Philippa Kaur

Zeder, M. A. The domestication of animals. J. Anthropol. Res. 68, 161–190 (2012).Article 

Google Scholar 
Zohary, D. & Hopf, M. Domestication of Plants in the Old World: The Origin and Spread of Cultivated Plants in West Asia, Europe and the Nile Valley (Oxford University Press, 2000).
Google Scholar 
Epanchin-Niell, R., McAusland, C., Liebhold, A., Mwebaze, P. & Springborn, M. R. Biological invasions and international trade: Managing a moving target. Rev. Environ. Econom. Policy 15, 180–190 (2021).Article 

Google Scholar 
Gippet, J. M. & Bertelsmeier, C. Invasiveness is linked to greater commercial success in the global pet trade. Proc. Natl. Acad. Sci. 118, e2016337118 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Bertelsmeier, C. Globalization and the anthropogenic spread of invasive social insects. Curr. Opin. Insect Sci. 46, 16–23 (2021).PubMed 
Article 

Google Scholar 
Charles, H. & Dukes, J. S. Biological Invasions 217–237 (Springer, 2008).
Google Scholar 
Bellard, C., Cassey, P. & Blackburn, T. M. Alien species as a driver of recent extinctions. Biol. Let. 12, 20150623 (2016).Article 

Google Scholar 
Bertolino, S. et al. Spatially explicit models as tools for implementing effective management strategies for invasive alien mammals. Mamm. Rev. 50, 187–199 (2020).Article 

Google Scholar 
Grimaldi, D., Engel, M. S., Engel, M. S. & Engel, M. S. Evolution of the Insects (Cambridge University Press, 2005).MATH 

Google Scholar 
Hill, M. P., Clusella-Trullas, S., Terblanche, J. S. & Richardson, D. M. Vol. 18, 883–891 (Springer, 2016).Sawicka, B. & Egbuna, C. Natural Remedies for Pest, Disease and Weed Control 1–16 (Elsevier, 2020).Book 

Google Scholar 
de la Vega, G. J. & Corley, J. C. Drosophila suzukii (Diptera: Drosophilidae) distribution modelling improves our understanding of pest range limits. Int. J. Pest Manag. 65, 217–227 (2019).Article 

Google Scholar 
Kriticos, D. J. et al. The potential distribution of invading Helicoverpa armigera in North America: Is it just a matter of time? PLoS ONE 10, e0119618 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Early, R., González-Moreno, P., Murphy, S. T. & Day, R. Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. NeoBiota 40, 25–50 (2018).Article 

Google Scholar 
Day, R. et al. Fall armyworm: Impacts and implications for Africa. Outlooks Pest Manag. 28, 196–201 (2017).Article 

Google Scholar 
Rose, D. D. & Page, W. W. The African Armyworm Handbook 304 (Chatham, 2000).
Google Scholar 
De Groote, H. et al. Spread and impact of fall armyworm (Spodoptera frugiperda JE Smith) in maize production areas of Kenya. Agric. Ecosyst. Environ. 292, 106804 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Cheke, R. & Tucker, M. An evaluation of potential economic returns from the strategic control approach to the management of African armyworm Spodoptera exempta (Lepidoptera: Noctuidae) populations in eastern Africa. Crop Prot. 14, 91–103 (1995).Article 

Google Scholar 
Fox, K. Migrant Lepidoptera in New Zealand 1972–1973. N. Z. Entomol. 5, 268–271 (1973).Article 

Google Scholar 
Baker, G. An Outbreak of Spodoptera exempta (Walker) (Lepidoptera: Noctuidae) in the Highlands of Papua New Guinea (1978).Haggis, M. J. Distribution, Frequency of Attack and Seasonal Incidence of the African Armyworm Spodoptera exempta (Walk.) (Lep.: Noctuidae), with Particular Reference to Africa and Southwestern Arabia (Tropical Development and Research Institute, 1984).
Google Scholar 
Brown, E. Control of the African armyworm, Spodoptera exempta (Walk.)—An appreciation of the problem. East Afr. Agric. For. J. 35, 237–245 (1970).Article 

Google Scholar 
Rose, D. & Rainey, R. C. The significance of low-density populations of the African armyworm Spodoptera exempta (Walk.). Philos. Trans. R. Soc. Lond. B Biol. Sci. 287, 393–402 (1979).ADS 
Article 

Google Scholar 
Tucker, M. & Pedgley, D. Rainfall and outbreaks of the African armyworm, Spodoptera exempta (Walker) (Lepidoptera: Noctuidae). Bull. Entomol. Res. 73, 195–199 (1983).Article 

Google Scholar 
Tucker, M. Forecasting the severity of armyworm seasons in East Africa from early season rainfall. Int. J. Trop. Insect Sci. 5, 51–55 (1984).Article 

Google Scholar 
Wilson, K. & Gatehouse, A. Seasonal and geographical variation in the migratory potential of outbreak populations of the African armyworm moth, Spodoptera exempta. J. Anim. Ecol. 62, 169–181 (1993).Article 

Google Scholar 
Odiyo, P. O. Development of the first outbreaks of the African armyworm, Spodoptera exempta (Walk.), between Kenya and Tanzania during the ‘off-season’ months of July to December. Int. J. Trop. Insect Sci. 1, 305–318 (1981).Article 

Google Scholar 
Haggis, M. Forecasting the severity of seasonal outbreaks of African armyworm, Spodoptera exempta (Lepidoptera: Noctuidae) in Kenya from the previous year’s rainfall. Bull. Entomol. Res. 86, 129–136 (1996).Article 

Google Scholar 
Harvey, A. & Mallya, G. Predicting the severity of Spodoptera exempta (Lepidoptera: Noctuidae) outbreak seasons in Tanzania. Bull. Entomol. Res. 85, 479–487 (1995).Article 

Google Scholar 
Holt, J., Mushobozi, W., Tucker, M. & Venn, J. Workshop on Research Priorities for Migrant Pests of Agriculture in Southern Africa, 151.Matthew Hill, T. C. M. Bloomberg (Online, 2017).Wilson, K. The Conversation (United Kingdom, 2017).Day, R. K. et al. WormBase: A data management and information system for forecasting Spodoptera exempta (Lepidoptera: Noctuidae) in eastern Africa. J. Econ. Entomol. 89, 1–10 (1996).Article 

Google Scholar 
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).PubMed 
Article 

Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).Article 

Google Scholar 
Bosso, L. et al. The rise and fall of an alien: Why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea. Biol. Invas. https://doi.org/10.1007/s10530-022-02838-y (2022).Article 

Google Scholar 
Sutherst, R. W. Pest species distribution modelling: Origins and lessons from history. Biol. Invas. 16, 239–256 (2014).Article 

Google Scholar 
Méndez-Vázquez, L. J., Lira-Noriega, A., Lasa-Covarrubias, R. & Cerdeira-Estrada, S. Delineation of site-specific management zones for pest control purposes: Exploring precision agriculture and species distribution modeling approaches. Comput. Electron. Agric. 167, 105101 (2019).Article 

