Ecology

1.Almond, R. E. A., Grooten, M. & Petersen, T. (eds) Living Planet Report 2020 – Bending the Curve of Biodiversity Loss (WWF, 2020).2.Leung, B. et al. Clustered versus catastrophic global vertebrate declines. Nature 588, 267–271 (2020).ADS 
CAS 
Article 

Google Scholar 
3.Deinet, S. et al. Wildlife Comeback in Europe: The Recovery of Selected Mammal and Bird Species (final report to Rewilding Europe by ZSL, BirdLife International and the European Bird Census Council) (2013).4.Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl Acad. Sci. USA 114, E6089–E6096 (2017).CAS 
Article 

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

Google Scholar 
6.Daskalova, G. N., Myers-Smith, I. H. & Godlee, J. L. Rare and common vertebrates span a wide spectrum of population trends. Nat. Commun. 11, 4394 (2020).ADS 
CAS 
Article 

Google Scholar 
7.Setiawan, R. et al. Preventing global extinction of the Javan rhino: tsunami risk and future conservation direction. Conserv. Lett. 11, e12366 (2018).Article 

Google Scholar 
8.Mondol, S., Bruford, M. W. & Ramakrishnan, U. Demographic loss, genetic structure and the conservation implications for Indian tigers. Proc. R. Soc. Lond. B 280, 20130496 (2013).
Google Scholar 
9.Milner-Gulland, E. J. & Beddington, J. R. The exploitation of elephants for the ivory trade: An historical perspective. Proc. R. Soc. Lond. B 252, 29–37 (1993).ADS 
Article 

Google Scholar 
10.Casas-Marce, M. et al. Spatiotemporal dynamics of genetic variation in the iberian lynx along its path to extinction reconstructed with ancient DNA. Mol. Biol. Evol. 34, 2893–2907 (2017).CAS 
Article 

Google Scholar 
11.Chase, M. J. et al. Continent-wide survey reveals massive decline in African savannah elephants. PeerJ 4, e2354 (2016).Article 

Google Scholar 
12.Jhala, Y. V, Qureshi, Q. & Nayak, A. K. (eds) Status of Tigers, Co-Predators and Prey in India 2018. Summary Report (National Tiger Conservation Authority, Government of India, New Delhi & Wildlife Institute of India, 2019).13.Sanderson, E. W. et al. The ecological future of the North American bison: conceiving long-term, large-scale conservation of wildlife. Conserv. Biol. 22, 252–266 (2008).Article 

Google Scholar  More

1.Laffoley, D. & Baxter, J.M. Ocean Deoxygenation: Everyone’s Problem-Causes, Impacts, Consequences and Solutions. (IUCN, 2019).2.Heinze, C. et al. The quiet crossing of ocean tipping points. Proc. Natl. Acad. Sci. 118(9), e2008478118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Ekau, W., Auel, H., Pörtner, H. O. & Gilbert, D. Impacts of hypoxia on the structure and processes in pelagic communities (zooplankton, macro-invertebrates and fish). Biogeosciences 7(5), 1669–1699 (2010).ADS 
CAS 

Google Scholar 
4.Gallo, N. D. & Levin, L. A. Fish ecology and evolution in the world’s oxygen minimum zones and implications of ocean deoxygenation. Adv. Mar. Biol. 74, 117–198 (2016).CAS 

Google Scholar 
5.Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359(6371), eaam7240 (2018).
Google Scholar 
6.Hoegh-Guldberg, O. et al. The human imperative of stabilizing global climate change at 1.5 C. Science 365(6459), eaaw6974 (2019).CAS 

Google Scholar 
7.Sampaio, E. et al. Impacts of hypoxic events surpass those of future ocean warming and acidification. Nat. Ecol. Evol. 5, 311–321 (2021).
Google Scholar 
8.Chan, F. et al. Emergence of anoxia in the California current large marine ecosystem. Science 319(5865), 920–920 (2008).ADS 
CAS 

Google Scholar 
9.Levin, L. A. et al. Effects of natural and human-induced hypoxia on coastal benthos. Biogeosciences 6, 2063–2098 (2009).ADS 
CAS 

Google Scholar 
10.Stramma, L., Schmidtko, S., Levin, L. A. & Johnson, G. C. Ocean oxygen minima expansions and their biological impacts. Deep Sea Res Part I Oceanogr. Res. Pap. 57(4), 587–595 (2010).ADS 
CAS 

Google Scholar 
11.Hoegh-Guldberg, O. et al. 2018: Impacts of 1.5 °C Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the Impacts of Global Warming of 1.5°C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty 175–311 (Intergovernmental Panel on Climate Change, 2019).12.Zhang, X. et al. In situ Raman-based measurements of high dissolved methane concentrations in hydrate-rich ocean sediments. Geophys. Res. Lett. 38, L08605 (2011).ADS 

Google Scholar 
13.Wright, J. J., Konwar, K. M. & Hallam, S. J. Microbial ecology of expanding oxygen minimum zones. Nat. Rev. Microbiol. 10, 381–394 (2012).CAS 

Google Scholar 
14.Kalvelage, T. et al. Nitrogen cycling driven by organic matter export in the South Pacific oxygen minimum zone. Nat. Geosci. 6, 228–234 (2013).ADS 
CAS 

Google Scholar 
15.Falkowski, P. G. Evolution of the nitrogen cycle and its influence on the biological sequestration of CO2 in the ocean. Nature 387(6630), 272–275 (1997).ADS 
CAS 

Google Scholar 
16.Zehr, J. P. & Kudela, R. M. Nitrogen cycle of the open ocean: From genes to ecosystems. Annu. Rev. Mar. Sci. 3, 197–225 (2011).ADS 

Google Scholar 
17.Pack, M. A. et al. Methane oxidation in the Eastern Tropical North Pacific Ocean water column. J. Geophys. Res. Biogeosci. 120, 1078–1092 (2015).CAS 

Google Scholar 
18.Lashof, D. A. & Ahuja, D. R. Relative contributions of greenhouse gas emissions to global warming. Nature 344, 529–531 (1990).ADS 
CAS 

Google Scholar 
19.Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).CAS 

Google Scholar 
20.Stramma, L., Johnson, G. C., Sprintall, J. & Mohrholz, V. Expanding oxygen-minimum zones in the tropical oceans. Science 320, 655–658 (2008).ADS 
CAS 

Google Scholar 
21.Keeling, R. E., Körtzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Ann. Rev. Mar. Sci. 2, 199–229 (2010).
Google Scholar 
22.Helm, K. P., Bindoff, N. L. & Church, J. A. Observed decreases in oxygen content of the global ocean. Geophys. Res. Lett. 38, L23602 (2011).ADS 

Google Scholar 
23.Kirschke, S. et al. Three decades of global methane sources and sinks. Nat. Geosci. 6, 813–823 (2013).ADS 
CAS 

Google Scholar 
24.Savrda, C. E. & Bottjer, D. J. Trace·fossil model for reconstruction of paleo-oxgenation in bottom waters. Geology 14, 3–6 (1986).ADS 
CAS 

Google Scholar 
25.Savrda, C. E. & Bottjer, D. J. The exaerobic zone, a new oxygen-deficient marine biofacies. Nature 327, 54–56 (1987).ADS 

Google Scholar 
26.Savrda, C. E. & Bottjer, D. J. Trace·fossil model for reconstructing oxygenation histories of ancient marine bottom waters: Application to Upper Cretaceous Niobrara Formation, Colorado. Palaeogeogr. Palaeoclimatol. Palaeoecol. 74, 49–74 (1989).
Google Scholar 
27.Kaiho, K. Morphotype changes of deep-sea benthic foraminifera during the Cenozoic Era and their paleoenvironmental implications. Kaseki (Fossils) 47, 1–23 (1989).
Google Scholar 
28.Kaiho, K. Global changes of Paleogene aerobic/anaerobic Benthic foraminifera and deep-sea circulation. Palaeogeogr. Palaeoclimatol. Palaeoecol. 83, 65–85 (1991).
Google Scholar 
29.Kaiho, K. Benthic foraminiferal dissolved-oxygen index and dissolved-oxygen levels in the modern ocean. Geology 22, 719–722 (1994).ADS 
CAS 

Google Scholar 
30.Schumacher, S., Jorissen, F. J., Dissard, D., Larkin, K. E. & Gooday, A. J. Live (Rose Bengal stained) and dead benthic foraminifera from the oxygen minimum zone of the Pakistan continental margin (Arabian Sea). Mar. Micropaleontol. 62, 45–73 (2007).ADS 

Google Scholar 
31.Abu-Zied, R. H. et al. Benthic foraminiferal response to changes in bottom-water oxygenation and organic carbon flux in the eastern Mediterranean during LGM to Recent times. Mar. Micropaleontol. 67, 46–68 (2008).ADS 

Google Scholar 
32.Grunert, P. et al. Upwelling conditions in the Early Miocene Central Paratethys Sea. Geol. Carpath. 61(2), 129–145 (2010).ADS 
MathSciNet 
CAS 

Google Scholar 
33.Kaminski, M. A. Calibration of the benthic foraminiferal oxygen index in the Marmara Sea. Geol. Q. 56(4), 757–764 (2012).
Google Scholar 
34.Ilies, I. A. et al. Early middle Miocene paleoenvironmental evolution in southwest Transylvania (Romania): Interpretation based on foraminifera. Geol. Carpath. 71(5), 444–461 (2020).
Google Scholar 
35.Bernhard, J. M. & Bowser, S. S. Benthic foraminifera of dysoxic sediments: Chloroplast sequestration and functional morphology. Earth Sci. Rev. 46(1–4), 149–165 (1999).ADS 
CAS 

Google Scholar 
36.Ohkushi, K. et al. Quantified intermediate water oxygenation history of the NE Pacific: A new benthic foraminiferal record from Santa Barbara basin. Paleoceanography 28(3), 453–467 (2013).ADS 

Google Scholar 
37.Lu, W. et al. I/Ca in epifaunal benthic foraminifera: A semi-quantitative proxy for bottom water oxygen in a multi-proxy compilation for glacial ocean deoxygenation. EPSL 533, 116055 (2020).CAS 

Google Scholar 
38.Rathburn, A. E., Willingham, J., Ziebis, W., Burkett, A. M. & Corliss, B. H. A new biological proxy for deep-sea paleo-oxygen: Pores of epifaunal benthic foraminifera. Sci. Rep. 8, 1–8 (2018).CAS 

Google Scholar 
39.Singh, A. D., Rai, A. K., Verma, K., Das, S. & Bharti, S. K. Benthic foraminiferal diversity response to the climate induced changes in the eastern Arabian Sea oxygen minimum zone during the last 30 ka BP. Quat. Int. 374, 118–125 (2015).
Google Scholar 
40.Palmer, H. M. et al. Southern California margin benthic foraminiferal assemblages record recent centennial-scale changes in oxygen minimum zone. Biogeosciences 17(11), 2923–2937 (2020).ADS 

Google Scholar 
41.Tetard, M., Licari, L., Ovsepyan, E., Tachikawa, K. & Beaufort, L. Toward a global calibration for quantifying past oxygenation in oxygen minimum zones using benthic Foraminifera. Biogeosciences 18(9), 2827–2841 (2021).ADS 
CAS 

Google Scholar 
42.Moffitt, S. E., Hill, T. M., Ohkushi, K., Kennett, J. P. & Behl, R. J. Vertical oxygen minimum zone oscillations since 20 ka in Santa Barbara Basin: A benthic foraminiferal community perspective. Paleoceanography 29, 44–57 (2014).ADS 

Google Scholar 
43.Hoogakker, B. A., Elderfield, H., Schmiedl, G., McCave, I. N. & Rickaby, R. E. Glacial–interglacial changes in bottom-water oxygen content on the Portuguese margin. Nat. Geosci. 8, 40–43 (2015).ADS 
CAS 

Google Scholar 
44.Glock, N., Liebetrau, V. & Eisenhauer, A. I/Ca ratios in benthic foraminifera from the Peruvian oxygen minimum zone: analytical methodology and evaluation as a proxy for redox conditions. Biogeosciences 11(23), 7077–7095 (2014).ADS 

