Ecology

Eddy, T. D. et al. One Earth 4, 1278–1285 (2021).Article 

Google Scholar 
Mumby, P. J. et al. Science 311, 98–101 (2006).CAS 
Article 

Google Scholar 
Maire, E. et al. Proc. R. Soc. Lond. B 285, 20181167 (2018).
Google Scholar 
Schiettekatte, N. M. D. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-0-01710-5 (2022).Article 

Google Scholar 
Woodhead, A. J., Hicks, C. C., Norström, A. V., Williams, G. J. & Graham, N. A. J. Funct. Ecol. 33, 1023–1034 (2019).
Google Scholar 
Naeem, S., Bunker, D. E., Hector, A., Loreau, M. & Perrings, C. Biodiversity, Ecosystem Functioning, and Human Wellbeing: An Ecological and Economic Perspective (Oxford Univ. Press, 2009).Villéger, S., Brosse, S., Mouchet, M., Mouillot, D. & Vanni, M. J. Aquat. Sci. 79, 783–801 (2017).Article 

Google Scholar 
Bascompte, J., Melián, C. J. & Sala, E. Proc. Natl Acad. Sci. USA 102, 5443–5447 (2005).CAS 
Article 

Google Scholar 
Houk, P. & Musburger, C. Mar. Ecol. Prog. Ser. 488, 23–34 (2013).Article 

Google Scholar 
Allgeier, J. E., Burkepile, D. E. & Layman, C. A. Glob. Change Biol. 23, 2166–2178 (2017).Article 

Google Scholar 
Meyer, J. L., Schultz, E. T. & Helfman, G. S. Science 220, 1047–1049 (1983).CAS 
Article 

Google Scholar 
Brandl, S. J. et al. Science 364, 1189–1192 (2019).CAS 
Article 

Google Scholar 
Morais, R. A., Siqueira, A. C., Smallhorn-West, P. F. & Bellwood, D. R. PLoS Biol. 19, e3001435 (2021).CAS 
Article 

Google Scholar 
Larned, S. T. Mar. Biol. 132, 409–421 (1998).Article 

Google Scholar 
McClanahan, T. R., Carreiro-Silva, M. & DiLorenzo, M. Mar. Pollut. Bull. 54, 1947–1957 (2007).CAS 
Article 

Google Scholar 
McLean, M. et al. Proc. Natl Acad. Sci. USA 118, e2012318118 (2021).CAS 
Article 

Google Scholar  More

Lindenmayer, D. et al. A checklist of attributes for effective monitoring of threatened species and threatened ecosystems. J. Environ. Manage. 262, 110312 (2020).PubMed 

Google Scholar 
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).PubMed 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2019-3. http://www.iucnredlist.org (2021).Adams, M. J. et al. Trends in amphibian occupancy in the United States. PLoS ONE 8, e64347 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Corn, P. S. Climate change and amphibians. Anim. Biodivers. Conserv. 28, 59–67 (2005).
Google Scholar 
Kiesecker, J. M., Blaustein, A. R. & Belden, L. K. Complex causes of amphibian population declines. Nature 410, 681–684 (2001).ADS 
CAS 

Google Scholar 
Baldwin, R. F. & deMaynadier, P. G. Assessing threats to pool-breeding amphibian habitat in an urbanizing landscape. Biol. Conserv. 142, 1628–1638 (2009).Borzée, A., Kyong, C. N., Kil, H. K. & Jang, Y. Impact of water quality on the occurrence of two endangered Korean anurans: Dryophytes suweonensis and Pelophylax chosenicus. Herpetologica 74, 1–7 (2018).
Google Scholar 
Stuart, S. N. et al. Status and trends of amphibian declines and extinctions worldwide. Science 306, 1783–1786 (2004).ADS 
CAS 

Google Scholar 
Caro, T., Rowe, Z., Berger, J., Wholey, P. & Dobson, A. An inconvenient misconception: climate change is not the principal driver of biodiversity loss. Conserv. Lett. e12868 (2022).Daszak, P. et al. Emerging infectious diseases and amphibian population declines. Emerg. Infect. 5, 735–748 (1999).CAS 

Google Scholar 
Fellers, G., Green, D. E. & Longcore, J. Oral chytridiomycosis in the mountain yellow-legged frog (Rana muscosa). Copeia 2001, 945–953Blaustein, A. R. et al. Effects of ultraviolet radiation on amphibians: field experiments. Am. Zool. 38, 799–812 (1999).
Google Scholar 
Langhelle, A., Lindell, M. J. & Nyström, P. Effects of ultraviolet radiation on amphibian embryonic and larval development. J. Herpetol. 33, 449–456 (1999).
Google Scholar 
Beebee, T. J. C. Amphibians breeding and climate. Nature 374, 219–220 (1995).ADS 
CAS 

Google Scholar 
Donnelly, M. A. & Crump, M. L. Potential effects of climate change on two neotropical amphibian assemblages. In Potential Impacts of Climate Change on Tropical Forest Ecosystems (ed. Markham, A.) 401–421 (Springer Netherlands, 1998).Carey, C. & Alexander, M. A. Climate change and amphibian declines: is there a link? Divers. Distrib. 9, 111–121 (2003).
Google Scholar 
Fisher, R. N. & Shaffer, H. B. The decline of amphibians in California’s Great Central Valley. Conserv. Biol. 10, 1387–1397 (1996).
Google Scholar 
Sparling, D. W., Donald, W., Linder, G. & Bishop, C. A. Ecotoxicology of Amphibians and Reptiles. (SETAC Press, 2000).Rouse, M. J. & Daellenbach, U. S. Rethinking research methods for the resource-based perspective: isolating sources of sustainable competitive advantage. Strat. Manag. J. 20, 487–494 (1999).
Google Scholar 
Bridges, C. M. & Boone, M. D. The interactive effects of UV-B and insecticide exposure on tadpole survival, growth and development. Biol. Conserv. 113, 49–54 (2003).
Google Scholar 
Schmeller, D. S. et al. National responsibilities in European species conservation: a methodological review. Conserv. Biol. 22, 593–601 (2008).PubMed 

Google Scholar 
Anderson, S. Area and endemism. Q. Rev. Biol. 69, 451–471 (1994).
Google Scholar 
Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: recent progress and future challenges. J. N. Am. Benthol. Soc. 29, 344–358 (2010).
Google Scholar 
Gorman, C. E., Potts, B. M., Schweitzer, J. A. & Bailey, J. K. Shifts in species interactions due to the evolution of functional differences between endemics and non-endemics: an endemic syndrome hypothesis. PLoS ONE 9, e111190 (2014).Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22, 1424–1442 (2008).PubMed 

Google Scholar 
Fontaine, B. et al. The European Union’s 2010 target: putting rare species in focus. Biol. Conserv. 139, 167–185 (2007).
Google Scholar 
Saeed, M. et al. Rise in temperature causes decreased fitness and higher extinction risks in endemic frogs at high altitude forested wetlands in northern Pakistan. J. Therm. Biol. 95, 102809 (2021).McDonald, L. L. Sampling rare populations. In Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters (ed. Thompson W. L.) 11–42 (Island Press, 2004).Dodd Jr. K. Monitoring Amphibians in Great Smoky Mountains National Park (USGS Survey Circular, 2003).Qu, C. & Stewart, K. A. Evaluating monitoring options for conservation : traditional and environmental DNA tools for a critically endangered mammal. Sci. Nat. 106, 9 (2019).
Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: transforming how we survey animal and plant communities. Mol. Ecol. 26, 5872–5895 (2017).PubMed 

Google Scholar 
Schmidt, B. R., Kery, M., Ursenbacher, S., Hyman, O. J. & Collins, J. P. Site occupancy models in the analysis of environmental DNA presence/absence surveys: a case study of an emerging amphibian pathogen. Methods Ecol. Evol. 4, 646–653 (2013).
Google Scholar 
Iknayan, K. J., Tingley, M. W., Furnas, B. J. & Beissinger, S. R. Detecting diversity: emerging methods to estimate species diversity. Trends Ecol. Evol. 29, 97–106 (2014).PubMed 

Google Scholar 
Kéry, M. & Schmidt, B. R. Imperfect detection and its consequences for monitoring for conservation. Community Ecol. 9, 207–216 (2008).
Google Scholar 
Mackenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).
Google Scholar 
Mackenzie, D. I. & Royle, J. A. Designing occupancy studies: general advice and allocating survey effort. J. Appl. Ecol. 42, 1105–1114 (2005).
Google Scholar 
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).PubMed 
PubMed Central 

Google Scholar 
Darling, J. A. & Mahon, A. R. From molecules to management: adopting DNA-based methods for monitoring biological invasions in aquatic environments. Environ. Res. 111, 978–988 (2011).CAS 
PubMed 

Google Scholar 
Goldberg, C. S., Pilliod, D. S., Arkle, R. S. & Waits, L. P. Molecular detection of vertebrates in stream water: a demonstration using Rocky Mountain tailed frogs and Idaho giant salamanders. PLoS ONE 6, e22746 (2011).Williams, M. R. et al. Isothermal amplification of environmental DNA (eDNA) for direct field-based monitoring and laboratory confirmation of Dreissena sp. PLoS ONE 12, e0186462 (2017).Agersnap, S. et al. Monitoring of noble, signal and narrow-clawed crayfish using environmental DNA from freshwater samples. PLoS ONE 12, e0179261 (2017).Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17 (2016).CAS 

Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29, 358–367 (2014).PubMed 

Google Scholar 
Sigsgaard, E. E., Carl, H., Møller, P. R. & Thomsen, P. F. Monitoring the near-extinct European weather loach in Denmark based on environmental DNA from water samples. Biol. Conserv. 183, 46–52 (2015).
Google Scholar 
Bedwell, M. E., Hopkins, K. V. S., Dillingham, C. & Goldberg, C. S. Evaluating Sierra Nevada yellow-legged frog distribution using environmental DNA. J. Wildl. Mangaement 85, 945–952 (2021).
Google Scholar 
Eiler, A., Löfgren, A., Hjerne, O., Nordén, S. & Saetre, P. Environmental DNA (eDNA) detects the pool frog (Pelophylax lessonae) at times when traditional monitoring methods are insensitive. Sci. Rep. 8, 5452 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Brozio, S. et al. Development and application of an eDNA method to detect the critically endangered Trinidad golden tree frog (Phytotriades auratus) in bromeliad phytotelmata. PLoS ONE 12, e0170619 (2017).Pellet, J. & Schmidt, B. R. Monitoring distributions using call surveys: estimating site occupancy, detection probabilities and inferring absence. Biol. Conserv. 123, 27–35 (2005).
Google Scholar 
Weir, L. A., Royle, J. A., Nanjappa, P. & Jung, R. E. Modeling anuran detection and site occupancy on North American Amphibian Monitoring Program (NAAMP) routes in Maryland. J. Herpetol. 39, 627–639 (2005).
Google Scholar 
Fiske, I. J. & Chandler, R. B. Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1–23 (2011).
Google Scholar 
Goldberg, C. S. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 7, 1299–1307 (2016).
Google Scholar 
Holland, M. M. & Parsons, T. J. Mitochondrial DNA sequence analysis – validation and use for forensic casework. Forensic Sci. Rev. 11, 21–50 (1999).CAS 
PubMed 

