Ecology

Sightings of some common bird species increased during the UK’s 2020 lockdown.Credit: Tolga Akmen/AFP via Getty

People weren’t the only ones who changed their ways during the COVID-19 pandemic — birds did, too. Four out of five of the most commonly observed birds in the United Kingdom altered their behaviour during the nation’s first lockdown of 2020, although they did so in different ways depending on the species, according to an analysis.The study, published in Proceedings of the Royal Society B on 21 September1, is one of several that used the disruptions brought about by the pandemic — from a reduction in the number of cars on the roads to the closure of some national parks — to quantify the impact that humanity has on the natural world. Although some research has found that lockdowns had a largely positive effect on wildlife2, the latest data from the United Kingdom provide a much more nuanced picture (see Bird Behaviour).

Credit: Warrington et al/Proceedings of the Royal Society B

“People didn’t disappear during the lockdown,” says co-author Miyako Warrington, a behavioural ecologist at the University of Manitoba in Winnipeg, Canada. “We changed our behaviour, and wildlife responded.”Rare experimentIn the early months of the pandemic, social media was abuzz with reports of wild animals being seen in unusual places. These claims were partially validated when Warrington and her colleagues reported that, in 2020, many bird species in the United States and Canada were spotted moving into spaces usually occupied by people2.To see how a COVID-19 lockdown affected birds in the United Kingdom, Warrington and her colleagues tallied sightings of the 25 most common birds between March and July 2020 — during the country’s first lockdown — and compared their data set with data from previous years. In total, the study included around 870,000 observations.The team then compared this information to data showing how people split their time between home, essential shops and parks: three places people in the United Kingdom were allowed to be during the lockdown.Because people spent more time at home and in parks than before March 2020, the analysis found that 20 of the 25 bird species examined behaved differently during lockdown. Parks — which were flooded with visitors — saw an an uptick in the numbers of corvids and gulls, whereas smaller birds, such as Eurasian blue tits (Cyanistes caeruleus) and house sparrows (Passer domesticus), were spotted less frequently than in previous years. And because people spent more time at home, the number of avian species that visited domestic gardens also dropped, by around one-quarter, compared with previous years.Other species, including rock pigeons (Columba livia), didn’t react to the lockdown at all. Warrington found this surprising, because pigeons are city dwellers, so she thought they would be affected by the changes in people’s behaviour. “But they don’t give a crap about what we do,” she says.Adapting to changeThe birds that altered their habits during the lockdown were probably responding to changes in human behaviour, says Warrington. Tits and other birds whose numbers dipped might have fled when people and their pets started spending more time in parks and gardens. The reverse could be true for scavengers, such as gulls and corvids, which might have benefited from park visitors leaving behind rubbish for them to feed on.When combined with the results of other studies, the behaviour of British birds reveals the complex ways in which wildlife was affected by lockdowns and underlines the importance of reducing the disturbance of animals by people, says Raoul Manenti, a conservation zoologist at the University of Milan in Italy.For Warrington, that means acknowledging that lockdowns were not universally good for wildlife. “Our relationship with nature is complicated,” she says. By developing a better understanding of this relationship, “we know we can affect positive change as long as we do it in a thoughtful manner”. More

Orr, D. W. Land use and climate change. Conserv. Biol. 22(6), 1372–1374 (2010).
Google Scholar 
Zhang, X. D. et al. Tropospheric ozone perturbations induced by urban land expansion in China from 1980 to 2017. Environ. Sci. Technol. https://doi.org/10.1021/ACS.EST.1C06664 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Noojipady, P. et al. Forest carbon emissions from cropland expansion in the Brazilian cerrado biome. Environ. Res. Lett. 12(2), 025004. https://doi.org/10.1088/1748-9326/aa5986 (2017).ADS 
CAS 
Article 

Google Scholar 
Zhu, B., Xun, Z., Ran, Z. & Zhao, X. Study of multiple land use planning based on the coordinated development of wetland farmland: A case study of Fuyuan City, China. Sustainability 11(1), 271. https://doi.org/10.3390/su11010271 (2019).Article 

Google Scholar 
Tong, D., Chu, J., Han, Q. & Liu, X. How land finance drives urban expansion under fiscal pressure: Evidence from Chinese cities. Land. 11(2), 253. https://doi.org/10.3390/land11020253 (2022).Article 

Google Scholar 
Chen, J., Chang, K. T., Karacsonyi, D. & Zhang, X. Comparing urban land expansion and its driving factors in Shenzhen and Dongguan, China. Habitat. Int. 43, 61–71. https://doi.org/10.1016/j.habitatint.2014.01.004 (2014).CAS 
Article 

Google Scholar 
Shu, B. R., Zhang, H. H., Li, Y. L., Qu, Y. & Chen, L. Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: A case study of port towns in Taicang City, China. Habitat. Int. 43, 181–190. https://doi.org/10.1016/j.habitatint.2014.02.004 (2014).Article 

Google Scholar 
Wang, R. Y., He, W. S., Wu, D., Zhang, L. & Li, Y. J. Urban Land expansion simulation considering the diffusional and aggregated growth simultaneously: A case study of Luoyang City. Sustainability. 13(17), 9781–9781. https://doi.org/10.3390/su13179781 (2021).Article 

Google Scholar 
Wei, Y. D. & Ye, X. Determinants of urban land expansion and environmental change in China. Stoch. Env. Res. Risk. A. 28(4), 757–765. https://doi.org/10.1007/s00477-013-0840-9 (2014).Article 

Google Scholar 
Yang, Q. K., Duan, X. J., Yang, L. & Wang, L. Spatial-Temporal patterns and driving factors of rapid urban land development in provincial China: A case study of Jiangsu. Sustainability. 9(12), 2371. https://doi.org/10.3390/su9122371 (2017).Article 

Google Scholar 
Zhong, Y., Lin, A. & Zhou, Z. Evolution of the pattern of spatial expansion of urban land use in the Poyang Lake ecological economic zone. Int. J. Environ. Res. Public. Health. 16(1), 117. https://doi.org/10.3390/ijerph16010117 (2019).Article 
PubMed Central 

Google Scholar 
Wu, C., Huang, X. & Chen, B. Telecoupling mechanism of urban land expansion based on transportation accessibility: A case study of transitional Yangtze River economic Belt, China. Land Use Policy 96, 104687. https://doi.org/10.1016/j.landusepol.2020.104687 (2020).Article 

Google Scholar 
Zhao, P. Sustainable urban expansion and transportation in a growing megacity: Consequences of urban sprawl for mobility on the urban fringe of Beijing. Habitat. Int. 34(2), 236–243. https://doi.org/10.1016/j.habitatint.2009.09.008 (2010).Article 

Google Scholar 
Cai, W. J. & Tu, F. Y. Spatiotemporal characteristics and driving forces of construction land expansion in Yangtze River economic belt, China. PLoS ONE 15(1), 0227299. https://doi.org/10.1371/journal.pone.0227299 (2020).CAS 
Article 

Google Scholar 
Salvati, L., Carlucci, M., Grigoriadis, E. & Chelli, F. M. Uneven dispersion or adaptive polycentrism? Urban expansion, population dynamics and employment growth in an “ordinary” city. Rev. Region. Res. 38(1), 1–25. https://doi.org/10.1007/s10037-017-0115-x (2017).Article 

Google Scholar 
Cao, Y., Ba, I. Z., Zhou, W. & Zhang, X. Analyses of traits and driving forces on urban land expansion in a typical coal-resource-based city in a loess area. Environ. Earth. Sci. 75(16), 1191.1-11911.3. https://doi.org/10.1007/s12665-016-5926-5 (2016).Article 

Google Scholar 
Davies, R. G., Barbosa, O. D. & Fuller, R. A. City-wide relationships between green spaces, urban land use and topography. Urban Ecosyst. 11(3), 269. https://doi.org/10.1007/s11252-008-0062-y (2008).Article 

Google Scholar 
Cheng, L. L., Liu, M. & Zhan, J. Q. Land use scenario simulation of mountainous districts based on Dinamica EGO model. J. Mt. Sci. 17(2), 289–303. https://doi.org/10.1007/s11629-019-5491-y (2020).Article 

Google Scholar 
Liu, J. Y., Zhan, J. Y. & Deng, X. Z. Spatio-temporal patterns and driving forces of urban land expansion in China during the economic reform era. Ambio 34, 450–455. https://doi.org/10.1579/0044-7447-34.6.450 (2005).Article 
PubMed 

Google Scholar 
Li, X. M., Zhou, W. & Quyang, Z. J. Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors?. Appl. Geogr. 38, 1–10. https://doi.org/10.1016/j.apgeog.2012.11.004 (2013).Article 

Google Scholar 
Wang, Z. W. & Lu, C. H. Urban land expansion and its driving factors of mountain cities in China during 1990–2015. J. Geogr. Sci. 28(8), 1152–1166. https://doi.org/10.1007/s11442-018-1547-0 (2018).MathSciNet 
Article 

Google Scholar 
Zhang, Y. W. & Xie, H. L. Interactive relationship among urban expansion, economic development, and population growth since the reform and opening up in China: An analysis based on a vector error correction model. Land 8(10), 153–153. https://doi.org/10.3390/land8100153 (2019).CAS 
Article 

Google Scholar 
Deng, X., Huang, J., Rozelle, S. & Uchid, E. Growth, population and industrialization, and urban land expansion of China. J. Urban. Econ. 63(1), 96–115. https://doi.org/10.1016/j.jue.2006.12.006 (2006).Article 

Google Scholar 
Luo, J., Zhang, X. & Wu, Y. Urban land expansion and the floating population in China: For production or for living?. Cities 74(4), 219–228. https://doi.org/10.1016/j.cities.2017.12.007 (2018).Article 

Google Scholar 
Salem, M., Tsurusaki, N. & Divigalpitiya, P. Analyzing the driving factors causing urban expansion in the peri-urban areas using logistic regression: A case study of the greater Cairo region. Infrastructures 4(1), 4. https://doi.org/10.3390/infrastructures4010004 (2019).Article 