Google Scholar 
Raffini, F. et al. From nucleotides to satellite imagery: Approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).CAS 
Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, 4858 (2019).ADS 
Article 

Google Scholar 
Hosmer, D. W. Jr., Lemeshow, S. & Sturdivant, R. X. Applied Logistic Regression Vol. 398 (Wiley, 2013).MATH 
Book 

Google Scholar 
Landis, J. R. & Koch, G. G. The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977).CAS 
PubMed 
MATH 
Article 

Google Scholar 
Kalisa, W. et al. Assessment of climate impact on vegetation dynamics over East Africa from 1982 to 2015. Sci. Rep. 9, 1–20 (2019).ADS 
CAS 
Article 

Google Scholar 
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 1–12 (2018).ADS 
Article 

Google Scholar 
Mayaux, P., Bartholomé, E., Fritz, S. & Belward, A. A new land-cover map of Africa for the year 2000. J. Biogeogr. 31, 861–877 (2004).Article 

Google Scholar 
Marchant, R. et al. Drivers and trajectories of land cover change in East Africa: Human and environmental interactions from 6000 years ago to present. Earth Sci. Rev. 178, 322–378 (2018).ADS 
Article 

Google Scholar 
Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).Article 

Google Scholar 
Pemberton, C. E. Highlights in the history of entomology in Hawaii 1778–1963. Pac. Insects 6, 689–729 (1964).
Google Scholar 
Andow, D. A. Vegetational diversity and arthropod population response. Annu. Rev. Entomol. 36, 561–586 (1991).Article 

Google Scholar 
Andow, D. The extent of monoculture and its effects on insect pest populations with particular reference to wheat and cotton. Agr. Ecosyst. Environ. 9, 25–35 (1983).Article 

Google Scholar 
Oliveira, C., Auad, A., Mendes, S. & Frizzas, M. Crop losses and the economic impact of insect pests on Brazilian agriculture. Crop Prot. 56, 50–54 (2014).Article 

Google Scholar 
Furlong, M. J., Wright, D. J. & Dosdall, L. M. Diamondback moth ecology and management: Problems, progress, and prospects. Annu. Rev. Entomol. 58, 517–541 (2013).CAS 
PubMed 
Article 

Google Scholar 
Howse, M. W., Haywood, J. & Lester, P. J. Bioclimatic modelling identifies suitable habitat for the establishment of the invasive European paper wasp (Hymenoptera: Vespidae) across the southern hemisphere. Insects 11, 784 (2020).PubMed Central 
Article 

Google Scholar 
Rose, D., Dewhurst, C., Page, W. & Fishpool, L. The role of migration in the life system of the African armyworm Spodoptera exempta. Int. J. Trop. Insect Sci. 8, 561–569 (1987).Article 

Google Scholar 
Dewhurst, C. F., Page, W. W. & Rose, D. J. The relationship between outbreaks, rainfall and low density populations of the African armyworm, Spodoptera exempta, Kenya. Entomol. Exp. et Appl. 98, 285–294 (2001).Article 

Google Scholar 
Aguilon, D. J. & Velasco, L. R. Effects of larval rearing temperature and host plant condition on the development, survival, and coloration of African armyworm, Spodoptera exempta Walker (Lepidoptera: Noctuidae). J. Environ. Sci. Manag. 18, 54 (2015).Article 

Google Scholar 
David, W. & Ellaby, S. The viability of the eggs of the African army-worm, Spodoptera exempta in laboratory cultures. Entomol. Exp. Appl. 18, 269–280 (1975).Article 

Google Scholar 
He, L., Zhao, S., Ali, A., Ge, S. & Wu, K. Ambient humidity affects development, survival, and reproduction of the invasive fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), China. J. Econ. Entomol. 114, 1145–1158 (2021).PubMed 
Article 

Google Scholar 
Janssen, J. Effects of the mineral composition and water content of intact plants on the fitness of the African armyworm. Oecologia 95, 401–409 (1993).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shahzad, M. S. et al. Modelling population dynamics of army worm (Spodoptera litura F.) (Lepidoptera: Noctuiidae) in relation to meteorological factors in Multan, Punjab, Pakistan. Int. J. Agron. Agric. Res. 5, 39–45 (2014).
Google Scholar 
Garcia, A. G., Ferreira, C. P., Godoy, W. A. & Meagher, R. L. A computational model to predict the population dynamics of Spodoptera frugiperda. J. Pest. Sci. 92, 429–441 (2019).Article 

Google Scholar 
Hickling, R., Roy, D. B., Hill, J. K., Fox, R. & Thomas, C. D. The distributions of a wide range of taxonomic groups are expanding polewards. Glob. Change Biol. 12, 450–455 (2006).ADS 
Article 

Google Scholar 
Vanhanen, H., Veteli, T. O., Paivinen, S., Kellomaki, S. & Niemela, P. Climate change and range shifts in two insect defoliators: Gypsy moth and nun moth-a model study. Silva Fennica 41, 621 (2007).Article 

Google Scholar 
Falk, W. & Hempelmann, N. Species favourability shift in Europe due to climate change: A case study for Fagus sylvatica L. and Picea abies (L.) Karst. based on an ensemble of climate models. J. Climatol. 2013, 1–18 (2013).Article 

Google Scholar 
Arora, R. & Dhawan, A. Climate Change and Insect Pest Management. Integrated Pest Management 44–60 (Scientific Publisher, 2013).
Google Scholar 
Andrew, N. R. & Hill, S. J. Effect of climate change on insect pest management. In Environmental Pest Management: Challenges for Agronomists, Ecologists, Economists and Policymakers, 197 (2017).De Boer, J. G. & Harvey, J. A. Range-expansion in processionary moths and biological control. Insects 11, 267 (2020).PubMed Central 
Article 

Google Scholar 
Bras, A. et al. A complex invasion story underlies the fast spread of the invasive box tree moth (Cydalima perspectalis) across Europe. J. Pest. Sci. 92, 1187–1202 (2019).Article 

Google Scholar 
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).PubMed 
Article 

Google Scholar 
Barford, E. Crop pests advancing with global warming. Nature 10, 13644 (2013).
Google Scholar 
Bebber, D. P., Ramotowski, M. A. & Gurr, S. J. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Change 3, 985–988 (2013).ADS 
Article 

Google Scholar 
Rubenstein, D. I. The greenhouse effect and changes in animal behavior: Effects on social structure and life-history strategies. In Global Warming and Biological Diversity, 180–192 (1992).Karuppaiah, V. & Sujayanad, G. Impact of climate change on population dynamics of insect pests. World J. Agric. Sci. 8, 240–246 (2012).
Google Scholar 
Jakhar, B. et al. Influence of climate change on Helicoverpa armigera (Hubner) in pigeonpea. J. Agric. Ecol. 2, 25–31 (2016).
Google Scholar 
Akbar, S. M., Pavani, T., Nagaraja, T. & Sharma, H. Influence of CO 2 and temperature on metabolism and development of Helicoverpa armigera (Noctuidae: Lepidoptera). Environ. Entomol. 45, 229–236 (2016).CAS 
PubMed 
Article 