Google Scholar 
45.Jorissen, F.J., Fontanier, C., & Thomas, E. Paleoceanographical proxies based on deep-sea benthic foraminiferal assemblage characteristics. In: Hillaire-Marcel, C., & De Vernal, A. Proxies in late Cenozoic paleoceanography. Dev. Mar. Geol., 1, 263–325 (2007).46.Diaz, R. J. Overview of hypoxia around the world. J. Environ. Qual. 30(2), 275–281 (2001).CAS 

Google Scholar 
47.Tetard, M., Licari, L., Tachikawa, K., Ovsepyan, E. & Beaufort, L. Toward a global calibration for quantifying past oxygenation in oxygen minimum zones using benthic Foraminifera. Biogeosci. Discuss. 18(9), 2827–2841 (2021).48.Diaz, R. J. & Rosenberg, R. Marine benthic hypoxia: A review of its ecological effects and the behavioural responses of benthic macrofauna. Oceanogr. Mar. Biol. 33, 245–303 (1995).
Google Scholar 
49.Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).ADS 
CAS 

Google Scholar 
50.Sen Gupta, B. K., Eugene Turner, R. & Rabalais, N. N. Seasonal oxygen depletion in continental-shelf waters of Louisiana: Historical record of benthic foraminifers. Geology 24(3), 227–230 (1996).ADS 

Google Scholar 
51.Schlanger, S. O. & Jenkyns, H. C. Cretaceous oceanic anoxic events: Causes and consequences. Geol. Mijnbouw 55, 179–184 (1976).
Google Scholar 
52.Jenkyns, H. C. Geochemistry of oceanic anoxic events. Geochem. Geophys. Geosyst. 11, Q03004 (2010).ADS 

Google Scholar 
53.Clark, P. U. et al. Consequences of twenty-first century policy for multi-millennial climate and sea-level change. Nat. Clim. Change 6, 360–369 (2016).ADS 

Google Scholar 
54.Clark, P. U. et al. Sea-level commitment as a gauge for climate policy. Nat. Clim. Change 8, 653–655 (2018).ADS 

Google Scholar 
55.Li, C., Held, H., Hokamp, S. & Marotzke, J. Optimal temperature overshoot profile found by limiting global sea level rise as a lower-cost climate target. Sci. Adv. 6(2), eaaw9490 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Berner, R. A. & Raiswell, R. Burial of organic carbon and pyrite sulfur in sediments over Phanerozoic time: A new theory. Geochim. Cosmochim. Acta 47(5), 855–862 (1983).ADS 
CAS 

Google Scholar 
57.Gautier, D. L. Cretaceous shales from the western interior of North America: Sulfur/carbon ratios and sulfur-isotope composition. Geology 14(3), 225–228 (1986).ADS 
CAS 

Google Scholar 
58.Kajiwara, Y. & Kaiho, K. Oceanic anoxia at the Cretaceous/Tertiary boundary supported by the sulfur isotopic record. Palaeogeogr. Palaeoclimatol. Palaeoecol. 99, 151–162 (1992).
Google Scholar 
59.Anderson, R. F., LeHuray, A. P., Fleisher, M. Q. & Murray, J. W. Uranium deposition in ancouv inlet sediments, ancouver island. Geochim. Cosmochim. Acta 53(9), 2205–2213 (1989).ADS 
CAS 

Google Scholar 
60.Kaiho, K., Fujiwara, O. & Motoyama, I. Mid-Cretaceous faunal turnover of intermediate-water benthic foraminifera in the northwestern Pacific Ocean margin. Mar. Micropaleontol. 23, 13–49 (1993).ADS 

Google Scholar 
61.Kaiho, K., Morgans, H. E. & Okada, H. Faunal turnover of intermediate-water benthic foraminifera during the Paleogene in New Zealand. Mar. Micropaleontol. 23, 51–86 (1993).ADS 

Google Scholar 
62.Alegret, L., Molina, E. & Thomas, E. Benthic foraminiferal turnover across the Cretaceous/Paleogene boundary at Agost (southeastern Spain): Paleoenvironmental inferences. Mar. Micropaleontol. 48(3–4), 251–279 (2003).ADS 

Google Scholar 
63.Morigi, C. Benthic environmental changes in the Eastern Mediterranean Sea during sapropel S5 deposition. Palaeogeogr. Palaeoclimatol. Palaeoecol. 273(3–4), 258–271 (2009).
Google Scholar 
64.Cetean, C. G., Bălc, R., Kaminski, M. A. & Filipescu, S. Integrated biostratigraphy and palaeoenvironments of an upper Santonian—upper Campanian succession from the southern part of the Eastern Carpathians, Romania. Cretac. Res. 32(5), 575–590 (2011).
Google Scholar 
65.Drinia, H. & Anastasakis, G. Benthic foraminifer palaeoecology of the Late Quaternary continental outer shelf of a landlocked marine basin in central Aegean Sea, Greece. Quat. Int. 261, 43–52 (2012).
Google Scholar 
66.Baas, J. H., Schönfeld, J. & Zahn, R. Mid-depth oxygen drawdown during Heinrich events: Evidence from benthic foraminiferal community structure, trace-fossil tiering, and benthic δ13C at the Portuguese Margin. Mar. Geol. 152(1–3), 25–55 (1998).ADS 
CAS 

Google Scholar 
67.Kaiho, K. Global climatic forcing of deep-sea benthic foraminiferal test size during the past 120 my. Geology 26(6), 491–494 (1998).ADS 

Google Scholar 
68.Wang, N., Huang, B. & Dong, Y. The evolution of deepwater dissolved oxygen in the Northern South China Sea during the past 400 ka. In AGU Fall Meeting Abstracts 2016, PP43A-2297 (2016).69.Ukpong, A. J. & Macaulay, E. O. Evaluation of paleo-oxygen conditions of Priabonian-Rupelian sediments of the Agbada Formation, Niger delta based on Fisher’s Diversity Index and Benthic Foraminifera Oxygen Index. IJRD. 2(12), 65–80 (2017).
Google Scholar 
70.Harzhauser, M. et al. Miocene lithostratigraphy of the northern and central Vienna Basin (Austria). Aust. J. Earth Sci. 113, 169–199 (2020).ADS 

Google Scholar 
71.Kranner, M. et al. Miocene ecology of the central and northern Vienna Basin (Austria), based on foraminiferal ecology. Palaeogeogr. Palaeoclimatol. Palaeoecol. 581, 110640 (2021).
Google Scholar 
72.Loeblich, A. R. & Tappan, H. Foraminiferal Genera and Their Classification (Von Nostrand Reinhold Co., 1987).
Google Scholar 
73.Kaminski, M. A. The year 2010 classification of the agglutinated foraminifera. Micropaleontology 60, 89–108 (2014).
Google Scholar 
74.Pawlowski, J., Lejzerowicz, F. & Esling, P. Next-generation environmental diversity surveys of foraminifera: Preparing the future. Biol. Bull. 227(2), 93–106 (2014).CAS 

Google Scholar 
75.Boersma, A. Foraminifera. In Introduction to Marine Micropaleontology. 19–77 (Elsevier Science BV, 1998).76.Piller, W. E. & Haunold, T. G. The Northern Bay of Safaga (Red Sea, Egypt): An Actuopalaeontological Approach V. Foraminifera (Waldemar Kramer Verlag, 1998).
Google Scholar 
77.Amao, A. O. et al. Distribution of benthic foraminifera along the Iranian coast. Mar. Biodivers. 49, 399–945 (2019).
Google Scholar 
78.Charrieau, L. M. et al. The effects of multiple stressors on the distribution of coastal benthic foraminifera: A case study from the Skagerrak-Baltic Sea region. Mar. Micropaleontol. 139, 42–56 (2018).ADS 

Google Scholar 
79.Charrieau, L. M. et al. Rapid environmental responses to climate-induced hydrographic changes in the Baltic Sea entrance. Biogeosciences 16, 3835–3852 (2019).ADS 
CAS 

Google Scholar 
80.Groeneveld, J. et al. Assessing proxy signatures of temperature, salinity, and hypoxia in the Baltic Sea through foraminifera-based geochemistry and faunal assemblages. J. Micropalaeontol. 37, 403–429 (2018).ADS 

Google Scholar 
81.García-Gallardo, Á. et al. Benthic foraminifera-based reconstruction of the first Mediterranean-Atlantic exchange in the early Pliocene Gulf of Cadiz. Palaeogeogr. Palaeoclimatol. Palaeoecol. 472, 93–107 (2017).
Google Scholar 
82.Rupp, C. & Ćorić, S. Zur Eferding-Formation. Jahrb. Geol. Bundesanst. 155, 33–95 (2015).
Google Scholar 
83.Murray, J. W. Ecology and Applications of Benthic Foraminifera (Cambridge University Press, 2006).
Google Scholar 
84.Jorissen, F. J., de Stigter, H. C. & Widmark, J. G. A conceptual model explaining benthic foraminiferal microhabitats. Mar. Micropaleontol. 26, 3–15 (1995).ADS 

Google Scholar 
85.Garcia, H.E. et al. World Ocean Atlas 2013. Vol. 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation. (NOAA Atlas NESDIS 75, 2013).86.Murray, J. W. Ecology and Palaeoecology of Benthic Foraminifera. (Longman Scientific and Technical, 1991).87.Reymond, C. E., Lloyd, A., Kline, D. I., Dove, S. G. & Pandolfi, J. M. Decline in growth of foraminifer Marginopora rossi under eutrophication and ocean acidification scenarios. Glob. Change Biol. 19, 291–302 (2013).ADS 

Google Scholar 
88.Titelboim, D. et al. Selective responses of benthic foraminifera to thermal pollution. Mar. Pollut. Bull. 105, 324–333 (2016).CAS 

Google Scholar 
89.Renema, W. Terrestrial influence as a key driver of spatial variability in large benthic foraminiferal assemblage composition in the Central Indo-Pacific. Earth-Sci. Rev. 177, 514–544 (2018).ADS 

Google Scholar 
90.Koho, K. A. et al. Sedimentary labile organic carbon and pore water redox control on species distribution of benthic foraminifera: A case study from Lisbon-Setúbal Canyon (southern Portugal). Prog. Oceanogr. 79, 55–82 (2008).ADS 