Google Scholar 
Salem, M., Bose, A. & Chowdhury, I. R. Urban expansion simulation based on various driving factors using a logistic regression model: Delhi as a case study. Sustainability 13(19), 1–17. https://doi.org/10.3390/su131910805 (2021).Article 

Google Scholar 
Su, Z. W. et al. Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China. Geomat. Nat. Hazards. Risk. 9(1), 1207–1229. https://doi.org/10.1080/19475705.2018.1505667 (2018).Article 

Google Scholar 
Hu, Y. & Hu, Y. Land cover changes and their driving mechanisms in central Asia from 2001 to 2017 supported by google earth engine. Remote. Sens-Basel. 11(5), 554. https://doi.org/10.3390/rs11050554 (2019).ADS 
Article 

Google Scholar 
Liu, Y., Song, W. & Deng, X. Understanding the spatiotemporal variation of urban land expansion in oasis cities by integrating remote sensing and multi-dimensional dpsir-based indicators. Ecol. Indic. 2(96), 23–37. https://doi.org/10.1016/j.ecolind.2018.01.029 (2019).CAS 
Article 

Google Scholar 
Tian, C., Cheng, L. L., Wang, Y. F., Sun, H. Y. & Yin, T. T. Comprehensive effectiveness evaluation and obstacle diagnosis of mining villages in the transition period. Trans. CSAE. 38(5), 241–249. https://doi.org/10.11975/j.issn.1002-6819.2022.05.029 (2022).Article 

Google Scholar 
Cheng, L. L., Sun, H. Y., Zhang, Y. & Zhen, S. Spatial structure optimization of mountainous abandoned mine land reuse based on system dynamics model and CLUE-S model. Int. J. Coal. Sci. Techn. 6, 113–126. https://doi.org/10.1007/s40789-019-0241-x (2019).CAS 
Article 

Google Scholar 
Tian, C., Cheng, L. L. & Yin, T. T. Impacts of anthropogenic and biophysical factors on ecological land using logistic regression and random forest: A case study in Mentougou District, Beijing, China. J. Mt. Sci. 19, 433–445. https://doi.org/10.1007/s11629-021-7022-x (2022).Article 

Google Scholar 
Gorelick, N., Hanchr, M., Dixon, M., Ilyushchenko, S. & Moore, R. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote. Sens. Environ. 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031 (2017).ADS 
Article 

Google Scholar 
Feng, R. D., Wang, F. Y. & Wang, K. Y. Quantifying influences of anthropogenic-natural factors on ecological land evolution in mega-urban agglomeration: A case study of Guangdong-Hong Kong-Macao Greater Bay area. J. Clean. Prod. 283(9), 125304. https://doi.org/10.1016/j.jclepro.2020.125304 (2021).Article 

Google Scholar 
Sun, X., Lu, Z., Li, F. & Crittenden, J. C. Analyzing spatio-temporal changes and tradeoffs to support the supply of multiple ecosystem services in Beijing, China. Ecol. Indicat. 94, 117–129. https://doi.org/10.1016/j.ecolind.2018.06.049 (2018).Article 

Google Scholar 
Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A. & Pereira, J. Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. Forest. Ecol. Manag. 275, 117–129. https://doi.org/10.1016/j.foreco.2012.03.003 (2012).Article 

Google Scholar 
Ugur, A. Dynamic land cover mapping of urbanized cities with Landsat 8 multi-temporal images: Comparative evaluation of classification algorithms and dimension reduction methods. Isprs Int. J. Geo-Inf. 8(3), 139. https://doi.org/10.3390/ijgi8030139 (2019).Article 

Google Scholar 
Chapelle, O. Training a support vector machine in the primal. Neural. Comput. 19(5), 1155. https://doi.org/10.1162/neco.2007.19.5.1155 (2007).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
Lin, Q. Y., Guo, J. Y., Yan, J. F. & Wang, H. Land use and landscape pattern changes of Weihai, China based on object-oriented SVM classification from Landsat MSS/TM/OLI images. Eur. J. Remote. Sens. 51(1), 1036–1048. https://doi.org/10.1080/22797254.2018.1534532 (2018).Article 

Google Scholar 
Devos, O., Ruckebusch, C., Duponchel, L. & Huvenne, J. P. Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation. Chemometr. Intell. Lab. 96(1), 27–33. https://doi.org/10.1016/j.chemolab.2008.11.005 (2009).CAS 
Article 

Google Scholar 
Heumann, B. W. An object-based classification of mangroves using a hybrid decision tree-support vector machine approach. Remote. Sens-Basel. 3(11), 2440–2460. https://doi.org/10.3390/rs3112440 (2011).ADS 
Article 

Google Scholar 
Hsu, C., Chang, C. C. & Lin, C. J. A practical guide to support vector classification, 15. Department of Computer Science, National Taiwan University. https://doi.org/10.1111/j.1365-3016.1995.tb00168.x (2009).Aspinall, R. Modelling land use change with generalized linear models-a multi-model analysis of change between 1860 and 2000 in Gallatin valley, Montana. J. Environ. Manage. 72(1–2), 91–103. https://doi.org/10.1016/j.jenvman.2004.02.009 (2004).Article 
PubMed 

Google Scholar 
Wu, W. & Zhang, J. Comparison of spatial and non-spatial logistic regression models for modeling the occurrence of cloud cover in north-eastern Puerto Rico. Appl. Geogr. 37, 52–62. https://doi.org/10.1016/j.apgeog.2012.10.012 (2013).Article 

Google Scholar 
Thomas, D. R., Zhu, P. C. & Decady, Y. J. Point estimates and confidence intervals for variable importance in multiple linear regression. J. Educ. Behav. Stat. 32(1), 61–91. https://doi.org/10.1002/bimj.201100134 (2007).Article 

Google Scholar 
Huang, B. & Boutros, P. C. The parameter sensitivity of random forests. BMC Bioinform. 17, 331. https://doi.org/10.1186/s12859-016-1228-x (2016).Article 

Google Scholar 
Pang, J., Chen, Y., He, S., Qiu, H. & Mao, L. Classification of friction and wear state of wind turbine gearboxes using decision tree and random forest algorithms. J. Tribol-T. Asme. 143(9), 1–28. https://doi.org/10.1115/1.4049257 (2020).CAS 
Article 

Google Scholar 
Liu, M., Hu, S., Ge, Y., Heuvelink, G. & Huang, X. Using multiple linear regression and random forests to identify spatial poverty determinants in rural China. Spat. Stat.-Neth. 42, 100461. https://doi.org/10.1016/j.spasta.2020.100461 (2020).MathSciNet 
Article 

Google Scholar 
Jutidamrongphan, W. Determine the land-use land-cover changes, urban expansion and their driving factors for sustainable development in Gazipur Bangladesh. Atmosphere 12(10), 1353. https://doi.org/10.3390/atmos12101353 (2021).ADS 
Article 

Google Scholar 
Liu, M. & Tian, H. China’s land cover and land use change from 1700 to 2005: estimations from high-resolution satellite data and historical archives. Glob. Biogeochem. Cycles https://doi.org/10.1029/2009GB003687 (2010).Article 

Google Scholar 
Tong, Z., Yao, S., Hu, W. & Cui, F. Simulation of urban expansion in Guangzhou Foshan metropolitan area under the influence of accessibility. Scientia. Geographica. Sinica. 38(5), 737–746 (2018).
Google Scholar 
Poelmans, L. & Rompaey, A. V. Complexity and performance of urban expansion models. Comput. Environ. Urban Syst. 34(1), 17–27. https://doi.org/10.1016/j.compenvurbsys.2009.06.001 (2010).Article 

Google Scholar 
Galinato, S. P. & Gregma, I. The effects of government spending on deforestation due to agricultural land expansion and CO2 related emissions. Ecol. Econ. 122, 43–53. https://doi.org/10.1016/j.ecolecon.2015.10.025 (2016).Article 

Google Scholar 
Xie, X. F., Wu, T., Zhu, M., Jiang, G. J. & Xw, E. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol. Indic. 120, 106925. https://doi.org/10.1016/j.ecolind.2020.106925 (2021).CAS 
Article 

Google Scholar 
Miller, M. D. The mpacts of Atlanta’s urban sprawl on forest cover and fragmentation. Appl. Geogr. 34, 171–179. https://doi.org/10.1016/j.apgeog.2011.11.010 (2012).ADS 
Article 

Google Scholar 
Güneralp, B. & Seto, K. C. Futures of global urban expansion: uncertainties and implications for biodiversity conservation. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/8/1/014025 (2013).Article 

Google Scholar 
Qiao, W. et al. Multi-dimensional expansion of urban space through the lens of land use: The case study of Nanjing city, China. J. Geogr. Sci. 29(5), 749–761. https://doi.org/10.1007/s11442-019-1625-y (2019).Article 

Google Scholar 
Yza, B., Lt, A. & Hw, A. An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. J. Clean. Prod. 329, 129488. https://doi.org/10.1016/j.jclepro.2021.129488 (2021).Article 

Google Scholar  More

Murphy, G. E. P., Romanuk, T. N. & Worm, B. Cascading effects of climate change on plankton community structure. Ecol. Evol. 10, 2170–2181. https://doi.org/10.1002/ece3.6055 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Woodward, G., Daniel, M., Perkins, D. M. & Brown, L. E. Climate change and freshwater ecosystems: Impacts across multiple levels of organization. Philos. Trans. R. Soc. B 365, 2093–2106. https://doi.org/10.1098/rstb.2010.0055 (2010).Article 

Google Scholar 
Lampert, W. Zooplankton research: The contribution of limnology to general ecological paradigms. Aquat. Ecol. 31, 19–27. https://doi.org/10.1023/A:1009943402621 (1997).Article 

Google Scholar 
Gannon, J. E. & Stemberger, R. S. Zooplankton (especially crustaceans and rotifers) as indicators of water quality. Trans. Am. Microsc. Soc. 97, 16–35. https://doi.org/10.2307/3225681 (1978).Article 

