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

Liu, W. et al. African origin of the malaria parasite Plasmodium vivax. Nat. Commun. 5, 3346 (2014).PubMed 

Google Scholar 
Liu, W. et al. Multigenomic delineation of Plasmodium species of the Laverania subgenus infecting wild-living chimpanzees and gorillas. Genome Biol. Evolution 8, 1929–1939 (2016).CAS 

Google Scholar 
Liu, W. et al. Single genome amplification and direct amplicon sequencing of Plasmodium spp. DNA from ape fecal specimens. Protocol Exchange 1–14 (2010).Liu, W. et al. Wild bonobos host geographically restricted malaria parasites including a putative new Laverania species. Nat. Commun. 8, 1635 (2017).PubMed 
PubMed Central 

Google Scholar 
Prugnolle, F. et al. African great apes are natural hosts of multiple related malaria species, including Plasmodium falciparum. Proc. Natl Acad. Sci. USA 107, 1458–1463 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sharp, P. M., Plenderleith, L. J. & Hahn, B. H. Ape origins of human malaria. Annu. Rev. Microbiol. 74, 39–63 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, W. et al. Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature 467, 420–425 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Otto, T. D. et al. Genomes of all known members of a Plasmodium subgenus reveal paths to virulent human malaria. Nat. Microbiol. 3, 687–697 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boundenga, L. et al. Diversity of malaria parasites in great apes in Gabon. Malar. J. 14, 1–8 (2015).CAS 

Google Scholar 
Délicat-Loembet, L. et al. No evidence for ape Plasmodium infections in humans in gabon. Plos One 10, e0126933 (2015).PubMed 
PubMed Central 

Google Scholar 
Sundararaman, S. A. et al. Plasmodium falciparum-like parasites infecting wild apes in southern Cameroon do not represent a recurrent source of human malaria. Proc. Natl Acad. Sci. USA 110, 7020–7025 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Junker, J. et al. Recent decline in suitable environmental conditions for African great apes. Diversity Distrib. 18, 1077–1091 (2012).
Google Scholar 
de Nys, H. M. et al. Age-related effects on malaria parasite infection in wild chimpanzees. Biol. Lett. 9, 20121160 (2013).PubMed 
PubMed Central 

Google Scholar 
de Nys, H. M. et al. Malaria parasite detection increases during pregnancy in wild chimpanzees. Malar. J. 13, 413 (2014).PubMed 
PubMed Central 

Google Scholar 
Kaiser, M. et al. Wild chimpanzees infected with 5 Plasmodium species. Emerg. Infect. Dis. 16, 1956–1959 (2010).PubMed 
PubMed Central 

Google Scholar 
Paupy, C. et al. Anopheles moucheti and Anopheles vinckei are candidate vectors of ape Plasmodium parasites, including Plasmodium praefalciparum in Gabon. PLoS ONE 8, e57294 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Makanga, B. et al. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc. Natl Acad. Sci. USA 113, 5329–5334 (2016).Loy, D. E. et al. Investigating zoonotic infection barriers to ape Plasmodium parasites using faecal DNA analysis. Int. J. Parasitol. 48, 531–542 (2018).Martin, M., Rayner, J., Gagneux, P., Barnwell, J. & Varki, A. Evolution of human–chimpanzee differences in malaria susceptibility: Relationship to human genetic loss of N-glycolylneuraminic acid. Proc. Natl Acad. Sci. USA 102, 12819–12824 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Scully, E. J., Kanjee, U. & Duraisingh, M. T. Molecular interactions governing host-specificity of blood stage malaria parasites. Curr. Opin. Microbiol. 40, 21–31 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sundararaman, S. A. et al. Genomes of cryptic chimpanzee Plasmodium species reveal key evolutionary events leading to human malaria. Nat. Commun. 7, 11078 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wanaguru, M., Liu, W., Hahn, B. H., Rayner, J. C. & Wright, G. J. RH5-Basigin interaction plays a major role in the host tropism of Plasmodium falciparum. Proc. Natl Acad. Sci. USA 110, 20735–20740 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ngoubangoye, B. et al. The host specificity of ape malaria parasites can be broken in confined environments. Int. J. Parasitol. 46, 737–744 (2016).PubMed 