Google Scholar 
Magandana, T. P., Hassen, A. & Tesfamariam, E. H. Seasonal herbaceous structure and biomass production response to rainfall reduction and resting period in the semi-arid grassland area of South Africa. Agronomy 10, 1807 (2020).CAS 
Article 

Google Scholar 
Gherardi, L. A. & Sala, O. E. Enhanced precipitation variability decreases grass-and increases shrub-productivity. Proc. Natl. Acad. Sci. 112, 12735–12740 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scheiter, S. & Higgins, S. I. Impacts of climate change on the vegetation of Africa: An adaptive dynamic vegetation modelling approach. Glob. Change Biol. 15, 2224–2246 (2009).ADS 
Article 

Google Scholar 
Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).Article 

Google Scholar 
Jiménez-Valverde, A., Lobo, J. & Hortal, J. The effect of prevalence and its interaction with sample size on the reliability of species distribution models. Community Ecol. 10, 196–205 (2009).Article 

Google Scholar 
Renault, D., Laparie, M., McCauley, S. J. & Bonte, D. Environmental adaptations, ecological filtering, and dispersal central to insect invasions. Annu. Rev. Entomol. 63, 345–368 (2018).CAS 
PubMed 
Article 

Google Scholar 
Ellis, S. New pest response guidelines: Spodoptera. USDA/APHIS/PPQ/PDMP (2004).Waage, J. & Mumford, J. D. Agricultural biosecurity. Philos. Trans. R. Soc. B Biol. Sci. 363, 863–876 (2008).CAS 
Article 

Google Scholar 
Anand, M. A systems approach to agricultural biosecurity. Health Secur. 16, 58–68 (2018).PubMed 
Article 

Google Scholar 
MacLeod, A., Pautasso, M., Jeger, M. J. & Haines-Young, R. Evolution of the international regulation of plant pests and challenges for future plant health. Food Secur. 2, 49–70 (2010).Article 

Google Scholar 
Jiménez-Valverde, A. et al. Use of niche models in invasive species risk assessments. Biol. Invas. 13, 2785–2797 (2011).Article 

Google Scholar 
Oluwole, F. A., Sambo, J. M. & Sikhalazo, D. Long-term effects of different burning frequencies on the dry savannah grassland in South Africa. Afr. J. Agric. Res. 3, 147–153 (2008).
Google Scholar 
Kalleshwaraswamy, C. et al. First Report of the Fall Armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), an Alien Invasive Pest on Maize in India (2018).Bentivenha, J., Baldin, E., Hunt, T., Paula-Moraes, S. & Blankenship, E. Intraguild competition of three noctuid maize pests. Environ. Entomol. 45, 999–1008 (2016).CAS 
PubMed 
Article 

Google Scholar 
Chapman, J. W. et al. Fitness consequences of cannibalism in the fall armyworm, Spodoptera frugiperda. Behav. Ecol. 10, 298–303 (1999).Article 

Google Scholar 
Divya, J., Kalleshwaraswamy, C., Mallikarjuna, H. & Deshmukh, S. Does recently invaded fall armyworm, Spodoptera frugiperda displace native lepidopteran pests of maize in India? Curr. Sci. 120, 1358 (2021).Article 

Google Scholar 
Hailu, G. et al. Could fall armyworm, Spodoptera frugiperda (JE Smith) invasion in Africa contribute to the displacement of cereal stemborers in maize and sorghum cropping systems. Int. J. Trop. Insect Sci. 41, 1753–1762 (2021).Article 

Google Scholar 
Srivastava, V., Lafond, V. & Griess, V. C. Species distribution models (SDM): Applications, benefits and challenges in invasive species management. CAB Rev. 14, 1–13 (2019).Article 

Google Scholar 
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 
Eyring, V. et al. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).ADS 
Article 

Google Scholar 
Wu, T. et al. The Beijing Climate Center climate system model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev. 12, 1573–1600 (2019).ADS 
Article 

Google Scholar 
O’Neill, B. C. et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2017).Article 

Google Scholar 
Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. & Guisan, A. Selecting predictors to maximize the transferability of species distribution models: Lessons from cross-continental plant invasions. Glob. Ecol. Biogeogr. 26, 275–287 (2017).Article 

Google Scholar 
Cano, J. et al. Modelling the spatial distribution of aquatic insects (Order Hemiptera) potentially involved in the transmission of Mycobacterium ulcerans in Africa. Parasit. Vectors 11, 1–16 (2018).Article 

Google Scholar 
Gómez-Undiano, I. Modelos y patrones de distribución geográfica de especies de Culicidae (Culex pipiens, Mansonia africana y Mansonia uniformis) vectores de filariasis linfática en ámbitos urbanos y periurbanos del África subsahariana. Máster en Zoología thesis, Universidad Complutense de Madrid (2018).R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).Phillips, S. J. et al. Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).PubMed 
Article 

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

Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many? Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

Google Scholar 
Thuiller, W. et al. Package ‘biomod2’. Species Distribution Modeling Within an Ensemble Forecasting Framework (2016).Acevedo, P., Jiménez-Valverde, A., Lobo, J. M. & Real, R. Delimiting the geographical background in species distribution modelling. J. Biogeogr. 39, 1383–1390 (2012).Article 

Google Scholar 
VanDerWal, J., Shoo, L. P., Graham, C. & Williams, S. E. Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know? Ecol. Model. 220, 589–594 (2009).Article 

Google Scholar 
Hijmans, R., Phillips, S., Leathwick, J. & Elith, J. (2012).Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).PubMed 
Article 

Google Scholar 
Gama, M., Crespo, D., Dolbeth, M. & Anastácio, P. M. Ensemble forecasting of Corbicula fluminea worldwide distribution: Projections of the impact of climate change. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 675–684 (2017).Article 

Google Scholar 
Liu, C., White, M., Newell, G. & Griffioen, P. Species distribution modelling for conservation planning in Victoria, Australia. Ecol. Model. 249, 68–74 (2013).Article 