Google Scholar 
Willerslev, E. et al. Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science 300, 791–795 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Waits, L. P. & Paetkau, D. Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. J. Wildl. Manage. 69, 1419–1433 (2006).
Google Scholar 
Shokralla, S. et al. Next-generation DNA barcoding: using next-generation sequencing to enhance and accelerate DNA barcode capture from single specimens. Mol. Ecol. Resour. 14, 892–901 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mills, L. S., Pilgrim, K. L., Schwartz, M. K. & McKelvey, K. Identifying lynx and other North American felids based on mtDNA analysis. Conserv. Genet. 1, 285–288 (2000).CAS 

Google Scholar 
Hajibabaei, M. et al. A minimalist barcode can identify a specimen whose DNA is degraded. Mol. Ecol. Notes 6, 959–964 (2006).CAS 

Google Scholar 
Kim, P., Kim, D., Yoon, T. J. & Shin, S. Early detection of marine invasive species, Bugula neritina (Bryozoa: Cheilostomatida), using species-specific primers and environmental DNA analysis in Korea. Mar. Environ. Res. 139, 1–10 (2018).CAS 
PubMed 

Google Scholar 
Dejean, T. et al. Persistence of environmental DNA in freshwater ecosystems. PLoS ONE 6, e23398 (2011).Xia, Z. et al. Early detection of a highly invasive bivalve based on environmental DNA (eDNA). Biol. Invasions 20, 437–447 (2018).
Google Scholar 
Torresdal, J. D., Farrell, A. D. & Goldberg, C. S. Environmental DNA detection of the golden tree frog (Phytotriades auratus) in bromeliads. PLoS ONE 12, e0168787 (2017).Biggs, J. et al. Using eDNA to develop a national citizen science-based monitoring programme for the great crested newt (Triturus cristatus). Biol. Conserv. 183, 19–28 (2015).
Google Scholar 
Pilliod, D. S., Goldberg, C. S., Arkle, R. S. & Waits, L. P. Estimating occupancy and abundance of stream amphibians using environmental DNA from filtered water samples. Can. J. Fish. Aquat. Sci. 70, 1123–1130 (2013).CAS 

Google Scholar 
Smith, D. H. V., Jones, B., Randall, L. & Prescott, D. R. C. Difference in detection and occupancy between two anurans: the importance of species-specific monitoring. Herpetol. Conserv. Biol. 9, 267–277 (2014).
Google Scholar 
Bayley, P. B. & Peterson, J. T. An approach to estimate probability of presence and richness of fish species. Trans. Am. Fish. Soc. 130, 620–633 (2004).
Google Scholar 
Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S. & Venette, R. C. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61, 237–245 (2007).
Google Scholar 
Scott, Jr., N. J. & Woodward, B. D. Surveys at breeding sites. In Measuring and Monitoring Biological Diversity: Standard Methods for Amphibians (eds. Heyer, W. R., Donnelly, M. A., McDiarmid, R. W., Hayek, L. C., & Foster, M. S.) 118–125 (Smithsonian Institution Press, 1994).Dejean, T. et al. Improved detection of an alien invasive species through environmental DNA barcoding: the example of the American bullfrog Lithobates catesbeianus. J. Appl. Ecol. 49, 953–959 (2012).
Google Scholar 
Goldberg, C. S., Sepulveda, A., Ray, A., Baumgardt, J. & Waits, L. P. Environmental DNA as a new method for early detection of New Zealand mudsnails (Potamopyrgus antipodarum). Freshw. Sci. 32, 792–800 (2013).
Google Scholar 
Mahon, A. R. et al. Validation of eDNA surveillance sensitivity for detection of Asian carps in controlled and field experiments. PLoS ONE 8, e58316 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan, M. S. Amphibians and Reptiles of Pakistan (Krieger Publishing Company, 2006).Ruppert, K. M., Davis, D. R., Rahman, M. S. & Kline, R. J. Development and assessment of an environmental DNA (eDNA) assay for a cryptic Siren (Amphibia: Sirenidae). Environ. Adv. 7, 100163 (2022).
Google Scholar 
Hobbs, J., Round, J. M., Allison, M. J. & Helbing, C. C. Expansion of the known distribution of the coastal tailed frog, Ascaphus truei, in British Columbia, Canada, using robust eDNA detection methods. PLoS ONE 14, e0213849 (2019).Barata, I. M., Griffiths, R. A., Fogell, D. J. & Buxton, A. S. Comparison of eDNA and visual surveys for rare and cryptic bromeliad-dwelling frogs. Herpetol. J. 31, 1–9 (2021).
Google Scholar 
Ahmed, W. et al. Site occupancy of two endemic stream frogs in different forest types in Pakistan. Herpetol. Conserv. Biol. 15, 506–511 (2020).
Google Scholar 
Richmond, O. M. W., Hines, J. E. & Beissinger, S. R. Two-species occupancy models: a new parameterization applied to co-occurrence of secretive rails. Ecol. Appl. 20, 2036–2046 (2010).PubMed 

Google Scholar 
Shea, C. P., Eaton, M. J. & MacKenzie, D. I. Implementation of an occupancy-based monitoring protocol for a widespread and cryptic species, the New England cottontail (Sylvilagus transitionalis). Wildl. Res. 46, 222–235 (2019).
Google Scholar 
Rota, C. T. et al. A multispecies occupancy model for two or more interacting species. Methods Ecol. Evol. 7, 1164–1173 (2016).
Google Scholar 
Ohler, A. & Dubois, A. Phylogenetic relationships and generic taxonomy of the tribe Paini (Amphibia, Anura, Ranidae, Dicroglossinae). Zoosystema 28, 769–784 (2006).
Google Scholar 
Jiang, J. et al. Phylogenetic relationships of the tribe Paini (Amphibia, Anura, Ranidae) based on partial sequences of mitochondrial 12s and 16s rRNA genes. Zool. Res. 362, 353–362 (2005).
Google Scholar 
Rais, M. et al. A note on recapture of Nanorana vicina (Anura: Amphibia) from Murree, Pakistan. J. Anim. Plant Sci. 24, 455–458 (2014).
Google Scholar 
Siddiqui, M. F., Ahmed, M., Khan, N. & Khan, I. A. A quantitative description of moist temperate conifer forests of Himalayan region of Pakistan and Azad Kashmir. Int. J. Biotechnol. 7, 175–185 (2010).
Google Scholar 
Beck, H. E. et al. Present and future köppen-geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).Sheikh, M. I. & Hafeez, S. M. Forest and Forestry in Pakistan (A-one Publishers, 2001).Lodhi, A. Conservation of leopards in Ayubia National Park, Pakistan (MS Thesis) (University of Montana, 2007).Palumbi, S. R. Nucleic acids II: the polymerase chain reaction. In Molecular Systematics, 2nd Edition (eds. Hillis, D. M. et al.) 205–247 (Sinauer, 1996).Vences, M., Thomas, M., Van Der Meijden, A., Chiari, Y. & Vieites, D. R. Comparative performance of the 16S rRNA gene in DNA barcoding of amphibians. Front. Zool. 2, 5 (2005).Pounds, J. A. & Crump, M. L. Amphibian declines and climate disturbance: the case of the golden toad and the harlequin frog. Conserv. Biol. 8, 72–85 (1994).
Google Scholar 
R Core Team. R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/ (2021).Hutchinson, R. A., Valente, J. J., Emerson, S. C., Betts, M. G. & Dietterich, T. G. Penalized likelihood methods improve parameter estimates in occupancy models. Methods Ecol. Evol. 6, 949–959 (2015).
Google Scholar 
Clipp, H. L., Evans, A. L., Kessinger, B. E., Kellner, K., & Rota, C. T. A penalized likelihood for multispecies occupancy models improves predictions of species interactions. Ecology 102, e03520 (2021).PubMed 

Google Scholar  More

Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin peatland complex. Nature 542, 86–90 (2017).CAS 
Article 

Google Scholar 
Horn, C. M., Vargas Paredes, V. H., Gilmore, M. P. & Endress, B. A. Spatio-temporal patterns of Mauritia flexuosa fruit extraction in the Peruvian Amazon: implications for conservation and sustainability. Appl. Geogr. 97, 98–108 (2018).Article 

Google Scholar 
Virapongse, A., Endress, B. A., Gilmore, M. P., Horn, C. & Romulo, C. Ecology, livelihoods, and management of the Mauritia flexuosa palm in South America. Glob. Ecol. Conserv. 10, 70–92 (2017).Article 

Google Scholar 
van der Hoek, Y., Solas, S. Á. & Peñuela, M. C. The palm Mauritia flexuosa, a keystone plant resource on multiple fronts. Biodivers. Conserv. 28, 539–551 (2019).Article 

Google Scholar 
Roucoux, K. H. et al. Threats to intact tropical peatlands and opportunities for their conservation. Conserv. Biol. 31, 1283–1292 (2017).CAS 
Article 

Google Scholar 
Dargie, G. C. et al. Congo Basin peatlands: threats and conservation priorities. Mitig. Adapt. Strateg. Glob. Change 24, 669–686 (2019).Article 

Google Scholar 
Pandey, A. K., Tripathi, Y. C. & Kumar, A. Non timber forest products (NTFPs) for sustained livelihood: challenges and strategies. Res. J. For. 10, 1–7 (2016).CAS 

Google Scholar 
Kor, L., Homewood, K., Dawson, T. P. & Diazgranados, M. Sustainability of wild plant use in the Andean Community of South America. Ambio 50, 1681–1697 (2021).Draper, F. C. et al. The distribution and amount of carbon in the largest peatland complex in Amazonia. Environ. Res. Lett. 9, 124017 (2014).Article 

Google Scholar 
Freitas, L. Impacto del aprovechamiento en la estructura, producción y valor de uso del aguaje en la Amazonía peruana. Recur. Naturales y Ambient. 67, 35–45 (2012).
Google Scholar 
Aprovechamiento de los Residuos de Mauritia flexuosa (ITP-CITE, 2018).Falen, L. Y. & Honorio Coronado, E. N. Assessment of the techniques use to harvest buriti fruits (Mauritia flexuosa L.f.) in the district of Jenaro Herrera, Loreto, Peru. Folia Amazónica 27, 131–150 (2018).Article 

Google Scholar 
Draper, F. C. et al. Peatland forests are the least diverse tree communities documented in Amazonia, but contribute to high regional beta-diversity. Ecography 41, 1256–1269 (2018).Article 

Google Scholar 
Bejarano, P. & Piana, R. Plan de Manejo de los Aguajales Aledaños al Caño Parinari (WWF-AIF/DK – Reserva Nacional Pacaya Samiria, 2002).Manzi, M. & Coomes, O. T. Managing Amazonian palms for community use: a case of aguaje palm (Mauritia flexuosa) in Peru. For. Ecol. Manage. 257, 510–517 (2009).Article 

