Philippa Kaur

Edwards, R. B., Naylor, R. L., Higgins, M. M. & Falcon, W. P. Causes of Indonesia’s forest fires. World Dev. 127, 104717 (2020).Article 

Google Scholar 
Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Chang. Biol. 17, 798–818 (2011).Article 
ADS 

Google Scholar 
Page, S., et al. Tropical Fire Ecology Ch. 9 (Springer, 2009).Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 371, 20150176 (2016).Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 1–8 (2016).Article 

Google Scholar 
Kusumaningtyas, S. D. A. & Aldrian, E. Impact of the June 2013 Riau province Sumatera smoke haze event on regional air pollution. Environ. Res. Lett. 11, 075007 (2016).Article 
ADS 

Google Scholar 
Gaveau, D. L. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: Evidence from the 2013 Sumatran fires. Sci. Rep. 4, 1–7 (2014).Article 

Google Scholar 
Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640–643 (2016).Article 
ADS 

Google Scholar 
Posa, M. R. C., Wijedasa, L. S. & Corlett, R. T. Biodiversity and conservation of tropical peat swamp forests. Bioscience 61, 49–57 (2011).Article 

Google Scholar 
Harrison, M. E. & Rieley, J. O. Tropical peatland biodiversity and conservation in Southeast Asia. Mires Peat 22, 1–7 (2018).
Google Scholar 
Purnomo, H. et al. Fire economy and actor network of forest and land fires in Indonesia. For. Policy Econ. 78, 21–31 (2017).Article 

Google Scholar 
Wösten, J. H. M., Clymans, E., Page, S. E., Rieley, J. O. & Limin, S. H. Peat–water interrelationships in a tropical peatland ecosystem in Southeast Asia. CATENA 73, 212–224 (2008).Article 

Google Scholar 
Taufik, M., Setiawan, B. I. & Van Lanen, H. A. Increased fire hazard in human-modified wetlands in Southeast Asia. Ambio 48, 363–373 (2019).Article 

Google Scholar 
Taufik, M. et al. Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nat. Clim. Chang. 7, 428–431 (2017).Article 
ADS 

Google Scholar 
Fanin, T. & Werf, G. R. Precipitation–fire linkages in Indonesia (1997–2015). Biogeosciences 14, 3995–4008 (2017).Article 
ADS 

Google Scholar 
Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl. Acad. Sci. U.S.A. 113, 9204–9209 (2016).Article 
ADS 
CAS 

Google Scholar 
Hirano, T. et al. Effects of disturbances on the carbon balance of tropical peat swamp forests. Glob. Chang. Biol. 18, 3410–3422 (2012).Article 
ADS 

Google Scholar 
Ohkubo, S., Hirano, T. & Kusin, K. Influence of fire and drainage on evapotranspiration in a degraded peat swamp forest in Central Kalimantan Indonesia. J. Hydrol. 603, 126906 (2021).Article 

Google Scholar 
Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. D. & Mezbahuddin, S. Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun. Earth Environ. 1, 1–8 (2020).Article 

Google Scholar 
Lin, Y., Wijedasa, L. S. & Chisholm, R. A. Singapore’s willingness to pay for mitigation of transboundary forest-fire haze from Indonesia. Environ. Res. Lett. 12, 024017 (2017).Article 
ADS 

Google Scholar 
Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. & Mezbahuddin, S. ProbFire: A probabilistic fire early warning system for Indonesia. Nat. Hazards Earth Syst. Sci. 22, 303–322 (2022).Article 
ADS 

Google Scholar 
Taufik, M., Veldhuizen, A. A., Wösten, J. H. M. & van Lanen, H. A. J. Exploration of the importance of physical properties of Indonesian peatlands to assess critical groundwater table depths, associated drought and fire hazard. Geoderma 347, 160–169 (2019).Article 
ADS 

Google Scholar 
Sloan, S., Tacconi, L. & Cattau, M. E. Fire prevention in managed landscapes: Recent success and challenges in Indonesia. Mitig. Adapt. Strateg. Glob. Chang. 26, 1–30 (2021).Article 

Google Scholar 
Lestari, I., Murdiyarso, D. & Taufik, M. Rewetting tropical peatlands reduced net greenhouse gas emissions in Riau Province Indonesia. Forests 13, 505 (2022).Article 

Google Scholar 
Spessa, A. C. et al. Seasonal forecasting of fire over Kalimantan Indonesia. Nat. Hazards Earth Syst. Sci. 15, 429–442 (2015).Article 
ADS 

Google Scholar 
Mezbahuddin, M., Grant, R. F. & Hirano, T. How hydrology determines seasonal and interannual variations in water table depth, surface energy exchange, and water stress in a tropical peatland: Modeling versus measurements. J. Geophys. Res. Biogeosci. 120, 2132–2157 (2015).Article 

Google Scholar 
Mezbahuddin, M., Grant, R. F. & Hirano, T. Modelling effects of seasonal variation in water table depth on net ecosystem CO2 exchange of a tropical peatland. Biogeosciences 11, 577–599 (2014).Article 
ADS 

Google Scholar 
Cobb, A. R. & Harvey, C. F. Scalar simulation and parameterization of water table dynamics in tropical peatlands. Water Resour. Res. 55, 9351–9377 (2019).Article 
ADS 

Google Scholar 
Dadap, N. C., Cobb, A. R., Hoyt, A. M., Harvey, C. F. & Konings, A. G. Satellite soil moisture observations predict burned area in Southeast Asian peatlands. Environ. Res. Lett. 14, 094014 (2019).Article 
ADS 

Google Scholar 
Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra Indonesia. Geoderma 338, 410–421 (2019).Article 
ADS 

Google Scholar 
Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).Article 
ADS 
CAS 

Google Scholar 
Couwenberg, J. & Hooijer, A. Towards robust subsidence-based soil carbon emission factors for peat soils in south-east Asia, with special reference to oil palm plantations. Mires Peat 12, 1–13 (2013).
Google Scholar 
Khasanah, N. M. & van Noordwijk, M. Subsidence and carbon dioxide emissions in a smallholder peatland mosaic in Sumatra Indonesia. Mitig. Adapt. Strateg. Glob. Chang. 24, 147 (2019).Article 

Google Scholar 
Marwanto, S., Watanabe, T., Iskandar, W., Sabiham, S. & Funakawa, S. Effects of seasonal rainfall and water table movement on the soil solution composition of tropical peatland. Soil Sci. Plant Nutr. 64, 386–395 (2018).Article 
CAS 

Google Scholar 
Lubis, M. E. S. et al. Changes in water table depth in an oil palm plantation and its surrounding regions in Sumatra Indonesia. J. Agron. 13, 140–146 (2014).Article 

Google Scholar 
Page, S. E., Rieley, J. O. & Wüst, R. Developments in Earth Surface Processes (Volume 9) Ch. 3 (Elsevier, 2006).Haffiez, N. et al. Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion. Sci. Total Environ. 839, 156211 (2022).Article 
ADS 
CAS 

Google Scholar 
Grant, R. F., Desai, A. R. & Sulman, B. N. Modelling contrasting responses of wetland productivity to changes in water table depth. Biogeosciences 9, 4215–4231 (2012).Article 
ADS 
CAS 

Google Scholar 
Mezbahuddin, M., Grant, R. F. & Flanagan, L. B. Modeling hydrological controls on variations in peat water content, water table depth, and surface energy exchange of a boreal western Canadian fen peatland. J. Geophys. Res. Biogeosci. 121, 2216–2242 (2016).Article 

Google Scholar 
Dimitrov, D. D., Grant, R. F., Lafleur, P. M. & Humphreys, E. R. Modeling the effects of hydrology on gross primary productivity and net ecosystem productivity at Mer Bleue bog. J. Geophys. Res. Biogeosci. 116, G04010 (2011).Article 
ADS 

Google Scholar 
Dimitrov, D. D., Bhatti, J. S. & Grant, R. F. The transition zones (ecotone) between boreal forests and peatlands: Modelling water table along a transition zone between upland black spruce forest and poor forested fen in central Saskatchewan. Ecol. Modell. 274, 57–70 (2014).Article 

Google Scholar 
Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 

Google Scholar 
Hodnett, M. G. & Tomasella, J. Marked differences between van Genuchten soil water-retention parameters for temperate and tropical soils: A new water-retention pedo-transfer functions developed for tropical soils. Geoderma 108, 155–180 (2002).Article 
ADS 
CAS 

Google Scholar 
Funk, C. et al. The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).Article 

Google Scholar 
Osaki, M., Hirose, K., Segah, H. & Helmy, F. Tropical Peatland Ecosystems Ch. 9 (Springer, 2016).Razavi, S. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling. Environ. Modell. Softw. 144, 105159 (2021).Article 

Google Scholar  More

Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

Google Scholar 
Díaz, S., et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).Article 

Google Scholar 
Locke, H., et al. A Nature-Positive World: The Global Goal for Nature (Wildlife Conservation Society, 2020); https://library.wcs.org/doi/ctl/view/mid/33065/pubid/DMX3974900000.aspxOpen-ended Working Group on the Post-2020 Global Biodiversity Framework. First Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/3/3 (Convention on Biological Diversity, 2021).Open-Ended Working Group on the Post-2020 Global Biodiversity Framework. Draft Recommendation Submitted by the Co-Chairs CBD/WG2020/4/L.2-ANNEX (Convention on Biological Diversity, 2022).Environment Act 2021 (UK) (HM Government, 2021); https://www.legislation.gov.uk/ukpga/2021/30/contents/enactedBull, J. W. & Strange, N. The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798 (2018).Article 

