fungal infections. J. Am. Board Fam. Med. 25, 734–744 (2012).Article 

Google Scholar 
Khan, N. A. Acanthamoeba: Biology and increasing importance in human health. FEMS Microbiol. Rev. 30, 564–595 (2006).Article 

Google Scholar 
Lindsay, R. G., Watters, G., Johnson, R., Ormonde, S. E. & Snibson, G. R. Acanthamoeba keratitis and contact lens wear. Clin. Exp. Optom. 90, 351–360 (2007).Article 

Google Scholar 
Fields, Barry, Robert, Benson & Besser, R. Legionella and Legionnaires’ Disease: 25 Years of Investigation – Comparative study of selective media for isolation of Legionella pneumophila from potable water. Clin. Microbiol. Rev. 15, 506 (2002).Article 

Google Scholar 
Van Elsas, J. D. et al. Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc. Natl. Acad. Sci. USA 109, 1159–1164 (2012).Article 

Google Scholar 
Chen, X. D. et al. Soil biodiversity and biogeochemical function in managed ecosystems. Soil Res. 58, 1–20 (2019).Article 

Google Scholar 
Hernando-Amado, S., Coque, T. M., Baquero, F. & Martínez, J. L. Defining and combating antibiotic resistance from One Health and Global Health perspectives. Nat. Microbiol. 4, 1432–1442 (2019).Article 
CAS 

Google Scholar 
Wang, F. H. et al. High throughput profiling of antibiotic resistance genes in urban park soils with reclaimed water irrigation. Environ. Sci. Technol. 48, 9079–9085 (2014).Article 
CAS 

Google Scholar 
Cave, R., Cole, J. & Mkrtchyan, H. V. Surveillance and prevalence of antimicrobial resistant bacteria from public settings within urban built environments: Challenges and opportunities for hygiene and infection control. Environ. Int. 157, 106836 (2021).Article 
CAS 

Google Scholar 
Alharbi, J. S., Alawadhi, Q. & Leather, S. R. Monomorium ant is a carrier for pathogenic and potentially pathogenic bacteria. BMC Res. Notes 12, 230 (2019).Article 

Google Scholar 
Guimaraes, A. J., Gomes, K. X., Cortines, J. R., Peralta, J. M. & Peralta, R. H. S. Acanthamoeba spp. as a universal host for pathogenic microorganisms: One bridge from environment to host virulence. Microbiol. Res. 193, 30–38 (2016).Article 

Google Scholar 
Vieira, A., Ramesh, A., Seddon, A. M. & Karlyshev, A. V. CmeABC multidrug efflux pump promotes Campylobacter jejuni survival and multiplication in Acanthamoeba polyphaga. Appl. Environ. Microbiol. 83, 1–13 (2017).Article 

Google Scholar 
Wyres, K. L. & Holt, K. E. Klebsiella pneumoniae as a key trafficker of drug resistance genes from environmental to clinically important bacteria. Curr. Opin. Microbiol. 45, 131–139 (2018).Article 
CAS 

Google Scholar 
Holt, K. E. et al. Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health. Proc. Natl. Acad. Sci. USA 112, E3574–E3581 (2015).Article 
CAS 

Google Scholar 
Bethony, J. et al. Soil-transmitted helminth infections: ascariasis, trichuriasis, and hookworm. Lancet 367, 1521–1532 (2006).Pullan, R. L., Smith, J. L., Jasrasaria, R. & Brooker, S. J. Global numbers of infection and disease burden of soil-transmitted helminth infections in 2010. Parasites and Vectors 7, 1–19 (2014).Article 

Google Scholar 
Kemp, S. F. et al. Expanding habitat of the imported fire ant (Solenopsis invicta): A public health concern. J. Allergy Clin. Immunol. 105, 683–691 (2000).Article 
CAS 

Google Scholar 
Estrada-Peña, A. & Jongejan, F. Ticks feeding on humans: a review of records on human-biting Ixodoidea with special reference to pathogen transmission Climate, niche, ticks, and models: what they are and how we should interpret them. Exp. Appl. Acarol. 23, 685–715 (1999).Article 

Google Scholar 
Nasir, S., Akram, W., Khan, R. R., Arshad, M. & Nasir, I. Paederusbeetles: The agent of human dermatitis. J. Venom. Anim. Toxins Incl. Trop. Dis. 21, 1–6 (2015).Article 

Google Scholar 
Santos, M. N. Research on termites in urban areas: approaches and gaps. https://doi.org/10.1007/s11252-020-00944-0 (2020).National Academies of Sciences, Engineering, and M. Advancing urban sustainability in China and the United States. (The National Academies Press, https://doi.org/10.17226/25794 2020).Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).Article 
CAS 

Google Scholar 
Velasco, E., Segovia, E., Choong, A. M. F., Lim, B. K. Y. & Vargas, R. Carbon dioxide dynamics in a residential lawn of a tropical city. J. Environ. Manage. 280, 111752 (2021).Article 
CAS 

Google Scholar 
Thakur, M. P. & Geisen, S. Trophic regulations of the soil microbiome. Trends Microbiol. 27, 771–780 (2019).Article 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).Article 

Google Scholar 
Jiao, S., Lu, Y. & Wei, G. Soil multitrophic network complexity enhances the link between biodiversity and multifunctionality in agricultural systems. Glob. Chang. Biol. 28, 140–153 (2022).Article 
CAS 

Google Scholar 
Hu, J. et al. Rhizosphere microbiome functional diversity and pathogen invasion resistance build up during plant development. Environ. Microbiol. 22, 5005–5018 (2020).Article 

Google Scholar 
Jayaraman, S. et al. Disease-suppressive soils—beyond food production: a critical review. J. Soil Sci. Plant Nutr. 21, 1437–1465 (2021).Article 

Google Scholar 
Chen, Q. L. et al. Loss of soil microbial diversity exacerbates spread of antibiotic resistance. Soil Ecol. Lett. 1, 3–13 (2019).Article 

Google Scholar 
Innocenti, G. & Sabatini, M. A. Collembola and plant pathogenic, antagonistic and arbuscular mycorrhizal fungi: a review. Bull. Insectology 71, 71–76 (2018).
Google Scholar 
Jones, M. S. et al. Organic farms conserve a dung beetle species capable of disrupting fly vectors of foodborne pathogens. Biol. Control 137, 104020 (2019).Huang, K. et al. Elimination of antibiotic resistance genes and human pathogenic bacteria by earthworms during vermicomposting of dewatered sludge by metagenomic analysis. Bioresour. Technol. 297, 122451 (2020).Article 
CAS 

Google Scholar 
Li, G., Sun, G. X., Ren, Y., Luo, X. S. & Zhu, Y. G. Urban soil and human health: a review. Eur. J. Soil Sci. 69, 196–215 (2018).Article 

Google Scholar 
Cachada, A., Pato, P., Rocha-Santos, T., da Silva, E. F. & Duarte, A. C. Levels, sources and potential human health risks of organic pollutants in urban soils. Sci. Total Environ. 430, 184–192 (2012).Article 
CAS 

Google Scholar 
Chen, M. et al. Bioremediation of soils contaminated with polycyclic aromatic hydrocarbons, petroleum, pesticides, chlorophenols, and heavy metals by composting: Applications, microbes and future research needs. Biotechnol. Adv. 33, 745–755 (2015).Article 
CAS 

Google Scholar 
González Henao, S. & Ghneim-Herrera, T. Heavy metals in soils and the remediation potential of bacteria associated With the plant microbiome. Front. Environ. Sci. 9, 1–17 (2021).Article 

Google Scholar 
Meynet, P. et al. Effect of activated carbon amendment on bacterial community structure and functions in a PAH impacted urban soil. Environ. Sci. Technol. 46, 5057–5066 (2012).Article 
CAS 

Google Scholar 
Xiong, W., Delgado-Baquerizo, M., Shen, Q. & Geisen, S. Pedogenesis shapes predator-prey relationships within soil microbiomes. Sci. Total Environ. 828, 154405 (2022).Article 
CAS 

Google Scholar 
Duan, G. et al. Interactions among soil biota and their applications in synergistic bioremediation of heavy-metal contaminated soils. Shengwu Gongcheng Xuebao/Chinese J. Biotechnol. 36, 455–470 (2020).CAS 

Google Scholar 
Beesley, L. & Dickinson, N. Carbon and trace element fluxes in the pore water of an urban soil following green waste compost, woody and biochar amendments, inoculated with the earthworm Lumbricus terrestris. Soil Biol. Biochem. 43, 188–196 (2011).Article 
CAS 

Google Scholar 
Zhu, D. et al. Deciphering potential roles of earthworms in mitigation of antibiotic resistance in the soils from diverse ecosystems. Environ. Sci. Technol. 55, 7445–7455 (2021).Article 
CAS 

Google Scholar 
Bowers, R. M., McLetchie, S., Knight, R. & Fierer, N. Spatial variability in airborne bacterial communities across land-use types and their relationship to the bacterial communities of potential source environments. ISME J. 5, 601–612 (2011).Article 
CAS 

Google Scholar 
Selway, C. A. et al. Transfer of environmental microbes to the skin and respiratory tract of humans after urban green space exposure. Environ. Int. 145, 106084 (2020).Article 

