Philippa Kaur

Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).Article 
ADS 
CAS 
PubMed 

Google Scholar 
González, C. E., Medellín-Mora, J. & Escribano, R. Environmental gradients and spatial patterns of calanoid copepods in the southeast pacific. Front. Ecol. Evol. 8, 1–16 (2020).Article 

Google Scholar 
Rombouts, I. et al. Global latitudinal variations in marine copepod diversity and environmental factors. Proc. R. Soc. B Biol. Sci. 276, 3053–3062 (2009).Article 

Google Scholar 
Brandão, M. C. et al. Macroscale patterns of oceanic zooplankton composition and size structure. Sci. Rep. 11, 1–19 (2021).
Google Scholar 
Mcclain, C. R. & Barry, J. P. Habitat heterogeneity, disturbance, and productivity work in concert to regulate biodiversity in deep submarine canyons. Ecology 91, 964–976 (2010).Article 
PubMed 

Google Scholar 
Escribano, R. & Rodriguez, L. Life cycle of Calanus chilensis Brodsky in Bay of San Jorge, Antofagasta Chile. Hydrobiologia 292–293, 289–294 (1994).Article 

Google Scholar 
Strub, P. T., Mesías, M. J., Montecino, V., Rutllant, J. & Salinas, S. Coastal ocean circulation off western South America coastal segment. Sea 11, 273–313 (1998).
Google Scholar 
Montecino, V. & Lange, C. The Humboldt current system: Ecosystem components and processes, fisheries, and sediment studies. Prog. Oceanogr. 83, 65–79 (2009).Article 
ADS 

Google Scholar 
Miloslavich, P. et al. Marine biodiversity in the Atlantic and Pacific coasts of South America: Knowledge and gaps. PLoS ONE 6, e14631 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Marín, V., Espinoza, S. & Fleminger, A. Morphometric study of Calanus chilensis males along the Chilean coast. Hydrobiologia 292, 75–80 (1994).Article 

Google Scholar 
Escribano, R. & McLaren, I. Production of Calanus chilensis in the upwelling area of Antofagasta Northern Chile. Mar. Ecol. Prog. Ser. 177, 147–156 (1999).Article 
ADS 

Google Scholar 
Escribano, R. & Hidalgo, P. Spatial distribution of copepods in the north of the Humboldt Current region off Chile during coastal upwelling. J. Mar. Biol. Assoc. U. K. 80, 283–290 (2000).Article 

Google Scholar 
Hirche, H. J., Barz, K., Ayon, P. & Schulz, J. High resolution vertical distribution of the copepod Calanus chilensis in relation to the shallow oxygen minimum zone off northern Peru using LOKI, a new plankton imaging system. Deep Res. I Oceanogr. Res. Pap. 88, 63–73 (2014).Article 
ADS 
CAS 

Google Scholar 
Sabatini, M., rez, F. & Martos, P. Distribution pattern and population structure of Calanus australis Brodsky, 1959 over the southern Patagonian Shelf off Argentina in summer. ICES J. Mar. Sci. 57, 1856–1866 (2000).Article 

Google Scholar 
Escribano, R. Population dynamics of Calanus chilensis in the Chilean Eastern Boundary Humboldt Current. Fish. Oceanogr. 7, 245–251 (1998).Article 

Google Scholar 
Hidalgo, P. et al. Patterns of copepod diversity in the Chilean coastal upwelling system. Deep Sea Res. Part II Top. Stud. Oceanogr. 57, 2089–2097 (2010).Article 
ADS 

Google Scholar 
Hidalgo, P., Escribano, R., Fuentes, M., Jorquera, E. & Vergara, O. How coastal upwelling influences spatial patterns of size-structured diversity of copepods off central-southern Chile (summer 2009). Prog. Oceanogr. 92–95, 134–145 (2012).Article 
ADS 

Google Scholar 
Giraldo, A., Escribano, R. & Marin, V. Spatial distribution of Calanus chilensis off Mejillones Peninsula (northern Chile): Ecological consequences upon coastal upwelling. Mar. Ecol. Prog. Ser. 230, 225–234 (2002).Article 
ADS 