Google Scholar 
Prendeville, S., Cherim, E. & Bocken, N. Circular cities: mapping six cities in transition. Environ. Innov. Soc. Transit. 26, 171–194 (2018).de Silva, G. C., Regan, E. C., Pollard, E. H. B. & Addison, P. F. E. The evolution of corporate no net loss and net positive impact biodiversity commitments: understanding appetite and addressing challenges. Bus. Strategy Environ. 28, 1481–1495 (2019).Article 

Google Scholar 
zu Ermgassen, S. O. S. E. et al. Exploring the ecological outcomes of mandatory biodiversity net gain using evidence from early‐adopter jurisdictions in England. Conserv. Lett. 14, e12820 (2021).Article 

Google Scholar 
McGlyn, J., et al. Science-Based Targets for Nature: Initial Guidance for Business (Science Based Targets Network, 2020); https://sciencebasedtargetsnetwork.org/resource-repository/zu Ermgassen, S. O. S. E. et al. Are corporate biodiversity commitments consistent with delivering ‘nature-positive’ outcomes? A review of ‘nature-positive’ definitions, company progress and challenges. J. Clean. Prod. 379, 134798 (2022).Article 

Google Scholar 
Addison, P. F. E., Bull, J. W. & Milner‐Gulland, E. J. Using conservation science to advance corporate biodiversity accountability. Conserv. Biol. 33, 307–318 (2019).Article 

Google Scholar 
Smith, T. et al. Biodiversity means business: reframing global biodiversity goals for the private sector. Conserv. Lett. 13, e12690 (2020).Article 

Google Scholar 
Maron, M. et al. Setting robust biodiversity goals. Conserv. Lett. https://doi.org/10.1111/conl.12816 (2021).Newing, H. & Perram, A. What do you know about conservation and human rights? Oryx 53, 595–596 (2019).Article 

Google Scholar 
Standard on Biodiversity Offsets (The Business and Biodiversity Offsets Programme, 2012).Arlidge, W. N. S., et al. A mitigation hierarchy approach for managing sea turtle captures in small-scale fisheries. Front. Mar. Sci. 7, 49 (2020).Squires, D. & Garcia, S. The least-cost biodiversity impact mitigation hierarchy with a focus on marine fisheries and bycatch issues. Conserv. Biol. 32, 989–997 (2018).Article 

Google Scholar 
Booth, H., Squires, D. & Milner-Gulland, E. J. The mitigation hierarchy for sharks: a risk-based framework for reconciling trade-offs between shark conservation and fisheries objectives. Fish Fish. 21, 269–289 (2020).Article 

Google Scholar 
Gupta, T. et al. Mitigation of elasmobranch bycatch in trawlers: a case study in Indian fisheries. Front. Mari. Sci. 7, 571 (2020).Budiharta, S. et al. Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices. Global Environ. Change 52, 152–161 (2018).Article 

Google Scholar 
Bull, J. W. et al. Net positive outcomes for nature. Nat. Ecol. Evol. 4, 4–7 (2020).Article 

Google Scholar 
Arlidge, W. N. S. et al. A global mitigation hierarchy for nature conservation. BioScience 68, 336–347 (2018).Article 

Google Scholar 
Milner-Gulland, E. J. et al. Four steps for the Earth: mainstreaming the post-2020 global biodiversity framework. One Earth 4, 75–87 (2021).Article 
ADS 

Google Scholar 
Wolff, A., Gondran, N. & Brodhag, C. Detecting unsustainable pressures exerted on biodiversity by a company. Application to the food portfolio of a retailer. J. Clean. Prod. 166, 784–797 (2017).Article 

Google Scholar 
FAOSTAT Analytical Brief 15 Land Use and Land Cover Statistics: Global, Regional and Country Trends, 1990–2018 (FAO, 2020).Williams, D. R. et al. Proactive conservation to prevent habitat losses to agricultural expansion. Nat. Sustain. 4, 314–322 (2021).Article 

Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
ADS 

Google Scholar 
Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

Google Scholar 
Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
ADS 
CAS 

Google Scholar 
Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).Article 

Google Scholar 
Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987 (2018).Article 
ADS 
CAS 

Google Scholar 
Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Article 
ADS 
CAS 

Google Scholar 
Benton, T. G. et al. A ‘net zero’ equivalent target is needed to transform food systems. Nat. Food 2, 905–906 (2021). 2021.Article 

Google Scholar 
Crenna, E., Sinkko, T. & Sala, S. Biodiversity impacts due to food consumption in Europe. J. Clean. Prod. 227, 378–391 (2019).Article 
CAS 

Google Scholar 
Bull, J. W., et al. Analysis: the biodiversity footprint of the University of Oxford. Nature 604, 420–424 (2022).Harrington, R. A., Adhikari, V., Rayner, M. & Scarborough, P. Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure. BMJ Open 9, e026652 (2019).Article 

Google Scholar 
Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
ADS 
CAS 

Google Scholar 
Winter, L., Lehmann, A., Finogenova, N. & Finkbeiner, M. Including biodiversity in life cycle assessment—state of the art, gaps and research needs. Environ. Impact Assess. Rev. 67, 88–100 (2017).Article 

Google Scholar 
Chaudhary, A. & Kastner, T. Land use biodiversity impacts embodied in international food trade. Global Environ. Change 38, 195–204 (2016).Article 

Google Scholar 
Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).Article 
ADS 
CAS 

Google Scholar 
Bates, B., et al. National Diet and Nutrition Survey Years 1 to 9 of the Rolling Programme (2008/2009–2016/2017): Time Trend and Income Analyses (Public Health England & Food Standards Agency, 2019).Stewart, C., Piernas, C., Cook, B. & Jebb, S. A. Trends in UK meat consumption: analysis of data from years 1–11 (2008–09 to 2018–19) of the National Diet and Nutrition Survey rolling programme. Lancet Planet. Health 5, e699–e708 (2021).Article 

Google Scholar 
Nielsen, K. S. et al. Improving climate change mitigation analysis: a framework for examining feasibility. One Earth 3, 325–336 (2020).Article 
ADS 

Google Scholar 
Selinske, M. J. et al. We have a steak in it: eliciting interventions to reduce beef consumption and its impact on biodiversity. Conserv. Lett. 13, e12721 (2020).Article 

Google Scholar 
Hollands, G. J. et al. The TIPPME intervention typology for changing environments to change behaviour. Nat. Hum. Behav. 1, 1–9 (2017).Article 

Google Scholar 
Marteau, T. M., Hollands, G. J. & Fletcher, P. C. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science 337, 1492–1495 (2012).Article 
ADS 
CAS 

Google Scholar 
Michie, S., van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).Article 

Google Scholar 
Moran, D., Giljum, S., Kanemoto, K. & Godar, J. From satellite to supply chain: new approaches connect earth observation to economic decisions. One Earth 3, 5–8 (2020).Article 
ADS 

Google Scholar 
Godar, J., Suavet, C., Gardner, T. A., Dawkins, E. & Meyfroidt, P. Balancing detail and scale in assessing transparency to improve the governance of agricultural commodity supply chains. Environ. Res. Lett. 11, 035015 (2016).Article 
ADS 

Google Scholar 
DeFries, R. S., Fanzo, J., Mondal, P., Remans, R. & Wood, S. A. Is voluntary certification of tropical agricultural commodities achieving sustainability goals for small-scale producers? A review of the evidence. Environ. Res. Lett. 12, 033001 (2017).Article 
ADS 

Google Scholar 
Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J. & Milner-Gulland, E. J. Biodiversity offsets in theory and practice. Oryx 47, 369–380 (2013).Article 

Google Scholar 
zu Ermgassen, S. O. S. E. et al. The ecological outcomes of biodiversity offsets under “no net loss” policies: a global review. Conserv. Lett. 12, e12664 (2019).Article 

Google Scholar 
Waddock, S. Achieving sustainability requires systemic business transformation. Glob. Sustain. 3, e12 (2020).Travers, H., Walsh, J., Vogt, S., Clements, T. & Milner-Gulland, E. J. Delivering behavioural change at scale: what conservation can learn from other fields. Biol. Conserv. 257, 109092 (2021).Article 

Google Scholar 
Gaupp, F. et al. Food system development pathways for healthy, nature-positive and inclusive food systems. Nat. Food 2, 928–934 (2021).Article 

Google Scholar 
Astill, J. et al. Transparency in food supply chains: a review of enabling technology solutions. Trends Food Sci. Technol. 91, 240–247 (2019).Article 
CAS 

Google Scholar 
Poore, J & Nemecek, T. Full Excel model: life-cycle environmental impacts of food drink products. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:a63fb28c-98f8-4313-add6-e9eca99320a5 (2018).Clark, M., et al. Estimating the environmental impacts of 57,000 food products. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).Clark, M., et al. Supplemental Data for ‘Estimating the environmental impacts of 57,000 food products’. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:4ad0b594-3e81-4e61-aefc-5d869c799a87 (2022).Bianchi, F., Dorsel, C., Garnett, E., Aveyard, P. & Jebb, S. A. Interventions targeting conscious determinants of human behaviour to reduce the demand for meat: a systematic review with qualitative comparative analysis. IJBNPA 15, 102 (2018).
Google Scholar 
Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P. & Jebb, S. A. Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. Lancet Planet. Health 2, e384–e397 (2018).Article 

