Ecology

A large-scale field study finds that different bee species experience different levels of risk from pesticides, depending on how much land is farmed within their foraging range. For bumblebees and solitary bees, more seminatural habitat means less risk from pesticides, but this is not true for honeybees.In the discussion of how to protect bees from pesticides, bees are often treated as a monolith. It is assumed that what is good for one species is good for all, and that pesticides or changes to agricultural landscapes would affect all bee species equally. This is often taken one step further, with the western honeybee (Apis mellifera) being used as a surrogate species for all bees. Yet despite this simplification there are around 2,000 species of bee in Europe1 and 20,000 worldwide2 with a dazzling diversity of niches and life histories. With this in mind, the question arises of how valid the assumption is that honeybees represent a good surrogate species. In this issue of Nature Ecology & Evolution, Knapp et al.3 investigate this question by measuring how three species of bee with differing life histories respond to different agricultural land-use intensities, and find that a species’ foraging range plays a big part in pesticide exposure risk. More

Department of Ecology and Evolutionary Biology and Eversource Energy Center, University of Connecticut, Storrs, CT, USACory MerowDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USABrad Boyle & Brian J. EnquistDepartment of Geography, Florida State University, Tallahassee, FL, USAXiao FengBiodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, JapanJamie M. KassDepartment of Geography, University at Buffalo, Buffalo, NY, USABrian S. Maitner & Adam M. WilsonSchool of Biology and Ecology, University of Maine, Orono, ME, USABrian McGillMitchell Center for Sustainability Solutions, University of Maine, Orono, ME, USABrian McGillCenter for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Copenhagen, DenmarkHannah OwensFlorida Museum of Natural History, University of Florida, Gainesville, FL, USAHannah OwensDepartment of Biological Sciences, Purdue University, West Lafayette, IN, USADaniel S. ParkPurdue Center for Plant Biology, Purdue University, West Lafayette, IN, USADaniel S. ParkDepartment of Environmental Systems Science, Institute of Integrative Biology, ETH Zürich, Zurich, SwitzerlandAndrea PazDepartment of Biology, City College of the City University of New York, New York, NY, USAGonzalo E. Pinilla-BuitragoPhD Program in Biology, Graduate Center of the City University of New York, New York, NY, USAGonzalo E. Pinilla-BuitragoDepartment of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USAMark C. UrbanCenter of Biological Risk, University of Connecticut, Storrs, CT, USAMark C. UrbanDepartamento de Ecoloxía e Bioloxía Animal, Universidade de Vigo, Vigo, SpainSara Varela More

Abrahms, B. Human–wildlife conflict under climate change. Science 373, 484–485 (2021).Article 
CAS 

Google Scholar 
Nyhus, P. J. Human–wildlife conflict and coexistence. Annu. Rev. Environ. Resour. 41, 143–171 (2016).Article 

Google Scholar 
Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl Acad. Sci. USA 114, 10678–10683 (2017).Article 
CAS 

Google Scholar 
Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).Article 
CAS 

Google Scholar 
Abrahms, B. et al. Data from: Climate change as an amplifier of human–wildlife conflict. Github https://github.com/Abrahms-Lab/Climate-Conflict-Review (2022).IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Sydeman, W. J., Santora, J. A., Thompson, S. A., Marinovic, B. & Lorenzo, E. D. Increasing variance in North Pacific climate relates to unprecedented ecosystem variability off California. Glob. Change Biol. 19, 1662–1675 (2013).Article 

Google Scholar 
Wang, G. et al. Continued increase of extreme El Niño frequency long after 1.5 °C warming stabilization. Nat. Clim. Change 7, 568–572 (2017).Article 

Google Scholar 
Filazzola, A., Blagrave, K., Imrit, M. A. & Sharma, S. Climate change drives increases in extreme events for lake ice in the Northern Hemisphere. Geophys. Res. Lett. 47, e2020GL089608 (2020).Marzeion, B., Cogley, J. G., Richter, K. & Parkes, D. Attribution of global glacier mass loss to anthropogenic and natural causes. Science 345, 919–921 (2014).Article 
CAS 

Google Scholar 
Martin, J. T. et al. Increased drought severity tracks warming in the United States’ largest river basin. Proc. Natl Acad. Sci. USA 117, 11328–11336 (2020).Article 
CAS 

Google Scholar 
Laufkötter, C., Zscheischler, J. & Frölicher, T. L. High-impact marine heatwaves attributable to human-induced global warming. Science 369, 1621–1625 (2020).Article 

Google Scholar 
Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A. & Maher, N. More extreme precipitation in the world’s dry and wet regions. Nat. Clim. Change 6, 508–513 (2016).Article 

Google Scholar 
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).Article 
CAS 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).Article 

Google Scholar 
Lin, D., Xia, J. & Wan, S. Climate warming and biomass accumulation of terrestrial plants: a meta‐analysis. New Phytol. 188, 187–198 (2010).Article 

Google Scholar 
Kharouba, H. M. & Wolkovich, E. M. Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Change 10, 406–415 (2020).Article 

Google Scholar 
Marinovic, B. B., Croll, D. A., Gong, N., Benson, S. R. & Chavez, F. P. Effects of the 1997–1999 El Niño and La Niña events on zooplankton abundance and euphausiid community composition within the Monterey Bay coastal upwelling system. Prog. Oceanogr. 54, 265–277 (2002).Article 

Google Scholar 
Kardol, P. et al. Climate change effects on plant biomass alter dominance patterns and community evenness in an experimental old‐field ecosystem. Glob. Change Biol. 16, 2676–2687 (2010).Article 

Google Scholar 
Prugh, L. R. et al. Ecological winners and losers of extreme drought in California. Nat. Clim. Change 8, 819–824 (2018).Article 

Google Scholar 
Sorte, C. J. B., Williams, S. L. & Zerebecki, R. A. Ocean warming increases threat of invasive species in a marine fouling community. Ecology 91, 2198–2204 (2010).Article 

Google Scholar 
Muehlenbein, M. P. Human–environment interactions, current and future directions. Hum. Environ. Interact. 1, 79–94 (2012).
Google Scholar 
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).Article 
CAS 

Google Scholar 
Mason, T. H. E., Keane, A., Redpath, S. M. & Bunnefeld, N. The changing environment of conservation conflict: geese and farming in Scotland. J. Appl. Ecol. 55, 651–662 (2018).Article 

Google Scholar 
Pérez-Flores, J., Mardero, S., López-Cen, A., Contreras-Moreno, F. M. & Pérez-Flores, J. Human–wildlife conflicts and drought in the greater Calakmul Region, Mexico: implications for tapir conservation. Neotrop. Biol. Conserv. 16, 539–563 (2021).Article 

Google Scholar 
Mariki, S. B., Svarstad, H. & Benjaminsen, T. A. Elephants over the cliff: explaining wildlife killings in Tanzania. Land Use Policy 44, 19–30 (2015).Article 

Google Scholar 
Mukeka, J. M., Ogutu, J. O., Kanga, E. & Roskaft, E. Spatial and temporal dynamics of human–wildlife conflicts in the Kenya Greater Tsavo Ecosystem. Hum. Wildl. Interact. 14, 255–272 (2020).
Google Scholar 
Popp, J. N., Hamr, J., Chan, C. & Mallory, F. F. Elk (Cervus elaphus) railway mortality in Ontario. Can. J. Zool. 96, 1066–1070 (2018).Article 

Google Scholar 
Olson, D. D. et al. How does variation in winter weather affect deer–vehicle collision rates? Wildl. Biol. 21, 80–87 (2015).Article 

Google Scholar 
Nyhus, P. & Tilson, R. Agroforestry, elephants, and tigers: balancing conservation theory and practice in human-dominated landscapes of Southeast Asia. Agric. Ecosyst. Environ. 104, 87–97 (2004).Article 

Google Scholar 
Laufenberg, J. S., Johnson, H. E., Doherty, P. F. & Breck, S. W. Compounding effects of human development and a natural food shortage on a black bear population along a human development–wildland interface. Biol. Conserv 224, 188–198 (2018).Article 

Google Scholar 
Blondin, H., Abrahms, B., Crowder, L. B. & Hazen, E. L. Combining high temporal resolution whale distribution and vessel tracking data improves estimates of ship strike risk. Biol. Conserv. 250, 108757 (2020).Article 

Google Scholar 
Abrahms, B. et al. Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species. Divers. Distrib. 25, 1182–1193 (2019).Article 

Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).Article 
CAS 

Google Scholar 
Kabir, M., Ghoddousi, A., Awan, M. S. & Awan, M. N. Assessment of human–leopard conflict in Machiara National Park, Azad Jammu and Kashmir, Pakistan. Eur. J. Wildl. Res. 60, 291–296 (2014).Article 

Google Scholar 
Soto, J. R. Patterns and Determinants of Human–Carnivore Conflicts in the Tropical Lowlands of Guatemala (Univ. of Florida, 2008).Honda, T. & Kozakai, C. Mechanisms of human–black bear conflicts in Japan: in preparation for climate change. Sci. Total Environ. 739, 140028 (2020).Article 
CAS 

Google Scholar 
Mukeka, J. M., Ogutu, J. O., Kanga, E. & Røskaft, E. Human–wildlife conflicts and their correlates in Narok County, Kenya. Glob. Ecol. Conserv. 18, e00620 (2019).Article 

Google Scholar 
Kuiper, T. R. et al. Seasonal herding practices influence predation on domestic stock by African lions along a protected area boundary. Biol. Conserv. 191, 546–554 (2015).Article 

Google Scholar 
Funston, P. J., Mills, M. G. L. & Biggs, H. C. Factors affecting the hunting success of male and female lions in the Kruger National Park. J. Zool. 253, 419–431 (2001).Article 

Google Scholar 
Schiess-Meier, M., Ramsauer, S., Gabanapelo, T. & Konig, B. Livestock predation—insights from problem animal control registers in Botswana. J. Wildl. Manag. 71, 1267–1274 (2007).Article 

Google Scholar 
Wilder, J. M. et al. Polar bear attacks on humans: implications of a changing climate. Wildl. Soc. B 41, 537–547 (2017).Article 

Google Scholar 
Towns, L., Derocher, A. E., Stirling, I., Lunn, N. J. & Hedman, D. Spatial and temporal patterns of problem polar bears in Churchill, Manitoba. Polar Biol. 32, 1529–1537 (2009).Article 

Google Scholar 
Schmidt, A. & Clark, D. ‘It’s just a matter of time:’ lessons from agency and community responses to polar bear-inflicted human injury. Conserv. Soc. 16, 64 (2018).Article 

Google Scholar 
Koenig, J., Shine, R. & Shea, G. The dangers of life in the city: patterns of activity, injury and mortality in suburban lizards (Tiliqua scincoides). J. Herpetol. 36, 62–68 (2002).Article 