Google Scholar 
Mapua, M. I. et al. A comparative molecular survey of malaria prevalence among Eastern chimpanzee populations in Issa Valley (Tanzania) and Kalinzu (Uganda). Malar. J. 15, 423 (2016).PubMed 
PubMed Central 

Google Scholar 
Wu, D. F. et al. Seasonal and inter-annual variation of malaria parasite detection in wild chimpanzees. Malar. J. 17, 1–5 (2018).CAS 

Google Scholar 
Craig, M., le Sueur, D. & Snow, B. A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol. Today 15, 105–111 (1999).CAS 
PubMed 

Google Scholar 
Mordecai, E. A. et al. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol. Lett. 16, 22–30 (2013).PubMed 

Google Scholar 
Paaijmans, K. P. et al. Influence of climate on malaria transmission depends on daily temperature variation. Proc. Natl Acad. Sci. USA 107, 15135–15139 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Parham, P. E. & Michael, E. Modeling the effects of weather and climate change on malaria transmission. Environ. Health Perspect. 118, 620–626 (2010).PubMed 

Google Scholar 
LaPointe, D. A., Goff, M. L. & Atkinson, C. T. Thermal constraints to the sporogonic development and altitudinal distribution of avian malaria Plasmodium relictum in Hawai’i. J. Parasitol. 96, 318–324 (2010).PubMed 

Google Scholar 
Vanderberg, J. P. & Yoeli, M. Effects of temperature on sporogonic development of Plasmodium berghei. J. Parasitol. 52, 559–564 (1966).Macdonald, G. The Epidemiology and Control of Malaria (Oxford University Press, 1957).Ryan, S. J. et al. Mapping physiological suitability limits for malaria in Africa under climate change. Vector-Borne Zoonotic Dis. 15, 718–725 (2015).PubMed 
PubMed Central 

Google Scholar 
Gemperli, A. et al. Mapping malaria transmission in West and Central Africa. Tropical Med. Int. Health 11, 1032–1046 (2006).
Google Scholar 
Gething, P. W. et al. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasites Vectors 4, 92 (2011).PubMed 
PubMed Central 

Google Scholar 
Weiss, D. J. et al. Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000–2012: a high-resolution spatiotemporal prediction. Malar. J. 13, 171 (2014).PubMed 
PubMed Central 

Google Scholar 
Lyons, C. L., Coetzee, M. & Chown, S. L. Stable and fluctuating temperature effects on the development rate and survival of two malaria vectors, Anopheles arabiensis and Anopheles funestus. Parasites Vectors 6, 104 (2013).PubMed 
PubMed Central 

Google Scholar 
Paaijmans, K. P., Wandago, M. O., Githeko, A. K. & Takken, W. Unexpected high losses of Anopheles gambiae larvae due to rainfall. PLoS One 2, e1146 (2007).PubMed 
PubMed Central 

Google Scholar 
Faust, C. & Dobson, A. P. Primate malarias: diversity, distribution and insights for zoonotic Plasmodium. One Health 1, 66–75 (2015).PubMed 
PubMed Central 

Google Scholar 
Tucker Lima, J. M., Vittor, A., Rifai, S. & Valle, D. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 372, 20160125 (2017).
Google Scholar 
Borner, J. et al. Phylogeny of haemosporidian blood parasites revealed by a multi-gene approach. Mol. Phylogenetics Evolution 94, 221–231 (2016).CAS 

Google Scholar 
Emery Thompson, M., Muller, M. N., Machanda, Z. P., Otali, E. & Wrangham, R. W. The Kibale Chimpanzee Project: over thirty years of research, conservation, and change. Biol. Conserv. 252, 108857 (2020).
Google Scholar 
Langergraber, K. E., Mitani, J. C. & Vigilant, L. The limited impact of kinship on cooperation in wild chimpanzees. Proc. Natl Acad. Sci. USA 104, 7786–7790 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arandjelovic, M. et al. Two-step multiplex polymerase chain reaction improves the speed and accuracy of genotyping using DNA from noninvasive and museum samples. Mol. Ecol. Resour. 9, 28–36 (2009).CAS 
PubMed 