Google Scholar 
Gonzalez, A. & Marin, V. Distribution and life cycle of Calanus chilensis and Centropages brachiatus (Copepoda) in Chilean coastal waters: A GIS approach. Mar. Ecol. Prog. Ser. 165, 109–117 (1998).Article 
ADS 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Change Biol. 13, 1860–1872 (2007).Article 
ADS 

Google Scholar 
Visser, M. E. & Both, C. Shifts in phenology due to global climate change: The need for a yardstick. Proc. R. Soc. B Biol. Sci. 272, 2561–2569 (2005).Article 

Google Scholar 
Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl. Acad. Sci. 118, e2015094118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferrier, S., Drielsma, M., Manion, G. & Watson, G. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biodivers. Conserv. 11, 2309–2338 (2002).Article 

Google Scholar 
Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Peterson, A. T. et al. Ecological Niches and Geographic Distributions (MPB-49) (Princeton University Press, 2011). https://doi.org/10.2307/j.ctt7stnh.Book 

Google Scholar 
Franklin, J. Spatial Inference and Prediction. Mapping Species Distributions Vol. 141 (Cambridge University Press, 2010).Book 

Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R. Ecology Biodiversity and Conservation (Cambridge University Press, 2017). https://doi.org/10.1017/9781139028271.Book 

Google Scholar 
Freer, J. J., Partridge, J. C., Tarling, G. A., Collins, M. A. & Genner, M. J. Predicting ecological responses in a changing ocean: The effects of future climate uncertainty. Mar. Biol. 165, 7 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E. & Lundquist, C. J. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front. Mar. Sci. 4, 421 (2017).Article 

Google Scholar 
Pennino, M. G. et al. Accounting for preferential sampling in species distribution models. Ecol. Evol. 9, 653–663 (2019).Article 
PubMed 

Google Scholar 
Coll, M., Pennino, M. G., Steenbeek, J., Sole, J. & Bellido, J. M. Predicting marine species distributions: Complementarity of food-web and Bayesian hierarchical modelling approaches. Ecol. Model. 405, 86–101 (2019).Article 

Google Scholar 
Stock, B. C. et al. Comparing predictions of fisheries bycatch using multiple spatiotemporal species distribution model frameworks. Can. J. Fish. Aquat. Sci. 77, 146–163 (2019).Article 

Google Scholar 
Lezama-Ochoa, N. et al. Spatio-temporal distribution of the spinetail devil ray mobula mobular in the Eastern tropical Atlantic ocean. Endanger. Species Res. 43, 447–460 (2020).Article 

Google Scholar 
Marshall, C. E., Glegg, G. A. & Howell, K. L. Species distribution modelling to support marine conservation planning: The next steps. Mar. Policy 45, 330–332 (2014).Article 

Google Scholar 
Hunt, T. N., Allen, S. J., Bejder, L. & Parra, G. J. Identifying priority habitat for conservation and management of Australian humpback dolphins within a marine protected area. Sci. Rep. 10, 1–14 (2020).Article 

Google Scholar 
Champion, C., Brodie, S. & Coleman, M. A. Climate-driven range shifts are rapid yet variable among recreationally important coastal-pelagic fishes. Front. Mar. Sci. 8, 1–13 (2021).Article 

Google Scholar 
Przeslawski, R., Falkner, I., Ashcroft, M. B. & Hutchings, P. Using rigorous selection criteria to investigate marine range shifts. Estuar. Coast. Shelf Sci. 113, 205–212 (2012).Article 
ADS 