Google Scholar 
Whitaker, P. B. & Shine, R. Responses of free-ranging brownsnakes (Pseudonaja textilis: Elapidae) to encounters with humans. Wildl. Res. 26, 689–704 (1999).Article 

Google Scholar 
Saberwal, V., Gibbs, J., Chellam, R. & Johnsingh, A. Lion–human conflict in the Gir Forest, India. Conserv. Biol. 8, 501–507 (1994).Article 

Google Scholar 
Ferreira, S. M. & Viljoen, P. African large carnivore population changes in response to a drought. Afr. J. Wildl. Res. https://hdl.handle.net/10520/ejc-wild2-v52-n1-a1 (2022).Masiaine, S. et al. Landscape-level changes to large mammal space use in response to a pastoralist incursion. Ecol. Indic. 121, 107091 (2021).Article 

Google Scholar 
Kiria, E. A Spatial Multi-criteria Analysis of Land Use, Land Cover and Climate Changes on Wildlife Ecosystems Planning and Management in Meru Conservation Area (Chuka Univ., 2018).Benansio, J., Demaya, G., Dendi, D. & Luiselli, L. Attacks by Nile crocodiles (Crocodylus nilotticus) on humans and livestock in the Sudd wetlands, South Sudan. Russ. J. Herpetol. https://doi.org/10.30906/1026-2296-2022-29-4-199-205 (2022).Melia, N., Haines, K. & Hawkins, E. Sea ice decline and 21st century trans‐Arctic shipping routes. Geophys. Res. Lett. 43, 9720–9728 (2016).Article 

Google Scholar 
Ivanova, S. V. et al. Shipping alters the movement and behavior of Arctic cod (Boreogadus saida), a keystone fish in Arctic marine ecosystems. Ecol. Appl. 30, e02050 (2020).Article 

Google Scholar 
Hauser, D. D. W., Laidre, K. L. & Stern, H. L. Vulnerability of Arctic marine mammals to vessel traffic in the increasingly ice-free Northwest Passage and Northern Sea Route. Proc. Natl Acad. Sci. USA 5, 201803543–201803546 (2018).
Google Scholar 
Hovelsrud, G. K., McKenna, M. & Huntington, H. P. Marine mammal harvests and other interactions with humans. Ecol. Appl. 18, S135–S147 (2008).Article 

Google Scholar 
Santora, J. A. et al. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 11, 536 (2020).Samhouri, J. F. et al. Marine heatwave challenges solutions to human–wildlife conflict. Proc. R. Soc. B 288, 20211607 (2021).Article 

Google Scholar 
Chapman, B. K. & McPhee, D. Global shark attack hotspots: identifying underlying factors behind increased unprovoked shark bite incidence. Ocean Coast. Manag. 133, 72–84 (2016).Article 

Google Scholar 
Burgess, G., Buch, R., Carvalho, F., Garner, B. & Walker, C. in Sharks and Their Relatives II: Biodiversity, Adaptive Physiology, and Conservation (eds Carrier, J. C. et al.) 541–565 (CRC Press, 2010).Woodward, A. R., Leone, E. H., Dutton, H. J., Waller, J. E. & Hord, L. Characteristics of American alligator bites on people in Florida. J. Wildl. Manag. 83, 1437–1453 (2019).Article 

Google Scholar 
Khorozyan, I., Soofi, M., Ghoddousi, A., Hamidi, A. K. & Waltert, M. The relationship between climate, diseases of domestic animals and human–carnivore conflicts. Basic Appl. Ecol. 16, 703–713 (2015).Article 

Google Scholar 
Treves, A. & Bruskotter, J. Tolerance for predatory wildlife. Science 344, 476–477 (2014).Article 
CAS 

Google Scholar 
Carpenter, S. Exploring the impact of climate change on the future of community‐based wildlife conservation. Conserv. Sci. Pract. 4, e585 (2022).Nisi, A. Cryptic Neighbors: Connecting Movement Ecology and Population Dynamics for a Large Carnivore in a Human-dominated Landscape (Univ. California, 2021). .Asiyanbi, A. P. A political ecology of REDD+: property rights, militarised protectionism, and carbonised exclusion in Cross River. Geoforum 77, 146–156 (2016).Article 

Google Scholar 
Dawson, N. M. et al. Barriers to equity in REDD+: deficiencies in national interpretation processes constrain adaptation to context. Environ. Sci. Policy 88, 1–9 (2018).Article 

Google Scholar 
Rabaiotti, D. et al. High temperatures and human pressures interact to influence mortality in an African carnivore. Ecol. Evol. 11, 8495–8506 (2021).Article 

Google Scholar 
Vargas, S. P., Castro-Carrasco, P. J., Rust, N. A. & F, J. L. R. Climate change contributing to conflicts between livestock farming and guanaco conservation in central Chile: a subjective theories approach. Oryx 55, 275–283 (2021).Article 

Google Scholar 
Heemskerk, S. et al. Temporal dynamics of human–polar bear conflicts in Churchill, Manitoba. Glob. Ecol. Conserv. 24, e01320 (2020).Article 

Google Scholar 
Schell, C. J. et al. The evolutionary consequences of human–wildlife conflict in cities. Evol. Appl. 14, 178–197 (2021).Article 

Google Scholar 
Clark, J. A. & May, R. M. Taxonomic bias in conservation research. Science 297, 191–192 (2002).Article 
CAS 

Google Scholar 
Ravenelle, J. & Nyhus, P. J. Global patterns and trends in human–wildlife conflict compensation. Conserv. Biol. 31, 1247–1256 (2017).Article 

Google Scholar 
Zack, C. S., Milne, B. T. & Dunn, W. Southern oscillation index as an indicator of encounters between humans and black bears in New Mexico. Wildl. Soc. Bull. 31, 517–520 (2003).
Google Scholar 
Acosta-Jamett, G., Gutiérrez, J. R., Kelt, D. A., Meserve, P. L. & Previtali, M. A. El Niño Southern Oscillation drives conflict between wild carnivores and livestock farmers in a semiarid area in Chile. J. Arid. Environ. 126, 76–80 (2016).Article 

Google Scholar 
Timmermann, A. et al. El Niño–Southern Oscillation complexity. Nature 559, 535–545 (2018).Article 
CAS 

Google Scholar 
Wittemyer, G., Elsen, P., Bean, W. T., Burton, A. C. O. & Brashares, J. S. Accelerated human population growth at protected area edges. Science 321, 123–126 (2008).Article 
CAS 

Google Scholar 
Powell, G., Versluys, T. M. M., Williams, J. J., Tiedt, S. & Pooley, S. Using environmental niche modelling to investigate abiotic predictors of crocodilian attacks on people. Oryx 54, 639–647 (2020).Article 

Google Scholar 
Maxwell, S. M. et al. Dynamic ocean management: defining and conceptualizing real-time management of the ocean. Mar. Policy 58, 42–50 (2015).Article 

Google Scholar 
Maxwell, S. M., Gjerde, K. M., Conners, M. G. & Crowder, L. B. Mobile protected areas for biodiversity on the high seas. Science 367, 252–254 (2020).Article 
CAS 

Google Scholar 
Pons, M. et al. Trade-offs between bycatch and target catches in static versus dynamic fishery closures. Proc. Natl Acad. Sci. USA 119, e2114508119 (2022).Article 

Google Scholar 
Oestreich, W. K., Chapman, M. S. & Crowder, L. B. A comparative analysis of dynamic management in marine and terrestrial systems. Front. Ecol. Environ. 18, 496–504 (2020).Article 

Google Scholar 
Mason, N., Ward, M., Watson, J. E. M., Venter, O. & Runting, R. K. Global opportunities and challenges for transboundary conservation. Nat. Ecol. Evol. 4, 694–701 (2020).Article 

Google Scholar 
Dickman, A. J., Macdonald, E. A. & Macdonald, D. W. A review of financial instruments to pay for predator conservation and encourage human–carnivore coexistence. Proc. Natl Acad. Sci. USA 108, 13937–13944 (2011).Article 
CAS 

Google Scholar 
Ej, N. G. et al. A Future for All: The Need for Human–Wildlife Coexistence (UNEP, 2021).Lankford, A. J., Svancara, L. K., Lawler, J. J. & Vierling, K. Comparison of climate change vulnerability assessments for wildlife. Wildl. Soc. Bull. 38, 386–394 (2014).Article 

Google Scholar 
Syombua, M. An Analysis of Human–Wildlife Conflicts in Tsavo West-Amboseli Agro-Ecosystem Using an Integrated Geospatial Approach: A Case Study of Taveta District (Univ. of Nairobi, 2013).Malhi, Y. et al. The role of large wild animals in climate change mitigation and adaptation. Curr. Biol. 32, R181–R196 (2022).Article 
CAS 

Google Scholar 
Aryal, A., Brunton, D. & Raubenheimer, D. Impact of climate change on human–wildlife–ecosystem interactions in the Trans-Himalaya region of Nepal. Theor. Appl. Climatol. 115, 517–529 (2013).Article 

Google Scholar 
Aryal, A., Brunton, D., Ji, W., Barraclough, R. K. & Raubenheimer, D. Human–carnivore conflict: ecological and economical sustainability of predation on livestock by snow leopard and other carnivores in the Himalaya. Sustain. Sci. 9, 321–329 (2014).Article 

Google Scholar 
Aryal, A. et al. Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya. Ecol. Evol. 6, 4065–4075 (2016).Article 

Google Scholar 
Nowell, K., Li, J., Paltsyn, M. & Sharma, R. An Ounce of Prevention: Snow Leopard Crime Revisited (Traffic Report, 2016). More

Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).Article 
CAS 
PubMed 

Google Scholar 
Tilman, D. et al. Forecasting agriculturally driven global environmental change. Science 292, 281–284 (2001).Article 
CAS 
PubMed 

Google Scholar 
IPBES: Summary for Policymakers. In The Assessment Report on Pollinators, Pollination and Food Production (eds Potts, S. G. et al.) (IPBES, 2016).Potts, S. G. et al. Safeguarding pollinators and their values to human well-being. Nature 540, 220–229 (2016).Article 
CAS 
PubMed 

Google Scholar 
Sgolastra, F. et al. Synergistic mortality between a neonicotinoid insecticide and an ergosterol-biosynthesis-inhibiting fungicide in three bee species. Pest Manag Sci. 73, 1236–1243 (2016).Article 
PubMed 

Google Scholar 
Whitehorn, P. R., O’Connor, S., Wackers, F. L. & Goulson, D. Neonicotinoid pesticide reduces bumble bee colony growth and queen production. Science 336, 351–352 (2012).Article 
CAS 
PubMed 

Google Scholar 
Rundlöf, M. et al. Seed coating with a neonicotinoid insecticide negatively affects wild bees. Nature 521, 77–80 (2015).Article 
PubMed 