Google Scholar 
Baker, T. R. et al. How can ecologists help realise the potential of payments for carbon in tropical forest countries? J. Appl. Ecol. 47, 1159–1165 (2010).Article 

Google Scholar 
Padoch, C. Marketing of non-timber forest products in Western Amazonia: general observations and research priorities. Adv. Econ. Bot. 9, 43–50 (1192).
Google Scholar 
Delgado, C., Couturierb, G. & Mejía, K. Mauritia flexuosa (Arecaceae: Calamoideae), an Amazonian palm with cultivation purposes in Peru. Fruits 62, 157–169 (2007).Article 

Google Scholar 
Living Planet Index 2020—Bending the Curve of Biodiversity Loss (WWF, 2020).Gentry, A. H. & Vasquez, R. Where have all the ceibas gone? A case history of mismanagement of a tropical forest resource. For. Ecol. Manage. 23, 73–76 (1988).Article 

Google Scholar 
Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
Article 

Google Scholar 
Soga, M. & Gaston, K. J. Shifting baseline syndrome: causes, consequences, and implications. Front. Ecol. 16, 222–230 (2018).Article 

Google Scholar 
Nic Lughadha, E. et al. Extinction risk and threats to plants and fungi. Plants People Planet 2, 389–408 (2020).Article 

Google Scholar 
Ter Steege, H. et al. Estimating the global conservation status of more than 15,000 Amazonian tree species. Sci. Adv. 1, e1500936 (2015).Article 

Google Scholar 
Khan, F. & de Granville, J. J. Palms in Forest Ecosystems of Amazonia (Springer-Verlag, 1992).Freitas, L., Zárate, Z., Bardales, R. & Del Castillo, D. Efecto de la densidad de siembra en el desarrollo vegetativo del aguaje (Mauritia flexuosa L.f.) en plantaciones forestales. Rev. Peru. de. Biol. 26, 227–234 (2019).Article 

Google Scholar 
Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).Article 

Google Scholar 
Endress, B. A., Gilmore, M. P., Vargas, V. H. & Horn, C. Data on spatio-temporal patterns of wild fruit harvest from the economically important palm Mauritia flexuosa in the Peruvian Amazon. Data Brief 20, 132–139 (2018).Article 

Google Scholar 
Ahrends, A. et al. Predictable waves of sequential forest degradation and biodiversity loss spreading from an African city. Proc. Natl Acad. Sci. USA 107, 14556–14561 (2010).Article 

Google Scholar 
Hardin, G. The tragedy of the commons. Science 162, 1243–1248 (1968).CAS 
Article 

Google Scholar 
Ostrom, E. in The New Palgrave Dictionary of Economics Online (eds Durlauf, N.S. & Blume, L.E.) (Palgrave Macmillan, 2008); https://hdl.handle.net/10535/5887Dietz, T., Ostrom, E. & Stern, P. C. The struggle to govern the commons. Science 302, 1907–1912 (2003).CAS 
Article 

Google Scholar 
Isaza, C., Bernal, R., Galeano, G. & Martorell, C. Demography of Euterpe precatoria and Mauritia flexuosa in the Amazon: application of integral projection models for their harvest. Biotropica 49, 653–664 (2017).Article 

Google Scholar 
Chuquinbalqui, C. M. et al. Diagnóstico socioeconómico de la población organizada para el manejo de recursos naturales en las cuencas Yanayacu Pucate y Pacaya en la Reserva Nacional Pacaya Samiria (Reserva Nacional Pacaya Samiria – SERNANP, 2014).Koh, L. & Wilcove, D. Cashing in palm oil for conservation. Nature 448, 993–994 (2007).CAS 
Article 

Google Scholar 
Murdiyarso, D., Suryadiputra, I. N. & Wahyunto. Tropical peatlands management and climate change: a case study in Sumatra, Indonesia. In Proc. 12th International Peat Congress on Wise Use of Peatlands Vol. 1 (ed. Paivanen, J.) 698–706 (International Peat Society, 2004).Freitas, M. A. B. et al. Intensification of açaí palm management largely impoverishes tree assemblages in the Amazon estuarine forest. Biol. Conserv. 261, 109251 (2021).Article 

Google Scholar 
Plan Operativo de Castaña Región Madre de Dios (MINCETUR, 2007).La Industria de la Madera en el Perú. Identificación de las Barreras y Oportunidades para el Comercio Interno de Productos Responsables de Madera, Provenientes de Fuentes Sostenibles y Legales en las MIPYMES del Perú (FAO, 2018).Transferencias por Tipo de Canon, Regalías, y Otros (Congreso Perú, 2019).Peters, C. M., Gentry, A. H. & Mendelsohn, R. O. Valuation of an Amazonian rainforest. Nature 339, 655–656 (1989).Article 

Google Scholar 
Sheil, D. & Wunder, S. The value of tropical forest to local communities: complications, caveats, and cautions. Conserv. Ecol. 6, 9 (2002).Belcher, B. & Schreckenberg, K. Commercialisation of non-timber forest products: a reality check. Dev. Policy Rev. 25, 355–377 (2007).Article 

Google Scholar 
López, M. et al. What Do We Know about Peruvian Peatlands? (CIFOR, 2020).Gilmore, M. P., Endress, B. A. & Horn, C. M. The socio-cultural importance of Mauritia flexuosa palm swamps (aguajales) and implications for multi-use management in two Maijuna communities of the Peruvian Amazon. J. Ethnobiol. Ethnomed. 9, 29 (2013).Article 

Google Scholar 
Tagle Casapia, X. et al. Identifying and quantifying the abundance of economically important palms in tropical moist forest using UAV imagery. Remote Sens 12, 9 (2020).Article 

Google Scholar 
Bruenig, E. F. Conservation and Management of Tropical Rainforests: An integrated Approach to Sustainability 2nd edn (CABI, 2016).de Mello, N. G., Gulinckb, H., Van den Broeckc, P. & Parra, P. Social-ecological sustainability of non-timber forest products: a review and theoretical considerations for future research. For. Policy Econ. 112, 102109 (2020).Article 

Google Scholar 
van Lent, J. Land-Use Change and Greenhouse Gas Emissions in the Tropics: Forest Degradation on Peat Soils. PhD thesis, Wageningen Univ. Res. (2020).Baker, T. R. et al. in Peru: Deforestation in Times of Climate Change (ed. Chirif, A.) 155–174 (IWGIA, Servindi, ONAMIAP & COHARYIMA, 2019).Bhomia, R. K. et al. Impacts of Mauritia flexuosa degradation on the carbon stocks of freshwater peatlands in the Pastaza-Marañón river basin of the Peruvian Amazon. Mitig. Adapt Strateg. Glob. Change 24, 645–668 (2019).Article 

Google Scholar 
Marengo, J. in Geoecología y Desarrollo Amazónico: Estudio Integrado en la Zona de Iquitos Biológica – Geographica – Geológica (eds Kalliola, R. & Flores, S.) 35–57 (Univ. Turku Press, 1998).Koolen, H. H. F., Da Silva, F. M. A., Da Silva, V. S. V., Paz, W. H. P. & Bataglion, G. A. in Exotic Fruits (eds Rodrigues, S. et al.) 61–67 (Elsevier, 2018).Malleux, O. J. Inventarios Forestales en Bosques Tropicales (Universidad Nacional Agraria La Molina, 1982).Del Castillo, D., Otárola, E. & Freitas, L. Aguaje, La Maravillosa Palmera de la Vida (Instituto de Investigaciones de la Amazonía Peruana, 2006).Khorsand Rosa, M., Barbosa, R. & Koptur, S. Which factors explain reproductive output of Mauritia flexuosa (Arecaceae) in forest and savanna habitats of northern Amazonia? Int. J. Plant Sci. 175, 307–318 (2014).Article 

Google Scholar 
Quinteros, Y., Roca, F. & Quinteros, V. in XIV. Morichales y cananguchales y otros palmares inundables de Suramérica. Parte II: Colombia, Venezuela, Brasil, Perú, Bolivia, Paraguay, Uruguay y Argentina Vol. XIV Serie recursos hidrobiológicos y pesqueros continentales de Colombia (eds Lasso, C. A. et al.) 265–282 (Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, 2016).Hergoualc’h, K., Gutiérrez-Vélez, V. H., Menton, M. & Verchot, L. V. Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon. For. Ecol. Manage. 393, 63–73 (2017).Article 

Google Scholar 
Honorio Coronado, E. N. et al. Intensive field sampling increases the known extent of carbon-rich Amazonian peatland pole forests. Environ. Res. Lett. 16, 074048 (2021).Article 

Google Scholar 
de Jong, J. The Impact of Indigenous and Local Communities in the Peruvian Amazon: Integrating Forest Inventory and Remote Sensing. MSc thesis, Wageningen Univ. Res. (2019).Alvarado, L. Estudio del Potencial de las Embarcaciones Solares en la Amazonía. Caso de Estudio Río Napo. MA thesis, Universidad Politécnica Madrid (2017).ArcGIS Desktop v.10.4 (ESRI, 2015).Directorio Nacional de Centrol Poblados – Censos Nacionales 2017- XII de Poblacion, VII de vivienda y III de Comunidades indigenas (Instituto Nacional de Estadítica e Informática, 2018).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).R Core Team. R: A Language and Environment for Statistical Computing. R version 3.5.3 (R Foundation for Statistical Computing, 2019).Taylor, J. R. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements 2nd edn (University Science Books, 1997).Consumer Price Index (Peru) (World Bank Group, 2020); https://data.worldbank.org/indicator/FP.CPI.TOTL?locations=PE More

Cury, P. et al. Small pelagics in upwelling systems: patterns of interaction and structural changes in “wasp-waist” ecosystems. ICES J. Mar. Sci. 57, 603–618 (2000).
Google Scholar 
Fréon, P., Cury, P., Shannon, L. & Roy, C. Sustainable exploitation of small pelagic fish stocks challenged by environmental and ecosystem changes: a review. Bull. Mar. Sci. 76, 385–462 (2005).
Google Scholar 
The State of Mediterranean and Black Sea Fisheries 2020. (FAO, 2020). doi:https://doi.org/10.4060/cb2429en.Checkley, D. M., Asch, R. G. & Rykaczewski, R. R. Climate, Anchovy, and Sardine. Annu. Rev. Mar. Sci. 9, 469–493 (2017).ADS 

Google Scholar 
Bakun, A. Patterns in the ocean: Ocean processes and marine population dynamics. Oceanogr. Lit. Rev. 5, 530 (1997).
Google Scholar 
Houde, E. D. Recruitment variability. Fish. Reprod. Biol. 1, 91–171 (2009).
Google Scholar 
Cole, J. & McGlade, J. Clupeoid population variability, the environment and satellite imagery in coastal upwelling systems. Rev. Fish Biol. Fish. 8, 445–471 (1998).
Google Scholar 
Pörtner, H. O. & Peck, M. A. Climate change effects on fishes and fisheries: towards a cause-and-effect understanding. J. Fish Biol. 77, 1745–1779 (2010).PubMed 