Google Scholar 
Hillier-Brown, F. C. et al. The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review. Obes. Rev. 18, 227–246 (2017).Article 
CAS 

Google Scholar 
von Philipsborn, P. et al. Environmental interventions to reduce the consumption of sugar-sweetened beverages and their effects on health. Cochrane Database Syst. Rev. 6, Cd012292 (2019).
Google Scholar 
Attwood, S., Voorheis, P., Mercer, C., Davies, K. & Vennard, D. Playbook for Guiding Diners toward Plant-Rich Dishes in Food Service (World Resources Institute, 2020); https://www.wri.org/research/playbook-guiding-diners-toward-plant-rich-dishes-food-serviceGarnett, E. E., Balmford, A., Sandbrook, C., Pilling, M. A. & Marteau, T. M. Impact of increasing vegetarian availability on meal selection and sales in cafeterias. Proc. Natl Acad. Sci. USA 116, 20923 (2019).Article 
ADS 
CAS 

Google Scholar 
Reinders, M. J., Huitink, M., Dijkstra, S. C., Maaskant, A. J. & Heijnen, J. Menu-engineering in restaurants—adapting portion sizes on plates to enhance vegetable consumption: a real-life experiment. IJBNPA 14, 41 (2017).
Google Scholar 
Brunner, F., Kurz, V., Bryngelsson, D. & Hedenus, F. Carbon label at a university restaurant—label implementation and evaluation. Ecol. Econ. 146, 658–667 (2018).Article 

Google Scholar 
McClain, A. D., Hekler, E. B. & Gardner, C. D. Incorporating prototyping and iteration into intervention development: a case study of a dining hall-based intervention. J. Am. Coll. Health 61, 122–131 (2013).Article 

Google Scholar 
de Vaan, J. Eating Less Meat: How to Stimulate the Choice for a Vegetarian Option without Inducing Reactance. MSc thesis, Radboud Univ. (2018). More

Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73. https://doi.org/10.1038/nature22900 (2017).Article 
ADS 
CAS 

Google Scholar 
Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116. https://doi.org/10.1016/j.tree.2013.12.001 (2014).Article 

Google Scholar 
Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. J. Sci. Adv. 1, e1400253. https://doi.org/10.1126/sciadv.1400253 (2015).Article 
ADS 

Google Scholar 
Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, e197. https://doi.org/10.1371/journal.pbio.0020197 (2004).Article 

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

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 124–148 (2014).Article 

Google Scholar 
Bauer, H. et al. Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas. Proc. Natl. Acad. Sci. 112, 14895–14899 (2015).Article 
ADS 

Google Scholar 
Bauer, H., Page-Nicholson, S., Hinks, A. & Dickman, A. Guidelines for the Conservation of lion in Africa 17–24 (IUCN SSC Cat Specialist Group, 2018).
Google Scholar 
Lindsey, P. A., Roulet, P. A. & Romanach, S. S. Economic and conservation significance of the trophy hunting industry in sub-Saharan Africa. Biol. Conserv. 134, 455–469. https://doi.org/10.1016/j.biocon.2006.09.005 (2007).Article 

Google Scholar 
Vucetich, J. A. et al. The value of argument analysis for understanding ethical considerations pertaining to trophy hunting and lion conservation. Biol. Conserv. 235, 260–272. https://doi.org/10.1016/j.biocon.2019.04.012 (2019).Article 

Google Scholar 
Dube, N. Voices from the village on trophy hunting in Hwange district, Zimbabwe. Ecol. Econ. 159, 335–343. https://doi.org/10.1016/j.ecolecon.2019.02.006 (2019).Article 

Google Scholar 
Murombedzi, J. African wildlife and livelihoods. In The Promise and Performance of Community Conservation (eds Hulme, D. & Murphree, M.) 244–255 (James Currey, 2001).
Google Scholar 
Leader-Williams, N., Baldus, R. D. & Smith, R. J. Recreational hunting. In Conservation and Rural Livelihoods (eds Dickson, B. et al.) 296–316 (Blackwell Publishing Ltd., 2009).Chapter 

Google Scholar 
DiMinin, E., Leader-Williams, N. & Bradshaw, C. J. A. Banning trophy hunting will exacerbate biodiversity loss. Trends Ecol. Evol. 31, 99–102 (2016).Article 

Google Scholar 
Whitman, K., Starfield, A. M., Quadling, H. S. & Packer, C. Sustainable trophy hunting of African lions. Nature 428, 175–178 (2004).Article 
ADS 
CAS 

Google Scholar 
Packer, C. et al. Sport hunting, predator control and conservation of large carnivores. PLoS ONE 4, e5941. https://doi.org/10.1371/journal.pone.0005941 (2009).Article 
ADS 
CAS 

Google Scholar 
Mweetwa, T. et al. Quantifying lion (Panthera leo) demographic response following a three-year moratorium on trophy hunting. PLoS ONE 13, e0197030. https://doi.org/10.1371/journal.pone.0197030 (2018).Article 
CAS 

Google Scholar 
Loveridge, A. J. et al. Conservation of large predator populations: Demographic and spatial responses of African lions to the intensity of trophy hunting. Biol. Conserv. 204, 247–254. https://doi.org/10.1016/j.biocon.2016.10.024 (2016).Article 

Google Scholar 
Starfield, A. M., Shiell, J. D. & Smuts, G. L. Simulation of lion control strategies in a large game reserve. Ecol. Model. 13, 17–28 (1981).Article 

Google Scholar 
Venter, J. & Hopkins, M. E. Use of a simulation model in the management of a lion population. S. Afr. J. Wildl. Res. 18, 126–130 (1988).
Google Scholar 
Starfield, A. M. & Bleloch, A. L. Modelling the effect of contraception on part of the lion population in Etosha National Park. Applied Mathematic Dept. Report R3/82, Witwaterstrand University, South Africa. 7 (1982).Dickman, A., Becker, M., Begg, C., Loveridge, A. J. & Macdonald, D. W. Guidelines for the Conservation of Lions in Africa, Ch. 6 69–75 (IUCN SSC Cat Specialist Group, 2018).
Google Scholar 
Creel, S. et al. Assessing the sustainability of lion trophy hunting with recomendations for policy. Ecol. Appl. 26, 2347–2357. https://doi.org/10.1002/eap.1377 (2016).Article 

Google Scholar 
Barthold, J., Loveridge, A. J., Macdonald, D. W., Packer, C. & Colchero, F. Bayesian estimates of male and female African lion mortality for future use in population management. J. Appl. Ecol. 53, 295–304 (2016).Article 

Google Scholar 
Loveridge, A. J., Valeix, M., Elliot, N. B. & Macdonald, D. W. The landscape of anthropogenic mortality: How African lions respond to spatial variation in risk. J. Appl. Ecol. 54, 815–825. https://doi.org/10.1111/1365-2664.12794 (2017).Article 

Google Scholar 
Loveridge, A. J. et al. Evaluating the spatial intensity and demographic impacts of wire-snare bush-meat poaching on large carnivores. Biol. Conserv. 244, 108504 (2020).Article 

Google Scholar 
Becker, M. S. et al. Estimating past and future male loss in three Zambian lion populations. J. Wildl. Manag. 77, 128–142 (2013).Article 

Google Scholar 
Kiffner, C., Meyer, B., Muhlenberg, M. & Waltert, M. Plenty of prey, few predators: What limits lions Panthera leo in Katavi National park, western Tanzania?. Oryx 43, 52–59 (2009).Article 

Google Scholar 
Loveridge, A. J., Searle, A. W., Murindagomo, F. & Macdonald, D. W. The impact of sport hunting on the population dynamics of an African lion population in a protected area. Biol. Conserv. 134, 548–558 (2007).Article 

Google Scholar 
Miller, J. R. B. et al. Aging traits and sustainable trophy hunting of African lions. Biol. Conserv. 201, 160–168 (2016).Article 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
ADS 
CAS 

Google Scholar 
Gervasi, V., Linnell, J. D. C., Brøseth, H. & Gimenez, O. Failure to coordinate management in transboundary populations hinders the achievement of national management goals: The case of wolverines in Scandinavia. J. Appl. Ecol. 56, 1905–1915. https://doi.org/10.1111/1365-2664.13379 (2019).Article 

Google Scholar 
Breitenmoser, U. & Nobbe, C. Guidelines for the Conservation of Lions in Africa (ed IUCN CSG/SSC) 29–30 (IUCN, 2018).du Preez, B. & Lopez-Bao, J. V. Guidelines for the Conservation of the Lion in Africa (ed IUCN CSG/SSC) 76–78 (IUCN, 2018).Loveridge, A. J., Hemson, G., Davidson, Z. & Macdonald, D. W. African lions on the edge: reserve boundaries as ‘attractive sinks’ In Biology and Conservation of Wild Felids, Ch. 11 (eds Macdonald, D. W. & Loveridge, A. J.) 283–304 (Oxford University Press, London, 2010).