Google Scholar 
Woodcock, B. et al. Impacts of neonicotinoid use on long-term population changes in wild bees in England. Nat. Commun. 7, 12459 (2016).Stuligross, C. & Williams, N. Past insecticide exposure reduces bee reproduction and population growth rate. Proc. Natl Acad. Sci. USA 118, e2109909118 (2021).Stanley, D. A. et al. Neonicotinoid pesticide exposure impairs crop pollination services provided by bumblebees. Nature 528, 548–550 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tamburini, G. et al. Fungicide and insecticide exposure adversely impacts bumblebees and pollination services under semi-field conditions. Environ. Int. 157, 106813 (2021).Article 
CAS 
PubMed 

Google Scholar 
Sponsler, D. B. et al. Pesticides and pollinators: a socioecological synthesis. Sci. Total Environ. 662, 1012–1027 (2019).Article 
CAS 
PubMed 

Google Scholar 
Meehan, T. D., Werling, B. P., Landis, D. A. & Gratton, C. Agricultural landscape simplification and insecticide use in the Midwestern United States. Proc. Natl Acad. Sci. USA 108, 11500–11505 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nicholson, C. C. & Williams, N. M. Cropland heterogeneity drives frequency and intensity of pesticide use. Environ. Res. 16, 074008 (2021).CAS 

Google Scholar 
Böhme, F., Bischoff, G., Zebitz, C. P. W., Rosenkranz, P. & Wallner, K. Pesticide residue survey of pollen loads collected by honeybees (Apis mellifera) in daily intervals at three agricultural sites in South Germany. PLoS ONE 13, e0199995 (2018).Larsen, A. E. & Noack, F. Impact of local and landscape complexity on the stability of field-level pest control. Nat. Sustain. 4, 120–128 (2021).Article 

Google Scholar 
Botías, C. et al. Neonicotinoid residues in wildflowers, a potential route of chronic exposure for bees. Environ. Sci. Technol. 49, 12731–12740 (2015).Article 
PubMed 

Google Scholar 
Krupke, C. H., Holland, J. D., Long, E. Y. & Eitzer, B. D. Planting of neonicotinoid-treated maize poses risks for honey bees and other non-target organisms over a wide area without consistent crop yield benefit. J. Appl. Ecol. 54, 1449–1458 (2017).Article 
CAS 

Google Scholar 
Wintermantel, D. et al. Neonicotinoid-induced mortality risk for bees foraging on oilseed rape nectar persists despite EU moratorium. Sci. Total Environ. 704, 135400 (2020).Article 
CAS 
PubMed 

Google Scholar 
Krupke, C. H., Hunt, G. J., Eitzer, B. D., Andino, G. & Given, K. Multiple routes of pesticide exposure for honey bees living near agricultural fields. PLoS ONE 7, e29268 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Long, E. Y. & Krupke, C. H. Non-cultivated plants present a season-long route of pesticide exposure for honey bees. Nat. Commun. 7, 11629 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
David, A. et al. Widespread contamination of wildflower and bee-collected pollen with complex mixtures of neonicotinoids and fungicides commonly applied to crops. Environ. Int. 88, 169–178 (2016).Article 
CAS 
PubMed 

Google Scholar 
Heinrich, B. The foraging specializations of individual bumblebees. Ecol. Monogr. 46, 105–128 (1976).Article 

Google Scholar 
Bolin, A., Smith, H. G., Lonsdorf, E. V. & Olsson, O. Scale-dependent foraging tradeoff allows competitive coexistence. Oikos 127, 1575–1585 (2018).Article 

Google Scholar 
Cresswell, J. E., Osborne, J. L. & Goulson, D. An economic model of the limits to foraging range in central place foragers with numerical solutions for bumblebees. Ecol. Entomol. 25, 249–255 (2000).Article 

Google Scholar 
Rundlöf, M. et al. Flower plantings support wild bee reproduction and may also mitigate pesticide exposure effects. J. Appl. Ecol. 59, 2117–2127 (2022).Article 

Google Scholar 
Graham, K. K. et al. Identities, concentrations, and sources of pesticide exposure in pollen collected by managed bees during blueberry pollination. Sci. Rep. 11, 16857 (2021).Centrella, M. et al. Diet diversity and pesticide risk mediate the negative effects of land use change on solitary bee offspring production. J. Appl. Ecol. 57, 1031–1042 (2020).Article 
CAS 

Google Scholar 
De Palma, A. et al. Ecological traits affect the sensitivity of bees to land-use pressures in European agricultural landscapes. J. Appl. Ecol. 52, 1567–1577 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Sponsler, D. B. & Johnson, R. M. Mechanistic modeling of pesticide exposure: the missing keystone of honey bee toxicology. Environ. Toxicol. Chem. 36, 871–881 (2017).Article 
CAS 
PubMed 

Google Scholar 
Holzschuh, A., Dormann, C. F., Tscharntke, T. & Steffan-Dewenter, I. Mass-flowering crops enhance wild bee abundance. Oecologia 172, 477–484 (2013).Article 
PubMed 

Google Scholar 
McArt, S. H., Fersch, A. A., Milano, N. J., Truitt, L. L. & Böröczky, K. High pesticide risk to honey bees despite low focal crop pollen collection during pollination of a mass blooming crop. Sci. Rep. 7, 46554 (2017).Sanchez-Bayo, F. & Goka, K. Pesticide residues and bees—a risk assessment. PLoS ONE 9, e94482 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Zioga, E., Kelly, R., White, B. & Stout, J. C. Plant protection product residues in plant pollen and nectar: a review of current knowledge. Environ. Res. 189, 109873 (2020).Article 
CAS 
PubMed 

Google Scholar 
The European Green Deal (European Commission, 2019).More, S. J., Auteri, D., Rortais, A. & Pagani, S. EFSA is working to protect bees and shape the future of environmental risk assessment. EFSA J. 19, e190101 (2021).Schmolke, A. et al. Assessment of the vulnerability to pesticide exposures across bee species. Environ. Toxicol. Chem. 40, 2640–2651 (2021).Article 
CAS 
PubMed 

Google Scholar 
Rollin, O. et al. Differences of floral resource use between honey bees and wild bees in an intensive farming system. Agric. Ecosyst. Environ. 179, 78–86 (2013).Article 

Google Scholar 
Persson, A. S. & Smith, H. G. Seasonal persistence of bumblebee populations is affected by landscape context. Agric. Ecosyst. Environ. 165, 201–209 (2013).Article 

Google Scholar 
Samuelson, A. E., Schürch, R. & Leadbeater, E. Dancing bees evaluate central urban forage resources as superior to agricultural land. J. Appl. Ecol. 59, 79–88 (2022).Article 

Google Scholar 
Milner, A. M. & Boyd, I. L. Toward pesticidovigilance. Science 357, 1232–1234 https://doi.org/10.1126/science.aan2683 (2017).Nowell, L. H., Norman, J. E., Moran, P. W., Martin, J. D. & Stone, W. W. Pesticide toxicity index—a tool for assessing potential toxicity of pesticide mixtures to freshwater aquatic organisms. Sci. Total Environ. 476–477, 144–157 (2014).Article 
PubMed 

Google Scholar 
Mullin, C. A., Frazier, M., Frazier, J. L., Ashcraft, S. & Simonds, R. High levels of miticides and agrochemicals in North American apiaries: implications for honey bee health. PLoS ONE 5, 9754 (2010).Article 

Google Scholar 
Pettis, J. S. et al. Crop pollination exposes honey bees to pesticides which alters their susceptibility to the gut pathogen Nosema ceranae. PLoS ONE 8, e70182 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Végh, R., Sörös, C., Majercsik, N. & Sipos, L. Determination of pesticides in bee pollen: validation of a multiresidue high-performance liquid chromatography-mass spectrometry/mass spectrometry method and testing pollen samples of selected botanical origin. J. Agric. Food Chem. 70, 1507–1515 (2022).Article 
PubMed 

Google Scholar 
Park, M. G., Blitzer, E. J., Gibbs, J., Losey, J. E. & Danforth, B. N. Negative effects of pesticides on wild bee communities can be buffered by landscape context. Proc. R. Soc. B 282, 20150299 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Graham, K. K. et al. Pesticide risk to managed bees during blueberry pollination is primarily driven by off-farm exposures. Sci. Rep. 12, 7189 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yourstone, J., Karlsson, M., Klatt, B. K., Olsson, O. & Smith, H. G. Effects of crop and non-crop resources and competition: high importance of trees and oilseed rape for solitary bee reproduction. Biol. Conserv. 261, 109249 (2021).Persson, A. S., Mazier, F. & Smith, H. G. When beggars are choosers—how nesting of a solitary bee is affected by temporal dynamics of pollen plants in the landscape. Ecol. Evol. 8, 5777–5791 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood, T. J., Holland, J. M. & Goulson, D. Providing foraging resources for solitary bees on farmland: current schemes for pollinators benefit a limited suite of species. J. Appl. Ecol. 54, 323–333 (2016).Garthwaite, D. et al. Collection of Pesticide Application Data in View of Performing Environmental Risk Assessments for Pesticides (EFSA, 2017).de Oliveira, R. C., Nascimento Queiroz, S. C., Pinto da Luz, C. F., Silveira Porto, R. & Rath, S. Bee pollen as a bioindicator of environmental pesticide contamination. Chemosphere 163, 525–534 (2016).Article 
PubMed 

Google Scholar 
Arena, M. & Sgolastra, F. A meta-analysis comparing the sensitivity of bees to pesticides. Ecotoxicology 23, 324–334 (2014).Article 
CAS 
PubMed 

Google Scholar 
Douglas, M. R., Sponsler, D. B., Lonsdorf, E. V. & Grozinger, C. M. County-level analysis reveals a rapidly shifting landscape of insecticide hazard to honey bees (Apis mellifera) on US farmland. Sci. Rep. 10, 797 (2020).Commission Implementing Regulation (EU) 2021/2081 of 26 November 2021 concerning the non-renewal of approval of the active substance indoxacarb, in accordance with Regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market, and amending Commission Implementing Regulation (EU) No 540/2011 (EUR-Lex, 2021); http://data.europa.eu/eli/reg_impl/2021/2081/ojCommission Implementing Regulation (EU) 2020/23 of 13 January 2020 concerning the non-renewal of the approval of the active substance thiacloprid, in accordance with Regulation (EC) No. 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market, and amending the Annex to Commission Implementing Regulation (EU) No 540/2011 (EUR-Lex, 2020); http://data.europa.eu/eli/reg_impl/2020/23/ojCommission Implementing Regulation (EU) 2018/783 of 29 May 2018 amending Implementing Regulation (EU) No 540/2011 as regards the conditions of approval of the active substance imidacloprid (EUR-Lex, 2018); http://data.europa.eu/eli/reg_impl/2018/783/ojHerbertsson, L., Jonsson, O., Kreuger, J., Smith, H. G. & Rundlöf, M. Scientific note: imidacloprid found in wild plants downstream permanent greenhouses in Sweden. Apidologie 52, 946–949 (2021).Article 

