Ecology

1.Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
Article 

Google Scholar 
2.Lionello, P. et al. In Mediterranean Climate Variability Vol. 4 (eds Lionello, P. et al.) 1–26 (Elsevier, 2006).3.Molina, M., Sánchez, E. & Gutiérrez, C. Future heat waves over the Mediterranean from an euro-coRDeX regional climate model ensemble. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
4.Bucchignani, E., Mercogliano, P., Panitz, H.-J. & Montesarchio, M. Climate change projections for the Middle East-North Africa domain with COSMO-CLM at different spatial resolutions. Adv. Clim. Change 9, 66–80 (2018).Article 

Google Scholar 
5.García, N., Cuttelod, A. & Malak, D. A. The Status and Distribution of Freshwater Biodiversity in Northern Africa (IUCN, 2010).6.Di Castri, F. & Mooney, H. A. Mediterranean Type Ecosystems: Origin and Structure Vol. 7 (Springer Science & Business Media, 2012).7.Stoks, R., Geerts, A. N. & De Meester, L. Evolutionary and plastic responses of freshwater invertebrates to climate change: Realized patterns and future potential. Evol. Appl. 7, 42–55 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Wellborn, G. A., Skelly, D. K. & Werner, E. E. Mechanisms creating community structure across a freshwater habitat gradient. Annu. Rev. Ecol. Evol. Syst. 27, 337–363 (1996).Article 

Google Scholar 
9.Arribas, P. et al. Dispersal ability rather than ecological tolerance drives differences in range size between lentic and lotic water beetles (Coleoptera: Hydrophilidae). J. Biogeogr. 39, 984–994 (2012).Article 

Google Scholar 
10.Hof, C., Brändle, M. & Brandl, R. Lentic odonates have larger and more northern ranges than lotic species. J. Biogeogr. 33, 63–70 (2006).Article 

Google Scholar 
11.Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).Article 

Google Scholar 
12.Ibàñez, C., Prat, N. & Canicio, A. Changes in the hydrology and sediment transport produced by large dams on the lower Ebro river and its estuary. Regul. Rivers Res. Manag. 12, 51–62 (1996).Article 

Google Scholar 
13.Kondolf, G., Rubin, Z. & Minear, J. Dams on the Mekong: Cumulative sediment starvation. Water Resour. Res. 50, 5158–5169 (2014).ADS 
Article 

Google Scholar 
14.Pringle, C. M., Freeman, M. C. & Freeman, B. J. Regional effects of hydrologic alterations on riverine macrobiota in the new world: Tropical-temperate comparisons. Bioscience 50, 807–823 (2000).Article 

Google Scholar 
15.Liu, X. et al. Effects of dams and their environmental impacts on the genetic diversity and connectivity of freshwater mussel populations in Poyang Lake Basin, China. Freshw. Biol. 65, 264–277 (2020).Article 

Google Scholar 
16.Barbarossa, V. et al. Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proc. Natl. Acad. Sci. U.S.A. 117, 3648–3655 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.López-Moreno, J. I. et al. Dam effects on droughts magnitude and duration in a transboundary basin: The Lower River Tagus, Spain and Portugal. Water Resour. Res. 45, W02405 (2009).ADS 
Article 

Google Scholar 
18.McMahon, T. & Finlayson, B. Droughts and anti-droughts: The low flow hydrology of Australian rivers. Freshw. Biol. 48, 1147–1160 (2003).Article 

Google Scholar 
19.Aguiar, F. C. & Ferreira, M. T. Human-disturbed landscapes: effects on composition and integrity of riparian woody vegetation in the Tagus River basin, Portugal. Environ. Conserv. 32, 30–41 (2005).Article 

Google Scholar 
20.Costa, M. J., Vasconcelos, R., Costa, J. & Cabral, H. River flow influence on the fish community of the Tagus estuary (Portugal). Hydrobiologia 587, 113–123 (2007).Article 

Google Scholar 
21.Dallas, H. F. The influence of biotope availability on macroinvertebrate assemblages in South African rivers: Implications for aquatic bioassessment. Freshw. Biol. 52, 370–380 (2007).Article 

Google Scholar 
22.Demars, B. O., Kemp, J. L., Friberg, N., Usseglio-Polatera, P. & Harper, D. M. Linking biotopes to invertebrates in rivers: Biological traits, taxonomic composition and diversity. Ecol. Indic. 23, 301–311 (2012).Article 

Google Scholar 
23.Wallace, J. B. Recovery of lotic macroinvertebrate communities from disturbance. Environ. Manag. 14, 605–620 (1990).ADS 
Article 

Google Scholar 
24.Boulton, A. J. Parallels and contrasts in the effects of drought on stream macroinvertebrate assemblages. Freshw. Biol. 48, 1173–1185 (2003).Article 

Google Scholar 
25.Desrosiers, M. et al. Assessing anthropogenic pressure in the St. Lawrence River using traits of benthic macroinvertebrates. Sci. Total Environ. 649, 233–246 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Durance, I. & Ormerod, S. J. Climate change effects on upland stream macroinvertebrates over a 25-year period. Glob. Change Biol. 13, 942–957 (2007).ADS 
Article 

Google Scholar 
27.Santos, R. et al. Impacts of climate change and land-use scenarios on Margaritifera margaritifera, an environmental indicator and endangered species. Sci. Total Environ. 511, 477–488 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Junior, R. F. V. et al. Impacts of land use conflicts on riverine ecosystems. Land Use Policy 43, 48–62 (2015).Article 

Google Scholar 
29.Fonseca, A., Fernandes, L. S., Fontainhas-Fernandes, A., Monteiro, S. & Pacheco, F. The impact of freshwater metal concentrations on the severity of histopathological changes in fish gills: A statistical perspective. Sci. Total Environ. 599, 217–226 (2017).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
30.Ferreira, A., Fernandes, L. S., Cortes, R. & Pacheco, F. Assessing anthropogenic impacts on riverine ecosystems using nested partial least squares regression. Sci. Total Environ. 583, 466–477 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Fernandes, L. S., Fernandes, A., Ferreira, A., Cortes, R. & Pacheco, F. A partial least squares—Path modeling analysis for the understanding of biodiversity loss in rural and urban watersheds in Portugal. Sci. Total Environ. 626, 1069–1085 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
32.Intergovernmental Panel on Climate Change. Climate Change 2014–Impacts, Adaptation and Vulnerability: Regional Aspects (Cambridge University Press, 2014).33.Khelifa, R. Flight period, apparent sex ratio and habitat preferences of the Maghribian endemic Calopteryx exul Selys, 1853 (Odonata: Zygoptera). Revue d’Ecologie (La Terre et La Vie) 68, 37–45 (2013).
Google Scholar 
34.Khelifa, R. & Mellal, M. K. Host-plant-based restoration as a potential tool to improve conservation status of odonate specialists. Insect Conserv. Divers. 10(2), 151–160. https://doi.org/10.1111/icad.12212 (2017).Article 

Google Scholar 
35.Khelifa, R. et al. A hotspot for threatened Mediterranean odonates in the Seybouse River (Northeast Algeria): Are IUCN population sizes drastically underestimated?. Int. J. Odonatol. 19, 1–11. https://doi.org/10.1080/13887890.2015.1133331 (2016).Article 

Google Scholar 
36.Boudot, J.-P. Calopteryx exul. The IUCN Red List of Threatened Species 2018 e.T60287A72725790. https://doi.org/10.2305/IUCN.UK.2018-2301.RLTS.T60287A72725790.en. Downloaded on 72725729 January 72722021. (2018).37.Martin, R. Contribution à l’étude des Neuroptères de l’Afrique. II. Les odonates du département de Constantine. Ann. Soc. Entomol. Fr. 79, 95–104 (1910).
Google Scholar 
38.Chelli, A., Zebsa, R. & Khelifa, R. Discovery of a new population of the endangered Calopteryx exul in central North Algeria (Odonata: Calopterygidae). Not. Odonatol. 9, 150–154 (2019).
Google Scholar 
39.Feyen, L. & Dankers, R. Impact of global warming on streamflow drought in Europe. J. Geophys. Res. Atmos. 114, D17116 (2009).ADS 
Article 

Google Scholar 
40.Schneider, C., Laizé, C., Acreman, M. & Florke, M. How will climate change modify river flow regimes in Europe?. Hydrol. Earth Syst. Sci. 17, 325–339 (2013).ADS 
Article 

Google Scholar 
41.Dudgeon, D. et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Strayer, D. L. & Dudgeon, D. Freshwater biodiversity conservation: Recent progress and future challenges. J. North Am. Benthol. Soc. 29, 344–358 (2010).Article 

Google Scholar 
43.Van Vliet, M. & Zwolsman, J. Impact of summer droughts on the water quality of the Meuse river. J. Hydrol. 353, 1–17 (2008).ADS 
Article 