Google Scholar 
Ottman, N. et al. Soil exposure modifies the gut microbiota and supports immune tolerance in a mouse model. J. Allergy Clin. Immunol. 143, 1198–1206.e12 (2019).Article 
CAS 

Google Scholar 
Roslund, M. I. et al. Long-term biodiversity intervention shapes health-associated commensal microbiota among urban day-care children. Environ. Int. 157, 106811 (2021).Article 

Google Scholar 
Roslund, M. I. et al. A Placebo-controlled double-blinded test of the biodiversity hypothesis of immune-mediated diseases: Environmental microbial diversity elicits changes in cytokines and increase in T regulatory cells in young children. Ecotoxicol. Environ. Saf. 242, 113900 (2022).Rook, G., Bäckhed, F., Levin, B. R., McFall-Ngai, M. J. & McLean, A. R. Evolution, human-microbe interactions, and life history plasticity. Lancet 390, 521–530 (2017).Article 

Google Scholar 
Flandroy, L. et al. The impact of human activities and lifestyles on the interlinked microbiota and health of humans and of ecosystems. Sci. Total Environ. 627, 1018–1038 (2018).Article 
CAS 

Google Scholar 
Reber, S. O. et al. Immunization with a heat-killed preparation of the environmental bacterium Mycobacterium vaccae promotes stress resilience in mice. Proc. Natl. Acad. Sci. USA 113, E3130–E3139 (2016).Article 
CAS 

Google Scholar 
Ege, M. J. Exposure to environmental microorganisms and childhood asthma. N. Engl. J. Med. 364, 701–9 (2011).Article 
CAS 

Google Scholar 
Stein, M. M. et al. Innate immunity and asthma risk in amish and hutterite farm children. N. Engl. J. Med. 375, 411–421 (2016).Article 
CAS 

Google Scholar 
Roslund, M. I. et al. Environmental Studies biodiversity intervention enhances immune regulation and health-associated commensal microbiota among daycare children. Sci. Adv. 6, eaba2578 (2020).Article 
CAS 

Google Scholar 
Hanski, I. et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc. Natl. Acad. Sci. USA 109, 8334–8339 (2012).Article 
CAS 

Google Scholar 
Franklin, P. J. Indoor air quality and respiratory health of children. Paediatr. Respir. Rev. 8, 281–286 (2007).Article 

Google Scholar 
Adams, R. I. et al. Microbial exposures in moisture-damaged schools and associations with respiratory symptoms in students: A multi-country environmental exposure study. Indoor Air 31, 1952–1966 (2021).Article 
CAS 

Google Scholar 
Dunn, R. R., Reese, A. T. & Eisenhauer, N. Biodiversity–ecosystem function relationships on bodies and in buildings. Nat. Ecol. Evol. 3, 7–9 (2019).Article 

Google Scholar 
Gilbert, J. A. & Stephens, B. Microbiology of the built environment. Nat. Rev. Microbiol. 16, 661–670 (2018).Article 
CAS 

Google Scholar 
Flies, E. J., Clarke, L. J., Brook, B. W. & Jones, P. Urbanisation reduces the abundance and diversity of airborne microbes – but what does that mean for our health? A systematic review. Sci. Total Environ. 738, 140337 (2020).Article 
CAS 

Google Scholar 
Berg, G., Mahnert, A. & Moissl-Eichinger, C. Beneficial effects of plant-associated microbes on indoor microbiomes and human health? Front. Microbiol. 5, 1–5 (2014).Article 

Google Scholar 
Parajuli, A. et al. Urbanization reduces transfer of diverse environmental microbiota indoors. Front. Microbiol. 9, 1–13 (2018).Article 

Google Scholar 
Kirjavainen, P. V. et al. Farm-like indoor microbiota in non-farm homes protects children from asthma development. Nat. Med. 25, 1089–1095 (2019).Article 
CAS 

Google Scholar 
Sonnenburg, E. D. & Sonnenburg, J. L. The ancestral and industrialized gut microbiota and implications for human health. Nat. Rev. Microbiol. 17, 383–390 (2019).Article 
CAS 

Google Scholar 
Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).Article 
CAS 

Google Scholar 
Blum, W. E. H., Zechmeister-Boltenstern, S. & Keiblinger, K. M. Does soil contribute to the human gut microbiome? Microorganisms 7, 287 (2019).Article 

Google Scholar 
Liddicoat, C. et al. Naturally-diverse airborne environmental microbial exposures modulate the gut microbiome and may provide anxiolytic benefits in mice. Sci. Total Environ. 701, 134684 (2020).Article 
CAS 

Google Scholar 
Tun, H. M. et al. Exposure to household furry pets influences the gut microbiota of infants at 3-4 months following various birth scenarios. Microbiome 5, 1–14 (2017).Article 

Google Scholar 
Brame, J. E., Liddicoat, C., Abbott, C. A. & Breed, M. F. The potential of outdoor environments to supply beneficial butyrate-producing bacteria to humans. Sci. Total Environ. 777, 146063 (2021).Article 
CAS 

Google Scholar 
Elmqvist, T. et al. Urbanization, biodiversity and ecosystem services: challenges and opportunities: a global assessment. https://doi.org/10.1007/978-94-007-7088-1_23 (2013).Breed, M. F. et al. Ecosystem Restoration: A Public Health Intervention. Ecohealth 18, 269–271 (2021).Article 

Google Scholar 
Aronson, M. F. J. et al. Biodiversity in the city: key challenges for urban green space management. Front. Ecol. Environ. 15, 189–196 (2017).Article 

Google Scholar 
Contos, P., Wood, J. L., Murphy, N. P. & Gibb, H. Rewilding with invertebrates and microbes to restore ecosystems: Present trends and future directions. Ecol. Evol. 11, 7187–7200 (2021).Article 

Google Scholar 
Auclerc, A. et al. Fostering the use of soil invertebrate traits to restore ecosystem functioning. Geoderma 424, 116019 (2022).Article 

Google Scholar 
Mills, J. G. et al. Revegetation of urban green space rewilds soil microbiotas with implications for human health and urban design. Restor. Ecol. 28, S322–S334 (2020).Article 

Google Scholar  More

In this study, we provide the first description of the bacterial communities of cryoconite holes from South American glaciers, in particular from both small high-elevation glaciers of the Central Andes in the Santiago Metropolitan Region (Chile), and from the tongues of two large glaciers in Patagonian Andes that reach low altitudes. These pieces of information fill a large geographical gap in our knowledge of glacier environments because this is the first description of the microbial communities of supraglacial environments in South America, a continent with about 30,000 km2 covered by ice29. Results showed that the large Patagonian glaciers (Exploradores and Perito Moreno) had the highest oxygen concentrations, while Iver and East Iver had the lowest ones and Morado an intermediate value. This pattern could be related to the different altitudes of the glaciers. Indeed, since water temperature in cryoconite holes is always quite low and stable at all altitudes, oxygen solubility in these environments is related to the atmospheric partial pressure of oxygen that decreases at increasing altitude30. This result is consistent with [O2] values we found in our samples. Indeed, Exploradores and Perito Moreno are located in Patagonia at low altitudes ( 40%), whereas mining is also an additional important black carbon source50. Their similarity can therefore derive also from being exposed to the same general ecological conditions, including high UV radiation, oxidative stress, anthropic pressures, and probably, also from similar sources of bacteria. These results therefore highlight that correlative studies like the present ones can hardly disentangle the effects of geographical positions and ecological conditions on the structure of cryoconite hole bacterial communities, and further studies should be designed to add insight into this still open question.Analyses of alpha diversity indices indicated that cryoconite holes on Exploradores glacier showed the highest richness and evenness. Samples on the Exploradores were collected close to the glacier terminus, surrounded by a rich evergreen broadleaf vegetation, and in an area with abundant supraglacial debris and frequented by tourists. The higher biodiversity of this large, low-altitude glacier, compared to that of the small, high-altitude Iver and East Iver glaciers is not surprising, as the rich evergreen broadleaf forest that surrounds the tongue of the first glacier can be the source of a richer and more diverse bacterial community than the bare ground surrounding the other ones. However, it is more surprising that the alpha biodiversity of the large, low-altitude Perito Moreno was intermediate and similar to that of the Morado glacier. Interestingly, Perito Moreno was the southernmost glacier among those we collected, and was surrounded by a less diverse forest, dominated by southern beeches, Nothofagus ssp. than that of Exploradores, while Morado was the glacier where samples were collected at the lowest altitude among the three glaciers near Santiago. We may therefore speculate that a broad gradient related to altitude and general climate conditions of the area surrounding the glacier may somehow affect its biodiversity. For instance, among the most abundant orders, Cytophagales were more abundant on high than on low-elevation glaciers (Fig. 5b). A similar pattern was observed for the Micrococcales and Chitinophagales (Fig. 5c–k) with the only exception of Iver.In summary, we provide the first-ever description of the bacterial communities of cryoconite holes of glaciers in South America, specifically in the Southern Andes. This study thus fills an important gap of knowledge as almost no information was previously available on the cryoconite holes of this continent, and opens the possibility of future biogeography analyses including samples from almost every important glacial area of the world. The five glaciers we investigated are still a too small sample for thoroughly assessing the ecological processes that control cryoconite hole bacterial communities, and a larger set of environmental variables should also be considered, but we hope this study can be the basis for further investigations aiming at a deeper understanding of these extreme environments. More