Google Scholar  More

Various estimates for great crested newt pond occupancy rates have been published with most relating to site or regional scale assessments. A naïve occupancy rate of 0.13 has been identified for a data set from the northwest of England29, while estimates based on conventional occupancy16 modelling of 0.31 for southeast England and between 0.32 and 0.33 for mid Wales were presented by Sewell et al.13. The only other national data which the authors are aware of are within the Freshwater Habitat National PondNet Study, which estimates a naïve pond occupancy of between 13 and 18%30, and the Amphibian and Reptile Conservation Trust National Amphibian and Reptile Recording Scheme, which suggests a 12% occupancy rate for the UK31. Using data from nearly 5000 ponds sampled across England, here we provide a more extensive national-level analysis while accounting for imperfect detection in the eDNA sampling protocol. Assuming a threshold of just one positive qPCR replicate in a sample, the naïve occupancy estimate of 0.30 is similar to the localised regional estimates made by Sewell et al.13 using direct observation methods. The posterior mean estimates of 0.198 for occupancy are comparable to most other estimates for great crested newt pond occupancy in the UK, but lower than the naïve estimate. The lower modelled estimates of occupancy than the naïve estimate suggest that false positives should not be ignored and need to be accounted for statistically using methodologies such as the eDNAShinyApp package used here23,24,27.The goodness of fit analysis was based on the MCMC output for each site and observed covariate levels in the data set. Some lack of fit was observed, with a predicted peak in amplification at 10 qPCR replicates but an observed peak at 12 qPCR replicates. There are several potential causes for this. For example, variation between laboratories could not be accounted for as these metadata were not made available. The assumption that error rates are the same across all laboratories may therefore not apply and could contribute to poorer model fit. Secondly, we did not consider error rates as functions of covariates, and this may also have contributed to a poorer fit.Stage 1 error was found to be smaller than Stage 2 error for both false positive and false negative error. However, Stage 2 error operates on individual qPCR replicates and not at the site level. If there was no error at Stage 2, we would observe either zero qPCR replicates amplifying or all qPCR replicates amplifying (i.e. 12 in the case of the data presented here). The majority of samples showed zero qPCR amplification (3429 samples), and this was strongly linked to absence of newts. For sites with amplification, we observed a greater number of samples amplifying between 1 and 11 qPCR replicates (1074 samples) than we did amplifying with all 12 qPCR replicates (422 samples). The qPCR replicates that do not amplify in samples containing target DNA are erroneous, even if other replicates within that sample do amplify and contribute to this high Stage 2 false negative error in the model output. Data simulated from the fitted model show that the frequency of samples that contain DNA at Stage 2 amplifying in less than five of the 12 qPCR replicates is very low (Fig. 2b). Given that all replicates need to be erroneous to alter the naïve assignment of a sample containing DNA to negative, Stage 2 false negatives at this sampling level are unlikely. However, this does not rule out Stage 1 false negative error which we estimate to be 5.2% (with wide credible intervals between 0.1% and 25.1%).Higher levels of Stage 2 replication remove lab-based false negative error. If eDNA is present within a sample and a high number of replicates are used, it is highly unlikely that all qPCR replicates will be erroneously negative, even when the false negative rate at the replicate level is high. Conversely, high levels of Stage 2 replication increase the likelihood of false positive error occurring32. Stage 2 false positive results are of greater consequence than the 2% the model output would suggest. Unlike false negative error where all Stage 2 replicates need to be erroneous to change the naïve assignment of occupancy of a sample, when a threshold of one amplifying replicate is applied, only a single replicate needs to be an erroneous to generate a false positive. With 12 qPCR replicates at Stage 2 and a 2% false positive error per replicate, a sample with no DNA present has a 24% chance of producing at least one amplification. Assuming this error is randomly distributed through samples with no DNA present and qPCR replicates, it is more likely that samples with small numbers of replicates amplifying would be erroneous than where large numbers of replicates amplify. This was confirmed in the goodness of fit analysis with the distribution of Stage 2 false positive replicates making up all samples amplifying with one or two positive qPCR replicates, while negligible false positive amplification was seen with four amplifying replicates or above (Fig. 2a). With only a single sample at Stage 1, false positive error is limited to the 1.5% per sample, as per the ({theta }_{10}) value in the occupancy model output.We would recommend that, where possible, results from individual sites are interpreted as a probability of site occupancy, based on modelled outputs such as those produced by the eDNAShinyApp R package23,27. The precision of these models is dependent on sample size. Where sample size is large, a reduced bias and narrower credible interval range is observed24. However, using occupancy modelling, Buxton et al.24 demonstrated that studies that contain only a small number of sites are unlikely to produce accurate and precise estimates. As a result, such assessments will need to continue to rely on a threshold value of amplifying qPCR replicates to define site occupancy. A naïve amplification threshold for assigning occupancy of one positive qPCR replicate is unwise and should be increased to reduce Stage 2 false positive error. Indeed, a threshold of three positive qPCR replicates would reduce false positive error, without increasing false negative error. Alternatively, redistributing the replication between Stage 1 and Stage 224, would also reduce the credible interval width and generate a more precise posterior mean estimate at Stage 1, in turn reducing the uncertainty around the occupancy estimate. A redistribution of replication leading to two samples collected from each site, both analysed using up to six qPCR replicates, as opposed to one sample analysed using twelve qPCR replicates, has been suggested24.Equal weighting of the ten covariates used in the traditional great crested newt HSI assessment25 may be ecologically unrealistic29. This is supported by the observations here, with only some of the HSI covariates identified as important for occupancy. The model applied by the eDNAShinyApp package23,27 successfully identified several covariates known to influence great crested newt occupancy, that are included within the HSI assessment25. These included occurrence of fish, water quality, shade, pond density, macrophyte cover, frequency of drying and geographic area; although our analysis was based on Easting and Northing, rather than the broad-scale suitability map used in deriving the original HSI25. However, several traditionally used HSI variables emerged as unimportant, i.e., waterfowl, terrestrial habitat quality, and area of pond; while ground frost, rainfall, surface wind and land cover type, are not included within the HSI assessment but were important.The importance and influence of the HSI suitability indices of fish, shade, pond density, water quality, macrophyte cover, and frequency of drying on pond occupancy were all as expected with wide literature support25,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48. The negative pond occupancy response to climatic covariates of ground frost and high precipitation are supported in relation to annual survival47. ‘It is worth noting that the PIP value for wind speed was only just over the threshold for inclusion as important. Although ponds are shallow with limited stratification possible, wind speed has been shown to influence the distribution of eDNA in deeper waterbodies49,50. Estimating the presence of fish in a pond by direct observation for the traditional HSI may be problematical, and metabarcoding approaches to eDNA surveys which offer information on presence of other species would improve the accuracy of covariates, such as fish presence40. Indeed, assigning “Possible” fish presence within the HSI when scoring a pond accounted for the same percentage (33.1%) of both positive and negative eDNA samples. This suggests that when surveyors are not confident of fish presence, they are using this category in equal proportions for both occupied and unoccupied ponds. Landcover and bedrock were also important for pond level occupancy. This is expected given the importance of terrestrial habitat, and water retention to the species (Figs. S1, S3)35,51. However, with very unbalanced sample sizes between the categories (Figs. S2, S4), and influence of nearby land cover types uncaptured by the data, this variable is difficult to interpret, and we suggest further examination. Nevertheless, the positive associations with woodland and grassland reflect established knowledge of habitat preferences36. Equally, as freshwater predominantly relates to rivers and lakes rather than ponds in the landcover dataset used, negative relationships reflect the lower suitability of these habitats36.Several covariates, however, did not exhibit the expected response for pond occupancy. Terrestrial habitat was not found to be important despite the species being only semi-aquatic, and previous studies emphasising the importance of this variable36. This may be a result of the original Oldham et al.25 terrestrial habitat assessment being simplified into four subjective categories in the ARG UK26 protocol: this may not be nuanced enough to differentiate terrestrial habitat usage using statistical modelling. Waterfowl were not identified by the model as important predictors of great crested newt pond occupancy, where they have been elsewhere29,41, with one study suggesting a positive relationship between waterfowl species richness and great crested newt occupancy40. The lack of importance demonstrated in this data set may indicate that other covariates outweigh waterfowl in terms of occupancy importance, or they may only become important predictors of occupancy at very high waterfowl densities rarely observed in this data set. Similarly, pond area was not found to be an important predictor of pond occupancy. There was no difference in the mean area for occupied or unoccupied ponds; however, no occupied ponds were found above 10,000 m2. We would anticipate that both very small and very large ponds to be unsuitable for great crested newts25,52.Northing but not Easting was found to be an important predictor of pond occupancy. A distribution gradient with latitude is a common feature of biodiversity generally, and in the UK great crested newts are much more patchily distributed in Scotland than in England53,54. Pond occupancy estimates varied by year, with a greater occupancy in 2018 than the other years considered. This is likely linked to climatic conditions and may relate to the timings of ponds drying in relation to eDNA sample collection. This may therefore be an artefact of unoccupied ponds being more likely to dry early in the season and therefore being excluded from occupancy estimates for dry years, or local migration to less suitable habitat if core ponds start to dry, however long term analysis of individuals within a metapopulation shows little support for this47. As a result, in very dry years, we would expect an increase in pond occupancy to be observed in the data. Although average early spring rainfall for England in 2018 was higher than in either 2017 or 2019, rainfall during the main eDNA survey window of May and June was considerably less in 2018 than in the other two years (Fig. S5). Similar variation in year on year occupancy rate has been observed elsewhere30.As with all sampling methods, imperfect detection is a general feature of eDNA surveys. When high levels of qPCR replicates are used, false negative error is predominantly due to failure to collect DNA in a sample rather than failure to detect DNA within the lab. False positive error can occur at both stages and is exaggerated at Stage 2 by high levels of replication; Stage 2 false positive error is most likely in samples with a low proportion of replicates amplifying. We recommend using statistical models to estimate the occupancy of individual sites, taking into consideration sampling error. Failing that, a naïve occupancy threshold of two or three amplifying qPCR replicates, adjusting for total levels of replication, should be applied before assigning a site as occupied or not.With specific reference to great crested newts, we estimate approximately 20% of ponds through their natural range within England are occupied. We estimate that eDNA sampling failed to collect DNA from approximately 5% of sites where it was present. However, if eDNA is collected it is highly unlikely to be missed during the laboratory phase using the present protocol. We estimate that eDNA is erroneously collected in approximately 1.5% of water samples causing Stage 1 false positive results. However, false positives at the laboratory phase were found to be 2% per qPCR replicate; it is likely that this error would account for the majority of samples amplifying with one or two qPCR replicates, as a result these need to be treated with caution. To maximise accuracy, we recommend redistributing replication between the two stages, as is recommended elsewhere, and that thresholds to define a replicate as positive are further examined24,55. It is important to recognise that visual surveys also experience imperfect detection13, with observation errors likely to be similar to or greater than the error experienced using eDNA methods, particularly if the recommendations presented here are put in place to minimise laboratory stage false positive error. The benefits associated with eDNA over traditional methods allowing rapid collection of large scale distribution data are invaluable and should not be devalued in relation to traditional methods15. Although not identified within the models as important predictors, waterfowl, terrestrial habitat, and pond area may remain important habitat features for great crested newts. These covariates may be less important than the other HSI covariates, may not be measured in a sufficiently nuanced way to enable their importance to be identified, or may have influence on a local but not national scale29,40. However, equal weighing of the ten HSI variables is an oversimplification with the effect of some variables, for example pond area, overinflated within the HSI analysis, whereas others are undervalued, for example fish intensity. It is important to measure HSI covariates accurately and consistently to allow them to be utilised in statistical analysis such as this, and a review of the covariates and weighting is warranted now large occupancy data sets are becoming available. More

Acute cold shock causes drastic phenotypic alterationsThe duration of cold exposure for young adult hermaphrodite C. elegans at 2 °C is negatively correlated to post-shock survival rates15. Wild-type hermaphrodite worms exposed to a 4-h cold shock (CS) do not initially display high mortality rates (Fig. 1a); this allows observation of a range of phenotypic transitions as they recover from the limited-duration cold stress at their preferred temperature of 20℃. One of the most striking phenotypes exhibited in post-cold shock (post-CS) animals during the recovery period is a dramatic decrease in pigmentation in the normally highly pigmented intestine, so that the body becomes almost entirely clear (Fig. 1b, c)15. This is often accompanied by motor and reproductive disruptions such as mobility loss, withering of the gonad arms, decreased number of internal embryos, and the eventual death of about 30% of the population (Fig. 1a–d)15. It should be noted that these phenotypic responses do not appear to be due to any relative heat shock following the transition from 2 to 20 °C as the expression of GFP-tagged HSP-4 (heat shock protein) is not induced following cold shock (Fig. 1e). Neither is the reduced pigmentation following cold shock due to a period of starvation presumably experienced by the worms while they are at 2 °C. At this extreme cold temperature, the worms enter a “chill coma” in which pharyngeal pumping and virtually all other movement ceases15,16; however, a total absence of food for a similar time period does not induce a comparable clearing phenotype (Supplemental Fig. S1). Interestingly, some CS wild-type animals regain pigmentation after clearing; these worms do not die and display a general reversal of the other negative impacts of cold shock (Fig. 1b)15. We sought to better understand the factors regulating the post-CS recovery program in wild-type worms, focusing particularly on the functional role of pigmentation loss and the genetic components involved in producing it.Figure 1Cold-shocked worms show decrease in survival and characteristic phenotypic alterations. N2 young adult hermaphrodites were shifted from 20 to 2 °C for a 4 h cold shock (CS) and thereafter recovered at 20 °C for 96 h with assessment of (a) survival and (b) phenotypic alterations (n = 177). Death and immobility were assayed by nose tap; worms were considered to be immobile if the tap elicited slight movement in the head region but no other body movement, and dead worms were completely unresponsive (Chi-squared Test for Homogeneity: P  More