Google Scholar 
Borrego, N., Ozgul, A., Slotow, R. & Packer, C. Lion population dynamics: Do nomadic males matter?. Behav. Ecol. 29, 660–666. https://doi.org/10.1093/beheco/ary018%JBehavioralEcology (2018).Article 

Google Scholar 
Packer, C. et al. The case for fencing remains intact. Ecol. Lett. https://doi.org/10.1111/ele.12171 (2013).Balme, G. et al. Big cats at large: Density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267. https://doi.org/10.1002/1438-390x.1023 (2019).Article 

Google Scholar 
Grünewald, C., Schleuning, M. & Böhning-Gaese, K. Biodiversity, scenery and infrastructure: Factors driving wildlife tourism in an African savannah national park. Biol. Conserv. 201, 60–68. https://doi.org/10.1016/j.biocon.2016.05.036 (2016).Article 

Google Scholar 
Pulliam, H. R. Sources, sinks, and population. Regulation 132, 652–661. https://doi.org/10.1086/284880 (1988).Article 

Google Scholar 
Lamb, C. T. et al. The ecology of human–carnivore coexistence. Proc. Natl. Acad. Sci. 117, 17876–17883. https://doi.org/10.1073/pnas.1922097117 (2020).Article 
ADS 
CAS 

Google Scholar 
Robinson, H. S., Weilgus, R. B., Cooley, H. & Cooley, S. Source—sink populations in carnivore management: cougar demography and immigration in a hunted population. Ecol. Appl. 18, 1028–1037 (2008).Article 

Google Scholar 
Creel, S. et al. Questionable policy for large carnivore hunting. Science 350, 1473–1475 (2015).Article 
ADS 
CAS 

Google Scholar 
Cushman, S. A. et al. Prioritizing core areas, corridors and conflict hotspots for lion conservation in southern Africa. PLoS ONE 13, e0196213. https://doi.org/10.1371/journal.pone.0196213 (2018).Article 
CAS 

Google Scholar 
Kelly, M. J. & Durant, S. M. Viability of the Serengeti cheetah population. Conserv. Biol. 14, 786–797 (2000).Article 

Google Scholar 
Skalski, J. R., Ryding, K. & Millspaug, J. J. Wildlife Demography: Analysis of Sex, Age, and Count Data (Elsevier Academic Press, 2005).
Google Scholar 
Hamlin, K. L., Pac, D. F., Sime, C. A., DeSimone, R. M. & Dusek, G. L. Evaluating the accuracy of ages obtained by two methods for montana ungulates. J. Wildl. Manag. 64, 441–449. https://doi.org/10.2307/3803242 (2000).Article 

Google Scholar 
Storm, D. J. et al. Estimating ages of white-tailed deer: Age and sex patterns of error using tooth wear-and-replacement and consistency of cementum annuli. Wildl Soc Bull 38, 849–856. https://doi.org/10.1002/wsb.457 (2014).Article 
ADS 

Google Scholar 
Balme, G. A., Hunter, L. & Braczkowski, A. R. Applicability of age-based hunting regulations for African Leopards. PLoS ONE 7, e35209. https://doi.org/10.1371/journal.pone.0035209 (2012).Article 
ADS 
CAS 

Google Scholar 
Gipson, P. S., Ballard, W. B., Nowak, R. M. & Mech, L. D. Accuracy and precision of estimating age of gray wolves by tooth wear. J. Wildl. Manag. 64, 752–758. https://doi.org/10.2307/3802745 (2000).Article 

Google Scholar 
Hiller, T. L. Comparison of two age-estimation techniques for cougars. J. Northwest. Nat. 77–82, 76 (2014).
Google Scholar 
Begg, C. M., Miller, J. R. B. & Begg, K. S. Effective implementation of age restrictions increases selectivity of sport hunting of the African lion. J. Appl. Ecol. 55, 139–146. https://doi.org/10.1111/1365-2664.12951 (2018).Article 

Google Scholar 
Mandisodza-Chikerema, R., Jooste, D. & Funston, P. J. Lion aging and adaptive quota management report: Ages of lions hunted and recommended quotas for 2019 in Zimbabwe. 12 (Unpublished report, Zimbabwe Parks and Wildlife Management and Panthera, Harare, Zimbabwe, 2019).Smuts, G. L., Anderson, J. L. & Austin, J. C. Age determination of the African lion (Panthera leo). J. Zool. Lond. 185, 115–146 (1978).Article 

Google Scholar 
Lindsey, P. A. et al. The trophy hunting of African lions: Scale, current management practices and factors undermining sustainability. PLoS ONE 8, 1–11 (2013).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Packer, C. et al. Effects of trophy hunting on lion and leopard populations in Tanzania. Conserv. Biol. 25, 142–153 (2011).Article 
CAS 

Google Scholar 
Mace, G. M. & Reynolds, J. Exploitation as a conservation issue. In Conservation of Exploited Species, Ch. 1 (eds Reynolds, J. et al.) 3–15 (Cambridge University Press, Cambridge, 2001).
Google Scholar 
Struhsaker, T. T. A biologists perspective on the role of sustainable harvest in conservation. Conserv. Biol. 12, 930–932 (1998).Article 

Google Scholar  More

Humpenöder, F. et al. Projected environmental benefits of replacing beef with microbial protein. Nature 605, 90–96 (2022).Article 

Google Scholar 
Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).Article 
CAS 

Google Scholar 
Clark, M. A. et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science 370, 705–708 (2020).Article 
CAS 

Google Scholar 
Eisler, M. C. et al. Agriculture: steps to sustainable livestock. Nature 507, 32–34 (2014).Article 

Google Scholar 
Kamke, J. et al. Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation. Microbiome 4, 56 (2016).Article 

Google Scholar 
Kruger Ben Shabat, S. et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 10, 2958–2972 (2016).Article 

Google Scholar 
Janssen, P. H. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim. Feed Sci. Technol. 160, 1–22 (2010).Article 
CAS 

Google Scholar 
Wallace, R. J. et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5, eaav8391 (2019).Article 
CAS 

Google Scholar 
Urrutia, N. L. & Harvatine, K. J. Acetate dose-dependently stimulates milk fat synthesis in lactating dairy cows. J. Nutr. 147, 763–769 (2017).Article 
CAS 

Google Scholar 
Seshadri, R. et al. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nat. Biotechnol. 36, 359–367 (2018).Article 
CAS 

Google Scholar 
Anderson, C. J., Koester, L. R. & Schmitz-Esser, S. Rumen epithelial communities share a core bacterial microbiota: a meta-analysis of 16S rRNA Gene Illumina MiSeq sequencing datasets. Front. Microbiol. 12, 625400 (2021).Wallace, R. J., Cheng, K.-J., Dinsdale, D. & Ørskov, E. R. An independent microbial flora of the epithelium and its role in the ecomicrobiology of the rumen. Nature 279, 424–426 (1979).Article 
CAS 

Google Scholar 
Mann, E., Wetzels, S. U., Wagner, M., Zebeli, Q. & Schmitz-Esser, S. Metatranscriptome sequencing reveals insights into the gene expression and functional potential of rumen wall bacteria. Front. Microbiol. 9, 43 (2018).Pacífico, C. et al. Unveiling the bovine epimural microbiota composition and putative function. Microorganisms 9, 342 (2021).Article 

Google Scholar 
VanInsberghe, D., Arevalo, P., Chien, D. & Polz, M. F. How can microbial population genomics inform community ecology?. Phil. Trans. R. Soc. B 375, 20190253 (2020).Article 

Google Scholar 
Hunt, D. E. et al. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 320, 1081–1085 (2008).Article 
CAS 

Google Scholar 
Fraser, C., Hanage, W. P. & Spratt, B. G. Recombination and the nature of bacterial speciation. Science 315, 476–480 (2007).Article 
CAS 

Google Scholar 
Shapiro, B. J. et al. Population genomics of early events in the ecological differentiation of bacteria. Science 335, 48–51 (2012).Article 

Google Scholar 
Cadillo-Quiroz, H. et al. Patterns of gene flow define species of thermophilic Archaea. PLoS Biol. 10, e1001265 (2012).Article 
CAS 

Google Scholar 
Koeppel, A. et al. Identifying the fundamental units of bacterial diversity: a paradigm shift to incorporate ecology into bacterial systematics. Proc. Natl Acad. Sci. USA 105, 2504–2509 (2008).Article 
CAS 

Google Scholar 
Arevalo, P., VanInsberghe, D., Elsherbini, J., Gore, J. & Polz, M. F. A reverse ecology approach based on a biological definition of microbial populations. Cell 178, 820–834.e14 (2019).Article 
CAS 

Google Scholar 
Wetzels, S. U. et al. Epimural bacterial community structure in the rumen of Holstein cows with different responses to a long-term subacute ruminal acidosis diet challenge. J. Dairy Sci. 100, 1829–1844 (2017).Article 
CAS 

Google Scholar 
Neubauer, V. et al. Effects of clay mineral supplementation on particle-associated and epimural microbiota, and gene expression in the rumen of cows fed high-concentrate diet. Anaerobe 59, 38–48 (2019).Article 
CAS 

Google Scholar 
Stewart, R. D. et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nat. Biotechnol. 37, 953–961 (2019).Article 
CAS 

Google Scholar 
Waite, D. W. et al. Comparative genomic analysis of the class Epsilonproteobacteria and proposed reclassification to Epsilonbacteraeota (phyl. nov.). Front. Microbiol. 8, 682 (2017).Article 

Google Scholar 
Rodriguez-R, L. M. & Konstantinidis, K. T. Bypassing cultivation to identify bacterial species. Microbe Mag. 9, 111–118 (2014).Article 

Google Scholar 
Bendall, M. L. et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 10, 1589–1601 (2016).Article 