Bradshaw, W. E. & Holzapfel, C. M. Evolution of animal photoperiodism. Annu. Rev. Ecol. Evol. System. 2007, 1–25 (2007).Article 

Google Scholar 
Way, M., Hopkins, B. & Smith, P. Photoperiodism and diapause in insects. Nature 164, 615–615 (1949).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Bromage, N., Porter, M. & Randall, C. Reproductive Biotechnology in Finfish Aquaculture 63–98 (Elsevier, 2001).Book 

Google Scholar 
Weil, Z. M. & Crews, D. Photoperiodism in Amphibians and Reptiles (ed. Nelson, R. J. et al.) 399–419 (Oxford University Press, 2010).Vera, L., Davie, A., Taylor, J. & Migaud, H. Differential light intensity and spectral sensitivities of Atlantic salmon, European sea bass and Atlantic cod pineal glands ex vivo. Gen. Comp. Endocrinol. 165, 25–33 (2010).Article 
CAS 
PubMed 

Google Scholar 
Smith, K. A., Schoen, M. W. & Czeisler, C. A. Adaptation of human pineal melatonin suppression by recent photic history. J. Clin. Endocrinol. Metabol. 89, 3610–3614 (2004).Article 
CAS 

Google Scholar 
Refinetti, R. Enhanced circadian photoresponsiveness after prolonged dark adaptation in seven species of diurnal and nocturnal rodents. Physiol. Behav. 90, 431–437 (2007).Article 
CAS 
PubMed 

Google Scholar 
Chang, A.-M., Scheer, F. A. & Czeisler, C. A. The human circadian system adapts to prior photic history. J. Physiol. 589, 1095–1102 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aschoff, J. & Daan, S. Human time perception in temporal isolation: Effects of illumination intensity. Chronobiol. Int. 14, 585–596 (1997).Article 
CAS 
PubMed 

Google Scholar 
Tast, A. et al. The photophase light intensity does not affect the scotophase melatonin response in the domestic pig. Anim. Reprod. Sci. 65, 283–290 (2001).Article 
CAS 
PubMed 

Google Scholar 
Migaud, H. et al. A comparative ex vivo and in vivo study of day and night perception in teleosts species using the melatonin rhythm. J. Pineal Res. 41, 42–52 (2006).Article 
CAS 
PubMed 

Google Scholar 
Nisembaum, L. G., Martin, P., Lecomte, F. & Falcón, J. Melatonin and osmoregulation in fish: A focus on Atlantic salmon Salmo salar smoltification. J. Neuroendocrinol. 33, e12955 (2021).Article 
CAS 
PubMed 

Google Scholar 
Iigo, M. et al. Lack of circadian regulation of in vitro melatonin release from the pineal organ of salmonid teleosts. Gen. Comp. Endocrinol. 154, 91–97 (2007).Article 
CAS 
PubMed 

Google Scholar 
Iigo, M., Azuma, T. & Iwata, M. Lack of circadian regulation of melatonin rhythms in the sockeye salmon (Oncorhynchus nerka) in vivo and in vitro. Zool. Sci. 24, 67–70 (2007).Article 
CAS 

Google Scholar 
Huang, T., Ruoff, P. & Fjelldal, P. G. Diurnal expression of clock genes in pineal gland and brain and plasma levels of melatonin and cortisol in Atlantic salmon parr and smolts. Chronobiol. Int. 27, 1697–1714 (2010).Article 
CAS 
PubMed 

Google Scholar 
Fjelldal, P. G., Hansen, T. & Huang, T. Continuous light and elevated temperature can trigger maturation both during and immediately after smoltification in male Atlantic salmon (Salmo salar). Aquaculture 321, 93–100 (2011).Article 

Google Scholar 
Leclercq, E., Taylor, J., Sprague, M. & Migaud, H. The potential of alternative lighting-systems to suppress pre-harvest sexual maturation of 1+ Atlantic salmon (Salmo salar) post-smolts reared in commercial sea-cages. Aquacult. Eng. 44, 35–47 (2011).Article 

Google Scholar 
Fjelldal, P. G. et al. Development of supermale and all-male Atlantic salmon to research the vgll3 allele-puberty link. BMC Genet. 21, 1–13 (2020).Article 

Google Scholar 
Ricker, W. E. Computation and interpretation of biological statistics of fish populations. Bull. Fisher. Res. 191, 1–382 (1975).
Google Scholar 
Fjelldal, P. G. et al. Sexual maturation and smoltification in domesticated Atlantic salmon (Salmo salar L.)-is there a developmental conflict?. Physiol. Rep. 6, e13809 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for. R J. 9, 378–400 (2017).Article 

Google Scholar 
Lenth, R. emmeans: Estimated Marginal Means, Aka Least-Squares Means. R package version 1. 4. 3. 01 (2019).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Huang, T., Ruoff, P. & Fjelldal, P. G. Effect of continuous light on daily levels of plasma melatonin and cortisol and expression of clock genes in pineal gland, brain, and liver in Atlantic salmon postsmolts. Chronobiol. Int. 27, 1715–1734 (2010).Article 
CAS 
PubMed 

Google Scholar 
Davie, A., Minghetti, M. & Migaud, H. Seasonal variations in clock-gene expression in Atlantic salmon (Salmo salar). Chronobiol. Int. 26, 379–395 (2009).Article 
CAS 
PubMed 

Google Scholar 
Max, M. & Menaker, M. Regulation of melatonin production by light, darkness, and temperature in the trout pineal. J. Comp. Physiol. Part A 170, 479–489 (1992).CAS 

Google Scholar 
Randall, C. & Bromage, N. Photoperiodic history determines the reproductive response of rainbow trout to changes in daylength. J. Comp. Physiol. Part A 183, 651–660 (1998).Article 

Google Scholar 
Randall, C., Bromage, N., Duston, J. & Symes, J. Photoperiod-induced phase-shifts of the endogenous clock controlling reproduction in the rainbow trout: A circannual phase-response curve. Reproduction 112, 399–405 (1998).Article 
CAS 

Google Scholar 
Duston, J. & Bromage, N. Photoperiodic mechanisms and rhythms of reproduction in the female rainbow trout. Fish Physiol. Biochem. 2, 35–51 (1986).Article 
CAS 
PubMed 

Google Scholar 
Duston, J. & Bromage, N. Circannual rhythms of gonadal maturation in female rainbow trout (Oncorhynchus mykiss). J. Biol. Rhythms 6, 49–53 (1991).Article 
CAS 
PubMed 

Google Scholar 
Taranger, G. L. et al. Abrupt changes in photoperiod affect age at maturity, timing of ovulation and plasma testosterone and oestradiol-17β profiles in Atlantic salmon, Salmo salar. Aquaculture 162, 85–98 (1998).Article 

Google Scholar 
Melo, M. C. et al. Salinity and photoperiod modulate pubertal development in Atlantic salmon (Salmo salar). J. Endocrinol. 220, 319–332 (2014).Article 
CAS 
PubMed 

Google Scholar 
Hansen, T. J., Fjelldal, P. G., Folkedal, O., Vågseth, T. & Oppedal, F. Effects of light source and intensity on sexual maturation, growth and swimming behaviour of Atlantic salmon in sea cages. Aquac. Environ. Interact. 9, 193–204 (2017).Article 

Google Scholar 
Oppedal, F., Taranger, G. L., Juell, J.-E., Fosseidengen, J. E. & Hansen, T. Light intensity affects growth and sexual maturation of Atlantic salmon (Salmo salar) postsmolts in sea cages. Aquat. Living Resour. 10, 351–357 (1997).Article 

Google Scholar 
Harvey, A. C. et al. Inferring Atlantic salmon post-smolt migration patterns using genetic assignment. R. Soc. Open Sci. 6, 190426 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, J. J., Gurarie, E., Bracis, C., Burke, B. J. & Laidre, K. L. Modeling climate change impacts on phenology and population dynamics of migratory marine species. Ecol. Model. 264, 83–97 (2013).Article 

Google Scholar 
Ljungstrӧm, G., Langbehn, T. J. & Jørgensen, C. Light and energetics at seasonal extremes limit poleward range shifts. Nat. Clim. Chang. 11, 530–536 (2021).Article 
ADS 

Google Scholar 
Naish, K. A. & Hard, J. J. Bridging the gap between the genotype and the phenotype: Linking genetic variation, selection and adaptation in fishes. Fish Fish. 9, 396–422 (2008).Article 

Google Scholar 
Lehnert, S. J. et al. Genomic signatures and correlates of widespread population declines in salmon. Nat. Commun. 10, 1–10 (2019).Article 
CAS 