Google Scholar 
44.Caruso, B. Temporal and spatial patterns of extreme low flows and effects on stream ecosystems in Otago, New Zealand. J. Hydrol. 257, 115–133 (2002).ADS 
CAS 
Article 

Google Scholar 
45.Stanley, E. H., Fisher, S. G. & Grimm, N. B. Ecosystem expansion and contraction in streams. Bioscience 47, 427–435 (1997).Article 

Google Scholar 
46.Truchy, A. et al. Habitat patchiness, ecological connectivity and the uneven recovery of boreal stream ecosystems from an experimental drought. Glob. Change Biol. 26, 3455–3472 (2020).ADS 
Article 

Google Scholar 
47.Boulton, A. J. & Lake, P. S. Effects of drought on stream insects and its ecological consequences. Aquatic insects: Challenges to populations 81–102 (CABI, 2008).48.Andersen, C. B., Lewis, G. P. & Sargent, K. A. Influence of wastewater-treatment effluent on concentrations and fluxes of solutes in the Bush River, South Carolina, during extreme drought conditions. Environ. Geosci. 11, 28–41 (2004).Article 

Google Scholar 
49.Wada, Y., Van Beek, L. P., Wanders, N. & Bierkens, M. F. Human water consumption intensifies hydrological drought worldwide. Environ. Res. Lett 8, 034036 (2013).ADS 
Article 

Google Scholar 
50.Aldous, A., Fitzsimons, J., Richter, B. & Bach, L. Droughts, floods and freshwater ecosystems: Evaluating climate change impacts and developing adaptation strategies. Mar. Freshw. Res. 62, 223–231 (2011).CAS 
Article 

Google Scholar 
51.Conley, D. J. et al. Controlling eutrophication: Nitrogen and phosphorus. Science 123, 1014–1015 (2009).Article 

Google Scholar 
52.Park, T.-J. et al. Development of water quality criteria of ammonia for protecting aquatic life in freshwater using species sensitivity distribution method. Sci. Total Environ. 634, 934–940 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Reggam, A., Bouchelaghem, E.-H., Hanane, S. & Houhamdi, M. Effects of anthropogenic activities on the quality of surface water of Seybouse River (northeast of the Algeria). Arab. J. Geosci. 10, 219 (2017).Article 
CAS 

Google Scholar 
54.Khelifa, R. et al. Long-range movements of an endangered endemic damselfly Calopteryx exul Selys, 1853 (Calopterygidae: Odonata). Afr. J. Ecol. 52, 375–377 (2014).
Google Scholar 
55.Khelifa, R. Partial bivoltinism and emergence patterns in the North African endemic damselfly Calopteryx exul: Conservation implications. Afr. J. Ecol. 55, 145–151 (2017).Article 

Google Scholar 
56.Adams, H. D. et al. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-chang-type drought. Proc. Natl. Acad. Sci. U.S.A. 106, 7063–7066 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Scrimgeour, G. J. & Winterbourn, M. J. Effects of floods on epilithon and benthic macroinvertebrate populations in an unstable New Zealand river. Hydrobiologia 171, 33–44 (1989).Article 

Google Scholar 
58.Giller, P., Sangpradub, N. & Twomey, H. Catastrophic flooding and macroinvertebrate community structure. Verh. Int. Ver. Theor. Angew. Limnol. 24, 1724–1729 (1991).
Google Scholar 
59.Siva-Jothy, M. T., Gibbons, D. W. & Pain, D. Female oviposition-site preference and egg hatching success in the damselfly Calopteryx splendens xanthostoma. Behav. Ecol. Sociobiol. 37, 39–44 (1995).Article 

Google Scholar 
60.Stettmer, C. Colonisation and dispersal patterns of banded (Calopteryxsplendens) and beautiful demoiselles (C. virgo) (Odonata: Calopterygidae) in south-east German streams. Eur. J. Entomol. 93, 579–593 (1996).
Google Scholar 
61.Chaput-Bardy, A., Grégoire, A., Baguette, M., Pagano, A. & Secondi, J. Condition and phenotype-dependent dispersal in a damselfly, Calopteryx splendens. PLoS ONE 5, e10694 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Ward, L. & Mill, P. Long range movements by individuals as a vehicle for range expansion in Calopteryx splendens (Odonata: Zygoptera). Eur. J. Entomol. 104, 195 (2007).Article 

Google Scholar 
63.Mellal, M. K., Bensouilah, M., Houhamd, M. & Khelifa, R. Reproductive habitat provisioning promotes survival and reproduction of the endangered endemic damselfly Calopteryx exul. J. Insect Conserv. 22, 563–570 (2018).Article 

Google Scholar 
64.Cordero-Rivera, A. & Stoks, R. In Dragonflies and Damselflies: Model Organisms for Ecological and Evolutionary Research (ed. Córdoba-Aguilar, A.) 7–20 (Oxford University Press, 2008).65.Iglesias, A., Garrote, L., Flores, F. & Moneo, M. Challenges to manage the risk of water scarcity and climate change in the Mediterranean. Water Resour. Manag. 21, 775–788 (2007).Article 

Google Scholar 
66.Barnett, T. P. et al. Human-induced changes in the hydrology of the western United States. Science 319, 1080–1083 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Samways, M. J. et al. Value of artificial ponds for aquatic insects in drought-prone southern Africa: A review. Biodivers. Conserv. 29, 3131–3150 (2020).Article 

Google Scholar 
68.Deacon, C., Samways, M. J. & Pryke, J. S. Aquatic insects decline in abundance and occupy low-quality artificial habitats to survive hydrological droughts. Freshw. Biol. 64, 1643–1654 (2019).Article 

Google Scholar 
69.Briggs, A. J., Pryke, J. S., Samways, M. J. & Conlong, D. E. Complementarity among dragonflies across a pondscape in a rural landscape mosaic. Insect Conserv. Divers. 12, 241–250 (2019).Article 

Google Scholar 
70.Geist, J. Integrative freshwater ecology and biodiversity conservation. Ecol. Indic. 11, 1507–1516 (2011).Article 

Google Scholar 
71.Brooks, A. J., Chessman, B. C. & Haeusler, T. Macroinvertebrate traits distinguish unregulated rivers subject to water abstraction. J. North Am. Benthol. Soc. 30, 419–435 (2011).Article 

Google Scholar 
72.Garibaldi, L. A. et al. Working landscapes need at least 20% native habitat. Conserv. Lett. https://doi.org/10.1111/conl.12773 (2020).Article 

Google Scholar 
73.Vincent, A. & Fleury, P. Development of organic farming for the protection of water quality: Local projects in France and their policy implications. Land Use Policy 43, 197–206 (2015).Article 

Google Scholar 
74.Bengtsson, J., Ahnström, J. & Weibull, A. C. The effects of organic agriculture on biodiversity and abundance: A meta-analysis. J. Appl. Ecol. 42, 261–269 (2005).Article 

Google Scholar 
75.Lichtenberg, E. M. et al. A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Glob. Change Biol. 23, 4946–4957 (2017).ADS 
Article 

Google Scholar 
76.ABHCSM. A.G.I.R.E (Agence nationale de la gestion intégrée des ressources en eau) (2016). Rapport sur l’analyse de l’année hydrologique (2015–2016) du barrage Hammam Debagh. Agence de bassin hydrographique Constantinois-Seybouse-Mellegue (2016).77.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
78.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
79.Wildlife Conservation Society—WCS and Center for International Earth Science Information Network—CIESIN—Columbia University (NASA Socioeconomic Data and Applications Center (SEDAC), 2005).80.Vicente-Serrano, S. M. & Staff. The Climate Data Guide: Standardized Precipitation Evapotranspiration Index (SPEI). Retreived from https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-evapotranspiration-index-spei (2015).81.D’Orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Change Biol. 24, 2339–2351 (2018).ADS 
Article 

Google Scholar 
82.Khelifa, R. Females ‘assist’ sneaker males to dupe dominant males in a rare endemic damselfly: Sexual conflict at its finest. Ecology 100, e02811 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
83.R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).84.Laake, J. RMark: An R Interface for Analysis of Capture–Recapture Data with MARK, AFSC Processed Rep 2013-01 (Alaska Fish. Sci. Cent., NOAA, National Marine Fisheries Service, 2013).85.Burnham, K. P. Design and Analysis Methods for Fish Survival Experiments Based on Release-Recapture Vol. 5 (America Fisheries Society Monograph, 1987).86.Amstrup, S. C., McDonald, T. L. & Manly, B. F. Handbook of Capture–Recapture Analysis (Princeton University Press, 2010). More

1.Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamò, M. First report of outbreaks of the fall armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in West and Central Africa. PLoS ONE 11, e0165632 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Nagoshi, R. N. et al. Comparative molecular analyses of invasive fall armyworm in Togo reveal strong similarities to populations from the eastern United States and the Greater Antilles. PLoS ONE 12, e0181982 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Nagoshi, R. N., Goergen, G., Plessis, H. D., van den Berg, J. & Meagher, R. Genetic comparisons of fall armyworm populations from 11 countries spanning sub-Saharan Africa provide insights into strain composition and migratory behaviors. Sci. Rep. 9, 1–11 (2019).CAS 
Article 