Google Scholar 
Tosi, S. et al. Long-term field-realistic exposure to a next-generation pesticide, flupyradifurone, impairs honey bee behaviour and survival. Commun. Biol. 4, 805 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Siviter, H. & Muth, F. Do novel insecticides pose a threat to beneficial insects?: novel insecticides harm insects. Proc. R. Soc. B 287, 20201265 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
EFSA. Guidance on the risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees). EFSA J. 11, 3295 (2013).Guidance for Assessing Pesticide Risks to Bees (US EPA, 2014).Boyle, N. K. et al. Workshop on pesticide exposure assessment paradigm for non-apis bees: foundation and summaries. Environ. Entomol. 48, 4–11 (2019).Article 
PubMed 

Google Scholar 
EFSA. Analysis of the evidence to support the definition of specific protection goals for bumble bees and solitary bees. EFSA J. 19, EN-7125 (2022).Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611 (2013).Article 
CAS 
PubMed 

Google Scholar 
Tscharntke, T., Grass, I., Wanger, T. C. & Westphal, C. Restoring biodiversity needs more than reducing pesticides. Trends Ecol. Evol. 37, 115–116 (2022).Article 
PubMed 

Google Scholar 
Topping, C. J. et al. Holistic environmental risk assessment for bees. Science 37, 897 (2021).Article 

Google Scholar 
Tsvetkov, N. et al. Chronic exposure to neonicotinoids reduces honey bee health near corn crops. Science 356, 1395–1397 (2017).Article 
CAS 
PubMed 

Google Scholar 
Jonsson, O., Fries, I. & Kreuger, J. Utveckling av Analysmetoder och Screening av Växtskyddsmedel i bin och Pollen (CKB, 2013).Sawyer, R. Pollen Identification for Beekeepers (Univ. Cardiff Press, 1981).IUPAC Pesticide Properties Data Base (Univ. of Hertfordshire, 2022).EFSA Scientific Committee & More, S.J. et al. Guidance on harmonised methodologies for human health, animal health and ecological risk assessment of combined exposure to multiple chemicals. EFSA J. 17, e05634 (2019).Martin, O. et al. Ten years of research on synergisms and antagonisms in chemical mixtures: a systematic review and quantitative reappraisal of mixture studies. Environ. Int. 146, 106206 (2021).Article 
CAS 
PubMed 

Google Scholar 
DiBartolomeis, M., Kegley, S., Mineau, P., Radford, R. & Klein, K. An assessment of acute insecticide toxicity loading (AITL) of chemical pesticides used on agricultural land in the United States. PLoS ONE 14, e0220029 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Test No. 213: Honeybees, Acute Oral Toxicity Test (OECD, 1998); https://doi.org/10.1787/9789264070165-enPrice, P. S. & Han, X. Maximum cumulative ratio (MCR) as a tool for assessing the value of performing a cumulative risk assessment. Int. J. Environ. Res. Public Health 8, 2212–2225 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Oksanen, J. et al. vegan community ecology package version 2.6-2 (2022).Lenth, R. emmeans: Estimated marginal means, aka least-squares means (2022).Lüdecke, D., Ben-shachar, M. S., Patil, I. & Makowski, D. performance: an R package for assessment, comparison and testing of statistical models statement of need. J. Open Source Softw. 6, 3139 (2021).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Kendall, L. K. et al. The potential and realized foraging movements of bees are differentially determined by body size and sociality. Ecology 103, e3809 (2022).Parreño, M. A. et al. Critical links between biodiversity and health in wild bee conservation. Trends Ecol. Evol. 37, 309–321 (2022).Article 
PubMed 

Google Scholar  More

Semin, G. R., Scandurra, A., Baragli, P., Lanatà, A. & D’Aniello, B. Inter- and intra-species communication of emotion: Chemosignals as the neglected medium. Animals 9, 887 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Désiré, L., Boissy, A. & Veissier, I. Emotions in farm animals: A new approach to animal welfare in applied ethology. Behav. Processes 60, 165–180 (2002).Article 
PubMed 

Google Scholar 
Briefer, E. F. & Le Comber, S. Vocal expression of emotions in mammals: mechanisms of production and evidence. J. Zool. 288, 1–20 (2012).Article 

Google Scholar 
Jardat, P. & Lansade, L. Cognition and the human–animal relationship: a review of the sociocognitive skills of domestic mammals toward humans. Anim. Cogn. 25, 369–384 (2022).Article 
PubMed 

Google Scholar 
Galvan, M. & Vonk, J. Man’s other best friend: domestic cats (F. silvestris catus) and their discrimination of human emotion cues. Anim. Cogn. 19, 193–205 (2016).Nawroth, C., Albuquerque, N., Savalli, C., Single, M. S. & McElligott, A. G. Goats prefer positive human emotional facial expressions. R. Soc. Open Sci. 5, 180491 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Proops, L., Grounds, K., Smith, A. V. & McComb, K. Animals remember previous facial expressions that specific humans have exhibited. Curr. Biol. 28, 1428-1432.e4 (2018).Article 
CAS 
PubMed 

Google Scholar 
Smith, A. V., Proops, L., Grounds, K., Wathan, J. & McComb, K. Functionally relevant responses to human facial expressions of emotion in the domestic horse (Equus caballus). Biol. Lett. 12, 20150907 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., D’Ingeo, S., Fornelli, S. & Quaranta, A. Lateralized behavior and cardiac activity of dogs in response to human emotional vocalizations. Sci. Rep. 8, 77 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., D’Ingeo, S. & Quaranta, A. Orienting asymmetries and physiological reactivity in dogs’ response to human emotional faces. Learn. Behav. 46, 574–585 (2018).Article 
PubMed 

Google Scholar 
Smith, A. V. et al. Domestic horses (Equus caballus) discriminate between negative and positive human nonverbal vocalisations. Sci. Rep. 8, 13052 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Trösch, M. et al. Horses categorize human emotions cross-modally based on facial expression and non-verbal vocalizations. Animals 9, 862 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Quaranta, A., D’ingeo, S., Amoruso, R. & Siniscalchi, M. Emotion recognition in cats. Animals 10, 1107 (2020).Nakamura, K., Takimoto-Inose, A. & Hasegawa, T. Cross-modal perception of human emotion in domestic horses (Equus caballus). Sci. Rep. 8, 8660 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Albuquerque, N. et al. Dogs recognize dog and human emotions. Biol. Lett. 12, 20150883 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Briefer, E. F. Vocal contagion of emotions in non-human animals. Proc. R. Soc. B Biol. Sci. 285 (2018).Sabiniewicz, A., Tarnowska, K., Świątek, R., Sorokowski, P. & Laska, M. Olfactory-based interspecific recognition of human emotions: Horses (Equus ferus caballus) can recognize fear and happiness body odour from humans (Homo sapiens). Appl. Anim. Behav. Sci. 230, 105072 (2020).Article 

Google Scholar 
Baba, C., Kawai, M. & Takimoto-Inose, A. Are horses (Equus caballus) sensitive to human emotional cues?. Animals 9, 630 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Brennan, P. A. & Kendrick, K. M. Mammalian social odours: attraction and individual recognition. Philos. Trans. R. Soc. B Biol. Sci. 361, 2061–2078 (2006).Saslow, C. A. Understanding the perceptual world of horses. Appl. Anim. Behav. Sci. 78, 209–224 (2002).Article 

Google Scholar 
Péron, F., Ward, R. & Burman, O. Horses (Equus caballus) discriminate body odour cues from conspecifics. Anim. Cogn. 17, 1007–1011 (2014).Article 
PubMed 

Google Scholar 
Krueger, K. & Flauger, B. Olfactory recognition of individual competitors by means of faeces in horse (Equus caballus). Anim. Cogn. 14, 245–257 (2011).Article 
PubMed 

Google Scholar 
Boissy, A., Terlouw, C. & Le Neindre, P. Presence of cues from stressed conspecifics increases reactivity to aversive events in cattle: evidence for the existence of alarm substances in urine. Physiol. Behav. 63, 489–495 (1998).Article 
CAS 
PubMed 

Google Scholar 
Vieuille-Thomas, C. & Signoret, J. P. Pheromonal transmission of an aversive experience in domestic pig. J. Chem. Ecol. 18, 1551–1557 (1992).Article 
CAS 
PubMed 

Google Scholar 
Siniscalchi, M., D’Ingeo, S. & Quaranta, A. The dog nose ‘KNOWS’ fear: Asymmetric nostril use during sniffing at canine and human emotional stimuli. Behav. Brain Res. 304, 34–41 (2016).Article 
PubMed 

Google Scholar 
Calvi, E. et al. The scent of emotions: A systematic review of human intra- and interspecific chemical communication of emotions. Brain Behav. 10 (2020).de Groot, J. H. B., Semin, G. R. & Smeets, M. A. M. On the communicative function of body odors: A theoretical integration and review. Perspect. Psychol. Sci. 12, 306–324 (2017).Article 
PubMed 

Google Scholar 
de Groot, J. H. B. et al. A sniff of happiness. Psychol. Sci. 26, 684–700 (2015).Article 
PubMed 

Google Scholar 
Destrez, A. et al. Male mice and cows perceive human emotional chemosignals: A preliminary study. Anim. Cogn. 24, 1205–1214 (2021).Article 
PubMed 

Google Scholar 
Wilson, Id. Dogs can discriminate between human baseline and psychological stress condition odours. PLoS ONE 17, e0274143 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
D’Aniello, B., Semin, G. R., Alterisio, A., Aria, M. & Scandurra, A. Interspecies transmission of emotional information via chemosignals: from humans to dogs (Canis lupus familiaris). Anim. Cogn. 21, 67–78 (2018).Article 
PubMed 

Google Scholar 
D’Aniello, B. et al. Sex differences in the behavioral responses of dogs exposed to human chemosignals of fear and happiness. Anim. Cogn. 24, 299–309 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., d’Ingeo, S., Quaranta, A., D’Ingeo, S. & Quaranta, A. Lateralized emotional functioning in domestic animals. Appl. Anim. Behav. Sci. 237, 105282 (2021).Article 