Google Scholar  More

Forest Inventory dataTree growth, tree survival, and plot-level basal area data were compiled from the Forest Inventory and Analysis (FIA) program database (accessed on January 24, 2017, FIA phase 2 manual version 6.1; http://www.fia.fs.fed.us/). Aboveground tree biomass was estimated from tree diameter measurements44 and then multiplied by 0.5 to estimate aboveground C. Tree growth rates were calculated from the difference in estimated aboveground C between the latest and first live measurement of every tree and divided by the elapsed time between measurements to the day. Tree species that had at least 2000 individual trees after the data filters were applied were retained for further growth and survival evaluation. The probability of tree survival was calculated using the first measurement to the last measurement of a plot. Trees that were alive at both measurements were assigned a value of 1 (survived) and trees alive at the first and dead at the last measurement were assigned a value of 0 (dead). The duration between the first and last measurement was used to determine the annual probability of tree survival. Trees that were recorded as dead at both measurement inventories and trees that were harvested were excluded from the survival analysis.Predictor data: Climate, deposition, size, and competitionThere were six predictors that were related to the response rate of growth or survival for each individual tree: mean annual temperature, mean annual precipitation, mean annual total nitrogen deposition, mean annual total S deposition, tree size, and plot-level competition.To obtain total N and S deposition rates for each tree, we used spatially modeled N and S deposition data from the National Atmospheric Deposition Program’s Total Deposition Science Committee32. Annual N and S deposition rates were then averaged from the first year of measurement to the last year of measurement for every tree so that each tree had an individualized average N deposition based on the remeasurement years, and each species had an individualized range of average N deposition exposure based on its distribution. Monthly mean temperature and precipitation values were obtained in a gridded (4 x 4 km) format from the PRISM Climate Group at Oregon State45 for the contiguous US and averaged between measurement periods for each tree in a similar manner. Tree size was represented by estimated aboveground tree C (previously described). Because the climate and deposition predictors were tailored to each plot, the years assessed varied by plot, but spanned 2000–2016. Thus, the results from the earlier study6 used conditions from the 1980–1990s, whereas the results from this study used more recent environmental and stand conditions. Tree competition was represented by a combination two factors: (1) plot basal area and (2) the basal area of trees larger than the focal tree being modeled. How all six variables were statistically modeled is discussed below.Modeling tree growth and survivalWe developed in ref. 20 multiple models to predict tree growth (G; kg C year−1) and survival (P(s); annual probability of survival). Our growth model (Eq. 1 and 2) assumes that there is a potential maximum growth rate (a) that is modified by up to six predictors in our study (which are multipliers from 0 to 1): temperature (T), precipitation (P), N deposition (N), S deposition (S), tree size (m), and competition. The potential full growth model included all six terms (Eq. 1 for the general form and Eq. 2 for the specific form). The size effect was modeled as a power function (z) based on the aboveground biomass (m). N deposition may affect the allometric relationships between tree diameter and aboveground tree biomass46, but these relationships are not yet accounted for in U.S. inventories44. Competition between trees was modeled as a function of plot basal area (BA) and the basal area of trees larger than that of the tree of interest (BAL) similar to the methods of47. The environmental factors (N deposition, S deposition, temperature, precipitation) were modeled as two-term lognormal functions (e.g., t1 and t2 for temperature effects, n1 and n2 for nitrogen deposition effects). The two-term lognormal functions allowed for flexibility in both the location of the peak (determined by t1 for temperature, for example), and the steepness of the curve (determined by t2 for temperature, for example). Thus, the full growth model is presented in Eq. 2.$$G=potentialgrowthratetimes competitiontimes temperaturetimes precipitationtimes {S}_{dep}times {N}_{dep}$$
(1)
$$G=a* {m}^{z}* {e}^{({c}_{1}* BAL+{c}_{2}* {{{{mathrm{ln}}}}}(BA))}* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}$$
(2)
We examined a total of five different growth models: (1) a full model with the size, competition, climate, S deposition, and N deposition terms (Eq. 2); (2) a model with all terms except the N deposition term; (3) a model with all terms except the S deposition term; (4) a model with all terms but without S and N deposition terms; and (5) a null model that estimated a single parameter for the mean growth parameter (a in Eq. 2).The annual probability of survival (P(s)) was estimated similarly as for growth, except that the probability was a function of time and we explored two different representations for competition. The general form of the model is shown in Eq. 3, and the full survival model in Eqs. 4, 5 for the two competition forms.$$P(s)={[acdot {{{{{rm{size}}}}}}times competitiontimes temperaturetimes precipitationtimes {N}_{dep}times {S}_{dep}]}^{time}$$
(3)
$$P(s)= {left[a* [((1-z{c}_{1}{e}^{-z{c}_{2}* m})* {e}^{-z{c}_{3}* {m}^{z{c}_{4}}})({e}^{-b{r}_{1}* B{A}_{ratio}{,}^{br2}* B{A}^{b{r}_{3}}})]vphantom{{left.* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}right]}}^{time}right.}\ {left.* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}right]}^{time}$$
(4)
$$P(s)= {left[a* left({e}^{-0.5* {left(frac{ln(m/{m}_{1})}{{m}_{2}}right)}^{2}* -0.5* {left(frac{ln(BA/b{a}_{1})}{b{a}_{2}}right)}^{2}* -0.5* {left(frac{ln(BAL+1/b{l}_{1}+1)}{b{l}_{2}}right)}^{2}}right)vphantom{{left.* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}right]}^{time}}right.}\ {left.* {e}^{-0.5* {left(frac{ln(T/{t}_{1})}{{t}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(P/{p}_{1})}{{p}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(N/{n}_{1})}{{n}_{2}}right)}^{2}}* {e}^{-0.5* {left(frac{ln(S/{s}_{1})}{{s}_{2}}right)}^{2}}right]}^{time}$$
(5)
A total of nine survival models were examined: four using the formulation for size and competition in Eq. 4 (with the same combinations of predictors as above for growth), four using formulation for size and competition in Eq. 5, and a null survival model in which a mean annual estimate of survival (a) was raised to the exponent of the elapsed time.Parameters for each of the growth and survival models above were fit for a given species using maximum likelihood estimates through simulated annealing with 100,000 iterations via the likelihood package (v2.1.1) in Program R. Akaike’s Information Criteria (AIC) was estimated for all models. The best model was the model with the lowest AIC, and statistically indistinguishable models are those with a delta AIC  More

Fraser, W., Trivelpiece, W., Ainley, D. & Trivelpiece, S. Increases in Antarctic penguin populations: Reduced competition with whales or a loss of sea ice due to environmental warming?. Polar Biol. 11, 525–531 (1992).Article 

Google Scholar 
Trivelpiece, W. et al. Variability in krill biomass links harvesting and climate warming to penguin population changes in Antarctica. PNAS 108, 7625–7628 (2011).Article 
CAS 

Google Scholar 
Fraser, W. & Hofmann, E. A predator’s perspective on causal links between climate change, physical forcing and ecosystem response. Mar. Ecol. Prog. Ser. 265, 1–15 (2003).Article 

Google Scholar 
Hinke, J., Salwicka, K., Trivelpiece, S., Watters, G. & Trivelpiece, W. Divergent responses of Pygoscelis penguins reveal common environmental driver. Oecologia 153, 845–855 (2007).Article 

Google Scholar 
Poncet, S. & Poncet, J. Censuses of penguin populations of the Antarctic Peninsula, 1983–87. Br. Antarct. Surv. Bull. 77, 109–129 (1987).
Google Scholar 
Fraser, W. R. & Trivelpiece, W. Z. Factors controlling the distribution of seabirds: Winter-summer heterogeneity in the distribution of Adélie penguin populations. Found. For. Ecol. Res. West Antarct. Penins. 70, 257–272 (1996).Article 

Google Scholar 
Humphries, G. R. W. et al. Mapping application for penguin populations and projected dynamics (MAPPPD): Data and tools for dynamic management and decision support. Polar Rec. 53, 160–166 (2017).Article 

Google Scholar 
Lynch, H., Naveen, R., Trathan, P. N. & Fagan, W. F. Spatially integrated assessment reveals widespread changes in penguin populations on the Antarctic Peninsula. Ecology 93, 1367–1377 (2012).Article 

Google Scholar 
Elliot, D. H., Watts, D. R., Alley, R. B. & Gracanin, T. M. Bird and seal observations at Joinville Island and offshore islands. Antarct. J. USA 13, 154–155 (1978).
Google Scholar 
Bender, N. A., Crosbie, K. & Lynch, H. Patterns of tourism in the Antarctic Peninsula region: A twenty-year re-analysis. Antarct. Sci. 28, 194–203 (2016).Article 

Google Scholar 
Lynch, H. J. & Schwaller, M. R. Mapping the abundance and distribution of Adélie penguins using Landsat-7: First steps towards an integrated multi-sensor pipeline for tracking populations at the continental scale. PLoS ONE 9, 1–8 (2014).Article 

Google Scholar 
Lynch, H. J. & LaRue, M. A. First global census of the Adélie penguin. Auk 131, 457–466 (2014).Article 

Google Scholar 
Parkinson, C. L. & Cavalieri, D. J. Antarctic sea ice variability and trends, 1979–2010. Cryosphere 6, 871–880 (2012).Article 

Google Scholar 
Parkinson, C. L. Trends in the length of the Southern Ocean sea-ice season, 1979–99. Ann. Glaciol. 34, 435–440 (2002).Article 

Google Scholar 
Jena, B. et al. Record low sea ice extent in the Weddell Sea, Antarctica in April/May 2019 driven by intense and explosive polar cyclones. NPJ Clim. Atmos. Sci. 5, 1–15 (2022).Article 

Google Scholar 
Kumar, A., Yadav, J. & Mohan, R. Seasonal sea-ice variability and its trend in the Weddell Sea sector of West Antarctica. Environ. Res. Lett. 16, 024046 (2021).
Google Scholar 
Strass, V. H., Rohardt, G., Kanzow, T., Hoppema, M. & Boebel, O. Multidecadal warming and density loss in the deep Weddell Sea. Antarct. J. Clim. 33, 9863–9881 (2020).Article 