Google Scholar 
European Commission. Joint Research Centre. Institute for the Protection and the Security of the Citizen. Scientific, technical and economic committee for fisheries (STECF) : Assessment of mediterranean stocks (Part I). (Publications Office, 2010).García, A. & Palomera, I. Anchovy early life history and its relation to its surrounding environment in the Western Mediterranean basin. 12 (1996).Tsikliras, A. C., Antonopoulou, E. & Stergiou, K. I. Spawning period of Mediterranean marine fishes. 40 (2010).Fernández-Corredor, E., Albo-Puigserver, M., Pennino, M. G., Bellido, J. M. & Coll, M. Influence of environmental factors on different life stages of European anchovy (Engraulis encrasicolus) and European sardine (Sardina pilchardus) from the Mediterranean Sea: A literature review. Reg. Stud. Mar. Sci. 41, 1006 (2021).
Google Scholar 
Agostini, V. N. & Bakun, A. Ocean triads’ in the Mediterranean Sea: physical mechanisms potentially structuring reproductive habitat suitability (with example application to European anchovy, Engraulis encrasicolus). Fish. Oceanogr. 11, 129–142 (2002).
Google Scholar 
Lasker, R. The relation between oceanographic conditions and larval anchovy food in the California Current: identification of factors contributing to recruitment failure. Rapp. P-V Reun. Cons. Int. Explor. Mer. 173, 212–230 (1978).
Google Scholar 
Cushing, D. H. Plankton Production and Year-class Strength in Fish Populations: an Update of the Match/Mismatch Hypothesis. in Advances in Marine Biology (eds. Blaxter, J. H. S. & Southward, A. J.) vol. 26 249–293 (Academic Press, 1990).Patti, B. et al. Anchovy (Engraulis encrasicolus) early life stages in the Central Mediterranean Sea: connectivity issues emerging among adjacent sub-areas across the Strait of Sicily. Hydrobiologia 821, 25–40 (2018).
Google Scholar 
Maynou, F., Sabatés, A. & Raya, V. Changes in the spawning habitat of two small pelagic fish in the Northwestern Mediterranean. Fish. Oceanogr. 29, 201–213 (2020).
Google Scholar 
Basilone, G. et al. Spawning site selection by European anchovy ( Engraulis encrasicolus ) in relation to oceanographic conditions in the Strait of Sicily. Fish. Oceanogr. 22, 309–323 (2013).
Google Scholar 
Malavolti, S. et al. Distribution of Engraulis encrasicolus eggs and larvae in relation to coastal oceanographic conditions (the South-western Adriatic Sea case study). Mediterr. Mar. Sci. 19, 180 (2018).
Google Scholar 
Zorica, B. et al. Spatiotemporal distribution of anchovy early life stages in the eastern part of the Adriatic Sea in relation to some oceanographic features. J. Mar. Biol. Assoc. UK 99, 1205–1211 (2019).
Google Scholar 
Punt, A. E. et al. Fisheries management under climate and environmental uncertainty: control rules and performance simulation. ICES J. Mar. Sci. 71, 2208–2220 (2014).
Google Scholar 
Szuwalski, C. S. & Hilborn, R. Environment drives forage fish productivity. Proc. Natl. Acad. Sci. 112, E3314–E3315 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ganias, K. Biology and Ecology of Sardines and Anchovies. 391.Palomera, I. et al. Small pelagic fish in the NW Mediterranean Sea: An ecological review. Prog. Oceanogr. 74, 377–396 (2007).ADS 

Google Scholar 
Patti, B., Torri, M. & Cuttitta, A. General surface circulation controls the interannual fluctuations of anchovy stock biomass in the Central Mediterranean Sea. Sci. Rep. 10, 1554 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pisano, A. et al. New evidence of mediterranean climate change and variability from sea surface temperature observations. Rem. Sens. 12, 132 (2020).ADS 

Google Scholar 
Regner, S. Effects of environmental changes on early stages and reproduction of anchovy in the Adriatic Sea. Sci. Mar. 60, 167–177 (1996).
Google Scholar 
Lavigne, H. et al. Enhancing the comprehension of mixed layer depth control on the Mediterranean phytoplankton phenology: Mediterranean Phytoplankton Phenology. J. Geophys. Res. Oceans 118, 3416–3430 (2013).ADS 

Google Scholar 
Morote, E., Olivar, M. P., Villate, F. & Uriarte, I. A comparison of anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) larvae feeding in the Northwest Mediterranean: Influence of prey availability and ontogeny. ICES J. Mar. Sci. 67, 897–908 (2010).
Google Scholar 
Basilone, G. et al. Spawning ecology of the European anchovy (Engraulis encrasicolus) in the Strait of Sicily: Linking variations of zooplankton prey, fish density, growth, and reproduction in an upwelling system. Prog. Oceanogr. 184, 102330 (2020).
Google Scholar 
McBride, R. S. et al. Energy acquisition and allocation to egg production in relation to fish reproductive strategies. Fish Fish. 16, 23–57 (2015).
Google Scholar 
d’Ortenzio, F. & Ribera d’Alcalà, M. On the trophic regimes of the Mediterranean Sea: a satellite analysis. Biogeosciences 6, 139–148 (2009).ADS 

Google Scholar 
Escribano, A., Aldanondo, N., Cotano, U., Boyra, G. & Urtizberea, A. Size- and density-dependent overwinter mortality of anchovy juveniles in the Bay of Biscay. Cont. Shelf Res. 183, 28–37 (2019).ADS 

Google Scholar 
Bellido, J. M. et al. Identifying essential fish habitat for small pelagic species in Spanish Mediterranean waters. Hydrobiologia 612, 171–184 (2008).
Google Scholar 
Van Beveren, E. et al. The fisheries history of small pelagics in the Northern Mediterranean. ICES J. Mar. Sci. 73, 1474–1484 (2016).
Google Scholar 
Edwards, M., Beaugrand, G., Helaouët, P., Alheit, J. & Coombs, S. Marine ecosystem response to the Atlantic Multidecadal Oscillation. PLoS ONE 8, e57212 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Erauskin-Extramiana, M. et al. Historical trends and future distribution of anchovy spawning in the Bay of Biscay. Deep Sea Res. Part II Top. Stud. Oceanogr. 159, 169–182 (2019).
Google Scholar 
Bertrand, A. et al. Schooling behaviour and environmental forcing in relation to anchoveta distribution: An analysis across multiple spatial scales. Prog. Oceanogr. 79, 264–277 (2008).ADS 

Google Scholar 
Giannoulaki, M. et al. Characterizing the potential habitat of European anchovy Engraulis encrasicolus in the Mediterranean Sea, at different life stages: Habitat of anchovy in the Mediterranean. Fish. Oceanogr. 22, 69–89 (2013).
Google Scholar 
Durant, J., Hjermann, D., Ottersen, G. & Stenseth, N. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. 33, 271–283 (2007).
Google Scholar 
Kharouba, H. M. & Wolkovich, E. M. Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Change 10, 406–415 (2020).ADS 

Google Scholar 
Kodama, T. et al. Improvement in recruitment of Japanese sardine with delays of the spring phytoplankton bloom in the Sea of Japan. Fish. Oceanogr. 27, 289–301 (2018).
Google Scholar 
Platt, T., Fuentes-Yaco, C. & Frank, K. T. Spring algal bloom and larval fish survival. Nature 423, 398–399 (2003).ADS 
CAS 
PubMed 

Google Scholar 
Schweigert, J. F. et al. Factors linking Pacific herring (Clupea pallasi) productivity and the spring plankton bloom in the Strait of Georgia, British Columbia. Canada. Prog. Oceanogr. 115, 103–110 (2013).ADS 

Google Scholar 
Laurel, B. J. et al. Regional warming exacerbates match/mismatch vulnerability for cod larvae in Alaska. Progress Oceanogr. 193, 1055 (2021).
Google Scholar 
Brosset, P. et al. A fine-scale multi-step approach to understand fish recruitment variability. Sci. Rep. 10, 16064 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferreira, A. et al. Match-mismatch dynamics in the Norwegian-Barents Sea system. Mar. Ecol. Prog. Ser. 650, 81–94 (2020).ADS 

Google Scholar 
Ferreira, A. S. A., Hátún, H., Counillon, F., Payne, M. R. & Visser, A. W. Synoptic-scale analysis of mechanisms driving surface chlorophyll dynamics in the North Atlantic. Biogeosciences 12, 3641–3653 (2015).ADS 

Google Scholar 
Taboada, F. G. & Anadón, R. Determining the causes behind the collapse of a small pelagic fishery using Bayesian population modeling. Ecol. Appl. 26, 886–898 (2016).
Google Scholar 
Katara, I., Pierce, G. J., Illian, J. & Scott, B. E. Environmental drivers of the anchovy/sardine complex in the Eastern Mediterranean. Hydrobiologia 670, 49–65 (2011).
Google Scholar 
Robinson, A. R. et al. The Atlantic Ionian Stream. J. Mar. Syst. 20, 129–156 (1999).
Google Scholar 
Di Lorenzo, M., Sinerchia, M. & Colloca, F. The North sector of the Strait of Sicily: A priority area for conservation in the Mediterranean Sea. Hydrobiologia 821, 235–253 (2018).
Google Scholar 
García Lafuente, J. et al. Hydrographic phenomena influencing early life stages of the Sicilian Channel anchovy: Hydrographic relations with the Sicilian Channel anchovy. Fish. Oceanogr. 11, 31–44 (2002).
Google Scholar 
Patti, B. et al. Role of physical forcings and nutrient availability on the control of satellite-based chlorophyll a concentration in the coastal upwelling area of the Sicilian Channel. Sci. Mar. 74, 577–588 (2010).
Google Scholar 
Marini, M., Jones, B. H., Campanelli, A., Grilli, F. & Lee, C. M. Seasonal variability and Po River plume influence on biochemical properties along western Adriatic coast. J. Geophys. Res. 113, C0590 (2008).
Google Scholar 
Artegiani et al. The Adriatic Sea General Circulation. Part I Air–.pdf. (1997)Artegiani, A. et al. The Adriatic Sea general circulation. Part II: Baroclinic circulation structure. J. Phys. Oceanogr. 27, 18 (1997).
Google Scholar 
Millot, C. The Gulf of Lions’ hydrodynamics. Cont. Shelf Res. 10, 885–894 (1990).ADS 

Google Scholar 
Petrenko, A., Leredde, Y. & Marsaleix, P. Circulation in a stratified and wind-forced Gulf of Lions, NW Mediterranean Sea: In situ and modeling data. Cont. Shelf Res. 25, 7–27 (2005).ADS 

Google Scholar 
Sverdrup, H. U. On conditions for the vernal blooming of phytoplankton. J. Cons. Int. Explor. Mer. 18, 287–295 (1953).
Google Scholar 
Wilcox, R. R. Introduction to robust estimation and hypothesis testing. (Elsevier, 2016).Pernet, C. R., Wilcox, R. R. & Rousselet, G. A. Robust correlation analyses: False positive and power validation using a new open source Matlab toolbox. Front. Psychol. 1, 1 (2013).
Google Scholar 
Mair, P. & Wilcox, R. Robust statistical methods in R using the WRS2 package. Behav. Res. Methods 52, 464–488 (2020).PubMed 