Google Scholar 
4.Ganiger, P. C. et al. Occurrence of the new invasive pest, fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), in the maize fields of Karnataka, India. Curr. Sci. 115, 621 (2018).CAS 
Article 

Google Scholar 
5.Deshmukh, S. et al. First report of the fall armyworm, Spodoptera frugiperda (J E Smith) (Lepidoptera: Noctuidae), an alien invasive pest on maize in India. Pest Manag. Hortic. Ecosyst. 24, 23–29 (2018).
Google Scholar 
6.Shylesha, A. N. et al. Studies on new invasive pest Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) and its natural enemies. J. Biol. Control 32, 145–151 (2018).Article 

Google Scholar 
7.Swamy, H. M. M. et al. Prevalence of “R” strain and molecular diversity of fall army worm Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) in India. Indian J. Entomol. 80, 544 (2018).Article 

Google Scholar 
8.Chormule, A. et al. First report of the fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera, Noctuidae) on sugarcane and other crops from Maharashtra, India. J. Entomol. Zool. Stud. 7, 114–117 (2019).
Google Scholar 
9.Visalakshi, M. et al. Report of the invasive fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) and its natural enemies on maize and other crops from Andhra Pradesh, India. J. Entomol. Zool. Stud. 7, 1348–1352 (2019).MathSciNet 

Google Scholar 
10.Srikanth, J. et al. First report of occurrence of fall armyworm Spodoptera frugiperda in sugarcane from Tamil Nadu, India. J. Sugarcane Res. 8, 195–202 (2019).
Google Scholar 
11.Babu, S. R. et al. Report of an exotic invasive pest the fall armyworm, Spodoptera frugiperda (J.E. Smith) on maize in Southern Rajasthan. J. Entomol. Zool. Stud. 7, 1296–1300 (2019).
Google Scholar 
12.Pashley, D. P. Host-associated genetic differentiation in fall armyworm (Lepidoptera: Noctuidae): a sibling species complex?. Ann. Entomol. Soc. Am. 79, 898–904 (1986).Article 

Google Scholar 
13.Pashley, D. P., Sparks, T. C., Quisenberry, S. S., Jamjanya, T. & Dowd, P. F. Two fall armyworm strains feed on corn, rice and Bermuda-grass. La. Agric. 30, 8–9 (1987).
Google Scholar 
14.Pashley, D. P. & Martin, J. A. Reproductive incompatibility between host strains of the fall armyworm (Lepidoptera: Noctuidae). Ann. Entomol. Soc. Am. 80, 731–733 (1987).Article 

Google Scholar 
15.Lima, E. R. & McNeil, J. N. Female sex pheromones in the host races and hybrids of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae). Chemoecology 19, 29–36 (2009).CAS 
Article 

Google Scholar 
16.Levy, H. C., Garcia-Maruniak, A. & Maruniak, J. E. Strain identification of Spodoptera frugiperda (Lepidoptera: Noctuidae) insects and cell line: PCR-RFLP of cytochrome oxidase C subunit-I gene. Fla. Entomol. 85, 186–190 (2002).CAS 
Article 

Google Scholar 
17.Nagoshi, R. N. The fall armyworm triose phosphate isomerase (Tpi) gene as a marker of strain identity and interstrain mating. Ann. Entomol. Soc. Am. 103, 283–292 (2010).CAS 
Article 

Google Scholar 
18.Nagoshi, R. N. et al. Genetic characterization of fall armyworm infesting South Africa and India indicate recent introduction from a common source population. PLoS ONE 14, e0217755 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Nagoshi, R. N. et al. Southeastern Asia fall armyworms are closely related to populations in Africa and India, consistent with common origin and recent migration. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
20.Zhang, L. et al. High-depth resequencing reveals hybrid population and insecticide resistance characteristics of fall armyworm (Spodoptera frugiperda) invading China; https://doi.org/10.1101/813154 (2019).21.Yainna, S. et al. Genomic balancing selection is key to the invasive success of the fall armyworm; https://doi.org/10.22541/au.160363803.32074105/v1 (2020).22.Tay, W. T. et al. Global FAW population genomic signature supports complex introduction events across the Old World. bioRxiv; https://doi.org/10.1101/2020.06.12.147660 (2020).23.South, A. rworldmap: a new R package for mapping global data. R J. 3(1), 35–43 (2011).MathSciNet 
Article 

Google Scholar 
24.Wickham, et al. Welcome to the tidyverse. J. Open Source Softw. 4(43), 1686 (2019).ADS 
Article 

Google Scholar 
25.Nagoshi, R. N. et al. Using haplotypes to monitor the migration of fall armyworm (Lepidoptera: Noctuidae) corn-strain populations from Texas and Florida. J. Econ. Entomol. 101, 742–749 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Pedersen, T. L. patchwork: the composer of plots; https://CRAN.R-project.org/package=patchwork (2020).27.Yan, L. ggvenn: draw Venn diagram by ‘ggplot2’; https://CRAN.R-project.org/package=ggvenn (2020).28.Marchese, C. Biodiversity hotspots: a shortcut for a more complicated concept. Glob. Ecol. Conserv. 3, 297–309 (2015).Article 

Google Scholar 
29.Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
30.Behere, G. T., Tay, W. T., Russell, D. A., Kranthi, K. R. & Batterham, P. Population genetic structure of the cotton bollworm Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) in India as inferred from EPIC-PCR DNA markers. PLoS ONE 8, e53448 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Chowda-Reddy, R. et al. Bemisia tabaci phylogenetic groups in India and the relative transmission efficacy of tomato leaf curl Bangalore virus by an indigenous and an exotic population. J. Integr. Agric. 11, 235–248 (2012).Article 

Google Scholar 
32.Naik, V. C. B. et al. Evidence for population expansion of cotton pink bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae) in India. Sci. Rep. 10, 1–11 (2020).Article 
CAS 

Google Scholar 
33.Ciborowski, K. L. et al. Rare and fleeting: an example of interspecific recombination in animal mitochondrial DNA. Biol. Lett. 3, 554–557 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Andolfatto, P., Scriber, J. M. & Charlesworth, B. No association between mitochondrial DNA haplotypes and a female-limited mimicry phenotype in Papilio glaucus. Evolution 57, 305 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Gantenbein, B., Fet, V., Gantenbein-Ritter, I. A. & Balloux, F. Evidence for recombination in scorpion mitochondrial DNA (Scorpiones: Buthidae). Proc. R. Soc. B Biol. Sci. 272, 697–704 (2005).CAS 
Article 

Google Scholar 
36.Hebert, P. D. N., Cywinska, A., Ball, S. L. & Dewaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321 (2003).CAS 
Article 

Google Scholar 
37.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
40.Paradis, E. pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Templeton, A. R., Crandall, K. A. & Sing, C. F. A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation. Genetics 132, 619–633 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

Google Scholar 
45.Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinform. Oxf. Engl. 24, 1403–1405 (2008).CAS 
Article 

Google Scholar  More

1.Priddel, D. & Carlile, N. J. An artificial nest box for burrow-nesting seabirds. Emu-Austral Ornithol. 95, 290–294 (1995).Article 

Google Scholar 
2.Burton, N. H., Evans, P. R. & Robinson, M. A. Effects on shorebird numbers of disturbance, the loss of a roost site and its replacement by an artificial island at Hartlepool, Cleveland. Biol. Conserv. 77, 193–201 (1996).Article 

Google Scholar 
3.Chambers, C. L., Alm, V., Siders, M. S. & Rabe, M. J. Use of artificial roosts by forest-dwelling bats in northern Arizona. Wildl. Soc. B 30, 1085–1091 (2002).
Google Scholar 
4.Lausen, C. L. & Barclay, R. M. Benefits of living in a building: Big brown bats (Eptesicus fuscus) in rocks versus buildings. J. Mammal. 87, 362–370 (2006).Article 

Google Scholar 
5.Kelm, D. H., Wiesner, K. R. & Helversen, O. V. Effects of artificial roosts for frugivorous bats on seed dispersal in a Neotropical forest pasture mosaic. Biol. Conserv. 22, 733–741 (2008).Article 

Google Scholar 
6.Agnelli, P., Maltagliati, G., Ducci, L. & Cannicci, S. J. H. Artificial roosts for bats: education and research. The” Be a bat’s friend” project of the Natural History Museum of the University of Florence. Ital. J. Mammal. 22, 733–741 (2010).
Google Scholar 
7.Rueegger, N. Bat boxes: A review of their use and application, past, present and future. Acta Chiropterol. 18, 279–299 (2016).Article 