Google Scholar 
Herbert, A. et al. Malaria-like symptoms associated with a natural Plasmodium reichenowi infection in a chimpanzee. Malar. J. 14, 220 (2015).PubMed 
PubMed Central 

Google Scholar 
Torres, J. R. Therapy of Infectious Diseases 597–613 (2003).Trampuz, A., Jereb, M., Muzlovic, I. & Prabhu, R. M. Clinical review: severe malaria. Crit. Care 7, 315 (2003).PubMed 
PubMed Central 

Google Scholar 
Akim, N. I. et al. Dynamics of P. falciparum gametocytemia in symptomatic patients in an area of intense perennial transmission in Tanzania. Am. J. Tropical Med. Hyg. 63, 199–203 (2000).CAS 

Google Scholar 
Mackinnon, M. J. & Read, A. F. Genetic relationships between parasite virulence and transmission in the rodent malaria Plasmodium chabaudi. Evolution 53, 689–703 (1999).PubMed 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
PubMed 

Google Scholar 
Prugnolle, F. et al. African monkeys are infected by Plasmodium falciparum nonhuman primate-specific strains. Proc. Natl Acad. Sci. USA 108, 11948–11953 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ayouba, A. et al. Ubiquitous Hepatocystis infections, but no evidence of Plasmodium falciparum-like malaria parasites in wild greater spot-nosed monkeys (Cercopithecus nictitans). Int. J. Parasitol. 42, 709–713 (2012).PubMed 
PubMed Central 

Google Scholar 
Martinsen, E. S., Perkins, S. L. & Schall, J. J. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): Evolution of life-history traits and host switches. Mol. Phylogenetics Evolution 47, 261–273 (2008).CAS 

Google Scholar 
Thurber, M. I. et al. Co-infection and cross-species transmission of divergent Hepatocystis lineages in a wild African primate community. Int. J. Parasitol. 43, 613–619 (2013).PubMed 
PubMed Central 

Google Scholar 
Baayen, R. H. Analyzing Linguistic Data: A Practical Introduction to Statistics (Cambridge University Press, 2008).Stanisic, D. I. et al. Acquisition of antibodies against Plasmodium falciparum merozoites and malaria immunity in young children and the influence of age, force of infection, and magnitude of response. Infect. Immun. 83, 646–660 (2015).PubMed 
PubMed Central 

Google Scholar 
Taylor, R. R., Allen, S. J., Greenwood, B. M. & Riley, E. M. IgG3 antibodies to Plasmodium falciparum merozoite surface protein 2 (MSP2): increasing prevalence with age and association with clinical immunity to malaria. Am. J. Tropical Med. Hyg. 58, 406–413 (1998).CAS 

Google Scholar 
World Malaria Report (World Health Organization, 2015).Shaman, J. Letter to the Editor: Caution needed when using gridded meteorological data products for analyses in Africa. Eur. Surveill. 19, 20930 (2014).
Google Scholar 
Tatem, A. J., Goetz, S. J. & Hay, S. I. Terra and Aqua: new data for epidemiology and public health. Int. J. Appl. Earth Observation Geoinf. 6, 33–46 (2004).
Google Scholar 
Adler, R. F. et al. The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeorol. 4, 1147–1167 (2003).
Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 

Google Scholar 
Carter, R. & Mendis, K. N. Evolutionary and historical aspects of the burden of malaria. Clin. Microbiol. Rev. 15, 564–594 (2002).PubMed 
PubMed Central 

