Ecology

Hutchinson, G. E. An Introduction to Population Ecology (Yale University, 1978).MATH 

Google Scholar 
Erb, J., Boyce, M. S. & Stenseth, N. C. Population dynamics of large and small mammals. Oikos 92, 3–12 (2001).
Google Scholar 
Gaillard, J. M., Festa-Bianchet, M. & Yoccoz, N. G. Population dynamics of large herbivores: Variable recruitment with constant adult survival. Trends Ecol. Evol. 13, 58–63 (1998).CAS 
PubMed 

Google Scholar 
Dennis, B., Ponciano, J. M., Lele, S. R., Taper, M. L. & Staples, D. F. Estimating density dependence, process noise, and observation error. Ecol. Monogr. 76, 323–341 (2006).
Google Scholar 
Rockwood, L. L. Introduction to Population Ecology (Wiley, 2015).
Google Scholar 
Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).
Google Scholar 
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Ripple, W. J. et al. Collapse of the world’s largest herbivores. Sci. Adv. 1, e1400103 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dickinson, A. B. A History of Sealing in the Falkland Island and Dependencies, 1764–1972. Doctoral dissertation, Scott Polar Research Institute, University of Cambridge. (1987).Clapham, P. J. & Baker, C. S. Whaling, modern. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 1070–1074 (Academic Press, 2018).
Google Scholar 
Reeves, R. R. The origins and character of ‘aboriginal subsistence’ whaling: a global review. Mamm. Rev. 32, 71–106 (2002).
Google Scholar 
Reeves, R. R. Hunting of marine mammals. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 585–588 (Academic Press, 2009).
Google Scholar 
Magera, A. M., Flemming, J. E. M., Kaschner, K., Christensen, L. B. & Lotze, H. K. Recovery trends in marine mammal populations. PLoS One 8, e77908 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tulloch, V. J., Plagányi, É. E., Matear, R., Brown, C. J. & Richardson, A. J. Ecosystem modelling to quantify the impact of historical whaling on Southern Hemisphere baleen whales. Fish Fish. 19, 117–137 (2017).
Google Scholar 
Best, P. B. Increase rates in severely depleted stocks of baleen whales. ICES J. Mar. Sci. 50, 169–186 (1993).
Google Scholar 
Townsend, C. H. The distribution of certain whales as shown by logbook records of American whaleships. Zool. Sci. Contr. New York Zool. Soc. 19, 1–50 (1935).MathSciNet 

Google Scholar 
Du Pasquier, J. T. Les baleiniers français au XIXe siècle, 1814–1868 (Terre et mer, 1982).Richards, R. Into the South Seas: The Southern Whale Fishery Comes of Age on the Brazil Banks 1765 to 1812 (The Paramatta Press, 1993).
Google Scholar 
International Whaling Commission. Report of the workshop on the comprehensive assessment of right whales: A worldwide comparison. J. Cetacean Res. Manag. (Special Issue) 2, 1–60 (2001).
Google Scholar 
Jackson, J., Patenaude, N., Carroll, E. & Baker, C. S. How few whales were there after whaling? Inference from contemporary mtDNA diversity. Mol. Ecol. 17, 236–251 (2008).CAS 
PubMed 

Google Scholar 
Tormosov, D. D. et al. Soviet catches of southern right whales Eubalaena australis, 1951–1971. Biological data and conservation implications. Biol. Conserv. 86, 185–197 (1998).
Google Scholar 
International Whaling Commission. Report of the IWC workshop on the assessment of Southern Right whales. J. Cetacean Res. Manag. (Supp) 14, 439–462 (2013).
Google Scholar 
Carroll, E. L. et al. Accounting for female reproductive cycles in a superpopulation capture–recapture framework. Ecol. Appl. 23, 1677–1690 (2013).CAS 
PubMed 

Google Scholar 
Brandão, A., Vermeulen, E., Ross-Gillespie, A., Findlay, K. & Butterworth, D. Updated application of a photo-identification based assessment model to southern right whales in South African waters, focussing on inferences to be drawn from a series of appreciably lower counts of calving females over 2015 to 2017. IWC SC/67b/SH/22 (2018).Bannister, J. L. Project A7- Monitoring Population Dynamics of ‘Western’ Right Whales off Southern Australia 2015–2018. Final report to National Environment Science Program, Australian Commonwealth Government. (2017).Stamation, K., Watson, M., Moloney, P., Charlton, C. & Bannister, J. L. Population estimate and rate of increase of Southern Right whales Eubalaena australis in Southeastern Australia. Endanger. Species Res. 41, 373–383 (2020).
Google Scholar 
Baker, C. S., Patenaude, N. J., Bannister, J. L., Robins, J. & Kato, H. Distribution and diversity of mtDNA lineages among southern right whales (Eubalaena australis) from Australia and New Zealand. Mar. Biol. 134, 1–7 (1999).CAS 

Google Scholar 
Patenaude, N. J. et al. Mitochondrial DNA diversity and population structure among southern right whales (Eubalaena australis). J. Hered. 98, 147–157 (2007).CAS 
PubMed 

Google Scholar 
Carroll, E. L. et al. Population structure and individual movement of southern right whales around New Zealand and Australia. Mar. Ecol. Prog. Ser. 432, 257–268 (2011).ADS 
CAS 

Google Scholar 
Cooke, J. G., Rowntree, V. J. & Payne, R. Estimates of demographic parameters for southern right whales (Eubalaena australis) observed off Península Valdés, Argentina. J. Cetacean Res. Manag. (Special Issue) 2, 125–132 (2001).
Google Scholar 
Cooke, J., Rowntree, V. & Sironi, M. Southwest Atlantic right whales: interim updated population assessment from photo-id collected at Península Valdéz, Argentina. IWC SC/66a/BRG/23 (2015).Crespo, E. A. et al. The southwestern Atlantic southern right whale, Eubalaena australis, population is growing but at a decelerated rate. Mar. Mamm. Sci. 35, 93–107 (2019).
Google Scholar 
Groch, K. R., Palazzo, J. T. Jr., Flores, P. A. C., Adler, F. R. & Fabian, M. E. Recent rapid increases in the right whale (Eubalaena australis) population off southern Brazil. LAJAM 4, 41–47 (2005).
Google Scholar 
Danilewicz, D., Moreno, I. B., Tavares, M. & Sucunza, F. Southern right whales (Eubalaena australis) off Torres, Brazil: group characteristics, movements, and insights into the role of the Brazilian-Uruguayan wintering ground. Mammalia 81, 225–234 (2017).
Google Scholar 
Costa, P., Praderi, R., Piedra, M. & Franco-Fraguas, P. Sightings of southern right whales, Eubalaena australis, off Uruguay. LAJAM 4, 157–161 (2005).
Google Scholar 
Lodi, L. & Tardelli, R. M. Southern right whale on the coast of Rio de Janeiro State, Brazil: Conflict between conservation and human activity. J. Mar. Biol. Ass. UK 87, 105–107 (2007).
Google Scholar 
Belgrano, J., Iñíguez, M., Gibbons, J., García, C. & Olavarría, C. South-west Atlantic right whales Eubalaena australis (Desmoulins, 1822) distribution nearby the Magellan Strait. Anales Instituto Patagonia (Chile) 36, 69–74 (2008).
Google Scholar 
Belgrano, J., Kröhling, F., Arcucci, D., Melcón, M. & Iñíguez, M. First Southern right whale aerial surveys in Golfo San Jorge, Santa Cruz, Argentina. IWC SC/63/BRG11 (2011).Arias, M. et al. Southern right whale Eubalaena australis in Golfo San Matías (Patagonia, Argentina): Evidence of recolonisation. PloS One 13(12), e0207524 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mandiola, M. A. et al. Half a century of sightings data of southern right whales in Mar del Plata (Buenos Aires, Argentina). J. Mar. Biol. Ass. UK 100, 165–171 (2020).
Google Scholar 
Weir, C. R. & Stanworth, A. The Falkland Islands (Malvinas) as sub-Antarctic foraging, migratory and wintering habitat for southern right whales. J. Mar. Biol. Ass. UK 100, 153–163 (2020).
Google Scholar 
Du Pasquier, T. Catch history of French right whaling mainly in the South Atlantic. Right whales: Past and present status. Rep. Int. Whaling Commission (Special Issue) 10, 269–274 (1986).
Google Scholar 
Best, P. B. Estimates of the landed catch of right (and other whalebone) whales in the American fishery, 1805–1909. Fish. Bull. 85, 403–418 (1987).
Google Scholar 
Rowntree, V., Groch, K. R., Vilches, F. & Sironi, M. Sighting Histories of 124 Southern Right Whales Recorded off Both Southern Brazil and Península Valdés, Argentina, between 1971 and 2017. IWC SC/68B/CMP/20 (2020).Zerbini, A. N., et al. Satellite tracking of Southern right whales (Eubalaena australis) from Golfo San Matias, Rio Negro Province, Argentina. IWC SC/67b/CMP/17 (2018).Rowntree, V. J., Valenzuela, L. O., Fraguas, P. F. & Seger, J. Foraging behaviour of Southern Right Whales (Eubalaena australis) inferred from variation of carbon stable isotope ratios in their baleen. IWC SC/60/BRG23 (2008).Vighi, M. A. et al. Stable isotopes indicate population structuring in the Southwest Atlantic population of right whales (Eubalaena australis). PLoS One 9, e90489 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Valenzuela, L. O., Rowntree, V. J., Sironi, M. & Seger, J. Stable isotopes (d15N, d13C, d34S) in skin reveal diverse food sources used by southern right whales Eubalaena australis. Mar. Ecol. Prog. Ser. 603, 243–255 (2018).ADS 
CAS 

Google Scholar 
Ott, P. H., Flores, P. A. C., Freitas, T. R. O. & White, B. N. Genetic diversity and population structure of southern right whales, Eubalaena australis, from the Atlantic coast of South America. IWC SC/S11/RW25 (2011).Carroll, E. L. et al. Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile-Peru wintering grounds and the South Georgia (Islas Georgias del Sur) feeding ground. J. Hered. 111, 263–276 (2020).PubMed 
PubMed Central 

Google Scholar 
Smith, T. D., Reeves, R. R., Josephson, E. A. & Lund, J. N. Spatial and seasonal distribution of American whaling and whales in the age of sail. PLoS One 7, e34905 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
International Whaling Commission. Report of the Scientific Committee of the International Whaling Commission. J. Cetacean Res. Manage. 20 (Suppl.), 675 (2019).Peterson, B. W. South Atlantic whaling: 1603–1830. Ph.D. thesis, University of California. (1948).Palazzo, J. T. Jr., Groch, K. R. & Silveira, H. A. Projeto Baleia Franca: 25 anos de pesquisa e conservação, 1982–2007 (2007).Reeves, R. R. & Smith, T. D. A taxonomy of world whaling. In Whales, Whaling, and Ocean Ecosystems (eds Estes, J. A. et al.) 82–101 (University of California Press, 2006).
Google Scholar 
Richards, R. Past and present distributions of southern right whales (Eubalaena australis). N. Z. J. Zool. 36, 447–459 (2009).
Google Scholar 
Harcourt, R., Van Der Hoop, J., Kraus, S. & Carroll, E. L. Future directions in Eubalaena spp.: comparative research to inform conservation. Front. Mar. Sci. 5, 530 (2019).Cooke, J. G. & Zerbini, A. N. Eubalaena australis. IUCN Red List of Threatened Species 2018: e. T8153A50354147 (2018).Ellis, M. Aspectos da pesca da baleia no Brasil colonial. Rev. Hist. Sao Paulo 14, 1–126 (1958).
Google Scholar 
Ellis, M. A baleia no Brasil colonial (Melhoramentos, 1969).Palazzo, J. T., Jr. & Carter, L. A. A caça de baleias no Brasil (AGAPAN, 1983).Furniss, H. W. Whaling in Brazil. Bull. Int. Union Am. Republ. 2, 1048–1054 (1909).
Google Scholar 
Acosta y Lara, E. F. Un ballenero inglés en la Cisplatina. Hoy es Historia 24, 82–88 (1987).Moore, M. J. et al. Relative abundance of large whales around South Georgia (1979–1998). Mar. Mamm. Sci. 15, 1287–1302 (1999).
Google Scholar 
Comerlato, F. A baleia como recurso energético no Brasil. Simpósio Internacional de História Ambiental e Migrações 1119–1138 (2010).Comerlato, F. As armações baleeiras na configuração da costa catarinense em tempos coloniais. Tempos Históricos 15, 481–501 (2011).
Google Scholar 
de Morais, I. O. B. et al. From the southern right whale hunting decline to the humpback whaling expansion: A review of whale catch records in the tropical western South Atlantic Ocean. Mammal Rev. 47, 11–23 (2017).
Google Scholar 
American Whaling Logbook Data: A Database, by New Bedford Whaling Museum and Mystic Seaport Museum, Inc. Compilers Judith Lund and Tim Smith. Hosted at whalinghistory.org. (2020).BSWF Databases. 2020. Compilers–A. G. E. Jones; Dale Chatwin; Rhys Richards. Contributors–Jane Clayton; Mark Howard. Hosted at whalinghistory.orgFrench Offshore Whaling Voyages: A Database, https://whalinghistory.org/fv/, Mystic Seaport Museum, Inc. and New Bedford Whaling Museum.Allison, C. IWC summary catch database Version 6.1. (2016).Vighi, M. et al. The missing whales: relevance of “struck and lost” rates for the impact assessment of historical whaling in the southwestern Atlantic Ocean. ICES J. Mar. Sci. 78, 14–24 (2021).
Google Scholar 
McCullagh, P. & Nelder, J. Generalized Linear Models Monographs on Statistics and Applied Probability (Chapman and Hall, 1989).MATH 

Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Model and Extension in Ecology with R (Springer, 2009).MATH 