Google Scholar 
Rogers, L. & Vallortigara, G. Lateralized Brain Functions: Methods in Human and Non-Human Species. vol. 122 (2017).D’Ingeo, S. et al. Horses associate individual human voices with the valence of past interactions: A behavioural and electrophysiological study. Sci. Rep. 9, 11568 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Siniscalchi, M., Padalino, B., Aubé, L. & Quaranta, A. Right-nostril use during sniffing at arousing stimuli produces higher cardiac activity in jumper horses. Laterality 20, (2015).De Boyer Des Roches, A., Richard-Yris, M.-A. A., Henry, S., Ezzaouïa, M. & Hausberger, M. Laterality and emotions: Visual laterality in the domestic horse (Equus caballus) differs with objects’ emotional value. Physiol. Behav. 94, 487–490 (2008).Albrecht, J. et al. Smelling chemosensory signals of males in anxious versus nonanxious condition increases state anxiety of female subjects. Chem. Senses 36, 19–27 (2011).Article 
CAS 
PubMed 

Google Scholar 
Derrickson, S. Sinister (VF)—YouTube. (Wild Bunch SA, 2001).Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing (2021).Wickham, H. Ggplot2: Elegant graphics for data analysis. (2016).Hothorn, T., Winell, H., Hornik, K., van de Wiel, M. A. & Zeileis, A. coin: Conditional inference procedures in a permutation test framework (2021).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models (2021).Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. (2022).Hothersall, B., Harris, P., Sörtoft, L. & Nicol, C. J. Discrimination between conspecific odour samples in the horse (Equus caballus). Appl. Anim. Behav. Sci. 126, 37–44 (2010).Article 

Google Scholar 
Smeets, M. A. M. et al. Chemical fingerprints of emotional body odor. Metabolites 10, (2020).Sabiniewicz, A. et al. A preliminary investigation of interspecific chemosensory communication of emotions: Can Humans (Homo sapiens) recognise fear- and non-fear body odour from horses (Equus ferus caballus). Animal 11, 3499 (2021).Article 

Google Scholar 
Zhou, W. & Chen, D. Entangled chemosensory emotion and identity: Familiarity enhances detection of chemosensorily encoded emotion. Soc. Neurosci. 6, 270–276 (2011).Article 
PubMed 

Google Scholar 
Starling, M., McLean, A. & McGreevy, P. The contribution of equitation science to minimising horse-related risks to humans. Animal 6, 15 (2016).Article 

Google Scholar 
Basile, M. et al. Socially dependent auditory laterality in domestic horses (Equus caballus). Anim. Cogn. 12, 611–619 (2009).Article 
PubMed 

Google Scholar 
Hatfield, E., Cacioppo, J. T. & Rapson, R. L. Emotional contagion. Curr. Dir. Psychol. Sci. 2, 96–100 (1993).Article 

Google Scholar 
Austin, N. P. & Rogers, L. J. Limb preferences and lateralization of aggression, reactivity and vigilance in feral horses Equus caballus. Anim. Behav. 83, 239–247 (2012).Article 

Google Scholar 
Larose, C., Richard-Yris, M.-A., Hausberger, M. & Rogers, L. J. Laterality of horses associated with emotionality in novel situations Laterality Asymmetries Body. Brain Cogn. 11, 355–367 (2006).
Google Scholar 
Farmer, K., Krüger, K., Byrne, R. W. & Marr, I. Sensory laterality in affiliative interactions in domestic horses and ponies (Equus caballus). Anim. Cogn. 21, 631–637 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, D. & Haviland-Jones, J. Human olfactory communication of emotion. Percept. Mot. Skills 91, 771–781 (2000).Article 
CAS 
PubMed 

Google Scholar 
de Groot, J. H. B., Semin, G. R. & Smeets, M. A. M. Chemical communication of fear: A case of male-female asymmetry. J. Exp. Psychol. Gen. 143, 1515–1525 (2014).Article 
PubMed 

Google Scholar 
Marinier, S. L., Alexander, A. J. & Waring, G. H. Flehmen behaviour in the domestic horse: Discrimination of conspecific odours. Appl. Anim. Behav. Sci. 19, 227–237 (1988).Article 

Google Scholar 
Lansade, L., Pichard, G. & Leconte, M. Sensory sensitivities: Components of a horse’s temperament dimension. Appl. Anim. Behav. Sci. 114, 534–553 (2008).Article 

Google Scholar 
Lansade, L., Bouissou, M. F. & Erhard, H. W. Fearfulness in horses: A temperament trait stable across time and situations. Appl. Anim. Behav. Sci. 115, 182–200 (2008).Article 

Google Scholar 
Hoenen, M., Wolf, O. T. & Pause, B. M. The impact of stress on odor perception. https://doi.org/10.1177/030100661668870746,366-376 (2017).Article 
PubMed 

Google Scholar 
Rørvang, M. V., Nicova, K. & Yngvesson, J. Horse odor exploration behavior is influenced by pregnancy and age. Front. Behav. Neurosci. 16, 295 (2022).Article 

Google Scholar 
Doty, R. L. & Cameron, E. L. Sex differences and reproductive hormone influences on human odor perception. Physiol. Behav. 97, 213–228 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar  More

Study populations and sequencing strategyDNA libraries were prepared for 1261 D. sylvestris individuals from 115 populations (5–20 individuals per population) under a modified protocol49 of the Illumina Nextera DNA library preparation kit (Supplementary Methods S1.1, Supplementary Data 1). Individuals were indexed with unique dual-indexes (IDT Illumina Nextera 10nt UDI – 384 set) from Integrated DNA Technologies Co, to avoid index-hopping50. Libraries were sequenced (150 bp paired-end sequencing) in four lanes of an Illumina NovaSeq 6000 machine at Novogene Co. This resulted in an average coverage of ca. 2x per individual. Sequenced individuals were trimmed for adapter sequences (Trimmomatic version 0.3551), mapped (BWA-MEM version 0.7.1752,53) against a reference assembly54 (ca. 440 Mb), had duplicates marked and removed (Picard Toolkit version 2.0.1; http://broadinstitute.github.io/picard), locally realigned around indels (GATK version 3.555), recalibrated for base quality scores (ATLAS version 0.956) and had overlapping read pairs clipped (bamUtil version 1.0.1457) (Supplementary Methods S1.1). Population genetic analyses were performed on the resultant BAM files via genotype likelihoods (ANGSD version 0.93358 and ATLAS versions 0.9–1.056), to accommodate the propagation of uncertainty from the raw sequence data to population genetic inference.Population genetic structure and biogeographic barriersTo investigate the genetic structure of our samples (Fig. 2A, Supplementary Fig. S2), we performed principal component analyses (PCA) on all 1261 samples (“full” dataset) via PCAngsd version 0.9859, following conversion of the mapped sequence data to ANGSD genotype likelihoods in Beagle format (Supplementary Methods S1.2). To visualise PCA results in space (Supplementary Fig. S4), individuals’ principal components were projected on a map, spatially interpolated (linear interpolation, akima R package version 0.6.260) and had the first two principal components represented as green and blue colour channels. Given that uneven sampling can bias the inference of structure in PCA, PCA was also performed on a balanced dataset comprising a common, down-sampled size of 125 individuals per geographic region (“balanced” dataset; Fig. 2B, Supplementary Fig. S3; Supplementary Methods S1.2; Supplementary Data 1). Individual admixture proportions and ancestral allele frequencies were estimated using PCAngsd (-admix model) for K = 2–6, using the balanced dataset to avoid potential biases related to imbalanced sampling22,23 and an automatic search for the optimal sparseness regularisation parameter (alpha) soft-capped to 10,000 (Supplementary Methods S1.2). To visualise ancestry proportions in space, population ancestry proportions were spatially interpolated (kriging) via code modified from Ref. 61 (Supplementary Fig. S5).To test if between-lineage admixture underlies admixture patterns inferred by PCAngsd or if the data is better explained by alternative scenarios such as recent bottlenecks, we used chromosome painting and patterns of allele sharing to construct painting palettes via the programmes MixPainter and badMIXTURE (unlinked model)28 and compared this to the PCAngsd-inferred palettes (Fig. 2B, C; Supplementary Methods S1.2). We referred to patterns of residuals between these palettes to inform of the most likely underlying demographic scenario. For assessing Alpine–Balkan palette residuals (and hence admixture), 65 individuals each from the French Alps (inferred as pure Alpine ancestry in PCAngsd), Monte Baldo (inferred with both Alpine and Balkan ancestries in PCAngsd) and Julian Alps (inferred as pure Balkan ancestry in PCAngsd) were analysed under K = 2 in PCAngsd and badMIXTURE (Fig. 2C). For assessing Apennine–Balkan admixture, 22 individuals each from the French pre-Alps (inferred as pure Apennine ancestry in PCAngsd), Tuscany (inferred with both Apennine and Balkan ancestries in PCAngsd) and Julian Alps (inferred as pure Balkan ancestry in PCAngsd) were analysed under K = 2 in PCAngsd and badMIXTURE.To construct a genetic distance tree (Supplementary Fig. S1), we first calculated pairwise genetic distances between 549 individuals (5 individuals per population for all populations) using ATLAS, employing a distance measure (weight) reflective of the number of alleles differing between the genotypes (Supplementary Methods S1.2; Supplementary Data 1). A tree was constructed from the resultant distance matrix via an initial topology defined by the BioNJ algorithm with subsequent topological moves performed via Subtree Pruning and Regrafting (SPR) in FastME version 2.1.6.162. This matrix of pairwise genetic distances was also used as input for analyses of effective migration and effective diversity surfaces in EEMS25. EEMS was run setting the number of modelled demes to 1000 (Fig. 2A, Supplementary Fig. S8). For each case, ten independent Markov chain Monte Carlo (MCMC) chains comprising 5 million iterations each were run, with a 1 million iteration burn-in, retaining every 10,000th iteration. Biogeographic barriers (Fig. 2A, Supplementary Fig. S7) were further identified via applying Monmonier’s algorithm24 on a valuated graph constructed via Delauney triangulation of population geographic coordinates, with edge values reflecting population pairwise FST; via the adegenet R package version 2.1.163. FST between all population pairs were calculated via ANGSD, employing a common sample size of 5 individuals per population (Supplementary Fig. S6; Supplementary Methods S1.2; Supplementary Data 1). 100 bootstrap runs were performed to generate a heatmap of genetic boundaries in space, from which a weighted mean line was drawn (Supplementary Fig. S7). All analyses in ANGSD were performed with the GATK (-GL 2) model, as we noticed irregularities in the site frequency spectra (SFS) with the SAMtools (-GL 1) model similar to that reported in Ref. 58 with particular BAM files. All analyses described above were performed on the full genome.Ancestral sequence reconstructionTo acquire ancestral states and polarise site-frequency spectra for use in the directionality index ψ and demographic inference, we reconstructed ancestral genome sequences at each node of the phylogenetic tree of 9 Dianthus species: D. carthusianorum, D. deltoides, D. glacialis, D. sylvestris (Apennine lineage), D. lusitanus, D. pungens, D. superbus alpestris, D. superbus superbus, and D. sylvestris (Alpine lineage). This tree topology was extracted from a detailed reconstruction of Dianthus phylogeny based on 30 taxa by Fior et al. (Fior, Luqman, Scharmann, Zemp, Zoller, Pålsson, Gargano, Wegmann & Widmer; paper in preparation) (Supplementary Methods S1.3). For ancestral sequence reconstruction, one individual per species was sequenced at medium coverage (ca. 10x), trimmed (Trimmomatic), mapped against the D. sylvestris reference assembly (BWA-MEM) and had overlapping read pairs clipped (bamUtil) (Supplementary Methods S1.3). For each species, we then generated a species-specific FASTA using GATK FastaAlternateReferenceMaker. This was achieved by replacing the reference bases at polymorphic sites with species-specific variants as identified by freebayes64 (version 1.3.1; default parameters), while masking (i.e., setting as “N”) sites (i) with zero depth and (ii) that didn’t pass the applied variant filtering criteria (i.e., that are not confidently called as polymorphic; Supplementary Methods S1.3). Species FASTA files were then combined into a multi-sample FASTA. Using this, we probabilistically reconstructed ancestral sequences at each node of the tree via PHAST (version 1.4) prequel65, using a tree model produced by PHAST phylofit under a REV substitution model and the specified tree topology (Supplementary Methods S1.3). Ancestral sequence FASTA files were then generated from the prequel results using a custom script.Expansion signalTo calculate the population pairwise directionality index ψ for the Alpine lineage, we utilised equation 1b from Peter and Slatkin (2013)31, which defines ψ in terms of the two-population site frequency spectrum (2D-SFS) (Supplementary Methods S1.4). 2D-SFS between all population pairs (10 individuals per population; Supplementary Data 1) were estimated via ANGSD and realSFS66 (Supplementary Methods S1.4), for unfolded spectra. Unfolding of spectra was achieved via polarisation with respect to the ancestral state of sites defined at the D. sylvestris (Apennine lineage) – D. sylvestris (Alpine lineage) ancestral node. Correlation of pairwise ψ and (great-circle) distance matrices was tested via a Mantel test (10,000 permutations). To infer the geographic origin of the expansion (Fig. 3), we employed a time difference of arrival (TDOA) algorithm following Peter and Slatkin (2013);31 performed via the rangeExpansion R package version 0.0.0.900031,67. We further estimated the strength of the founder of this expansion using the same package.Demographic inferenceTo evaluate the demographic history of D. sylvestris, a set of candidate demographic models was formulated. To constrain the topology of tested models, we first inferred the phylogenetic tree of the three identified evolutionary lineages of D. sylvestris (Alpine, Apennine and Balkan) as embedded within the larger phylogeny of the Eurasian Dianthus clade (note that the phylogeny from Fior et al. (Fior, Luqman, Scharmann, Zemp, Zoller, Pålsson, Gargano, Wegmann & Widmer; paper in preparation) excludes Balkan representatives of D. sylvestris). Trees were inferred based on low-coverage whole-genome sequence data of 1–2 representatives from each D. sylvestris lineage, together with whole-genome sequence data of 7 other Dianthus species, namely D. carthusianorum, D. deltoides, D. glacialis, D. lusitanus, D. pungens, D. superbus alpestris and D. superbus superbus, that were used to root the D. sylvestris clade (Supplementary Methods S1.5). We estimated distance-based phylogenies using ngsDist68 that accommodates genotype likelihoods in the estimation of genetic distances (Supplementary Methods S1.5). Genetic distances were calculated via two approaches: (i) genome-wide and (ii) along 10 kb windows. For the former, 110 bootstrap replicates were calculated by re-sampling over similar-sized genomic blocks. For the alternative strategy based on 10 kb windows, window trees were combined using ASTRAL-III version 5.6.369 to generate a genome-wide consensus tree accounting for potential gene tree discordance (Supplementary Methods S1.5). Trees were constructed from matrices of genetic distances from initial topologies defined by the BioNJ algorithm with subsequent topological moves performed via Subtree Pruning and Regrafting (SPR) in FastME version 2.1.6.162. We rooted all resultant phylogenetic trees with D. deltoides as the outgroup70. Both approaches recovered a topology with the Balkan lineage diverging prior to the Apennine and Alpine lineages (Supplementary Fig. S9). This taxon topology for D. sylvestris was supported by high ASTRAL-III posterior probabilities ( >99%), ASTRAL-III quartet scores ( >0.5) and bootstrap values ( >99%). Topologies deeper in the tree were less well-resolved (with quartet scores More