Google Scholar  More

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

Changes in temperature and extreme environmental conditions can affect the dynamics of vector-borne pathogens. These include leishmaniasis, transmitted by phlebotomine sandflies, as well as mosquitoes that spread arboviruses like dengue, encephalitis, yellow fever, West Nile fever, and lymphatic filariasis19,20,21.The CCA analysis showed that maximum temperature significantly influenced the abundance of mosquito populations in the study area. In addition, the NMDS showed two different groupings that consisted of samples collected during the rainy and dry seasons. Accordingly, Refs.22,23 report that changes in temperature and relative humidity determine the abundance of mosquitoes, which can disappear entirely during the dry season. Moreover, Refs.22,24,25 note that certain species of mosquitoes increase proportionally with the regional rainfall regime. This is consistent with Ref.10, who find alternating patterns in tropical and temperate climates in some Brazilian regions.As shown by the geometric regression, there is a positive correlation between cumulative rainfall in the days before collection and the number of species found in the study period. Likewise, Ref.26 reported that under the conditions observed in the Serra do Mar State Park, climate variables directly influenced the abundance of Cq. chrysonotum and Cq. venezuelensis, favoring the occurrence of culicids during the more warm, wet, and rainy months.The current climate scenario and future projections about climate, environmental, demographic, and meteorological factors directly influence the distribution and abundance of mosquito vectors and/or diseases27,28,29,30. Environmental temperature alters mosquito population dynamics, thereby affecting the development of immature stages as well as reproduction31. While temperature has an important effect on population dynamics, rainfall and drought also affect the density and dispersal of mosquitoes in temperate and tropical regions32.To be sure, environmental changes other than climate can modify the behavior of vector insects and, subsequently, the mechanism of transmission of parasites20. Specifically, human impacts on the environment can result in drastically different disease transmission cycles in and around inhabited areas33.A previous study34 reported that changes in land use influence the mosquito communities with potential implications for the emergence of arboviruses. Another study35 noted that environmental changes negatively affect natural ecosystems with accelerated biodiversity loss. This is due to the modification and loss of natural habitat and unsustainable land use, which leads to the spread of pathogens and disease vectors.Hence, understanding the relationship between humans and the environment becomes increasingly critical, given the way in which climate changes can lead to alterations in the epidemiology of diseases such as dengue in areas considered free of the disease, as well as in endemic areas36.We found that the abundance and diversity of Mansoniini were directly influenced by the effect of the rainy season and other climatic factors. The rainfall regime has been shown to affect the development of immature forms12,37; explaining the greater frequency of these specimens in the warmer and wetter months38,39,40. According to Ref.41, stable ecosystems such as forests contain great species diversity. On the other hand, diversity tends to be reduced in biotic communities suffering from stress.Studies of insect populations in natural areas are important because they allow a direct analysis of how environmental factors influence phenomena such as the choice of breeding sites by females for oviposition, hematophagous behavior, and the distribution of species along a vegetation gradient12,26,42,43.Throughout the experimental period of the present study, we observed that Shannon light traps are an effective method for catching mosquitoes from the Mansoniini tribe. Interestingly, Ref.44 reported a species richness pattern strongly influenced by Coquillettidia fasciolata (Lynch Arribálzaga, 1891) on mosquito samples from different capture points by using CDC and Shannon light traps as sampling methods. In contrast to the results of Ref.44, where the highest population density of mosquitoes was captured with CDC traps, we observed that these traps were not effective at capturing specimens of Mansoniini in spite of being used in large numbers in the present study. Moreover, Ref.45 conducted another study on faunal diversity in an Atlantic Forest remnant of the state of Rio de Janeiro and observed the highest abundance of Cq. chrysonotum (Peryassú, 1922) and Cq. venezuelensis by using Shannon light traps, while the numbers of captures of Ma. titillans were very similar using CDC and Shannon traps.The results of this study indicate that the makeup of culicid fauna remains quite similar throughout the year, despite seasonal variations in abundance, though there was a lower variability of fauna in the dry season. Therefore, although the seasonality did not affect the temporal variation of the faunal composition in a generalized way, it was possible to detect a partial effect of the seasonality on fauna abundance.
Reference46 report that the incidence peaks of mosquitoes in the warmer and wetter months, as well as mosquito populations remaining between tolerance limits for most of the year, indicate the sensitivity of some species to the local climate.The elevated abundance and diversity of species of Mansoniini in the study area were influenced by the favorable maintenance of breeding sites, including specific water accumulations with emerging vegetation that remain present throughout the year and the well-defined rainy season in the region. In addition, the representatives of Mansoniini, which prefer breeding sites containing macrophytes, made up nearly all of the species collected7.Besides providing a greater awareness of mosquito populations’ ecological and biological aspects, research carried out in wild areas also provides information on the relationship between species diversity and the area in which they are found. Considering that wild insects may become potential vectors of diseases, research in wild areas also provides helpful information for understanding relevant epidemiological aspects. These studies facilitate the identification, monitoring, and control of mosquito populations following environmental changes caused by direct human action, which can lead to major epidemics26.We observed considerable heterogeneity among Mansoniini fauna, and the months with the highest rainfall directly influence the structure of the communities and contribute to the increase in mosquito diversity and abundance, possibly due to variations in the availability of habitat for their immature forms. 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

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

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

Morens, D. M. et al. The origin of COVID-19 and why it matters. Am. J. Trop. Med. Hyg. 103, 955–959 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pierson, T. C. & Diamond, M. S. The emergence of Zika virus and its new clinical syndromes. Nature 560, 573–581 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gates, B. The next epidemic—Lessons from Ebola., https://doi.org/10.1056/NEJMp1502918 (2015).World Health Organization. Lassa fever research and development (R&D) roadmap. https://www.who.int/publications/m/item/lassa-fever-research-and-development-(r-d)-roadmap (2018).World Health Organization. Prioritizing diseases for research and development in emergency contexts. https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts.Akpede, G. O. et al. Caseload and case fatality of Lassa fever in Nigeria, 2001–2018: A specialist center’s experience and its implications. Front. Public Health 7, https://doi.org/10.3389/fpubh.2019.00170 (2019).Eberhardt, K. A. et al. Ribavirin for the treatment of Lassa fever: A systematic review and meta-analysis. Int. J. Infect. Dis. 87, 15–20 (2019).CAS 
PubMed 
Article 

Google Scholar 
Lukashevich, I. S., Paessler, S. & de la Torre, J. C. Lassa virus diversity and feasibility for universal prophylactic vaccine. F1000Res 8, https://doi.org/10.12688/f1000research.16989.1 (2019).Purushotham, J., Lambe, T. & Gilbert, S. C. Vaccine platforms for the prevention of Lassa fever. Immunol. Lett. 215, 1–11 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mateo, M. et al. A single-shot Lassa vaccine induces long-term immunity and protects cynomolgus monkeys against heterologous strains. Sci. Transl. Med. 13, eabf6348 (2021).CAS 
PubMed 
Article 

Google Scholar 
McCormick, J. B. et al. Lassa Fever. N. Engl. J. Med. 314, 20–26 (1986).CAS 
PubMed 
Article 

Google Scholar 
Bell-Kareem, A. R. & Smither, A. R. Epidemiology of Lassa fever. in 1–23 (Springer, 2021). https://doi.org/10.1007/82_2021_234.Nigeria Centre for Disease Control. https://ncdc.gov.ng/diseases/sitreps/?cat=5&name=An%20update%20of%20Lassa%20fever%20outbreak%20in%20Nigeria.Manning, J. T., Forrester, N. & Paessler, S. Lassa virus isolates from Mali and the Ivory Coast represent an emerging fifth lineage. Front. Microbiol. 6, https://doi.org/10.3389/fmicb.2015.01037 (2015).Dzotsi, E. K. et al. The first cases of Lassa fever in Ghana. Ghana. Med. J. 46, 166–170 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Patassi, A. A. et al. Emergence of Lassa fever disease in northern Togo: Report of two cases in Oti District in 2016. Case Rep. Infect. Dis. 2017, 8242313 (2017).PubMed 
PubMed Central 