Google Scholar 
Morioka, Y. & Behera, S. K. Remote and local processes controlling decadal sea ice variability in the Weddell Sea. J. Geophys. Res. Ocean 126, e2020JC017036 (2021).Article 

Google Scholar 
Veytia, D. et al. Circumpolar projections of Antarctic krill growth potential. Nat. Clim. Chang. 10, 568–575 (2020).Article 

Google Scholar 
Humphries, G. R. et al. Predicting the future is hard and other lessons from a population time series data science competition. Ecol. Inf. 48, 1–11 (2018).Article 

Google Scholar 
Borowicz, A. et al. A multi-modal survey of Adèlie penguin megacolonies reveals the Danger Islands as a seabird hotspot. Sci. Rep. 8, 3926 (2018).Article 

Google Scholar 
Cimino, M., Lynch, H., Saba, V. & Oliver, M. Projected asymmetric response of Adèlie penguins to Antarctic climate change. Sci. Rep. 6, 28785 (2016).Article 
CAS 

Google Scholar 
McClintock, J., Silva-Rodriguez, P. & Fraser, W. Southerly breeding in gentoo penguins for the eastern Antarctic Peninsula: Further evidence for unprecedented climate change. Antarct. Sci. 22, 285–286 (2010).Article 

Google Scholar 
Lynch, H. J., Naveen, R. & Fagan, W. F. Censuses of penguin, blue-eyed shag Phalacrocorax atriceps and southern giant petrel Macronectes giganteus populations on the Antarctic Peninsula, 2001–2007. Mar. Ornithol. 36, 83–97 (2008).
Google Scholar 
Dunn, M. J. et al. Population size and decadal trends of three penguin species nesting at Signy Island, South Orkney Islands. PLoS ONE 11, e0164025 (2016).Article 

Google Scholar 
Delegations of Argentina and Chile. Domain 1 Marine Protected Area Preliminary Proposal Part A-1, Priority Areas for Conservation. SC-CAMLR- XXXVI/17. Retrieved from https://meetings.ccamlr.org/en/sc-camlr-xxxvi/18 (2018).Delegations of Argentina and Chile. Domain 1 Marine Protected Area Preliminary Proposal Part A-2, Priority Areas for Conservation. SC-CAMLR- XXXVI/18. Retrieved from https://meetings.ccamlr.org/en/sc-camlr-xxxvi/18 (2018).Teschke, K. et al. Planning marine protected areas under the CCAMLR regime—the case of the Weddell Sea (Antarctica). Mar. Policy 124, 104370 (2021).Article 

Google Scholar 
Herman, R. et al. Update on the global abundance and distribution of breeding Gentoo Penguins (Pygoscelis papua). Polar Biol. 43, 1947–1956 (2020).Article 

Google Scholar 
Korczak-Abshire, M., Hinke, J. T., Milinevsky, G., Juáres, M. A. & Watters, G. M. Coastal regions of the northern Antarctic Peninsula are key for gentoo populations. Biol. Lett. 17, 20200708 (2021).Article 

Google Scholar 
Miller, A. K., Karnovsky, N. J. & Trivelpiece, W. Z. Flexible foraging strategies of gentoo penguins Pygoscelis papua over 5 years in the South Shetland Islands. Antarct. Mar. Biol. 156, 2527–2537 (2009).Article 

Google Scholar 
Herman, R. W. et al. Seasonal consistency and individual variation in foraging strategies differ among and within Pygoscelis penguin species in the Antarctic Peninsula region. Mar. Biol. 164, 1–13 (2017).Article 
CAS 

Google Scholar 
Cimino, M. A., Fraser, W. R., Irwin, A. J. & Oliver, M. J. Satellite data identify decadal trends in the quality of Pygoscelis penguin chick-rearing habitat. Glob. Chang. Biol. 19, 136–148 (2013).Article 

Google Scholar 
Black, C. E. A comprehensive review of the phenology of Pygoscelis penguins. Polar Biol. 39, 405–432 (2016).Article 

Google Scholar 
Croxall, J. P. & Kirkwood, E. The Distribution of Penguins on the Antarctic Peninsula and Islands of the Scotia Sea (British Antarctic Survey, Cambridge, UK, 1979).
Google Scholar 
Naveen, R. et al. Censuses of penguin, blue-eyed shag, and southern giant petrel populations in the Antarctic Peninsula region, 1994–2000. Polar Rec. 36, 323–334 (2000).Article 

Google Scholar 
Woehler,E. J. The Distribution and Abundance of Antarctic and Subantarctic Penguins. In SCAR Comm. on Antarctic Res. Bird Biol. Subcomm. (Cambridge University Press, 1993).Naveen, R., Lynch, H. J., Forrest, S., Mueller, T. & Polito, M. First direct, site-wide penguin survey at Deception Island, Antarctica, suggests significant declines in breeding chinstrap penguins. Polar Biol. 35, 1879–1888 (2012).
Google Scholar 
Hallermann, N., Morgenthal, G. & Rodehorst, V. Unmanned aerial systems (UAS)–case studies of vision based monitoring of ageing structures. In Int. Symp. Non-Destructive Test. Civ. Eng. (NDT-CE) 15–17 (2015).Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L. & Carbonneau, P. E. Topographic structure from motion: A new development in photogrammetric measurement. Earth Surf. Process. Landf. 38, 421–430 (2013).Article 

Google Scholar 
Cavalieri, D. J., Germain, K. M. S. & Swift, C. T. Reduction of weather effects in the calculation of sea-ice concentration with the DMSP SSM/I. J. Glaciol. 41, 455–464 (1995).Article 

Google Scholar 
Fetterer, F., Knowles, K., Meier, W., Savoie, M. & Windnagel, A. Updated daily Sea Ice Index, Version 3. In Boulder, Colo.USA. NSIDC: Natl. Snow Ice Data Cent (2017).Iles, D. T. et al. Sea ice predicts long-term trends in Adélie penguin population growth, but not annual fluctuations: Results from a range-wide multiscale analysis. Glob. Change Biol. 26, 3788–3798 (2020).Article 

Google Scholar 
Plummer, M., Stukalov, A. & Denwood, M. rjags: Bayesian graphical models using mcmc. R package version 4. https://rdrr.io/cran/rjags/ (2016).Plummer, M. et al. Jags: A program for analysis of bayesian graphical models using gibbs sampling. In Proceedings of the 3rd International Workshop on Distributed Statistical Computing, vol. 124, 1–10 (Vienna, Austria., 2003).Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).MathSciNet 

Google Scholar 
Youngflesh, C. MCMCvis: Tools to visualize, manipulate, and summarize MCMC output. J. Open Sourc. Softw. 3, 640 (2018).Article 

Google Scholar 
Wickham, H. ggplot2. Wiley Interdiscipl. Rev. Comput. Stat. 3, 180–185 (2011).Article 

Google Scholar 
Kellner,K. jagsUI: A wrapper around rjags to streamline JAGS analyses. R package version 1, 2015 (2015).Herman, R. & Lynch, H. Age-structured model reveals prolonged immigration is key for colony establishment in Gentoo Penguins. Ornithol. Appl. 124, duac04 (2022).
Google Scholar 
Polito, M. J., Lynch, H. J., Naveen, R. & Emslie, S. D. Stable isotopes reveal regional heterogeneity in the pre-breeding distribution and diets of sympatrically breeding Pygoscelis spp. penguins. Mar. Ecol. Prog. Ser. 421, 265–277 (2011).Article 

Google Scholar 
Ballerini, T., Tavecchia, G., Olmastroni, S., Pezzo, F. & Focardi, S. Nonlinear effects of winter sea ice on the survival probabilities of Adélie penguins. Oecologia 161, 253–265 (2009).Article 

Google Scholar 
Wilson, P. et al. Adélie penguin population change in the pacific sector of Antarctica: Relation to sea-ice extent and the Antarctic Circumpolar Current. Mar. Ecol. Prog. Ser. 213, 301–309 (2001).Article 

Google Scholar 
Wienecke, B. et al. Adélie penguin foraging behaviour and krill abundance along the Wilkes and Adélie land coasts, Antarctica. Deep. Sea Res. Part II Top. Stud. Oceanogr. 47, 2573–2587 (2000).Article 

Google Scholar 
Ainley, D. G. The Adélie Penguin: Bellwether of Climate Change (Columbia University Press, 2002).Book 

Google Scholar 
Cherel, Y. Isotopic niches of emperor and Adélie penguins in Adélie Land. Antarct. Mar. Biol. 154, 813–821 (2008).Article 

Google Scholar 
Ainley, D. G. et al. Post-fledging survival of Adélie penguins at multiple colonies: Chicks raised on fish do well. Mar. Ecol. Prog. Ser. 601, 239–251 (2018).Article 

Google Scholar 
Ashford, J., Zane, L., Torres, J. J., La Mesa, M. & Simms, A. R. Population structure and life history connectivity of Antarctic silverfish (Pleuragramma antarctica) in the Southern Ocean ecosystem. In The Antarctic Silverfish: A Keystone Species in a Changing Ecosystem 193–234 (Springer, 2017).Pakhomov, E. & Perissinotto, R. Antarctic neritic krill Euphausia crystallorophias: Spatio-temporal distribution, growth and grazing rates. Deep. Sea Res. Part I Oceanogr. Res. Pap. 43, 59–87 (1996).Article 