Google Scholar 
8.Brittingham, M. C. & Williams, L. M. Bat boxes as alternative roosts for displaced bat maternity colonies. Wildl. Soc. B 28, 197–207 (2000).
Google Scholar 
9.Lambrechts, M. M. et al. Nest box design for the study of diurnal raptors and owls is still an overlooked point in ecological, evolutionary and conservation studies: A review. J. Ornithol. 153, 23–34 (2012).Article 

Google Scholar 
10.Easterling, D. R. et al. Observed variability and trends in extreme climate events: A brief review. Bull. Am. Meteorol. Soc. 81, 417–426 (2000).ADS 
Article 

Google Scholar 
11.Welbergen, J. A., Klose, S. M., Markus, N. & Eby, P. Climate change and the effects of temperature extremes on Australian flying-foxes. Proc. R. Soc. B 275, 419–425 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
12.Adams, R. A. Bat reproduction declines when conditions mimic climate change projections for western North America. Ecology 91, 2437–2445 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Ratti, J. T. & Reese, K. P. J. T. Preliminary test of the ecological trap hypothesis. J. Wildl. Manage 52, 484–491 (1988).Article 

Google Scholar 
14.Flaquer, C. et al. Could overheating turn bat boxes into death traps. Barb 7, 46–53 (2014).
Google Scholar 
15.Bideguren, G. M. et al. Bat boxes and climate change: Testing the risk of over-heating in the Mediterranean region. Biodivers. Conserv. 28, 21–35 (2019).Article 

Google Scholar 
16.Griffiths, S. R. et al. Surface reflectance drives nest box temperature profiles and thermal suitability for target wildlife. PLoS ONE 12, e0176951 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Rowland, J. A., Briscoe, N. J. & Handasyde, K. A. Comparing the thermal suitability of nest-boxes and tree-hollows for the conservation-management of arboreal marsupials. Biol. Conserv. 209, 341–348 (2017).Article 

Google Scholar 
18.Zahn, A. Reproductive success, colony size and roost temperature in attic-dwelling bat Myotis myotis. J. Zool. 247, 275–280 (1999).Article 

Google Scholar 
19.Ruczyński, I. Influence of temperature on maternity roost selection by noctule bats (Nyctalus noctula) and Leisler’s bats (N. leisleri) in Białowieża Primeval Forest Poland. Can. J. Zool. 84, 900–907 (2006).Article 

Google Scholar 
20.Wilcox, A. & Willis, C. K. Energetic benefits of enhanced summer roosting habitat for little brown bats (Myotis lucifugus) recovering from white-nose syndrome. Conserv. Physiol. 4, 070 (2016).Article 

Google Scholar 
21.Thiollay, J.-M. Comparative foraging success of insectivorous birds in tropical and temperate forests: Ecological implications. Oikos 53, 17–30 (1988).Article 

Google Scholar 
22.Ransome, R. Population changes of greater horseshoe bats studied near Bristol over the past twenty-six years. Biol. J. Linn. Soc. 38, 71–82 (1989).Article 

Google Scholar 
23.O’Shea, T. J. et al. Recruitment in a Colorado population of big brown bats: Breeding probabilities, litter size, and first-year survival. J. Mammal. 91, 418–428 (2010).Article 

Google Scholar 
24.Nurul-Ain, E., Rosli, H. & Kingston, T. Resource availability and roosting ecology shape reproductive phenology of rain forest insectivorous bats. Biotropica 49, 382–394 (2017).Article 

Google Scholar 
25.Racey, P. Environmental factors affecting the length of gestation in heterothermic bats. J. Reprod. Fertil. 19, 175–189 (1973).CAS 

Google Scholar 
26.Racey, P. & Swift, S. M. Variations in gestation length in a colony of pipistrelle bats (Pipistrellus pipistrellus) from year to year. J. Reprod. Fertil. 61, 123–129 (1981).CAS 
PubMed 
Article 

Google Scholar 
27.Wilde, C. J., Knight, C. H. & Racey, P. A. Influence of torpor on milk protein composition and secretion in lactating bats. J. Exp. Zool. A 284, 35–41 (1999).CAS 
Article 

Google Scholar 
28.Beer, J. R. & Richards, A. G. Hibernation of the big brown bat. J. Mammal. 37, 31–41 (1956).Article 

Google Scholar 
29.Pagels, J. F. Temperature regulation, body weight and changes in total body fat of the free-tailed bat, Tadarida brasiliensis cynocephala (Le Conte). Comp. Biochem. Phys. A 50, 237–246 (1975).CAS 
Article 

Google Scholar 
30.Henry, M., Thomas, D. W., Vaudry, R. & Carrier, M. Foraging distances and home range of pregnant and lactating little brown bats (Myotis lucifugus). J. Mammal. 83, 767–774 (2002).Article 

Google Scholar 
31.Studier, E. H. & O’Farrell, M. J. Biology of Myotis thysanodes and M. lucifugus (Chiroptera: Vespertilionidae)—III. Metabolism, heart rate, breathing rate, evaporative water loss and general energetics. Comp. Biochem. Phys. A 54, 423–432 (1976).CAS 
Article 

Google Scholar 
32.Henry, M. Étude de l’écologie d’une population de petites chauves-souris brunes (Myotis Lucifugus) en vue d’un programme de conservation. Master’s thesis. Sherbrooke University. https://savoirs.usherbrooke.ca/handle/11143/4513 (2001).33.Flaquer, C., Torre, I. & Ruiz-Jarillo, R. The value of bat-boxes in the conservation of Pipistrellus pygmaeus in wetland rice paddies. Biol. Conserv. 128, 223–230 (2006).Article 

Google Scholar 
34.Mickleburgh, S. P., Hutson, A. M. & Racey, P. A. A review of the global conservation status of bats. Oryx 36, 18–34 (2002).Article 

Google Scholar 
35.Boyles, J. G., Cryan, P. M., McCracken, G. F. & Kunz, T. H. Economic importance of bats in agriculture. Science 332, 41–42 (2011).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Barclay, R. M., Harder, L. D., Kunz, T. & Fenton, M. Life histories of bats: life in the slow lane. In Bat Ecology (eds Kunz, T. & Fenton, M.) 209–253 (The University of Chicago Press, 2003).
Google Scholar 
37.Keen, R. & Hitchcock, H. B. Survival and longevity of the little brown bat (Myotis lucifugus) in southeastern Ontario. J. Mammal. 61, 1–7 (1980).Article 

Google Scholar 
38.Kunz, T. H. Censusing bats: challenges, solutions, and sampling biases in Monitoring Trends in Bat Populations of the United States and Territories: Problems and Prospects (Eds TJ O’Shea, and MA Bogan). 9–20 (US Geological Survey, Sciences Division, Biological Resources Discipline, Information and Technology Report USGS/BRD/ITR-2003–003, 2003).39.Campbell, L. A., Hallett, J. G. & O’Connell, M. A. Conservation of bats in managed forests: Use of roosts by Lasionycteris noctivagans. J. Mammal. 77, 976–984 (1996).Article 

Google Scholar 
40.Entwistle, A., Racey, P. & Speakman, J. R. Roost selection by the brown long-eared bat Plecotus auritus. J. Appl. Ecol. 34, 399–408 (1997).Article 

Google Scholar 
41.Kerth, G., Weissmann, K. & König, B. Day roost selection in female Bechstein’s bats (Myotis bechsteinii): A field experiment to determine the influence of roost temperature. Oecologia 126, 1–9 (2001).ADS 
PubMed 
Article 

Google Scholar 
42.Lourenço, S. I. & Palmeirim, J. M. Influence of temperature in roost selection by Pipistrellus pygmaeus (Chiroptera): Relevance for the design of bat boxes. Biol. Conserv. 2, 237–243 (2004).Article 

Google Scholar 
43.Webber, Q. M. & Willis, C. K. An experimental test of effects of ambient temperature and roost quality on aggregation by little brown bats (Myotis lucifugus). J. Therm. Biol. 74, 174–180 (2018).PubMed 
Article 

Google Scholar 
44.Mering, E. D. & Chambers, C. L. Thinking outside the box: A review of artificial roosts for bats. Wildl. Soc. B 38, 741–751 (2014).Article 

Google Scholar 
45.Mackintosh, M. Bats and licensing: A report on the success of maternity roost compensation measures. Scottish Natural Heritage Commissioned Report No. 928. https://www.nature.scot/sites/default/files/Publication%202016%20-%20SNH%20Commissioned%20Report%20928%20-%20Bats%20and%20Licensing%20-%20A%20report%20on%20the%20success%20of%20maternity%20roost%20compensation%20measures.pdf (2016).46.López-Baucells, A. et al. Bat boxes in urban non-native forests: A popular practice that should be reconsidered. Urban Ecosyst. 20, 217–225 (2017).Article 

Google Scholar 
47.Neilson, A. L. & Fenton, M. B. Responses of little brown myotis to exclusion and to bat houses. Wildl. Soc. B 22, 8–14 (1994).
Google Scholar 
48.White, E. P. Factors affecting bat house occupancy in Colorado. Southwest Nat. 49, 344–349 (2004).Article 