Google Scholar 
Januario, S. M., Estay, S. A., Labra, F. A. & Lima, M. Combining environmental suitability and population abundances to evaluate the invasive potential of the tunicate Ciona intestinalis along the temperate South American coast. PeerJ 3, e1357. https://doi.org/10.7717/peerj.1357 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Pinochet, J., Rivera, R., Neill, P. E., Brante, A. & Hernández, C. E. Spread of the non-native anemone Anemonia alicemartinae Häussermann & Försterra, 2001 along the Humboldt-current large marine ecosystem: An ecological niche model approach. PeerJ https://doi.org/10.7717/peerj.7156 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Lh, G., Rj, R. & Brante, A. One step ahead of sea anemone invasions with ecological niche modeling: Potential distributions and niche dynamics of three successful invasive species. Mar. Ecol. Prog. Ser. 690, 83–95 (2022).Article 

Google Scholar 
Allynid, A. J. et al. Comparing and synthesizing quantitative distribution models and qualitative vulnerability assessments to project marine species distributions under climate change. PLoS ONE 15, 1–28 (2020).
Google Scholar 
Pennino, M. G. et al. Current and future influence of environmental factors on small pelagic fish distributions in the northwestern mediterranean sea. Front. Mar. Sci. 7, 1–20 (2020).Article 

Google Scholar 
Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecol. Model. 415, 108837 (2020).Article 

Google Scholar 
Rosa, R., Dierssen, H. M., Gonzalez, L. & Seibel, B. A. Ecological biogeography of cephalopod molluscs in the Atlantic Ocean: Historical and contemporary causes of coastal diversity patterns. Glob. Ecol. Biogeogr. 17, 600–610 (2008).Article 

Google Scholar 
Barton, A. D., Dutkiewicz, S., Flierl, G., Bragg, J. & Follows, M. J. Patterns of diversity in marine phytoplankton. Science 327, 1509–1511 (2010).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Rodríguez-Ramos, T., Marañón, E. & Cermeño, P. Marine nano- and microphytoplankton diversity: Redrawing global patterns from sampling-standardized data. Glob. Ecol. Biogeogr. 24, 527–538 (2015).Article 

Google Scholar 
Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, eaau6253 (2022).Article 
ADS 

Google Scholar 
Busseni, G. et al. Large scale patterns of marine diatom richness: Drivers and trends in a changing ocean. Glob. Ecol. Biogeogr. 29, 1915–1928 (2020).Article 

Google Scholar 
Ruz, P. M., Hidalgo, P., Yáñez, S., Escribano, R. & Keister, J. E. Egg production and hatching success of Calanus chilensis and Acartia tonsa in the northern Chile upwelling zone (23°S) Humboldt Current System. J. Mar. Syst. 148, 200–212 (2015).Article 

Google Scholar 
Ashlock, L., García-Reyes, M., Gentemann, C., Batten, S. & Sydeman, W. Temperature and patterns of occurrence and abundance of key copepod taxa in the Northeast Pacific. Front. Mar. Sci. 8, 1–10 (2021).
Article 
ADS 

Google Scholar 
Campbell, M. D. et al. Testing Bergmann’s rule in marine copepods. Ecography 44, 1283–1295 (2021).Article 

Google Scholar 
Soberón, J. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10, 1115–1123 (2007).Article 
PubMed 

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

Google Scholar 
Morales, C. E. et al. Mesoscale structure of copepod assemblages in the coastal transition zone and oceanic waters off central-southern Chile. Prog. Oceanogr. 84, 158–173 (2010).Article 
ADS 

Google Scholar 
Gonzalez, R. R. & Quiñones, R. A. Ldh activity in Euphausia mucronata and Calanus chilensis: Implications for vertical migration behaviour. J. Plankton Res. 24, 1349–1356 (2002).Article 
CAS 

Google Scholar 
Escribano, R., Hidalgo, P. & Krautz, C. Zooplankton associated with the oxygen minimum zone system in the northern upwelling region of Chile during March 2000. Deep Sea Res. Part II Top. Stud. Oceanogr. 56, 1083–1094 (2009).Article 
ADS 

Google Scholar 
Fernández-Urruzola, I. et al. Plankton respiration in the Atacama Trench region: Implications for particulate organic carbon flux into the hadal realm. Limnol. Oceanogr. 66, 3134–3148 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Steinberg, D. K. & Landry, M. R. Zooplankton and the ocean carbon cycle. Ann. Rev. Mar. Sci. 9, 413–444 (2017).Article 
PubMed 