Google Scholar 
Ferdous, Z. & Muktadir, S. K. M. A review: Potentiality of zooplankton as bioindicator. Am. J. Appl. Sci. 6, 1815–1819 (2009).Article 

Google Scholar 
Ejsmont-Karabin, J. The usefulness of zooplankton as lake ecosystem indicators: Rotifer Trophic State Index. Pol. J. Ecol. 60, 339–350 (2012).
Google Scholar 
Gillooly, J. F. Effect of body size and temperature on generation time in zooplankton. J. Plankton Res. 22(2), 241–251 (2000).Article 

Google Scholar 
Lewandowska, A. M., Hillebrand, H., Lengfellner, K. & Sommer, U. Temperature effects on phytoplankton diversity—The zooplankton link. J. Sea Res. 85, 359–364. https://doi.org/10.1016/j.seares.2013.07.003 (2014).ADS 
Article 

Google Scholar 
Carter, J. L. & Schindler, D. L. Responses of zooplankton populations to four decades of climate warming in Lakes of Southwestern Alaska. Ecosystems 15, 1010–1026. https://doi.org/10.1007/s10021-012-9560-0 (2012).CAS 
Article 

Google Scholar 
Ejsmont-Karabin, J. & Węgleńska, T. Disturbances in zooplankton seasonality in Lake Gosławskie (Poland) affected by permanent heating and heavy fish stocking. Ekol. Pol. 36, 245–260 (1988).
Google Scholar 
Ejsmont-Karabin, J. et al. Rotifers in Heated Konin Lakes—A review of long-term observations. Water 12, 1660. https://doi.org/10.3390/w12061660 (2020).Article 

Google Scholar 
Evans, L. E., Hirst, A. G., Kratina, P. & Beaugrand, G. Temperature-mediated changes in zooplankton body size: Large scale temporal and spatial analysis. Ecography 43, 581–590. https://doi.org/10.1111/ecog.04631 (2020).Article 

Google Scholar 
Wang, L. et al. Is zooplankton body size an indicator of water quality in (sub)tropical reservoirs in China?. Ecosystems 25, 656–662. https://doi.org/10.1007/s10021-021-00656-2 (2021).CAS 
Article 

Google Scholar 
Williamson, C. E., Saros, J. E., Vincent, W. F. & Smol, J. P. Lakes and reservoirs as sentinels, integrators, and regulators of climate change. Limnol. Oceanogr. 54(6), 2273–2282 (2009).ADS 
Article 

Google Scholar 
Richardson, A. J. In hot water: Zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295. https://doi.org/10.1093/icesjms/fsn028 (2008).Article 

Google Scholar 
Visconti, A., Manca, M. & De Bernardi, R. Eutrophication-like response to climate warming: An analysis of Lago Maggiore (N. Italy) zooplankton in contrasting years. J. Limnol. 67(2), 87–92 (2008).Article 

Google Scholar 
Vandysh, O. I. The effect of thermal flow of large power facilities on zooplankton community under subarctic conditions. Water Res. 36(3), 310–318. https://doi.org/10.1134/S0097807809030063 (2009).CAS 
Article 

Google Scholar 
Alric, B. et al. Local forcings affect lake zooplankton vulnerability and response to climate warming. Ecology 94(12), 2767–2780 (2013).Article 

Google Scholar 
Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. PNAS 106(31), 12788–12793. https://doi.org/10.1073/pnas.0902080106 (2009).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gutierrez, M. F. et al. Is recovery of large-bodied zooplankton after nutrient loading reduction hampered by climate warming? A long-term study of shallow hypertrophic Lake Søbygaard, Denmark. Water 8, 341. https://doi.org/10.3390/w8080341 (2016).ADS 
CAS 
Article 

Google Scholar 
Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884. https://doi.org/10.1038/nature02808 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Thackeray, S. J., Jones, I. D. & Maberly, S. C. Long-term change in the phenology of spring phytoplankton: Species-specific responses to nutrient enrichment and climatic change. J. Ecol. 96, 523–535. https://doi.org/10.1111/j.1365-2745.2008.01355.x (2008).Article 

Google Scholar 
Adrian, A., Wilhelm, S. & Gerten, D. Life-history traits of lake plankton species may govern their phenological response to climate warming. Life-history traits of lake plankton species may govern their phenological response to climate warming. Glob. Change Biol. 12, 652–661. https://doi.org/10.1111/j.1365-2486.2006.01125.x (2006).ADS 
Article 

Google Scholar 
Costello, J. H., Sullivan, B. K. & Gifford, D. J. A physical–biological interaction underlying variable phenological responses to climate change by coastal zooplankton. J. Plankton Res. 28(11), 1099–1105. https://doi.org/10.1093/plankt/fbl042 (2006).Article 

Google Scholar 
Lewandowska, A. M. et al. Effects of sea surface warming on marine plankton. Ecol. Lett. 17, 614–623. https://doi.org/10.1111/ele.12265 (2014).Article 
PubMed 

Google Scholar 
Wagner, C. & Adrian, R. Exploring lake ecosystems: Hierarchy responses to long-term change?. Glob. Change Biol. 15, 1104–1115. https://doi.org/10.1111/j.1365-2486.2008.01833.x (2009).ADS 
Article 

Google Scholar 
Hart, R. C. Zooplankton feeding rates in relation to suspended sediment content: Potential influences on community structure in a turbid reservoir. Fresh. Biol. 19, 123–139. https://doi.org/10.1111/j.1365-2427.1988.tb00334.x (1988).Article 

Google Scholar 
Carter, J. L., Schindler, D. E. & Francis, T. B. Effects of climate change on zooplankton community interactions in an Alaskan lake. Climate Change Resp. 4, 3. https://doi.org/10.1186/s40665-017-0031-x (2017).Article 

Google Scholar 
Calbet, A. The trophic roles of microzooplankton in marine systems. ICES J. Mar. Sci. 65, 325–331 (2008).Article 

Google Scholar 
Wollrab, S. et al. Climate change-driven regime shifts in a planktonic food web. Am. Natur. 197, 281–295. https://doi.org/10.1086/712813 (2021).Article 
PubMed 

Google Scholar 
Recknagel, F., Adrian, R. & Köhler, J. Quantifying phenological asynchrony of phyto- and zooplankton in response to changing temperature and nutrient conditions in Lake Müggelsee (Germany) by means of evolutionary computation. Environ. Model. Softw. 146, 105224. https://doi.org/10.1016/j.envsoft.2021.105224 (2021).Article 

Google Scholar 
EEA. Projected changes in annual, summer and winter temperature. European Environmental Agency. https://www.eea.europa.eu/data-and-maps/figures/projected-changes-in-annual-summer-1 (2014).IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2021).Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427. https://doi.org/10.1101/SQB.1957.022.01.039 (1957).Article 

Google Scholar 
Ferrario, A. & Hämmerli, R. On Boosting: Theory and Applications. SSRN: https://ssrn.com/abstract=3402687 (2019).Meysman, F. J. R. & Bruers, S. Ecosystem functioning and maximum entropy production: A quantitative test of hypotheses. Philos. Trans. R. Soc. B 365, 1405–1416. https://doi.org/10.1098/rstb.2009.0300 (2010).CAS 
Article 

Google Scholar 
Yu, Q., Ji, W., Prihodko, L., Anchang, J. Y. & Hanan, N. P. Study becomes insight: Ecological learning from machine learning. Methods Ecol. Evol. 12, 217–2128. https://doi.org/10.1111/2041-210X.13686 (2021).Article 

Google Scholar 
Park, J. et al. Interpretation of ensemble learning to predict water quality using explainable artificial intelligence. Sci. Total Environ. 832, 155070. https://doi.org/10.1016/j.scitotenv.2022.155070 (2022).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Grbčić, L. et al. Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis. Environ. Model. Softw. 155, 105458. https://doi.org/10.1016/j.envsoft.2022.105458 (2022).Article 

Google Scholar 
Kruk, M., Artiemjew, P. & Paturej, E. The application of game theory-based machine learning modelling to assess climate variability effects on the sensitivity of lagoon ecosystem parameters. Ecol. Inf. 66, 101462. https://doi.org/10.1016/j.ecoinf.2021.101462 (2021).Article 

Google Scholar 
Hebert, P. D. N. Competition in zooplankton communities. Ann. Zool. Fennici 19, 349–356 (1982).
Google Scholar 
Eigen, M. & Winkler, R. Laws of the Game. How the Principles of Nature Govern Chance (Princeton University Press, 1993).
Google Scholar 
Tilman, A. R., Plotkin, J. B. & Akçay, E. Evolutionary games with environmental feedbacks. Nat. Commun. 11, 915. https://doi.org/10.1038/s41467-020-14531-6 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Shapley, L. S. A Value for n-Person Games. In Contributions to the Theory of Games II (eds Kuhn, H. W. & Tucker, A. W.) 315–317 (Princeton University Press, 1953).
Google Scholar 
Lundberg, S. M. & Lee, S. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30, 4765–4774 (2017).
Google Scholar 
Štrumbelj, E. & Kononenko, I. An efficient explanation of individual classifications using game theory. J. Mach. Learn. Res. 11, 1–18 http://dl.acm.org/citation.cfm?id=1756006.1756007 (2010).Gan, G., Ma, C. & Wu, J. Data clustering: Theory, algorithms, and applications. ASA-SIAM Ser. Stat. Appl. Math. https://doi.org/10.1137/1.9780898718348 (2007).Article 
MATH 

Google Scholar 
Riechert, S. E. & Hammerstein, P. Game theory in the ecological context. Ann. Rev. Ecol. Syst. 14, 377–409. https://doi.org/10.1146/annurev.es.14.110183.002113 (1983).Article 

Google Scholar 
Maynard-Smith, J. Evolution and the Theory of Games (Cambridge University Press, 1982).Book 

Google Scholar 
Nowak, M. A. & Sigmund, K. Evolutionary dynamics of biological games. Science 303(5659), 793–799. https://doi.org/10.1126/science.1093411 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Maloney, K. O., Schmid, M. & Weller, D. E. Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages. Methods Ecol. Evol. 3, 116–128. https://doi.org/10.1111/j.2041-210X.2011.00124.x (2012).Article 