Modular components of the DFTe energy functionalThe central ingredient of DFTe is an energy functional E, assembled according to Eq. (1). The methodology of DFTe can be understood by inspecting the dispersal and environmental energies in Eqs. (2) and (3) without interactions. In our first case study, illustrated in Fig. 2 and Supplementary Fig. 2, we demonstrate that equation (3), in conjunction with Eq. (2), can realistically describe the influence of the environment on species’ distributions. Mechanisms that alter the trade-off between dispersal and environment can be introduced as part of Eint. For instance, back reactions on the environment could be modelled with a bifunctional Ebr[Venv, n] that yields the equilibrated modified environment ({V}_{s}^{{{{{{{{rm{env}}}}}}}}}+delta {E}_{{{{{{{{rm{br}}}}}}}}}[{{{{{{{{bf{V}}}}}}}}}^{{{{{{{{rm{env}}}}}}}}},{{{{{{{bf{n}}}}}}}}]/delta {n}_{s}({{{{{{{bf{r}}}}}}}})), cf. Eq. (5).In the following we make explicit the interaction and resource energies that enter Eq. (1) and are used in our case studies of Figs. 2–7. We let Eint[n] include all possible bipartite interactions$${E}_{gamma }[{{{{{{{bf{n}}}}}}}}]=mathop{sum }limits_{{s,{s}^{{prime} }!=!1}atop {{s}^{{prime} }ne s}}^{S}{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}})({{{{{{{rm{d}}}}}}}}{{{{{{{{bf{r}}}}}}}}}^{{prime} }){n}_{s}{({{{{{{{bf{r}}}}}}}})}^{{alpha }_{s}},{gamma }_{s{s}^{{prime} }}({{{{{{{bf{r}}}}}}}},, {{{{{{{{bf{r}}}}}}}}}^{{prime} }){n}_{{s}^{{prime} }}{({{{{{{{{bf{r}}}}}}}}}^{{prime} })}^{{beta }_{{s}^{{prime} }}},$$
(6)
which include amensalism, commensalism, mutualism, and so forth. Here, ({alpha }_{s},, {beta }_{{s}^{{prime} }}ge 0), and the interaction kernels ({gamma }_{s{s}^{{prime} }}) are assembled from fitness proxies of species s and ({s}^{{prime} }) (Supplementary Table 1). Higher-order interactions can be introduced, for example, through (i) terms like ({n}_{s},{gamma }_{s{s}^{{prime} }},{n}_{{s}^{{prime} }},{gamma }_{{s}^{{prime} }{s}^{{primeprime} }}^{{prime} },{n}_{{s}^{{primeprime} }}) that build on pairwise interactions or (ii) genuinely multipartite expressions like ({gamma }_{s{s}^{{prime} }{s}^{{primeprime} }}{n}_{s},,{n}_{{s}^{{prime} }},{n}_{{s}^{{primeprime} }}). Multi-partite interactions based on bipartite interactions do not seem to be an uncommon scenario48. However, there may be systems where nonzero coefficients ({gamma }_{s{s}^{{prime} }{s}^{{primeprime} }}) couple all species. This poses a challenge for mechanistic theories in general. Then, ‘simpler subsystems’ that have to be included in the DFTe workflow of Fig. 1a can only refer to situations where other energy components are absent, such as resource terms or complex environments. For example, the coefficients ({gamma }_{s{s}^{{prime} }{s}^{{primeprime} }}) could be extracted in an experiment with a controlled simple environment and then used to model the interacting species in a real-world setting. For (({alpha }_{s},, {beta }_{{s}^{{prime} }})=(1,1)) we identify the contact interaction in physics as ({gamma }_{s{s}^{{prime} }}propto delta ({{{{{{{bf{r}}}}}}}}-{{{{{{{{bf{r}}}}}}}}}^{{prime} })) with the two-dimensional delta function δ( ), while the Coulomb interaction amounts to setting ({gamma }_{s{s}^{{prime} }}propto 1/|{{{{{{{bf{r}}}}}}}}-{{{{{{{{bf{r}}}}}}}}}^{{prime} }|). The mechanistic effect of these interaction kernels on the density distributions is the same in ecology as it is in physics—a mathematical insight that inspired us to build ecological analogues to the phenomenology of quantum gases, which feature functionals of the kind in Eq. (6). Note that we do not introduce any quantum effects into ecology despite the fact that the mathematical structure of DFTe is borrowed in part from quantum physics. While the contact interaction is a suitable candidate for plants and especially microbes52, we expect long-range interactions (for example, repulsion of Coulomb type) to be more appropriate for species with long-range sensors, such as eyes. Both types of interactions feature in describing the ecosystems addressed in this work.In a natural setting the equilibrium abundances are ultimately constrained by the accessible resources. It is within these limits of resource availability that environment as well as intra- and inter-specific interactions can shape the density distributions. An energy term for penalising over- and underconsumption of resources is thus of central importance. Each species consumes resources from some of the K provided resources, indexed by k. A subset of species consumes the locally available resource density ρk(r) according to the resource requirements νks, which represent the absolute amount of resource k consumed by one individual (or aggregated constituent) of species s. The simple quadratic functional$${E}_{{{{{{{{rm{Res}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]={int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}})mathop{sum }limits_{k=1}^{K}{{{{{{{{mathcal{L}}}}}}}}}_{k}left({{{{{{{bf{n}}}}}}}},, {rho }_{k}right)equiv zeta {int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}})mathop{sum }limits_{k=1}^{K}{w}_{k}({{{{{{{bf{r}}}}}}}}){left[mathop{sum }limits_{s=1}^{S}{nu }_{ks}{n}_{s}({{{{{{{bf{r}}}}}}}})-{rho }_{k}({{{{{{{bf{r}}}}}}}})right]}^{2}$$
(7)
proves appropriate. Here, νksns is the portion of resource density ρk that is consumed by species s. That is, νks  > 0 indicates that s requires resource k. If Eq. (7) is the total energy functional, then a single-species system with a single resource equilibrates with density n1(r) = ρ1(r)/ν11 at every position r, and additional DFTe energy components would modify this equilibrium. Predator–prey relationships are introduced by making species k a resource ({rho }_{k}=left]{n}_{k}right[), where (left]nright[) declares n a constant w.r.t. the functional differentiation of E, that is, the predator tends to align with the prey, not the prey with the predator. In view of the energy minimisation, the quadratic term in Eq. (7) entails that regions of low resource density ρk are less important than regions of high ρk. The different resources k have the same ability to limit the abundances, such that the limiting resource k = l at r has to come with the largest of weights wl(r), irrespective of the absolute amounts of resources at r. For example, the weights wk have to ensure that an essential but scarce mineral has (a priori) the same ability to limit the abundances as a resource like water, which might be abundant in absolute terms. To that end, we specify the weights$${w}_{k}({{{{{{{bf{r}}}}}}}})=frac{1}{{bar{rho }}_{k}^{2}}mathop{sum}limits_{s}eta ({nu }_{ks})exp left[sigma left(frac{{lambda }_{ks}}{{lambda }_{ls}}-1right)right],$$
(8)