Google Scholar 
Ward, E., Zerbini, A., Kinas, P. G., Engel, M. H. & Adriolo, A. Estimates of growth rates of humpback whales (Megaptera novaeangliae) in the wintering grounds off the coast of Brazil (Breeding Stock A). J. Cetacean Res. Manag. (Special Issue) 3, 145–149 (2011).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2019).Rowntree, V., Payne, R. & Schell, D. M. Changing patterns of habitat use by southern right whales (Eubalaena australis) on their nursery ground at Península Valdés, Argentina, and in their long-range movements. J. Cetacean Res. Manag. (Special Issue) 2, 133–143 (2001).
Google Scholar 
de Valpine, P. & Hastings, A. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72, 57–76 (2002).
Google Scholar 
Pella, J. J. & Tomlinson, P. K. A generalised stock production model. Int.-Am. Trop. Tuna Commun. Bull. 13, 421–496 (1969).
Google Scholar 
Zerbini, A. N. et al. Assessing the recovery of an Antarctic predator from historical exploitation. R. Soc. Open Sci. 6, 190368 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Halley, J. & Inchausti, P. Lognormality in ecological time series. Oikos 99, 518–530 (2002).
Google Scholar 
Best, J. K. & Punt, A. E. Parameterizations for Bayesian state-space surplus production models. Fish. Res. 222, 105411 (2020).
Google Scholar 
Walters, C. & Ludwig, D. Calculation of Bayes posterior probability distributions for key population parameters. Can. J. Fish. Aquat. Sci. 51, 713–722 (1994).
Google Scholar 
International Whaling Commission. Annex I: report of the sub-committee on stock definition. J. Cetacean Res. Manag. 13(Supp.), 233–241 (2012).Butterworth, D. S. & Punt, A. E. On the Bayesian approach suggested for the assessment of the Bering-Chukchi-Beaufort Seas stock of bowhead whales. Rep. Int. Whaling Commun. 45, 303–311 (1995).
Google Scholar 
McAllister, M. K., Pikitch, E. K., Punt, A. E. & Hilborn, R. Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Can. J. Fish. Aquat. Sci. 12, 2673–2687 (1999).
Google Scholar 
Best, P., Brandao, A. & Butterworth, D. Demographic parameters of southern right whales off South Africa. J. Cetacean Res. Manag. (Special Issue) 2, 161–169 (2001).
Google Scholar 
Punt, A. E. et al. Robustness of potential biological removal to monitoring, environmental, and management uncertainties. ICES J. Mar. Sci. 77, 2491–2507 (2020).
Google Scholar 
Pace, R. M., Corkeron, P. J. & Kraus, S. D. State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales. Ecol. Evol. 7, 8730–8741 (2017).PubMed 
PubMed Central 

Google Scholar 
Sueyro, N., Crespo, E. A., Arias, M. & Coscarella, M. A. Density-dependent changes in the distribution of Southern right whales (Eubalaena australis) in the breeding ground Peninsula Valdés. PeerJ 6, e5957 (2018).PubMed 
PubMed Central 

Google Scholar 
Wilberg, M. J., Thorson, J. T., Linton, B. C. & Berkson, J. Incorporating time-varying catchability into population dynamic stock assessment models. Rev. Fish. Sci. 18, 7–24 (2010).
Google Scholar 
Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).MathSciNet 
MATH 

Google Scholar 
Chang, Y. J. et al. Model selection and multi-model inference for Bayesian surplus production models: a case study for Pacific blue and striped marlin. Fish. Res. 166, 129–139 (2015).CAS 

Google Scholar 
Barker, D. & Sibly, R. M. The effects of environmental perturbation and measurement error on estimates of the shape parameter in the theta-logistic model of population regulation. Ecol. Model. 219, 170–177 (2008).
Google Scholar 
Dennis, B., Ponciano, J. M. & Taper, M. L. Replicated sampling increases efficiency in monitoring biological populations. Ecology 91, 610–620 (2010).PubMed 

Google Scholar 
Punt, A. E. Review of contemporary cetacean stock assessment models. J. Cetacean Res. Manag. 17, 35–56 (2017).
Google Scholar 
Punt, A. E., Butterworth, D. S., de Moor, C. L., De Oliveira, J. A. & Haddon, M. Management strategy evaluation: best practices. Fish Fish. 17, 303–334 (2016).
Google Scholar 
Baker, C. S. & Clapham, P. J. Modelling the past and future of whales and whaling. Trends Ecol. Evol. 19, 365–371 (2004).
Google Scholar 
Lotze, H. K. & Worm, B. Historical baselines for large marine animals. Trends Ecol. Evol. 24, 254–262 (2009).PubMed 

Google Scholar 
Foley, C. M. & Lynch, H. J. A method to estimate pre-exploitation population size. Conserv. Biol. 34, 256–265 (2020).PubMed 

Google Scholar 
Collins, A. C., Böhm, M. & Collen, B. Choice of baseline affects historical population trends in hunted mammals of North America. Biol. Conserv. 242, 108421 (2020).
Google Scholar 
Jackson, J. A. et al. An integrated approach to historical population assessment of the great whales: Case of the New Zealand southern right whale. R. Soc. Open Sci. 3, 150669 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Richards, R. & Du Pasquier, T. Bay whaling off southern Africa, c. 1785–1805. S. Afr. J. Mar. Sci. 8, 231–250 (1989).
Google Scholar 
Brandão, A., Best, P. B. & Butterworth, D. S. Estimates of demographic parameters for southern right whales off South Africa from survey data from 1979 to 2006. IWC SC/62/BRG30 (2010).Rowntree, V. J. et al. Unexplained recurring high mortality of southern right whale Eubalaena australis calves at Península Valdés, Argentina. Mar. Ecol. Progr. Ser. 493, 275–289 (2013).ADS 

Google Scholar 
Valenzuela, L. O., Sironi, M., Rowntree, V. J. & Seger, J. Isotopic and genetic evidence for culturally inherited site fidelity to feeding grounds in southern right whales (Eubalaena australis). Mol. Ecol. 18, 782–791 (2009).CAS 
PubMed 

Google Scholar 
Clapham, P. J., Aguilar, A. & Hatch, L. T. Determining spatial and temporal scales for management: lessons from whaling. Mar. Mamm. Sci. 24, 183–201 (2008).
Google Scholar 
Baker, C. S. et al. Strong maternal fidelity and natal philopatry shape genetic structure in North Pacific humpback whales. Mar. Ecol. Prog. Ser. 494, 291–306 (2013).ADS 

Google Scholar 
González, C. V., Piola, A., O’Brien, T. D., Tormosov, D. D. & Acha, E. M. Circumpolar frontal systems as potential feeding grounds of Southern Right whales. Prog. Oceanogr. 176, 102123 (2019).
Google Scholar 
Leaper, R. et al. Global climate drives southern right whale (Eubalaena australis) population dynamics. Biol. Lett. 2, 289–292 (2006).PubMed 
PubMed Central 

Google Scholar 
Seyboth, E. et al. Southern right whale (Eubalaena australis) reproductive success is influenced by krill (Euphausia superba) density and climate. Sci. Rep. 6, 28205 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Punt, A. E. Extending production models to include process error in the population dynamics. Can. J. Fish. Aquat. Sci. 60, 1217–1228 (2003).
Google Scholar 
Tulloch, V. J., Plagányi, É. E., Brown, C., Richardson, A. J. & Matear, R. Future recovery of baleen whales is imperiled by climate change. Glob. Change Biol. 25, 1263–1281 (2019).ADS 

Google Scholar 
Witting, L. Selection-delayed population dynamics in baleen whales and beyond. Popul. Ecol. 55, 377–401 (2013).
Google Scholar 
Pante, E. & Benoit, S.-B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8, e73051 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar  More

Müller, K. Investigations on the organic drift in north Swedish streams. Rep. Inst. Freshw. Res. Drottningholm 35, 133–148 (1954).
Google Scholar 
Müller, K. Stream drift as a chronobiological phenomenon in running water ecosystems. Annu. Rev. Ecol. Syst. 5, 309–323 (1974).Article 

Google Scholar 
Waters, T. F. Interpretation of invertebrate drift in streams. Ecology 46, 327–334. https://doi.org/10.2307/1936336 (1965).Article 

Google Scholar 
Waters, T. F. The drift of stream insects. Annu. Rev. Entomol. 17, 253–272 (1972).Article 

Google Scholar 
Thiesmeier, B. Der Feuersalamander (Laurenti, 2004).
Google Scholar 
Hughes, D. A. Some factors affecting drift and upstream movements of Gammarus pulex. Ecology 51, 301–305. https://doi.org/10.2307/1933668 (1970).Article 

Google Scholar 
Humphries, S. & Ruxton, G. D. Is there really a drift paradox?. J. Anim. Ecol. 71, 151–154 (2002).Article 

Google Scholar 
Altig, R. & McDiarmid, R. W. In Tadpoles: The Biology of Anuran Larvae (eds McDiarmid, R. W. & Altig, R.) 24–51 (University of Chicago Press, 2000).
Google Scholar 
Sherratt, E., Vidal-García, M., Anstis, M. & Keogh, J. S. Adult frogs and tadpoles have different macroevolutionary patterns across the Australian continent. Nat. Ecol. Evol. 1, 1385–1391. https://doi.org/10.1038/s41559-017-0268-6 (2017).Article 
PubMed 

Google Scholar 
Griffiths, R. A. Newts and Salamanders of Europe (Poyser Natural History, 1996).
Google Scholar 
Cecala, K. K., Price, S. J. & Dorcas, M. E. Evaluating existing movement hypotheses in linear systems using larval stream salamanders. Can. J. Zool. 87, 292–298. https://doi.org/10.1139/z09-013 (2009).Article 

Google Scholar 
Grant, E. H. C., Nichols, J. D., Lowe, W. H. & Fagan, W. F. Use of multiple dispersal pathways facilitates amphibian persistence in stream networks. Proc. Natl. Acad. Sci. USA 107, 6936–6940. https://doi.org/10.1073/pnas.1000266107 (2010).ADS 
Article 

Google Scholar 
Lowe, W. H. Linking dispersal to local population dynamics: A case study using a headwater salamander system. Ecology 84, 2145–2154. https://doi.org/10.1890/0012-9658(2003)084[2145:LDTLPD]2.0.CO;2 (2003).Article 

Google Scholar 
Bruce, R. C. Upstream and downstream movements of Eurycea bislineata and other salamanders in a southern appalachian stream. Herpetologica 42, 149–155 (1986).
Google Scholar 
Thiesmeier, B. Untersuchungen zur Phänologie und Populationsdynamik des Feuersalamanders (Salamandra salamandra terrestris Lacépède, 1788) im Niederbergische Land (BRD). Zool. Jahrbücher Abteilung für Systematik Ökologie Geographie der Tiere 117, 331–353 (1990).
Google Scholar 
Thiesmeier, B. & Schuhmacher, H. Causes of larval drift of the fire salamander, Salamandra salamandra terrestris, and its effects on population dynamics. Oecologia 82, 259–263. https://doi.org/10.1007/BF00323543 (1990).ADS 
Article 
PubMed 

Google Scholar 
Reinhardt, T., Baldauf, L., Ilic, M. & Fink, P. Cast away: Drift as the main determinant for larval survival in western fire salamanders (Salamandra salamandra) in headwater streams. J. Zool. 306, 171–179 (2018).Article 

Google Scholar 
Baumgartner, N., Waringer, A. & Waringer, J. Hydraulic microdistribution patterns of larval fire salamanders (Salamandra salamandra salamandra) in the Weidlingbach near Vienna, Austria. Freshw. Biol. 41, 31–41. https://doi.org/10.1046/j.1365-2427.1999.00378.x (1999).Article 

Google Scholar 
Krause, E. T., Steinfartz, S. & Caspers, B. A. Poor nutritional conditions during the early larval stage reduce risk-taking activities of fire salamander larvae (Salamandra salamandra). Ethology 117, 416–421. https://doi.org/10.1111/j.1439-0310.2011.01886.x (2011).Article 

Google Scholar 
Veith, M. et al. Drift compensation in larval European fire salamanders, Salamandra salamandra (Amphibia: Urodela)?. Hydrobiologia 828, 315–325. https://doi.org/10.1007/s10750-018-3820-8 (2019).Article 

Google Scholar 
Arnold, A. Zur Verbreitung des Feuersalamanders im Tal der Zwickauer Mulde. Veröffentlichungen aus dem Museum für Naturkunde Karl-Marx-Stadt 71–79 (1983).Thiesmeier, B. Ökologie des Feuersalamanders (Westarp Wissenschaften, 1992).
Google Scholar 
Thiesmeier-Hornberg, B. Zur Ökologie und Populationsdynamik des Feuersalamanders (Salamandra salamandra terrestris Lacépède, 1788) im niederbergischen Land unter besonderer Berücksichtigung der Larvalphase. PhD thesis, Universität-Gesamthochschule Essen (1988).Thiesmeier, B. & Grossenbacher, K. Salamandra salamandra (Linnaeus, 1758)—Feuersalamander. In Die Amphibien und Reptilien Europas. Schwanzlurche IIB (eds Thiesmeier, B. & Grossenbacher, K.) 1059–1132 (Aula, 2004).
Google Scholar 
Reques, R. & Tejedo, M. Intraspecific aggressive behaviour in fire salamander larvae (Salamandra salamandra): The effects of density and body size. Herpetol. J. 6, 15–19 (1996).
Google Scholar 
Thiesmeier, B. & Günther, R. Feuersalamander: Salamandra salamandra (Linnaeus, 1758). In Die Amphibien und Reptilien Deutschlands (ed. Günther, R.) 82–104 (Fischer, 1996).
Google Scholar 
Wagner, N., Pfrommer, J. & Veith, M. Comparison of different methods to estimate abundances of larval fire salamanders (Salamandra salamandra) in first-order creeks. Salamandra 56, 265–274 (2020).
Google Scholar 
Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891. https://doi.org/10.1111/j.1600-0706.2009.17643.x (2009).Article 

Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).Article 

Google Scholar 
Otis, D. L., Burnham, K. P., White, G. C. & Anderson, D. R. Statistical inference from capture data on closed animal populations. Wildl. Monogr. 62, 3–135 (1978).MATH 

Google Scholar 
Schwarz, C. J. & Arnason, A. N. A general methodology for the analysis of capture-recapture experiments in open populations. Biometrics 52, 860–873 (1996).MathSciNet 
Article 

Google Scholar 
Seber, G. A. A note on the multiple-recapture census. Biometrika 52, 249–259 (1965).MathSciNet 
CAS 
Article 

Google Scholar 
Jolly, G. M. Explicit estimates from capture-recapture data with both death and immigration-stochastic model. Biometrika 52, 225–247 (1965).MathSciNet 
CAS 
Article 

Google Scholar 
Cormack, R. Estimates of survival from the sighting of marked animals. Biometrika 51, 429–438 (1964).Article 

Google Scholar 
Razali, N. M. & Wah, Y. B. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. J. Stat. Model. Anal. 2, 21–33 (2011).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Google Scholar 
Segev, O. & Blaustein, L. Influence of water velocity and predation risk on fire salamander (Salamandra infraimmaculata) larval drift among temporary pools in ephemeral streams. Freshw. Sci. 33, 950–957. https://doi.org/10.1086/676634 (2014).Article 

Google Scholar 
Montori, A., Llorente, G. & Richter-Boix, À. Habitat features affecting the small-scale distribution and longitudinal migration patterns of Calotriton asper in a Pre-Pyrenean population. Amphibia-Reptilia 29, 371–381. https://doi.org/10.1163/156853808785112048 (2008).Article 

Google Scholar 
Zakrzewski, M. Effect of definite temperature ranges on development metamorphosis and procreation of the spotted salamander larvae, Salamandra salamandra (L.). Acta Biol. Crac. Ser. Zool. 29, 77–83 (1987).
Google Scholar 
Degani, G., Goldenberg, S. & Warburg, M. R. Cannibalistic phenomena in Salamandra salamandra larvae in certain water bodies and under experimental conditions. Hydrobiologia 75, 123–128. https://doi.org/10.1007/BF00007425 (1980).Article 