Google Scholar 
Cao, H., Recknagel, F. & Orr, P. T. Parameter optimization algorithms for evolving rule models applied to freshwater ecosystems. IEEE Trans. Evol. Comput. 18, 793–806. https://doi.org/10.1109/TEVC.2013.2286404 (2014).Article 

Google Scholar 
Naqshbandi, N., Iranmanesh, M. & Askari Hesni, M. Effects of environmental factors on species diversity of rotifers using biodiversity indicators and canonical correlation analysis (CCA). J. Aquat. Ecol. 7, 66–75 https://www.sid.ir/en/journal/ViewPaper.aspx?id=661950 (2017).Weisse, M. & Frahm, A. Species-specific interactions between small planctonic ciliates (Urotricha spp.) and rotifers (Keratella spp.). J. Plank. Res. 23, 1329–1338 (2001).Article 

Google Scholar 
Sokal, R. R. & Rohlf, F. J. The comparison of dendrograms by objective methods. Taxon 11, 33–40 (1962).Article 

Google Scholar 
Pomerleau, C., Sastri, A. R. & Beisner, B. E. Evaluation of functional trait diversity for marine zooplankton communities in the Northeast subarctic Pacific Ocean. J. Plankton Res. 37, 712–726. https://doi.org/10.1093/plankt/fbv045 (2015).Article 

Google Scholar 
Hopcroft, R. R., Kosobokova, K. N. & Pinchuk, A. I. Zooplankton community patterns in the Chukchi Sea during summer 2004. Deep-Sea Res. II(57), 27–39. https://doi.org/10.1016/j.dsr2.2009.08.003 (2010).ADS 
Article 

Google Scholar 
Neumann, L. S. et al. Connectivity between coastal and oceanic zooplankton from Rio Grande do Norte in the Tropical Western Atlantic. Front. Mar. Sci. 6, 00287. https://doi.org/10.3389/fmars.2019.00287 (2019).Article 

Google Scholar 
Benedetti, F., Ayata, S.-D., Irisson, J.-O., Adloff, F. & Guilhaumon, F. Climate change may have minor impact on zooplankton functional diversity in the Mediterranean Sea. Divers. Distrib. 25, 568–581. https://doi.org/10.1111/ddi.12857 (2019).Article 

Google Scholar 
Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 70, 1063–1085 (1972).
Google Scholar 
O’Neil, J. M., Davis, T. W., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14, 313–334. https://doi.org/10.1016/j.hal.2011.10.027 (2012).CAS 
Article 

Google Scholar 
Irigoien, X., Huisman, J. & Harris, R. P. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429, 863–867 (2004).ADS 
CAS 
Article 

Google Scholar 
Jasnos, K., Kołba, P., Biernat, H. & Noga, B. The results of the hydrogeological research leading to know and develop the resources of thermal water in the Kleszczów district. Modelowanie Inżynierskie 45, 14 (2012).
Google Scholar 
Rybak, J. I. & Błędzki, L. A Freshwater Planktonic Crustaceans (Warsaw University Press, 2010).
Google Scholar 
Kim, H.-W., Hwang, S.-J. & Joo, G.-J. Zooplankton grazing on bacteria and phytoplankton in a regulated large river (Nakdong River, Korea). J. Plankton Res. 22, 1559–1577 (2000).CAS 
Article 

Google Scholar 
Moreira, F. W. A. et al. Assessing the impacts of mining activities on zooplankton functional diversity. Acta Limn. Bras. 28, e7. https://doi.org/10.1590/S2179-975X0816 (2016).Article 

Google Scholar 
Obertegger, U. & Flaim, G. Taxonomic and functional diversity of rotifers, what do they tell us about community assembly?. Hydrobiologia 823, 79–91. https://doi.org/10.1007/s10750-018-3697-6 (2018).Article 

Google Scholar 
Ejsmont-Karabin, J., Radwan, S. & Bielańska-Grajner, I. Rotifers. Monogononta–atlas of species. Polish freshwater fauna (University of Łódź, Łódź, 2004).
Google Scholar 
Rose, J. M. & Caron, D. A. Does low temperature constrain the growth rates of heterotrophic protists? Evidence and implications for algal blooms in cold waters. Limnol Oceanogr. 52, 886–895. https://doi.org/10.4319/lo.2007.52.2.0886 (2007).ADS 
Article 

Google Scholar 
Huntley, M. E. & Lopez, M. D. Temperature-dependent production of marine copepods: A global synthesis. Am. Nat. 140, 201–242. https://doi.org/10.1086/285410 (1992).CAS 
Article 
PubMed 

Google Scholar 
Olonscheck, D., Hofmann, M., Worm, B. & Schellnhuber, H. J. Decomposing the effects of ocean warming on chlorophyll a concentrations into physically and biologically driven contributions. Environ. Res. Lett. 8, 014043. https://doi.org/10.1088/1748-9326/8/1/014043 (2013).ADS 
CAS 
Article 

Google Scholar 
Hillebrand, H. et al. Goldman revisited: Faster-growing phytoplankton has lower N:P and lower stoichiometric flexibility. Limnol. Oceanogr. 58, 2076–2088. https://doi.org/10.4319/lo.2013.58.6.2076 (2013).ADS 
CAS 
Article 

Google Scholar 
Kruk, M., Kobos, J., Nawrocka, L. & Parszuto, K. Positive and negative feedback loops in nutrient phytoplankton interactions related to climate dynamics factors in a shallow temperate estuary (Vistula Lagoon, southern Baltic). J. Mar. Syst. 180, 49–58. https://doi.org/10.1016/j.jmarsys.2018.01.003 (2018).Article 

Google Scholar 
Santer, B. & Hansen, A.-M. Diapause of Cyclops vicinus (Uljanin) in Lake Søbyga˚ rd: Indication of a risk-spreading strategy. Hydrobiologia 560, 217–226. https://doi.org/10.1007/s10750-005-1067-7 (2006).Article 

Google Scholar 
Mayer, J. et al. Seasonal successions and trophic relations between phytoplankton, zooplankton, ciliate and bacteria in a hypertrophic shallow lake in Vienna, Austria. Hydrobiologia 342(343), 165–174 (1997).Article 

Google Scholar 
Galir Balkić, A., Ternjej, I. & Špoljar, M. Hydrology driven changes in the rotifer trophic structure and implications for food web interactions. Ecohydrology 11, 1917. https://doi.org/10.1002/eco.1917 (2018).Article 

Google Scholar 
Goździejewska, A. M., Gwoździk, M., Kulesza, S., Bramowicz, M. & Koszałka, J. Effects of suspended micro- and nanoscale particles on zooplankton functional diversity of drainage system reservoirs at an open-pit mine. Sci. Rep. 9, 16113. https://doi.org/10.1038/s41598-019-52542-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Goździejewska, A. M., Skrzypczak, A. R., Koszałka, J. & Bowszys, M. Effects of recreational fishing on zooplankton communities of drainage system reservoirs at an open-pit mine. Fish. Manag. Ecol. 27, 279–291. https://doi.org/10.1111/fme.12411 (2020).Article 

Google Scholar 
Goździejewska, A. M., Skrzypczak, A. R., Paturej, E. & Koszałka, J. Zooplankton diversity of drainage system reservoirs at an opencast mine. Knowl. Manag. Aquat. Ecosyst. 419, 33. https://doi.org/10.1051/kmae/2018020 (2018).Article 

Google Scholar 
von Flössner, D. Krebstiere (Branchiopoda, Fischläuse, Branchiura (VEB Gustav Fischer Verlag, Jena, 1972).
Google Scholar 
Koste, W. Rotatoria. Die Rädertiere Mitteleuropas. Überordnung Monogononta. I Textband, II Tafelband, 52–570, (Gebrüder Borntraeger, Berlin, 1978).Streble H. & Krauter D. Das Leben im Wassertropfen. Mikroflora und Mikrofauna des Süβwassers. (Kosmos Gesellschaft der Naturfreunde Franckh’sche Verlagshandlung, Stuttgart, 1978).Błędzki, L. A. & Rybak, J. I. Freshwater crustacean zooplankton of Europe: Cladocera & Copepoda (Calanoida, Cyclopoida). Key to species identification with notes on ecology, distribution, methods and introduction to data analysis. (Springer, Switzerland, 2016).Bottrell, H. H. et al. Review of some problems in zooplankton production studies. Norw. J. Zool. 24, 419–456 (1976).
Google Scholar 
Ejsmont-Karabin, J. Empirical equations for biomass calculation of planktonic rotifers. Pol. Arch. Hydr. 45, 513–522 (1998).
Google Scholar 
APHA. Standard methods for the examination of water and wastewater, 20th ed.. (American Public Health Association, Washington, DC, 1999).Wei, Z.-G. et al. Comparison of methods for picking the operational taxonomic units from amplicon sequences. Front. Microbiol. 24, 644012. https://doi.org/10.3389/fmicb.2021.644012 (2021).Article 

Google Scholar 
Sgalella. Kaggle. https://www.kaggle.com/sgalella/correlation-heatmaps-with-hierarchical-clustering (2019).Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).MathSciNet 
Article 

Google Scholar 
Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System. 22 ACM SIGKDD Conference on Knowledge, Discovery and Data mining, 12–17 August, San Francisco. https://doi.org/10.1145/2939672.2939785 (2016).Kirpal, E. Kaggle. https://www.kaggle.com/eshaan90/ensembles-and-model-stacking (2019).Brownlee, J. Github. https://github.com/datamangit/codes_for_articles/blob/master/Explain%20your%20model%20with%20the%20SHAP%20values%20for%20article.ipynb (2021).Rathi, P. Toward Data Science. https://towardsdatascience.com/a-novel-approach-to-feature-importance-shapley-additive-explanations-d18af30fc21 (2020). More

Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Seibold, S. et al. The contribution of insects to global forest deadwood decomposition. Nature 597, 77–81 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Filipiak, M. Nutrient dynamics in decomposing dead wood in the context of wood eater requirements: The ecological stoichiometry of saproxylophagous insects. In Saproxylic Insects (ed. Ulyshen, M. D.) 429–470 (Springer, 2018).
Google Scholar 
Weedon, J. T. et al. Global meta-analysis of wood decomposition rates: A role for trait variation among tree species?. Ecol. Lett. 12, 45–56 (2009).PubMed 

Google Scholar 
Oberle, B. et al. Accurate forest projections require long-term wood decay experiments because plant trait effects change through time. Glob. Change Biol. 26, 864–875 (2020).ADS 

Google Scholar 
Guo, C., Yan, E. & Cornelissen, J. H. C. Size matters for linking traits to ecosystem multifunctionality. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2022.06.003 (2022).Article 
PubMed 

Google Scholar 
Ulyshen, M. D. Wood decomposition as influenced by invertebrates. Biol. Rev. 91, 70–85 (2016).PubMed 

Google Scholar 
Lustenhouwer, N. et al. A trait-based understanding of wood decomposition by fungi. Proc. Natl. Acad. Sci. U.S.A. 117, 1–8 (2020).
Google Scholar 
Tláskal, V. et al. Complementary roles of wood-Inhabiting fungi and bacteria facilitate deadwood decomposition. mSystems 6, e01078-20 (2021).PubMed 
PubMed Central 

Google Scholar 
Schmidt, O. Wood and Tree Fungi: Biology, Damage, Protection and Use (Springer, 2006).
Google Scholar 
Arantes, V. & Goodell, B. Current understanding of brown-rot fungal biodegradation mechanisms: A review. ACS Symp. Ser. 1158, 3–21 (2014).CAS 

Google Scholar 
Jacobsen, R. M., Sverdrup-Thygeson, A., Kauserud, H., Mundra, S. & Birkemoe, T. Exclusion of invertebrates influences saprotrophic fungal community and wood decay rate in an experimental field study. Funct. Ecol. 32, 2571–2582 (2018).
Google Scholar 
Fukami, T. et al. Assembly history dictates ecosystem functioning: Evidence from wood decomposer communities. Ecol. Lett. 13, 675–684 (2010).PubMed 

Google Scholar 
Wang, J. Y. et al. Durability of mass timber structures: A review of the biological risks. Wood Fiber Sci. 50, 110–127 (2018).CAS 

Google Scholar 
Venugopal, P., Junninen, K., Linnakoski, R., Edman, M. & Kouki, J. Climate and wood quality have decayer-specific effects on fungal wood decomposition. For. Ecol. Manag. 360, 341–351 (2016).
Google Scholar 
Ulyshen, M. D. & Wagner, T. L. Quantifying arthropod contributions to wood decay. Methods Ecol. Evol. 4, 345–352 (2013).
Google Scholar 
Freschet, G. T., Weedon, J. T., Aerts, R., van Hal, J. R. & Cornelissen, J. H. C. Interspecific differences in wood decay rates: Insights from a new short-term method to study long-term wood decomposition. J. Ecol. 100, 161–170 (2012).
Google Scholar 
Chang, C. et al. Methodology matters for comparing coarse wood and bark decay rates across tree species. Methods Ecol. Evol. 11, 828–838 (2020).
Google Scholar 
Hervé, V., Mothe, F., Freyburger, C., Gelhaye, E. & Frey-Klett, P. Density mapping of decaying wood using X-ray computed tomography. Int. Biodeterior. Biodegrad. 86, 358–363 (2014).
Google Scholar 
Williamson, G. B. & Wiemann, M. C. Measuring wood specific gravity…Correctly. Am. J. Bot. 97, 519–524 (2010).PubMed 

Google Scholar 
Van Der Wal, A., Gunnewiek, P. J. A. K., Cornelissen, J. H. C., Crowther, T. W. & De Boer, W. Patterns of natural fungal community assembly during initial decay of coniferous and broadleaf tree logs. Ecosphere 7, e01393 (2016).
Google Scholar 
Saint-Germain, M., Buddle, C. M. & Drapeau, P. Substrate selection by saprophagous wood-borer larvae within highly variable hosts. Entomol. Exp. Appl. 134, 227–233 (2010).
Google Scholar 
Lettenmaier, L. et al. Beetle diversity is higher in sunny forests due to higher microclimatic heterogeneity in deadwood. Oecologia https://doi.org/10.1007/s00442-022-05141-8 (2022).Article 
PubMed 

Google Scholar 
Gao, S. et al. A critical analysis of methods for rapid and nondestructive determination of wood density in standing trees. Ann. For. Sci. 74, 1–13 (2017).
Google Scholar 
Arnstadt, T. et al. Dynamics of fungal community composition, decomposition and resulting deadwood properties in logs of Fagus sylvatica, Picea abies and Pinus sylvestris. For. Ecol. Manag. 382, 129–142 (2016).
Google Scholar 
Gessner, M. O. Ergosterol as a measure of fungal biomass. In Methods to Study Litter Decomposition (eds Bärlocher, F. et al.) 247–255 (Springer, 2020). https://doi.org/10.1007/978-3-030-30515-4_27.Chapter 

Google Scholar 
Baldrian, P. et al. Responses of the extracellular enzyme activities in hardwood forest to soil temperature and seasonality and the potential effects of climate change. Soil Biol. Biochem. 56, 60–68 (2013).CAS 

Google Scholar 
Strid, Y., Schroeder, M., Lindahl, B., Ihrmark, K. & Stenlid, J. Bark beetles have a decisive impact on fungal communities in Norway spruce stem sections. Fungal Ecol. 7, 47–58 (2014).
Google Scholar 
Hagge, J. et al. Bark coverage shifts assembly processes of microbial decomposer communities in dead wood. Proc. R. Soc. B Biol. Sci. 286, 20191744 (2019).
Google Scholar 
Birkemoe, T., Jacobsen, R. M., Sverdrup-Thygeson, A. & Biedermann, P. H. W. Insect–fungus interactions in dead wood. In Saproxylic Insects (ed. Ulyshen, M. D.) 377–427 (Springer, 2018).
Google Scholar 
Leach, J. G., Ork, L. W. & Christensen, C. Further studies on the interrelationship of insects and fungi in the deterioration of felled Norway pine logs. J. Agric. Res. 55, 129–140 (1937).
Google Scholar 
Ulyshen, M. D., Wagner, T. L. & Mulrooney, J. E. Contrasting effects of insect exclusion on wood loss in a temperate forest. Ecosphere 5, art47 (2014).
Google Scholar 
Shigo, A. L. & Marx, H. G. Compartmentalization of decay in trees (1977).De Ligne, L. et al. Studying the spatio-temporal dynamics of wood decay with X-ray CT scanning. Holzforschung 76, 408–420 (2022).
Google Scholar 
Freyburger, C., Longuetaud, F., Mothe, F., Constant, T. & Leban, J. M. Measuring wood density by means of X-ray computer tomography. Ann. For. Sci. 66, 804 (2009).
Google Scholar 
Wei, Q., Leblon, B. & La Rocque, A. On the use of X-ray computed tomography for determining wood properties: A review. Can. J. For. Res. 41, 2120–2140 (2011).
Google Scholar 
Fuchs, A., Schreyer, A., Feuerbach, S. & Korb, J. A new technique for termite monitoring using computer tomography and endoscopy. Int. J. Pest Manag. 50, 63–66 (2004).
Google Scholar 
Choi, B., Himmi, S. K. & Yoshimura, T. Quantitative observation of the foraging tunnels in Sitka spruce and Japanese cypress caused by the drywood termite Incisitermes minor (Hagen) by 2D and 3D X-ray computer tomography (CT). Holzforschung 71, 535–542 (2017).CAS 

Google Scholar 
Bélanger, S. et al. Effect of temperature and tree species on damage progression caused by whitespotted sawyer (Coleoptera: Cerambycidae) larvae in recently burned logs. J. Econ. Entomol. 106, 1331–1338 (2013).PubMed 

Google Scholar 
Pereira Junior, A. & Garcia de Carvalho, M. An initial study in wood tomographic image classification using the SVM and CNN techniques. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) Vol. 4 575–581 (2022).Kautz, M., Peter, F. J., Harms, L., Kammen, S. & Delb, H. Patterns, drivers and detectability of infestation symptoms following attacks by the European spruce bark beetle. J. Pest Sci. https://doi.org/10.1007/s10340-022-01490-8 (2022).Article 

Google Scholar 
Ehnström, B. & Axelsson, R. Insektsgnag i bark och ved (ArtDatabanken SLU, 2002).
Google Scholar 
Philpott, T. J., Prescott, C. E., Chapman, W. K. & Grayston, S. J. Nitrogen translocation and accumulation by a cord-forming fungus (Hypholoma fasciculare) into simulated woody debris. For. Ecol. Manag. 315, 121–128 (2014).
Google Scholar 
Kahl, T. et al. Wood decay rates of 13 temperate tree species in relation to wood properties, enzyme activities and organismic diversities. For. Ecol. Manag. 391, 86–95 (2017).
Google Scholar 
Deflorio, G., Johnson, C., Fink, S. & Schwarze, F. W. M. R. Decay development in living sapwood of coniferous and deciduous trees inoculated with six wood decay fungi. For. Ecol. Manag. 255, 2373–2383 (2008).
Google Scholar 
Fuhr, M. J., Schubert, M., Schwarze, F. W. M. R. & Herrmann, H. J. Modelling the hyphal growth of the wood-decay fungus Physisporinus vitreus. Fungal Biol. 115, 919–932 (2011).CAS 
PubMed 