which are inspired by the smooth minimum function, where σ  λls irrelevant at r. Using ({E}_{{{{{{{{rm{Res}}}}}}}}}), we show that an analytically solvable minimal example of two amensalistically interacting species already exhibits a plethora of resource-dependent equilibrium states (see Supplementary Notes and Supplementary Fig. 1).We specify the DFTe energy functional in Eq. (1) by summing Eqs. (2), (3), (6), and (7) and by (optionally) constraining the abundances to N via Lagrange multipliers μ:$$E[{{{{{{{bf{n}}}}}}}},, {{{{{{{boldsymbol{mu }}}}}}}}]({{{{{{{bf{N}}}}}}}}) equiv E[{{{{{{{bf{n}}}}}}}}]+{E}_{{{{{{{{boldsymbol{mu }}}}}}}}}[{{{{{{{bf{n}}}}}}}}]({{{{{{{bf{N}}}}}}}})\ equiv {E}_{{{{{{{{rm{dis}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+{E}_{{{{{{{{rm{env}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+{E}_{gamma }[{{{{{{{bf{n}}}}}}}}]+{E}_{{{{{{{{rm{Res}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+mathop{sum }limits_{s=1}^{S}{mu }_{s}left({N}_{s}-{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),{n}_{s}right).$$
(9)
Uniform situations are characterised by spatially constant ingredients ns = Ns/A, ρk = Rk/A, coefficients τs, etc. for the DFTe energy, such that Eq. (9) reduces to a function E(N) with building blocks$${E}_{{{{{{{{rm{dis}}}}}}}}}longrightarrow frac{1}{2,A}mathop{sum }limits_{s=1}^{S}{tau }_{s},{N}_{s}^{2},$$
(10)
$${E}_{{{{{{{{rm{env}}}}}}}}}longrightarrow mathop{sum }limits_{s=1}^{S}{V}_{s}^{{{{{{{{rm{env}}}}}}}}},{N}_{s},$$
(11)
$${E}_{gamma }longrightarrow mathop{sum }limits_{{s,{s}^{{prime} }!=!1}atop {{s}^{{prime} }ne s}}^{S}frac{{N}_{s}^{{alpha }_{s}},{gamma }_{s{s}^{{prime} }},{N}_{{s}^{{prime} }}^{{beta }_{{s}^{{prime} }}}}{{A}^{{alpha }_{s}+{beta }_{{s}^{{prime} }}-1}},$$
(12)
$${E}_{{{{{{{{rm{Res}}}}}}}}}longrightarrow Amathop{sum }limits_{k=1}^{K}{{{{{{{{mathcal{L}}}}}}}}}_{k}left({{{{{{{bf{N}}}}}}}}/A,, {R}_{k}/Aright).$$
(13)
Ecosystem equilibria from the DFTe energy functionalThe general form of Eq. (9) gives rise to two types of minimisers (viz., equilibria): First, we term$${{{{{{{mathcal{H}}}}}}}}({{{{{{{bf{N}}}}}}}})equiv E[tilde{{{{{{{{bf{n}}}}}}}}}]equiv mathop{min }limits_{{{{{{{{bf{n}}}}}}}}}left{E[{{{{{{{bf{n}}}}}}}}],left|,{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),{{{{{{{bf{n}}}}}}}}({{{{{{{bf{r}}}}}}}})={{{{{{{bf{N}}}}}}}},{{{{{{{rm{(fixed)}}}}}}}}right.right}$$
(14)
the ‘DFTe hypersurface’, with (tilde{{{{{{{{bf{n}}}}}}}}}) the energy-minimising spatial density profiles for given (fixed) N. Second, the ecosystem equilibrium is attained at the equilibrium abundances (hat{{{{{{{{bf{N}}}}}}}}}={int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),hat{{{{{{{{bf{n}}}}}}}}}({{{{{{{bf{r}}}}}}}})), which yield the global energy minimum$${{{{{{{mathcal{H}}}}}}}}(hat{{{{{{{{bf{N}}}}}}}}})=mathop{min }limits_{{{{{{{{bf{N}}}}}}}}},{{{{{{{mathcal{H}}}}}}}}({{{{{{{bf{N}}}}}}}}),$$
(15)
where the minimisation samples all admissible abundances, that is, ({{{{{{{bf{N}}}}}}}}in {left({{mathbb{R}}}_{0}^{+}right)}^{times S}) if no further constraints are imposed.The direct minimisation of E[n] is most practical for uniform systems, which only require us to minimise E(N) over an S-dimensional space of abundances. For the general nonuniform case, we adopt a two-step strategy that reflects Eqs. (14) and (15). First, we obtain the equilibrated density distributions on ({{{{{{{mathcal{H}}}}}}}}) for fixed N from the computational DPFT framework26,27,28,29,30,31. Second, a conjugate gradient descent searches ({{{{{{{mathcal{H}}}}}}}}({{{{{{{bf{N}}}}}}}})) for the global minimiser (hat{{{{{{{{bf{N}}}}}}}}}). Technically, we perform the computationally more efficient descent in μ-space. Local minima are frequently encountered, and we identify the best candidate for the global minimum from many individual runs that are initialised with random μ. Note that system realisations with energies close to the global minimum, especially local minima, are likely observable in reality, assuming that the system can equilibrate at all. There is always an equilibrium if the energy functional is bounded from below, together with the fact that the support (abundances/densities) of the energy functional is finite in any practical application. If some DFTe energy components are chosen (too) negative, the system can be unstable, in which case the energy functional has no minimum and is inappropriate for modelling the equilibrium in question. This means that another energy functional has to be considered, or, in the worst case, that DFTe is incapable of simulating this system. We also caution that no numerical optimisation algorithms for non-convex black-box functions can guarantee to find the global minimum, not even approximately. Without analytically available characteristics of the global minimum, all one may hope for are candidates of the minimiser, and those may not even be local minima—there is no way to be certain that an optimum proposed by a numerical optimisation algorithm is stable.Density-potential functional theory (DPFT) in Thomas–Fermi (TF) approximationDefining$${V}_{s}({{{{{{{bf{r}}}}}}}})={mu }_{s}-frac{delta {E}_{{{{{{{{rm{dis}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]}{delta {n}_{s}({{{{{{{bf{r}}}}}}}})}$$
(16)
for all s, we obtain the reversible Legendre transform$${E}_{{{{{{{{rm{dis}}}}}}}}}^{{{{{{{{rm{L}}}}}}}}}[{{{{{{{bf{V}}}}}}}}-{{{{{{{boldsymbol{mu }}}}}}}}]={E}_{{{{{{{{rm{dis}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+mathop{sum }limits_{s=1}^{S}{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),({V}_{s}-{mu }_{s}),{n}_{s}$$
(17)
of the dispersal energy and thereby supplement the total energy with the additional variables V:$$E[{{{{{{{bf{V}}}}}}}},, {{{{{{{bf{n}}}}}}}},, {{{{{{{boldsymbol{mu }}}}}}}}]({{{{{{{bf{N}}}}}}}})={E}_{{{{{{{{rm{dis}}}}}}}}}^{{{{{{{{rm{L}}}}}}}}}[{{{{{{{bf{V}}}}}}}}-{{{{{{{boldsymbol{mu }}}}}}}}]-{int}_{A}({{{{{{{rm{d}}}}}}}}{{{{{{{bf{r}}}}}}}}),{{{{{{{bf{n}}}}}}}}cdot ({{{{{{{bf{V}}}}}}}}-{{{{{{{{bf{V}}}}}}}}}^{{{{{{{{rm{env}}}}}}}}})+{E}_{{{{{{{{rm{int}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]+{{{{{{{boldsymbol{mu }}}}}}}}cdot {{{{{{{bf{N}}}}}}}}.$$
(18)
This density-potential functional is equivalent to (but more flexible than) the density-only functional E[n,  μ](N). The minimisers of E[n] are thus among the stationary points of Eq. (18) and are obtained by solving$${n}_{s}[{V}_{s}-{mu }_{s}]({{{{{{{bf{r}}}}}}}})=frac{delta {E}_{{{{{{{{rm{dis}}}}}}}}}^{{{{{{{{rm{L}}}}}}}}}[{V}_{s}-{mu }_{s}]}{delta {V}_{s}({{{{{{{bf{r}}}}}}}})}$$
(19)
and$${V}_{s}[{{{{{{{bf{n}}}}}}}}]({{{{{{{bf{r}}}}}}}})={V}_{s}^{{{{{{{{rm{env}}}}}}}}}({{{{{{{bf{r}}}}}}}})+frac{delta {E}_{{{{{{{{rm{int}}}}}}}}}[{{{{{{{bf{n}}}}}}}}]}{delta {n}_{s}({{{{{{{bf{r}}}}}}}})}$$
(20)
self-consistently for all ns while enforcing ∫A(dr) ns(r) = Ns. Specifically, starting from V(0) = Venv, such that ({n}_{s}^{(0)}={n}_{s}[{V}_{s}^{(0)}-{mu }_{s}^{(0)}]), we iterate$${n}_{s}^{(i)}mathop{longrightarrow }limits^{{{{{{{{rm{equation}}}}}}}},(20)}{V}_{s}^{(i+1)}={V}_{s}[{{{{{{{{bf{n}}}}}}}}}^{(i)}]mathop{longrightarrow }limits^{{{{{{{{rm{equation}}}}}}}},(19)}{n}_{s}^{(i+1)}=(1-{theta }_{s}),{n}_{s}^{(i)}+{theta }_{s},{n}_{s}left[{V}_{s}^{(i+1)}-{mu }_{s}^{(i+1)}right]$$
(21)
until all ns are converged sufficiently. This self-consistent loop establishes a trade-off between dispersal energy and effective environment V by forcing an initial out-of-equilibrium density distribution to equilibrate at fixed N. We adjust ({mu }_{s}^{(i)}) in each iteration i such that ({n}_{s}^{(i)}) integrates to Ns. Small enough density admixtures, with 0  More