Study site and experimental designThe study site, experimental design, and annual sampling protocol have been described in previous publications15,22,47 but a summary will be provided here. The experiment was conducted in a remote study site approximately 20 km northeast of Kangerlussuaq, Greenland, at 67.11° N latitude and 50.34° W longitude, approximately 160 km inland from Baffin Bay. Annual growing season (May through July) mean temperature and total precipitation at the study site during the duration of this experiment (2002–2017) were 8.62 ± 0.20 °C and 43 ± 6.78 mm, respectively47. The surrounding area has functioned as an important caribou (Rangifer tarandus) migration corridor, calving ground, and Indigenous Peoples hunting site for at least approximately 4000 years48, and was designated as a UNESCO World Heritage Site, Aasivissuit—Nipisat, by the United Nations in 2018. Caribou are present in greatest numbers seasonally, with most of the animals that use the site migrating into it during late winter and early spring and migrating out of it in mid to late summer; some male caribou remain at the site through winter. Muskoxen (Ovibos moschatus) are present at the site year-round. Arctic hares (Lepus arcticus) and rock ptarmigan (Lagopus muta) occupy the site in low numbers. In contrast to other locations in the Arctic where they are important herbivores, this site does not harbor voles or lemmings.In June 2002 we erected six exclosures constructed of woven wire fencing material supported by steel t-posts; each exclosure was circular and measured 800 m2. Adjacent to each exclosure, and separated from it by approximately 20–50 m, we located a comparable control site. Exclosure sites and adjacent control sites covered a range of elevations from approximately 275–300 m above sea level. In early May 2003, prior to onset of the plant growing season, we installed passive, open-topped warming chambers constructed of UV neutral glazing material on three plots inside and three plots outside of one exclosure site and three plots inside and four plots outside of a second exclosure site. In early May 2004, we added three warming chambers inside and three warming chambers outside one of the sites equipped in 2003, and we installed an additional three warming chambers on plots inside and three warming chambers on plots outside of a third exclosure site, thus resulting in a total of 12 warmed plots distributed among three exclosure sites and 13 warmed plots distributed among three control (grazed) sites. An ambient (control) plot was located near, but not closer than 2 m to, each warmed plot, thus resulting in 25 warmed plots and 25 ambient plots distributed among three exclosures and adjacent grazed sites. No plot was located closer than 2 m to the edge of any exclosure. Warming chambers were constructed according to the International Tundra Experiment (ITEX) protocol49, were 1.5 m in basal diameter, and encompassed 1.77 m2. Warming chambers were installed in early May each year, anchored to plots using metal garden stakes, and removed annually at the time of vegetation sampling, which was intended to coincide with peak aboveground abundance at mid to late July in most years (except in 2006, when sampling was conducted in mid-June, and in 2003 and 2011 when sampling was conducted in mid-August)47. Warming chambers significantly elevated near surface temperature by approximately 1.5–3.0 °C, and resulted in a non-significant reduction of soil moisture22,50.Vegetation samplingVegetation sampling was conducted non-destructively using a square Plexiglas tabletop point frame on adjustable aluminum legs. The point frame measured 0.25 m2 and was centered within each plot for sampling. The corners of each plot were equipped with hollow aluminum tubes sunk into the soil surface at the cardinal directions, and the legs of the point frame were inserted into these tubes to ensure consistent orientation and location of the frame during sampling. Once the frame was positioned, a steel welding pin was lowered through each of 20 randomly located holes in the point frame tabletop, and each encounter by the tip of the pin with vegetation was recorded until the pin struck soil, litter, or rock. In 2003 and 2004, vegetation was recorded at the species level for deciduous shrubs (Betula nana and Salix glauca) and at the functional group level for graminoids (including grasses, rushes, and sedges of the genera Calamagrostis sp., Poa sp., Festuca sp., Hierochloë sp., Trisetum spicatum, Luzula sp., Carex sp., and Kobresia sp.), forbs, mosses, lichens, and fungi. Beginning in 2005, vegetation was recorded at the species level for forbs, in addition to deciduous shrubs, and at the genus level for lichens (Peltigera sp.), fungi [Calvatia sp.; most likely C. cretacea51], and mosses (Aulacomnium sp.). Graminoids were not resolved to the genus or species levels due to concerns about consistent identification. All taxa were identified in the field by the authors on the basis of visual inspection of live individuals in consultation with reference guides52,53,54,55. In adherence with the Guidelines for Professional Ethics established by the Botanical Society of America, sampling and identification were done non-destructively, and no voucher specimens were collected.Commonness estimationEcologically meaningful estimation of commonness is inherently relative; a taxon is only common or rare in relation to other taxa5. While there exist a considerable array of quantitative indices of commonness56, we opted for one that integrates abundance and occurrence by assigning equal weight to each. Using annual abundance sums obtained during point frame sampling, we calculated commonness for each taxon as the product of its proportional abundance across all plots within each treatment and its proportional occurrence across all plots within each treatment. Hence, the commonness (C) of an individual taxon, i, in a given year, t, can be expressed as the product of its proportional abundance (A) and proportional occurrence (O) in that year:$$C_{it} = A_{it} *O_{it}$$
(1)
in which proportional abundance of taxon i in year t is the sum of point frame pin intercepts, h, for that taxon in that year across all plots sampled that year divided by the total number of point frame pin intercepts, H, of live vegetation biomass recorded across all plots sampled that year:$$A_{it} = h_{it} /H_{t}$$
(2)
and in which proportional occurrence of taxon i in year t is the sum of the number of plots, p, on which point frame pin intercepts of taxon i were recorded in year t divided by the total number of plots, P, sampled in year t:$$O_{it} = p_{it} /P_{t}$$
(3)
This index was used to estimate taxon-specific commonness within each experimental treatment combination (i.e., exclosed ambient, exclosed warmed, grazed ambient, and grazed warmed treatments), as well as across the entire site (sitewide commonness) for derivation of baseline commonness. To derive baseline commonness for subsequent analysis of its contribution to taxon-specific trends in commonness over the course of the experiment, we used sitewide commonness of each taxon in the year 2006. As described above, greater taxonomic resolution beyond functional group was not widely applied in our sampling until the third year of the experiment, 2005. However, we decided against using 2005 as a baseline for commonness at the site because it also happened to be the final year of a two-year outbreak of caterpillar larvae of a noctuid moth, Eurois occulta, that reduced aboveground abundance of nearly all taxa on our plots22,57. Except for the fungus C. cretacea, all taxa, whether recorded by pin intercepts during point-frame sampling or not, were observed on at least one plot under each of the four experimental treatment combinations. The rarest forb in this study, Pyrola grandiflora, was observed on a single plot under each of the exclosed ambient, exclosed warmed, and grazed warmed treatments, and on two plots under the grazed warmed treatment, but was not recorded during point frame sampling of exclosed ambient or grazed ambient plots. Hence, any conclusions about the effects of warming on this species must be limited. Similarly, the lichen Peltigera sp., which was also very rare in this study, was recorded during point frame sampling on plots under each treatment combination, but was not detected by sampling on exclosed warmed plots after 2005 even though it was observed on one exclosed warmed plot after that. This might be considered corroboration of the negative effect on this genus of warming under herbivore exclusion reported in the Results, but caution may also be warranted. The fungus C. cretacea first appeared under the grazed ambient treatment in 2008 and then under the exclosed ambient treatment in 2012, but was not recorded under the grazed warmed or exclosed warmed treatments. This might in and of itself suggest a negative effect of warming on the establishment or occurrence of this species, or fungi in general, and might be consistent with limiting effects of reduced moisture availability under warming. However, we urge caution with this interpretation because fungi may not form fruiting bodies every growing season, and such fruiting bodies may emerge aboveground in different locations from one growing season to the next, thereby potentially confounding repeated detection by sampling methods such as ours.Analysis of experimental treatment effects on plant functional group abundanceWe used a Gaussian generalized linear model (GLM) with an identity link function to analyze variation in functional group abundance among experimental treatment combinations. This GLM included total annual abundance, for the period 2003–2017, of deciduous shrubs (comprising summed abundances of Betula nana and Salix glauca leaf and stem point frame pin intercepts), graminoids (comprising all grass, rush, and sedge tissue point frame pin intercepts), forbs, mosses, lichens, or fungi, in separate models with the two experimental treatments (warming and herbivore exclusion) and their interaction as factors, year as a factor, and day of year of sampling as a continuous covariate. Significance of individual treatment effects of warming and herbivore exclusion, as well as their interaction, was determined based on Wald Chi-square statistics and associated two-tailed P-values (with significance indicated at P ≤ 0.05).Analysis of experimental treatment effects on commonnessAnalyses of commonness data were performed at higher taxonomic resolution than were analyses of abundance data, and so were limited to analysis of data from the last 12 years of the experiment, 2006–2017. Using Eq. (1), commonness was estimated for 14 taxa, including two species of deciduous shrubs, Betula nana and Salix glauca; graminoids, comprising at least eight non-distinguished genera of grasses, rushes, and sedges listed above in the sub-section Vegetation sampling; eight species of forbs, including Equisetum arvense, Stellaria longipes, Cerastium alpinum, Bistorta vivipara, Draba nivalis, Campanula gieseckiana, Viola canina, and Pyrola grandiflora; one genus of moss, Aulacomnium sp.; one genus of fungus, Calvatia sp.; and one genus of lichen, Peltigera sp.We first investigated general characteristics of and treatment effects on commonness across the study site. We examined the skewness of commonness to determine whether the distribution of the 14 focal taxa was significantly right-skewed, indicating greater numbers of rare than of common taxa2. We obtained an estimate of skewness and its standard error across pooled data for the period 2003–2017, derived a 95% confidence interval, and compared it to zero. Next, we examined experimental treatment effects on sitewide commonness. To do this, we used a Gaussian GLM with identity link function to analyze pooled commonness of all taxa for the period 2006–2017, with commonness as the dependent variable and the two experimental treatments and their interaction as factors, year as a factor, taxon as a factor, and day of year of sampling as a covariate. We determined significance of individual treatment effects and their interaction by examining Wald Chi-square statistics, with significance indicated if the two-tailed P ≤ 0.05. We then tested for experimental treatment effects on individual taxa using the same analytical approach, but with taxon-specific commonness as the dependent variable, and treatment and year as factors, with day of year of sampling as a covariate.Analysis of trends in commonness and skewness of commonness over the last 12 years of the experimentWe next investigated whether common and rare taxa displayed different trends in commonness over the course of the last 12 years of the experiment. This was motivated by a presupposition that warming and/or herbivore exclusion might have differentially altered commonness of common vs. rare species. We first examined linear trends in sitewide commonness of all 14 taxa pooled across experimental treatments by testing for significance of linear regressions of taxon-specific commonness vs. year for the period 2006–2017. We then conducted the same analysis for each taxon individually under each experimental treatment combination to determine whether our experimental manipulations contributed to trends differentially in common vs. rare taxa. We then investigated whether the distribution of commonness across the 14 focal taxa displayed directional change over the course of the final 12 years of the experiment, and whether it might have done so differently in relation to experimental treatment combinations. To do this, we tested for significance of linear regressions of treatment-specific skewness of commonness vs. year for the period 2006–2017. Finally, we examined whether trends in commonness were related to baseline commonness for the 13 taxa resolved to the genus or species level, excluding graminoids because this group comprised multiple unresolved genera. This analysis was motivated by interest in determining whether taxa that were common at the beginning of the experiment tended to become more common and taxa that were rare at the beginning of the experiment tended to become rarer, thus indicating that degree of commonness itself might be an important driver of changes in commonness over the course of a multi-annual experiment such as ours. To do this, we fit a non-linear regression model using a von Bertalanffy equation to quantify the relationship between taxon-specific commonness trend (standardized coefficient from the regression of commonness vs. year, ranging between − 1 and 1) and baseline commonness by treatment. This equation took the form:$$Y = 1 – left( {1 – a} right)e^{ – bX}$$
(4)
In which Y = taxon- and treatment-specific commonness trend, estimated in this case using the standardized coefficient from a linear regression of commonness of taxon i under a given experimental treatment combination vs. year; a = the Y-intercept; b = the slope; and X = baseline commonness of taxon i under the same treatment combination in 2006. Significance of regressions for each treatment was determined by calculating an F-statistic using corrected model sums of squares, error sums of squares, model degrees of freedom, and error degrees of freedom. Non-linear regression models were considered significant if the F-associated P ≤ 0.05. More