Google Scholar 
Kwiatkowski, D. P. How malaria has affected the human genome and what human genetics can teach us about malaria. Am. J. Hum. Genet. 77, 171–192 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tarello, W. A fatal Plasmodium reichenowi infection in a chimpanzee? Rev. de. Med. Veterinaire 156, 503–505 (2005).
Google Scholar 
Taylor, D. W. et al. Parasitologic and immunologic studies of experimental Plasmodium falciparum infection in nonsplenectomized chimpanzees (Pan troglodytes). Am. J. Tropical Med. Hyg. 34, 36–44 (1985).CAS 

Google Scholar 
Krief, S., Martin, M., Grellier, P., Kasenene, J. & Sevenet, T. Novel antimalarial compounds isolated in a survey of self-medicative behavior of wild chimpanzees in Uganda. Antimicrobial Agents Chemother. 48, 3196–3199 (2004).CAS 

Google Scholar 
Cox-Singh, J. et al. Plasmodium knowlesi malaria in humans is widely distributed and potentially life threatening. Clin. Infect. Dis. 46, 165–171 (2008).CAS 
PubMed 

Google Scholar 
Singh, B. & Daneshvar, C. Human infections and detection of Plasmodium knowlesi. Clin. Microbiol. Rev. 26, 165–184 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brasil, P. et al. Outbreak of human malaria caused by Plasmodium simium in the Atlantic Forest in Rio de Janeiro: a molecular epidemiological investigation. Lancet Global Health 5, e1038–e1046 (2017).Krief, S. et al. On the diversity of malaria parasites in African apes and the origin of Plasmodium falciparum from bonobos. PLoS Pathog. 6, e1000765 (2010).Pacheco, M. A., Cranfield, M., Cameron, K. & Escalante, A. A. Malarial parasite diversity in chimpanzees: the value of comparative approaches to ascertain the evolution of Plasmodium falciparum antigens. Malar. J. 12, 328 (2013).PubMed 
PubMed Central 

Google Scholar 
Etienne, L. et al. Noninvasive follow-up of simian immunodeficiency virus infection in wild-living nonhabituated western lowland gorillas in Cameroon. J. Virol. 86, 9760–9772 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Keele, B. F. et al. Chimpanzee reservoirs of pandemic and nonpandemic HIV-1. Science 313, 523–526 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Keele, B. F. et al. Increased mortality and AIDS-like immunopathology in wild chimpanzees infected with SIVcpz. Nature 460, 515–519 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. et al. Eastern chimpanzees, but not bonobos, represent a simian immunodeficiency virus reservoir. J. Virol. 86, 10776–10791 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Neel, C. et al. Molecular epidemiology of simian immunodeficiency virus infection in wild-living gorillas. J. Virol. 84, 1464–1476 (2010).CAS 
PubMed 

Google Scholar 
Rudicell, R. S. et al. Impact of simian immunodeficiency virus infection on chimpanzee population dynamics. PLoS Pathog. 6, 1–17 (2010).
Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bates, D. & Maechler, M. Lme4: linear mixed-effects models using s4 classes. Cran R Project Website (2010). 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

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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

Liu LP. Characteristics of blue algal bloom in Dianchi Lake and analysis on its cause. Res Environ Sci. 1999;12:36–37.
Google Scholar 
Liu YM, Chen W, Li DH, Shen YW, Liu YD, Song LR. Analysis of paralytic shellfish toxins in Aphanizomenon DC-1 from Lake Dianchi, China. Environ Toxicol. 2006;21:289–95.CAS 
PubMed 
Article 

Google Scholar 
Dziallas C, Grossart HP. Temperature and biotic factors influence bacterial communities associated with the cyanobacterium Microcystis sp. Environ Microbiol. 2011;13:1632–41.PubMed 
Article 

Google Scholar 
Parveen B, Ravet V, Djediat C, Mary I, Quiblier C, Debroas D, Humbert JF. Bacterial communities associated with Microcystis colonies differ from free-living communities living in the same ecosystem. Environ Microbiol Rep. 2013;5:716–24.CAS 
PubMed 

Google Scholar 
Shi LM, Cai YF, Kong FX, Yu Y. Specific association between bacteria and buoyant Microcystis colonies compared with other bulk bacterial communities in the eutrophic Lake Taihu, China. Environ Microbiol Rep. 2012;4:669–78.CAS 
PubMed 