Google Scholar 
Yadouleton, A. et al. Lassa fever in Benin: Description of the 2014 and 2016 epidemics and genetic characterization of a new Lassa virus. Emerg. Microbes Infect. 9, 1761–1770 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCormick, J. B. & Fisher-Hoch, S. P. Lassa fever. Curr. Top. Microbiol. Immunol. 262, 75–109 (2002).CAS 
PubMed 

Google Scholar 
Monath, T. P., Newhouse, V. F., Kemp, G. E., Setzer, H. W. & Cacciapuoti, A. Lassa virus isolation from Mastomys natalensis rodents during an epidemic in Sierra Leone. Science 185, 263–265 (1974).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Stephenson, E. H., Larson, E. W. & Dominik, J. W. Effect of environmental factors on aerosol-induced Lassa virus infection. J. Med. Virol. 14, 295–303 (1984).CAS 
PubMed 
Article 

Google Scholar 
Wozniak, D. M. et al. Inoculation route-dependent Lassa virus dissemination and shedding dynamics in the natural reservoir – Mastomys natalensis. Emerg. Microbes Infect. 10, 2313–2325 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ter Meulen, J. et al. Hunting of peridomestic rodents and consumption of their meat as possible risk factors for rodent-to-human transmission of Lassa virus in the Republic of Guinea. Am. J. Trop. Med. Hyg. 55, 661–666 (1996).PubMed 
Article 

Google Scholar 
Downs, I. L. et al. Natural history of aerosol induced Lassa fever in non-human primates. Viruses 12, 593 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Lecompte, E. et al. Mastomys natalensis and Lassa Fever, West Africa. Emerg. Infect. Dis. 12, 1971–1974 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
Smither, A. R. & Bell-Kareem, A. R. Ecology of Lassa Virus. in 1–20 (Springer, 2021). https://doi.org/10.1007/82_2020_231.Ogbu, O., Ajuluchukwu, E. & Uneke, C. J. Lassa fever in West African sub-region: An overview. J. Vector Borne Dis. 44, 1–11 (2007).CAS 
PubMed 

Google Scholar 
Fichet-Calvet, E. et al. Fluctuation of abundance and Lassa virus prevalence in Mastomys natalensis in Guinea, West Africa. Vector Borne Zoonotic Dis. 7, 119–128 (2007).PubMed 
Article 

Google Scholar 
Fichet-Calvet, E., Becker-Ziaja, B., Koivogui, L. & Günther, S. Lassa serology in natural populations of rodents and horizontal transmission. Vector Borne Zoonotic Dis. 14, 665–674 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Lo Iacono, G. et al. Using modelling to disentangle the relative contributions of zoonotic and anthroponotic transmission: The case of Lassa fever. PLoS Negl. Trop. Dis. 9, e3398 (2015).Siddle, K. J. et al. Genomic analysis of Lassa virus during an increase in cases in Nigeria in 2018. N. Engl. J. Med. 379, 1745–1753 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kafetzopoulou, L. E. et al. Metagenomic sequencing at the epicenter of the Nigeria 2018 Lassa fever outbreak. Science 363, 74–77 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Andersen, K. G. et al. Clinical sequencing uncovers origins and evolution of Lassa virus. Cell 162, 738–750 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lalis, A. & Wirth, T. Mice and men: An evolutionary history of Lassa fever. in Biodiversity and Evolution (eds. Grandcolas, P. & Maurel, M.-C.) 189–212, https://doi.org/10.1016/B978-1-78548-277-9.50011-5 (Elsevier, 2018).Mylne, A. Q. N. et al. Mapping the zoonotic niche of Lassa fever in Africa. Trans. R. Soc. Trop. Med. Hyg. 109, 483–492 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Colangelo, P. et al. A mitochondrial phylogeographic scenario for the most widespread African rodent, Mastomys natalensis. Biol. J. Linn. Soc. 108, 901–916 (2013).Article 

Google Scholar 
Gryseels, S. et al. When viruses don’t go viral: The importance of host phylogeographic structure in the spatial spread of arenaviruses. PLoS Path 13, e1006073 (2017).Article 

Google Scholar 
Cuypers, L. N. et al. Three arenaviruses in three subspecific natal multimammate mouse taxa in Tanzania: Same host specificity, but different spatial genetic structure? Virus Evol. https://doi.org/10.1093/ve/veaa039 (2020).Vazeille, M., Gaborit, P., Mousson, L., Girod, R. & Failloux, A.-B. Competitive advantage of a dengue 4 virus when co-infecting the mosquito Aedes aegypti with a dengue 1 virus. BMC Infect. Dis. 16, 318 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Chan, K. F. et al. Investigating viral interference between influenza A virus and human respiratory syncytial virus in a ferret model of infection. J. Infect. Dis. 218, 406–417 (2018).CAS 
PubMed 
Article 

Google Scholar 
Meunier, D. Y., McCormick, J. B., Georges, A. J., Georges, M. C. & Gonzalez, J. P. Comparison of Lassa, Mobala, and Ippy virus reactions by immunofluorescence test. Lancet 1, 873–874 (1985).CAS 
PubMed 
Article 

Google Scholar 
Howard, C. R. Antigenic diversity among the Arenaviruses. in The Arenaviridae (ed. Salvato, M. S.) 37–49, https://doi.org/10.1007/978-1-4615-3028-2_3 (Springer US, 1993).Bhattacharyya, S., Gesteland, P. H., Korgenski, K., Bjørnstad, O. N. & Adler, F. R. Cross-immunity between strains explains the dynamical pattern of paramyxoviruses. Proc. Natl Acad. Sci. U. S. A. 112, 13396–13400 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Luis, A. D., Douglass, R. J., Mills, J. N. & Bjørnstad, O. N. Environmental fluctuations lead to predictability in Sin Nombre hantavirus outbreaks. Ecology 96, 1691–1701 (2015).Article 

Google Scholar 
Anderson, R. M., Jackson, H. C., May, R. M. & Smith, A. M. Population dynamics of fox rabies in Europe. Nature 289, 765–771 (1981).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Tian, H. et al. Anthropogenically driven environmental changes shift the ecological dynamics of hemorrhagic fever with renal syndrome. PLoS Pathog. 13, e1006198 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Redding, D. W., Moses, L. M., Cunningham, A. A., Wood, J. & Jones, K. E. Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever. Methods Ecol. Evol. 7, 646–655 (2016).Article 

Google Scholar 
Peterson, A. T., Moses, L. M. & Bausch, D. G. Mapping transmission risk of Lassa fever in West Africa: the importance of quality control, sampling bias, and error weighting. PLoS One 9, e100711 (2014).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fichet-Calvet, E. & Rogers, D. J. Risk maps of Lassa fever in West Africa. PLoS. Negl. Trop. Dis. 3, e388 (2009).Basinski, A. J. et al. Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa. PLoS Comput. Biol. 17, e1008811 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Iacono, G. L. et al. A unified framework for the infection dynamics of zoonotic spillover and spread. PLoS Negl. Trop. Dis. 10, e0004957 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).ADS 
Article 