Google Scholar 
Tutasi, P. & Escribano, R. Zooplankton diel vertical migration and downward~C flux into the oxygen minimum zone in the highly productive upwelling region off northern Chile. Biogeosciences 17, 455–473 (2020).Article 
ADS 
CAS 

Google Scholar 
Gonzalez, A. & Marín, V. H. Distribution and life cycle of Calanus chilensis and Centropages brachiatus (Copepoda) in chilean coastal waters: A GIS approach. Mar. Ecol. Prog. Ser. 165, 109–117 (1998).Article 
ADS 

Google Scholar 
Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).Article 

Google Scholar 
Dias, P. C. Sources and sinks in population biology. Trends Ecol. Evol. 11, 326–330 (1996).Article 
CAS 
PubMed 

Google Scholar 
Ding, M., Lin, P., Liu, H., Hu, A. & Liu, C. Lagrangian eddy kinetic energy of ocean mesoscale eddies and its application to the Northwestern Pacific. Sci. Rep. 10, 12791 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morales, C. E. et al. The distribution of chlorophyll-a and dominant planktonic components in the coastal transition zone off Concepción, central Chile, during different oceanographic conditions. Prog. Oceanogr. 75, 452–469 (2007).Article 
ADS 

Google Scholar 
Escribano, R. & Rodriguez, L. Life cycle of Calanus chilensis Brodsky in Bay of San Jorge, Antofagasta Chile. Hydrobiologia 292, 289–294 (1994).Article 

Google Scholar 
Hidalgo, P. & Escribano, R. Coupling of life cycles of the copepods Calanus chilensis and Centropages brachiatus to upwelling induced variability in the central-southern region of Chile. Prog. Oceanogr. 75, 501–517 (2007).Article 
ADS 

Google Scholar 
Sobarzo, M., Bravo, L., Donoso, D., Garcés-Vargas, J. & Schneider, W. Coastal upwelling and seasonal cycles that influence the water column over the continental shelf off central Chile. Prog. Oceanogr. 75, 363–382 (2007).Article 
ADS 

Google Scholar 
Carlson, C. J. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods Ecol. Evol. 11, 850–858 (2020).Article 

Google Scholar 
Gelfand, A. et al. Explaining species distribution patterns through hierarchical modeling. Bayesian Anal. https://doi.org/10.1214/06-BA102 (2006).Article 
MathSciNet 
MATH 

Google Scholar 
Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

Google Scholar 
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).Article 

Google Scholar 
van Proosdij, A. S. J., Sosef, M. S. M., Wieringa, J. J. & Raes, N. Minimum required number of specimen records to develop accurate species distribution models. Ecography 39, 542–552 (2016).Article 

Google Scholar 
Gaul, W. et al. Data quantity is more important than its spatial bias for predictive species distribution modelling. PeerJ 8, e10411 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Beck, J., Böller, M., Erhardt, A. & Schwanghart, W. Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecol. Inform. 19, 10–15 (2014).Article 

Google Scholar 
Breiner, F. T., Guisan, A., Bergamini, A. & Nobis, M. P. Overcoming limitations of modelling rare species by using ensembles of small models. Methods Ecol. Evol. 6, 1210–1218 (2015).Article 

Google Scholar 
Breiner, F. T., Nobis, M. P., Bergamini, A. & Guisan, A. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods Ecol. Evol. 9, 802–808 (2018).Article 

Google Scholar 
Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl. Acad. Sci. 117, 12891–12896 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Richardson, A., Schoeman, D., Richardson, A. J. & Schoeman, D. S. Climate impact on plankton ecosystems in the Northeast Atlantic. Science 305, 1609–1612 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Chiba, S., Sugisaki, H., Nonaka, M. & Saino, T. Geographical shift of zooplankton communities and decadal dynamics of the Kuroshio-Oyashio currents in the western North Pacific. Glob. Change Biol. 15, 1846–1858 (2009).Article 
ADS 