Google Scholar 
Fox, J., & Weisberg, S. “Robust regression.” An R and S-Plus companion to applied regression 91 (2002).Crawley, M. J. The R book. John Wiley & Sons (2012).Fox, J. Bootstrapping Regression Models Appendix to An R and S-PLUS Companion to Applied Regression (2002). More

Seasonal conditions during crop growthThe groundnut crop produces optimum yield in the regions receiving rainfall between 200 to 1000 mm8. The total rainfall during groundnut growing season was 257.10 mm and 403.10 mm at Baljigapade in 2018 and 2019, respectively, wherein Pavagada (2018) total rainfall was 53.90 mm (Fig. 1). In 2018, both Pavagada and Baljigapade received very low and negligible rainfall during the reproduction and harvest stage of groundnut. Optimum temperature for groundnut production ranges between 20 to 30 °C and growth and pod formation limited below 16 °C and above 32 °C9. The monthly mean atmospheric temperature was ranged from 23 to 27 °C and 21 to 26 °C at Baljigapade in 2018 and 2019, respectively, wherein Pavagada ranged from 25 to 28 °C. At all three locations, the monthly mean atmospheric temperature was slightly high during the early vegetative growth of groundnut and it was progressively decreased as the crop reaches its maturity stage (Fig. 1). All three locations recorded higher and lower mean monthly sunshine hours (hours day−1) during peg initiation to pod filling stage (September and October) and early vegetative growth of groundnut (July and August), respectively.Growth parameters of groundnutAnalysis of variance revealed that treatment, location, and their interaction had a significant effect on plant height and number of branches at harvest (P  More

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I’m from a fishing family. My grandfather was a fisherman when he was a young man, working out of Fano, the Italian town where I grew up and still live. I’m used to the smell of fish.I’m pictured during an overnight shift on the fishing boat RIMAS. I work from 5 p.m. until 9 a.m. with fishermen from nearby Cesenatico on the north Adriatic Sea. It’s a small boat: there’s only six or so of us on board. At night, the fish are most active and we can avoid other vessels.The nets scrape the sea bed for the catch but sometimes they also catch turtles who often die in the nets or on board. That’s where I come in. The net I’m holding is designed to allow turtles to escape: it has a hole at the top they can swim out of. We call it TED — short for ‘turtle excluder device’. The TED is made from a high-strength plastic, and is based on decades of work and research aimed at reducing the bycatch of turtles from trawling. Turtles and some larger fish can leave through the escape hatch, but the current holds most of the catch in the net.I ensure that the net is working, and that the fishermen we’re collaborating with can still catch enough for their livelihoods while protecting turtles. The work is part of research by the Cetacea Foundation, based in Riccione, Italy, in collaboration with the University of Pisa, where I’m a field researcher. It is financed by the LIFE programme, the European Union’s funding instrument for the environment and climate action.I love this work. It means I’m not stuck in an office all day and instead can enjoy the ocean and work closely with people who live by the sea. I get to be a researcher who works outside, rather than being hunched over a microscope.When my grandfather was fishing in the 1970s, there were more fish and more turtles around. At the foundation, we save 50–60 turtles a year, most of them harmed because of fishing. If we can protect turtles by rolling out this device to fishermen all across the Adriatic, I’d see this work as a success.

Nature 604, 210 (2022)
doi: https://doi.org/10.1038/d41586-022-00930-w

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Mabhaudhi, T. et al. Prospects of orphan crops in climate change. Planta 250, 695–708. https://doi.org/10.1007/s00425-019-03129-y (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Singh, M., Bisht, I. S., Dutta, M., Springer. India, 221. https://doi.org/10.1007/978-81-322-2023-7(2014).Litrico, I. & Violle, C. Diversity in plant breeding: A new conceptual framework. Trends Plant Sci. 20, 604–613. https://doi.org/10.1016/j.tplants.2015.07.007 (2015).CAS 
Article 
PubMed 

Google Scholar 
Govindaraj, M., Vetriventhan, M. & Srinivasan, M. Importance of genetic diversity assessment in crop plants and its recent advances: An overview of its analytical perspectives. Genet. Res. Intern. 431–487, 2015. https://doi.org/10.1155/2015/431487 (2015).Article 

Google Scholar 
Akohoué, F., Sibiya, J. & Achigan-Dako, E. G. On-farm practices, mapping, and uses of genetic resources of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] across ecological zones in Benin and Togo. Genet. Resour. Crop. Evol. 66, 195–214. https://doi.org/10.1007/s10722-018-0705-7 (2018).CAS 
Article 

Google Scholar 
Assogba, P. et al. Indigenous knowledge and agro-morphological evaluation of the minor crop Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] cultivars of Benin. Genet. Resour. Crop. Evol. 63, 513–529. https://doi.org/10.1007/s10722-015-0268-9 (2015).Article 

Google Scholar 
Adu-Gyamfi, R., Fearon, J., Bayorbor, T. B., Dzomeku, I. K. & Avornyo, V. K. The Status of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Marechal and Baudet]. Outlook Agric. 40, 259–262. https://doi.org/10.5367/oa.2011.0050 (2011).Article 

Google Scholar 
Obasi, M. O. & Agbatse, A. Evaluation of nutritive value and some functional properties of Kersting’s groundnut seeds for optimum utilisation as a food and feed resource. E. Afr. Agric. For. J. 68, 173–181. https://doi.org/10.4314/eaafj.v68i4.1794 (2003).Article 

Google Scholar 
Ajayi, O. B. & Oyetayo, F. L. Potentials of Kerstingiella geocarpa as a health food. J. Med. Food 12, 184–187. https://doi.org/10.1089/jmf.2008.0100 (2009).CAS 
Article 
PubMed 

Google Scholar 
Mohammed, M., Jaiswal, S. K., Sowley, E. N. K., Ahiabor, B. D. K. & Dakora, F. D. Symbiotic N2 fixation and grain yield of endangered Kersting’s groundnut landraces in response to soil and plant associated bradyrhizobium inoculation to promote ecological resource-use efficiency. Front. Microbiol. 9, 1–14. https://doi.org/10.3389/fmicb.2018.02105 (2018).CAS 
Article 

Google Scholar 
Tamini, Z. Étude ethnobotanique de la Lentille de Terre [Macrotyloma geocarpum Maréchal & Baudet] au Burkina Faso. J. Agric. Trad. Bot. Appl. 37, 187–199. https://doi.org/10.3406/jatba.1995.3569 (1995).Article 

Google Scholar 
Coulibaly, M., Agossou, C. O. A., Akohoué, F., Sawadogo, M. & Achigan-Dako, E. G. Farmers’ preferences for genetic resources of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] in the production systems of Burkina Faso and Ghana. Agronomy 10, 1–20. https://doi.org/10.3390/agronomy10030371 (2020).CAS 
Article 

Google Scholar 
AchiganDako, E. G. & Vodouhe, S. R. Macrotyloma geocarpum (Harms) Marechal & Baudet. In Plant Resources of Tropical Africa 1: Cereals and Pulses (ed. Brink, M. B. G.) 111–114 (Backhuys Publishers CTA, PROTA, 2006).
Google Scholar 
Mergeai, G. Influence des facteurs sociologiques sur la conservation des ressources phytogenetiques: Le cas de la lentille de terre [Macrotyloma geocarpum (Harms) Marechal et Baudet] au Togo. Bull Rech Agron 28, 487–500 (1993).
Google Scholar 
Long, S. P., Marshall-Colon, A. & Zhu, X. G. Meeting the global food demand of the future by engineering crop photosynthesis and yield potential. Cell 161, 56–66. https://doi.org/10.1016/j.cell.2015.03.019 (2015).CAS 
Article 
PubMed 

Google Scholar 
Akohoue, F., Achigan-Dako, E. G., Sneller, C., Van Deynze, A. & Sibiya, J. Genetic diversity, SNP-trait associations and genomic selection accuracy in a west African collection of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Marechal & Baudet]. PLoS ONE 15, 1–24. https://doi.org/10.1371/journal.pone.0234769 (2020).CAS 
Article 

Google Scholar 
Schierenbeck, K. A. Population-level genetic variation and climate change in a biodiversity hotspot. Ann. Bot. 119, 215–228. https://doi.org/10.1093/aob/mcw214 (2017).Article 
PubMed 

Google Scholar 
Araújo, M. B., Whittaker, R. J., Ladle, R. J. & Erhard, M. Reducing uncertainty in projections of extinction risk from climate change. Glob. Ecol. Biogeogr. 14, 529–538. https://doi.org/10.1111/j.1466-822X.2005.00182.x (2005).Article 

Google Scholar 
Araujo, M. B. & Peterson, A. T. Uses and misuses of bioclimatic envelope modeling. Ecology 93, 1527–1539. https://doi.org/10.1890/11-1930.1 (2012).Article 
PubMed 

Google Scholar 
Martínez-Meyer, E. Climate change and biodiversity: Some considerations in forecasting shifts in species’ potential distributions. Biodiv. Inform. 2, 42–55. https://doi.org/10.17161/bi.v2i0.8 (2005).Article 

Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

Google Scholar 
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Chang. 9, 758–763. https://doi.org/10.1038/s41558-019-0585-7 (2019).ADS 
Article 

Google Scholar 
Ramirez-Cabral, N. Y. Z., Kumar, L. & Taylor, S. Crop niche modeling projects major shifts in common bean growing areas. Agric. For. Meteor. 218–219, 102–113. https://doi.org/10.1016/j.agrformet.2015.12.002 (2016).Article 

Google Scholar 
Syfert, M. M. et al. Crop wild relatives of the brinjal eggplant (Solanum melongena): Poorly represented in genebanks and many species at risk of extinction. Am. J. Bot. 103, 1–17. https://doi.org/10.3732/ajb.1500539 (2016).CAS 
Article 

Google Scholar 
Blonder, B. Hypervolume concepts in niche- and trait-based ecology. Ecography 41, 1441–1455. https://doi.org/10.1111/ecog.03187 (2018).Article 

Google Scholar 
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 8, 461–467. https://doi.org/10.1111/j.1461-0248.2005.00739.x (2005).Article 
PubMed 

Google Scholar 
Leimu, R. & Fischer, M. A meta-analysis of local adaptation in plants. PLoS ONE 3, 1–8. https://doi.org/10.1371/journal.pone.0004010 (2008).CAS 
Article 

Google Scholar 
Hereford, J. A quantitative survey of local adaptation and fitness trade-offs. Am. Naturalist. 173, 579–588. https://doi.org/10.1086/597611 (2009).Article 

Google Scholar 
Shaw, R. G. & Etterson, J. R. Rapid climate change and the rate of adaptation: Insight from experimental quantitative genetics. New Phytol. 195, 752–765. https://doi.org/10.1111/j.1469-8137.2012.04230.x (2012).Article 
PubMed 

Google Scholar 
Gotelli, N. J. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585. https://doi.org/10.1111/jbi.12562 (2015).Article 