Google Scholar 
Sommer, C., Straehle, C., Köthe, U. & Hamprecht, F. A. Ilastik: Interactive learning and segmentation toolkit. In IEEE International Symposium on Biomedical Imaging: From Nano to Macro 230–233. https://doi.org/10.1109/ISBI.2011.5872394 (2011).Dodds, K. J., Graber, C. & Stephen, F. M. Facultative intraguild predation by larval Cerambycidae (Coleoptera) on bark beetle larvae (Coleoptera: Scolytidae). Environ. Entomol. 30, 17–22 (2001).
Google Scholar 
Graham, S. A. Temperature as a limiting factor in the life of subcortical insects. J. Econ. Entomol. 17, 377–383 (1924).
Google Scholar 
Baldrian, P. et al. Estimation of fungal biomass in forest litter and soil. Fungal Ecol. 6, 1–11 (2013).
Google Scholar 
Šnajdr, J. et al. Spatial variability of enzyme activities and microbial biomass in the upper layers of Quercus petraea forest soil. Soil Biol. Biochem. 40, 2068–2075 (2008).
Google Scholar 
Möller, G. Struktur- und Substratbindung holzbewohnender Insekten, Schwerpunkt Coleoptera—Käfer. Dissertation at Freien Universität Berlin (Freie Universität Berlin, 2009).
Google Scholar 
Baldrian, P. Forest microbiome: Diversity, complexity and dynamics. FEMS Microbiol. Rev. 41, 109–130 (2017).CAS 
PubMed 

Google Scholar 
Steger, C., Ulrich, M. & Wiedemann, C. Machine Vision Algorithms and Applications (Wiley, 2008).
Google Scholar 
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional Networks for Biomedical Image Segmentation (Springer, 2015).
Google Scholar 
Jansche, M. Maximum expected F-measure training of logistic regression models. In Proceedings of the conference on human language technology and empirical meth-ods in natural language processing 692–699 (Association for Computational Linguistics, 2005).Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).
Google Scholar 
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chollet, F. Keras. https://github.com/fchollet/keras (2015).Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems. Tensorflow.org. (2015).R Core Team. R: A language and environment for statistical computing (2020). More

Seneviratne, S. I. et al. Changes in climate extremes and their impacts on the natural physical environment. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change (Field, C.B. et al. eds) vol. 9781107025 109–230 (Cambridge University Press, 2012).Tan, X., Gan, T. Y. & Horton, D. E. Projected timing of perceivable changes in climate extremes for terrestrial and marine ecosystems. Glob. Chang. Biol. 24, 4696–4708 (2018).ADS 
PubMed 
Article 

Google Scholar 
Parmesan, C., Root, T. L. & Willig, M. R. Impacts of extreme weather and climate on terrestrial biota. Bull. Am. Meteorol. Soc. 81, 443–450 (2000).ADS 
Article 

Google Scholar 
Van de Pol, M., Jenouvrier, S., Cornelissen, J. H. C. & Visser, M. E. Behavioural, ecological and evolutionary responses to extreme climatic events: challenges and directions. Philos. Trans. R. Soc. B Biol. Sci. 372, 1–16 (2017).Smith, M. D. An ecological perspective on extreme climatic events: a synthetic definition and framework to guide future research. J. Ecol. 99, 656–663 (2011).Article 

Google Scholar 
Wingfield, J. C. et al. How birds cope physiologically and behaviourally with extreme climatic events. Philos. Trans. R. Soc. B Biol. Sci. 372, 1–10 (2017).Sergio, F., Blas, J. & Hiraldo, F. Animal responses to natural disturbance and climate extremes: a review. Glob. Planet. Change. 161, 28–40 (2018).ADS 
Article 

Google Scholar 
Aghakouchak, A. et al. Climate Extremes and Compound Hazards in a Warming World. Annu. Rev. Earth Planet. Sci. 48, 519–548 (2020).ADS 
CAS 
Article 

Google Scholar 
Schewe, J. et al. State-of-the-art global models underestimate impacts from climate extremes. Nat. Commun. 10, 1–14 (2019).CAS 
Article 

Google Scholar 
Boyce, M. S. et al. Demography in an increasingly variable world. Trends Ecol. Evol. 21, 141–148 (2006).PubMed 
Article 

Google Scholar 
Lindström, J. Early development and fitness in birds and mammals. Trends Ecol. Evol. 14, 343–348 (1999).PubMed 
Article 

Google Scholar 
Monaghan, P. Early growth conditions, phenotypic development and environmental change. Philos. Trans. R. Soc. B Biol. Sci. 363, 1635–1645 (2008).Article 

Google Scholar 
Nussey, D. H., Kruuk, L. E. B., Morris, A. & Clutton-Brock, T. H. Environmental conditions in early life influence ageing rates in a wild population of red deer. Curr. Biol. 17, 1000–1001 (2007).Article 
CAS 

Google Scholar 
Van De Pol, M., Bruinzeel, L. W., Heg, D., Van Der Jeugd, H. P. & Verhulst, S. A silver spoon for a golden future: long-term effects of natal origin on fitness prospects of oystercatchers (Haematopus ostralegus). J. Anim. Ecol. 75, 616–626 (2006).PubMed 
Article 

Google Scholar 
Reid, J. M., Bignal, E. M., Bignal, S., McCracken, D. I. & Monaghan, P. Environmental variability, life-history covariation and cohort effects in the red-billed chough Pyrrhocorax pyrrhocorax. J. Anim. Ecol. 72, 36–46 (2003).Article 

Google Scholar 
Hamel, S., Gaillard, J. M., Festa-Bianchet, M. & Côté, S. D. Individual quality, early-life conditions, and reproductive success in contrasted populations of large herbivores. Ecology 90, 1981–1995 (2009).PubMed 
Article 

Google Scholar 
Kordosky, J. R. et al. Landscape of stress: tree mortality influences physiological stress and survival in a native mesocarnivore. PLoS One. 16, 1–22 (2021).Article 
CAS 

Google Scholar 
Millon, A., Petty, S. J., Little, B. & Lambin, X. Natal conditions alter age-specific reproduction but not survival or senescence in a long-lived bird of prey. J. Anim. Ecol. 80, 968–975 (2011).PubMed 
Article 

Google Scholar 
Mugabo, M., Marquis, O., Perret, S. & Le Galliard, J. F. Immediate and delayed life history effects caused by food deprivation early in life in a short-lived lizard. J. Evol. Biol. 23, 1886–1898 (2010).CAS 
PubMed 
Article 

Google Scholar 
Taborsky, B. The influence of juvenile and adult environments on life-history trajectories. Proc. R. Soc. B Biol. Sci. 273, 741–750 (2006).Article 

Google Scholar 
Hayward, A. D., Rickard, I. J. & Lummaa, V. Influence of early-life nutrition on mortality and reproductive success during a subsequent famine in a preindustrial population. Proc. Natl Acad. Sci. 110, 13886–13891 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Korpimäki, E. & Lagerström, M. Survival and natal dispersal of fledglings of Tengmalm’s owl in relation to fluctuating food conditions and hatching date. J. Anim. Ecol. 57, 433–441 (1988).Article 

Google Scholar 
Gluckman, P. D., Hanson, M. A. & Spencer, H. G. Predictive adaptive responses and human evolution. Trends Ecol. Evol. 20, 527–533 (2005).PubMed 
Article 

Google Scholar 
Gluckman, P. D., Hanson, M. A., Spencer, H. G. & Bateson, P. Environmental influences during development and their later consequences for health and disease: implications for the interpretation of empirical studies. Proc. R. Soc. B Biol. Sci. 272, 671–677 (2005).Article 

Google Scholar 
Grafen, A. On the uses of data on lifetime reproductive success. in Reproductive Success (ed. T. H. Clutton-Brock) 454–471 (Chicago University Press, 1988).Jenouvrier, S., Péron, C. & Weimerskirch, H. Extreme climate events and individual heterogeneity shape lifehistory traits and population dynamics. Ecol. Monogr. 85, 605–623 (2015).Article 

Google Scholar 
McNamara, J. M. & Houston, A. I. State-dependent life histories. Nature 380, 215–221 (1996).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Douhard, M. et al. Fitness consequences of environmental conditions at different life stages in a long-lived vertebrate. Proc. R. Soc. B Biol. Sci. 281, 1–8 (2014).Monaghan, P. Organismal stress, telomeres and life histories. J. Exp. Biol. 217, 57–66 (2014).PubMed 
Article 

Google Scholar 
Zimmer, C., Larriva, M., Boogert, N. J. & Spencer, K. A. Transgenerational transmission of a stress-coping phenotype programmed by early-life stress in the Japanese quail. Sci. Rep. 7, 1–19 (2017).Article 
CAS 

Google Scholar 
Krause, E. T., Honarmand, M., Wetzel, J. & Naguib, M. Early fasting is long lasting: differences in early nutritional conditions reappear under stressful conditions in adult female zebra finches. PLoS One. 4, 1–6 (2009).Article 
CAS 

Google Scholar 
Martin, T. G. et al. Acting fast helps avoid extinction. Conserv. Lett. 5, 274–280 (2012).Article 

Google Scholar 
Lewontin, R. C. & Cohen, D. On population growth in a randomly varying environment. Proc. Natl Acad. Sci. 62, 1056–1060 (1969).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sæther, B. E. & Bakke, Ø. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).Article 

Google Scholar 
Morris, W. F. & Doak, D. F. Buffering of Life Histories against Environmental Stochasticity: Accounting for a Spurious Correlation between the Variabilities of Vital Rates and Their Contributions to Fitness. Am. Nat. 163, 579–590 (2004).PubMed 
Article 

Google Scholar 
Rodríguez-Caro, R. C. et al. The limits of demographic buffering in coping with environmental variation. Oikos 130, 1346–1358 (2021).Article 

Google Scholar 
Bakker, V. J., Doak, D. F. & Ferrara, F. J. Understanding extinction risk and resilience in an extremely small population facing climate and ecosystem change. Ecosphere 12, 1–20 (2021).Beissinger, S. R. Modeling extinction in periodic environments: Everglades water levels and Snail Kite population viability. Ecol. Appl. 5, 618–631 (1995).Article 