1.Hoffmann, A. A. & Willi, Y. Detecting genetic responses to environmental change. Nat. Rev. Genet. 9, 421–432 (2008).CAS 
PubMed 

Google Scholar 
2.Endler, J. A. Geographic Variation, Speciation and Clines (Princeton, 1977).
Google Scholar 
3.Huey, R. B. Rapid evolution of a geographic cline in size in an introduced fly. Science. 287, 308–309 (2000).ADS 
CAS 
PubMed 

Google Scholar 
4.Bridle, J. R. & Vines, T. H. Limits to evolution at range margins: when and why does adaptation fail?. Trends Ecol. Evol. 22, 140–147 (2007).PubMed 

Google Scholar 
5.Holt, R. D. & Gomulkiewik, R. How does immigration influence local adaptation? A reexamination of a familiar paradim. Am. Nat. 149, 563–572 (1997).
Google Scholar 
6.Ronce, O. & Kirkpatrick, M. When sources become sinks: Migrational meltdown in heterogeneous habitats. Evolution 55, 1520–1531 (2001).CAS 
PubMed 

Google Scholar 
7.Bridle, J. R., Gavaz, S. & Kennington, W. J. Testing limits to adaptation along altitudinal gradients in rainforest Drosophila. Proc. R. Soc. B Biol. Sci. 276, 1507–1515 (2009).
Google Scholar 
8.Bridle, J. R., Polechová, J., Kawata, M. & Butlin, R. K. Why is adaptation prevented at ecological margins? New insights from individual-based simulations. Ecol. Lett. 13, 485–494 (2010).PubMed 

Google Scholar 
9.Holt, R. D. & Keitt, T. H. Alternative causes for range limits: A metapopulation perspective. Ecol. Lett. 3, 41–47 (2000).
Google Scholar 
10.Takahashi, Y. et al. Lack of genetic variation prevents adaptation at the geographic range margin in a damselfly. Mol. Ecol. 25, 4450–4460 (2016).PubMed 

Google Scholar 
11.Arnaud-Haond, S. et al. Genetic structure at range edge: Low diversity and high inbreeding in Southeast Asian mangrove (Avicennia marina) populations. Mol. Ecol. 15, 3515–3525 (2006).CAS 
PubMed 

Google Scholar 
12.Pujol, B. & Pannell, J. R. Reduced responses to selection after species range expansion. Science 321, 96 (2008).ADS 
CAS 
PubMed 

Google Scholar 
13.Cahill, A. E. & Levinton, J. S. Genetic differentiation and reduced genetic diversity at the northern range edge of two species with different dispersal modes. Mol. Ecol. 25, 515–526 (2016).PubMed 

Google Scholar 
14.Bachmann, J. C., van Rensburg, A. J., Cortazar-Chinarro, M., Laurila, A. & Van Buskirk, J. Gene flow limits adaptation along steep environmental gradients. Am. Nat. 195, E67–E86 (2020).PubMed 

Google Scholar 
15.Polechová, J. & Barton, N. H. Limits to adaptation along environmental gradients. Proc. Natl Acad. Sci. U. S. A. 112, 6401–6406 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
16.Sexton, J. P., Strauss, S. Y. & Rice, K. J. Gene flow increases fitness at the warm edge of a species’ range. Proc. Natl. Acad. Sci. U. S. A. 108, 11704–11709 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Angert, A. L., Bontrager, M. G. & Aringgren, J. What do we really know about adaptation at range edges?. Annu. Rev. Ecol. Evol. Syst. 51, 341–361 (2020).
Google Scholar 
18.Ciborowski, J. J. H. Downstream and lateral transport of nymphs of two mayfly species (Ephemeroptera). Can. J. Fish. Aquat. Sci. 40, 2025–2029 (1983).
Google Scholar 
19.Bilton, D. T., Freeland, J. R. & Okamura, B. Dispersal in freshwater invertebrates. Annu. Rev. Ecol. Syst. 32, 159–181 (2001).
Google Scholar 
20.Markwith, S. H. & Scanlon, M. J. Multiscale analysis of Hymenocallis coronaria (Amaryllidaceae) genetic diversity, genetic structure, and gene movement under the influence of unidirectional stream flow. Am. J. Bot. 94, 151–160 (2007).PubMed 

Google Scholar 
21.Congdon, B. C. Unidirectional gene flow and maintenance of genetic diversity in mosquitofish Gambusia holbrooki (Teleostei: Poeciliidae). Copeia 1995, 162 (1995).
Google Scholar 
22.Schaefer, J. Riffles as barriers to interpool movement by three cyprinids (Notropis boops, Campostoma anomalum and Cyprinella venusta). Freshw. Biol. 46, 379–388 (2001).
Google Scholar 
23.Moore, J. S., Gow, J. L., Taylor, E. B. & Hendry, A. P. Quantifying the constraining influence of gene flow on adaptive divergence in the lake-stream threespine stickleback system. Evolution 61, 2015–2026 (2007).PubMed 

Google Scholar 
24.Urabe, M. Diel change of activity and movement on natural river beds in Semisuleospira reiniana. VENUS 57, 17–27 (1998).
Google Scholar 
25.Hastie, L. C., Boon, P. J., Young, M. R. & Way, S. The effects of a major flood on an endangered freshwater mussel population. Biol. Conserv. 98, 107–115 (2001).
Google Scholar 
26.Alp, M., Keller, I., Westram, A. M. & Robinson, C. T. How river structure and biological traits influence gene flow: A population genetic study of two stream invertebrates with differing dispersal abilities. Freshw. Biol. 57, 969–981 (2012).
Google Scholar 
27.Terui, A. et al. Asymmetric dispersal structures a riverine metapopulation of the freshwater pearl mussel Margaritifera laevis. Ecol. Evol. 4, 3004–3014 (2014).PubMed 
PubMed Central 

Google Scholar 
28.Holomuzki, J. R. & Biggs, B. J. F. Distributional responses to flow disturbance by a stream-dwelling snail. Oikos 87, 36 (1999).
Google Scholar 
29.Urabe, M. Phenotypic modulation by the substratum of shell sculpture in Semisulcospira reiniana (Prosobranchia: Pleuroceridae). J. Molluscan Stud. 66, 53–60 (2000).
Google Scholar 
30.Gu, Q. H., Husemann, M., Ding, B., Luo, Z. & Xiong, B. X. Population genetic structure of Bellamya aeruginosa (Mollusca: Gastropoda: Viviparidae) in China: Weak divergence across large geographic distances. Ecol. Evol. 5, 4906–4919 (2015).PubMed 
PubMed Central 

Google Scholar 
31.Davis, C. D., Epps, C. W., Flitcroft, R. L. & Banks, M. A. Refining and defining riverscape genetics: How rivers influence population genetic structure. Wiley Interdiscip. Rev. Water 5(2), e1269 (2018).
Google Scholar 
32.De Wit, P. & Palumbi, S. R. Transcriptome-wide polymorphisms of red abalone (Haliotis rufescens) reveal patterns of gene flow and local adaptation. Mol. Ecol. 22, 2884–2897 (2013).PubMed 

Google Scholar 
33.Sun, Y.-B. et al. Species groups distributed across elevational gradients reveal convergent and continuous genetic adaptation to high elevations. Proc. Natl. Acad. Sci. 115, 201813593 (2018).
Google Scholar 
34.Willoughby, J. R., Harder, A. M., Tennessen, J. A., Scribner, K. T. & Christie, M. R. Rapid genetic adaptation to a novel environment despite a genome-wide reduction in genetic diversity. Mol. Ecol. 27, 4041–4051 (2018).CAS 
PubMed 

Google Scholar 
35.De Wit, P. et al. The simple fool’s guide to population genomics via RNA-Seq: An introduction to high-throughput sequencing data analysis. Mol. Ecol. Resour. 12, 1058–1067 (2012).PubMed 

Google Scholar 
36.Yokomizo, T. & Takahashi, Y. Changes in transcriptomic response to salinity stress induce the brackish water adaptation in a freshwater snail. Sci. Rep. 10, 1–9 (2020).
Google Scholar 
37.Kottler, E. J., Dickman, E. E., Sexton, J. P., Emery, N. C., & Franks, S. J. Draining the swamping hypothesis: Little evidence that gene flow reduces fitness at range edges. Trends Ecol. Evol. 1–12 (2021).38.Moore, J. S. & Hendry, A. P. Can gene flow have negative demographic consequences? Mixed evidence from stream threespine stickleback. Philos. Trans. R. Soc. B Biol. Sci. 364, 1533–1542 (2009).
Google Scholar 
39.Ingvarsson, P. K. Restoration of genetic variation lost – The genetic rescue hypothesis. Trends Ecol. Evol. 16, 62–63 (2001).PubMed 

Google Scholar 
40.Shimada, K. & Urabe, M. Drift and upstream movement of Semisulcospira libertina (Caenogastropoda: Pleuroceridae) in a natural stream. Vinus 63, 49–59 (2004).
Google Scholar 
41.Nyitray, L., Goodwin, E. B. & Szent-Gyorgyi, A. G. Complete primary structure of a scallop striated muscle myosin heavy chain: Sequence comparison with other heavy chains reveals regions that might be critical for regulation. J. Biol. Chem. 266, 18469–18476 (1991).CAS 
PubMed 

Google Scholar 
42.Ponder, W. F., Lindberg, D. R. & Ponder, J. M. Shell, Body, and Muscles (CRC Press, Taylor and Francis Group, Boca Raton, 2019).
Google Scholar 
43.Lesoway, M. P., Abouheif, E. & Collin, R. Comparative transcriptomics of alternative developmental phenotypes in a marine gastropod. J. Exp. Zool. Part B Mol. Dev. Evol. 326, 151–167 (2016).CAS 

Google Scholar 
44.Sexton, J. P., McIntyre, P. J., Angert, A. L. & Rice, K. J. Evolution and ecology of species range limits. Ann. Rev. Ecol. Evol. Syst. 40, 415–436 (2009).
Google Scholar 
45.Berger, V. J. & Kharazova, A. D. Mechanisms of salinity adaptations in marine molluscs. Hydrobiologia 355, 115–126 (1997).CAS 

Google Scholar 
46.Rivera-Ingraham, G. A. & Lignot, J. H. Osmoregulation, bioenergetics and oxidative stress in coastal marine invertebrates: Raising the questions for future research. J. Exp. Biol. 220, 1749–1760 (2017).PubMed 

Google Scholar 
47.Jo, P. G., Choi, Y. K., An, K. W. & Choi, C. Y. Osmoregulation and mRNA expression of a heat shock protein 68 and glucose-regulated protein 78 in the Pacific oyster Crassostrea gigas in response to salinity changes. J. Aquac. 20, 205–211 (2007).CAS 

Google Scholar 
48.Eierman, L. E. & Hare, M. P. Transcriptomic analysis of candidate osmoregulatory genes in the eastern oyster Crassostrea virginica. BMC Genomics 15, 1–15 (2014).
Google Scholar 
49.X. Zhao, H. Yu, L. Kong, Q. Li, Transcriptomic responses to salinity stress in the pacific oyster Crassostrea gigas. PLoS ONE 7 (2012).50.Zhang, Y. et al. Proteomic basis of stress responses in the gills of the pacific oyster Crassostrea gigas. J. Proteome Res. 14, 304–317 (2015).CAS 
PubMed 