Google Scholar 
Kouzuma A, Watanabe K. Exploring the potential of algae/bacteria interactions. Curr Opin Biotech. 2015;33:125–9.CAS 
PubMed 
Article 

Google Scholar 
Cooper MB, Smith AG. Exploring mutualistic interactions between microalgae and bacteria in the omics age. Curr Opin Plant Biol. 2015;26:147–53.PubMed 
Article 

Google Scholar 
Yang L, Xiao L. Outburst, jeopardize and control of cyanobacterial bloom in lakes. Beijing: Science Press; 2011. p. 71–212.
Google Scholar 
de-Bashan LE, Antoun H, Bashan Y. Involvement of indole-3-acetic-acid produced by the growth-promoting bacterium Azospirillum spp. in promoting growth of Chlorella vulgaris. J Phycol. 2008;44:938–47.CAS 
PubMed 
Article 

Google Scholar 
Xiao Y, Wang L, Wang X, Chen M, Chen J, Tian BY, Zhang BH. Nocardioides lacusdianchii sp. nov., an attached bacterium of Microcystis aeruginosa. Antonie van Leeuwenhoek. 2022;115:141–53.PubMed 
Article 

Google Scholar 
Shirling EB, Gottlieb D. Methods for characterization of Streptomyces species. Int J Syst Bacteriol. 1966;16:313–40.Article 

Google Scholar 
Zhang BH, Chen W, Li HQ, Zhou EM, Hu WY, Duan YQ, Mohamad OA, Gao R, Li WJ. An antialgal compound produced by Streptomyces jiujiangensis JXJ 0074T. Appl Microbiol Biotechnol. 2015;99:7673–83.CAS 
PubMed 
Article 

Google Scholar 
Zhang BH, Salam N, Cheng J, Xiao M, Li HQ, Yang JY, Zha DM, Li WJ. Citricoccus lacusdiani sp. nov., an actinobacterium promoting Microcystis growth with limited soluble phosphorus. Antonie Van Leeuwenhoek. 2016;109:1457–65.CAS 
PubMed 
Article 

Google Scholar 
Zhang BH, Salam N, Cheng J, Li HQ, Yang JY, Zha DM, Guo QG, Li WJ. Microbacterium lacusdiani sp. nov., a phosphate–solubilizing novel actinobacterium isolated from mucilaginous sheath of Microcystis. J Antibiot. 2017;70:147–51.Article 

Google Scholar 
Smibert RM, Krieg NR. Phenotypic characterization. In: Gerhardt P, Murray RGE, Wood WA, Krieg NR, editors. Methods for general and molecular bacteriology. Washington, DC: American Society for Microbiology; 1994. p. 607–54.Dong XZ, Cai MY. Manual of systematic identification of common bacteria. Beijing: Science Press; 2001. p. p349–89.
Google Scholar 
Minnikin DE, Collins MD, Goodfellow M. Fatty acid and polar lipid composition in the classification of Cellulomonas, Oerskovia and related taxa. J Appl Bacteriol. 1979;47:87–95.CAS 
Article 

Google Scholar 
Tamaoka J, Katayama-Fujimura Y, Kuraishi H. Analysis of bacterial menaquinone mixtures by high performance liquid chromatography. J Appl Bacteriol. 1983;54:31–36.CAS 
Article 

Google Scholar 
Schleifer KH, Kandler O. Peptidoglycan types of bacterial cell walls and their taxonomic implications. Bacteriol Rev. 1972;36:407–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tang SK, Wang Y, Chen Y, Lou K, Cao LL, Xu LH, Li WJ. Zhihengliuella alba sp. nov., and emended description of the genus Zhihengliuella. Int J Syst Evol Microbiol. 2009;59:2025–32.CAS 
PubMed 
Article 

Google Scholar 
Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, Seo H, Chun J. Introducing EzBiocloud: a taxonomically united database of 16S rRNA gene sequences and whole–genome assemblies. Int J Syst Evol Microbiol. 2017;67:1613–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28:2731–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Saitou N, Nei M. The neighbor–joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4:406–42.CAS 
PubMed 