Google Scholar 
49.Michaelsen, T. C., Jensen, K. H. & Högstedt, G. R. Roost site selection in pregnant and lactating soprano pipistrelles (Pipistrellus pygmaeus Leach, 1825) at the species northern extreme: The importance of warm and safe roosts. Acta Chiropterol. 16, 349–357 (2014).Article 

Google Scholar 
50.Bartonicka, T. & Řehák, Z. Influence of the microclimate of bat boxes on their occupation by the soprano pipistrelle Pipistrellus pygmaeus: Possible cause of roost switching. Acta Chiropterol. 9, 517–526 (2007).Article 

Google Scholar 
51.Ralegaonkar, R. V. & Gupta, R. Review of intelligent building construction: A passive solar architecture approach. Renew. Sust. Energy Rev. 14, 2238–2242 (2010).Article 

Google Scholar 
52.Morrissey, J., Moore, T. & Horne, R. E. Affordable passive solar design in a temperate climate: An experiment in residential building orientation. Renew. Energy 36, 568–577 (2011).Article 

Google Scholar 
53.Sodha, M. S., Bansal, N. K., Bansal, P. K., Kumar, A., and Malik, M. Solar passive building: Science and Design (ed. Ilustrated), (Pergamon Press, 1986).54.Griffiths, S. R. et al. Bat boxes are not a silver bullet conservation tool. Mammal. Rev. 47, 261–265 (2017).Article 

Google Scholar 
55.Arias, M., Gignoux-Wolfsohn, S., Kerwin, K. & Maslo, B. Use of artificial roost boxes installed as alternative habitat for bats evicted from buildings. Northeast Nat. 27, 201–214 (2020).Article 

Google Scholar 
56.Tuttle, M. D., Kiser, M. & Kiser, S. The Bat House Builder’s handbook (Eds Tuttle, M. D., Kiser, M. & Kiser, S.). (University of Texas Press, 2005).57.Kiser, M. & Kiser, S. A decade of bat house discovery. Bat House Res. 12, 1–12 (2004).
Google Scholar 
58.Long, R., Kiser, W. & Kiser, S. Well-placed bat houses can attract bats to Central Valley farms. Calif. Agric. 60, 91–94 (2006).Article 

Google Scholar 
59.Dillingham, C. P., Cross, S. P. & Dillingham, P. W. Two environmental factors that influence usage of bat houses in managed forests of southwest Oregon. Northwest Nat. 84, 20–23 (2003).Article 

Google Scholar 
60.Horncastle, V., Frary, V., Ingraldi, M. P. Progress report—forest-dwelling bat responses to forest restoration (Arizona Game and Fish Department, 2008).61.Ardia, D. R., Pérez, J. H. & Clotfelter, E. D. Nest box orientation affects internal temperature and nest site selection by Tree Swallows. J. Field. Ornithol. 77, 339–344 (2006).Article 

Google Scholar 
62.Hooge, P. N., Stanback, M. T. & Koenig, W. D. Nest-site selection in the Acorn Woodpecker. Auk 116, 45–54 (1999).Article 

Google Scholar 
63.Wiebe, K. L. Microclimate of tree cavity nests: Is it important for reproductive success in Northern Flickers?. Auk 118, 412–421 (2001).Article 

Google Scholar 
64.Godinho, L. N., Lumsden, L. F., Coulson, G. & Griffiths, S. R. Flexible roost selection by Gould’s wattled bats (Chalinolobus gouldii) using bat boxes in an urban landscape. Aust. J. Zool. 10, e1071 (2020).
Google Scholar 
65.Goldingay, R. L., Rueegger, N. N., Grimson, M. J. & Taylor, B. D. Specific nest box designs can improve habitat restoration for cavity-dependent arboreal mammals. Restor. Ecol. 23, 482–490 (2015).Article 

Google Scholar 
66.Summers, R. & Taylor, W. Use by tits of nest boxes of different designs in pinewoods. Bird Study 43, 138–141 (1996).Article 

Google Scholar 
67.Hoeh, J. P. S., Bakken, G. S., Mitchell, W. A. & O’Keefe, J. M. In artificial roost comparison, bats show preference for rocket box style. PLoS ONE 13, e0205701 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Rueegger, N., Goldingay, R., Law, B. & Gonsalves, L. Testing multichambered bat box designs in a habitat-offset area in eastern Australia: Influence of material, colour, size and box host. Pac. Conserv. Biol. 26, 13–21 (2020).Article 

Google Scholar 
69.Campbell, S., Coulson, G. & Lumsden, L. F. Divergent microclimates in artificial and natural roosts of the large-footed myotis (Myotis macropus). Acta Chiropterol. 12, 173–185 (2010).Article 

Google Scholar 
70.Bat Conservation International, Bat houses https://www.batcon.org/about-bats/bat-houses/ (2021).71.Geiser, F. & Drury, R. L. Radiant heat affects thermoregulation and energy expenditure during rewarming from torpor. J. Comp. Physiol. B 173, 55–60 (2003).CAS 
PubMed 
Article 

Google Scholar 
72.Turbill, C., Körtner, G. & Geiser, F. Natural use of heterothermy by a small, tree-roosting bat during summer. Physiol. Biochem. Zool. 76, 868–876 (2003).PubMed 
Article 

Google Scholar 
73.Dzal, Y. A. & Brigham, R. M. The tradeoff between torpor use and reproduction in little brown bats (Myotis lucifugus). J. Comp. Physiol. B 183, 279–288 (2013).PubMed 
Article 

Google Scholar 
74.Speakman, J. R., Thomas, D. W., Kunz, T. & Fenton, M. B. Physiological ecology and energetics of bats. in Bat Ecology (Eds Kunz, T. & Fenton, M. B.). 430–490 (The University of Chicago Press, 2003).75.Besler, N. K. & Broders, H. G. Combinations of reproductive, individual, and weather effects best explain torpor patterns among female little brown bats (Myotis lucifugus). Ecol. Evol. 9, 5158–5171 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
76.Willis, C. K. & Brigham, R. M. Social thermoregulation exerts more influence than microclimate on forest roost preferences by a cavity-dwelling bat. Behav. Ecol. Sociobiol. 62, 97–108 (2007).Article 

Google Scholar 
77.Kurta, A., Bell, G. P., Nagy, K. A. & Kunz, T. H. Energetics of pregnancy and lactation in freeranging little brown bats (Myotis lucifugus). Physiol. Zool. 62, 804–818 (1989).Article 

Google Scholar 
78.Lewis, S. E. Roost fidelity of bats: A review. J. Mammal. 76, 481–496 (1995).Article 

Google Scholar 
79.Kerth, G. & Konig, B. Fission, fusion and nonrandom associations in female Bechstein’s bats (Myotis bechsteinii). Behaviour 136, 1187–1202 (1999).Article 

Google Scholar 
80.Boye, P. & Dietz, M. Development of good practice guidelines for woodland management for bats. English Nature Report to The Bat Conservation Trust (2005).81.Fukui, D., Okazaki, K., Miyazaki, M. & Maeda, K. The effect of roost environment on roost selection by non-reproductive and dispersing Asian parti-coloured bats Vespertilio sinensis. Mammal. Stud. 35, 99–109 (2010).Article 

Google Scholar 
82.Fabianek, F., Simard, M. A., Racine, E. B. & Desrochers, A. Selection of roosting habitat by male Myotis bats in a boreal forest. Can. J. Zool. 93, 539–546 (2015).Article 

Google Scholar 
83.Hamilton, I. M. & Barclay, R. M. Patterns of daily torpor and day-roost selection by male and female big brown bats (Eptesicus fuscus). Can. J. Zool. 72, 744–749 (1994).Article 

Google Scholar 
84.Grinevitch, L., Holroyd, S. & Barclay, R. Sex differences in the use of daily torpor and foraging time by big brown bats (Eptesicus fuscus) during the reproductive season. J. Zool. 235, 301–309 (1995).Article 

Google Scholar 
85.Dietz, M. & Kalko, E. K. Seasonal changes in daily torpor patterns of free-ranging female and male Daubenton’s bats (Myotis daubentonii). J. Comp. Physiol. B 176, 223–231 (2006).PubMed 
Article 

Google Scholar 
86.Barclay, R. M. Night roosting behavior of the little brown bat, Myotis lucifugus. J. Mammal. 63, 464–474 (1982).Article 

Google Scholar 
87.Jonasson, K. A. & Willis, C. K. R. Changes in body condition of hibernating bats support the thrifty female hypothesis and predict consequences for populations with white-nose syndrome. PLoS ONE 6, e21061 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Willis, C. R., Turbill, C. & Geiser, F. Torpor and thermal energetics in a tiny Australian vespertilionid, the little forest bat (Vespadelus vulturnus). J. Comp. Physiol. B 175, 479–486 (2005).PubMed 
Article 