Google Scholar 
51.Veiga, M. P. T., Gutierre, S. M. M., Castellano, G. C. & Freire, C. A. Tolerance of high and low salinity in the intertidal gastropod Stramonita brasiliensis (Muricidae): Behaviour and maintenance of tissue water content. J. Molluscan Stud. 82, 154–160 (2016).
Google Scholar 
52.Muraeva, O. A., Maltseva, A. L., Mikhailova, N. A. & Granovitch, A. I. Mechanisms of adaption to salinity stress in marine gastropods Littorina saxatilis: a proteomic analysis. Cell Tissue Biol. 10, 160–169 (2016).
Google Scholar 
53.Muraeva, O., Maltseva, A., Varfolomeeva, M., Mikhailova, N. & Granovitch, A. Mild osmotic stress in intertidal gastropods Littorina saxatilis and Littorina obtusata (Mollusca: Caenogastropoda): A proteomic analysis. Biol. Commun. 62, 202–213 (2017).
Google Scholar 
54.Maynard, A., Bible, J. M., Pespeni, M. H., Sanford, E. & Evans, T. G. Transcriptomic responses to extreme low salinity among locally adapted populations of Olympia oyster (Ostrea lurida). Mol. Ecol. 27, 4225–4240 (2018).CAS 
PubMed 

Google Scholar 
55.Ma, E., Gu, X. Q., Wu, X., Xu, T. & Haddad, G. G. Mutation in pre-mRNA adenosine deaminase markedly attenuates neuronal tolerance to O2 deprivation in Drosophila melanogaster. J. Clin. Invest. 107, 685–693 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Jepson, J. E. C. et al. Engineered alterations in RNA editing modulate complex behavior in Drosophila: Regulatory diversity of adenosine deaminase acting on RNA (ADAR) targets. J. Biol. Chem. 286, 8325–8337 (2011).CAS 
PubMed 

Google Scholar 
57.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14(4), 1–13 (2013).
Google Scholar 
61.Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Dobin, A. et al. STAR: ULTRAFAST universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 

Google Scholar 
63.McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Rsearch 20, 1297–1303 (2010).CAS 

Google Scholar 
64.Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).CAS 

Google Scholar 
66.Chang, C. C. et al. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4, 1–16 (2015).
Google Scholar 
67.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 
PubMed Central 

Google Scholar 
68.Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).PubMed 
PubMed Central 

Google Scholar 
69.Mussmann, S. M., Douglas, M. R., Chafin, T. K. & Douglas, M. E. BA3-SNPs: contemporary migration reconfigured in BayesAss for next-generation sequence data. Methods Ecol. Evol. 10, 1808–1813 (2019).
Google Scholar 
70.Frichot, E. & François, O. LEA: an R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).
Google Scholar 
71.Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G. & François, O. Fast and efficient estimation of individual ancestry coefficients. Genetics 196, 973–983 (2014).PubMed 
PubMed Central 

Google Scholar 
72.Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
PubMed Central 

Google Scholar 
73.Mi, H. et al. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res. 49, D394–D403 (2021).CAS 
PubMed 

Google Scholar 
74.Parrish, N., Hormozdiari, F., & Eskin, E. Assembly of non-unique insertion content using next-generation sequencing. BMC Bioinformatics. 12, S3 (2011).75.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).PubMed 
PubMed Central 

Google Scholar 
77.Sun, J., Nishiyama, T., Shimizu, K. & Kadota, K. TCC: An R package for comparing tag count data with robust normalization strategies. BMC Bioinformatics 14(1), 1–14 (2013).CAS 

Google Scholar  More

1.Canadell, J. G. et al. Factoring out natural and indirect human effects on terrestrial carbon sources and sinks. Environ. Sci. Policy 10, 370–384. https://doi.org/10.1016/j.envsci.2007.01.009 (2007).Article 

Google Scholar 
2.Bradford, M. A., Fierer, N. & Reynolds, J. F. Soil carbon stocks in experimental mesocosms are dependent on the rate of labile carbon, nitrogen and phosphorus inputs to soils. Funct. Ecol. 22, 964–974. https://doi.org/10.1111/j.1365-2435.2008.01404.x (2008).Article 

Google Scholar 
3.He, S., Liang, Z., Han, R., Wang, Y. & Liu, G. Soil carbon dynamics during grass restoration on abandoned sloping cropland in the hilly area of the Loess Plateau China. Catena 137, 679–685. https://doi.org/10.1016/j.catena.2015.01.027 (2016).CAS 
Article 

Google Scholar 
4.Schuman, G. E., Janzen, H. H. & Herrick, J. E. Soil carbon dynamics and potential carbon sequestration by rangelands. Environ. Pollut. 116, 391–396. https://doi.org/10.1016/s0269-7491(01)00215-9 (2002).CAS 
Article 

Google Scholar 
5.Duan, C. et al. Reveal the response of enzyme activities to heavy metals through in situ zymography. Ecotoxicol. Environ. Saf. 156, 106–115. https://doi.org/10.1016/j.ecoenv.2018.03.015 (2018).CAS 
Article 
PubMed 

Google Scholar 
6.Yang, J. et al. A proposal of “core enzyme” bioindicator in long-term Pb-Zn ore pollution areas based on topsoil property analysis. Environ. Pollut. 213, 760–769. https://doi.org/10.1016/j.envpol.2016.03.030 (2016).CAS 
Article 
PubMed 

Google Scholar 
7.Fierer, N. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590. https://doi.org/10.1038/nrmicro.2017.87 (2017).CAS 
Article 

Google Scholar 
8.Wan, Z. & Song, C. Advance on response of soil enzyme activity to ecological environment. Chin. J. Soil Sci. 40(4), 951–956 (2009).CAS 

Google Scholar 
9.Liu, G. et al. Soil enzymes as indicators of saline soil fertility under various soil amendments. Agric. Ecosyst. Environ. 237, 274–279 (2017).CAS 
Article 

Google Scholar 
10.Li, Z., Chaonian, F., Mengjie, L. & Huanchao, Z. Nutrient and biological characteristics of different salinized soils in coastal areas of northern Jiangsu Province. J. Anhui Agric. Univ. 46, 86–92 (2019).
Google Scholar 
11.Bueis, T., Turrion, M. B., Bravo, F., Pando, V. & Muscolo, A. Factors determining enzyme activities in soils under Pinus halepensis and Pinus sylvestris plantations in Spain: A basis for establishing sustainable forest management strategies. Ann. For. Sci. https://doi.org/10.1007/s13595-018-0720-z (2018).Article 

Google Scholar 
12.Sinsabaugh, R. L. et al. Stoichiometry of soil enzyme activity at global scale. Ecol. Lett. 11, 1252–1264. https://doi.org/10.1111/j.1461-0248.2008.01245.x (2008).Article 
PubMed 

Google Scholar 
13.Marx, M. C., Kandeler, E., Wood, M., Wermbter, N. & Jarvis, S. C. Exploring the enzymatic landscape: Distribution and kinetics of hydrolytic enzymes in soil particle-size fractions. Soil Biol. Biochem. 37, 35–48. https://doi.org/10.1016/j.soilbio.2004.05.024 (2005).CAS 
Article 

Google Scholar 
14.Bais, et al. The role of root exudates in rhizosphere interations with plants and other organisms. Annu. Rev. Plant Biol. 2006(57), 233–266 (2006).Article 

Google Scholar 
15.Qu, Y. et al. Soil enzyme activity and microbial metabolic function diversity in soda saline–alkali rice paddy fields of northeast China. Sustainability 12, 15. https://doi.org/10.3390/su122310095 (2020).CAS 
Article 

Google Scholar 
16.Salinas-Garcia, J. R. et al. Tillage effects on microbial biomass and nutrient distribution in soils under rain-fed corn production in central-western Mexico. Soil Till. Res. 66, 143–152. https://doi.org/10.1016/s0167-1987(02)00022-3 (2002).Article 

Google Scholar 
17.Roldán, A., Salinas-García, J. R., Alguacil, M. M. & Caravaca, F. Changes in soil enzyme activity, fertility, aggregation and C sequestration mediated by conservation tillage practices and water regime in a maize field. Appl. Soil Ecol. 30, 11–20. https://doi.org/10.1016/j.apsoil.2005.01.004 (2005).Article 

Google Scholar 
18.Lenton, T. M. & Huntingford, C. Global terrestrial carbon storage and uncertainties in its temperature sensitivity examined with a simple model. Glob. Change Biol. 9, 1333–1352. https://doi.org/10.1046/j.1365-2486.2003.00674.x (2003).ADS 
Article 

Google Scholar 
19.Rey, A., Petsikos, C., Jarvis, P. G. & Grace, J. Effect of temperature and moisture on rates of carbon mineralization in a Mediterranean oak forest soil under controlled and field conditions. Eur. J. Soil Sci. 56(5), 589–599 (2005).CAS 
Article 

Google Scholar 
20.Wang, X., Zhag, Y., Lv, J. & Fan, X. Effect of long term different fertilization on properties of soil organic matter and humic acids. Sci. Agric. Sinica 33, 78–84 (2000).
Google Scholar 
21.Wei, Y. et al. Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River. China. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.135387 (2019).Article 

Google Scholar 
22.Huang, L. H. et al. Impact of cultivation year, nitrogen fertilization rate and irrigation water quality on soil salinity and soil nitrogen in saline-sodic paddy fields in Northeast China. J. Agric. Sci. 154, 632–646. https://doi.org/10.1017/s002185961500057x (2015).Article 

Google Scholar 
23.Liu, Q., Cui, B. & Yang, Z. Dynamics of the soil water and solute in the sodic saline soil in the Songnen Plain China. Environ. Earth Sci. 59, 837–845. https://doi.org/10.1007/s12665-009-0079-4 (2009).CAS 
Article 

Google Scholar 
24.Lu, Y. & Xu, H. Distribution characteristic of soil organic carbon fraction in different types of wetland in Hongze Lake of China. Sci. World J. 2014, 487961. https://doi.org/10.1155/2014/487961 (2014).CAS 
Article 

Google Scholar 
25.Nitsch, P., Kaupenjohann, M. & Wulf, M. Forest continuity, soil depth and tree species are important parameters for SOC stocks in an old forest (Templiner Buchheide, northeast Germany). Geoderma 310, 65–76. https://doi.org/10.1016/j.geoderma.2017.08.041 (2018).ADS 
CAS 
Article 

Google Scholar 
26.Zhang, Z., Lu, X., Song, X., Guo, Y. & Xue, Z. Soil C, N and P stoichiometry of deyeuxia angustifolia and carex lasiocarpa wetlands in Sanjiang Plain, northeast China. J. Soil Sediment. 12, 1309–1315. https://doi.org/10.1007/s11368-012-0551-8 (2012).CAS 
Article 

Google Scholar 
27.Bian, J., Tang, J., Zhang, L., Ma, H. & Zhao, J. Arsenic distribution and geological factors in the western Jilin province China. J. Geochem. Explor. 112, 347–356. https://doi.org/10.1016/j.gexplo.2011.10.003 (2012).CAS 
Article 

Google Scholar 
28.Zheng, B. Technical Guide for Soil Analysis (China Agriculture Press, 2013).
Google Scholar 
29.Paul, E. A. The nature and dynamics of soil organic matter: Plant inputs, microbial transformations, and organic matter stabilization. Soil Biol. Biochem. 98, 109–126. https://doi.org/10.1016/j.soilbio.2016.04.001 (2016).CAS 
Article 

Google Scholar 
30.Solly, E. F., Schoning, I., Herold, N., Trumbore, S. E. & Schrumpf, M. No depth-dependence of fine root litter decomposition in temperate beech forest soils. Plant Soil 393, 273–282. https://doi.org/10.1007/s11104-015-2492-7 (2015).CAS 
Article 

Google Scholar 
31.Steinweg, J. M., Kostka, J. E., Hanson, P. J. & Schadt, C. W. Temperature sensitivity of extracellular enzymes differs with peat depth but not with season in an ombrotrophic bog. Soil Biol. Biochem. 125, 244–250. https://doi.org/10.1016/j.soilbio.2018.07.001 (2018).CAS 
Article 