Google Scholar 
La Mesa, M. & Eastman, J. T. Antarctic silverfish: Life strategies of a key species in the high-Antarctic ecosystem. Fish Fish 13, 241–266 (2012).Article 

Google Scholar 
Davis, L. B., Hofmann, E. E., Klinck, J. M., Piñones, A. & Dinniman, M. S. Distributions of krill and Antarctic silverfish and correlations with environmental variables in the western Ross Sea. Antarct. Mar. Ecol. Prog. Ser. 584, 45–65 (2017).Article 
CAS 

Google Scholar 
Chapman, E. W., Hofmann, E. E., Patterson, D. L., Ribic, C. A. & Fraser, W. R. Marine and terrestrial factors affecting Adélie penguin Pygoscelis adeliae chick growth and recruitment off the western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 436, 273–289 (2011).Article 

Google Scholar 
La Mesa, M., Piñones, A., Catalano, B. & Ashford, J. Predicting early life connectivity of Antarctic silverfish, an important forage species along the Antarctic Peninsula. Fish. Oceanogr. 24, 150–161 (2015).Article 

Google Scholar 
La Mesa, M., Riginella, E., Mazzoldi, C. & Ashford, J. Reproductive resilience of ice-dependent Antarctic silverfish in a rapidly changing system along the Western Antarctic Peninsula. Mar. Ecol. 36, 235–245 (2015).Article 

Google Scholar 
Handley, J. et al. Marine important bird and biodiversity areas for penguins in Antarctica, targets for conservation action. Front. Mar. Sci. 7, 256 (2021).Article 

Google Scholar 
Brooks, C. et al. Workshop on identifying key biodiversity areas for the Southern Ocean using tracking data. In Tech.Rep. SC-CAMLR-41/BG/22, CCAMLR (2022).Lynch, H. J., Naveen, R. & Casanovas, P. Antarctic site inventory breeding bird survey data, 1994–2013: Ecological Archives E094–243. Ecology 94, 2653–2653 (2013).Article 

Google Scholar 
Myrcha, A., Tatur, A. & Valle, R. D. V. Numbers of Adélie penguins breeding at Hope Bay and Seymour Island rookeries (West Antarctica) in 1985. Pol. Polar Res. 8, 411–422 (1987).
Google Scholar 
Montalti, D. & Soave, G. E. The birds of Seymour Island, Antarctica. Ornitol. Neotrop. 13, 267–271 (2002).
Google Scholar 
Perchivale, P. J. et al. Updated estimate of the Breeding Population of Adélie penguins (Pygoscelis adeliae) at Penguin Point, Marambio/Seymour Island within the proposed Weddell Sea Marine Protected Area (2022). https://www.researchsquare.com/article/rs-2117503/v1.Che-Castaldo, C. et al. Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise. Nat. Commun. 8, 832 (2017).Article 

Google Scholar 
Hinke, J. T., Trivelpiece, S. G. & Trivelpiece, W. Z. Variable vital rates and the risk of population declines in Adélie penguins from the Antarctic Peninsula region. Ecosphere 8, e01666 (2017).Article 

Google Scholar  More

Brown, A. R. et al. Assessing risks and mitigating impacts of harmful algal blooms on mariculture and marine fisheries. Rev. Aquac. 12, 1663–1688 (2020).
Google Scholar 
Bechard, A. Red tide at morning, tourists take warning? County-level economic effects of HABS on tourism dependent sectors. Harmful Algae 85, 101689–101689 (2019).Article 

Google Scholar 
Landsberg, J. H. The effects of harmful algal blooms on aquatic organisms. Rev. Fish. Sci. 10, 113–390 (2002).Article 

Google Scholar 
Flewelling, L. J. et al. Brevetoxicosis: Red tides and marine mammal mortalities. Nature 435, 755–756 (2005).Article 
CAS 

Google Scholar 
Gannon, D. P. et al. Effects of Karenia brevis harmful algal blooms on nearshore fish communities in southwest Florida. Mar. Ecol. Prog. Ser. 378, 171–186 (2009).Article 
CAS 

Google Scholar 
Driggers, W. B. et al. Environmental conditions and catch rates of predatory fishes associated with a mass mortality on the West Florida Shelf. Estuar. Coast. Shelf Sci. 168, 40–49 (2016).Article 
CAS 

Google Scholar 
Hallett, C. S., Valesini, F. J., Clarke, K. R. & Hoeksema, S. D. Effects of a harmful algal bloom on the community ecology, movements and spatial distributions of fishes in a microtidal estuary. Hydrobiologia 763, 267–284 (2016).Article 

Google Scholar 
Anderson, D. M. et al. Marine harmful algal blooms (HABs) in the United States: History, current status and future trends. Harmful Algae 102, 101975–101975 (2021).Article 
CAS 

Google Scholar 
Steidinger, K. A. & Haddad, K. Biologic and hydrographic aspects of red tides. Bioscience 31, 814–819 (1981).Article 

Google Scholar 
Soto, I. M. et al. Advection of Karenia brevis blooms from the Florida Panhandle towards Mississippi coastal waters. Harmful Algae 72, 46–64 (2018).Article 

Google Scholar 
Steidinger, K. A. & Ingle, R. M. Observations on the 1971 summer red tide in tampa bay, Florida1. Environ. Lett. 3, 271–278 (1972).Article 
CAS 

Google Scholar 
Liu, Y. et al. Offshore forcing on the “pressure point” of the West Florida Shelf: Anomalous upwelling and its influence on harmful algal blooms. J. Geophys. Res. 121, 5501–5515 (2016).Article 

Google Scholar 
Liu, Y., Weisberg, R. H., Zheng, L., Heil, C. A. & Hubbard, K. A. Termination of the 2018 Florida red tide event: A tracer model perspective. Estuar. Coast. Shelf Sci. 272, 107901 (2022).Article 

Google Scholar 
Weisberg, R. H. & Liu, Y. Local and deep-ocean forcing effects on the West Florida continental shelf circulation and ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.863227 (2022).Article 

Google Scholar 
Walsh, J. J. et al. Red tides in the Gulf of Mexico: Where, when, and why? Journal of Geophysical Research: Oceans 111, (2006).Lapointe, B. E., Herren, L. W., Debortoli, D. D. & Vogel, M. A. Evidence of sewage-driven eutrophication and harmful algal blooms in Florida’s Indian River Lagoon. Harmful Algae 43, 82–102 (2015).Article 
CAS 

Google Scholar 
Medina, M. et al. Nitrogen-enriched discharges from a highly managed watershed intensify red tide (Karenia brevis) blooms in southwest Florida. Sci. Total Environ. 827, 154149–154149 (2022).Article 
CAS 

Google Scholar 
Perkins, S. Ramping up the fight against Florida’s red tides. Proc. Natl. Acad. Sci. U.S.A. 116, 6510–6512 (2019).Article 
CAS 

Google Scholar 
Skripnikov, A. et al. Using localized Twitter activity to assess harmful algal bloom impacts of Karenia brevis in Florida, USA. Harmful Algae 110, 102118–102118 (2021).Article 
CAS 

Google Scholar 
SEDAR. SEDAR 33 Update – Gulf of Mexico gag grouper stock assessment report, 123. https://sedarweb.org/docs/suar/GagUpdateAssessReport_Final_0.pdf (2016).SEDAR. SEDAR 61 – Gulf of Mexico red grouper stock assessment report, 285. https://sedarweb.org/docs/sar/S61_Final_SAR.pdf (2019).SEDAR. SEDAR 10 Stock Assessment Report 2: Gulf of Mexico Gag Grouper, 250. www.sedarweb.org (2004).SEDAR. SEDAR 10 Update – Gulf of Mexico gag grouper stock assessment report. http://www.sedarweb.org (2009).SEDAR. SEDAR 72—Gulf of Mexico gag grouper stock assessment report, 318–318. https://sedarweb.org/docs/sar/S72_SAR_FINAL.pdf%0A (2021).Geary, W. L. et al. A guide to ecosystem models and their environmental applications. Nat. Ecol. Evol. 4, 1459–1471 (2020).Article 

Google Scholar 
Steenbeek, J. et al. Making spatial-temporal marine ecosystem modelling better—A perspective. Environ. Model. Softw. 145, 105209–105209 (2021).Article 

Google Scholar 
Gray DiLeone, A. M. & Ainsworth, C. H. Effects of Karenia brevis harmful algal blooms on fish community structure on the West Florida Shelf. Ecol. Model. 392, 250–267 (2019).Article 

Google Scholar 
Perryman, H. A. et al. A revised diet matrix to improve the parameterization of a West Florida Shelf Ecopath model for understanding harmful algal bloom impacts. Ecol. Model. 416, 108890–108890 (2020).Article 

Google Scholar 
Mayer-Pinto, M., Ledet, J., Crowe, T. P. & Johnston, E. L. Sublethal effects of contaminants on marine habitat-forming species: A review and meta-analysis. Biol. Rev. 95, 1554–1573 (2020).Article 