Google Scholar 
Reygondeau, G. & Beaugrand, G. Future climate-driven shifts in distribution of Calanus finmarchicus. Glob. Change Biol. 17, 756–766 (2011).Article 
ADS 

Google Scholar 
Beaugrand, G., Lindley, J. A., Helaouet, P. & Bonnet, D. Macroecological study of Centropages typicus in the North Atlantic Ocean. Prog. Oceanogr. 72, 259–273 (2007).Article 
ADS 

Google Scholar 
Hirche, H. J., Barz, K., Ayon, P. & Schulz, J. High resolution vertical distribution of the copepod Calanus chilensis in relation to the shallow oxygen minimum zone off northern Peru using LOKI, a new plankton imaging system. Deep Sea Res. I Oceanogr. Res. Pap. 88, 63–73 (2014).Article 
ADS 
CAS 

Google Scholar 
Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).Article 

Google Scholar 
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).Article 

Google Scholar 
Riquelme-Bugueño, R. et al. The influence of upwelling variation on the spatially-structured euphausiid community off central-southern Chile in 2007–2008. Prog. Oceanogr. 92–95, 146–165 (2012).Article 
ADS 

Google Scholar 
Soberón, J. & Peterson, A. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. https://doi.org/10.17161/bi.v2i0.4 (2005).Article 

Google Scholar 
Provoost, P. & Bosch, S. robis: Ocean Biodiversity Information System (OBIS) Client (2020).Chamberlain, S. & Oldoni, D. rgbif: Interface to the Global Biodiversity Information Facility API (2021).R Core Team. R: A Language and Environment for Statistical Computing (2021).Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).Article 

Google Scholar 
ESRI. ArcGIS Desktop: Release 10.4.1 (Envrionmental Systems Research Institute, 2016).
Google Scholar 
De Marco, P. & Nóbrega, C. C. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLoS ONE 13, e202403 (2018).Article 

Google Scholar 
Feng, X. et al. A checklist for maximizing reproducibility of ecological niche models. Nat. Ecol. Evol. 3, 1382–1395 (2019).Article 
PubMed 

Google Scholar 
Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography 37, 191–203 (2014).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).Article 

Google Scholar 
Pinto-Ledezma, J. N. & Cavender-Bares, J. Predicting species distributions and community composition using satellite remote sensing predictors. Sci. Rep. 11, 16448 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ellison, A. M. Bayesian inference in ecology. Ecol. Lett. 7, 509–520 (2004).Article 

Google Scholar 
Pennino, M. G., Muñoz, F., Conesa, D., López-Quílez, A. & Bellido, J. M. Bayesian spatio-temporal discard model in a demersal trawl fishery. J. Sea Res. 90, 44–53 (2014).Article 

Google Scholar 
Di Cola, V. et al. ecospat: An R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).Article 

Google Scholar 
Engler, R., Guisan, A. & Rechsteiner, L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41, 263–274 (2004).Article 

Google Scholar 
Pearce, J. & Ferrier, S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model. 133, 225–245 (2000).Article 

Google Scholar 
Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199, 142–152 (2006).Article 

Google Scholar 
Warren, D. & Dinnage, R. ENMTools: Analysis of Niche Evolution using Niche and Distribution Models (2020).Assis, J. et al. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2018).Article 

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

Google Scholar 
Osorio-Olvera, L. et al. ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches. Methods Ecol. Evol. 11, 1199–1206 (2020).Article 

Google Scholar 
Bosch, S., Tyberghein, L. & De Clerck, O. ‘sdmpredictors’: Species distribution modelling predictor datasets. R package version 0.2.6. R Packag. version 0.2.6 (2018).Thuiller, W., Georges, D., Engler, R. & Breiner, F. biomod2: Ensemble platform for species distribution modeling (2020).Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography 43, 1261–1277 (2020).Article 

Google Scholar  More

Robertson, G. P. et al. Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes. Sci. (80-.) 356, 1–9 (2017).Article 