Google Scholar 
Birky, C. W., Adams, J., Gemmel, M. & Perry, J. Using population genetic theory and DNA sequences for species detection and identification in asexual organisms. PLoS ONE 5, e10609 (2010).Article 

Google Scholar 
Li, W.-H. Unbiased estimation of the rates of synonymous and nonsynonymous substitution. J. Mol. Evol. 36, 96–99 (1993).Article 
CAS 

Google Scholar 
Novichkov, P. S., Wolf, Y. I., Dubchak, I. & Koonin, E. V. Trends in prokaryotic evolution revealed by comparison of closely related bacterial and archaeal genomes. J. Bacteriol. 191, 65–73 (2009).Article 
CAS 

Google Scholar 
Tilman, D. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58, 338–348 (1977).Article 
CAS 

Google Scholar 
Yawata, Y. et al. Competition–dispersal tradeoff ecologically differentiates recently speciated marine bacterioplankton populations. Proc. Natl Acad. Sci. USA 111, 5622–5627 (2014).Article 
CAS 

Google Scholar 
Basan, M. et al. A universal trade-off between growth and lag in fluctuating environments. Nature 584, 470–474 (2020).Article 
CAS 

Google Scholar 
Flamholz, A., Noor, E., Bar-Even, A., Liebermeister, W. & Milo, R. Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc. Natl Acad. Sci. USA 110, 10039–10044 (2013).Article 
CAS 

Google Scholar 
Szymanski, C. M., Yao, R., Ewing, C. P., Trust, T. J. & Guerry, P. Evidence for a system of general protein glycosylation in Campylobacter jejuni. Mol. Microbiol. 32, 1022–1030 (1999).Article 
CAS 

Google Scholar 
Roux, D. et al. Identification of poly-N-acetylglucosamine as a major polysaccharide component of the Bacillus subtilis biofilm matrix. J. Biol. Chem. 290, 19261–19272 (2015).Article 
CAS 

Google Scholar 
Troutman, J. M. & Imperiali, B. Campylobacter jejuni PglH is a single active site processive polymerase that utilizes product inhibition to limit sequential glycosyl transfer reactions. Biochemistry 48, 2807–2816 (2009).Article 
CAS 

Google Scholar 
Hehemann, J. H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat. Commun. 7, 12860 (2016).Article 
CAS 

Google Scholar 
Treangen, T. J. & Rocha, E. P. C. Horizontal transfer, not duplication, drives the expansion of protein families in prokaryotes. PLoS Genet. 7, e1001284 (2011).Article 
CAS 

Google Scholar 
Castric, P. pilO, a gene required for glycosylation of Pseudomonas aeruginosa 1244 pilin. Microbiology 141, 1247–1254 (1995).Article 
CAS 

Google Scholar 
Mourkas, E. et al. Host ecology regulates interspecies recombination in bacteria of the genus Campylobacter. eLife 11, e73552 (2022).Article 
CAS 

Google Scholar 
Sheppard, S. K. et al. Genome-wide association study identifies vitamin B 5 biosynthesis as a host specificity factor in Campylobacter. Proc. Natl Acad. Sci. USA 110, 11923–11927 (2013).Article 
CAS 

Google Scholar 
Bobay, L.-M. & Ochman, H. Biological species are universal across life’s domains. Genome Biol. Evol. https://doi.org/10.1093/gbe/evx026 (2017).Dieho, K. et al. Morphological adaptation of rumen papillae during the dry period and early lactation as affected by rate of increase of concentrate allowance. J. Dairy Sci. 99, 2339–2352 (2016).Article 
CAS 

Google Scholar 
Lawson, C. E. et al. Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13C and 2H metabolic network mapping. ISME J. 15, 673–687 (2021).Article 
CAS 

Google Scholar 
Kwong, W. K., Zheng, H. & Moran, N. A. Convergent evolution of a modified, acetate-driven TCA cycle in bacteria. Nat. Microbiol. 2, 17067 (2017).Article 
CAS 

Google Scholar 
Kather, B., Stingl, K., van der Rest, M. E., Altendorf, K. & Molenaar, D. Another unusual type of citric acid cycle enzyme in Helicobacter pylori: the malate:quinone oxidoreductase. J. Bacteriol. 182, 3204–3209 (2000).Article 
CAS 

Google Scholar 
Mullins, E. A. & Kappock, T. J. Crystal structures of Acetobacter aceti succinyl-coenzyme A (CoA):acetate CoA-transferase reveal specificity determinants and illustrate the mechanism used by class I CoA-transferases. Biochemistry 51, 8422–8434 (2012).Article 
CAS 

Google Scholar 
Letten, A. D., Hall, A. R. & Levine, J. M. Using ecological coexistence theory to understand antibiotic resistance and microbial competition. Nat. Ecol. Evol. 5, 431–441 (2021).Article 

Google Scholar 
Park, S. Y. et al. Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite. Cell 185, 513–529.e21 (2022).Article 
CAS 

Google Scholar 
Kim, C. H. Control of lymphocyte functions by gut microbiota-derived short-chain fatty acids. Cell Mol. Immunol. 18, 1161–1171 (2021).Article 
CAS 

Google Scholar 
Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes 7, 189–200 (2016).Article 

Google Scholar 
Frampton, J., Murphy, K. G., Frost, G. & Chambers, E. S. Short-chain fatty acids as potential regulators of skeletal muscle metabolism and function. Nat. Metab. 2, 840–848 (2020).Article 
CAS 

Google Scholar 
Good, B. H., McDonald, M. J., Barrick, J. E., Lenski, R. E. & Desai, M. M. The dynamics of molecular evolution over 60,000 generations. Nature 551, 45–50 (2017).Article 

Google Scholar 
Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013).Article 
CAS 

Google Scholar 
Shapiro, B. J. & Polz, M. F. Microbial speciation. Cold Spring Harb. Perspect. Biol. 7, a018143 (2015).Article 

Google Scholar 
Sheppard, S. K. et al. Evolution of an agriculture-associated disease causing Campylobacter coli clade: evidence from national surveillance data in Scotland. PLoS ONE 5, e15708 (2010).Article 
CAS 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).Article 
CAS 

Google Scholar 
Pacífico, C. et al. Bovine rumen epithelial miRNA–mRNA dynamics reveals post-transcriptional regulation of gene expression upon transition to high-grain feeding and phytogenic supplementation. Genomics 114, 110333 (2022).Article 

Google Scholar 
Rivera-Chacon, R. et al. Supplementing a phytogenic feed additive modulates the risk of subacute rumen acidosis, rumen fermentation and systemic inflammation in cattle fed acidogenic diets. Animals 12, 1201 (2022).Article 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).Article 
CAS 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).Article 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
CAS 

Google Scholar 
Putri, G. H., Anders, S., Pyl, P. T., Pimanda, J. E. & Zanini, F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics 38, 2943–2945 (2022).Article 
CAS 

Google Scholar 
O’doherty, A. et al. Development of nalidixic acid amphotericin B vancomycin (NAV) medium for the isolation of Campylobacter ureolyticus from the stools of patients presenting with acute gastroenteritis. Br. J. Biomed. Sci. 71, 6–12 (2014).Article 

Google Scholar 
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).Article 
CAS 

Google Scholar 
Karst, S. M., Kirkegaard, R. H. & Albertsen, M. mmgenome: a toolbox for reproducible genome extraction from metagenomes. Preprint at bioRxiv https://doi.org/10.1101/059121 (2014).Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).Article 
CAS 

Google Scholar 
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 

Google Scholar 
Jukes, T. H. & Cantor, C. R. in Mammalian Protein Metabolism (ed. Munro, H. N.) 21–132 (Elsevier, 1969).Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).Article 
CAS 

Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).Article 
CAS 

Google Scholar 
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).Article 
CAS 

Google Scholar 
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).Article 
CAS 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).Article 
CAS 

Google Scholar 
Le, S. Q. & Gascuel, O. An improved general amino acid replacement matrix. Mol. Biol. Evol. 25, 1307–1320 (2008).Article 
CAS 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).Article 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).Article 
CAS 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Article 

Google Scholar 
Tan, R. S. G., Zhou, M., Li, F. & Guan, L. L. Identifying active rumen epithelial associated bacteria and archaea in beef cattle divergent in feed efficiency using total RNA-seq. Curr. Res. Microbial Sci. 2, 100064 (2021).Article 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics https://doi.org/10.1093/bioinformatics/btz848 (2019).Brewer, M. T., Anderson, K. L., Yoon, I., Scott, M. F. & Carlson, S. A. Amelioration of salmonellosis in pre-weaned dairy calves fed Saccharomyces cerevisiae fermentation products in feed and milk replacer. Vet. Microbiol. 172, 248–255 (2014).Article 

Google Scholar  More

Newmark, W. D. A land-bridge island perspective on mammalian extinctions in western North American parks. Nature 325, 430–432 (1987).Article 
ADS 
CAS 

Google Scholar 
Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).Article 

Google Scholar 
Radeloff, V. C. et al. Housing growth in and near United States protected areas limits their conservation value. Proc. Natl. Acad. Sci. U. S. A. 107, 940–945 (2010).Article 
ADS 
CAS 

Google Scholar 
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).Article 
CAS 

Google Scholar 
Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. https://doi.org/10.1126/sciadv.aay0814 (2020).Article 

Google Scholar 
Wasser, S. K. et al. Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science 349, 84–87 (2015).Article 
ADS 
CAS 