Google Scholar 
Manenti, R., Ficetola, G. F. & De Bernardi, F. Water, stream morphology and landscape: Complex habitat determinants for the fire salamander Salamandra salamandra. Amphibia-Reptilia 30, 7–15. https://doi.org/10.1163/156853809787392766 (2009).Article 

Google Scholar 
Klewen, R. Landsalamander Europa: Teil1. Die Gattungen Salamandra und Mertensiella 2nd edn. (Ziemsen-Verlag, 1991).
Google Scholar 
Orth, R., Zscheischler, J. & Seneviratne, S. I. Record dry summer in 2015 challenges precipitation projections in Central Europe. Sci. Rep. 6, 1–8 (2016).Article 

Google Scholar 
Degani, G. Temperature selection in Salamandra salamandra (L.) larvae and juveniles from different habitats. Biol. Behav. 9, 175–183 (1984).
Google Scholar  More

Castelle CJ, Banfield JF. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell. 2018;172:1181–97.CAS 
PubMed 

Google Scholar 
Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 

Google Scholar 
Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y. et al. Isolation of an archaeon at the prokaryote-eukaryote interface. Nature. 2020;577:519–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Spang A, Saw JH, Jorgensen SL, Zaremba-Niedzwiedzka K, Martijn J, Lind AE. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature. 2015;521:173–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature. 2021;593:553–7.CAS 
PubMed 

Google Scholar 
Huber H, Stetter KO Thermoplasmatales. In: M Dworkin, S Falkow, E Rosenberg, KH Schleifer, E Stackebrandt (eds). The Prokaryotes, 3rd edn. (Springer, New York, 2006), pp 101–12.Inagaki F, Suzuki M, Takai K, Oida H, Sakamoto T, Aoki K, et al. Microbial communities associated with geological horizons in coastal subseafloor sediments from the Sea of Okhotsk. Appl Environ Microbiol. 2003;69:7224–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
Vetriani C, Jannasch HW, MacGregor AJ, Stahl DA, Reysenbach AR. Population structure and phylogenetic characterization of marine benthic archaea in deep-sea sediments. Appl Environ Microbiol. 1999;65:4375–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Orsi WD, Vuillemin A, Rodriguez P, Coskun OK, Gomez-Saez GV, Lavik G, et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat Microbiol. 2019;5:248–55.PubMed 

Google Scholar 
Yu T, Wu W, Liang W, Lever MA, Hinrichs KU, Wang F. Growth of sedimentary Bathyarchaeota on lignin as an energy source. Proc Natl Acad Sci USA. 2018;115:6022–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin X, Cai M, Liu Y, Zhou G, Richter-Heitmann T, Aromokeye DA, et al. Subgroup level differences of physiological activities in marine Lokiarchaeota. ISME J. 2020;15:848–61.PubMed 
PubMed Central 

Google Scholar 
Lloyd KG, Schreiber L, Petersen DG, Kjeldsen KU, Lever MA, Steen AD. et al. Predominant archaea in marine sediments degrade detrital proteins. Nature. 2013;496:215–8.CAS 
PubMed 

Google Scholar 
Lin X, Handley KM, Gilbert JA, Kostka JE. Metabolic potential of fatty acid oxidation and anaerobic respiration by abundant members of Thaumarchaeota and Thermoplasmata in deep anoxic peat. ISME J. 2015;9:2740–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
He Y, Li M, Perumal V, Feng X, Fang J, Xie J, et al. Genomic and enzymatic evidence for acetogenesis among multiple lineages of the archaeal phylum Bathyarchaeota widespread in marine sediments. Nat Microbiol. 2016;1:16035.CAS 
PubMed 

Google Scholar 
Zhou Z, Liu Y, Lloyd KG, Pan J, Yang Y, Gu J-D, et al. Genomic and transcriptomic insights into the ecology and metabolism of benthic archaeal cosmopolitan, Thermoprofundales (MBG-D archaea). ISME J. 2019;13:885–901.CAS 
PubMed 

Google Scholar 
Lazar CS, Baker BJ, Seitz K, Hyde AS, Dick GJ, Hinrichs KU, et al. Genomic evidence for distinct carbon substrate preferences and ecological niches of Bathyarchaeota in estuarine sediments. Environ Microbiol. 2016;18:1200–11.CAS 
PubMed 

Google Scholar 
Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.CAS 
PubMed 

Google Scholar 
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol. 2019;4:1138–48.CAS 
PubMed 

Google Scholar 
Baker BJ, Appler KE, Gong X. New microbial biodiversity in marine sediments. Ann Rev Mar Sci. 2020;13:161–75.PubMed 

Google Scholar 
Gorke B, Stulke J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol. 2008;6:613–24.PubMed 

Google Scholar 
Siliakus MF, van der Oost J, Kengen SWM. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure. Extremophiles. 2017;21:651–70.CAS 
PubMed 
PubMed Central 

Google Scholar 
Takano Y, Chikaraishi Y, Ogawa NO, Nomaki H, Morono Y, Inagaki F. et al. Sedimentary membrane lipids recycled by deep-sea benthic archaea. Nat Geosci. 2010;3:858–61.CAS 

Google Scholar 
Li M, Baker BJ, Anantharaman K, Jain S, Breier JA, Dick GJ. Genomic and transcriptomic evidence for scavenging of diverse organic compounds by widespread deep-sea archaea. Nat Commun. 2015;6:8933.CAS 
PubMed 

Google Scholar 
Dekas AE, Parada AE, Mayali X, Fuhrman JA, Wollard J, Weber PK, et al. Characterizing chemoautotrophy and heterotrophy in marine Archaea and Bacteria with single-cell multi-isotope NanoSIP. Front Microbiol. 2019;10:2682.PubMed 
PubMed Central 

Google Scholar 
Vuillemin A, Wankel SD, Coskun ÖK, Magritsch T, Vargas S, Estes ER. et al. Archaea dominate oxic subseafloor communities over multimillion-year time scales. Sci Adv. 2019;5:eaaw4108PubMed 
PubMed Central 

Google Scholar 
Könneke M, Bernhard AE, de la Torre JR, Walker CB, Waterbury JB, Stahl DA. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature. 2005;437:543–6.PubMed 

Google Scholar 
Qin W, Amin SA, Martens-Habbena W, Walker CB, Urakawa H, Devol AH, et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc Natl Acad Sci USA. 2014;111:12504–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aoyagi T, Hanada S, Itoh H, Sato Y, Ogata A, Friedrich MW, et al. Ultra-high-sensitivity stable-isotope probing of rRNA by high-throughput sequencing of isopycnic centrifugation gradients. Environ Microbiol Rep. 2015;7:282–7.CAS 
PubMed 

Google Scholar 
Yin X, Wu W, Maeke M, Richter-Heitmann T, Kulkarni AC, Oni OE, et al. CO2 conversion to methane and biomass in obligate methylotrophic methanogens in marine sediments. ISME J. 2019;13:2107–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oni O, Miyatake T, Kasten S, Richter-Heitmann T, Fischer D, Wagenknecht L, et al. Distinct microbial populations are tightly linked to the profile of dissolved iron in the methanic sediments of the Helgoland mud area, North Sea. Front Microbiol. 2015;6:365.PubMed 
PubMed Central 

Google Scholar 
Bohrmann G, Aromokeye AD, Bihler V, Dehning K, Dohrmann I, Gentz T, et al. R/V METEOR Cruise Report M134, Emissions of free gas from cross-shelf troughs of South Georgia: distribution, quantification, and sources for methane ebullition sites in sub-Antarctic waters, Port Stanley (Falkland Islands) – Punta Arenas (Chile). Ber aus dem MARUM und dem Fachbereich Geowissenschaften der Univät Brem. 2017;317:1–220.
Google Scholar 
Yin X, Kulkarni AC, Friedrich MW DNA and RNA stable isotope probing of methylotrophic methanogenic archaea. In: Dumont M, Hernández García M (eds), Stable Isotope Probing, Methods in Molecular Biology, (Humana Press New York, 2019) pp 189–206.Danovaro R, Dell¹Anno A, Fabiano M. Bioavailability of organic matter in the sediments of the Porcupine Abyssal Plain, northeastern Atlantic. Mar Ecol Prog Ser. 2001;220:25–32.CAS 

Google Scholar 
Yang T, Jiang S-Y, Yang J-H, Lu G, Wu N-Y, Liu J, et al. Dissolved inorganic carbon (DIC) and its carbon isotopic composition in sediment pore waters from the Shenhu area, northern South China Sea. J Oceanogr. 2008;64:303–10.CAS 

Google Scholar 
Lueders T, Manefield M, Friedrich MW. Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients. Environ Microbiol. 2003;6:73–8.
Google Scholar 
Ovreas L, Forney L, Daae FL, Torsvik V. Distribution of bacterioplankton in meromictic Lake Saelenvannet, as determined by denaturing gradient gel electrophoresis of PCR-amplified gene fragments coding for 16S rRNA. Appl Environ Microbiol. 1997;63:3367–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Takai K, Horikoshi K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microbiol. 2000;66:5066–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
Aromokeye DA, Richter-Heitmann T, Oni OE, Kulkarni A, Yin X, Kasten S, et al. Temperature controls crystalline iron oxide utilization by microbial communities in methanic ferruginous marine sediment incubations. Front Microbiol. 2018;9:2574.PubMed 
PubMed Central 

Google Scholar 
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
Wegener G, Kellermann MY, Elvert M. Tracking activity and function of microorganisms by stable isotope probing of membrane lipids. Curr Opin Biotechnol. 2016;41:43–52.CAS 
PubMed 

Google Scholar 
Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, Pel R. et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392:801–5.CAS 

Google Scholar 
Sturt HF, Summons RE, Smith K, Elvert M, Hinrichs KU. Intact polar membrane lipids in prokaryotes and sediments deciphered by high-performance liquid chromatography/electrospray ionization multistage mass spectrometry-new biomarkers for biogeochemistry and microbial ecology. Rapid Commun Mass Spectrom. 2004;18:617–28.CAS 
PubMed 

Google Scholar 
Liu XL, Lipp JS, Simpson JH, Lin YS, Summons RE, Hinrichs KU. Mono- and dihydroxyl glycerol dibiphytanyl glycerol tetraethers in marine sediments: Identification of both core and intact polar lipid forms. Geochim Cosmochim Acta. 2012;89:102–15.CAS 

Google Scholar 
Ertefai TF, Heuer VB, Prieto-Mollar X, Vogt C, Sylva SP, Seewald J, et al. The biogeochemistry of sorbed methane in marine sediments. Geochim Cosmochim Acta. 2010;74:6033–48.CAS 

Google Scholar 
Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.CAS 
PubMed 

Google Scholar 
Hu W, Pan J, Wang B, Guo J, Li M, Xu M. Metagenomic insights into the metabolism and evolution of a new Thermoplasmata order (Candidatus Gimiplasmatales). Environ Microbiol. 2020;23:3695–709.PubMed 

Google Scholar 
Spang A, Stairs CW, Dombrowski N, Eme L, Lombard J, Caceres EF, et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat Microbiol. 2019;4:1138–48.CAS 
PubMed 

Google Scholar 
Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019;37:420–3.CAS 
PubMed 

Google Scholar 
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158PubMed 
PubMed Central 

Google Scholar 
Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
PubMed 

Google Scholar 
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2015;32:605–7.PubMed 

Google Scholar 
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359–e.PubMed 
PubMed Central 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. J Bioinform. 2019;36:1925–7.
Google Scholar 
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.
Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
PubMed 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2006;23:127–8.PubMed 

Google Scholar 
Zhou Z, Pan J, Wang F, Gu JD, Li M. Bathyarchaeota: globally distributed metabolic generalists in anoxic environments. FEMS Microbiol Rev. 2018;42:639–55.CAS 
PubMed 

Google Scholar 
Lee MD. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics. 2019;35:4162–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319PubMed 
PubMed Central 

Google Scholar 
Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 

Google Scholar 
Manefield M, Whiteley AS, Ostle N, Ineson P, Bailey MJ. Technical considerations for RNA- based stable isotope probing an approach to associating microbial diversity with microbial community function. Rapid Commun Mass Spectrom. 2002;16:2179–83.CAS 
PubMed 

Google Scholar 
Lazar CS, Baker BJ, Seitz KW, Teske AP. Genomic reconstruction of multiple lineages of uncultured benthic archaea suggests distinct biogeochemical roles and ecological niches. ISME J. 2017;11:1118–29.CAS 
PubMed 
PubMed Central 

Google Scholar 
Villanueva L, Damste JS, Schouten S. A re-evaluation of the archaeal membrane lipid biosynthetic pathway. Nat Rev Microbiol. 2014;12:438–48.CAS 
PubMed 

Google Scholar 
Konstantinidis KT, Rossello-Mora R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
PubMed Central 

Google Scholar 
Hedges JI, Keil RG. Sedimentary organic matter preservation: an assessment and speculative synthesis. Mar Chem. 1995;49:81–115.CAS 

Google Scholar 
Arndt S, Jørgensen BB, LaRowe DE, Middelburg JJ, Pancost RD, Regnier P. Quantifying the degradation of organic matter in marine sediments: a review and synthesis. Earth-Sci Rev. 2013;123:53–86.CAS 

Google Scholar 
LaRowe DE, Arndt S, Bradley JA, Estes ER, Hoarfrost A, Lang SQ, et al. The fate of organic carbon in marine sediments – New insights from recent data and analysis. Earth-Sci Rev. 2020;204:103146.CAS 

Google Scholar 
Zhu Q-Z, Elvert M, Meador TB, Becker KW, Heuer VB, Hinrichs KU. Stable carbon isotopic compositions of archaeal lipids constrain terrestrial, planktonic, and benthic sources in marine sediments. Geochim Cosmochim Acta. 2021;307:319–37.CAS 

Google Scholar 
Jain S, Caforio A, Driessen AJ. Biosynthesis of archaeal membrane ether lipids. Front Microbiol. 2014;5:641.PubMed 
PubMed Central 

Google Scholar 
Yang S, Lv Y, Liu X, Wang Y, Fan Q, Yang Z, et al. Genomic and enzymatic evidence of acetogenesis by anaerobic methanotrophic archaea. Nat Commun. 2020;11:3941.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zinke LA, Evans PN, Santos-Medellín C, Schroeder AL, Parks DH, Varner RK, et al. Evidence for non-methanogenic metabolisms in globally distributed archaeal clades basal to the Methanomassiliicoccales. Environ Microbiol. 2021;23:340–57.CAS 
PubMed 

Google Scholar 
Bhatnagar L, Jain MK, Aubert JP, Zeikus JG. Comparison of assimilatory organic nitrogen, sulfur, and carbon sources for growth of methanobacterium species. Appl Environ Microbiol. 1984;48:785–90.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maupin-Furlow JA. Proteolytic systems of archaea: slicing, dicing, and mincing in the extreme. Emerg Top Life Sci. 2018;2:561–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pohlschroder M, Pfeiffer F, Schulze S, Abdul Halim MF. Archaeal cell surface biogenesis. FEMS Microbiol Rev. 2018;42:694–717.CAS 
PubMed 
PubMed Central 