Google Scholar 
Fitch WM. Toward defining the course of evolution: minimum change for a specific tree topology. Syst Zool. 1971;20:406–16.Article 

Google Scholar 
Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol. 1981;17:368–76.CAS 
PubMed 
Article 

Google Scholar 
Felsenstein J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution. 1985;39:783–91.PubMed 
Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Massouras A, Hens K, Gubelmann C, Uplekar S, Decouttere F, Rougemont J, Cole ST, Deplancke B. Primer-initiated sequence synthesis to detect and assemble structural variants. Nat Methods. 2010;7:485–6.CAS 
PubMed 
Article 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
PubMed 
Article 

Google Scholar 
Bland C, Ramsey TL, Sabree F, Lowe M, Brown K, Kyrpides NC, Hugenholtz P. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinforma. 2007;8:209.Article 

Google Scholar 
Meier-Kolthoff JP, Auch AF, Klenk HP, Göker M. Genome sequence–based species delimitation with confidence intervals and improved distance functions. BMC Bioinforma. 2013;14:60.Article 

Google Scholar 
Xiao Y, Chen J, Chen M, Deng SJ, Xiong ZQ, Tian BY, Zhang BH. Mycolicibacterium lacusdiani sp. nov., an attached bacterium of Microcystis aeruginosa. Front Microbiol. 2022;13:861291.PubMed 
PubMed Central 
Article 

Google Scholar 
Vaz-Moreira I, Lopes AR, Faria C, Spröer C, Schumann P, Nunes OC, Manaia CM. Microbacterium invictum sp. nov., isolated from homemade compost. Int J Syst Evol Microbiol. 2009;59:2036–41.PubMed 
Article 

Google Scholar 
Ohta Y, Ito T, Mori K, Nishi S, Shimane Y, Mikuni K, Hatada Y. Microbacterium saccharophilum sp. nov., isolated from a sucrose-refining factory. Int J Syst Evol Microbiol. 2013;63:2765–9.CAS 
PubMed 
Article 

Google Scholar 
Kageyama A, Takahashi Y, Ōmura S. Microbacterium deminutum sp. nov., Microbacterium pumilum sp. nov. and Microbacterium aoyamense sp. nov. Int J Syst Evol Microbiol. 2006;56:2113–7.CAS 
PubMed 
Article 

Google Scholar 
Stackebrandt E, Ebers J. Taxonomic parameters revisited: tarnished gold standards. Microbiol Today. 2006;33:152–5.
Google Scholar 
Meier-Kolthoff JP, Auch AF, Klenk HP, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinforma. 2013;14:60.Article 

Google Scholar 
Kim M, Oh HS, Park SC, Chun J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int J Syst Evol Microbiol. 2014;64:346–51.CAS 
PubMed 
Article 

Google Scholar 
Chun J, Oren A, Ventosa A, Christensen H, Arahal DR, da Costa MS, Rooney AP, Yi H, Xu XW, De Meyer S, Trujillo ME. Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. Int J Syst Evol Microbiol. 2018;68:461–6.CAS 
PubMed 
Article 

Google Scholar 
Hoke AK, Reynoso G, Smith MR, Gardner MI, Lockwood DJ, Gilbert NE, Wilhelm SW, Becker IR, Brennan GJ, Crider KE, Farnan SR, Mendoza V, Poole AC, Zimmerman ZP, Utz LK, Wurch LL, Steffen MM. Genomic signatures of Lake Erie bacteria suggest interaction in the Microcystis phycosphere. PLoS ONE. 2021;16:e0257017.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang BH, Salam N, Cheng J, Li HQ, Yang JY, Zha DM, Zhang YQ, Ai MJ, Hozzein WN, Li WJ. Modestobacter lacusdianchii sp. nov., a phosphate-solubilizing actinobacterium with ability to promote Microcystis growth. PLoS ONE. 2016;11:e0161069.PubMed 
PubMed Central 
Article 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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