Google Scholar 
Davis, C. R. & Hansen, A. J. Trajectories in land use change around U,S. national parks and challenges and opportunities for management. Ecol. Appl. 21, 3299–3316 (2011).Article 

Google Scholar 
Newmark, W. D. Extinction of mammal populations in western North American national parks. Conserv. Biol. 9, 512–526 (1995).Article 

Google Scholar 
Newmark, W. D. Insularization of Tanzanian parks and the local extinction of large mammals. Conserv. Biol. 10, 1549–1556 (1996).Article 

Google Scholar 
Brashares, J. S., Arcese, P. & Sam, M. K. Human demography and reserve size predict wildlife extinction in West Africa. Proc. R. Soc. B Biol. Sci. 268, 2473–2478 (2001).Article 
CAS 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
ADS 
CAS 

Google Scholar 
Turner, M. G. & Dale, V. H. Comparing large, infrequent disturbances: What have we learned?. Ecosystems 1, 493–496 (1998).Article 

Google Scholar 
Berger, J. The last mile: How to sustain long-distance migration in mammals. Conserv. Biol. 18, 320–331 (2004).Article 

Google Scholar 
Bolger, D. T., Newmark, W. D., Morrison, T. A. & Doak, D. F. The need for integrative approaches to understand and conserve migratory ungulates. Ecol. Lett. 11, 63–77 (2008).
Google Scholar 
Sawyer, H., Kauffman, M. J., Nielson, R. M. & Horne, J. S. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecol. Appl. 19, 2016–2025 (2009).Article 

Google Scholar 
Tucker, M. A. et al. Moving in the anthropocene: Global reductions in terrestrial mammalian movements. Science 469, 466–469 (2018).Article 
ADS 

Google Scholar 
Soulé, M. E. & Terborgh, J. Conserving nature at regional and continental scales-a scientific program for North America. Bioscience 49, 809–817 (1999).Article 

Google Scholar 
Hilty, J. et al. Guidelines for conserving connectivity through ecological networks and corridors. Best Pract. Prot. Area Guidel. Ser. 30, 122 (2020).
Google Scholar 
Haddad, N. & Tewksbury, J. Impacts of corridors on populations and communities. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 390–415 (Cambridge University Press, 2010).
Google Scholar 
Ramiadantsoa, T., Ovaskainen, O., Rybicki, J. & Hanski, I. Large-scale habitat corridors for biodiversity conservation: A forest corridor in Madagascar. PLoS One 10, 1–18 (2015).Article 
CAS 

Google Scholar 
Newmark, W. D., Jenkins, C. N., Pimm, S. L., McNeally, P. B. & Halley, J. M. Targeted habitat restoration can reduce extinction rates in fragmented forests. Proc. Natl. Acad. Sci. USA. 114, 9635–9640 (2017).Article 
ADS 
CAS 

Google Scholar 
Diamond, J. M. Biogeographic kinetics: Estimation of relaxation times for avifaunas of southwest Pacific islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).Article 
ADS 
CAS 

Google Scholar 
Terborgh, J. Preservation of natural diversity: The problem of extinction prone species. Bioscience 24, 715–722 (1974).Article 

Google Scholar 
Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt revisited. Nature 371, 65–66 (1994).Article 
ADS 

Google Scholar 
Halley, J. M., Monokrousos, N., Mazaris, A. D., Newmark, W. D. & Vokou, D. Dynamics of extinction debt across five taxonomic groups. Nat. Commun. 7, 1–6 (2016).Article 

Google Scholar 
Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian amazon. Science 337, 228–232 (2012).Article 
ADS 
CAS 

Google Scholar 
Hanski, I. Extinction debt and species credit in boreal forests: Modelling the consequences of different approaches to conservation. Ann. Zool. Fennici 37, 271–280 (2000).
Google Scholar 
LaBarbera, M. Analyzing body size as a factor in ecology and evolution. Annu. Rev. Ecol. Syst. 20, 97–117 (1989).Article 

Google Scholar 
Oakleaf, J. K. et al. Habitat selection by recolonizing wolves in the northern Rocky mountains of the United States. J. Wildl. Manage. 70, 554–563 (2006).Article 

Google Scholar 
Cushman, S. A., McKelvey, K. S. & Schwartz, M. K. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23, 368–376 (2009).Article 

Google Scholar 
Schwartz, M. K. et al. Wolverine gene flow across a narrow climatic niche. Ecology 90, 3222–3232 (2014).Article 

Google Scholar 
McKelvey, K. S. et al. Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors. Ecol. Appl. 21, 2882–2897 (2011).Article 

Google Scholar 
Carroll, C., Mcrae, B. H. & Brookes, A. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conserv. Biol. 26, 78–87 (2012).Article 

Google Scholar 
Parks, S. A., McKelvey, K. S. & Schwartz, M. K. Effects of weighting schemes on the identification of wildlife corridors generated with least-cost methods. Conserv. Biol. 27, 145–154 (2013).Article 

Google Scholar 
Peck, C. P. et al. Potential paths for male-mediated gene flow to and from an isolated grizzly bear population. Ecosphere 8, e01969 (2017).Article 

Google Scholar 
Wild Migrations: Atlas of Wyoming’s Ungulates. (Oregon State University, 2018).Singleton, P. H., Gaines, W. L. & Lehmkuhl, J. F. Landscape permeability for large carnivores in Washington: A geographic information system weighted-distance and least-cost corridor assessment. (2002).Long, R. A. et al. The Cascades carnivore connectivity project: A landscape genetic assessment of connectivity in Washington’s north Cascades ecosystem. Final report for the Seattle City Light Wildlife Research Program (2013).Diamond, J. M. The island dilemma: Lessons of modern biogeographic studies for the design of natural reserves. Biol. Conserv. 7, 129–146 (1975).Article 

Google Scholar 
Wilson, E. O. & Willis, E. O. Applied biogeography. In Ecological structure of ecological communities (eds. Cody, M. L, & Diamond, J. M.) 522–534 (Harvard University Press, 1975)
Google Scholar 
Halley, J. M. & Iwasa, Y. Neutral theory as a predictor of avifaunal extinctions after habitat loss. Proc. Natl. Acad. Sci. USA 108, 2316–2321 (2011).Article 
ADS 
CAS 

Google Scholar 
Cushman, S. A., Lewis, J. S. & Landguth, E. L. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov. Ecol. 1, 1–11 (2013).Article 

Google Scholar 
Singleton, P. H. & Lehmkuhl, J. F. I-90 Snoqualmie pass wildlife habitat linkage assessment. Final Report. USDA, Pacific Northwest Research Station. (2000).Craighead, L., Craighead, A., Oeschslia, L. & Kociolek, A. Bozeman pass post-fencing wildlife monitoring. Final Report. FHWA/MT-10-006/8173 (2011).Andis, A. Z., Huijser, M. P. & Broberg, L. Performance of arch-style road crossing structures from relative movement rates of large mammals. Front. Ecol. Evol. 5, 1–13 (2017).Article 

Google Scholar 
Millward, L. Small mammal microhabitat use and species composition at a wildlife crossing structure compared with nearby forest (Central Washington University, 2018).
Google Scholar 
Bischof, R., Steyaert, S. M. J. G. & Kindberg, J. Caught in the mesh: Roads and their network-scale impediment to animal movement. Ecography 40, 1369–1380 (2017).Article 

Google Scholar 
Balkenhol, N. & Waits, L. P. Molecular road ecology: Exploring the potential of genetics for investigating transportation impacts on wildlife. Mol. Ecol. 18, 4151–4164 (2009).Article 

Google Scholar 
Clevenger, A. P. & Wierzchowski, J. Maintaining and restoring connectivity in landscapes fragmented by roads. In Connectivity Conservation, (eds. Crooks, K. R. & Sanjayan, M.) 502–535 (Cambridge University Press, 2010.)
Google Scholar 
Sawaya, M. A., Kalinowski, S. T. & Clevenger, A. P. Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proc. R. Soc. B Biol. Sci. 281, 20131705 (2014).Article 

Google Scholar 
Sawaya, M. A., Clevenger, A. P. & Schwartz, M. K. Demographic fragmentation of a protected wolverine population bisected by a major transportation corridor. Biol. Conserv. 236, 616–625 (2019).Article 

Google Scholar 
Kamal, S., Grodzińska-Jurczak, M. & Brown, G. Conservation on private land: A review of global strategies with a proposed classification system. J. Environ. Plan. Manag. 58, 576–597 (2015).Article 

Google Scholar 
Wasserman, T. N., Cushman, S. A., Littell, J. S., Shirk, A. J. & Landguth, E. L. Population connectivity and genetic diversity of American marten (Martes americana) in the United States northern Rocky Mountains in a climate change context. Conserv. Genet. 14, 529–541 (2013).Article 

Google Scholar 
Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).Article 

Google Scholar 
Cushman, S. A., Landguth, E. L. & Flather, C. H. Evaluating the sufficiency of protected lands for maintaining wildlife population connectivity in the U.S. northern Rocky Mountains. Divers. Distrib. 18, 873–884 (2012).Article 

Google Scholar 
Beier, P., Spencer, W., Baldwin, R. F. & Mcrae, B. H. Toward best practices for developing regional connectivity maps. Conserv. Biol. 25, 879–892 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2020). More

Chase, J. M. et al. The interaction between predation and competition: A review and synthesis. Ecol. Lett. 5, 302–315. https://doi.org/10.1046/j.1461-0248.2002.00315.x (2002).Article 