Google Scholar 
32.Hartman, W. H., Richardson, C. J., Vilgalys, R. & Bruland, G. L. Environmental and anthropogenic controls over bacterial communities in wetland soils. P. Natl. Acad. Sci. USA 105, 17842–17847. https://doi.org/10.1073/pnas.0808254105 (2008).ADS 
Article 

Google Scholar 
33.Andersen, R., Chapman, S. J. & Artz, R. R. E. Microbial communities in natural and disturbed peatlands: A review. Soil Biol. Biochem. 57, 979–994. https://doi.org/10.1016/j.soilbio.2012.10.003 (2013).CAS 
Article 

Google Scholar 
34.Huang, B., Wang, J., Jin, H. & Xu, S. Effects of long- term application fertilizer on carbon storage in calcareous meadow soil. J. Agro-Environ. Sci. 25, 161–164 (2006).CAS 

Google Scholar 
35.Bacmaga, M., Wyszkowska, J. & Kucharski, J. Bioaugmentation of soil contaminated with azoxystrobin. Water Air Soil Poll. 228, 9. https://doi.org/10.1007/s11270-016-3200-9 (2017).ADS 
CAS 
Article 

Google Scholar 
36.Miller, G., Suzuki, N., Ciftci-Yilmaz, S. & Mittler, R. Reactive oxygen species homeostasis and signalling during drought and salinity stresses. Plant Cell Environ. 33, 453–467 (2010).CAS 
Article 
PubMed 

Google Scholar 
37.Pathak, H. & Rao, D. L. N. Carbon and nitrogen mineralization from added organic matter in saline and alkali soils. Soil Biol. Biochem. 30, 695–702. https://doi.org/10.1016/S0038-0717(97)00208-3 (1998).CAS 
Article 

Google Scholar 
38.Xiao, Y. et al. Response of soil labile organic carbon fractions to forest conversions in subtropical China. Trop. Ecol. 57, 691–699 (2016).CAS 

Google Scholar 
39.Broszat, M. et al. Wastewater irrigation increases the abundance of potentially harmful gammaproteobacteria in soils in Mezquital Valley. Mexico. Appl. Environ. Microbiol. 80, 5282–5291 (2014).ADS 
Article 
PubMed 

Google Scholar 
40.Liu, Y. et al. Alphaproteobacteria dominate active 2-methyl-4-chlorophenoxyacetic acid herbicide degraders in agricultural soil and drilosphere. Environ. Microbiol. 13, 991–1009 (2011).CAS 
Article 

Google Scholar 
41.Baumann, K. et al. Soil microbial diversity affects soil organic matter decomposition in a silty grassland soil. Biogeochemistry 114, 201–212 (2013).CAS 
Article 

Google Scholar 
42.Philippot, L. et al. Loss in microbial diversity affects nitrogen cycling in soil. ISME J. 7, 1609–1619 (2013).CAS 
Article 
PubMed 

Google Scholar 
43.Chambers, L. G., Guevara, R., Boyer, J. N., Troxler, T. G. & Davis, S. E. Effects of salinity and inundation on microbial community structure and fFunction in a mangrove peat soil. Wetlands 36, 361–371 (2016).Article 

Google Scholar 
44.Wong, V. N. L., Greene, R. S. B., Dalal, R. C. & Murphy, B. W. Soil carbon dynamics in saline and sodic soils: A review. Soil Use Manage. 26, 2–11 (2010).Article 

Google Scholar  More

1.Malthus, T. An Essay on the Principle of Population (Penguin Classics, 1798).
Google Scholar 
2.Northrup, J. M. & Wittemyer, G. Characterising the impacts of emerging energy development on wildlife, with an eye towards mitigation. Ecol. Lett. 16, 112–125 (2013).PubMed 

Google Scholar 
3.Hubbert, M. K. Nuclear energy and the fossil fuel. In Drilling and Production Practice. 1–57 (Shell Developmental Company, American Petroleum Institute, Houston, 1956).
Google Scholar 
4.Höök, M., Sivertsson, A. & Aleklett, K. Validity of the fossil fuel production outlooks in the IPCC Emission Scenarios. Nat. Resour. Res. 19, 63–81 (2010).
Google Scholar 
5.Höök, M. & Tang, X. Depletion of fossil fuels and anthropogenic climate change—A review. Energy Policy 52, 797–809 (2013).
Google Scholar 
6.Abbasi, S. A. & Abbasi, N. The likely adverse environmental impacts of renewable energy sources. Appl. Energy. 65, 121–144 (2000).
Google Scholar 
7.Vöhringer, M. Renewable energy and sustainable development: An impact assessment of micro and mini hydel projects in Gilgit-Baltistan, Pakistan. https://doi.org/10.17169/refubium-22914 (2010).8.Höök, M., Li, J., Johansson, K. & Snowden, S. Growth rates of global energy systems and future outlooks. Nat. Resour. Res. 21, 23–41 (2012).
Google Scholar 
9.Rotty, R. M. Growth in global energy demand and contribution of alternative supply systems. Energy 4, 881–890 (1979).
Google Scholar 
10.Aung, T. S., Overland, I. & Vakulchuk, R. Environmental performance of foreign firms: Chinese and Japanese firms in Myanmar. J. Clean. Prod. 312, 127701 (2021).
Google Scholar 
11.Lu, M. S., Chang, C. L., Lee, W. J. & Wang, L. Combining the wind power generation system with energy storage equipment. IEEE Trans. Ind. Appl. 45, 2109–2115 (2009).
Google Scholar 
12.Morinha, F. et al. Differential mortality of birds killed at wind farms in Northern Portugal. Bird Study 61, 255–259 (2014).
Google Scholar 
13.Arnett, E. B. & May, R. F. Mitigating wind energy impacts on wildlife: Approaches for multiple taxa. Hum.–Wild. Interact. 10, 5 (2016).
Google Scholar 
14.Powlesland, R. G. Impacts of wind farms on birds: A review. Sci. Conserv. 289, 1–53 (2009).
Google Scholar 
15.Marques, A. T. et al. Understanding bird collisions at wind farms: An updated review on the causes and possible mitigation strategies. Biol. Conserv. 179, 40–52 (2014).
Google Scholar 
16.Wellig, S. D. et al. Mitigating the negative impacts of tall wind turbines on bats: Vertical activity profiles and relationships to wind speed. PLoS ONE 13, e0192493. https://doi.org/10.1371/journal.pone.0192493 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Everaert, J. & Eric, W. M. S. Impact of wind turbines on birds in Zeebrugge (Belgium). Biodivers. Conserv. 16, 3345–3359 (2007).
Google Scholar 
18.Barrios, L. & Rodriguez, A. Behavioural and environmental correlates of soaring-bird mortality at on-shore wind turbines. J. Appl. Ecol. 41, 72–81 (2004).
Google Scholar 
19.Barrios, L. & Rodriguez, A. Spatiotemporal patterns of bird mortality at two wind farms of Southern Spain. in Birds and wind farms: Risk Assessment and Mitigation (eds De Lucas, M. et al.) 229–239 (2007).20.Meek, E. R., Ribbands, J. B., Christer, W. B., Davy, P. R. & Higginson, I. The effects of aero-generators onmoorland bird populations in the Orkney Islands, Scotland. Bird Study 40, 140–143 (1993).
Google Scholar 
21.Smallwood, K. S. & Thelander, C. Developing methods to reduce bird mortality in the Altamont Pass Wind Resource Area. Final report to the California Energy Commission. Public Interest Energy Research–Environmental Area, Contract (2004).22.Smallwood, K. S. & Thelander, C. Bird mortality in the Altamont Pass wind resource area, California. J. Wildl. Manag. 72, 215–223 (2008).
Google Scholar 
23.Drewitt, A. L. & Langston, R. H. Collision effects of wind-power generators and other obstacles on birds. Ann. N. Y. Acad. Sci. 1134, 233–266 (2008).ADS 
PubMed 

Google Scholar 
24.IWTMA—Indian Wind Turbine Manufacturers Association: INDIA—WIND POWER State wise Month wise Wind Power Installed Capacity 2019–2020. Accessed 7 May 2021. http://www.indianwindpower.com/wind-energy-in-india.php#tab1 (2020). 25.Smith, K. T., Taylor, K. L., Albeke, S. E. & Beck, J. L. Pronghorn winter resource selection before and after wind energy development in South-Central Wyoming. Rangel. Ecol. Manag. 73(2), 227–233 (2020).
Google Scholar 
26.Klich, D., Łopucki, R., Ścibior, A., Gołębiowska, D. & Wojciechowska, M. Roe deer stress response to a wind farms: methodological and practical implications. Ecol. Indic. 117, 106658 (2020).
Google Scholar 
27.Flydal, K., Eftestøl, S., Reimers, E. & Colman, J. E. Effects of wind turbines on area use and behaviour of semi-domestic reindeer in enclosures. Rangifer. 24(2), 55–66 (2004).
Google Scholar 
28.Rabin, L. A., Coss, R. G. & Owings, D. H. The effects of wind turbines on antipredator behavior in California ground squirrels (Spermophilus beecheyi). Biol. Conserv. 131(3), 410–420 (2006).
Google Scholar 
29.Łopucki, R. & Perzanowski, K. Effects of wind turbines on spatial distribution of the European hamster. Ecol. Indic. 84, 433–436 (2018).
Google Scholar 
30.Kumar, S. R., Ali, A. & Arun, P. R. Impact of wind turbines on birds: A case study from Gujarat, India. Sci. J. Environ. Sci. 228, 1–12 (2012).
Google Scholar 
31.Kumar, S. R., Ali, A. M. S. & Arun, P. R. Bat mortality due to collision with wind turbines in Kutch District, Gujarat, India. J. Threat. Taxa. 5(13), 4822–4824 (2013).
Google Scholar 
32.Kumar, V., Kumar, P. & Singh, J. An introduction to contaminants in agriculture and environment. In Contaminants in Agriculture and Environment: Health Risks and Remediation (eds. Kumar, V. et al.) 1–8 (Afro Environment India, Haridwar, India, 2019).
Google Scholar 
33.Pande, S. et al. CEPF Western Ghats Special Series: Avian collision threat assessment at Bhambarwadi Wind farm Plateau in northern Western Ghats. India. J. Threat. Taxa. 5(1), 3504–3515 (2013).
Google Scholar 
34.Narwade, S. et al. Mass mortality of wildlife due to hailstorms in Maharashtra, India. Bird Populations. 13, 28–35 (2014).
Google Scholar 
35.Champion, H. G. & Seth, S. K. A Revised Survey of the Forest Types of India (Manager of Publications, 1968).
Google Scholar 
36.Erickson, W. P. et al. Avian collisions with wind turbines: a summary of existing studies and comparisons to other sources of avian collision mortality in the United States. Western EcoSystems Technology, Inc., Cheyenne, WY (United States); RESOLVE, Inc., Washington, DC (United States). Accessed 20 Feb 2021. https://www.osti.gov/servlets/purl/822418/ (2001).37.Shoenfeld, P. Suggestions regarding avian mortality extrapolation. Prepared for the Mountaineer Wind Energy Center Technical Review Committee (2004).38.Buckland, S. T. et al. Introduction to Distance Sampling (Oxford University Press, 2001).
Google Scholar 
39.Bibby, C. J., Burgess, N. D. & Hill, D. A. Bird Census Techniques (Academic Press, 1992).
Google Scholar 
40.Cottam, G. & Curtis, J. T. The use of distance measures in phytosociological sampling. Ecology 37, 451–460 (1956).
Google Scholar 
41.Erickson, W. P., Jeffrey, J., Kronner, K. & Bay, K. Stateline wind project wildlife monitoring annual report, results for the period July 2001–December 2002. Technical report submitted to FPL Energy, the Oregon Office of Energy, and the Stateline Technical Advisory Committee (2003).42.Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
43.McCune, B., Grace, J. B. & Urban, D. L. Analysis of Ecological Communities Vol. 28 (MjM Software Design, 2002).
Google Scholar 
44.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. Ecol. 18, 117–143 (1993).
Google Scholar 
45.Baskaran, N., Desai, A. A. & Udhayan, A. Population distribution and conservation of the four-horned antelope (Tetracerus quadricornis) in the tropical forest of Southern India. J. Sci. Trans. Environ. Technol. 2, 139–144 (2009).
Google Scholar 
46.Isvaran, K. Intraspecific variation in group size in the blackbuck antelope: The roles of habitat structure and forage at different spatial scales. Oecologia 154(2), 435–444 (2007).ADS 
PubMed 