Google Scholar 
Reis Costa, P. Impact and effects of paralytic shellfish poisoning toxins derived from harmful algal blooms to marine fish. Fish Fish. 17, 226–248 (2016).Article 

Google Scholar 
Dahood, A., de Mutsert, K. & Watters, G. M. Evaluating Antarctic marine protected area scenarios using a dynamic food web model. Biol. Cons. 251, 108766–108766 (2020).Article 

Google Scholar 
de Mutsert, K. et al. Exploring effects of hypoxia on fish and fisheries in the northern Gulf of Mexico using a dynamic spatially explicit ecosystem model. Ecol. Model. 331, 142–150 (2016).Article 

Google Scholar 
de Mutsert, K., Lewis, K. A., White, E. D. & Buszowski, J. End-to-end modeling reveals species-specific effects of large-scale coastal restoration on living resources facing climate change. Front. Mar. Sci. 8, 104–104 (2021).Article 

Google Scholar 
Bauer, B. et al. Erratum: Reducing eutrophication increases spatial extent of communities supporting commercial fisheries: A model case study (ICES Journal of Marine Science (2018) DOI: https://doi.org/10.1093/icesjms/fsy003). ICES Journal of Marine Science, 75, 1155–1155 (2018).Sadchatheeswaran, S., Branch, G. M., Shannon, L. J., Coll, M. & Steenbeek, J. A novel approach to explicitly model the spatiotemporal impacts of structural complexity created by alien ecosystem engineers in a marine benthic environment. Ecol. Model. 459, 109731–109731 (2021).Article 

Google Scholar 
Coll, M. et al. Advancing global ecological modeling capabilities to simulate future trajectories of change in marine ecosystems. Front. Mar. Sci. 7, 741–741 (2020).Article 

Google Scholar 
Hernvann, P. Y. et al. The celtic sea through time and space: Ecosystem modeling to unravel fishing and climate change impacts on food-web structure and dynamics. Front. Mar. Sci. 7, 1018–1018 (2020).Article 

Google Scholar 
Walters, C. Ecospace: Prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems 2, 539–554 (1999).Article 

Google Scholar 
Christensen, V., Walters, C. J., Pauly, D. & Forrest, R. Ecopath with Ecosim version 6 user guide. Fish. Cent. Univ. Br. Columbia Vanc. Can. 281, 1–235 (2008).
Google Scholar 
Okey, T. A., Mahmoudi, B., Mackinson, S., Vasconcellos, M. & Vidal-Hernandez, L. An ecosystem model of the West Florida Shelf for use in fisheries management and ecological research: Volume II. Model construction. Fish. Manag. II, 163–163 (2002).
Google Scholar 
Liu, Y. & Weisberg, R. H. Seasonal variability on the West Florida Shelf. Prog. Oceanogr. 104, 80–98 (2012).Article 

Google Scholar 
Moretzsohn, F., Chávez-Sánchez, J. A. & J.W. Tunnell, Jr. GulfBase: Resource Database for Gulf of Mexico Research. World Wide Web electronic publication (2016).Murawski, S. A., Peebles, E. B., Gracia, A., Tunnell, J. W. & Armenteros, M. Comparative abundance, species composition, and demographics of continental shelf fish assemblages throughout the Gulf of Mexico. Mar. Coast. Fish. 10, 325–346 (2018).Article 

Google Scholar 
Darnell, R. M. The American sea: A natural history of the gulf of Mexico. The American Sea: A Natural History of the Gulf of Mexico, 557, https://doi.org/10.5860/choice.193769 (2015).Brochure, I. Marine recreational information program: Implementation plan (2008).Florida Fish and Wildlife Conservation Commission. Commercial fisheries landings summaries (2021).Murawski, S. A. et al. How did the deepwater horizon oil spill affect coastal and continental shelf ecosystems of the Gulf of Mexico?. Oceanography 29, 160–173 (2016).Article 

Google Scholar 
Chagaris, D. D., Patterson, W. F. & Allen, M. S. Relative effects of multiple stressors on reef food webs in the Northern Gulf of Mexico revealed via ecosystem modeling. Front. Mar. Sci. 7, 513–513 (2020).Article 

Google Scholar 
South, A. rnaturalearth: world map data from Natural Earth. R package version 0.1.0. The R Foundation. https://CRAN.R-project.org/package=rnaturalearth (2017).Colleter, M. et al. Global overview of the applications of the Ecopath with Ecosim modeling approach using the EcoBase models repository. Ecol. Model. 302, 42–53 (2015).Article 

Google Scholar 
Ahrens, R. N., Walters, C. J. & Christensen, V. Foraging arena theory. Fish fish. 13, 41–59 (2012).Article 

Google Scholar 
Christensen, V. et al. Representing variable habitat quality in a spatial food web model. Ecosystems 17, 1397–1412 (2014).Article 
CAS 

Google Scholar 
Steenbeek, J. et al. Bridging the gap between ecosystem modeling tools and geographic information systems: Driving a food web model with external spatial–temporal data. Ecol. Model. 263, 139–151 (2013).Article 

Google Scholar 
Walters, C., Christensen, V., Walters, W. & Rose, K. Representation of multistanza life histories in Ecospace models for spatial organization of ecosystem trophic interaction patterns. Bull. Mar. Sci. 86, 439–459 (2010).
Google Scholar 
Heymans, J. J. et al. Best practice in Ecopath with Ecosim food-web models for ecosystem-based management. Ecol. Model. 331, 173–184 (2016).Article 

Google Scholar 
Okey, T. Simulating community effects of sea floor shading by plankton blooms over the West Florida Shelf. Ecol. Model. 172, 339–359 (2004).Article 

Google Scholar 
Chagaris, D. D. Ecosystem-based Evaluation of Fishery Policies and Tradeoffs on the West Florida Shelf Vol. 53, 1699 (University of Florida, 2013).
Google Scholar 
Chagaris, D. D., Mahmoudi, B., Walters, C. J. & Allen, M. S. Simulating the trophic impacts of fishery policy options on the west florida shelf using ecopath with ecosim. Mar. Coast. Fish. 7, 44–58 (2015).Article 

Google Scholar 
Chagaris, D. et al. An ecosystem-based approach to evaluating impacts and management of invasive lionfish. Fisheries 42, 421–431 (2017).Article 

Google Scholar 
Chagaris, D. West Florida Shelf Ecosystem Model. University of Florida. https://ufdc.ufl.edu/IR00011604/00001%0A West Florida Shelf Ecosystem Model (2021).Vilas, D. Spatiotemporal Ecosystem Dynamics on the West Florida Shelf: Prediction, Validation, and Application to Red Tides and Stock Assessment (University of Florida, 2022).
Google Scholar 
Chassignet, E. P. et al. The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system. J. Mar. Syst. 65, 60–83 (2007).Article 

Google Scholar 
NOAA National Geophysical Data Center. U.S. Coastal Relief Model Vol. 3—Florida and East Gulf of Mexico. https://doi.org/10.7289/V5W66HP (2001).NASA. Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Earth Data (2018).Casey, L. Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1950–2020: U.S. Geological Survey, https://doi.org/10.5066/P9P2PF1N (2021).Chagaris, D. & Vilas, D. NOAA RESTORE Science Program: Ecosystem modeling to improve fisheries management in the Gulf of Mexico: model inputs and outputs for the West Florida Shelf, 1985–01–01 to 2018–12–31 (NCEI Accession 0242339), https://doi.org/10.25921/t26e-wj91. (2022).Püts, M. et al. Insights on integrating habitat preferences in process-oriented ecological models—A case study of the southern North Sea. Ecol. Model. 431, 109189–109189 (2020).Article 

Google Scholar 
Vilas, D., Fletcher, R. J. Jr., Siders, Z. A. & Chagaris, D. Understanding the temporal dynamics of estimated environmental niche hypervolumes for marine fishes. Ecol. Evol. 12, e9604 (2022).Article 

Google Scholar 
Grubbs, R. D., Musick, J. A., Conrath, C. L. & Romine, J. G. Long-term movements, migration, and temporal delineation of a summer nursery for Juvenile Sandbar Sharks in the Chesapeake Bay region. In Shark Nursery Grounds of the Gulf of Mexico and the East Coast Waters of the United States. American Fisheries Society Symposium 50 Vol. 50 (eds Grubbs, R. D. et al.) 87–107 (American Fisheries Society, 2007).
Google Scholar 
Addis, D. T., Patterson, W. F., Dance, M. A. & Ingram, G. W. Implications of reef fish movement from unreported artificial reef sites in the northern Gulf of Mexico. Fish. Res. 147, 349–358 (2013).Article 

Google Scholar 
Akins, J. L., Morris, J. A. & Green, S. J. In situ tagging technique for fishes provides insight into growth and movement of invasive lionfish. Ecol. Evol. 4, 3768–3777 (2014).Article 

Google Scholar 
Chen, Z., Xu, S., Qiu, Y., Lin, Z. & Jia, X. Modeling the effects of fishery management and marine protected areas on the Beibu Gulf using spatial ecosystem simulation. Fish. Res. 100, 222–229 (2009).Article 

Google Scholar 
Steenbeek, J. et al. Ecopath with ecosim as a model-building toolbox: Source code capabilities, extensions, and variations. Ecol. Model. 319, 178–189 (2016).Article 

Google Scholar 
Moriasi, D. N. et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50, 885–900 (2007).Article 