Google Scholar 
Droge, E., Creel, S., Becker, M. S. & M’Soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128. https://doi.org/10.1038/s41559-017-0220-9 (2017).Article 

Google Scholar 
Allesina, S. & Levine Jonathan, M. A competitive network theory of species diversity. Proc. Natl. Acad. Sci. U.S.A. 108, 5638–5642. https://doi.org/10.1073/pnas.1014428108 (2011).Article 
ADS 

Google Scholar 
Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285. https://doi.org/10.1038/ncomms12285 (2016).Article 
ADS 
CAS 

Google Scholar 
Letten, A. D. & Stouffer, D. B. The mechanistic basis for higher-order interactions and non-additivity in competitive communities. Ecol. Lett. 22, 423–436. https://doi.org/10.1111/ele.13211 (2019).Article 

Google Scholar 
Lotka, A. J. Elements of physical biology. Sci. Prog. Twent. Century (1919–1933) 21, 341–343 (1926).
Google Scholar 
Volterra, V. Variazioni e Fluttuazioni del Numero d’Individui in Specie Animali Conviventi. (Società Anonima Tipografica “Leonardo da Vinci”, 1926).Schmitz, O. J. Top predator control of plant biodiversity and productivity in an old-field ecosystem. Ecol. Lett. 6, 156–163. https://doi.org/10.1046/j.1461-0248.2003.00412.x (2003).Article 

Google Scholar 
Fey, K., Banks, P. B., Oksanen, L. & Korpimäki, E. Does removal of an alien predator from small islands in the Baltic Sea induce a trophic cascade?. Ecography 32, 546–552. https://doi.org/10.1111/j.1600-0587.2008.05637.x (2009).Article 

Google Scholar 
Terborgh John, W. Toward a trophic theory of species diversity. Proc. Natl. Acad. Sci. U.S.A. 112, 11415–11422. https://doi.org/10.1073/pnas.1501070112 (2015).Article 
ADS 
CAS 

Google Scholar 
Pringle, R. M. et al. Predator-induced collapse of niche structure and species coexistence. Nature 570, 58–64. https://doi.org/10.1038/s41586-019-1264-6 (2019).Article 
ADS 
CAS 

Google Scholar 
Sandom, C. et al. Mammal predator and prey species richness are strongly linked at macroscales. Ecology 94, 1112–1122. https://doi.org/10.1890/12-1342.1 (2013).Article 

Google Scholar 
Louette, G. & De Meester, L. Predation and priority effects in experimental zooplankton communities. Oikos 116, 419–426. https://doi.org/10.1111/j.2006.0030-1299.15381.x (2007).Article 

Google Scholar 
Johnston, N. K., Pu, Z. & Jiang, L. Predator identity influences metacommunity assembly. J. Anim. Ecol. 85, 1161–1170. https://doi.org/10.1111/1365-2656.12551 (2016).Article 

Google Scholar 
Karakoc, C., Radchuk, V., Harms, H. & Chatzinotas, A. Interactions between predation and disturbances shape prey communities. Sci. Rep. 8, 2968. https://doi.org/10.1038/s41598-018-21219-x (2018).Article 
ADS 
CAS 

Google Scholar 
Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) (Princeton University Press, 2011).Book 

Google Scholar 
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 2001).Book 

Google Scholar 
Daniel, J., Gleason, J. E., Cottenie, K. & Rooney, R. C. Stochastic and deterministic processes drive wetland community assembly across a gradient of environmental filtering. Oikos 128, 1158–1169. https://doi.org/10.1111/oik.05987 (2019).Article 

Google Scholar 
Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028 (2004).Article 
ADS 

Google Scholar 
Chase, J. M., Biro, E. G., Ryberg, W. A. & Smith, K. G. Predators temper the relative importance of stochastic processes in the assembly of prey metacommunities. Ecol. Lett. 12, 1210–1218. https://doi.org/10.1111/j.1461-0248.2009.01362.x (2009).Article 

Google Scholar 
Werner, E. E. & Peacor, S. D. A review of trait-mediated indirect interactions in ecological communities. Ecology 84, 1083–1100. https://doi.org/10.1890/0012-9658(2003)084[1083:AROTII]2.0.CO;2 (2003).Article 

Google Scholar 
Pearson, D. E., Ortega, Y. K., Eren, Ö. & Hierro, J. L. Community assembly theory as a framework for biological invasions. Trends Ecol. Evol. 33, 313–325. https://doi.org/10.1016/j.tree.2018.03.002 (2018).Article 

Google Scholar 
Duchesne, É. et al. Variable strength of predator-mediated effects on species occurrence in an arctic terrestrial vertebrate community. Ecography 44, 1236–1248. https://doi.org/10.1111/ecog.05760 (2021).Article 

Google Scholar 
Ryberg, W. A., Smith, K. G. & Chase, J. M. Predators alter the scaling of diversity in prey metacommunities. Oikos 121, 1995–2000. https://doi.org/10.1111/j.1600-0706.2012.19620.x (2012).Article 

Google Scholar 
Carrete Vega, G. & Wiens, J. J. Why are there so few fish in the sea?. Proc. R. Soc. B 279, 2323–2329. https://doi.org/10.1098/rspb.2012.0075 (2012).Article 

Google Scholar 
Barrett, M. et al. Living planet report 2018: Aiming higher. (2018).Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873. https://doi.org/10.1111/brv.12480 (2019).Article 

Google Scholar 
Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42. https://doi.org/10.1016/j.gecco.2017.01.008 (2017).Article 

Google Scholar 
Hammerschlag, N. et al. Ecosystem function and services of aquatic predators in the anthropocene. Trends Ecol. Evol. 34, 369–383. https://doi.org/10.1016/j.tree.2019.01.005 (2019).Article 

Google Scholar 
Wang, T. et al. Amur tigers and leopards returning to China: direct evidence and a landscape conservation plan. Landsc Ecol 31, 491–503. https://doi.org/10.1007/s10980-015-0278-1 (2016).Article 

Google Scholar 
Hong, S. et al. Stream health, topography, and land use influences on the distribution of the Eurasian otter Lutra lutra in the Nakdong River basin, South Korea. Ecol. Indic. 88, 241–249. https://doi.org/10.1016/j.ecolind.2018.01.004 (2018).Article 

Google Scholar 
Guter, A., Dolev, A., Saltz, D. & Kronfeld-Schor, N. Using videotaping to validate the use of spraints as an index of Eurasian otter (Lutra lutra) activity. Ecol. Indic. 8, 462–465. https://doi.org/10.1016/j.ecolind.2007.04.009 (2008).Article 

Google Scholar 
Sittenthaler, M., Bayerl, H., Unfer, G., Kuehn, R. & Parz-Gollner, R. Impact of fish stocking on Eurasian otter (Lutra lutra) densities: A case study on two salmonid streams. Mamm. Biol. 80, 106–113. https://doi.org/10.1016/j.mambio.2015.01.004 (2015).Article 

Google Scholar 
Zheng, B., Huang, H., Zhang, Y. & Dai, D. The Fishes of Tumen River (Jilin People’s Publishing House, 1980).
Google Scholar 
Fleishman, E., Murphy, D. D. & Brussard, P. F. A new method for selection of umbrella species for conservation planning. Ecol Appl 10, 569–579. https://doi.org/10.1890/1051-0761(2000)010[0569:ANMFSO]2.0.CO;2 (2000).Article 

Google Scholar 
Roberge, J.-M. & Angelstam, P. E. R. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18, 76–85. https://doi.org/10.1111/j.1523-1739.2004.00450.x (2004).Article 

Google Scholar 
McGowan, J. et al. Conservation prioritization can resolve the flagship species conundrum. Nat. Commun. 11, 994. https://doi.org/10.1038/s41467-020-14554-z (2020).Article 
ADS 
CAS 

Google Scholar 
Katano, I., Doi, H., Eriksson, B. K. & Hillebrand, H. A cross-system meta-analysis reveals coupled predation effects on prey biomass and diversity. Oikos 124, 1427–1435. https://doi.org/10.1111/oik.02430 (2015).Article 

Google Scholar 
Leibold, M. A. A graphical model of keystone predators in food webs: Trophic regulation of abundance, incidence, and diversity patterns in communities. Am. Nat. 147, 784–812. https://doi.org/10.1086/285879 (1996).Article 

Google Scholar 
McPeek, M. A. The consequences of changing the top predator in a food web: A comparative experimental approach. Ecol. Monogr. 68, 1–23. https://doi.org/10.1890/0012-9615(1998)068[0001:TCOCTT]2.0.CO;2 (1998).Article 

Google Scholar 
Chase, J. M. & Leibold, M. A. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, 2003).Book 

Google Scholar 
Gravel, D., Canham, C. D., Beaudet, M. & Messier, C. Reconciling niche and neutrality: The continuum hypothesis. Ecol. Lett. 9, 399–409. https://doi.org/10.1111/j.1461-0248.2006.00884.x (2006).Article 

Google Scholar 
Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306. https://doi.org/10.1038/nature01767 (2003).Article 
ADS 
CAS 

Google Scholar 
Yin, X., Wang, J., Yin, H. & Ruan, Y. Does inducible defense mitigate physiological stress responses of prey to predation risk?. Hydrobiologia 843, 173–181. https://doi.org/10.1007/s10750-019-04046-7 (2019).Article 