Google Scholar 
Yancey P, Clark M, Hand S, Bowlus R, Somero G. Living with water stress: evolution of osmolyte systems. Science. 1982;217:1214–22.CAS 
PubMed 

Google Scholar 
Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oni OE, Schmidt F, Miyatake T, Kasten S, Witt M, Hinrichs KU, et al. Microbial communities and organic matter composition in surface and subsurface sediments of the Helgoland Mud Area, North Sea. Front Microbiol. 2015;6:1290.PubMed 
PubMed Central 

Google Scholar 
Pelikan C, Wasmund K, Glombitza C, Hausmann B, Herbold CW, Flieder M, et al. Anaerobic bacterial degradation of protein and lipid macromolecules in subarctic marine sediment. ISME J. 2021;15:833–47.CAS 
PubMed 

Google Scholar 
Orsi WD, Schink B, Buckel W, Martin WF. Physiological limits to life in anoxic subseafloor sediment. FEMS Microbiol Rev. 2020;44:219–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
Heijnen JJ, Van, Dijken JP. In search of a thermodynamic description of biomass yields for the chemotrophic growth of microorganisms. Biotechnol Bioeng. 1992;39:833–58.CAS 
PubMed 

Google Scholar 
Braun S, Mhatre SS, Jaussi M, Røy H, Kjeldsen KU, Pearce C, et al. Microbial turnover times in the deep seabed studied by amino acid racemization modelling. Sci Rep. 2017;7:5680.PubMed 
PubMed Central 

Google Scholar  More

Konduri, V. S., Vandal, T. J., Ganguly, S. & Ganguly, A. R. Data science for weather impacts on crop yield. Front. Sustain. Food Syst. 4, 52. https://doi.org/10.3389/fsufs.2020.00052 (2020).Article 

Google Scholar 
Xu, X., Gao, P., Zhu, X., Guo, W., Ding, J., Li, C., … & Wu, X. Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China. Eco. Ind., 101, 943953. https://doi.org/10.1016/J.ECOLIND.2019.01.059 (2019).Moeinizade, S., Hu, G., Wang, L. & Schnable, P. S. Optimizing selection and mating in genomic selection with a look-ahead approach: An operations research framework. G3: Genes Genomes Genet. 9, 2123–2133. https://doi.org/10.1534/g3.118.200842 (2019).Article 

Google Scholar 
Basso, B. & Liu, L. Chapter four: Seasonal crop yield forecast: Methods, applications, and accuracies. In Advances in Agronomy Vol. 154 (ed. Sparks, D. L.) 201–255. https://doi.org/10.1016/bs.agron.2018.11.002 (2019)Zarindast, A., & Wood, J. A Data-Driven Personalized Lighting Recommender System. Front. in Big Data, 4. (2021).Shahhosseini, M., Hu, G., Khaki, S., & Archontoulis, S. V. Corn yield prediction with ensemble CNN-DNN. Front. Plant Sci., 12. (2021).Haghighat, A. K., Ravichandra-Mouli, V., Chakraborty, P., Esfandiari, Y., Arabi, S., & Sharma, A. Applications of deep learning in intelligent transportation systems. J. Big Data Anal. Transp. 2, 115–145. https://doi.org/10.1007/s42421-020-00020-1 (2020).Zarindast, A., Sharma, A., & Wood, J. Application of text mining in smart lighting literature-an analysis of existing literature and a research agenda. Int. J. Info. Mgmt. Data Insights, 1(2), 100032. (2021).Chlingaryan, A., Sukkarieh, S. & Whelan, B. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Comput. Electron. Agric. 151, 61–69. https://doi.org/10.1016/j.compag.2018.05.012 (2018).Article 

Google Scholar 
Zarindast, A., Poddar, S., & Sharma, A. A Data-Driven Method for Congestion Identification and Classification. J. Trans. Eng., Part A: Sys., 148(4), 04022012. (2022)Shahhosseini, M., Martinez-Feria, R. A., Hu, G. & Archontoulis, S. V. Maize yield and nitrate loss prediction with machine learning algorithms. Environ. Res. Lett. 14, 124026. https://doi.org/10.1088/1748-9326/ab5268 (2019).Article 
ADS 

Google Scholar 
Khaki, S. & Wang, L. Crop yield prediction using deep neural networks. Front. Plant Sci.https://doi.org/10.3389/fpls.2019.00621 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Feng, P., Wang, B., Li Liu, D., Waters, C., Xiao, D., Shi, L., & Yu, Q. Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agric. For. Meteorol. 285–286, 107922. https://doi.org/10.1016/j.agrformet.2020.107922 (2020).Article 
ADS 

Google Scholar 
Kang, Y., Ozdogan, M., Zhu, X., Ye, Z., Hain, C., & Anderson, M. Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest. Environ. Res. Lett. 15, 064005. https://doi.org/10.1088/1748-9326/ab7df9 (2020).Article 
ADS 

Google Scholar 
Van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: A systematic literature review. Comput. Electron. Agric. 177, 105709. https://doi.org/10.1016/j.compag.2020.105709 (2020).Article 

Google Scholar 
Khaki, S., Safaei, N., Pham, H. & Wang, L. WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting. arXiv:2103.09408 (2021).Hassan, M. A., Khalil, A., Kaseb, S. & Kassem, M. A. Exploring the potential of tree-based ensemble methods in solar radiation modeling. Appl. Energy 203, 897–916. https://doi.org/10.1016/j.apenergy.2017.06.104 (2017).Article 

Google Scholar 
Mishra, S. & Santra, D. M. A. G. H. Applications of machine learning techniques in agricultural crop production: A review paper. Indian J. Sci. Technol. 9, 1–14. https://doi.org/10.17485/ijst/2016/v9i38/95032 (2016).Article 

Google Scholar 
Kamilaris, A. & Prenafeta-Boldú, F. Deep learning in agriculture: A survey. Comput. Electron. Agric.https://doi.org/10.1016/j.compag.2018.02.016 (2018).Article 

Google Scholar 
Liakos, K. G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. Machine learning in agriculture: A review. Sensors 18, 2674. https://doi.org/10.3390/s18082674 (2018).Article 
PubMed Central 
ADS 

Google Scholar 
Wang, Y., Zhang, Z., Feng, L., Du, Q. & Runge, T. Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous United States. Remote Sens. 12, 1232. https://doi.org/10.3390/rs12081232 (2020).Article 
ADS 

Google Scholar 
Cao, J., Zhang, Z., Luo, Y., Zhang, L., Zhang, J., Li, Z., & Tao, F. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. Eur. J. Agron. 123, 126204. https://doi.org/10.1016/j.eja.2020.126204 (2021).Article 

Google Scholar 
FAO. FAOSTAT (2021).Zhao, G., Webber, H., Hoffmann, H., Wolf, J., Siebert, S., & Ewert, F. The implication of irrigation in climate change impact assessment: a European‐wide study. Glob. Chang. Biol. 21, 4031–4048. https://doi.org/10.1111/gcb.13008 (2015).Article 
PubMed 
ADS 

Google Scholar 
EUROSTAT. Glossary: Nomenclature of territorial units for statistics (NUTS) (2019).COPERNICUS. CORINE Land Cover (2006).Webber, H., Lischeid, G., Sommer, M., Finger, R., Nendel, C., Gaiser, T., & Ewert, F. No perfect storm for crop yield failure in Germany. Environ. Res. Lett. 15, 104012 (2020).Article 
ADS 

Google Scholar 
EUROSTAT. NUTS – Nomenclature of territorial units for statistics – Eurostat (2019).Ämter, S. Regionaldatenbank Deutschland (2020).DWD. Wetter und Klima – Deutscher Wetterdienst (2020).Shook, J., Gangopadhyay, T., Wu, L., Ganapathysubramanian, B., Sarkar, S., & Singh, A. K. Crop yield prediction integrating genotype and weather variables using deep learning. PLOS ONE 16, e0252402. https://doi.org/10.1371/journal.pone.0252402 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nevavuori, P., Narra, N., Linna, P. & Lipping, T. Crop yield prediction using multitemporal UAV data and spatio-temporal deep learning models. Remote Sens. 12, 4000. https://doi.org/10.3390/rs12234000 (2020).Article 
ADS 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).Article 
MATH 

Google Scholar 
Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27. https://doi.org/10.1109/TIT.1967.1053964 (1967).Article 
MATH 

Google Scholar 
Hu, L.-Y., Huang, M.-W., Ke, S.-W. & Tsai, C.-F. The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus 5, 1304. https://doi.org/10.1186/s40064-016-2941-7 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, 2013).Book 

Google Scholar 
Hoerl, A. E. & Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67. https://doi.org/10.1080/00401706.1970.10488634 (1970).Article 
MATH 

Google Scholar 
Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. Classification and Regression Trees 1st edn. (Chapman and Hall/CRC Press, 1984).MATH 

Google Scholar 
Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297. https://doi.org/10.1007/BF00994018 (1995).Article 
MATH 

Google Scholar 
Chen, T., & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794) (2016).Zhang, L., Zhang, Z., Luo, Y., Cao, J. & Tao, F. Combining optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield in China using machine learning approaches. Remote Sens. 12, 21. https://doi.org/10.3390/rs12010021 (2020).Article 
ADS 

Google Scholar 
Nigam, A., Garg, S., Agrawal, A. & Agrawal, P. Crop Yield Prediction Using Machine Learning Algorithms. 2019 Fifth International Conference on Image Information Processing (ICIIP) https://doi.org/10.1109/ICIIP47207.2019.8985951 (2019).LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).CAS 
Article 
ADS 

Google Scholar 
Khaki, S., Wang, L. & Archontoulis, S. V. A CNN-RNN framework for crop yield prediction. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01750 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Khaki, S., Pham, H., & Wang, L. Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning. Sci. Rep. 11(1), 1–14. (2021).Article 

Google Scholar 
Khaki, S., Khalilzadeh, Z. & Wang, L. Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach. PLoS ONE 15, e0233382. https://doi.org/10.1371/journal.pone.0233382 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lamorski, K., Pachepsky, Y., Sławiński, C. & Walczak, R. T. Using support vector machines to develop pedotransfer functions for water retention of soils in Poland. Soil Sci. Soc. Am. J. 72, 1243–1247. https://doi.org/10.2136/sssaj2007.0280N (2008).CAS 
Article 
ADS 

Google Scholar 
Merdun, H., Çınar, C., Meral, R. & Apan, M. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil Tillage Res. 1–2, 108–116. https://doi.org/10.1016/j.still.2005.08.011 (2006).Article 

Google Scholar 
Landeras, G., Ortiz-Barredo, A. & López, J. J. Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agric. Water Manag. 95, 553–565. https://doi.org/10.1016/j.agwat.2007.12.011 (2008).Article 

Google Scholar 
Yamaç, S. S. & Todorovic, M. Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agric. Water Manag. 228, 105875. https://doi.org/10.1016/j.agwat.2019.105875 (2020).Article 

Google Scholar 
Obsie, E. Y., Qu, H. & Drummond, F. Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms. Comput. Electron. Agric. 178, 105778. https://doi.org/10.1016/j.compag.2020.105778 (2020).Article 

Google Scholar 
Shapley, L. S. A Value for n-Person Games (Princeton University Press, 1953).MATH 

Google Scholar 
Vogel, E., Donat, M. G., Alexander, L. V., Meinshausen, M., Ray, D. K., Karoly, D., … & Frieler, K. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010. https://doi.org/10.1088/1748-9326/ab154b (2019).Article 
ADS 

Google Scholar 
Stokes, V. J., Morecroft, M. D. & Morison, J. I. L. Boundary layer conductance for contrasting leaf shapes in a deciduous broadleaved forest canopy. Agric. For. Meteorol. 139, 40–54. https://doi.org/10.1016/j.agrformet.2006.05.011 (2006).Article 
ADS 

Google Scholar 
Chen, X. & Chen, S. China feels the heat: Negative impacts of high temperatures on China’s rice sector. Aust. J. Agric. Resour. Econ. 62, 576–588. https://doi.org/10.1111/1467-8489.12267 (2018).Article 

Google Scholar 
Tao, F., Xiao, D., Zhang, S., Zhang, Z. & Rötter, R. P. Wheat yield benefited from increases in minimum temperature in the Huang–Huai–Hai Plain of China in the past three decades. Agric. For. Meteorol. 239, 1–14. https://doi.org/10.1016/j.agrformet.2017.02.033 (2017).Article 
ADS 

Google Scholar 
Zheng, C., Zhang, J., Chen, J., Chen, C., Tian, Y., Deng, A., … & Zhang, W. Nighttime warming increases winter-sown wheat yield across major Chinese cropping regions. Field Crops Res. 214, 202–210. https://doi.org/10.1016/j.fcr.2017.09.014 (2017).Article 
ADS 

Google Scholar 
Gibson, L. R. & Paulsen, G. M. Yield components of wheat grown under high temperature stress during reproductive growth. Crop Sci. 39, 1841–1846. https://doi.org/10.2135/cropsci1999.3961841x (1999).Article 

Google Scholar 
Lobell, D. B., Sibley, A. & Ivan Ortiz-Monasterio, J. Extreme heat effects on wheat senescence in India. Nat. Clim. Chang. 2, 186–189. https://doi.org/10.1038/nclimate1356 (2012).Article 
ADS 

Google Scholar 
Tashiro, T. & Wardlaw, I. A comparison of the effect of high temperature on grain development in wheat and rice. Ann. Bot. 64, 59–65. https://doi.org/10.1093/oxfordjournals.aob.a087808 (1989).Article 

Google Scholar 
Wollenweber, B., Porter, J. R. & Schellberg, J. Lack of interaction between extreme high-temperature events at vegetative and reproductive growth stages in wheat. J. Agron. Crop Sci. 189, 142–150. https://doi.org/10.1046/j.1439-037X.2003.00025.x (2003).Article 

Google Scholar 
Mäkinen, H., Kaseva, J., Trnka, M., Balek, J., Kersebaum, K. C., Nendel, C., … & Kahiluoto, H. Sensitivity of European wheat to extreme weather. Field Crops Res., 222, 209–217. https://doi.org/10.1016/j.fcr.2017.11.008 (2018).Article 

Google Scholar 
Farooq, M., Bramley, H., Palta, J. A. & Siddique, K. H. Heat stress in wheat during reproductive and grain-filling phases. Crit. Rev. Plant Sci. 30, 491–507. https://doi.org/10.1080/07352689.2011.615687 (2011).Article 

Google Scholar 
Peichl, M., Thober, S., Meyer, V. & Samaniego, L. The effect of soil moisture anomalies on maize yield in Germany. Nat. Hazards Earth Syst. Sci. 18, 889–906. https://doi.org/10.5194/nhess-18-889-2018 (2018).Article 
ADS 