Google Scholar 
47.Šálek, M. et al. Population densities and habitat use of the golden jackal (Canis aureus) in farmlands across the Balkan Peninsula. Eur. J. Wildl. Res. 60(2), 193–200 (2014).
Google Scholar 
48.Mukherjee, S., Goyal, S. P., Johnsingh, A. J. T. & Pitman, M. L. The importance of rodents in the diet of jungle cat (Felis chaus), caracal (Caracal caracal) and golden jackal (Canis aureus) in Sariska Tiger Reserve, Rajasthan, India. J. Zool. 262(4), 405–411 (2004).
Google Scholar 
49.Majumder, A., Sankar, K., Qureshi, Q. & Basu, S. Food habits and temporal activity patterns of the Golden Jackal Canis aureus and the Jungle Cat Felis chaus in Pench Tiger Reserve, Madhya Pradesh. J. Threat. Taxa. 3(11), 2221–2225 (2011).
Google Scholar 
50.Gaikwad, M. C. & Narwade, S. S. The status of Chinkara Gazella bennettii (Mammalia: Cetartiodactyla: Bovidae) at Mayureshwar Wildlife Sanctuary, Supe, Baramati, Pune and a note on its current distribution in the southwestern region of the Deccan Plateau of Maharashtra, India. J. Threat. Taxa. 8(3), 8590–8595 (2016).
Google Scholar 
51.Kumar, D. et al. Ecological determinants of occupancy and abundance of chinkara (Gazella bennettii) in Yadahalli Wildlife Sanctuary, Karnataka, India. Curr. Sci. 118(2), 264–270 (2020).
Google Scholar 
52.Anoop, V., Arun, P. R. & Jayapal, R. Do black-naped hares Lepus nigricollis (Mammalia: Lagomorpha: Leporidae) have synanthropic association with wind farms?. J. Threat. Taxa. 10(7), 11925–11927 (2018).
Google Scholar 
53.MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).
Google Scholar 
54.MacKenzie, D. I. et al. Occupancy Modelling and Estimation (Academic Press, 2006).
Google Scholar 
55.Akaike, H. Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60, 255–265 (1973).MathSciNet 
MATH 

Google Scholar 
56.Burnham, K. P. & Anderson, D. R. in Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn (Springer-Verlag, New York, 2002).MATH 

Google Scholar 
57.Kingsley, A. & Whittam, B. Potential Impacts of Wind Turbines on Birds at North Cape, Prince Edward Island (Bird Studies Canada, 2001).
Google Scholar 
58.Winkelman, J. E. Vogels en het Windpark nabij Urk (NOP): Aanvaringsslachtoffers en Verstoring van Pleisterende Eenden, Ganzen en Zwanen. RIN-report 89/15. Arnhem. (1989).59.Arun, P. R., Jayapal, R. & Anoop, V. Impact of Hara wind power project of CLP wind farms (India) ltd. On wildlife including migratory birds and raptors at Harpanahalli, Davangere, Karnataka. Final report Submitted to CLP Wind farms (India) Pvt. Ltd. SACON Report. 145 (2015).60.SGS Environment – Haverigg wind farm ornithological monitoring programme. Report to Windcluster Ltd. (1994).61.Tyler, S. J. Bird strike study at Bryn Titli wind farm, Rhayader. Report to National Wind Power Ltd (1995).62.Petterson, J. & Stalin, T. Influence of offshore windmill on migratory birds in southeast coast of Sweden. Report to GE Wind Energy. Piorkowski (2003).63.Grünkorn T., Diederichs A., Stahl B., Pöszig D. & Nehls G. Entwicklung einer Methode zur Abschätzung desKollisionsrisikos von Vögeln an Windenergie-anlagen. Bioconsult SH, Hockensbüll, Germany. Accessed 2 Feb 2021. http://www.umweltdaten.landsh.de/nuis/upool/gesamt/wea/voegel_wea.pdf (2005).64.Morrison, M. L. Avian Risk and Fatality Protocol (National Renewable Energy Lab., 1998).
Google Scholar 
65.Anderson, R. et al. Avian Monitoring and Risk Assessment at the San Gorgonio Wind Resource Area (National Renewable Energy Lab. (NREL), 2005).
Google Scholar 
66.Hunt, G. & Hunt, T. The trend of golden eagle territory occupancy in the vicinity of the Altamont Pass Wind Resource Area: 2005 survey. Unpublished report of the California Energy Commission. Accessed 2 Feb 2021. www.energy.ca.gov/2006publications/CEC-500-2006-056/CEC-500-2006-056.pdf (2006).67.Brown, M. J., Linton, E. & Rees, E. C. Causes of mortality among wild swans in Britain. Wildfowl. 43, 70–79 (1992).
Google Scholar 
68.Larsen, J. K. & Clausen, P. Potential wind park impacts on whooper swans in winter: The risk of collision. Waterbirds. 25, 327–330 (2002).
Google Scholar 
69.Drewitt, A. L. & Langston, R. H. Assessing the impacts of wind farms on birds. Ibis 148, 29–42 (2006).
Google Scholar 
70.Stewart, G. B., Pullin, A. S. & Coles, C. F. Poor evidence-base for assessment of wind farm impacts on birds. Environ. Conserv. 34, 1–11 (2007).
Google Scholar 
71.Kuvlesky, W. P. Jr. et al. Wind energy development and wildlife conservation: Challenges and opportunities. J. Wildl. Manag. 71, 2487–2498 (2007).
Google Scholar 
72.Behr, O. & Helversen. O. V. Gutachten zur Beeinträchtigung im freien Luftraum jagender und ziehender Fledermäuse durch bestehende Windkraftanlagen. Wirkungskontrolle zum Windpark “Roßkopf” (Freiburg i. Br.). Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Zoologie II: 1–42 (2005).73.Behr, O. et al. Gutachten zur Beeinträchtigung im freien Luftraum jagender und ziehender Fledermäuse durch bestehende Windkraftanlagen. Wirkungskontrolle zum Windpark ‘Ittenschwander Horn’ (Freiburg i Br.). Report to Windpark Fröhnd GmbH & Co. KG (2006).74.Brinkmann, R., Schauer-Weisshahn H., & Bontadina F. Untersuchungen zu möglichen betriebsbedingten Auswirkungen von Windkraftanlagen auf Fledermäuse im Regierungsbezirk Freiburg. Report to Regierungspräsidium Freiburg, Referat 56 Naturschutz und Landschaftspflege. Accessed 2 Feb 2021. http://www.rp-freiburg.de/servlet/PB/show/1158478/rpf-windkraft-fledermaeuse.pdf (2006).75.Grunwald, T. & Schäfer, F. Aktivität von Fledermäusen im Rotorbereich von Windenergieanlagen an bestehenden WEA in Südwestdeutschland – Teil 2: Ergebnisse. Nyctalus. 12, 182–198 (2007).
Google Scholar 
76.Bach, P., Niermann, I. & Bach, L. Impact of wind speed on the activity of bats-at the coast and inland. Accessed 2 Feb 2021. https://tethys.pnnl.gov/sites/default/files/publications/Bachetal2011.pdf (2011).77.Bach, P., Bach, L., Ekschmitt, K., Frey, K. & Gerhardt, U. Bat fatalities at different wind facilities in Northwest Germany. Accessed 2 Feb 2021. https://tethys.pnnl.gov/sites/default/files/publications/Bach-Bat-fatalities-Poster-2013.pdf (2013).78.Cryan, P. M. & Brown, A. C. Migration of bats past a remote island offers clues toward the problem of bat fatalities at wind turbines. Biol. Conserv. 139, 1–11 (2007).
Google Scholar 
79.Thaxter, C. B. et al. Bird and bat species’ global vulnerability to collision mortality at wind farms revealed through a trait-based assessment. Proc. R. Soc. B 284, 20170829. https://doi.org/10.1098/rspb.2017.0829 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
80.Kumar S. R. Impacts of wind farm on avifauna of Samakhiali region, Kutch, Gujarat, PhD thesis submitted to Bharathiyar University, Coimbatore (2017).81.De Lucas, M., Janss, G. F., Whitfield, D. P. & Ferrer, M. Collision fatality of raptors in wind farms does not depend on raptor abundance. J. Appl. Ecol. 45, 1695–1703 (2008).
Google Scholar 
82.Leddy, K. L., Higgins, K. F. & Naugle, D. E. Effects of wind turbines on upland nesting birds in Conservation Reserve Program grasslands. Wilson. Bull. 111, 100–104 (1999).
Google Scholar 
83.Pearce-Higgins, J. W., Stephen, L., Langston, R. H., Bainbridge, I. P. & Bullman, R. The distribution of breeding birds around upland wind farms. J. Appl. Ecol. 46, 1323–1331 (2009).
Google Scholar 
84.Zimmerling, J., Pomeroy, A., d’Entremont, M. & Francis, C. Canadian estimate of bird mortality due to collisions and direct habitat loss associated with wind turbine developments. Avian Conserv. Ecol. 8, 1–13 (2013).
Google Scholar 
85.Schaller, G. B. The Deer and the Tiger: A Study of Wildlife in India (University Chicago Press, 1967).
Google Scholar 
86.Prasad, N. Home range, dispersal, and movement of blackbuck (Antilope cervicapra) population in relation to seasonal change in Mudmal and environs. PhD thesis submitted to Saurashtra University, Rajkot, India (1981).87.Ranjitsinh, M. K. Indian Blackbuck (Natraj Publishers, 1989).
Google Scholar 
88.Kumara, H. N., Rathnakumar, S., Sasi, R. & Singh, M. Conservation status of wild mammals in Biligiri Rangaswamy Temple wildlife sanctuary, the Western Ghats, India. Curr. Sci. 103, 933–940 (2012).
Google Scholar 
89.Rathore, D. Blackbuck occupancy in Moyar valley, Tamil Nadu. MSc thesis submitted to TERI University, Delhi (2017).90.Arandhara, S., Sathishkumar, S., Gupta, S. & Baskaran, N. Influence of invasive Prosopis juliflora on the distribution and ecology of native blackbuck in protected areas of Tamil Nadu, India. Eur. J. Wildl. Res. 67, 1–16 (2021).
Google Scholar 
91.Prater, S. H. The Book of Indian Animals (Oxford University Press, 1971).
Google Scholar 
92.Roberts, T. J. The Mammals of Pakistan (Oxford University Press, 1997).
Google Scholar 
93.Rahmani, A. R. Distribution of the Indian gazelle or chinkara Gazella bennetti (Sykes) in India. Mammalia 54, 605–620 (1990).
Google Scholar 
94.Kumar, D. et al. Ecological determinants of occupancy and abundance of chinkara (Gazella bennettii) in Yadahalli Wildlife Sanctuary, Karnataka, India. Curr. Sci. 118, 264 (2020).
Google Scholar 
95.Gubbi, S., Seshadri, S. & Kumara, V. Counting the unmarked: Estimating animal population using count data. Electron. J. Appl. Stat. Anal. 12, 604–618 (2019).
Google Scholar 
96.Clutton-Brock, J., Corbet, G. B. & Hills, M. Review of the family Canidae, with a classification by numerical methods. Bull. Br. Mus. Nat. Hist. Zool. 29, 117–199 (1976).
Google Scholar 
97.Nowell, K. & Jackson, P. Wild Cats: Status Survey and Conservation Action Plan (IUCN, 1996).
Google Scholar 
98.Krishna, Y. C., Krishnaswamy, J. & Kumar, N. S. Habitat factors affecting site occupancy and relative abundance of four-horned antelope. J. Zool. 276, 63–70 (2008).
Google Scholar  More