Google Scholar 
Ma, L. et al. The impact of stand age and fertilization on the soil microbiome of Miscanthus × giganteus. Phytobiomes J. 5, 51–59 (2021).Article 

Google Scholar 
Hestrin, R., Lee, M. R., Whitaker, B. K. & Pett-Ridge, J. The switchgrass microbiome: a review of structure, function, and taxonomic distribution. Phytobiomes J. 5, 14–28 (2021).Article 

Google Scholar 
Heaton, E. A., Dohleman, F. G. & Long, S. P. Meeting US biofuel goals with less land: The potential of Miscanthus. Glob. Chang. Biol. 14, 2000–2014 (2008).Article 
ADS 

Google Scholar 
Langholtz, M., Stokes, B. & Eaton, L. 2016 billion-ton report: Advancing domestic resources for a thriving bioeconomy (Executive Summary). Ind. Biotechnol. 12, 282–289 (2016).Article 

Google Scholar 
Roley, S. S. et al. Associative nitrogen fixation (ANF) across a nitrogen input gradient. PLoS One 13, 1–19 (2018).Article 

Google Scholar 
Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).Article 
PubMed 

Google Scholar 
Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, 1–14 (2017).Article 

Google Scholar 
Wang, N. R. & Haney, C. H. Harnessing the genetic potential of the plant microbiome. Biochem. (Lond.) 42, 20–25 (2020).Article 
CAS 

Google Scholar 
Haskett, T. L., Tkacz, A. & Poole, P. S. Engineering rhizobacteria for sustainable agriculture. ISME J. 15, 949–964 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hacquard, S. et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17, 603–616 (2015).Article 
CAS 
PubMed 

Google Scholar 
Gopal, M. & Gupta, A. Microbiome selection could spur next-generation plant breeding strategies. Front. Microbiol. 7, 1971 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Hardoim, P. R. et al. The hidden world within plants: Ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol. Mol. Biol. Rev. 79, 293–320 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Andrews, J. H. & Harris, R. F. The ecology and biogeography of microorganisms on plant surfaces. Annu. Rev. Phytopathol. 38, 145–180 (2000).Article 
PubMed 

Google Scholar 
Bulgarelli, D., Schlaeppi, K., Spaepen, S., van Themaat, E. V. L. & Schulze-Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 64, 807–838 (2013).Article 
CAS 
PubMed 

Google Scholar 
Müller, D. B., Vogel, C., Bai, Y. & Vorholt, J. A. The Plant Microbiota: Systems-Level Insights and Perspectives. Annu. Rev. Genet. 50, 120215–034952 (2016).Article 

Google Scholar 
Kuzyakov, Y. & Razavi, B. S. Rhizosphere size and shape: Temporal dynamics and spatial stationarity. Soil Biol. Biochem. 135, 343–360 (2019).Article 
CAS 

Google Scholar 
Bell, T. H. et al. Manipulating wild and tamed phytobiomes: Challenges and opportunities. Phytobiomes J. 3, 3–21 (2019).Article 

Google Scholar 
Chen, T. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580, 653–657 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828–840 (2012).Article 
CAS 
PubMed 

Google Scholar 
Koskella, B. The phyllosphere. Curr. Biol. 30, R1143–R1146 (2020).Article 
CAS 
PubMed 

Google Scholar 
Lindow, S. E. & Brandl, M. T. Microbiology of the phyllosphere. Appl. Environ. Microbiol. 69, 1875–1883 (2003).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bringel, F. & Couée, I. Pivotal roles of phyllosphere microorganisms at the interface between plant functioning and atmospheric trace gas dynamics. Front. Microbiol. 6, 486 (2015).Dorokhov, Y. L., Sheshukova, E. V. & Komarova, T. V. Methanol in plant life. Front. Plant Sci. 871, 1–6 (2018).
Google Scholar 
Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Peñuelas, J. & Terradas, J. The foliar microbiome. Trends Plant Sci. 19, 278–280 (2014).Article 
PubMed 