Google Scholar 
Simberloff, D. Small and declining populations. in Conservation science and action (ed. Sutherland, W. J.) 116–134 (Blackwell, 1998).Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).Blake, J. G. & Loiselle, B. A. Enigmatic declines in bird numbers in lowland forest of eastern Ecuador may be a consequence of climate change. PeerJ. 2015, 1–20 (2015).
Google Scholar 
Whitfield, S. M. et al. Amphibian and reptile declines over 35 years at La Selva, Costa Rica. Proc. Natl Acad. Sci. 104, 8352–8356 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS One 12, (2017).González, L. M., Margalida, A., Sánchez, R. & Oria, J. Supplementary feeding as an effective tool for improving breeding success in the Spanish imperial eagle (Aquila adalberti). Biol. Conserv. 129, 477–486 (129AD).García, F. & Marín, C. Doñana: water and biosphere. (Spanish Ministry of the Environment, 2006).Díaz-Paniagua, C. & Aragonés, D. Permanent and temporary ponds in Doñana National Park (SW Spain) are threatened by desiccation. Limnetica 34, 407–424 (2015).
Google Scholar 
Schmidt, G. et al. The state of water in Doñana: an evaluation of the state of the water and of the ecosystems of the protected space. (WWF/Adena, Madrid, 2017).Camacho, C. et al. Groundwater extraction poses extreme threat to Doñana World Heritage Site. Nat. Ecol. Evol. 6, 654–655 (2022).Navedo, J. G., Piersma, T., Figuerola, J. & Vansteelant, W. Spain’s Doñana World Heritage Site in danger. Science 376, 144 (2022).ADS 
PubMed 
Article 

Google Scholar 
Giorgi, F. & Lionello, P. Climate change projections for the Mediterranean region. Glob. Planet. Change. 63, 90–104 (2008).ADS 
Article 

Google Scholar 
Goubanova, K. & Li, L. Extremes in temperature and precipitation around the Mediterranean basin in an ensemble of future climate scenario simulations. Glob. Planet. Change 57, 27–42 (2007).ADS 
Article 

Google Scholar 
Hertig, E. & Tramblay, Y. Regional downscaling of Mediterranean droughts under past and future climatic conditions. Glob. Planet. Change. 151, 36–48 (2017).ADS 
Article 

Google Scholar 
Bustamante, J., Pacios, F., Díaz-Delgado, R. & Aragonés, D. Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images. J. Environ. Manag. 90, 2219–2225 (2009).Article 

Google Scholar 
Veiga, J. P. & Hiraldo, F. Food habits and the survival and growth of nestlings in two sympatric kites (Milvus milvus and Milvus migrans). Ecography (Cop.). 13, 62–71 (1990).Article 

Google Scholar 
Viñuela, J. & Bustamante, J. Effect of growth and hatching asynchrony on the fledging age of Black and Red Kites. Auk 109, 748–757 (1992).Article 

Google Scholar 
Newton, I., Davis, P. E. & Davis, J. E. Age of first breeding, dispersal and survival of Red Kites Milvus milvus in Wales. Ibis (Lond. 1859). 131, 16–21 (1989).Article 

Google Scholar 
Katzenberger, J., Gottschalk, E., Balkenhol, N. & Waltert, M. Density-dependent age of first reproduction as a key factor for population dynamics: stable breeding populations mask strong floater declines in a long-lived raptor. Anim. Conserv. 24, 862–875 (2021).Article 

Google Scholar 
Sergio, F., Tavecchia, G., Blas, J., Tanferna, A. & Hiraldo, F. Demographic modeling to fine-tune conservation targets: importance of pre-adults for the decline of an endangered raptor. Ecol. Appl. 31, 1–12 (2021).Article 

Google Scholar 
Sergio, F. et al. Protected areas under pressure: decline, redistribution, local eradication and projected extinction of a threatened predator, the red kite, in Doñana National Park, Spain. Endanger. Species Res. 38, 189–204 (2019).Article 

Google Scholar 
Sergio, F. et al. Preservation of wide-ranging top predators by site-protection: black and red kites in Doñana National Park. Biol. Conserv. 125, 11–21 (2005).Article 

Google Scholar 
Sofaer, H. R., Chapman, P. L., Sillett, T. S. & Ghalambor, C. K. Advantages of nonlinear mixed models for fitting avian growth curves. J. Avian Biol. 44, 469–478 (2013).
Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R (Springer, New York, 2009).Lebreton, J. D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

Google Scholar 
Anderson, D. R. Model based inference in the life sciences: a primer on evidence (Springer, 2008).White, G. C. & Burnham, K. P. Program mark: survival estimation from populations of marked animals. Bird. Study. 46, S120–S139 (1999).Article 

Google Scholar 
Grosbois, V. & Tavecchia, G. Modeling dispersal with capture-recapture data: disentangling decisions of leaving and settlement. Ecology 84, 1225–1236 (2003).Article 

Google Scholar 
Caswell, H. Matrix population models (Sinauer, 2001).Ballerini, T., Tavecchia, G., Pezzo, F., Jenouvrier, S. & Olmastroni, S. Predicting responses of the Adélie penguin population of Edmonson Point to future sea ice changes in the Ross Sea. Front. Ecol. Evol. 3, 1–11 (2015).Bateson, P. et al. Developmental plasticity and human health. Nature 430, 419–421 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

The experiments were approved by the French Ethics Committee in charge of Animal Experimentation (no.2019072411491441) and were in accordance with institutional and ARRIVE guidelines.Animal collection and husbandryIn May 2019, juvenile European sea bass, Dicentrarchus labrax (Linnaeus 1758) (6 months old, mass 5 g), were transferred from a fish farm (Turbot Ichtus, Trédarzec, France) to the Ifremer rearing facility (Plouzané, France). Fish were kept in a common tank for 5 months, maintained under a 12 L: 12 D photoperiod, and fed at satiety three times a week using commercial pellets (Neo Start, Le Gouessant, Lamballe, France).In October 2019, fish (n = 40) were anaesthetized (Tricaïne; 125 mg L−1), weighed (41.5 ± 1.8 g, MCE11201S-2S00-0, Sartorius, Göttingen, Germany), and implanted subcutaneously with an identification tag (RFID; Biolog-id, Bernay, France). The fish were then randomly allocated to ten replicate 400 L tanks supplied with open-flow, fully aerated seawater (oxygen saturation  > 95%, salinity 32 ppt), thermo-regulated during winter to avoid falling below 13 °C, and fed at satiety three times a week. Temperature was recorded weekly. To account for the potential effect of temperature variation over the duration of the trial (15.5 ± 0.5 °C, range: 13.1–17.9 °C) on growth, a correlations analysis was performed between temperature and specific growth rate (SGR). No statistical relationship was found between SGR and temperature (Spearman R2 = 0.060, P = 0.596). Additional fish (n = 40) were present in the tanks (final density: n = 8 per tank) for the need of another project.Growth measurementsBody mass (BM) was measured about every four weeks from October 2019 to June 2020. The fish were fasted for 48 h and anesthetized before each BM measurement (± 0.1 g). The specific growth rate (% day-1) was estimated as follows:$${text{Specific~Growth~Rate}} = ~frac{{ln left( {final~BM} right) – ln left( {initial~BM} right)}}{{{text{days~elapsed}}}} times 100$$In March 2020, a red muscle biopsy sample was collected from fish to measure the mitochondrial metabolic traits. Past growth was defined as specific growth rates before the analysis of mitochondrial metabolic traits (past specific growth rate, SGRpast). SGRpast were calculated using the BM at the muscle biopsy as the final BM and the BM at 7, 11, 16, and 20 weeks before the muscle biopsy as the initial BM (Fig. 1). Future growth was defined as specific growth rates after analysis of mitochondrial metabolic traits (future specific growth rates, SGRfuture). SGRfuture were calculated using the BM at 4, 8, and 12 weeks after the muscle biopsy as the final BM and the BM at the muscle biopsy as the initial BM. In European sea bass, most of the somatic growth occur within the first 3 to 5 years of life, so several months of growth measurement at the juvenile stage might be representative of the overall growth of the animal.Figure 1Experimental design. Juvenile European sea bass (n = 40) were weighted about every four weeks over a 32-week period. At week 20, a biopsy of red muscle was used for mitochondrial assay. Specific growth rates (SGR) were calculated relative to the time of the biopsy. Past growth rate corresponds to SGR calculated before the biopsy, and future growth rate corresponds to SGR calculated after the biopsy.Full size imageMuscle biopsy procedureMuscle biopsy was performed as a non-lethal means of sampling tissue for the mitochondrial assay while allowing us to determine future growth rate. Fish were anaesthetized with tricaine (as above), weighed (76.7 ± 3.6 g), and biopsied. A skin incision ( More