Google Scholar 
Rezaei, E. E. et al. Quantifying the response of wheat yields to heat stress: The role of the experimental setup. Field Crops Res., 217, 93–103. https://doi.org/10.1016/j.fcr.2017.12.015 (2018).Article 

Google Scholar 
Cannell, R. Q., Belford, R. K., Gales, K., Dennis, C. W. & Prew, R. D. Effects of waterlogging at different stages of development on the growth and yield of winter wheat. J. Sci. Food Agric. 31, 117–132. https://doi.org/10.1002/jsfa.2740310203 (1980).Article 

Google Scholar 
Gömann, H. Wetterextreme: mögliche Folgen für die Landwirtschaft in Deutschland (2018).Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. Problems with Shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning (pp. 5491–5500). PMLR. (2020).Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. (2019). More

Tay, D. Vegetable hybrid seed production. in Seeds: Trade, Production and Technology. 18–139. (2002).Piquerez, S. J., Harvey, S. E., Beynon, J. L. & Ntoukakis, V. Improving crop disease resistance: Lessons from research on Arabidopsis and tomato. Front. Plant Sci. 5, 671. https://doi.org/10.3389/fpls.2014.00671 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Mason, A. S. & Batley, J. Creating new interspecific hybrid and polyploid crops. Trends Biotechnol. 33, 436–441. https://doi.org/10.1016/j.tibtech.2015.06.004 (2015).CAS 
Article 
PubMed 

Google Scholar 
Broussard, M. A., Mas, F., Howlett, B., Pattemore, D. & Tylianakis, J. M. Possible mechanisms of pollination failure in hybrid carrot seed and implications for industry in a changing climate. PLoS ONE 12, 180215.  https://doi.org/10.1371/journal.pone.0180215 (2017).CAS 
Article 

Google Scholar 
Wilcock, C. & Neiland, R. Pollination failure in plants: why it happens and when it matters. Trends Plant Sci. 7, 270–277. https://doi.org/10.1016/S1360-1385(02)02258-6 (2002).CAS 
Article 
PubMed 

Google Scholar 
Fijen, T. P., Scheper, J. A., Vogel, C., van Ruijven, J. & Kleijn, D. Insect pollination is the weakest link in the production of a hybrid seed crop. Agric. Ecosyst. Environ. 290, 106743. https://doi.org/10.1016/j.agee.2019.106743 (2020).CAS 
Article 

Google Scholar 
Batra, S. W. Male-fertile potato flowers are selectively buzz-pollinated only by Bombus terricola Kirby in upstate New York. J. Kans. Entomol. Soc. 1, 252–254 (1993).
Google Scholar 
Evans, L., Goodwin, R., Walker, M. & Howlett, B. Honey bee (Apis mellifera) distribution and behaviour on hybrid radish (Raphanus sativus L.) crops. N.Z. Plant Prot. 64, 32–36. https://doi.org/10.30843/nzpp.2011.64.5952 (2011).Article 

Google Scholar 
Estravis Barcala, M. C., Palottini, F. & Farina, W. M. Honey bee and native solitary bee foraging behavior in a crop with dimorphic parental lines. PLoS ONE 14, e0223865. https://doi.org/10.1371/journal.pone.0223865 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nye, W. P., Shasha’a, N., Campbell, W. & Hamson, A. Insect pollination and seed set of onions (Allium cepa L.). Utah State Univ. Agric. Exp. Station Res. Rep. 6, 1 (1973).
Google Scholar 
Zulkarnain, Z., Eliyanti, E. & Swari, E. I. Pollen viability and stigma receptivity in Swainsona formosa (G. Don) J. Thompson (Fabaceae), an ornamental legume native to Australia. Ornam. Hortic. 25, 158–167. https://doi.org/10.14295/oh.v25i2.2011 (2019).Article 

Google Scholar 
Ne’eman, G., Jürgens, A., Newstrom-Lloyd, L., Potts, S. G. & Dafni, A. A framework for comparing pollinator performance: Effectiveness and efficiency. Biol. Rev. 85, 435–451. https://doi.org/10.1111/j.1469-185X.2009.00108.x (2010).Article 
PubMed 

Google Scholar 
Bione, N. C. P., Pagliarini, M. S. & Toledo, J. F. F. D. Meiotic behavior of several Brazilian soybean varieties. Genet. Mol. 23, 623–631. https://doi.org/10.1590/S1415-47572000000300022 (2000).Article 

Google Scholar 
Levin, D. A. The exploitation of pollinators by species and hybrids of Phlox. Evolution 1, 367–377 (1970).Article 

Google Scholar 
Smith-Huerta, N. L. & Vasek, F. C. Pollen longevity and stigma pre-emption in Clarkia. Am. J. Bot. 71, 1183–1191 (1984).Article 

Google Scholar 
Ashman, T. L. & Arceo-Gómez, G. Toward a predictive understanding of the fitness costs of heterospecific pollen receipt and its importance in co-flowering communities. Am. J. Bot. 100, 1061–1070. https://doi.org/10.3732/ajb.1200496 (2013).Article 
PubMed 

Google Scholar 
Stanghellini, M., Schultheis, J. & Ambrose, J. Pollen mobilization in selected Cucurbitaceae and the putative effects of pollinator abundance on pollen depletion rates. J. Am. Soc. Hortic. Sci. 127, 729–736. https://doi.org/10.21273/Jashs.127.5.729 (2002).Article 

Google Scholar 
Jahed, K. R. & Hirst, P. M. Pollen tube growth and fruit set in apple. HortScience 52, 1054–1059. https://doi.org/10.21273/Hortsci11511-16 (2017).Article 

Google Scholar 
Erdtman, G. Pollen Morphology and Plant Taxonomy: Angiosperms. Vol. 1. (Brill Archive, 1986).Weber, R. W. Pollen identification. Ann. Allergy Asthma Immunol. 80, 141–148. https://doi.org/10.1016/S1081-1206(10)62947-X (1998).CAS 
Article 
PubMed 

Google Scholar 
Castro López, A. J. et al. Seedless watermelons: From the microscope to the table through the greenhouse. High Sch. Students Agric. Scii. Res.. 3. 27–32 (2013).
Google Scholar 
Laws, H. M. Pollen-grain morphology of polyploid Oenotheras. J. Hered. 56, 18–21 (1965).Article 

Google Scholar 
Shoemaker, J. S. Pollen development in the apple, with special reference to chromosome behavior. Bot. Gaz. 81, 148–172 (1926).Article 

Google Scholar 
Hao, L., Ma, H., da Silva, J. A. T. & Yu, X. Pollen morphology of herbaceous peonies with different ploidy levels. J. Am. Soc. Hortic. Sci. 141, 275–284. https://doi.org/10.21273/Jashs.141.3.275 (2016).CAS 
Article 

Google Scholar 
Jacob, Y. & Pierret, V. Pollen size and ploidy level in the genus Rosa. XIX International Symposium on Improvement of Ornamental Plants, Vol. 508. 289–292. (1998).Karlsdóttir, L., Hallsdóttir, M., Thórsson, A. T. & Anamthawat-Jónsson, K. Characteristics of pollen from natural triploid Betula hybrids. Grana 47, 52–59. https://doi.org/10.1080/00173130801927498 (2008).Article 

Google Scholar 
Wrońska-Pilarek, D., Danielewicz, W., Bocianowski, J., Maliński, T. & Janyszek, M. Comparative pollen morphological analysis and its systematic implications on three European Oak (Quercus L., Fagaceae) species and their spontaneous hybrids. PLoS ONE 11, e0161762. https://doi.org/10.1371/journal.pone.0161762 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, C., Viruel, M., Lora, J. & Hormaza, J. I. Polyploidy in fruit tree crops of the genus Annona (Annonaceae). Front. Plant Sci. 10, 99. https://doi.org/10.3389/fpls.2019.00099 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sedgley, M. & Scholefield, P. B. Stigma secretion in the watermelon before and after pollination. Bot. Gaz. 141, 428–434 (1980).Article 

Google Scholar 
Sedgley, M. Anatomy of the unpollinated and pollinated watermelon stigma. J. Cell Sci. 54, 341–355.  https://doi.org/10.1242/jcs.54.1.341 (1982).Article 

Google Scholar 
Sedgley, M. & Blesing, M. A. Foreign pollination of the stigma of watermelon (Citrullus lanatus [Thunb.] Matsum and Nakai). Bot. Gaz. 143, 210–215 (1982).Article 

Google Scholar 
Hiscock, S. J. & Allen, A. M. Diverse cell signalling pathways regulate pollen-stigma interactions: The search for consensus. New Phytol. 179, 286–317. https://doi.org/10.1111/j.1469-8137.2008.02457.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Swanson, R., Edlund, A. F. & Preuss, D. Species specificity in pollen-pistil interactions. Annu. Rev. Genet. 38, 793–818. https://doi.org/10.1146/annurev.genet.38.072902.092356 (2004).CAS 
Article 
PubMed 

Google Scholar 
Edlund, A. F., Swanson, R. & Preuss, D. Pollen and stigma structure and function: the role of diversity in pollination. Plant Cell 16, S84–S97. https://doi.org/10.1105/tpc.015800 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wehner, T. Cucurbit Breeding. https://cucurbitbreeding.wordpress.ncsu.edu/watermelon-breeding/seedless-watermelon-breeding/ (2011).Maynard, D. N. & Elmstrom, G.W. Triploid watermelon production practices and varieties. Acta Hort. 318, 169–173 (1992).Article 

Google Scholar 
Tupý, J. Callose formation in pollen tubes and incompatibility. Biol. Plant. 1, 192–198. https://doi.org/10.1007/BF02928684 (1959).Article 

Google Scholar 
Distefano, G. et al. Pollen tube behavior in different mandarin hybrids. J. Am. Soc. Hortic. Sci. 134, 583–588. https://doi.org/10.21273/Jashs.134.6.583 (2009).Article 

Google Scholar 
Glišić, I. et al. Examination of self-compatibility in promising plum (Prunus domestica L.) genotypes developed at the Fruit Research Institute. Čačak. Sci. Hortic. 224, 156–162. https://doi.org/10.1016/j.scienta.2017.06.006 (2017).Article 

Google Scholar 
Arndt, G. C., Rueda, J., Kidane-Mariam, H. & Peloquin, S. Pollen fertility in relation to open pollinated true seed production in potatoes. Am. Potato. J 67, 499–505. https://doi.org/10.1007/Bf03045112 (1990).Article 

Google Scholar 
Jing, S., Kryger, P., Markussen, B. & Boelt, B. Pollination and plant reproductive success of two ploidy levels in red clover (Trifolium pratense L.). Front. Plant Sci. 1, 1580.  https://doi.org/10.3389/fpls.2021.720069 (2021).Article 

Google Scholar 
Suárez-Mariño, A., Arceo-Gómez, G., Sosenski, P. & Parra-Tabla, V. Patterns and effects of heterospecific pollen transfer between an invasive and two native plant species: The importance of pollen arrival time to the stigma. Am. J. Bot. 106, 1308–1315. https://doi.org/10.1002/ajb2.1361 (2019).Article 
PubMed 

Google Scholar 
FAO. FAOSTAT. Food and Agriculture Organization of the United Nations, Rome, Italy. http://www.fao.org/faostat/en/#data (2017).Stanghellini, M., Ambrose, J. & Schultheis, J. Seed production in watermelon: A comparison between two commercially available pollinators. HortScience 33, 28–30.  https://doi.org/10.21273/Hortsci.33.1.28 (1998).Article 

Google Scholar 
Delaplane, K. S. A. M. D.F. Crop Pollination by Bees. (CABI Publishing, 2005).Wijesinghe, S., Evans, L., Kirkland, L. & Rader, R. A global review of watermelon pollination biology and ecology: The increasing importance of seedless cultivars. Sci. Hortic. 271, 109493. https://doi.org/10.1016/j.scienta.2020.109493 (2020).CAS 
Article 

Google Scholar 
AgMRC. Watermelon. https://www.agmrc.org/commodities-products/vegetables/watermelon (2018).Bomfim, I. G. A., Bezerra, A. D. D. M., Nunes, A. C., Freitas, B. M. & Aragão, F. A. S. D. Pollination requirements of seeded and seedless mini watermelon varieties cultivated under protected environment. Pesqui. Agropecu. Bras. 50, 44–53. https://doi.org/10.1590/s0100-204×2015000100005 (2015).Article 

Google Scholar 
Maynard, D. N. & Elmstrom, G. W. Triploid watermelon production practices and varieties. II International Symposium on Specialty and Exotic Vegetable Crops, Vol. 318. 169–178.Jones, G. D. Pollen analyses for pollination research, acetolysis. J. Pollinat. Ecol. 13, 203–217. https://doi.org/10.26786/1920-7603(2014)19 (2014).Article 

Google Scholar 
Kurtz, E. B. Jr. Pollen morphology of the Cactaceae. Grana 4, 367–372.  https://doi.org/10.1080/00173136309429110 (1963).Article 

Google Scholar 
Halbritter, H. et al. Illustrated Pollen Terminology. 97–127. (Springer, 2018).Punt, W., Hoen, P., Blackmore, S., Nilsson, S. & Le Thomas, A. Glossary of pollen and spore terminology. Rev. Palaeobot. Palynol. 143, 1–81.  https://doi.org/10.1016/j.revpalbo.2006.06.008 (2007).Article 

Google Scholar 
Kaya, Y., Mesut Pınar, S., Emre Erez, M., Fidan, M. & Riding, J. B. Identification of Onopordum pollen using the extreme learning machine, a type of artificial neural network. Palynology 38, 129–137. https://doi.org/10.1080/09500340.2013.868173 (2014).Article 

Google Scholar 
Pruesapan, K. & Van Der Ham, R. Pollen morphology of Trichosanthes (Cucurbitaceae). Grana 44, 75–90. https://doi.org/10.1080/00173130510010512 (2005).Article 

Google Scholar 
Sedgley, M. & Buttrose, M. Some effects of light intensity, daylength and temperature on flowering and pollen tube growth in the watermelon (Citrullus lanatus). Ann. Bot. 42, 609–616. https://doi.org/10.1093/oxfordjournals.aob.a085495 (1978).Article 

Google Scholar 
Martin, F. W. Staining and observing pollen tubes in the style by means of fluorescence. Stain Technol. 34, 125–128. https://doi.org/10.3109/10520295909114663 (1959).CAS 
Article 
PubMed 

Google Scholar 
Godini, A. Counting pollen grains of some almond cultivars by means of an haemocytometer. Riv. Studi Ital. 1, 173–178 (1981).
Google Scholar 
Howlett, B., Evans, L., Pattemore, D. & Nelson, W. Stigmatic pollen delivery by flies and bees: Methods comparing multiple species within a pollinator community. Basic Appl. Ecol. 19, 19–25. https://doi.org/10.1016/j.baae.2016.12.002 (2017).Article 