Google Scholar 
Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. USA. 112, E911–E920 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470 (2018).Article 
CAS 
PubMed 

Google Scholar 
Xu, L. et al. Drought delays development of the sorghum root microbiome and enriches for monoderm bacteria. Proc. Natl Acad. Sci. 115, E4284–E4293 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shade, A. & Stopnisek, N. Abundance-occupancy distributions to prioritize plant core microbiome membership. Curr. Opin. Microbiol. 49, 50–58 (2019).Article 
PubMed 

Google Scholar 
Stopnisek, N. & Shade, A. Persistent microbiome members in the common bean rhizosphere: an integrated analysis of space, time, and plant genotype. ISME J. 15, 2708–2722 (2021).Grady, K. L., Sorensen, J. W., Stopnisek, N., Guittar, J. & Shade, A. Assembly and seasonality of core phyllosphere microbiota on perennial biofuel crops. Nat. Commun. 10, 4135 (2019).Singer, E., Bonnette, J., Kenaley, S. C., Woyke, T. & Juenger, T. E. Plant compartment and genetic variation drive microbiome composition in switchgrass roots. Environ. Microbiol. Rep. 11, 185–195 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95 (2012).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Bahulikar, R. A., Torres-Jerez, I., Worley, E., Craven, K. & Udvardi, M. K. Diversity of nitrogen-fixing bacteria associated with switchgrass in the native tallgrass prairie of Northern Oklahoma. Appl. Environ. Microbiol. 80, 5636–5643 (2014).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Roley, S. S., Xue, C., Hamilton, S. K., Tiedje, J. M. & Robertson, G. P. Isotopic evidence for episodic nitrogen fixation in switchgrass (Panicum virgatum L.). Soil Biol. Biochem. 129, 90–98 (2019).Article 
CAS 

Google Scholar 
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yoon, S. H., Ha, S. M., Lim, J., Kwon, S. & Chun, J. A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie van. Leeuwenhoek, Int. J. Gen. Mol. Microbiol. 110, 1281–1286 (2017).Article 
CAS 

Google Scholar 
Julsing, M. K., Rijpkema, M., Woerdenbag, H. J., Quax, W. J. & Kayser, O. Functional analysis of genes involved in the biosynthesis of isoprene in Bacillus subtilis. Appl. Microbiol. Biotechnol. 75, 1377–1384 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Laskowska, E. & Kuczyńska-Wiśnik, D. New insight into the mechanisms protecting bacteria during desiccation. Curr. Genet. 66, 313–318 (2020).Article 
CAS 
PubMed 

Google Scholar 
Zou, H. et al. The metabolism and biotechnological application of betaine in microorganism. Appl. Microbiol. Biotechnol. 100, 3865–3876 (2016).Article 
CAS 
PubMed 

Google Scholar 
Rastogi, G., Coaker, G. L. & Leveau, J. H. J. New insights into the structure and function of phyllosphere microbiota through high-throughput molecular approaches. FEMS Microbiol. Lett. 348, 1–10 (2013).Article 
CAS 
PubMed 

Google Scholar 
Urrejola, C. et al. Genomic features for desiccation tolerance and sugar biosynthesis in the extremophile gloeocapsopsis sp. UTEX B3054. Front. Microbiol. 10, 1–11 (2019).Article 

Google Scholar 
Lacerda-Júnior, G. V. et al. Land use and seasonal effects on the soil microbiome of a Brazilian dry forest. Front. Microbiol. 10, 1–14 (2019).Article 

Google Scholar 
Dai, J. et al. Unraveling adaptation of Pontibacter korlensis to radiation and infertility in desert through complete genome and comparative transcriptomic analysis. Sci. Rep. 5, 1–9 (2015).Article 