Google Scholar 
Hu, C. et al. Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sens. Environ. 97, 311–321 (2005).Article 

Google Scholar 
Chagaris, D., Vilas, D., Siders, Z. A. & Sinnickson, D. Monthly maps of red tide on the West Florida Shelf 2002–2021: A simple approach combining remote sensing and in situ measurements (in prep).Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute. Statewide harmful algal bloom karenia brevis current status map (2022).Wickham, H. et al. ggplot2: Create elegant data visualisations using the grammar of graphics (2016).Landsberg, J. H., Flewelling, L. J. & Naar, J. Karenia brevis red tides, brevetoxins in the food web, and impacts on natural resources: Decadal advancements. Harmful Algae 8, 598–607 (2009).Article 
CAS 

Google Scholar 
Gianelli, I., Ortega, L. & Defeo, O. Modeling short-term fishing dynamics in a small-scale intertidal shellfishery. Fish. Res. 209, 242–250 (2019).Article 

Google Scholar 
Moore, S. K. et al. An index of fisheries closures due to harmful algal blooms and a framework for identifying vulnerable fishing communities on the U.S. West Coast. Mar. Policy 110, 103543–103543 (2019).Article 

Google Scholar 
GSMFC. SEAMAP: Environmental and Biological Atlas of the Gulf of Mexico. www.seamap.org (2020).Bechard, A. Harmful algal blooms and tourism: The economic impact to counties in Southwest Florida. Rev. Reg. Stud. 50, 170–188 (2020).
Google Scholar 
Foley, A. M. et al. Effects of Karenia brevis harmful algal blooms on nearshore fish communities in southwest Florida. Mar. Ecol. Prog. Ser. 378, 171–186 (2009).Article 

Google Scholar 
Karnauskas, M. et al. Timeline of severe red tide events on the West Florida Shelf: insights from oral histories. http://sedarweb.org/docs/wpapers/S61_WP_20_Karnauskasetal_red_tide.pdf (2019).Sagarese, S. R., Gruss, A., Karnauskas, M. & Walter, J. F. Ontogenetic spatial distributions of red grouper (Epinephelus morio) within the northeastern Gulf of Mexico and spatio‐ temporal overlap with red tide events, 35–35. http://sedarweb.org/docs/wpapers/S42_DW_04_Red_tide_distribution.pdf (2014).Sagarese, S. R., Vaughan, N. R., Walter, J. F. & Karnauskas, M. Enhancing single-species stock assessments with diverse ecosystem perspectives: A case study for gulf of mexico red grouper (epinephelus morio) and red tides. Can. J. Fish. Aquat. Sci. 78, 1168–1180 (2021).Article 

Google Scholar 
Sagarese, S. R. & Harford, W. J. Evaluating the risks of red tide mortality misspecification when modeling stock dynamics. Fish. Res. 250, 106271–106271 (2022).Article 

Google Scholar 
Whitehouse, G. A. & Aydin, K. Y. Assessing the sensitivity of three Alaska marine food webs to perturbations: An example of Ecosim simulations using Rpath. Ecol. Model. 429, 109074–109074 (2020).Article 

Google Scholar 
Walter, J. F. et al. Satellite derived indices of red tide severity for input for Gulf of Mexico Gag grouper stock assessment. SEDAR33-DW08 SEDAR. North Charlest. S. C. 43, 40–40 (2013).
Google Scholar 
Jackson, M. C., Pawar, S. & Woodward, G. The temporal dynamics of multiple stressor effects: From individuals to ecosystems. Trends Ecol. Evol. 36, 402–410 (2021).Article 

Google Scholar 
Walters, S., Lowerre-Barbieri, S., Bickford, J., Tustison, J. & Landsberg, J. H. Effects of Karenia brevis red tide on the spatial distribution of spawning aggregations of sand seatrout Cynoscion arenarius in Tampa Bay Florida. Mar. Ecol. Prog. Ser. 479, 191–202 (2013).Article 

Google Scholar 
Reynolds, D. A., Yoo, M. J., Dixson, D. L. & Ross, C. Exposure to the Florida red tide dinoflagellate, Karenia brevis, and its associated brevetoxins induces ecophysiological and proteomic alterations in Porites astreoides. PLoS One 15, e0228414–e0228414 (2020).Article 
CAS 

Google Scholar 
Bornman, E., Cowley, P. D., Adams, J. B. & Strydom, N. A. Daytime intra-estuary movements and harmful algal bloom avoidance by Mugil cephalus (family Mugilidae). Estuar. Coast. Shelf Sci. 260, 107492–107492 (2021).Article 
CAS 

Google Scholar 
Moreira-Santos, M., Ribeiro, R. & Araújo, C. V. M. What if aquatic animals move away from pesticide-contaminated habitats before suffering adverse physiological effects? A critical review. Crit. Rev. Environ. Sci. Technol. 49, 989–1025 (2019).Article 
CAS 

Google Scholar 
Schreck, C. B. & Tort, L. The concept of stress in fish. In Fish Physiology Vol. 35 (eds Schreck, C. B. & Tort, L.) 1–34 (Elsevier, 2016).
Google Scholar 
Madin, E. M. P., Dill, L. M., Ridlon, A. D., Heithaus, M. R. & Warner, R. R. Human activities change marine ecosystems by altering predation risk. Glob. Change Biol. 22, 44–60 (2016).Article 

Google Scholar 
Walsh, J. R., Carpenter, S. R. & Van Der Zanden, M. J. Invasive species triggers a massive loss of ecosystem services through a trophic cascade. Proc. Natl. Acad. Sci. U.S.A. 113, 4081–4085 (2016).Article 
CAS 

Google Scholar 
Short, J. W. et al. Evidence for ecosystem-level trophic cascade effects involving gulf menhaden (Brevoortia patronus) triggered by the Deepwater horizon blowout. J. Mar. Sci. Eng. 9, 1–20 (2021).Article 

Google Scholar 
Zohdi, E. & Abbaspour, M. Harmful algal blooms (red tide): A review of causes, impacts and approaches to monitoring and prediction. Int. J. Environ. Sci. Technol. 16, 1789–1806 (2019).Article 

Google Scholar 
Weisberg, R. H., Barth, A., Alvera-Azcarate, A. & Zheng, L. A coordinated coastal ocean observing and modeling system for the West Florida Continental Shelf. Harmful Algae 8, 585–597 (2009).Article 

Google Scholar 
Turley, B. D., Karnauskas, M., Campbell, M. D., Hanisko, D. S. & Kelble, C. R. Relationships between blooms of Karenia brevis and hypoxia across the West Florida Shelf. Harmful Algae 114, 102223 (2022).Article 

Google Scholar 
Fulton, E. A., Smith, A. D., Smith, D. C. & Johnson, P. An integrated approach is needed for ecosystem based fisheries management: Insights from ecosystem-level management strategy evaluation. PLoS One 9, e84242 (2014).Article 

Google Scholar 
Flynn, K. J. & McGillicuddy, D. J. Modeling marine harmful algal blooms: Current status and future prospects. Harmful Algal Blooms https://doi.org/10.1002/9781118994672.ch3 (2018).Article 

Google Scholar 
Thorson, J. T. Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161 (2019).Article 

Google Scholar 
Fossum, T. O., Travelletti, C., Eidsvik, J., Ginsbourger, D. & Rajan, K. Learning excursion sets of vector-valued gaussian random fields for autonomous ocean sampling. Ann. Appl. Stat. 15, 597–618 (2021).Article 
MathSciNet 
MATH 

Google Scholar 
Fu, F. X., Place, A. R., Garcia, N. S. & Hutchins, D. A. CO2 and phosphate availability control the toxicity of the harmful bloom dinoflagellate Karlodinium veneficum. Aquat. Microb. Ecol. 59, 55–65 (2010).Article 

Google Scholar 
Hardison, D. R., Sunda, W. G., Shea, D. & Litaker, R. W. Increased toxicity of Karenia brevis during phosphate limited growth: Ecological and evolutionary implications. PLoS One 8, e58545–e58545 (2013).Article 
CAS 

Google Scholar 
Errera, R. M., Yvon-Lewis, S., Kessler, J. D. & Campbell, L. Reponses of the dinoflagellate Karenia brevis to climate change: PCO2 and sea surface temperatures. Harmful Algae 37, 110–116 (2014).Article 
CAS 

Google Scholar 
Wells, M. L. et al. Future HAB science: Directions and challenges in a changing climate. Harmful Algae 91, 101632–101632 (2020).Article 

Google Scholar 
Wolny, J. L. et al. Current and future remote sensing of harmful algal blooms in the chesapeake bay to support the shellfish industry. Front. Mar. Sci. 7, 337–337 (2020).Article 

Google Scholar 
Reum, J. C. P. et al. It’s not the destination, It’s the journey: Multispecies model ensembles for ecosystem approaches to fisheries management. Front. Mar. Sci. 8, 75–75 (2021).Article 

Google Scholar 
Howell, D. et al. Combining ecosystem and single-species modeling to provide ecosystem-based fisheries management advice within current management systems. Front. Mar. Sci. 7, 607831 (2021).Article 

Google Scholar 
McPherson, W. C. and J. C. and J. A. and Y. X. and J. shiny: Web application framework for R. R package version 1.4.0, 115–115 (2019). More