Google Scholar 
Winfree, R., Williams, N. M., Dushoff, J. & Kremen, C. Native bees provide insurance against ongoing honey bee losses. Ecol. Lett. 10, 1105–1113. https://doi.org/10.1111/j.1461-0248.2007.01110.x (2007).Article 
PubMed 

Google Scholar 
Abdelgadir, H., Johnson, S. & Van Staden, J. Pollen viability, pollen germination and pollen tube growth in the biofuel seed crop Jatropha curcas (Euphorbiaceae). S. Afr. J. Bot. 79, 132–139. https://doi.org/10.1016/j.sajb.2011.10.005 (2012).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Pinheiro, J., Bates, D., Deb Roy, S. & Sarkar; D. R Core Team. nlme: Linear and nonlinear mixed effects models. R package version 3.1-117. http://CRAN.R-project.org/package=nlme (2014).Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: Estimated marginal means, aka least-squares means. R package. https://CRAN.R-project.org/package=emmeans (2018).Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol 24, 127–135. https://doi.org/10.1016/j.tree.2008.10.008 (2009).Article 
PubMed 

Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package v. 0.2. 0. (Regensburg: University of Regensburg, 2018). More

Marqués, M. J., Martínez-Conde, E., Rovira, J. V. & Ordóñez, S. Heavy metals pollution of aquatic ecosystems in the vicinity of a recently closed underground lead-zinc mine (Basque Country, Spain). Environ. Geol. 40, 1125–1137 (2001).
Google Scholar 
Bud, I., Duma, S., Denuţ, I. & Taşcu, I. Water pollution due to mining activity. Causes and consequences Wasserverunreinigung aufgrund von Bergbauaktivitäten. Ursachen und Konsequenzen. BHM Berg- Hüttenmännische Monatsh. 152, 326–328 (2007).CAS 

Google Scholar 
Ugya, Y. Assessment of ambient air quality resulting from anthropogenic emissions. Am. J. Prev. Med. Public Health https://doi.org/10.5455/ajpmph.20171030080402 (2017).Article 

Google Scholar 
Dore, E. Environment and society: Long-term trends in Latin American mining. Environ. Hist. Camb. 6, 1–29 (2000).
Google Scholar 
Zhou, Q. et al. Total concentrations and sources of heavy metal pollution in global river and lake water bodies from 1972 to 2017. Glob. Ecol. Conserv. 22, 925 (2020).
Google Scholar 
Graesser, J., Aide, T. M., Grau, H. R. & Ramankutty, N. Cropland/pastureland dynamics and the slowdown of deforestation in Latin America. Environ. Res. Lett. 10, 034017 (2015).ADS 

Google Scholar 
Ramírez, A., Pringle, C. M. & Wantzen, K. M. Tropical stream conservation. Trop. Stream Ecol. https://doi.org/10.1016/B978-012088449-0.50012-1 (2008).Article 

Google Scholar 
Uriarte, M., Yackulic, C. B., Lim, Y. & Arce-Nazario, J. A. Influence of land use on water quality in a tropical landscape: A multi-scale analysis. Landsc. Ecol. 26, 1151–1164 (2011).PubMed 
PubMed Central 

Google Scholar 
White, M. & Barquera, S. Mexico adopts food warning labels, why now?. Health Syst. Reform 6, e1752063 (2020).PubMed 

Google Scholar 
Koleff, P. et al. Biodiversity in Mexico: State of knowledge. in Global Biodiversity. 285–337. https://doi.org/10.1201/9780429433634-8. (Apple Academic Press, 2018).Armendáriz-Villegas, E. J. et al. Metal mining and natural protected areas in Mexico: Geographic overlaps and environmental implications. Environ. Sci. Policy 48, 9–19 (2015).
Google Scholar 
Montoya-Lopera, P. et al. New geological, geochronological and geochemical characterization of the San Dimas mineral system: Evidence for a telescoped Eocene-Oligocene Ag/Au deposit in the Sierra Madre Occidental, Mexico. Ore Geol. Rev. 118, 103195 (2020).
Google Scholar 
LeuraVicencio, A. K., CarrizalesYañez, L. & RazoSoto, I. Mercury pollution assessment of mining wastes and soils from former silver amalgamation area in North-Central Mexico. Rev. Int. Contam. Ambient. 33, 655–669 (2017).
Google Scholar 
Veiga, M. M. Introducing New Technologies for Abatement of Global Mercury Pollution in Latin America. United Nations Industrial Development Organization (UNIDO), University of British Columbia (UBC), Center of Mineral Technology (CETEM) (UNIDO, UBC, CETEM, 1997).Camacho, A. et al. Mercury mining in Mexico: I. Community engagement to improve health outcomes from artisanal mining. Ann. Glob. Health 82, 149 (2016).PubMed 

Google Scholar 
IUCN. Benefits Beyond Boundaries: Proceedings of the Vth IUCN World Parks Congress : Durban, South Africa. 8–17 September 2003. (Iucn, 2005).González, S. O., Almeida, C. A., Calderón, M., Mallea, M. A. & González, P. Assessment of the water self-purification capacity on a river affected by organic pollution: Application of chemometrics in spatial and temporal variations. Environ. Sci. Pollut. Res. 21, 10583–10593 (2014).
Google Scholar 
Rico-Sánchez, A. E. et al. Biological diversity in protected areas: Not yet known but already threatened. Glob. Ecol. Conserv. 22, e01006 (2020).
Google Scholar 
Harvey, C. A. et al. Integrating agricultural landscapes with biodiversity conservation in the Mesoamerican hotspot. Conserv. Biol. 22, 8–15 (2008).PubMed 

Google Scholar 
Messerli, B., Grosjean, M. & Vuille, M. Water availability, protected areas, and natural resources in the Andean desert altiplano. Mt. Res. Dev. 17, 229–238 (1997).
Google Scholar 
Servicio Geológico Mexicano. Conoce GeoInfoMex en 3D. https://www.gob.mx/sgm/articulos/conoce-el-sistema-de-consulta-de-informacion-geocientifica-geoinfomex?idiom=es. Accessed 18 Feb 2021. (2019).Resh, V. H. Which group is best? Attributes of different biological assemblages used in freshwater biomonitoring programs. Environ. Monit. Assess. 138, 131–138 (2008).PubMed 

Google Scholar 
Ruiz-Picos, R. A., Sedeño-Díaz, J. E. & López-López, E. Calibrating and validating the biomonitoring working party (BMWP) index for the bioassessment of water quality in neotropical streams. in Water Quality (InTech, 2017).Oertel, N. & Salánki, J. Biomonitoring and bioindicators in aquatic ecosystems. in Modern Trends in Applied Aquatic Ecology. 219–246. https://doi.org/10.1007/978-1-4615-0221-0_10. (Springer, 2011).Goodyear, K. L. & McNeill, S. Bioaccumulation of heavy metals by aquatic macro-invertebrates of different feeding guilds: A review. Sci. Total Environ. 229, 1–19 (1999).ADS 
CAS 

Google Scholar 
Clements, W. H. Small-scale experiments support causal relationships between metal contamination and macroinvertebrate community responses. Ecol. Appl. 14, 954–967 (2004).
Google Scholar 
Michailova, P., Warchałowska-Śliwa, E., Szarek-Gwiazda, E. & Kownacki, A. Does biodiversity of macroinvertebrates and genome response of Chironomidae larvae (Diptera) reflect heavy metal pollution in a small pond?. Environ. Monit. Assess. 184, 1–14 (2012).CAS 
PubMed 

Google Scholar 
Wright, I. A. & Ryan, M. M. Impact of mining and industrial pollution on stream macroinvertebrates: Importance of taxonomic resolution, water geochemistry and EPT indices for impact detection. Hydrobiologia 772, 103–115 (2016).CAS 

Google Scholar 
Wright, I. A. & Burgin, S. Comparison of sewage and coal-mine wastes on stream macroinvertebrates within an otherwise clean upland catchment, Southeastern Australia. Water Air Soil Pollut. 204, 227–241 (2009).ADS 
CAS 

Google Scholar 
Batty, L. C. The potential importance of mine sites for biodiversity. Mine Water Environ. 24, 101–103 (2005).
Google Scholar 
Dolédec, S. & Chessel, D. Co-inertia analysis: An alternative method for studying species–environment relationships. Freshw. Biol. 31, 277–294 (1994).
Google Scholar 
Thioulouse, J. et al. Multivariate Analysis of Ecological Data with ade4. Multivariate Analysis of Ecological Data with ade4. https://doi.org/10.1007/978-1-4939-8850-1. (Springer, 2018).Dodds, W. K., Clements, W. H., Gido, K., Hilderbrand, R. H. & King, R. S. Thresholds, breakpoints, and nonlinearity in freshwaters as related to management. J. N. Am. Benthol. Soc. 29, 988–997 (2010).
Google Scholar 
Sundermann, A., Gerhardt, M., Kappes, H. & Haase, P. Stressor prioritisation in riverine ecosystems: Which environmental factors shape benthic invertebrate assemblage metrics?. Ecol. Indic. 27, 83–96 (2013).
Google Scholar 
Gutiérrez-Yurrita, P. J., García-Serrano, L. A. & Plata, M. R. Is ecotourism a viable option to generate wealth in brittle environments? A reflection on the case of the Sierra Gorda Biosphere Reserve, México. WIT Trans. Ecol. Environ. 161, 141–151 (2012).
Google Scholar 
Vinson, M. R. Long-term dynamics of an invertebrate assemblage downstream from a large dam. Ecol. Appl. 11, 711–730 (2001).
Google Scholar 
Torres-Olvera, M. J., Durán-Rodríguez, O. Y., Torres-García, U., Pineda-López, R. & Ramírez-Herrejón, J. P. Validation of an index of biological integrity based on aquatic macroinvertebrates assemblages in two subtropical basins of central Mexico. Lat. Am. J. Aquat. Res. 46, 945–960 (2018).
Google Scholar 
Carabias Lillo, J., Provencio, E., de la Maza Elvira, J. & Ruiz Corzo, M. Programa de Manejo Reserva de la Biosfera Sierra Gorda. (México, Instituto Nacional de Ecologıa, SEMARNAT, 1999).Macedo, D. R. et al. The relative influence of catchment and site variables on fish and macroinvertebrate richness in cerrado biome streams. Landsc. Ecol. 29, 1001–1016 (2014).
Google Scholar 
Dutra, S. L. & Callisto, M. Macroinvertebrates as tadpole food: Importance and body size relationships. Rev. Bras. Zool. 22, 923–927 (2005).
Google Scholar 
Wang, Z. et al. River-groundwater interaction affected species composition and diversity perpendicular to a regulated river in an arid riparian zone. Glob. Ecol. Conserv. 27, e01595 (2021).
Google Scholar 
López-López, E., Sedeño-Díaz, J. E., Mendoza-Martínez, E., Gómez-Ruiz, A. & Ramírez, E. M. Water quality and macroinvertebrate community in dryland streams: The case of the Tehuacán-Cuicatlán Biosphere Reserve (México) facing climate change. Water (Switzerland) 11, 1376 (2019).
Google Scholar 
O’Connor, N. A. The effects of habitat complexity on the macroinvertebrates colonising wood substrates in a lowland stream. Oecologia 85, 504–512 (1991).ADS 
PubMed 

Google Scholar 
Milner, A. M. & Gloyne-Phillips, I. T. The role of riparian vegetation and woody debris in the development of macroinvertebrate assemblages in streams. River Res. Appl. 21, 403–420 (2005).
Google Scholar 
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).
Google Scholar 
Malmqvist, B. & Hoffsten, P.-O. Influence of drainage from old mine deposits on benthic macroinvertebrate communities in central Swedish streams. Water Res. 33, 2415–2423 (1999).CAS 

Google Scholar 
Jost, L. Independence of alpha and beta diversities. Ecology 91, 1969–1974 (2010).PubMed 

Google Scholar 
Cottenie, K. Integrating environmental and spatial processes in ecological community dynamics. Ecol. Lett. 8, 1175–1182 (2005).PubMed 

Google Scholar 
Jerves-Cobo, R. et al. Biological impact assessment of sewage outfalls in the urbanized area of the Cuenca River basin (Ecuador) in two different seasons. Limnologica 71, 8–28 (2018).CAS 

Google Scholar 
US Environmental Protection Agency. National Recommended Water Quality Criteria-Aquatic Life Criteria Table. Arsenic. (US Environmental Protection Agency, 1995).DeNicola, D. M. & Lellock, A. J. Nutrient limitation of algal periphyton in streams along an acid mine drainage gradient. J. Phycol. 51, 739–749 (2015).CAS 
PubMed 

Google Scholar 
Younos, T. & Schreiber, M. The Handbook of Environmental Chemistry 68. Tamim Younos, Madeline Schreiber, Katarina Kosič Ficco-Karst Water Environment-Springer International Publishing (2019).pdf. (Springer, 2019).Robles, I. et al. Characterization and remediation of soils and sediments polluted with Mercury: Occurrence, transformations, environmental considerations and San Joaquin’s Sierra Gorda case. in Environmental Risk Assessment of Soil Contamination. https://doi.org/10.5772/57284. (InTech, 2014).Hernández-Silva, G. et al. Presencia Del Hg total En Una Relación Suelo-Planta-Atmósfera Al Sur De La Sierra Gorda De Querétaro, México. TIP Rev. Espec. Ciencias Químico-Biol. 15, 5–15 (2012).
Google Scholar 
Campos, E. M. P. & Muñoz, A. J. H. Minas y mineros: Presencia de metales en sedimentos y restos humanos al sur de la sierra gorda de Querétaro en México. Chungara 45, 161–176 (2013).
Google Scholar 
Carrillo-Martínez, M. & Suter-Cargneluti, M. Tectónica de los alrededores de Zimapán, Hidalgo y Querétaro, Libro Guía de la excursión geológica a la región de Zimapán y áreas circundantes, estados de Hidalgo y Querétaro, Hidalgo, México. in VI Convención Geológica Nacional México, DF, Society Geológica Mexico. 1–20. (1982).Allan, J. D. Stream ecology: Structure and function of running waters. Stream Ecol. Struct. Funct. Run. Waters https://doi.org/10.2307/2261644 (2007).Article 

Google Scholar 
Trang, N. T. T., Shrestha, S., Shrestha, M., Datta, A. & Kawasaki, A. Evaluating the impacts of climate and land-use change on the hydrology and nutrient yield in a transboundary river basin: A case study in the 3S River Basin (Sekong, Sesan, and Srepok). Sci. Total Environ. 576, 586–598 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Khatri, N. & Tyagi, S. Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas. Front. Life Sci. 8, 23–39 (2015).CAS 