Google Scholar 
Coumou, D., Robinson, A. & Rahmstorf, S. Global increase in record-breaking monthly-mean temperatures. Clim. Change 118, 771–782 (2013).ADS 
Article 

Google Scholar 
Bathiany, S., Dakos, V., Scheffer, M. & Lenton, T. M. Climate models predict increasing temperature variability in poor countries. Sci. Adv. 4, eaar5809 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Arneth, A. Uncertain future for vegetation cover. Nature 524, 44–45 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Brandt, M. et al. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nat. Ecol. Evol. 1, 81 (2017).PubMed 
Article 

Google Scholar 
Herrmann, S. M., Brandt, M., Rasmussen, K. & Fensholt, R. Accelerating land cover change in West Africa over four decades as population pressure increased. Com. Earth Envir 1, 1–10 (2020).
Google Scholar 
Gibb, R., Moses, L. M., Redding, D. W. & Jones, K. E. Understanding the cryptic nature of Lassa fever in West Africa. Pathog. Glob. Health 111, 276–288 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
PubMed 
Article 

Google Scholar 
Frieler, K. et al. Assessing the impacts of 1.5 °C global warming—simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci. Model Dev. 10, 4321–4345 (2017).ADS 
Article 

Google Scholar 
Soberón, J. & Nakamura, M. Niches and distributional areas: Concepts, methods, and assumptions. Proc. Natl Acad. Sci. U. S. A. 106, 19644–19650 (2009).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lemey, P., Rambaut, A., Welch, J. J. & Suchard, M. A. Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evol. 27, 1877–1885 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lukashevich, I. S. Generation of reassortants between African arenaviruses. Virology 188, 600–605 (1992).CAS 
PubMed 
Article 

Google Scholar 
Vijaykrishna, D., Mukerji, R. & Smith, G. J. D. RNA virus reassortment: an evolutionary mechanism for host jumps and immune evasion. PLoS Path 11, e1004902 (2015).Article 

Google Scholar 
Whitmer, S. L. M. et al. New lineage of Lassa Virus, Togo, 2016. Emerg. Infect. Dis. 24, 599 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Ehichioya, D. U. et al. Phylogeography of Lassa virus in Nigeria. J. Virol. 93, e00929–19 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Dellicour, S., Rose, R., Faria, N. R., Lemey, P. & Pybus, O. G. SERAPHIM: studying environmental rasters and phylogenetically informed movements. Bioinformatics 32, 3204–3206 (2016).CAS 
PubMed 
Article 

Google Scholar 
Dellicour, S. et al. Using viral gene sequences to compare and explain the heterogeneous spatial dynamics of virus epidemics. Mol. Biol. Evol. 34, 2563–2571 (2017).CAS 
PubMed 
Article 

Google Scholar 
Dellicour, S. et al. Epidemiological hypothesis testing using a phylogeographic and phylodynamic framework. Nat. Commun. 11, 5620 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dijkstra, E. W. A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959).MathSciNet 
MATH 
Article 

Google Scholar 
Strahler, A. N. Quantitative analysis of watershed geomorphology. Eos, Trans. Am. Geophys. Union 38, 913–920 (1957).Article 

Google Scholar 
Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).MathSciNet 
MATH 
Article 

Google Scholar 
Ehichioya, D. U. et al. Current molecular epidemiology of Lassa virus in Nigeria. J. Clin. Microbiol. 49, 1157 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Oloniniyi, O. K. et al. Genetic characterization of Lassa virus strains isolated from 2012 to 2016 in southeastern Nigeria. PLoS Negl. Trop. Dis. 12, e0006971 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Olesen, J. E. et al. Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Clim. Change 81, 123–143 (2007).Article 

Google Scholar 
Simo Tchetgna, H. et al. Molecular characterization of a new highly divergent Mobala related arenavirus isolated from Praomys sp. rodents. Sci. Rep. 11, 10188 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Olayemi, A. et al. New hosts of the Lassa virus. Sci. Rep. 6, 25280 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zaidi, M. B. et al. Competitive suppression of dengue virus replication occurs in chikungunya and dengue co-infected Mexican infants. Parasit. Vectors 11, 378 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Olayemi, A. et al. Widespread arenavirus occurrence and seroprevalence in small mammals, Nigeria. Parasit. Vectors 11, 416 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Nigeria Centre for Disease Control. https://ncdc.gov.ng/diseases/sitreps/?cat=5&name=An%20update%20of%20Lassa%20fever%20outbreak%20in%20Nigeria.Norris, K. et al. Biodiversity in a forestagriculture mosaic: the changing face of west Africa rainforests. Biol. Conserv. 143, 2341–2350 (2010).Article 

Google Scholar 
Stocker, T. F. et al. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, Cambridge, United Kingdom and New York, 2013).Buba, M. I. et al. Mortality among confirmed Lassa fever cases during the 2015-2016 outbreak in Nigeria. Am. J. Public Health 108, 262–264 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Tobin, E. A. et al. Knowledge of secondary school children in Edo State on Lassa fever and its implications for prevention and control. West. Afr. J. Med. 34, 101–107 (2015).CAS 
PubMed 

Google Scholar 
Saez, A. M. et al. Rodent control to fight Lassa fever: Evaluation and lessons learned from a 4-year study in Upper Guinea. PLoS Negl. Trop. Dis. 12, e0006829 (2018).Article 

Google Scholar 
Ejembi, J. et al. Contact tracing in Lassa fever outbreak response, an effective strategy for control? Online J. Public Health Inf. 11, e378 (2019).
Google Scholar 
ECHO Flash List. https://erccportal.jrc.ec.europa.eu/ECHO-Flash/ECHO-Flash-List/yy/2018/mm/2.Pigott, D. M. et al. Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis. Lancet 390, 2662–2672 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Kraemer, M. U. G. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nature Microbiology https://doi.org/10.1038/s41564-019-0376-y (2019).Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

Google Scholar 
Dhingra, M. S. et al. Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation. eLife 5, e19571 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

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

Google Scholar 
Valavi, R., Elith, J., Lahoz‐Monfort, J. J. & Guillera‐Arroita, G. blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol. Evol. 10, 225–232 (2019).Article 

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Randin, C. F. et al. Are niche-based species distribution models transferable in space? J. Biogeogr. 33, 1689–1703 (2006).Article 

Google Scholar 
Lange, S. Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth Syst. Dyn. 9, 627–645 (2018).ADS 
Article 

Google Scholar 
Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).ADS 
Article 

Google Scholar 
Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).ADS 
Article 

Google Scholar 
Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).Article 

Google Scholar 
Watanabe, M. et al. Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).ADS 
Article 

Google Scholar 
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteor. Soc. 93, 485–498 (2012).ADS 
Article 