Google Scholar 
Chalcraft, D. R. & Resetarits, W. J. Jr. Predator identity and ecological impacts: Functional redundancy or functional diversity?. Ecology 84, 2407–2418. https://doi.org/10.1890/02-0550 (2003).Article 

Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758. https://doi.org/10.1111/j.1461-0248.2006.00924.x (2006).Article 

Google Scholar 
Burner, R. C. et al. Functional structure of European forest beetle communities is enhanced by rare species. Biol. Conserv. 267, 109491. https://doi.org/10.1016/j.biocon.2022.109491 (2022).Article 

Google Scholar  More

Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, ChinaQian Zhao, Yao Zhang & Shilong PiaoSchool of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengKey Laboratory of Earth Surface System and Human—Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengDepartment of Earth and Environment, Boston University, Boston, MA, USARanga B. MyneniCSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Catalonia, SpainJosep PeñuelasCREAF, Barcelona, Catalonia, SpainJosep PeñuelasState Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaShilong Piao More

Ethics statementAnimals’ care was in accordance with institutional guidelines. Ethical permit was issued by Malmö-Lund djurförsöksetiska nämnd 5.8.18-00848/2018.Field workWe carried out the field work in Sweden during four breeding seasons (2018–2021). Adult male willow warblers were captured in their breeding territories using mist nets and playback of a song. From each bird, we collected the innermost primary feather from the right wing. From the birds that returned with a logger we also collected ~20 μl of blood from the brachial wing vein. The blood was stored in SET buffer (0.015 M NaCl, 0.05 M Tris, 0.001 M of EDTA, pH 8.0) at room temperature until deposited for permanent storage at −20 °C. We deployed Migrate Technology Ltd geolocators (Intigeo-W30Z11-DIP 12 × 5 × 4 mm, 0.32 g) and used a nylon string to mount them on birds with the “leg-loop” harness method as outlined in our previous work24. The mass of the logger relative to that of the bird was on average 3.3% (range 2.7–3.8%).The tagged birds were ringed with a numbered aluminum ring, and two, colored plastic rings for later identification in the field. In total, we tagged 466 males (349 in 2018 and 117 in 2020) at breeding territories. During the first tagging season (2018), birds were trapped at 17 locations (average 22 birds per site; range 7–30) distributed across Sweden (Fig. S1). Three of the sites were in southern Sweden to document migration routes of allopatric trochilus and three sites were located above the Arctic circle to record migratory routes of allopatric acredula, whereas the remaining (239) loggers were spread over 11 sites located in the migratory divide. Given the observed densities and distribution of hybrids after analyzing returning birds in 2019, we deployed 117 more loggers at one single site (63.439°N, 14.831°E) in 2020. We successfully retrieved tracks from 57 birds tagged in 2019 and 16 from birds tagged in 2021. In search for birds with loggers, we checked circa 3000 willow warbler males and covered an area of at least 0.5 km radius around each site the year after tagging.Geolocator data treatmentThe R package GeoLight (version 2.0)25 was used to extract and analyze locations from raw geolocator data. All twilight events were obtained with light threshold of 3 lux. The most extreme outliers were trimmed with “loessFilter” function and a K value of 3. We used GeoLight’s function “getElevation” for estimating the sun elevation angle for the breeding period: these sets of locations were used to infer the positions for autumn departure direction. In addition, we carried out a “Hill-Ekström” calibration for the longest stationary winter site during the period before the spring equinox. Winter calibration produced location sets that better reflected the winter coordinates of the main winter site in sub-Saharan Africa26. We reduced some of the inherent geolocation “noise” by applying cantered 5-day rolling means to the coordinates. The equinox periods were visually identified by inspecting standard deviations in latitude. Latitudes from equinox periods were omitted (on average autumn equinox obscured data for 45 days (range 25–68). For the main winter site, we used the longest period at which bird stayed stationary and from which in all cases begun the spring migration (mean = 118, SD = 23 days). Timing of autumn departure was estimated by manual inspection of longitudes and latitudes plotted in time series. To estimate at which longitude the birds crossed the Mediterranean, we extracted the longitude when birds crossed latitude 35 N° (Mediterranean crossing longitude). For 29 birds, it was possible to directly extract the longitude at crossing latitude 35 N°. For the rest of the cases, the birds had not reached latitude 35 N° before the latitude was obscured by the equinox, we calculated the mean longitude of 10 days from the onset of fall equinox as a measure of the Mediterranean crossing. This measurement correlated highly with the winter longitude (r = 0.78, p = 2.8 × 10−16). To control for the birds relative breeding site longitude, we extracted the departure direction (1°–360°) relative from the tagging site to the location where the birds crossed the Mediterranean (departure direction). The departure data was of circular type (measured in 360°), however the variance did not span more than 180° degrees (range 151°–224°). Therefore, we proceeded with analyses using linear statistics. Geographic distances and departure direction were calculated using R package “geosphere” (version 1.5-10). Complete set of positions of each individual bird with equinoxes excluded is presented in Supplementary Data 1.Laboratory work and molecular data extractionWe extracted DNA from blood samples following the ammonium acetate protocol16. Genotyping for divergent regions on chromosome 1 (InvP-Ch1) and chromosome 5 (InvP-Ch5) was done using a qPCR SNP assay16, which is based on one informative SNP per region (SNP 65 for chromosome 1 and SNP 285 for chromosome 5). Probes and primers were produced by Thermo Fisher Scientific and were designed using the online Custom TaqMan® Assay Design tool (Table S4). We used Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) and the universal Fast-two-steps protocol: 95 °C, 15 min—40*(95 °C, 10 s–60 °C, 30 s, plate read. Both regions contain inversion polymorphisms that restrict recombination between subspecies-specific haplotypes and contain nearly all the SNPs separating the two subspecies13. For each region, we scored genotypes as either “Tro” (homozygous for trochilus haplotypes), “Acr” (homozygous for acredula haplotypes) or “Het” (heterozygous). The method that we used to assess the presence of MARB-a is based on a qPCR assay that quantifies the copy number of a novel TE (previously known as AFLP-WW212) that has expanded in acredula. The quantification of repeats by this method has been shown to be highly repeatable (R2 = 0.88) when comparing estimates obtained from DNA in blood and feathers15. We used the forward (5′-CCTTGCATACTTCTATTTCTCCC-3′) and reverse (5′-CATAGGACAGACATTGTTGAGG-3′) primers developed by Caballero-López et al.15 to amplify the TE motif. For reference of a single copy region we used the primers SFRS3F and SFRS3R27. We diluted DNA to 1 ng/μl−1 and used a Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) with SYBR-green-based detection. Total reaction volume was 25 μl of which 4 μl of DNA, 12.5 μl of SuperMix, 0.1 μl ROX, 1 μl of primer (forward and reverse), and 6.4 μl of double distilled H2O. We ran quantifications of the single copy gene and the TE variant found on MARB-a on separate plates with the following settings: 50 °C for 2 min as initial incubation, 95 °C for 2 min X 43 (94 °C for 30 s [55.3 °C SFRS3 and 55.5 °C for TE, 30 s] and 72 °C for 45 s). Each sample was run in duplicate and together with a two-fold serial standard dilution (2.5–7.8 × 10−2 ng). Allopatric trochilus have 0–6 copies whereas allopatric acredula have 8–45 copies15; a bimodal distribution was also confirmed in this new data set (Fig. S2). Accordingly, for the present analyses, we split the data in two groups: birds with ≤6 TE copies and birds with >7, translating into absence or presence of MARB-a, with the former assumed to be homozygous for the absence of MARB-a and the latter heterozygous or homozygous for the presence of MARB-a. Data from two investigated willow warbler families suggest a Mendelian inheritance pattern and provide support for our interpretation of how TE copy numbers reflect the three genotypes (Table S5). Moreover, the TE copy numbers within the hybrid swarm have a distribution similar to a combination of allopatric trochilus and acredula, further supporting that the copies are inherited as intact blocks (haplotypes). However, a precise distinction between heterozygotes and homozygotes on MARB-a is still not possible15.Statistical analysisWe used linear models with departure direction, winter longitude, migration distance and departure timing as response variables and the three genetic markers: MARB-a (a factor with two levels), InvP-Ch1 (a factor with three levels) and InvP-Ch5 (a factor with three levels) as explanatory variables. Models were constructed with R base package “stats”. We reported Type II ANOVA for models with more than one explanatory variable and no interactions and type III ANOVA results for models with interaction term by using R package “Car” (version 3.0-12)28. We initially constructed mixed effect models with timing of departure and tagging year as random factors however, this delivered singular fits due to insufficient sample sizes across categories. Normality of residuals was checked with a Shapiro–Wilk test. For carrying out circular statistics on autumn migration direction we used the R package “circular” (version 0.4-93). Watson’s U2 pairwise comparisons of different groups delivered the same results as linear models (Table S2 and Fig. S5). Circular means were identical to conventional linear means in our data set, which we take as another evidence that linear models are appropriate for the analysis of our data (Table S3 and Fig. S5). Maps in Figs. 1 and 2b and S1, S3 and S4 were created with R package “ggplot2” (version 3.3.6) using continent contours from Natural Earth, naturalearthdata.com/. Heat gradient over the maps in Fig. 1a–d were created with R package “gstat” (version 2.0-8) and the inverse distance weighting power of 3.0. Circular plots were created with ORIANA (version 4.02). All analyses were carried out with R version 4.1.1 (R Core Team 2021).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More