Google Scholar 
Simões, N. R. et al. Impact of reservoirs on zooplankton diversity and implications for the conservation of natural aquatic environments. Hydrobiologia 758, 3–17 (2015).
Google Scholar 
Dallas, H. F. & Rivers-Moore, N. A. Critical thermal maxima of aquatic macroinvertebrates: Towards identifying bioindicators of thermal alteration. Hydrobiologia 679, 61–76 (2012).
Google Scholar 
Struijs, J., De Zwart, D., Posthuma, L., Leuven, R. S. & Huijbregts, M. A. Field sensitivity distribution of macroinvertebrates for phosphorus in inland waters. Integr. Environ. Assess. Manag. 7, 280–286 (2011).CAS 
PubMed 

Google Scholar 
Molina, C. I. et al. Transfer of mercury and methylmercury along macroinvertebrate food chains in a floodplain lake of the Beni River, Bolivian Amazonia. Sci. Total Environ. 408, 3382–3391 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Corkum, L. D. Patterns of benthic invertebrate assemblages in rivers of northwestern North America. Freshw. Biol. 21, 191–205 (1989).
Google Scholar 
Dalu, T. et al. Assessing drivers of benthic macroinvertebrate community structure in African highland streams: An exploration using multivariate analysis. Sci. Total Environ. 601–602, 1340–1348 (2017).ADS 
PubMed 

Google Scholar 
Eriksen, T. E. et al. A global perspective on the application of riverine macroinvertebrates as biological indicators in Africa, South-Central America, Mexico and Southern Asia. Ecol. Indic. 126, 107609 (2021).
Google Scholar 
Mercado-Garcia, D. et al. Assessing the freshwater quality of a large-scale mining watershed: The need for integrated approaches. Water 11, 1797 (2019).CAS 

Google Scholar 
Gerhardt, A., Janssens De Bisthoven, L. & Soares, A. M. V. M. Effects of acid mine drainage and acidity on the activity of Choroterpes picteti (Ephemeroptera: Leptophlebiidae). Arch. Environ. Contam. Toxicol. 48, 450–458 (2005).CAS 
PubMed 

Google Scholar 
Qu, X., Wu, N., Tang, T., Cai, Q. & Park, Y.-S. Effects of heavy metals on benthic macroinvertebrate communities in high mountain streams. Ann. Limnol. Int. J. Limnol. 46, 291–302 (2010).
Google Scholar 
Soucek, D. J., Denson, B. C., Schmidt, T. S., Cherry, D. S. & Zipper, C. E. Impaired Acroneuria sp. (Plecoptera, Perlidae) populations associated with aluminum contamination in neutral pH surface waters. Arch. Environ. Contam. Toxicol. 42, 416–422 (2002).CAS 
PubMed 

Google Scholar 
Ankley, G. T. Evaluation of metal/acid-volatile sulfide relationships in the prediction of metal bioaccumulation by benthic macroinvertebrates. Environ. Toxicol. Chem. 15, 2138–2146 (1996).CAS 

Google Scholar 
Croteau, M. N., Luoma, S. N. & Stewart, A. R. Trophic transfer of metals along freshwater food webs: Evidence of cadmium biomagnification in nature. Limnol. Oceanogr. 50, 1511–1519 (2005).ADS 
CAS 

Google Scholar 
Specht, W. L., Cherry, D. S., Lechleitner, R. A. & Cairns, J. Structural, functional, and recovery responses of stream invertebrates to fly ash effluent. Can. J. Fish. Aquat. Sci. 41, 884–896 (1984).CAS 

Google Scholar 
Corbi, J. J., Froehlich, C. G., Strixino, S. T. & Dos Santos, A. Bioaccumulation of metals in aquatic insects of streams located in areas with sugar cane cultivation. Quim. Nova 33, 644–648 (2010).CAS 

Google Scholar 
Poff, N. L., Bledsoe, B. P. & Cuhaciyan, C. O. Hydrologic variation with land use across the contiguous United States: Geomorphic and ecological consequences for stream ecosystems. Geomorphology 79, 264–285 (2006).ADS 

Google Scholar 
Chang, F. H., Lawrence, J. E., Rios-Touma, B. & Resh, V. H. Tolerance values of benthic macroinvertebrates for stream biomonitoring: Assessment of assumptions underlying scoring systems worldwide. Environ. Monit. Assess. 186, 2135–2149 (2014).CAS 
PubMed 

Google Scholar 
Brittain, J. E. Life History Strategies in Ephemeroptera and Plecoptera. in Mayflies and Stoneflies: Life Histories and Biology. 1–12. https://doi.org/10.1007/978-94-009-2397-3_1 (Springer Netherlands, 1990).Bispo, P. C., Oliveira, L. G., Bini, L. M. & Sousa, K. G. Ephemeroptera, Plecoptera and Trichoptera assemblages from riffles in mountain streams of central Brazil: Environmental factors influencing the distribution and abundance of immatures. Braz. J. Biol. 66, 611–622 (2006).CAS 
PubMed 

Google Scholar 
Jacobsen, D. Tropical high-altitude streams. in Tropical Stream Ecology. 219–256. https://doi.org/10.1016/B978-012088449-0.50010-8 (Elsevier, 2008).Jacobsen, D., Rostgaard, S. & Vasconez, J. J. Are macroinvertebrates in high altitude streams affected by oxygen deficiency?. Freshw. Biol. 48, 2025–2032 (2003).
Google Scholar 
Courtney, L. A. & Clements, W. H. Assessing the influence of water and substratum quality on benthic macroinvertebrate communities in a metal-polluted stream: An experimental approach. Freshw. Biol. 47, 1766–1778 (2002).CAS 

Google Scholar 
Buss, D. F. & Salles, F. F. Using Baetidae species as biological indicators of environmental degradation in a Brazilian river basin. Environ. Monit. Assess. 130, 365–372 (2007).CAS 
PubMed 

Google Scholar 
Ristau, K., Faupel, M. & Traunspurger, W. The effects of nutrient enrichment on a freshwater meiofaunal assemblage. Freshw. Biol. 57, 824–834 (2012).CAS 

Google Scholar 
Cornelis, R. & Nordberg, M. General chemistry, sampling, analytical methods, and speciation. in Handbook on the Toxicology of Metals. 11–38. https://doi.org/10.1016/B978-012369413-3/50057-4 (Elsevier, 2007).Santore, R. C., Di Toro, D. M., Paquin, P. R., Allen, H. E. & Meyer, J. S. Biotic ligand model of the acute toxicity of metals. 2. Application to acute copper toxicity in freshwater fish and Daphnia. Environ. Toxicol. Chem. 20, 2397–2402 (2001).CAS 
PubMed 

Google Scholar 
Kozlova, T., Wood, C. M. & McGeer, J. C. The effect of water chemistry on the acute toxicity of nickel to the cladoceran Daphnia pulex and the development of a biotic ligand model. Aquat. Toxicol. 91, 221–228 (2009).CAS 
PubMed 

Google Scholar 
Valdez, R., Guzmán-Aranda, J. C., Abarca, F. J., Tarango-Arámbula, L. A. & Sánchez, F. C. Wildlife conservation and management in Mexico. Wildl. Soc. Bull. 34, 270–282 (2006).
Google Scholar 
INEGI. Por Actividad Económica. https://www.inegi.org.mx/temas/pib/. Accessed 6 Jan 2021. (2020).García, E. Modificaciones Al Sistema de Classificación Climática de Koppen. (Institute of Geography, UNAM, 1988).HACH. User Manual—HACH DR 3900. in 1–148 (2013).APHA. Standard Methods for the Examination of Water and Wastewater. (Association, American Public Health, 2005).NMX-AA-051-SCFI-2001. Análisis de agua—Determinación de metales por absorción atómica en aguas naturales, potables, residuales y residuales tratadas. Norma Mex. 1–47 (2001).Helsel, D. R. Less than obvious: Statistical treatment of data below the detection limit. Environ. Sci. Technol. 24, 1766–1774 (1990).ADS 
CAS 

Google Scholar 
Barbour, M. T., Stribling, J. B. & Verdonschot, P. F. M. The multihabitat approach of USEPA’s rapid bioassessment protocols: benthic macroinvertebrates. Limnetica 25, 839–850 (2006).
Google Scholar 
USEPA. National Rivers and Streams Assessment 2018/19: Field Operations Manual—Wadeable. Vol. EPA-841-B-. 169. (2017).Michaud, J. P. & Wierenga, M. Estimating Discharge and Stream Flows (Ecology Publication, 2005).
Google Scholar 
Hering, D., Moog, O., Sandin, L. & Verdonschot, P. F. M. Overview and application of the AQEM assessment system. Hydrobiologia 516, 1–20 (2004).
Google Scholar 
Merrit, R. & Cummins, K. W. An Introduction to the Aquatic Insects of North America 3rd edn. (Kendall Hunt, 1996).
Google Scholar 
Thorp, J. H. & Covich, A. P. Ecology and Classification of North American Freshwater Invertebrates (Academic Press, 2009).
Google Scholar 
Bueno-Soria, J. Guía de Identificación Ilustrada de Losgéneros de Larvas de Insectos del Orden Trichoptera de México (Universidad Nacional Autónoma de México, 2010).
Google Scholar 
Springer, M., Ramírez, A. & Hanson, P. Macroinvertebrados de agua dulce I. Rev. Biol. Trop. 58, 198 (2010).
Google Scholar 
Hamada, N., Thorp, J. H. & Rogers, D. C. Thorp and Covich’s Freshwater Invertebrates (Elsevier, 2018).
Google Scholar 
Jost, L. et al. Partitioning diversity for conservation analyses. Divers. Distrib. 16, 65–76 (2010).
Google Scholar 
Lavit, C., Escoufier, Y. & Sabatier, R. The ACT (STATIS method) J q G G Fl { q K q *. Comput. Stat. Data Anal. 18, 97–119 (1994).MATH 

Google Scholar  More

Barnagaud, J. Y., Barbaro, L., Hampe, A., Jiguet, F. & Archaux, F. Species’ thermal preferences affect forest bird communities along landscape and local scale habitat gradients. Ecography (Cop.) 36, 1218–1226 (2013).
Google Scholar 
LeRoy, P. N. Landscape filters and species traits: towards mechanistic understanding and prediction in stream ecology. J. North Am. Benthol. Soc. 391–409 (1997).Keddy, P. A. Assembly and response rules: two goals for predictive community ecology. J. Veg. Sci. 3, 157–164 (1992).
Google Scholar 
Whittaker, R. J., Willis, K. J. & Field, R. Scale and species richness: Towards a general, hierarchical theory of species diversity. J. Biogeogr. 28, 453–470 (2001).
Google Scholar 
Willis, K. J. & Whittaker, R. J. Species diversity – scale matters. Science (80-. ). 295, 1245–1247 (2002).Brockerhoff, E. G. et al. Forest biodiversity, ecosystem functioning. Biodivers. Conserv. 26, 3005–3035 (2017).
Google Scholar 
Dolek, M. et al. Ants on oaks: effects of forest structure on species composition. J. Insect Conserv. 13, 367–375 (2009).
Google Scholar 
Díaz, I. A., Armesto, J. J., Reid, S., Sieving, K. E. & Willson, M. F. Linking forest structure and composition: Avian diversity in successional forests of Chiloé Island Chile. Biol. Conserv. 123, 91–101 (2005).
Google Scholar 
Fady-Welterlen, B. Is there really more biodiversity in Mediterranean forest ecosystems?. Taxon 54, 905–910 (2005).
Google Scholar 
Peñuelas, J. et al. Impacts of global change on Mediterranean forests and their services. Forests 8, 1–37 (2017).
Google Scholar 
Resco De Dios, V., Fischer, C. & Colinas, C. Climate change effects on mediterranean forests and preventive measures. New For. 33, 29–40 (2007).Lindner, M. et al. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For. Ecol. Manage. 259, 698–709 (2010).
Google Scholar 
Cadieux, P. et al. Projected effects of climate change on boreal bird community accentuated by anthropogenic disturbances in western boreal forest Canada. Divers. Distrib. 26, 668–682 (2020).
Google Scholar 
Simmons, N. B. & Cirranello, A. L. Bat Species of the World: A taxonomic and geographic database. https://batnames.org/home.html (2020).Peixoto, F. P., Braga, P. H. P. & Mendes, P. A synthesis of ecological and evolutionary determinants of bat diversity across spatial scales. BMC Ecol. 18, 1–14 (2018).
Google Scholar 
Bats in forests: conservation and management. (The Johns Hopkins University Press, 2007).Barclay, R. M. R. & Kurta, A. Ecology and behavioyr of bats roosting in tree cavities and under bark. in Bats in forests: Conservation and management (eds. Lacki, M. J., Hayes, J. P. & Kurta, A.) (The Johns Hopkins University Press, 2007).Lacki, M. J., Amelon, S. K. & Baker, M. D. Foraging Ecology of Bats in Forests. in Bats in forests: Conservation and management (eds. Lacki, M. J., Hayes, J. P. & Kurta, A.) 329 (The Johns Hopkins University Press, 2007).Silvis, A., Ford, W. M. & Britzke, E. R. Day-roost tree selection by northern long-eared bats—What do non-roost tree comparisons and one year of data really tell us?. Glob. Ecol. Conserv. 3, 756–763 (2015).
Google Scholar 
Manual de conservación y seguimiento de los quirópteros forestales. in (eds. Guixe, D. & Camprodon, J.) 274 (Ministerio de Agricultura, Pesca y Alimentación y Ministerio para la Transición Ecológica., 2018).Patriquin, K. J. & Barclay, R. M. R. Foraging by bats in cleared, thinned and unharvested boreal forest. J. Appl. Ecol. 40, 646–657 (2003).
Google Scholar 
Carr, A., Weatherall, A. & Jones, G. The effects of thinning management on bats and their insect prey in temperate broadleaved woodland. For. Ecol. Manage. 457, 117682 (2020).Norberg, U. M. & Rayner, J. M. V. Ecological morphology and flight in bats (Mammalia; Chiroptera): wing adaptations, flight performance, foraging strategy and echolocation. Philos. Trans. R. Soc. London. B, Biol. Sci. 316, 335–427 (1987).Aldridge, H. D. J. N. & Rautenbach, I. L. Morphology, echolocation and resource partitioning in insectivorous bats. J. Anim. Ecol. 56, 763 (1987).
Google Scholar 
Dodd, L. E. et al. Forest structure affects trophic linkages: How silvicultural disturbance impacts bats and their insect prey. For. Ecol. Manage. 267, 262–270 (2012).
Google Scholar 
Lumsden, L. F. & Bennett, A. F. Scattered trees in rural landscapes: Foraging habitat for insectivorous bats in south-eastern Australia. Biol. Conserv. 122, 205–222 (2005).
Google Scholar 
Fahr, J. & Kalko, E. K. V. Biome transitions as centres of diversity: Habitat heterogeneity and diversity patterns of West African bat assemblages across spatial scales. Ecography (Cop.) 34, 177–195 (2011).
Google Scholar 
Ferreira, D. F. et al. Season-modulated responses of Neotropical bats to forest fragmentation. Ecol. Evol. 7, 4059–4071 (2017).PubMed 
PubMed Central 