Google Scholar 
Hurtt, G. C. et al. Harmonization of global land-use change and management for the period 850-2100 (LUH2) for CMIP6. Geosci. Model Dev. 1–65 https://doi.org/10.5194/gmd-2019-360 (2020)Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 084003 (2016).ADS 
Article 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Larsson, A. AliView: a fast and lightweight alignment viewer and editor for large datasets. Bioinformatics 30, 3276–3278 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ayres, D. L. et al. BEAGLE 3: Improved performance, scaling, and usability for a high-performance computing library for statistical phylogenetics. Syst. Biol. https://doi.org/10.1093/sysbio/syz020 (2019).Tavaré, S. Some probabilistic and statistical problems in the analysis of DNA sequences. Lect. Math. Life Sci. 17, 57–86 (1986).MathSciNet 
MATH 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Laenen, L. et al. Spatio-temporal analysis of Nova virus, a divergent hantavirus circulating in the European mole in Belgium. Mol. Ecol. 25, 5994–6008 (2016).CAS 
PubMed 
Article 

Google Scholar 
Dellicour, S. et al. Landscape genetic analyses of Cervus elaphus and Sus scrofa: comparative study and analytical developments. Heredity 123, 228–241 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Dellicour, S. et al. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. Nat. Commun. 9, 2222 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dellicour, S. et al. Phylogeographic and phylodynamic approaches to epidemiological hypothesis testing. bioRxiv https://doi.org/10.1101/788059 (2020).Dellicour, S., Rose, R. & Pybus, O. G. Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data. BMC Bioinform 17, 1–12 (2016).Article 

Google Scholar 
McRae, B. H. Isolation by resistance. Evolution 60, 1551–1561 (2006).PubMed 
Article 

Google Scholar 
Jacquot, M., Nomikou, K., Palmarini, M., Mertens, P. & Biek, R. Bluetongue virus spread in Europe is a consequence of climatic, landscape and vertebrate host factors as revealed by phylogeographic inference. Proc. R. Soc. Lond. B 284, 20170919 (2017).
Google Scholar 
Gill, M. S. et al. Improving Bayesian population dynamics inference: A coalescent-based model for multiple loci. Mol. Biol. Evol. 30, 713–724 (2013).CAS 
PubMed 
Article 

Google Scholar 
Karcher, M. D., Palacios, J. A., Bedford, T., Suchard, M. A. & Minin, V. N. Quantifying and mitigating the effect of preferential sampling on phylodynamic inference. PLoS Comput. Biol. 12, e1004789 (2016).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Case studyThe experimental setup for the field studies that provided the bacterial population and weather data used here was previously described by Belias et al. [9]. Briefly, baby spinach and lettuce plants were spray-inoculated with E. coli and S. enterica (Salmonella) onto field plots established in Davis, CA (University of California, Plant Sciences Field Research Facility); Freeville, NY (Homer C. Thompson Research Farm, Cornell University); and Murcia, Spain (La Matanza Research Farm). The spinach and lettuce varieties were selected based on their suitability for baby leaf production: lettuce var. Tamarindo, and spinach var. Acadia F1 and Seaside F1. Four replicate trials at different times of the regional growing season were carried out per location. The plants were spray-inoculated with a 104 CFU/mL cocktail of rifampin-resistant strains of commensal E. coli and attenuated S. enterica serovar Typhimurium (Salmonella), and samples were collected for bacterial cell quantification by plate counts on selective and differential media at 0, 4, 8, 24, 48, 72 and 96 h post-inoculation. Concurrent with leaf sample collection, weather variables (temperature, relative humidity (RH), solar radiation intensity, and wind velocity) were recorded hourly for the respective field locations. The hourly dew point (DP) was calculated as a function of both the hourly temperature and RH.Model for persister formation on plantsMathematical modeling to characterize the switch rate from a non-persister bacterial cell (hereafter termed “normal cell”) to a persister cell in the phyllosphere under laboratory conditions was performed as described in our previously published study [24]. Briefly, persister cell fractions were quantified in culturable EcO157 populations after inoculation onto young lettuce plants cultivated in plant growth chambers. Persister cells recovered from the lettuce phyllosphere were identified using the antibiotic lysing method [23]. The greatest persister fraction in the EcO157 population on lettuce in our laboratory investigation above was observed during population decline on leaf surfaces of plants left to dry after inoculation. Using mathematical modeling, we calculated the switch rate from an EcO157 normal to persister cell on dry lettuce plants based on these data [24]. Importantly, our laboratory conditions mimicked inoculation conditions in which E. coli arrived via water on leaves, the surfaces of which progressively dried like under prevailing weather conditions in the field.Based on the main dynamic observed in the field study data [9] and building on our previous study [24], we assumed that the total enteric pathogen population is composed of (i) non-persister (normal) cells consisting of two sub-populations, characterized by fast (n1) (CFU/100g) and slow (n2) (CFU/100g) decay, and (ii) the persister population, leading to the following model from Munther et al. [24]:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha _dn_1 + beta _dleft( {1 – sigma } right)hat p,$$
(1a)
$$frac{{dn_2}}{{dt}} = – theta _{n_2}n_2 – alpha _dn_2 + beta _dsigma hat p,$$
(1b)
$$frac{{dhat p}}{{dt}} = – mu _{hat p}hat p – beta _dhat p + alpha _dleft( {n_1 + n_2} right),$$
(1c)
$$n_1left( 0 right) = n_{10},n_2left( 0 right) = n_{20},, hat pleft( 0 right) = widehat {p_0},$$
(1d)
where (theta _{n_i})(1/h) is the death rate of the normal cells (subscript i = 1 for fast and i = 2 for slow), (hat p) (CFU/100 g) represents the persister cell population at time t (h), (mu _{hat p}) (1/h) reflects the persister population inactivation rate, αd (1/h) is the switch rate from normal to persister state, βd (1/h) is the switch rate from persister to the normal state, and σ ∈ (0,1) is a constant, describing the fraction of persister cells switching back to the normal, slowly decaying state. Equation (1a) and (1b) reflect the assumption that times between switching states are exponentially distributed, using the expected values (frac{1}{{alpha _d}}) (h) and (frac{1}{{beta _d}}) (h) of the respective distributions.Lacking data for potential persister populations from the field trials, we assumed the persister population is a fraction 1  > k  > 0 of the tail population, as observed in Munther et al. [24]. Regarding the model above, this implies that (hat p approx kn_2) for (t ge t^ ast), where (t^ ast approx frac{1}{{theta _{n_1}}}) (the time scale of survival for the fast-decaying population (n1)). In accord with bi-phasic decay, for (t ge t^ ast), the main dynamics for slow decaying population (n2) is dictated by (- theta _{n_2}n_2) in Eq. (1b). This suggests that the effective switch rates from n2 to (hat p) and from (hat p) back to n2 balance, so that (beta _dsigma hat p approx alpha _dn_2) in Eq. (1b). Following these ideas, we simplified the model in Eq. (1a)–(1d) to:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha _dn_1,$$
(2a)
$$frac{{dn_2}}{{dt}} = – theta _{n_2}n_2,$$
(2b)
$$frac{{dhat p}}{{dt}} = – theta _{hat p}hat p + alpha _dn_1,$$
(2c)
$$n_1left( 0 right) = n_{10},n_2left( 0 right) = n_{20},, hat pleft( 0 right) = widehat {p_0},$$
(2d)
where we ignored (beta _dleft( {1 – sigma } right)hat p) in (1a) since the decay rate ((theta _{n_1})) dominates. Also, by setting (theta _{hat p} = mu _{hat p} + beta _d(1 – sigma )), and using (beta _dsigma hat p approx alpha _dn_2), we obtained Eq. (2c). Furthermore, because (hat p approx kn_2) for (t ge t^ ast), (theta _{hat p} approx) (theta _{n_2}).In particular, the assumption that (hat p approx kn_2) for (t ge t^ ast) characterizes the switch rate from normal to persister cells, αd, as (alpha _d approx kalpha), where α is a hypothetical switch rate assuming that the population is composed only of fast decaying normal cells (n1) and a hypothetical persister cell population (p). In this case, the hypothetical population p starts small at (widehat {p_0}), initially increases due to switching from population n1 and then slowly decays as the n1 population is effectively inactivated (i.e., the tail of the total population is comprised entirely of p). From this perspective we utilized the following equations:$$frac{{dn_1}}{{dt}} = – theta _{n_1}n_1 – alpha n_1,$$
(3a)
$$frac{{dp}}{{dt}} = alpha n_1 – theta _pp.$$
(3b)
$$n_1left( 0 right) = n_0,, pleft( 0 right) = widehat {p_0},$$
(3c)
For mathematical justification regarding the relationship (alpha _d approx kalpha), please see the appendix (Supplementary Information).The utility of the relationship (alpha _d approx kalpha), is twofold. First, we used model fitting (Eqs. (3a)–(3c)) to determine α from the respective field study data [9]. Note that using Eqs. (3a)–(3c), we actually fit for (theta _{n_1}), θp, and α using the field study data [9]. Please reference the “model fitting procedure” section as well as the appendix for details concerning the unique determination of the aforementioned parameters, i.e., the practical identifiability of these parameters, and justification regarding the legitimacy of measured tail populations relative to the respective field trial data [9]. Second, because we wanted to examine Spearman’s correlations (corr) between αd and various weather factors, given a particular weather factor (vec w) across trials (i = 1, ldots ,n), let k be the maximum persister fraction (of the tail) across these n trials, that is, for each i, we have (alpha _{d_i} approx k_ialpha _i), so (alpha _{d_i} lesssim kalpha _i). Thus kαi represents the maximum persister switch rate for each trial i, and since corr((kvec alpha ,vec w)) =corr((vec alpha ,vec w)), we conducted the correlation analysis with the fitted α values in lieu of the actual persister switch rate αd.The assumptions behind our approach are summarized below:

A.

The tails of pathogen populations surviving on plants in the field study [9] are comprised of some fraction k ∈(0,1) of persister cells since their decay rate is quite small and they remain culturable.

B.

Because (alpha _d approx kalpha), we hereafter utilize α from model (3a)–(3c) as the representative persister switch rate.

C.

Given that the experimental context [24] for modeling persister switching occurred during population decline, we only employed trials from Belias et al. [9] that exhibited bi-phasic decay. Namely, we did not include trials in which significant bacterial growth was observed at the time scale of successive data points (the time scale in the field study is on the order of 4–16 h for the 1st day and then 24 h thereafter.)

D.

The switch rate from normal to persister cell is on average a monotonic function of some measure of environmental stress.

Based on assumptions A–D above, we applied the model (3a)–(3c) to published pathogen population size and weather data from four replicate trials in Spain, two in California, and one in NY [9]. More specifically, we fit model (3a)–(3c) to the respective population data in order to:

1.

determine values for the maximum switch rate α relative to the produce/bacteria type at the field scale,

2.

describe the correlative relationship between α and weather factors in the respective field trials.

Model fitting procedureIn model (3a)–(3c) above, we supposed dp/dtt = 0  > 0, i.e., we assumed that bacteria experience stress from the change in conditions from culture growth and inoculum suspension preparation to those on the plant surface and therefore, that persister formation increases in the phyllosphere immediately following inoculation. The report that EcO157 persister formation increases as early as 1 h after inoculation into leaf wash water [23], which could be considered as a proxy for the average oligotrophic environment that bacterial cells experience after spray inoculation onto leaves or through irrigation in the field, supports this assumption. To avoid identifiability issues between the initial persister population (widehat {p_0}) and α regarding the model fits above, we assumed that (widehat {p_0})= 1 ((widehat {p_0}) = 0 gives the same results). Thus, the initial persister population at inoculation is at its lowest, an assumption supported by Munther et al. [24], who observed an average fraction of EcO157 persisters of 0.0043% in the inoculum population. This imparts the largest possible switch rate, α, onto the population, corresponding to the largest and hence most conservative food safety risk.Let yk (CFU/100 g of produce) be the average bacteria population measurement at time tk (h) and let Pk,X (CFU/100 g of produce) represent the model prediction (total population) at time tk relative to the parameter vector (X = [ {theta _{n_1} , theta_p , alpha } ]^T). Following Eqs. (3a) and (3b), this means that ({{{{{{{mathrm{P}}}}}}}}_{k,X} = n_1left( {t_k,X} right) + p(t_k,X)). Since the population data spans multiple orders of magnitude, we calculated the residuals as (e_{k,X} = log _{10}y_k – log _{10}P_{k,X}). To determine the optimal model fit (see the appendix for details regarding a priori bounds on parameter ranges), we utilized the fminsearch function in MATLAB (MATLAB 2020b, The MathWorks, Inc., Natick, Massachusetts, United States) to determine the parameter vector X that minimizes the 2-norm of the following function F:$$| | Fleft( X right) | |_2 = left( {mathop {sum }limits_k e_{k,X}^2} right)^{frac{1}{2}}$$Correlation analysisTo provide a statistical foundation from which to relate the switch rate α and measured weather factors, we utilized Spearman and partial Spearman correlation. First, we calculated the Spearman correlation coefficients between α and each of the respective factors: 8-h average of temperature, RH, solar radiation, wind speed post-inoculation, and then we calculated the partial Spearman correlation coefficients for each respective weather factor, while controlling for the other three factors and simultaneously controlling for produce type (using lettuce =1 and spinach =0) (For details regarding why 8-h weather variables were used, see the “model fitting” subsection of the results.) The correlation coefficients were determined using the corr and partialcorr functions in MATLAB 2020b (The MathWorks, Inc., Natick, MA, USA). Considering the significant association of Salmonella α with RH and temperature, we also examined the correlation between α and dew point. Figure 1 presents a logical flow of the statistical analysis. Partial correlations with a P value of less than 0.05 were deemed significant. If the 8-h average of a weather factor exhibited a significant correlation with the switch rate, the 8-h minimum and range of the weather factor were also tested.Fig. 1: Logical flow diagram for statistical analysis.Factors in Step 1: UV (average ultraviolet radiation intensity), RH (average air relative humidity), Wind (average wind speed), and Temp (average air temperature). All weather data used in the statistical analysis were obtained over 8 h post-inoculation of E. coli and Salmonella onto lettuce and spinach leaves in the field.Full size image More