Google Scholar 
Ikeda, D. H. et al. Genetically informed ecological niche models improve climate change predictions. Glob. Change Biol. 23, 164–176. https://doi.org/10.1111/gcb.13470 (2016).ADS 
Article 

Google Scholar 
Alvarado-Serrano, D. F. & Knowles, L. L. Ecological niche models in phylogeographic studies: Applications, advances and precautions. Mol. Ecol. Resour. 14, 233–248. https://doi.org/10.1111/1755-0998.12184 (2014).Article 
PubMed 

Google Scholar 
Thoen, M. P. et al. Genetic architecture of plant stress resistance: Multi-trait genome-wide association mapping. New Phytol. 213, 1346–1362. https://doi.org/10.1111/nph.14220 (2017).CAS 
Article 
PubMed 

Google Scholar 
Kafoutchoni, K. M., Agoyi, E. E., Agbahoungba, S., Assogbadjo, A. E. & Agbangla, C. Genetic diversity and population structure in a regional collection of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal & Baudet]. Genet. Resour. Crop. Evol. https://doi.org/10.1007/s10722-021-01187-4 (2021).Article 

Google Scholar 
Brown, J. L. & Carnaval, A. C. A tale of two niches: Methods, concepts, and evolution. Front. Biogeogr. https://doi.org/10.21425/f5fbg44158 (2019).Article 

Google Scholar 
Marcer, A., Mendez-Vigo, B., Alonso-Blanco, C. & Pico, F. X. Tackling intraspecific genetic structure in distribution models better reflects species geographical range. Ecol. Evol. 6, 2084–2097. https://doi.org/10.1002/ece3.2010 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Oney, B., Reineking, B., O’Neill, G. & Kreyling, J. Intraspecific variation buffers projected climate change impacts on Pinus contorta. Ecol. Evol. 3, 437–449. https://doi.org/10.1002/ece3.426 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Tamini, Z. Etude ethnobotanique et analyses morphophysiologiques du développement de la lentille de terre [Macrotyloma geocarpum (harms) Maréchal et Baudet] (Université de Ouagadougou, 1997).
Google Scholar 
Yohannes, H. A review on relationship between climate change and agriculture. J. Earth Sci. Clim. Change 7, 1–8. https://doi.org/10.4172/2157-7617.1000335 (2015).Article 

Google Scholar 
Sileshi, G. et al. Variation in maize yield gaps with plant nutrient inputs, soil type and climate across sub-Saharan Africa. Field Crops Res. 116, 1–13. https://doi.org/10.1016/j.fcr.2009.11.014 (2010).Article 

Google Scholar 
Padi, F. K. & Ehlers, J. D. Effectiveness of early generation selection in cowpea for grain yield and agronomic characteristics in Semiarid West Africa. Crop Sci. 48, 533–540. https://doi.org/10.2135/cropsci2007.05.0265 (2008).Article 

Google Scholar 
Kouelo, K. A. F. et al. Impact du travail du sol et de la fertilisation minérale sur la productivité de [Macrotyloma geocarpum (Harms) Maréchal et Baudet] au centre du Bénin. J. Appl. Biosci. 51, 3625–3632 (2012).
Google Scholar 
Wellenreuther, M., Larson, K. W. & Svensson, E. I. Climatic niche divergence or conservatism? Environmental niches and range limits in ecologically similar damselflie. Ecology 93, 1353–1366. https://doi.org/10.1890/11-1181.1 (2012).Article 
PubMed 

Google Scholar 
Akohoue, F., Achigan-Dako, E. G., Coulibaly, M. & Sibiya, J. Correlations, path coefficient analysis and phenotypic diversity of a West African germplasm of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal & Baudet]. Genet. Resour. Crop Evol. 66, 1825–1842. https://doi.org/10.1007/s10722-019-00839-w (2019).Article 

Google Scholar 
Assogba, P. et al. Indigenous knowledge and agro-morphological evaluation of the minor crop Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal et Baudet] cultivars of Benin. Genet. Resour. Crop Evol. 63, 513–529. https://doi.org/10.1007/s10722-015-0268-9 (2015).Article 

Google Scholar 
Adu-Gyamfi, R., Dzomeku, I. K. & Lardi, J. Evaluation of growth and yield potential of genotypes of Kersting’s groundnut (Macrotyloma geocarpum Harms) in Northern Ghana. Int. Res. J. Agric. Sci. Soil Sci. 2, 509–515 (2012).
Google Scholar 
Burke, M. B., Lobell, D. B. & Guarino, L. Shifts in African crop climates by 2050, and the implications for crop improvement and genetic resources conservation. Glob. Environ. Change 19, 317–325. https://doi.org/10.1016/j.gloenvcha.2009.04.003 (2009).Article 

Google Scholar 
Ramirez-Cabral, N. Y. Z., Kumar, L. & Shabani, F. Global alterations in areas of suitability for maize production from climate change and using a mechanistic species distribution model (CLIMEX). Sci. Rep. 7, 1–13. https://doi.org/10.1038/s41598-017-05804-0 (2017).CAS 
Article 

Google Scholar 
Hancock, A. M. et al. Adaptation to climate across the Arabidopsis thaliana genome. Science 334, 83–86. https://doi.org/10.1126/science.1209244 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bellon, M. R. & van Etten, J. Climate change and on-farm conservation of crop landraces in centres of diversity. In Plant Genetic Resources and Climate Change Vol. 30 (eds Jackson, M. et al.) (CAB International, 2014).
Google Scholar 
Lane, A. & Jarvis, A. Changes in climate will modify the geography of crop suitability: Agricultural biodiversity can help with adaptation. ISI J 4, 12 (2007).
Google Scholar 
Vigouroux, Y., Barnaud, A., Scarcelli, N. & Thuillet, A. C. Biodiversity, evolution and adaptation of cultivated crops. C.R. Biol. 334, 450–457. https://doi.org/10.1016/j.crvi.2011.03.003 (2011).Article 
PubMed 

Google Scholar 
Coulibaly, M., Agossou, C. O. A., Akohoué, F., Sawadogo, M. & Achigan-Dako, E. G. Farmers’ preferences for genetic resources of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] in the production systems of Burkina Faso and Ghana. Agronomy 10, 371. https://doi.org/10.3390/agronomy10030371 (2020).CAS 
Article 

Google Scholar 
Sohn, N., Fernandez, M. H., Papes, M. & Anciães, M. Ecological Niche modeling in practice: Flagship species and regional conservation planning. Oecol. Aust. 17, 429–440. https://doi.org/10.4257/oeco.2013.1703.11 (2013).Article 

Google Scholar 
Amujoyegbe, B., Obisesan, I., Ajayi, A. & Aderanti, F. Disappearance of Kersting’s groundnut [Macrotyloma geocarpum (harms) Maréchal et Baudet] in South-Western Nigeria: An indicator of genetic erosion. Plant Gen Res News 152, 45–50 (2007).
Google Scholar 
Banta, J. A. et al. Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana. Ecol. Lett. 15, 769–777. https://doi.org/10.1111/j.1461-0248.2012.01796.x (2012).Article 
PubMed 

Google Scholar 
Kumar, J., Choudhary, A. K., Gupta, D. S. & Kumar, S. Towards exploitation of adaptive traits for climate-resilient smart pulses. Int. J. Mol. Sci. 20, 1–30. https://doi.org/10.3390/ijms20122971 (2019).ADS 
CAS 
Article 

Google Scholar 
Bohra, A., Mir, R. R., Jha, R., Maurya, A. K. & Varshney, R. K. Advances in genomics and molecular breeding for legume improvement. In Advancement in Crop Improvement Techniques (eds Bohra, A. et al.) 129–139 (Elsevier Inc, 2020).Chapter 

Google Scholar 
Gobu, R. et al. Accelerated crop breeding towards development of climate resilient varieties. In Climate Change and Indian Agriculture: Challenges and Adaptation Strategies (eds Srinivasarao, C. et al.) 49–69 (ICAR-National Academy of Agricultural Research Management, 2020).
Google Scholar 
Aliyu, S., Massawe, F. & Mayes, S. Genetic diversity and population structure of Bambara groundnut [Vigna subterranea (L.) Verdc.]: Synopsis of the past two decades of analysis and implications for crop improvement programmes. Genet. Resour. Crop. Evol. 63, 925–943. https://doi.org/10.1007/s10722-016-0406-z (2016).Article 

Google Scholar 
Al-Khayri, J. M., Jain, S. M., Johnson, D. V. Springer Nature Switzerland AG. Switzerland. https://doi.org/10.1007/978-3-030-23400-3(2019).Kilian, A. et al. Diversity arrays technology: a generic genome profiling technology on open platforms. Methods Mol. Biol. 888, 67–89. https://doi.org/10.1007/978-1-61779-870-2_5 (2012).Article 
PubMed 

Google Scholar 
Illumina, I. HiSeq®2500 Sequencing System: Unsurpassed power and efficiency for production scale sequencing. System Specification Sheet: Sequencing, 1–4. https://www.illumina.com/documents/products/datasheets/datasheet_hiseq2500.pdf (2015).Buckler, E. et al. User Manual for TASSEL Trait Analysis by association, Evolution and Linkage Version 5.0. The Buckler Lab at Cornell University. 1–70. https://www.maizegenetics.net/tassel (2014).Pritchard, J. K., Wen, X., Falush, D. Department of Human Genetics, University of Chicago. (2010).Francis, R. M. pophelper: An R package and web app to analyse and visualize population structure. Mol. Ecol. Resour. 17, 27–32. https://doi.org/10.1111/1755-0998.12509 (2016).CAS 
Article 
PubMed 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).CAS 
Article 
PubMed 

Google Scholar 
Jombart, T. & Ahmed, I. adegenet version 1.3-1: New tools for the analysis of genome-wide SNP data. Bioinformatics https://doi.org/10.1093/bioinformatics/btr521 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model 275, 73–77. https://doi.org/10.1016/j.ecolmodel.2013.12.012 (2014).Article 

Google Scholar 
Peterson, A. T. et al. NicheBook (Princeton University Press, 2011).
Google Scholar 
Kass, J. M., Pinilla-Buitrago, G. E., Vilela, B., Aiello-Lammens, M. E., Muscarella, R., Merow, C., Anderson, R. P., Wallace: A Modular Platform for Reproducible Modeling of Species Niches and Distributions. R package version 1.0.6.3. (2020).Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).Article 

Google Scholar 
Platts, P. J., Omeny, P. A. & Marchant, R. AFRICLIM: High-resolution climate projections for ecological applications in Africa. Afr. J. Ecol. 53, 103–108 (2014).Article 

Google Scholar 
Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241. https://doi.org/10.1007/s10584-011-0156-z (2011).ADS 
CAS 
Article 

Google Scholar 
IPCC, I. P. o. C. C. Cambridge University Press. https://doi.org/10.1017/CBO9781107415324(2013).Ramirez-Villegas, J. & Jarvis, A. Downscaling global circulation model outputs: The delta method decision and policy analysis, Working Paper No. 1. Policy Anal. Manag. 1, 1–18 (2010).
Google Scholar 
Hengl, T. et al. Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE 10, 1–26. https://doi.org/10.1371/journal.pone.0125814 (2015).CAS 
Article 