Google Scholar 
Fuentes-Montemayor, E., Goulson, D., Cavin, L., Wallace, J. M. & Park, K. J. Fragmented woodlands in agricultural landscapes: The influence of woodland character and landscape context on bats and their insect prey. Agric. Ecosyst. Environ. 172, 6–15 (2013).
Google Scholar 
Wood, H., Lindborg, R. & Jakobsson, S. European Union tree density limits do not reflect bat diversity in wood-pastures. Biol. Conserv. 210, 60–71 (2017).
Google Scholar 
Sagot, M. & Chaverri, G. Effects of roost specialization on extinction risk in bats. Conserv. Biol. 29, 1666–1673 (2015).PubMed 

Google Scholar 
Russo, D., Cistrone, L. & Jones, G. Spatial and temporal patterns of roost use by tree-dwelling barbastelle bats Barbastella barbastellus. Ecography (Cop.) 28, 769–776 (2005).
Google Scholar 
Popa-Lisseanu, A. G., Bontadina, F., Mora, O. & Ibáñez, C. Highly structured fission–fusion societies in an aerial-hawking, carnivorous bat. Anim. Behav. 75, 471–482 (2008).
Google Scholar 
Zambrana Pineda, J. F. & Ríos Jiménez, S. El sector primario andaluz en el siglo XX. Instituto de Estadística de Andalucía (2006).Nogueras, J., Garrido-García, J. A. & Fijo-León, A. Patrones de distribución del complejo “Myotis mystacinus” en la península Ibérica”. Barbastella 6, 24–30 (2013).
Google Scholar 
Boye, P. & Dietz, M. Development of good practice guidelines for woodland management for bats. English Nature Research Reports (2005) ISSN 0967-876X.Dietz, C. & Kiefer, A. Bats of Britain and Europe. (Bloomsbury Publishing, 2016).Estók, P., Gombkötő, P. & Cserkész, T. Roosting behaviour of the greater noctule Nyctalus lasiopterus Schreber, 1780 (Chiroptera, Vespertilionidae) in Hungary as revealed by radio-tracking. Mammalia 71, 1 (2007).
Google Scholar 
Walters, C. L. et al. A continental-scale tool for acoustic identification of European bats. J. Appl. Ecol. 49, 1064–1074 (2012).
Google Scholar 
Smeraldo, S. et al. Ignoring seasonal changes in the ecological niche of non-migratory species may lead to biases in potential distribution models: Lessons from bats. Biodivers. Conserv. 27, 2425–2441 (2018).
Google Scholar 
Crome, F. H. J. & Richards, G. C. Bats and gaps : Microchiropteran community structure in a queensland rain forest. Ecology 69, 1960–1969 (1988).
Google Scholar 
R core team. R: A language and environment for statistical computing. (2021).Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).
Google Scholar 
Franklin, J. F. & Pelt, R. Van. Spatial spects of structural complexity in old-growth forests. J. For. 22–28 (2004).Ishii, H. T., Tanabe, S. & Hiura, T. Canopy structure, stand productivity, and biodiversity of temperate forest ecosystems. For. Sci. 50, (2004).Pebesma, E. & Bivand, R. sp: Classes and methods for spatial data. (2021).Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the Geospatial Data Abstraction Library. (2021).Hijmans, R. J. raster: Geographic Data Analysis and Modeling. (2020).Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 6, 231–252 (2006).
Google Scholar 
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling. (2017).Muscarella, R. et al. ENMeval: Automated runs and evaluations of ecological niche models. (2018).Raes, N. & Ter Steege, H. A null-model for significance testing of presence-only species distribution models. Ecography (Cop.) 30, 727–736 (2007).
Google Scholar 
Wittmann, M. E., Barnes, M. A., Jerde, C. L., Jones, L. A. & Lodge, D. M. Confronting species distribution model predictions with species functional traits. Ecol. Evol. 6, 873–879 (2016).PubMed 
PubMed Central 

Google Scholar 
Hanspach, J., Kühn, I., Pompe, S. & Klotz, S. Predictive performance of plant species distribution models depends on species traits. Perspect. Plant Ecol. Evol. Syst. 12, 219–225 (2010).
Google Scholar 
Pöyry, J., Luoto, M., Heikkinen, R. K. & Saarinen, K. Species traits are associated with the quality of bioclimatic models. Glob. Ecol. Biogeogr. 17, 403–414 (2008).
Google Scholar 
van Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography (Cop.) 39, 542–552 (2016).
Google Scholar 
Froidevaux, J. S. P., Zellweger, F., Bollmann, K., Jones, G. & Obrist, M. K. From field surveys to LiDAR: Shining a light on how bats respond to forest structure. Remote Sens. Environ. 175, 242–250 (2016).ADS 

Google Scholar 
Edenius, L. & Elmberg, J. Landscape level effects of modern forestry on bird communities in North Swedish boreal forests. Landsc. Ecol. 11, 325–338 (1996).
Google Scholar 
Drapeau, P. et al. Landscape-scale disturbances and changes in bird communities of boreal mixed-wood forests. Ecol. Monogr. 70, 423–444 (2000).
Google Scholar 
McGarigal, K. & McComb, W. C. Relationships between landscape structure and breeding birds in the Oregon coast range. Ecol. Monogr. 65, 235–260 (1995).
Google Scholar 
Gil-Tena, A., Brotons, L. & Saura, S. Effects of forest landscape change and management on the range expansion of forest bird species in the Mediterranean region. For. Ecol. Manage. 259, 1338–1346 (2010).
Google Scholar 
Gil-tena, A., Brotons, L. & Saura, S. Mediterranean forest dynamics and forest bird distribution changes in the late 20th century. Glob. Chang. Biol. 15, 474–485 (2009).ADS 

Google Scholar 
Goiti, U., Garin, I., Almenar, D., Salsamendi, E. & Aihartza, J. Foraging by mediterranean horshoe bats (Rhinolophus euryale) in relation to prey distribution and edge habitat. J. Mammal. 89, 493–502 (2008).
Google Scholar 
Motte, G. & Libois, R. Conservation of the lesser horseshoe bat (Rhinolophus hipposideros Bechstein, 1800) (Mammalia: Chiroptera) in Belgium. A case study of feeding habitat requirements. Belgian J. Zool. 132, 49–54 (2002).Castro, E. B. Los bosques ibéricos: una interpretación geobotánica. (GeoPlaneta, Editorial, SA, 1997).Ozanne, C. M. P. A comparison of the canopy arthropod communities of coniferous and broad-leaved trees in the United Kingdom. Selbyana 20, 290–298 (1999).Vehviläinen, H., Koricheva, J. & Ruohomäki, K. Effects of stand tree species composition and diversity on abundance of predatory arthropods. Oikos 117, 935–943 (2008).
Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
Lisón, F. & Sánchez-Fernández, D. Low effectiveness of the Natura 2000 network in preventing land-use change in bat hotspots. Biodivers. Conserv. 26, 1989–2006 (2017).
Google Scholar 
Gillespie, T. W. & Walter, H. Distribution of bird species richness at a regional scale in tropical dry forest of central America. J. Biogeogr. 28, 651–662 (2001).
Google Scholar 
O’Brien, M. J. et al. Tree diversity drives diversity of arthropod herbivores, but successional stage mediates detritivores. Ecol. Evol. 7, 8753–8760 (2017).PubMed 
PubMed Central 

Google Scholar 
Zhang, J. et al. Tree diversity promotes generalist herbivore community patterns in a young subtropical forest experiment. Oecologia 183, 455–467 (2017).ADS 
PubMed 

Google Scholar 
Naďo, L. et al. Highly selective roosting of the giant noctule bat and its astonishing foraging activity by GPS tracking in a mountain environment. Mammal Res. 64, 587–594 (2019).
Google Scholar 
Begehold, H., Rzanny, M. & Flade, M. Forest development phases as an integrating tool to describe habitat preferences of breeding birds in lowland beech forests. J. Ornithol. 156, 19–29 (2015).
Google Scholar 
Hayes, J. P. Presence, relative abundance, and resource selection of bats in managed forest landscapes in western Oregon. vol. 53 (Oregon State University, 2007).Mortimer, G. Foraging, roosting and survival of natterer’s bats, Myotis nattereri, in a commercial coniferous plantation. (University of St Andrews, 2006).Kirkpatrick, L. et al. Bat use of commercial coniferous plantations at multiple spatial scales: Management and conservation implications. Biol. Conserv. 206, 1–10 (2017).
Google Scholar 
Napal, M. & Ibanez, C. Murcielagos y Bosques. in Manual de conservación y seguimiento de los quirópteros forestales (eds. Guixé, D. & Camprodon, J.) (Organismo Autónomo Parques Nacionales. Ministerio para la Transición Ecológica, 2018).Sleep, D. J. H. & Brigham, R. M. An experimental test of clutter tolerance in bats. J. Mammal. 84, 216–224 (2003).
Google Scholar 
Fukui, D., Murakami, M., Nakano, S. & Aoi, T. Effect of emergent aquatic insects on bat foraging in a riparian forest. J. Anim. Ecol. 75, 1252–1258 (2006).PubMed 

Google Scholar 
Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage. 259, 660–684 (2010).
Google Scholar 
Carnicer, J. et al. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. Proc. Natl. Acad. Sci. 108, 1474–1478 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rebelo, H., Tarroso, P. & Jones, G. Predicted impact of climate change on european bats in relation to their biogeographic patterns. Glob. Chang. Biol. 16, 561–576 (2010).ADS 

Google Scholar 
Amorim, F., Carvalho, S. B., Honrado, J. & Rebelo, H. Designing optimized multi-species monitoring networks to detect range shifts driven by climate change: A case study with bats in the North of Portugal. PLoS ONE 9, 1 (2014).
Google Scholar 
Mantyka-Pringle, C. S. et al. Climate change modifies risk of global biodiversity loss due to land-cover change. Biol. Conserv. 187, 103–111 (2015).
Google Scholar 
Jandl, R., Spathelf, P., Bolte, A. & Prescott, C. E. Forest adaptation to climate change—is non-management an option?. Ann. For. Sci. 76, 1–13 (2019).
Google Scholar 
Morán-Ordóñez, A. et al. Future trade-offs and synergies among ecosystem services in Mediterranean forests under global change scenarios. Ecosyst. Serv. 45, 1 (2020).
Google Scholar 
Wickham, H. et al. ggplot2: Create elegant data visualisations using the grammar of graphics. (2020). More

Albright, R. et al. Reversal of ocean acidification enhances net coral reef calcification. Nature 531, 362–365 (2016).CAS 
Article 

Google Scholar 
Pandolfi, J. M., Connolly, S. R., Marshall, D. J. & Cohen, A. L. Projecting coral reef futures under global warming and ocean acidification. Science 333, 418–422 (2011).CAS 
Article 

Google Scholar 
Davis, K. L., Colefax, A. P., Tucker, J. P., Kelaher, B. P. & Santos, I. R. Global coral reef ecosystems exhibit declining calcification and increasing primary productivity. Commun. Earth Environ. 2, 1–10 (2021).Article 

Google Scholar 
Silverman, J., Lazar, B., Cao, L., Caldeira, K. & Erez, J. Coral reefs may start dissolving when atmospheric CO2 doubles. Geophys. Res. Lett. 36, L05606 (2009).Anthony, K. R. N., Kleypas, J. A. & Gattuso, J. P. Coral reefs modify their seawater carbon chemistry – implications for impacts of ocean acidification. Global Change Biol. 17, 3655–3666 (2011).Article 

Google Scholar 
Eyre, B. D. et al. Coral reefs will transition to net dissolving before end of century. Science 359, 908–911 (2018).CAS 
Article 

Google Scholar 
Cantin, N. E., Cohen, A. L., Karnauskas, K. B., Tarrant, A. M. & McCorkle, D. C. Ocean warming slows coral growth in the central Red Sea. Science 329, 322–325 (2010).CAS 
Article 

Google Scholar 
Ries, J. B., Ghazaleh, M. N., Connolly, B., Westfield, I. & Castillo, K. D. Impacts of seawater saturation state (ΩA=0.4-4.6) and temperature (10, 25˚C) on the dissolution kinetics of whole-shell biogenic carbonates. Geochim. Cosmochim. Ac 192, 318–337 (2016).CAS 
Article 

Google Scholar 
Kornder, N. A., Riegl, B. M. & Figueiredo, J. Thresholds and drivers of coral calcification responses to climate change. Global Change Biol. 24, 5084–5095 (2018).Article 

Google Scholar 
Cyronak, T., Schulz, K. G. & Jokiel, P. L. The Omega myth: what really drives lower calcification rates in an acidifying ocean. Ices J Mar Sci 73, 558–562 (2016).Article 

Google Scholar 
Davis, K. L., McMahon, A., Kelaher, B., Shaw, E. & Santos, I. R. Fifty years of sporadic coral reef calcification estimates at One Tree Island, Great Barrier Reef: is it enough to imply long term trends? Front Marine Sci 6, 00282 (2019).Cyronak, T. et al. Taking the metabolic pulse of the world’s coral reefs. PLoS One 13, e0190872 (2018).Article 

Google Scholar 
Kinsey, D. W. Carbon turnover and accumulation by coral reefs, (University of Hawaii, 1979).Barnes, D. J. Profiling coral reef productivity and calcification using pH and oxygen electrodes. J. Exp. Mar. Biol. Ecol. 66, 149–161 (1983).CAS 
Article 

Google Scholar 
Albright, R., Langdon, C. & Anthony, K. R. N. Dynamics of seawater carbonate chemistry, production, and calcification of a coral reef flat, central Great Barrier Reef. Biogeosciences 10, 6747–6758 (2013).CAS 
Article 

Google Scholar 
Silverman, J. et al. Community calcification in Lizard Island, Great Barrier Reef: A 33 year perspective. Geochim. Cosmochim. Ac 144, 72–81 (2014).CAS 
Article 

Google Scholar 
Pichon, M. & Morrissey, J. Benthic zonation and community structure of South Island Reef, Lizard Island (Great Barrier Reef). B. Mar. Sci. 31, 581–593 (1981).
Google Scholar 
SCU. Declining growth rates of global coral reef ecosystems, Southern Cross University, June 2021. https://www.scu.edu.au/engage/news/latest-news/2021/declining-growth-rates-of-global-coral-reef-ecosystems.php (2021).Andersson, A. J., Yeakel, K. L., Bates, N. R. & de Putron, S. J. Partial offsets in ocean acidification from changing coral reef biogeochemistry. Nat. Clim. Change 4, 56–61 (2014).CAS 
Article 

Google Scholar 
Kapsenberg, L. & Cyronak, T. Ocean acidification refugia in variable environments. Global Change Biol. 25, 3201–3214 (2019).Article 

Google Scholar 
Cvitanovic, C. & Hobday, A. J. Building optimism at the environmental science-policy-practice interface through the study of bright spots. Nat. Commun. 9, 1–5 (2018).CAS 
Article 

Google Scholar  More