Google Scholar 
89.Hock, R. J. The metabolic rates and body temperatures of bats. Biol. Bull. 101, 475–479 (1951).Article 

Google Scholar 
90.Humphries, M. M., Thomas, D. W. & Speakman, J. R. Climate-mediated energetic constraints on the distribution of hibernating mammals. Nature 418, 313–316 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.Humphries, M.M., Speakman, J.R., & Thomas, D.W. Temperature, hibernation energetics, and the cave and continental distributions of little brown myotis. in Functional and Evolutionary Ecology of Bats (Zubaid, A., McCracken, G.F., Kunz, T.H.). 23–37 (Oxford University Press, 2005).92.Thomas, D. W., Dorais, M. & Bergeron, J. Winter energy budget and cost of arousals for hibernating little brown bats, Myotis lucifugus. J. Mammal. 71, 475–479 (1990).Article 

Google Scholar 
93.Stones, R. C. & Wiebers, J. E. A review of temperature regulation in bats (Chiroptera). Am. Midl. Nat. 74, 155–167 (1965).Article 

Google Scholar 
94.Campbell, K. L., McIntyre, I. W. & MacArthur, R. W. Postprandial heat increment does not substitute for active thermogenesis in cold challenged star-nosed moles (Condylura cristata). J. Exp. Biol. 203, 301–310 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Solving the problem of engineering water shortage is key to ensure water resource security in the karst area It can be seen from the subsystems of the indices sorted by the absolute MIV that the engineering water shortage subsystem had the greatest impact on water resource security in the karst area, which is the main reason to promote its transformation.The water shortage in karst areas is caused by poor natural conditions and inadequate engineering conditions, that is, “engineering water shortage”. It is a serious problem in the Guizhou karst area. The main reasons are as follows. First, the karst hydrogeological and geomorphic conditions, with high mountains and deep rivers, make Guizhou a water shortage area. Second, the karst area is rich in water resources, but it is difficult to develop and utilize these resources. Inter annual variations of rainfall are not significant, but there are large differences within the year, which can easily lead to seasonal drought. Third, the layout of water conservancy projects such as water retention, water storage, and water transfer is unreasonable or insufficient, resulting in conditions of shortage of irrigation and the inadequacy of drinking water for people and livestock. Therefore, the Guizhou karst area has become an area of water shortage, especially engineering water shortage. This is the main bottleneck restricting the coordinated development of the region’s social economy and ecology.Water conservancy projects can determine the diversion and allocation of water resources across time and district to achieve reasonable allocation, efficient utilization, and protection. This indicates the need for higher requirements for engineering water storage and improving water resource utilization efficiency. Therefore, the construction of water conservancy projects is key to ensure future water resource security.The modes of development and utilization of water resources are also significant in the karst area In the past 15 years, Guizhou Province has attached great importance to the development and utilization of water resources. The subsystems of water resource carrying capacity and vulnerability in the Guizhou karst area have risen steadily, which has improved water resource security. However, the development and utilization of water resources will cause changes in the quantity and structure of water usage. This has both optimization and constraints on regional development. Therefore, the geological, hydrological, and hydrogeological characteristics of the karst area must be investigated. The development and utilization of water resources in the karst area should involve appropriate technologies or methods in accordance with these different hydrogeological structures. Geology, geomorphology, rainwater, distributions of farmland and residences, and hydrogeological structures in the karst area are the major factors to consider for solving water shortages in this area35. Rain collection, underground reservoirs, a decentralized water supply and runoff gathering are significant modes of development in the karst area.The situation of water resource security in karst area of Guizhou is gradually getting better This is achieved through water conservation projects and technological measures for water resource exploitation, utilization, projection, and reasonable allocation and control. Meanwhile, Guizhou achieves the security of regional water resource utilization and development through adjusting the regional economic pattern, water resource utilization technology, and so forth.From 2001 to 2006, the status of water resource security was serious, and there was a moderate warning level. At that time, the industrialization of Guizhou province was developing rapidly, and the construction of water conservancy and other infrastructure was also advancing rapidly. Increased attention was given to soil erosion, desertification, water resource pollution, and other problems. Despite high water consumption, the water environment was gradually improving. However, rapid economic and social development has exceeded the carrying capacity of the water resources during this period. Some problems persist in the study area, such as inadequacy of urban sewage treatment facilities, outdated water conservancy facilities, and insufficient prevention of environmental pollution. Urban water pollution treatment facilities and garbage treatment facilities are seriously outdated and cannot meet the requirements of urban development and water environmental protection. These problems have led to a low starting point for water resource security utilization in Guizhou Province. Although the situation has been improved and alleviated year by year, it is still in a moderate warning level, and the water resource security situation is still severe.After reaching the critical safety level in 2007, the water resource security of Guizhou Province declined slightly in 2009 and 2013, although a critical safety level was maintained; the safety level further deteriorated to a moderate warning level in 2011. This deterioration occurred because Guizhou suffered its worst drought in a century from 2009 to 2011, and another drought in 2013. According to the information provided by single indices, the treatment rate of urban waste water, proportion of water supply for water lifting and diversion projects, qualifying rate of water environment function zones, qualifying rate of industrial waste water, degree of development and utilization of groundwater, and density of large and medium-sized reservoirs all showed increasing trends year by year or showed relatively high levels. In contrast, the indices of irrigation water consumption per unit area, above moderate rocky desertification area ratio, water consumption per ten thousand yuan GDP, and water consumption per ten thousand yuan industrial output decreased year by year. All of these indices played a driving role in water utilization and water resource security in the study area. Although the once-in-a-century drought reduced the amount of water, Guizhou Province improved the utilization rate of water resources in the dry years, which alleviated the impact of the reduction of water resources to a certain extent, and allowed the water resource security in the study area to barely maintain the critical safety level. This finding is consistent with previous research conclusions: the engineering water shortage subsystem had largest effect on water resource security in the karst area, whereas the water quantity subsystem had the least influence.It can be inferred that the requirements for ensuring water resource security in the karst area are a good economic development model, environmental protection, pollution control, and improvement of basic water conservancy facilities. These measures can be conducive to actively coping with the impact of abnormal climate changes on the utilization of water resources. More

1.Weiner, J. Physiological limits to sustainable energy budgets in birds and mammals: ecological implications. Trends Ecol. Evol. 7, 384–388 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Mcnab, B. K. Food habits, energetics, and the population biology of mammals. Am. Nat. 116, 106–124 (1980).Article 

Google Scholar 
3.Hovey, F. W. & Harestad, A. S. Estimating effects of snow on shrub availability for black-tailed deer in southwestern British Columbia. Wildl. Soc. Bull. 20, 308–313 (1992).
Google Scholar 
4.Post, E. & Stenseth, N. Climatic variability, plant phenology, and northern ungulates. Ecology 80, 1322–1339 (1999).Article 

Google Scholar 
5.Moen, A. N. Seasonal changes in heart rates, activity, metabolism, and forage intake of white-tailed deer. J. Wildl. Manag. 42, 715–738 (1978).Article 

Google Scholar 
6.Holand, Ø., Mysterud, A., Wannag, A. & Linnell, J. D. C. Roe deer in northern environments: physiology and behaviour. In The European Roe Deer: Biology of Success (eds Andersen, R. et al.) 117–137 (Scandinavian University Press, 1998).
Google Scholar 
7.Foromozov, A. N. Snow Cover as an Integral Factor of the Environment and Its Importance in the Ecology of Mammals and Birds (The University of Alberta, 1963).
Google Scholar 
8.Cagnacci, F. et al. Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors. Oikos 120, 1790–1802 (2011).Article 

Google Scholar 
9.Dussault, C., Courtois, R., Ouellet, J.-P. & Girard, I. Space use of moose in relation to food availability. Can. J. Zool. 83, 1431–1437 (2005).Article 

Google Scholar 
10.Mysterud, A. & Sæther, B.-E. Climate change and implications for the future distribution and management of ungulates in Europe. In Ungulate Management in Europe: Problems and Practices (eds Putman, R. et al.) 349–375 (Cambridge University Press, 2011).
Google Scholar 
11.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
12.Scherrer, S. C., Wüthrich, C., Croci-Maspoli, M., Weingartner, R. & Appenzeller, C. Snow variability in the Swiss Alps 1864–2009. Int. J. Climatol. 33, 3162–3173 (2013).Article 

Google Scholar 
13.Milner, J. M., van Beest, F. M., Schmidt, K. T., Brook, R. K. & Storaas, T. To feed or not to feed? Evidence of the intended and unintended effects of feeding wild ungulates. J. Wildl. Manag. 78, 1322–1334 (2014).Article 

Google Scholar 
14.Ossi, F. et al. Plastic response by a small cervid to supplemental feeding in winter across a wide environmental gradient. Ecosphere 8, e01629 (2017).Article 