Google Scholar 
Osorio-Olvera, L. et al. ntbox: An R package with graphical user interface for modeling and evaluating multidimensional ecological niches Methods. Ecol. Evol. 11, 1199–1206. https://doi.org/10.1111/2041-210X.13452 (2020).Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum Entropy modeling of species geographic distributions. Ecol. Model 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2005).Article 

Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Diversity Distrib. 17, 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x (2010).Article 

Google Scholar 
Richards, C. L., Carstens, B. C. & Lacey Knowles, L. Distribution modelling and statistical phylogeography: An integrative framework for generating and testing alternative biogeographical hypotheses. J. Biogeogr. 34, 1833–1845. https://doi.org/10.1111/j.1365-2699.2007.01814.x (2007).Article 

Google Scholar 
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31, 161–175. https://doi.org/10.1111/j.2007.0906-7590.05203.x (2008).Article 

Google Scholar 
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).Article 

Google Scholar 
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).Article 

Google Scholar 
Barve, N., Barve, V. ENMGadgets: Pre and post processing in ENM Workflow. R package version 0.1.0.1. (2019).Kass, J. M. et al. ENMeval 20: Redesigned for customizable and reproducible modeling of species’ niches and distributions. Ecol. Evolut. https://doi.org/10.1111/2041-210X.13628 (2021).Article 

Google Scholar 
Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342. https://doi.org/10.1890/10-1171.1 (2011).Article 
PubMed 

Google Scholar 
Warren, D. L., Glor, R. E. & Turelli, M. Environmental Niche equivalency versus conservatism: Quantitative approaches to Niche evolution. Evolution 62, 2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x (2008).Article 
PubMed 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497. https://doi.org/10.1111/j.1466-8238.2011.00698.x (2012).Article 

Google Scholar 
Benhamou, S. & Cornélis, D. Incorporating movement behavior and barriers to improve kernel home range space use estimates. J. Wildl. Manag. 74, 1353–1360. https://doi.org/10.2193/2009-441 (2010).Article 

Google Scholar  More

Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the World’s Marine Ecosystems. Science 328, 1523–1528 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Hewitt, J. E., Ellis, J. I. & Thrush, S. F. Multiple stressors, nonlinear effects and the implications of climate change impacts on marine coastal ecosystems. Glob. Change Biol. 22, 2665–2675 (2016).ADS 

Google Scholar 
Sweetman, A. K. et al. Major impacts of climate change on deep-sea benthic ecosystems. Elementa Sci. Anthropocene. https://doi.org/10.1525/elementa.203 (2017).Article 

Google Scholar 
Leslie, H. M. A synthesis of marine conservation planning approaches. Conserv. Biol. 19, 1701–1713 (2005).
Google Scholar 
Oppel, S. et al. Spatial scales of marine conservation management for breeding seabirds. Mar. Policy 98, 37–46 (2018).
Google Scholar 
Manea, E., Bianchelli, S., Fanelli, E., Danovaro, R. & Gissi, E. Towards an ecosystem-based marine spatial planning in the deep Mediterranean Sea. Sci. Total Environ. 715, 136884 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Aylesworth, L., Phoonsawat, R., Suvanachai, P. & Vincent, A. C. J. Generating spatial data for marine conservation and management. Biodivers. Conserv. 26, 383–399 (2017).
Google Scholar 
Lesser, M. P., Slattery, M. & Leichter, J. J. Ecology of mesophotic coral reefs. J. Exp. Mar. Biol. Ecol. 375, 1–8 (2009).
Google Scholar 
James, N. P., Ginsburg, R. N. & Ginsburg, R. N. The Seaward Margin of Belize Barrier and Atoll Reefs: Morphology, Sedimentology, Organism Distribution, and Late Quaternary History (Blackwell Scientific, 1979).
Google Scholar 
Ginsburg, R. N., Harris, P. M., Eberli, G. P. & Swart, P. K. The growth potential of a bypass margin, Great Bahama Bank. J. Sediment. Res. 61, 976–987 (1991).
Google Scholar 
Pyle, R. L. & Copus, J. M. Mesophotic coral ecosystems: Introduction and overview. In Mesophotic Coral Ecosystems. Coral Reefs of the World (eds Loya, Y. et al.) 3–27 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-319-92735-0_1.Chapter 

Google Scholar 
Kahng, S. E. et al. Community ecology of mesophotic coral reef ecosystems. Coral Reefs 29, 255–275 (2010).
Google Scholar 
Hinderstein, L. M. et al. Theme section on “Mesophotic coral ecosystems: Characterization, ecology, and management”. Coral Reefs 29, 247–251 (2010).ADS 

Google Scholar 
J. A. Turner, D. A. Andradi-Brown, A. Gori, P. Bongaerts, H. L. Burdett, C. Ferrier-Pagès, C. R. Voolstra, D. K. Weinstein, T. C. L. Bridge, F. Costantini, E. Gress, J. Laverick, Y. Loya, G. Goodbody-Gringley, S. Rossi, M. L. Taylor, N. Viladrich, J. D. Voss, J. Williams, L. C. Woodall, G. Eyal. in Mesophotic Coral Ecosystems, Coral Reefs of the World, 989–1003 (Y. Loya, K. A. Puglise, T. C. L. Bridge, Eds). (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-319-92735-0_52.Baker, E. K., Puglise, K. A., Harris, P. T., United Nations Environment Programme, GRID-Arendal. Mesophotic Coral Ecosystems: A Lifeboat for Coral Reefs? (United Nations Environment Programme and GRID-Arendal, 2016).
Google Scholar 
Lang, J. C. Biological Zonation at the Base of a Reef: Observations from the submersible Nekton Gamma have led to surprising revelations about the deep fore-reef and island slope at Discovery Bay, Jamaica. Am. Scientist. 62, 272–281 (1974).ADS 

Google Scholar 
J. K. Reed. Deepest distribution of Atlantic hermatypic corals discovered in the Bahamas. in Proceedings of the 5th International Coral Reef Symposium (1985), Vol. 6, 249–254.Hanisak, M. D. & Blair, S. M. The deep-water macroalgal community of the East Florida continental shelf (USA). Helgolander Meeresunters. 42, 133–163 (1988).
Google Scholar 
Aponte, N. E. & Ballantine, D. L. Depth distribution of algal species on the deep insular fore reef at Lee Stocking Island, Bahamas. Deep Sea Res. Part I 48, 2185–2194 (2001).
Google Scholar 
Fricke, H. W., Vareschi, E. & Schlichter, D. Photoecology of the coral Leptoseris fragilis in the Red Sea twilight zone (an experimental study by submersible). Oecologia 73, 371–381 (1987).ADS 
CAS 
PubMed 

Google Scholar 
Kahng, S. & Maragos, J. The deepest, zooxanthellate scleractinian corals in the world?. Coral Reefs 25, 254–254 (2006).ADS 

Google Scholar 
Maragos, J. E. & Jokiel, P. L. Reef corals of Johnston Atoll: One of the world’s most isolated reefs. Coral Reefs 4, 141–150 (1986).ADS 

Google Scholar 
Bridge, T. C. L. et al. Variability in mesophotic coral reef communities along the Great Barrier Reef, Australia. Mar. Ecol. Progress Series 428, 63–75 (2011).ADS 

Google Scholar 
Lesser, M. P. & Slattery, M. Phase shift to algal dominated communities at mesophotic depths associated with lionfish (Pterois volitans) invasion on a Bahamian coral reef. Biol. Invasions 13, 1855–1868 (2011).
Google Scholar 
Slattery, M. & Lesser, M. P. The Bahamas and Cayman Islands. In Mesophotic Coral Ecosystems (eds Loya, Y. et al.) 47–56 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-319-92735-0_3.Chapter 

Google Scholar 
Slattery, M., Lesser, M. P., Brazeau, D., Stokes, M. D. & Leichter, J. J. Connectivity and stability of mesophotic coral reefs. J. Exp. Mar. Biol. Ecol. 408, 32–41 (2011).
Google Scholar 
Lesser, M. P., Slattery, M., Laverick, J. H., Macartney, K. J. & Bridge, T. C. Global community breaks at 60 m on mesophotic coral reefs. Glob. Ecol. Biogeogr. 28, 1403–1416 (2019).
Google Scholar 
Tamir, R., Eyal, G., Kramer, N., Laverick, J. H. & Loya, Y. Light environment drives the shallow-to-mesophotic coral community transition. Ecosphere 10, e02839 (2019).
Google Scholar 
Laverick, J. H., Green, T. K., Burdett, H. L., Newton, J. & Rogers, A. D. Depth alone is an inappropriate proxy for physiological change in the mesophotic coral Agaricia lamarcki. J. Mar. Biol. Assoc. UK 99, 1535–1546 (2019).
Google Scholar 
Lesser, M. P., Mobley, C. D., Hedley, J. D. & Slattery, M. Incident light on mesophotic corals is constrained by reef topography and colony morphology. Mar. Ecol. Prog. Ser. 670, 49–60 (2021).ADS 

Google Scholar 
Cerrano, C. et al. Temperate mesophotic ecosystems: Gaps and perspectives of an emerging conservation challenge for the Mediterranean Sea. Eur. Zool. J. 86, 370–388 (2019).
Google Scholar 
Idan, T. et al. Shedding light on an East-Mediterranean mesophotic sponge ground community and the regional sponge fauna. Mediterr. Mar. Sci. 19, 84–106 (2018).
Google Scholar 
Idan, T., Goren, L., Shefer, S., Brickner, I. & Ilan, M. Does depth matter? Reproduction pattern plasticity in two common sponge species found in both mesophotic and shallow waters. Front. Mar. Sci. 7, 1078 (2020).
Google Scholar 
Enrichetti, F. et al. Megabenthic communities of the Ligurian deep continental shelf and shelf break (NW Mediterranean Sea). PLoS ONE 14, e0223949 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kahng, S. E. et al. Coral reefs of the world. In Mesophotic Coral Ecosystems (eds Loya, Y. et al.) 47–56 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-319-92735-0_42 (801–828).Chapter 

Google Scholar 
D’Ortenzio, F. & Ribera d’Alcalà, M. On the trophic regimes of the Mediterranean Sea: A satellite analysis. Biogeosciences 6, 139–148 (2009).ADS 

Google Scholar 
Christaki, U. et al. Microbial food webs and metabolic state across oligotrophic waters of the Mediterranean Sea during summer. Biogeosciences 8, 1839–1852 (2011).ADS 
CAS 

Google Scholar 
Rossi, V., Ser-Giacomi, E., López, C. & Hernández-García, E. Hydrodynamic provinces and oceanic connectivity from a transport network help designing marine reserves. Geophys. Res. Lett. 41, 2883–2891 (2014).ADS 