Study areaWe conducted field studies in both regions from August to March, each year from 2012 to 2016. In north India, we selected the two traditional breeding colonies of the Painted Storks, viz., the Delhi Zoo (28° 36′ N 77° 14′ E) and Keoladeo National Park (KNP) (27° 17′ N 77° 52′ E), Bharatpur, Rajasthan (Fig. 1). In the Delhi Zoo, close to the river Yamuna, the Painted Storks nest in the traditional heronries with other colonial nesters, Little Cormorant, Indian Cormorant, Black-headed Ibis, and Night Heron38. The KNP, a Ramsar site spread over 29 km2, situated at the confluence of the rivers Gambhir and Banganga on the western edge of the Gangetic basin, supports diverse fauna, flora, and a mosaic of habitats, wetlands, woodlands, scrub forests, grasslands, and heronries39. In 2013, we recorded 680 adults and 310 nests in the Delhi Zoo and 1584 adults and 430 nests of Painted Storks in the KNP.We selected the Vedanthangal Bird Sanctuary (VBS), the nesting colonies at Melmaruvathur Lake, and Koonthankulam Bird Sanctuary (KBS). The KBS & VBS are the newly declared Ramsar sites in Tamil Nadu, south India. The VBS (12° 32′ 02″ N and 79° 52′ 29″ E) is a 40.3-hectare community reserve effectively protected by the state Forest Department, Tamil Nadu, and Vedanthangal villagers40. It is the oldest breeding waterbird reserve in south India, located 85 km southwest of Chennai. More than 40 species of waterbirds, both residents and migrants, live here. Along with the other 17 heronry species, the Painted Storks build nests every year from November to April during its breeding season. The Painted Stork nesting colonies at Melmaruvathur Lake (12° 25′ 53″ N and 79° 49′ 36″ E) are about 20 km away from the VBS. Here, the Painted Storks build nests at 1.8–5 m above the water level, on trees of Acacia nilotica and Barringtonia acutangula on mounds surrounded by water41. In 2012, we recorded a total of 3185 nests in the VBS, with a maximum number of nests belonging to Spot-billed Pelican (1050 nests) followed by Painted Stork (550 nests), Asian Open-bill (770 nests), and others.Birds have been breeding in Melmaruvathur Lake since 2013, and we counted 80 nests of Spot-billed pelican, 45 nests of Oriental White Ibis, and 56 nests of Painted Stork during the winter of the year 2014. The Lake is spread over 0.19 km2 with islets (mounds) with four clusters of Acacia nilotica and Barringtonia acutangula trees. Rainwater and domestic sewage from the neighboring residential complex are the primary water source, and recreational boating attracts a large crowd visiting the Melmaruvathur temple41. KBS (8° 29′ 44″ N and 77° 45′ 30″ E) is about a 1.3 km2 protected area, declared a bird sanctuary in 1994 and an Important Bird Area40. It comprises Koonthankulam and Kadankulam irrigation tanks actively protected and managed by the local community. We noticed the frequent failures of breeding events due to water shortages related to monsoon failures in VBS and KBS. In 2015, we also observed Painted Storks’ breeding failure across northern India for unknown reasons; therefore, data could not be collected for those periods.Bioclimatic variablesWe obtained the bioclimatic variable, particularly temperature at 2 m height for all the four study sites, from the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) Project funded through the NASA Earth Science/Applied Science Program. The monthly average data from 2010 to 2020 was downloaded from the POWER Project’s Hourly 2.0.0 version on 2022/01/04.Digital images of Painted Storks collected under field conditionsUsing Binoculars (Olympus 10X50), Digital Cameras (Canon 5D Mark III and Sony handy-cam), we monitored and recorded all active nests with juveniles and adult Painted Storks twice a week. The nests were on trees, 3–7 m in height, and chicks and adults were visible, which aided the photography. Nests were numbered for our records by taking note of tree branching patterns, the nest’s position on the tree, and other local identification marks. Numbering the nests helped us identify the individuals associated with a given nest and avoided re-recording the same individual (pseudoreplication). Storks show site fidelity42,43, and hence we assumed the same breeding pairs occupied the same nesting site.During the initial months of the breeding seasons, pairing and copulations of the breeding pairs could be readily noticeable. We took consecutive photographs when they were copulating at the nest. After disengagement following the copulation, the birds (male and female) standing side by side at the nest were also photographed. The first author noted all the relevant spatial orientations of males and females during each copulation event in the field notes. Thus nearly 100 copulations involving different individuals of the Painted Storks pair were photographed. To minimize measurement errors, we selected for further analysis only the images of males and females standing parallel and close to each other, perpendicular to the camera. Since we used the digital images of the free-living Storks, we did not have the freedom to choose all morphological features resulting in some missing values. Therefore, we selected a hundred and forty-eight individuals for the analysis from nearly 1500 localized adults. The technique has an efficiency of less than 10% of the population, more efficient than the traditional capture, measure, and release of individuals. Though many individuals were recorded, only a few were subjected to the analyses. Moreover from the digital images, not all the morphological characters of the individuals were measured. The birds’ orientation towards the camera assumes importance because the correct direction ensures maximum exposure of body parts in the picture. In many pictures, correct orientation was missing as the birds were behind other individuals or branches of the trees or leaves. Therefore, selecting the right digital image becomes crucial. Keeping all the above criteria, we filtered images that were later included in the analysis.Calibrations of subject-distance using Exif MetadataWe extracted the EXIF metadata from each JPEG image of Painted Stork. EXIF metadata includes the filename, type, date, and time of the image captured, image width and height in pixels, camera model, lens information, field of view, focal length, and subject-distance. The subject-distance (Painted Stork distance from the camera) being a critical variable and its Exif metadata were standardized with the following equation.$${text{Subject{-}distance}} = 0.7864 times {text{(EXIF subject{-}distance)}}^{{1.0301}}$$
(1)
Using the Eq. (1) derived from an earlier study5, we regressed actual subject-distance with the Exif subject-distance from the images. Then multiplying with the field of view, available as Exif metadata (angle of view) with standardized subject-distance (Eq. 1), the total image size (length and width) in metric units was estimated. We excluded the cropped or manipulated images because Image (size) estimation is possible only for the images coming straight from the camera with EXIF tags. The methodological details for calibration and estimation of in-situ measurements of the morphological variables are given in Mahendiran et al.5.Measurements of the morphological variablesWe created a TPS file for JPEG images of Painted Storks with the TPSUtility Program44. Using the TPS file in the TPSDig (v. 2.17) program44, we measured the selected characters (morphological variables) in pixels. Later, it was used along with the total image size to estimate the size of the specific morphological features in metric units, following Mahendiran et al.5. Ten different morphological variables were measured: Bill length (upper and lower mandible), tibia & tarsus length of both legs, distances among the ear, nostril and corners of the mouth, and body length. We estimated the dimensions of the rigid body parts, viz., bill length, tibia, and tarsus using the given methodology13,15,21. Bill length is the distance from the tip of the upper mandible to the beginning of skin corners near nostrils, the proximal end of the beak (marked as ‘a’ in Fig. 3); Tibia length is the distance from the joint of the tibia-tarsus to the feathers (marked as ‘b’ in Fig. 3); Tarsus length is the distance between the tibia-tarsus joint and foot (marked as ‘c’ in Fig. 3). We took measurements of each individual’s right and left legs and other characters, viz., inter-distances among the nostril, corner of the eye, corner of the mouth on each side (marked as ‘d’, ‘e’, ‘f’ in Fig. 3). Body depth is the distance from the base of the neck near the breast to the tip of the tail (marked as ‘g’ in Fig. 3).Data analysisWe performed the statistical analysis in R45, primarily through the nlme, ggbiplot, nnet, tidyverse, devtools packages. We did not have the freedom to measure a few morphological variables due to the problems mentioned above, which led to missing values in the datasets. We filled the missing values with the impute function using the R Core team45 through mice & VIM packages. When the missing values are high in numbers, we discard the data rather than use the impute function. Since almost about 70% of the lower mandible values were missing, we discarded them and ended up having only nine morphological variables in the final analysis. Moreover, the lower mandible is movable, with the mouth being open and closed, producing a considerable variation in measurements.We designed the matrix (Individuals × Region × Sex) representing the intraspecific variations concerning the region and sexes of Painted Storks46. The individuals are in rows (R), their region in column (C1), and sex in column (C2). We considered the regional variations as a sequence of the latitudinal gradient of the study sites. The values of the individuals (R) were the selected morphological variables. This matrix helped us investigate the critical questions relating to eco-geographic variations and sexual dimorphism.To determine whether temperature varied between study sites, we conducted a two-way ANOVA to analyse the effect of study sites (between North India (DZ & KNP) and South India (VBS & KBS)) and months of the year on the temperature at 2 m. For each character, Dimorphism Index (DI) was calculated as a mean value of female divided by the mean male, multiplied by 100, following the method of Urfi and Kalam15. We estimated the general body size of Painted Storks from the selected morphological variables through Principal Component Analysis (PCA) and tested hypotheses on Eco-geographic variations (Bergmann’s or Allen’s rules)2,47 and the sexual dimorphism15,48. The dimension reduction through PCA was carried out after the imputation as there were a few missing values. Body depth was omitted only for the principal component analysis due to many missing values. However, the values of all the characters are presented in the summary statistics in Table 1. The first principal component is characterized as a measure of size, and subsequent components describe various aspects of shape; therefore, it is considered a measure of general body size15,48,49. The PC1 indicated the body size variation, and PC2 revealed leg length variation (tibia and tarsus). We used nested ANOVA to test their body size variation between regions and sexes. The sexes nested within the region explained the eco-geographic rules and sexual selection patterns.Using a multinomial logistic regression model, we compared the Painted Storks’ northern male (NM), southern male (SM), and female (SF) with the reference category, northern female (NF). Then, we classified the data through multinomial log-linear and feed-forward neural network models. We predicted the Painted Stork’s region and sex using the Machine Learning (ML) algorithms through open-source software Waikato Environment for Knowledge Analysis (WEKA.3.9.5) implemented in Java50. WEKA has standard Machine learning/data-mining algorithms with pre-processing tools generating insightful knowledge from the Painted Storks’ morphological data.Using the R and Python interfaces, we used different ML software frameworks, libraries, and computer programs, viz., TensorFlow and Keras, and extensively explored the WEKA workbench environment to predict the sex and region of the Painted Stork. We used the k-fold cross-validation (k = 10) to avoid overlapping test sets, including splitting the data into k subsets of equal size, using each subset for testing and the remainder for training. We analyzed using the WEKA on a Lenovo ThinkPad P53s Mobile Workstation with the 8th Gen Intel® Core i7 @ 1.80 GHz processor, 48 GB DDR4 Memory, NVIDIA® Quadro® P520 with 2 GB GDDR5 Graphics. The performance criteria for all the eight models were assessed by using the Precision (TP/(TP + FP)), Recall (TP/(TP + FN)), Area under Curve (AUC) = (Sensitivity + Specificity)/2, Accuracy = (TP + TN)/(TP + TN + FP + FN), where TP, TN, FN and FP are the acronyms of true positive, true negative, false negative and false positive, respectively. We used the WEKA experimenter environment to test the statistical significance of the selected Machine Learning algorithms. We performed the Paired T-tester based on the number of correctly classified instances and areas under the curve. More

Gigantic trees occur in only a few regions on Earth. Some of the world’s largest eucalypts, for example, are on the island of Tasmania, off southeastern Australia. As wildfires increase in severity and frequency as a result of climate change, we urge the authorities to protect these trees by adopting measures similar to those applied to safeguard California’s redwood forests.
Competing Interests
The authors declare no competing interests. More