Google Scholar 
15.Putman, R. & Staines, B. W. Supplementary winter feeding of wild red deer Cervus elaphus in Europe and North America: justifications, feeding practice and effectiveness. Mamm. Rev. 34, 285–306 (2004).Article 

Google Scholar 
16.Cagnacci, F., Boitani, L., Powell, R. A. & Boyce, M. S. Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Philos. Trans. R. Soc. B Biol. Sci. 365, 2157–2162 (2010).Article 

Google Scholar 
17.Peters, W. et al. Migration in geographic and ecological space by a large herbivore. Ecol. Monogr. 87, 297–320 (2017).Article 

Google Scholar 
18.Morellet, N. et al. Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within Europe. J. Anim. Ecol. 82, 1326–1339 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Johnson, D. H. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, 65–71 (1980).Article 

Google Scholar 
20.Ossi, F., Gaillard, J. M., Hebblewhite, M. & Cagnacci, F. Snow sinking depth and forest canopy drive winter resource selection more than supplemental feeding in an alpine population of roe deer. Eur. J. Wildl. Res. 61, 111–124 (2015).Article 

Google Scholar 
21.Mysterud, A. & Østbye, E. Bed-site selection by European roe deer (Capreolus capreolus) in southern Norway during winter. Can. J. Zool. 73, 924–932 (1995).Article 

Google Scholar 
22.Ramanzin, M., Sturaro, E. & Zanon, D. Seasonal migration and home range of roe deer (Capreolus capreolus) in the Italian eastern Alps. Can. J. Zool. 85, 280–289 (2007).Article 

Google Scholar 
23.Endrizzi, S., Gruber, S., Dall’Amico, M. & Rigon, R. GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects. Geosci. Model. Dev. 7, 2831–2857 (2014).Article 
ADS 

Google Scholar 
24.Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).Article 

Google Scholar 
25.Thomson, A. M. et al. RCP 4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).CAS 
Article 
ADS 

Google Scholar 
26.Riahi, K. et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).CAS 
Article 
ADS 

Google Scholar 
27.Thomas, C. D. Climate, climate change and range boundaries. Divers. Distrib. 16, 488–495 (2010).Article 

Google Scholar 
28.Penteriani, V. et al. Evolutionary and ecological traps for brown bears Ursus arctos in human-modified landscapes. Mamm. Rev. 48, 180–193 (2018).Article 

Google Scholar 
29.Sorensen, A., van Beest, F. M. & Brook, R. K. Impacts of wildlife baiting and supplemental feeding on infectious disease transmission risk: a synthesis of knowledge. Prev. Vet. Med. 113, 356–363 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Mysterud, A., Viljugrein, H., Solberg, E. J. & Rolandsen, C. M. Legal regulation of supplementary cervid feeding facing chronic wasting disease. J. Wildl. Manag. 83, 1667–1675 (2019).Article 

Google Scholar 
31.Ceacero, F. et al. Benefits for dominant red deer hinds under a competitive feeding system: food access behavior, diet and nutrient selection. PLoS ONE 7, e32780 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
32.Beever, E. A. et al. Behavioral flexibility as a mechanism for coping with climate change. Front. Ecol. Environ. 15, 299–308 (2017).Article 

Google Scholar 
33.Loe, L. E. et al. Behavioral buffering of extreme weather events in a high-Arctic herbivore. Ecosphere 7, e01374 (2016).Article 

Google Scholar 
34.Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
36.Mysterud, A. Still walking on the wild side? Management actions as steps towards ‘semi-domestication’ of hunted ungulates. J. Appl. Ecol. 47, 920–925 (2010).Article 

Google Scholar 
37.Felton, A. M. et al. Interactions between ungulates, forests, and supplementary feeding: the role of nutritional balancing in determining outcomes. Mamm. Res. 62, 1–7 (2017).Article 

Google Scholar 
38.Ricci, S. et al. Impact of supplemental winter feeding on ruminal microbiota of roe deer Capreolus capreolus. Wildl. Biol. 2019, wlb.00572 (2019).Article 

Google Scholar 
39.Lone, K. et al. Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans. Oikos 123, 641–651 (2014).Article 

Google Scholar 
40.Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science (80-) 346, 1517–1519 (2014).CAS 
Article 
ADS 

Google Scholar 
41.Milanesi, P., Breiner, F. T., Puopolo, F. & Holderegger, R. European human-dominated landscapes provide ample space for the recolonization of large carnivore populations under future land change scenarios. Ecography (Cop.) 40, 1359–1368 (2017).Article 

Google Scholar 
42.Pascual-Rico, R. et al. Is diversionary feeding a useful tool to avoid human-ungulate conflicts? A case study with the aoudad. Eur. J. Wildl. Res. 64, 1–7 (2018).Article 

Google Scholar 
43.van Beest, F. M., Loe, L. E., Mysterud, A. & Milner, J. M. Comparative space use and habitat selection of moose around feeding stations. J. Wildl. Manag. 74, 219–227 (2010).Article 

Google Scholar 
44.Jerina, K. Roads and supplemental feeding affect home-range size of Slovenian red deer more than natural factors. J. Mamm. 93, 1139–1148 (2012).Article 

Google Scholar 
45.Ranc, N. et al. Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability. Scientific Reports 10, 11946 (2020). 46.Brown, R. D. & Robinson, D. A. Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosphere 5, 219–229 (2011).Article 
ADS 

Google Scholar 
47.Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl. Acad. Sci. U. S. A. 109, 8606–8611 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
48.Gurarie, E. et al. A framework for modelling range shifts and migrations: asking when, whither, whether and will it return. J. Anim. Ecol. 86, 943–959 (2017).PubMed 
Article 

Google Scholar 
49.Rivrud, I. M. et al. Leave before it’s too late: anthropogenic and environmental triggers of autumn migration in a hunted ungulate population. Ecology 97, 1058–1065 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Courtois, R., Dussault, C., Potvin, F. & Daigle, G. Habitat selection by moose (Alces alces) in clear-cut landscapes. Alces 38, 177–192 (2002).
Google Scholar 
51.Gilbert, S. L., Hundertmark, K. J., Person, D. K., Lindberg, M. S. & Boyce, M. S. Behavioral plasticity in a variable environment: snow depth and habitat interactions drive deer movement in winter. J. Mamm. 98, 246–259 (2017).Article 

Google Scholar 
52.Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science (80-) 344, 1242552 (2014).CAS 
Article 

Google Scholar 
54.Mason, T. H. E., Stephens, P. A., Apollonio, M. & Willis, S. G. Predicting potential responses to future climate in an alpine ungulate: Interspecific interactions exceed climate effects. Glob. Change Biol. 20, 3872–3882 (2014).Article 
ADS 

Google Scholar 
55.Carnevali, L., Pedrotti, L., Riga, F. & Toso, S. Banca dati ungulati: Status, distribuzione, consistenza, gestione e prelievo venatorio delle popolazioni di ungulati in Italia. Rapporto 2001–2005 Vol. 117 (Biologia e Conservazione della Fauna, 2009).
Google Scholar 
56.Provincia Autonoma di Trento. Analisi delle consistenze e dei prelievi di ungulati, tetraonidi e coturnice. Stagione Venatoria 2018 (Provincia Autonoma di Trento, 2018).
Google Scholar 
57.Rockel, B., Will, A. & Hense, A. The regional climate model COSMO-CLM (CCLM). Meteorol. Z. 17, 347–348 (2008).Article 

Google Scholar 
58.Boyce, M. S. & McDonald, L. L. Relating populations to habitats using resource selection functions. Trends Ecol. Evol. 14, 268–272 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Modell. 157, 281–300 (2002).Article 

Google Scholar 
60.Benoit, T. & Achraf, E. suncalc: compute sun position, sunlight phases, moon position and lunar phase. R package version 0.5.0. https://cran.r-project.org/package=suncalc (2019).61.DeCesare, N. J. et al. Transcending scale dependece in identifying habitat with resource selection functions. Ecol. Appl. 22, 1068–1083 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Kendall, M. A new measure of rank correlation. Biometrika 30, 81–89 (1938).MATH 
Article 

Google Scholar 
63.Cohen, J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol. Bull. 70, 213–220 (1968).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Gamer, M., Lemon, J., Fellows, I. & Singh, P. irr: various coefficients of interrater reliability and agreement. R package version 0.84.1. https://cran.r-project.org/package=irr (2019).65.Lele, S. R., Keim, J. L. & Solymos, P. ResourceSelection: resource selection (probability) functions for use-availability data. R package version 0.3-5. https://cran.r-project.org/package=ResourceSelection (2019).66.Bivand, R., Keitt, T. & Rowlingson, B. rgdal: bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4-8. https://cran.r-project.org/package=rgdal (2019).67.McLeod, A. I. Kendall: Kendall rank correlation and Mann-Kendall trend test. R package version 2.2. https://cran.r-project.org/package=Kendall (2011).68.Bright Ross, J. G., Peters, W., Ossi, F., Moorcroft P. R., Cordano, E., Eccel, E., Bianchini, F., Ramanzin, M., and Cagnacci, F. Datasets for “Climate change and anthropogenic food manipulation interact in shifting the distribution of a large herbivore at its altitudinal range limit.” https://doi.org/10.5281/zenodo.4637674 (2021). More