Google Scholar 
Basterretxea, G., Font-Muñoz, J. S., Salgado-Hernanz, P. M., Arrieta, J. & Hernández-Carrasco, I. Patterns of chlorophyll interannual variability in Mediterranean biogeographical regions. Remote Sens. Environ. 215, 7–17 (2018).ADS 

Google Scholar 
Tanhua, T. et al. Repeat hydrography in the Mediterranean Sea, data from the Meteor cruise 84/3 in 2011. Earth Syst. Sci. Data 5, 289–294 (2013).ADS 

Google Scholar 
Bethoux, J. P. Budgets of the Mediterranean Sea-their dependance on the local climate and on the characteristics of the Atlantic waters. Oceanol. Acta 2, 157–163 (1979).
Google Scholar 
Azov, Y. Eastern Mediterranean—A marine desert?. Mar. Pollut. Bull. 23, 225–232 (1991).
Google Scholar 
Pinardi, N., Zavatarelli, M., Arneri, E., Crise, A. & Ravaioli, M. The physical, sedimentary and ecological structure and variability of shelf areas in the Mediterranean Sea. The Sea 14, 1243–1330 (2006).
Google Scholar 
Rodolfo-Metalpa, R. et al. Calcification is not the Achilles’ heel of cold-water corals in an acidifying ocean. Glob. Change Biol. 21, 2238–2248 (2015).ADS 

Google Scholar 
Bo, M. et al. Fishing impact on deep Mediterranean rocky habitats as revealed by ROV investigation. Biol. Cons. 171, 167–176 (2014).
Google Scholar 
Cau, A. et al. Deepwater corals biodiversity along roche du large ecosystems with different habitat complexity along the south Sardinia continental margin (CW Mediterranean Sea). Mar. Biol. 162, 1865–1878 (2015).
Google Scholar 
L. Bramanti, M. C. Benedetti, R. Cupido, S. Cocito, C. Priori, F. Erra, M. Iannelli, G. Santangelo. in Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots, 529–548 (S. Rossi, L. Bramanti, A. Gori, C. Orejas Eds.) (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-21012-4_13.Capdevila, P., Linares, C., Aspillaga, E., Riera, J. L. & Hereu, B. Effective dispersal and density-dependence in mesophotic macroalgal forests: Insights from the Mediterranean species Cystoseira zosteroides. PLoS ONE 13, e0191346 (2018).PubMed 
PubMed Central 

Google Scholar 
Angeletti, L. et al. A brachiopod biotope associated with rocky bottoms at the shelf break in the central Mediterranean Sea: Geobiological traits and conservation aspects. Aquat. Conserv. Mar. Freshwat. Ecosyst. 30, 402–411 (2020).
Google Scholar 
Angeletti, L. & Taviani, M. Offshore Neopycnodonte Oyster Reefs in the Mediterranean Sea. Diversity 12, 92 (2020).
Google Scholar 
Castellan, G., Angeletti, L., Taviani, M. & Montagna, P. The yellow coral Dendrophyllia cornigera in a warming ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00692 (2019).Article 

Google Scholar 
Corriero, G. et al. A Mediterranean mesophotic coral reef built by non-symbiotic scleractinians. Sci. Rep. 9, 3601 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Chimienti, G. Vulnerable Forests of the Pink Sea Fan Eunicella verrucosa in the Mediterranean Sea. Diversity 12, 176 (2020).
Google Scholar 
Gori, A. et al. Animal forests in deep coastal bottoms and continental shelf of the Mediterranean Sea. In Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds Rossi, S. et al.) 1–28 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-17001-5_5-2.Chapter 

Google Scholar 
Goren, L., Idan, T., Shefer, S. & Ilan, M. Macrofauna inhabiting massive demosponges from shallow and mesophotic habitats along the Israeli Mediterranean Coast. Front. Mar. Sci. 7, 1245 (2021).
Google Scholar 
Santín, A. et al. Sponge assemblages on the deep Mediterranean continental shelf and slope (Menorca Channel, Western Mediterranean Sea). Deep Sea Res. Part I 131, 75–86 (2018).
Google Scholar 
Martin, C. S. et al. Coralligenous and maërl habitats: predictive modelling to identify their spatial distributions across the Mediterranean Sea. Sci. Rep. 4, 5073 (2014).CAS 

Google Scholar 
D. Basso, L. Babbini, A. A. Ramos-Esplá, M. Salomidi. in Rhodolith/Maërl Beds: A Global Perspective, Coastal Research Library, 281–298 (R. Riosmena-Rodríguez, W. Nelson, J. Aguirre, Eds.) (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-29315-8_11.Foster, M. M., Amado Filho, G. M., Kamenos, N. A., Riosmena-Rodríguez, R. & Steller, D. L. Rhodoliths and rhodolith beds. Res. Discoveries Revolut. Sci. Through Scuba. 39, 143–155 (2013).
Google Scholar 
Littler, M. M., Littler, D. S. & Dennis Hanisak, M. Deep-water rhodolith distribution, productivity, and growth history at sites of formation and subsequent degradation. J. Exp. Mar. Biol. Ecol. 150, 163–182 (1991).
Google Scholar 
Ballesteros, E. Mediterranean coralligenous assemblages: a synthesis of present knowledge. Oceanogr. Mar. Biol. Annu. Rev. 44, 123–195 (2006).
Google Scholar 
Smith, T. B. et al. Benthic structure and cryptic mortality in a Caribbean mesophotic coral reef bank system, the Hind Bank Marine Conservation District, U. S. Virgin Islands. Coral Reefs 29, 289–308 (2010).ADS 

Google Scholar 
Markager, S. & Sand-Jensen, K. Light requirements and depth zonation of marine macroalgae. Mar. Ecol. Prog. Ser. 88, 83–92 (1992).ADS 

Google Scholar 
Runcie, J. W., Gurgel, C. F. D. & Mcdermid, K. J. In situ photosynthetic rates of tropical marine macroalgae at their lower depth limit. Eur. J. Phycol. 43, 377–388 (2008).CAS 

Google Scholar 
Bindoff, N. L., et al. Chapter 5: Changing ocean, marine ecosystems, and dependent communities. Intergovernmental panel of climate change. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate 447–587 (2019).Tweedley, J. R., Warwick, R. M. & Potter, I. C. The contrasting ecology of temperate macrotidal and microtidal estuaries. In Oceanography and Marine Biology: An Annual Review (eds Hughes, R. N. et al.) 73–171 (CRC Press, 2016).
Google Scholar 
Arias-Ortiz, A. et al. A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Change. 8, 338–344 (2018).ADS 
CAS 

Google Scholar 
Chen, N., Krom, M. D., Wu, Y., Yu, D. & Hong, H. Storm induced estuarine turbidity maxima and controls on nutrient fluxes across river-estuary-coast continuum. Sci. Total Environ. 628–629, 1108–1120 (2018).ADS 
PubMed 

Google Scholar 
Agusti, S., Lubián, L. M., Moreno-Ostos, E., Estrada, M. & Duarte, C. M. Projected changes in photosynthetic picoplankton in a warmer subtropical ocean. Front. Mar. Sci. 5, 506 (2019).
Google Scholar 
Lesser, M. P. & Slattery, M. Will coral reef sponges be winners in the Anthropocene?. Glob. Change Biol. 26, 3202–3211 (2020).ADS 

Google Scholar 
Ponti, M., Turicchia, E., Ferro, F., Cerrano, C. & Abbiati, M. The understorey of gorgonian forests in mesophotic temperate reefs. Aquat. Conserv. Mar. Freshwat. Ecosyst. 28, 1153–1166 (2018).
Google Scholar 
Enrichetti, F. et al. Assessing the environmental status of temperate mesophotic reefs: A new, integrated methodological approach. Ecol. Ind. 102, 218–229 (2019).
Google Scholar 
Soares, M. O., Tavares, T. C. L. & Carneiro, P. B. M. Mesophotic ecosystems: Distribution, impacts and conservation in the South Atlantic. Diversity Distributions. 25, 255–268 (2019).
Google Scholar 
Mobley, C. D. & Mobley, C. D. Light and Water: Radiative Transfer in Natural Waters (Academic Press, 1994).
Google Scholar 
Marty, J.-C. & Chiavérini, J. Seasonal and interannual variations in phytoplankton production at DYFAMED time-series station, northwestern Mediterranean Sea. Deep Sea Res. Part II 49, 2017–2030 (2002).ADS 
CAS 

Google Scholar 
Morel, A. & André, J.-M. Pigment distribution and primary production in the western Mediterranean as derived and modeled from coastal zone color scanner observations. J. Geophys. Res. Oceans. 96, 12685–12698 (1991).ADS 

Google Scholar 
Antoine, D., Morel, A. & André, J.-M. Algal pigment distribution and primary production in the eastern Mediterranean as derived from coastal zone color scanner observations. J. Geophys. Res. Oceans. 100, 16193–16209 (1995).ADS 

Google Scholar 
Mayot, N., D’Ortenzio, F., Ribera d’Alcalà, M., Lavigne, H. & Claustre, H. Interannual variability of the Mediterranean trophic regimes from ocean color satellites. Biogeosciences 13, 1901–1917 (2016).ADS 
CAS 

Google Scholar 
S. Kahng, J. M. Copus, D. Wagner. in Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots, 185–206 (S. Rossi, L. Bramanti, A. Gori, C. Orejas, Eds.) (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-21012-4_4.Chimienti, G. et al. Effects of global warming on Mediterranean coral forests. Sci. Rep. 11, 20703 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lesser, M. P., Slattery, M. & Mobley, C. D. Incident light and morphology determine coral productivity along a shallow to mesophotic depth gradient. Ecol. Evol. 11, 13445–13454 (2021).PubMed 
PubMed Central 

Google Scholar 
Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).
Google Scholar 
Danovaro, R. et al. Towards a marine strategy for the deep Mediterranean Sea: Analysis of current ecological status. Mar. Policy. 112, 103781 (2020).
Google Scholar 
Saulquin, B. et al. Estimation of the diffuse attenuation coefficient KdPAR using MERIS and application to seabed habitat mapping. Remote Sens. Environ. 128, 224–233 (2013).ADS 

Google Scholar 
Grinyó, J. et al. Soft corals assemblages in deep environments of the Menorca Channel (Western Mediterranean Sea). Progress Oceanogr. 188, 102435 (2020).
Google Scholar 
Artegiani, A. et al. The Adriatic Sea general circulation. Part I: Air–sea interactions and water mass structure. J. Phys. Oceanogr. 27, 1492–1514 (1997).ADS 

Google Scholar 
Morel, A. et al. Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach. Remote Sens. Environ. 111, 69–88 (2007).ADS 

Google Scholar 
Davies, A. J. & Guinotte, J. M. Global habitat suitability for framework-forming cold-water corals. PLoS ONE 6, e18483 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Georgian, S. E. et al. Habitat suitability modelling to predict the spatial distribution of cold-water coral communities affected by the Deepwater Horizon oil spill. J. Biogeogr. 47, 1455–1466 (2020).
Google Scholar 
R. C. Team, R: A language and environment for statistical computing (3. 5. 1)[Computer software]. R Foundation for Statistical Computing (2020). More