Martins, L. C. et al. Poluição atmosférica e atendimentos por pneumonia e gripe em São Paulo, Brasil. Revista de Saúde Pública 36, 88–94 (2002).Article 
PubMed 

Google Scholar 
Goudarzi, G. et al. Health risk assessment on human exposed to heavy metals in the ambient air PM10 in Ahvaz, Southwest Iran. Int. J. Biometeorol. 62, 1075–1083 (2018).Article 
ADS 
PubMed 

Google Scholar 
Makri, A. & Stilianakis, N. I. Vulnerability to air pollution health effects. Int. J. Hygiene Environ. Health 211, 326–336 (2008).Article 

Google Scholar 
Idani, E. et al. Characteristics, sources, and health risks of atmospheric PM10-bound heavy metals in a populated Middle Eastern City. Toxin Rev. 39, 266–274 (2020).Article 

Google Scholar 
Wang, J., Hu, Z., Chen, Y., Chen, Z. & Xu, S. Contamination characteristics and possible sources of PM10 and PM2.5 in different functional areas of Shanghai, China. Atmos. Environ. 68, 221–229 (2013).Article 
ADS 
CAS 

Google Scholar 
Guarnieri, M. & Balmes, J. R. Outdoor air pollution and asthma. Lancet 383, 1581–1592 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, J. O., Thundiyil, J. G. & Stolbach, A. Clearing the air: A review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175 (2012).Article 
CAS 
PubMed 

Google Scholar 
Roy, D., Seo, Y.-C., Kim, S. & Oh, J. Human health risks assessment for airborne PM10-bound metals in Seoul, Korea. Environ. Sci. Pollut. Res. 26, 24247–24261 (2019).Article 
CAS 

Google Scholar 
Maesano, C. et al. Impacts on human mortality due to reductions in PM10 concentrations through different traffic scenarios in Paris, France. Sci. The Total. Environ. 698, 134257 (2020).Article 
ADS 
CAS 

Google Scholar 
Maleki, H., Sorooshian, A., Goudarzi, G., Nikfal, A. & Baneshi, M. M. Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014. Aeolian Res. 22, 135–140 (2016).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Medina, S., Le Tertre, A. & Saklad, M. The Apheis project: Air pollution and health—A European information system. Air Qual. Atmos. Heal. 2, 185–198 (2009).Article 

Google Scholar 
Medina, S., Plasencia, A., Ballester, F., Mücke, H. & Schwartz, J. Apheis: Public health impact of PM10 in 19 European cities. J. Epidemiol. Community Heal. 58, 831–836 (2004).Article 
CAS 

Google Scholar 
Pérez-Martínez, P. J., de Fátima Andrade, M. & de Miranda, R. M. Traffic-related air quality trends in São Paulo, Brazil. J. Geophys. Res. Atmos. 120, 6290–6304 (2015).Article 
ADS 

Google Scholar 
Sánchez-Ccoyllo, O. R. et al. Vehicular particulate matter emissions in road tunnels in Sao Paulo, Brazil. Environ. Monitoring Assess. 149, 241–249 (2009).Article 

Google Scholar 
Ribeiro, H. & de Assunção, J. V. Historical overview of air pollution in São Paulo Metropolitan Area, Brazil: Influence of mobile sources and related health effects. WIT Trans. Built Environ. 52,10 (2001).Bravo, M. A. & Bell, M. L. Spatial heterogeneity of PM10 and O3 in São Paulo, Brazil, and implications for human health studies. J. Air Waste Manag. Assoc. 61, 69–77 (2011).Article 
CAS 
PubMed 

Google Scholar 
De Freitas, E. D., Martins, L. D., da Silva Dias, P. L. & de Fátima Andrade, M. A simple photochemical module implemented in rams for tropospheric ozone concentration forecast in the metropolitan area of Sao Paulo, Brazil: Coupling and validation. Atmos. Environ. 39, 6352–6361 (2005).Article 
ADS 

Google Scholar 
Encalada-Malca, A. A., Cochachi-Bustamante, J. D., Rodrigues, P. C., Salas, R. & López-Gonzales, J. L. A spatio-temporal visualization approach of PM10 concentration data in Metropolitan Lima. Atmosphere 12, 609 (2021).Article 
ADS 

Google Scholar 
do Meio Ambiente, C. N. Institutes the national air quality control programee. Tech. Rep., Official Journal of the Federative Republic of Brazil (1989).do Meio Ambiente, C. N. Sets standards of primary and secondary air quality and even the criteria for acute episodes of air pollution. Tech. Rep., Official Journal of the Federative Republic of Brazil (1990).Artaxo, P. O estado da qualidade do ar no brasil. Work. Pap. WRI Brasil 32 (2021).Costa, A. F., Hoek, G., Brunekreef, B. & Ponce de Leon, A. C. Air pollution and deaths among elderly residents of Sao Paulo, Brazil: An analysis of mortality displacement. Environ. Health Perspectives 125, 349–354 (2017).Article 
CAS 

Google Scholar 
Bravo, M. A., Son, J., De Freitas, C. U., Gouveia, N. & Bell, M. L. Air pollution and mortality in São Paulo, Brazil: Effects of multiple pollutants and analysis of susceptible populations. J. Exposure Sci. Environ. Epidemiol. 26, 150–161 (2016).Article 
CAS 

Google Scholar 
Chiarelli, P. S. et al. The association between air pollution and blood pressure in traffic controllers in Santo André, São Paulo, Brazil. Environ. Res. 111, 650–655 (2011).Article 
CAS 

Google Scholar 
Ventura, L. M. B., de Oliveira Pinto, F., Soares, L. M., Luna, A. S. & Gioda, A. Forecast of daily PM2.5 concentrations applying artificial neural networks and holt-winters models. Air Qual. Atmos. Heal. 12, 317–325 (2019).Article 
CAS 

Google Scholar 
Leão, M. L. P., Zhang, L. & da Silva Júnior, F. M. R. Effect of particulate matter (PM2.5 and PM10) on health indicators: Climate change scenarios in a Brazilian Metropolis. Environ. Geochem. Heal. 44, 1–12 (2022).Habermann, M. & Gouveia, N. Application of land use regression to predict the concentration of inhalable particular matter in São Paulo City, Brazil. Engenharia Sanit. e Ambiental 17, 155–162 (2012).Article 

Google Scholar 
Braga, A. L. F., Pereira, L. A. A., Procópio, M., André, P. A. D. & Saldiva, P. H. D. N. Association between air pollution and respiratory and cardiovascular diseases in Itabira, Minas Gerais State. Brazil. Cadernos de Saúde Pública 23, S570–S578 (2007).Article 
PubMed 

Google Scholar 
Pinto, W. D. P., Reisen, V. A. & Monte, E. Z. Previsão da concentração de material particulado inalável, na região da grande vitória, ES, Brasil, utilizando o modelo sarimax. Engenharia Sanitária e Ambiental 23, 307–318 (2018).Article 

Google Scholar 
Schornobay-Lui, E. et al. Prediction of short and medium term PM10 concentration using artificial neural networks. Manag. Environ. Qual. An Int. J. 30, 414–436 (2018).Neto, P. S. D. M. et al. Neural-based ensembles for particulate matter forecasting. IEEE Access 9, 14470–14490 (2021).Article 

Google Scholar 
Albuquerque Filho, F. S. D., Madeiro, F., Fernandes, S. M., de Mattos Neto, P. S. & Ferreira, T. A. Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks. Química Nova 36, 783–789 (2013).Lei, T. M., Siu, S. W., Monjardino, J., Mendes, L. & Ferreira, F. Using machine learning methods to forecast air quality: A case study in Macao. Atmosphere 13, 1412 (2022).Article 
ADS 
CAS 

Google Scholar 
Yu, T. et al. Study on the regional prediction model of PM2.5 concentrations based on multi-source observations. Atmos. Pollut. Res. 13, 101363 (2022).Article 
CAS 

Google Scholar 
Li, J., Xu, G. & Cheng, X. Combining spatial pyramid pooling and long short-term memory network to predict PM2.5 concentration. Atmos. Pollut. Res. 13, 101309 (2022).Article 
CAS 

Google Scholar 
Cordova, C. H. et al. Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru. Sci. Rep. 11, 1–19 (2021).Article 

Google Scholar 
Plocoste, T., Calif, R. & Jacoby-Koaly, S. Temporal multiscaling characteristics of particulate matter PM10 and ground-level ozone O3 concentrations in caribbean region. Atmos. Environ. 169, 22–35 (2017).Article 
ADS 
CAS 

Google Scholar 
Calif, R. & Schmitt, F. G. Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm. Nonlinear Process. Geophys. 21, 379–392 (2014).Article 
ADS 

Google Scholar 
Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: The forecast package for r. J. Stat. Softw. 27, 1–22 (2008).Article 

Google Scholar 
Harvey, A. C. Forecasting, structural time series models and the Kalman filter (Cambridge University Press, 1990).Book 
MATH 

Google Scholar 
Zhang, G. P. Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003).Article 
MATH 

Google Scholar 
Liao, T. W. Clustering of time series data—A survey. Pattern Recognit. 38, 1857–1874 (2005).Article 
ADS 
MATH 

Google Scholar 
Bell, M. L., Samet, J. M. & Dominici, F. Time-series studies of particulate matter. Annu. Rev. Public Heal. 25, 247–280 (2004).Article 

Google Scholar 
Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice (OTexts, 2018).
Google Scholar 
Box, G. E., Hillmer, S. C. & Tiao, G. C. Analysis and modeling of seasonal time series. in Seasonal analysis of economic time series, 309–344 (NBER, 1978).Sulandari, W., Suhartono, Subanar & Rodrigues, P. C. Exponential smoothing on modeling and forecasting multiple seasonal time series: An overview. Fluctuation Noise Lett. 20, 2130003 (2021).Article 
ADS 

Google Scholar 
Rodrigues, P. C., Awe, O. O., Pimentel, J. S. & Mahmoudvand, R. Modelling the behaviour of currency exchange rates with singular spectrum analysis and artificial neural networks. Stats 3, 137–157 (2020).Article 

Google Scholar 
Sako, K., Mpinda, B. N. & Rodrigues, P. C. Neural networks for financial time series forecasting. Entropy 24, 657 (2022).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Coelho, Leite et al. Statistical and artificial neural networks models for electricity consumption forecasting in the Brazilian industrial sector. Energies 15, 588 (2022).Article 

Google Scholar 
Sulandari, W., Subanar, S., Lee, M. H. & Rodrigues, P. C. Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks. MethodsX 7, 101015 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Sulandari, W. et al. Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks. Energy 190, 116408 (2020).Article 

Google Scholar 
Rodrigues, P. C. & Mahmoudvand, R. The benefits of multivariate singular spectrum analysis over the univariate version. J. Frankl. Inst. 355, 544–564 (2018).Article 
MathSciNet 
MATH 

Google Scholar  More