1.Alexandra-Maria, K. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).Article 

Google Scholar 
2.Winfree, R., Williams, N. M., Gaines, H., Ascher, J. S. & Kremen, C. Wild bee pollinators provide the majority of crop visitation across land-use gradients in New Jersey and Pennsylvania, USA. J. Appl. Ecol. 45, 793–802 (2008).Article 

Google Scholar 
3.Brosi, B. J. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc. Natl. Acad. Sci. 110, 13044–13048 (2013).CAS 
Article 
ADS 

Google Scholar 
4.Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).Article 

Google Scholar 
5.Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl. Acad. Sci. 108, 662–667 (2011).CAS 
Article 
ADS 

Google Scholar 
6.Koh, I. et al. Modeling the status, trends, and impacts of wild bee abundance in the United States. Proc. Natl. Acad. Sci. 113, 140–145 (2016).CAS 
Article 
ADS 

Google Scholar 
7.Cameron, S. A. & Sadd, B. M. Global trends in bumble bee health. Annu. Rev. Entomol. 65, 209–232 (2020).CAS 
Article 

Google Scholar 
8.Murray, T. E., Kuhlmann, M. & Potts, S. G. Conservation ecology of bees: populations, species and communities. Apidologie 40, 211–236 (2009).Article 

Google Scholar 
9.Michener, C. D. The Bees of the World (Johns Hopkins University Press, Baltimore, 2007).
Google Scholar 
10.Milam, J. et al. Validating morphometrics with DNA barcoding to reliably separate three cryptic species of bombus cresson (Hymenoptera: Apidae). Insects 11, 669 (2020).Article 

Google Scholar 
11.Williams, P. H. et al. Widespread polytypic species or complexes of local species? Revising bumblebees of the subgenus Melanobombus world-wide (Hymenoptera, Apidae, Bombus). Eur. J. Taxon. 719, 1–120 (2020).
Google Scholar 
12.Drew, L. W. Are we losing the science of taxonomy? As need grows, numbers and training are failing to keep up. Bioscience 61, 942–946 (2011).Article 

Google Scholar 
13.Portman, Z. M., Bruninga-Socolar, B. & Cariveau, D. P. The state of bee monitoring in the United States: A call to refocus away from bowl traps and towards more effective methods. Ann. Entomol. Soc. Am. 113, 337–342 (2020).Article 

Google Scholar 
14.Valan, M., Makonyi, K., Maki, A., Vondráček, D. & Ronquist, F. Automated taxonomic identification of insects with expert-level accuracy using effective feature transfer from convolutional networks. Syst. Biol. 68, 876–895 (2019).Article 

Google Scholar 
15.Gratton, C. & Zuckerberg, B. Citizen science data for mapping bumble bee populations, in Novel Quantitative Methods in Pollinator Ecology and Management (2019).16.MacPhail, V. J., Gibson, S. D., Hatfield, R. & Colla, S. R. Using Bumble Bee Watch to investigate the accuracy and perception of bumble bee (Bombus spp.) identification by community scientists. PeerJ 8, e9412 (2020).Article 

Google Scholar 
17.Weeks, P. J. D., Gauld, I. D., Gaston, K. J. & O’Neill, M. A. Automating the identification of insects: a new solution to an old problem. Bull. Entomol. Res. 87, 203–211 (1997).Article 

Google Scholar 
18.Schröder, S. et al. The new key to bees: Automated identification by image analysis of wings. in The Conservation Link Between Agriculture and Nature (eds. Kevan, P. & Imperatriz-Fonseca, V.) 209–216 (Ministry of Environment, 2002).19.MacLeod, N., Benfield, M. & Culverhouse, P. Time to automate identification. Nature 467, 154–155 (2010).CAS 
Article 
ADS 

Google Scholar 
20.Fuentes, A., Yoon, S., Kim, S. C. & Park, D. S. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17, 2022 (2017).Article 

Google Scholar 
21.Motta, D. et al. Application of convolutional neural networks for classification of adult mosquitoes in the field. PLoS ONE 14, e0210829 (2019).CAS 
Article 

Google Scholar 
22.Bojarski, M. et al. End to end learning for self-driving cars. arXxiv:1604.07316 (2016).23.Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A. & Mougiakakou, S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35, 1207–1216 (2016).Article 

Google Scholar 
24.Liu, Z., Gao, J., Yang, G., Zhang, H. & He, Y. Localization and classification of paddy field pests using a saliency map and deep convolutional neural network. Sci. Rep. 6, 20410 (2016).CAS 
Article 
ADS 

Google Scholar 
25.Martineau, M., Raveaux, R., Chatelain, C., Conte, D. & Venturini, G. Effective training of convolutional neural networks for insect image recognition. In Advanced Concepts for Intelligent Vision Systems, pp 426–437 (eds Blanc-Talon, J. et al.) (Springer International Publishing, Cham, 2018).
Google Scholar 
26.Marques, A. C. R. et al. Ant genera identification using an ensemble of convolutional neural networks. PLoS ONE 13, e0192011 (2018).Article 

Google Scholar 
27.Williams, P. H., Thorp, R. W., Richardson, L. L. & Colla, S. R. Bumble Bees of North America: An Identification Guide (Princeton University Press, Princeton, 2014).
Google Scholar 
28.He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. 2016 IEEE Conf. Comput. Vis. Pattern Recognit. CVPR 770–778 (2015).29.Zagoruyko, S. & Komodakis, N. Wide residual networks. arXxiv:1605.07146 (2017).30.Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. arXxiv:1512.00567 (2015).31.Tan, M. et al. MnasNet: Platform-aware neural architecture search for mobile. arXxiv:1807.11626 (2019).32.Deng, J. et al. ImageNet: A large-scale hierarchical image database. in 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (2009).33.Hernández-García, A. & König, P. Further advantages of data augmentation on convolutional neural networks. arXxiv:1906.11052 11139, 95–103 (2018).34.Fard, F. S., Hollensen, P., Mcilory, S. & Trappenberg, T. Impact of biased mislabeling on learning with deep networks. in 2017 International Joint Conference on Neural Networks (IJCNN) 2652–2657 (2017).35.Clare, J. D. J., Townsend, P. A. & Zuckerberg, B. Generalized model-based solutions to false positive error in species detection/non-detection data. Ecology 102, e03241 (2021).Article 

Google Scholar 
36.Clare, J. D. J. et al. Making inference with messy (citizen science) data: when are data accurate enough and how can they be improved?. Ecol. Appl. 29, e01849 (2019).Article 

Google Scholar 
37.Tian, Z. et al. Discriminative CNN via metric learning for hyperspectral classification. in IGARSS 2019 – 2019 IEEE International Geoscience and Remote Sensing Symposium 580–583 (2019).38.Nazki, H., Yoon, S., Fuentes, A. & Park, D. S. Unsupervised image translation using adversarial networks for improved plant disease recognition. Comput. Electron. Agric. 168, 105117 (2020).Article 

Google Scholar 
39.Wäldchen, J. & Mäder, P. Machine learning for image based species identification. Methods Ecol. Evol. 9, 2216–2225 (2018).Article 

Google Scholar 
40.Woodard, S. H. et al. Towards a U.S. national program for monitoring native bees. Biol. Conserv. 252, 108821 (2020).Article 

Google Scholar 
41.Wagner, D. L. Insect declines in the anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
Article 

Google Scholar 
42.Montgomery, G. A. et al. Is the insect apocalypse upon us? How to find out. Biol. Conserv. 241, 108327 (2020).Article 

Google Scholar 
43.Høye, T. T., Mann, H. M. R. & Bjerge, K. Camera-based monitoring of insects on green roofs. DCE – Natl. Cent. Environ. Energy 18 (2020).44.Ärje, J. et al. Automatic image-based identification and biomass estimation of invertebrates. Methods Ecol. Evol. 11, 922–931 (2020).Article 

Google Scholar 
45.Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl. Acad. Sci. 115, E5716–E5725 (2018).CAS 
Article 

Google Scholar 
46.Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).Article 

Google Scholar 
47.Ghisbain, G. et al. Substantial genetic divergence and lack of recent gene flow support cryptic speciation in a colour polymorphic bumble bee (Bombus bifarius) species complex. Syst. Ecol. 45, 635–652 (2020).
Google Scholar  More

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Experiment 1: Sponge fragmentsEvidence of bleaching first occurred 3 days after the treatment and was only evident in the group with fragments of T. hoshinota. No bleaching was detected in the other 2 groups with the black cloth (to block light) and white cloth (control) (Table 1). Chi-square tests confirmed that the occurrence of bleaching depended on the treatments (p  More