Google Scholar 
Harty, C. E. et al. Ethanol stimulates trehalose production through a SpoT-DksA-AlgU-dependent pathway in Pseudomonas aeruginosa. J. Bacteriol. 201, 1–21 (2019).Kimmerer, T. W. & MacDonald, R. C. Acetaldehyde and ethanol biosynthesis in leaves of plants. Plant Physiol. 84, 1204–1209 (1987).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferner, E., Rennenberg, H. & Kreuzwieser, J. Effect of flooding on C metabolism of flood-tolerant (Quercus robur) and non-tolerant (Fagus sylvatica) tree species. Tree Physiol. 32, 135–145 (2012).Article 
CAS 
PubMed 

Google Scholar 
Kimmerer, T. W. & Kozlowski, T. T. Ethylene, ethane, acetaldehyde, and ethanol production by plants under stress. Plant Physiol. 69, 840–847 (1982).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, Y. et al. Assessment of drought tolerance of 49 switchgrass (Panicum virgatum) genotypes using physiological and morphological parameters. Biotechnol. Biofuels 8, 1–18 (2015).Article 

Google Scholar 
Wingler, A. et al. Trehalose 6-phosphate is required for the onset of leaf senescence associated with high carbon availability. Plant Physiol. 158, 1241–1251 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gottschlich, L., Geiser, P., Bortfeld-Miller, M., Field, C. M. & Vorholt, J. A. Complex general stress response regulation in Sphingomonas melonis Fr1 revealed by transcriptional analyses. Sci. Rep. 9, 1–13 (2019).Article 
CAS 

Google Scholar 
Chen, C., Li, S., McKeever, D. R. & Beattie, G. A. The widespread plant-colonizing bacterial species Pseudomonas syringae detects and exploits an extracellular pool of choline in hosts. Plant J. 75, 891–902 (2013).Article 
CAS 
PubMed 

Google Scholar 
Valenzuela-Soto, E. M. & Figueroa-Soto, C. G. Biosynthesis and degradation of glycine betaine and its potential to control plant growth and development. in Osmoprotectant-Mediated Abiotic Stress Tolerance in Plants (eds. Anwar Hossain, M., Kumar, V., Burritt, D. J., Fujita, M. & Makela, P. S. A.) 241–256 (Springer, 2019). https://doi.org/10.1007/978-3-030-27423-8_5.Kerchev, P., De Smet, B., Waszczak, C., Messens, J. & Van Breusegem, F. Redox strategies for crop improvement. Antioxid. Redox Signal 23, 1186–1205 (2015).Article 
CAS 
PubMed 

Google Scholar 
Considine, M. J. & Foyer, C. H. Redox regulation of plant development. Antioxid. Redox Signal. 21, 1305–1326 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Spaepen, S., Vanderleyden, J. & Remans, R. Indole-3-acetic acid in microbial and microorganism-plant signaling. FEMS Microbiol. Rev. 31, 425–448 (2007).Article 
CAS 
PubMed 

Google Scholar 
Egamberdieva, D., Wirth, S. J., Alqarawi, A. A., Abd-Allah, E. F. & Hashem, A. Phytohormones and beneficial microbes: Essential components for plants to balance stress and fitness. Front. Microbiol. 8, 1–14 (2017).Article 

Google Scholar 
Lajoie, G., Maglione, R. & Kembel, S. W. Adaptive matching between phyllosphere bacteria and their tree hosts in a neotropical forest. Microbiome 8, 1–10 (2020).Article 

Google Scholar 
McGenity, T. J., Crombie, A. T. & Murrell, J. C. Microbial cycling of isoprene, the most abundantly produced biological volatile organic compound on Earth. ISME J. 12, 931–941 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sharkey, T. D., Wiberley, A. E. & Donohue, A. R. Isoprene emission from plants: Why and how. Ann. Bot. 101, 5–18 (2008).Article 
CAS 
PubMed 

Google Scholar 
Zuo, Z. et al. Isoprene acts as a signaling molecule in gene networks important for stress responses and plant growth. Plant Physiol. 180, 124–152 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sharkey, T. D. & Yeh, S. Isoprene emission from plants. Plant Mol. Biol. 52, 407–436 (2001).CAS 

Google Scholar 
Sharkey, T. D., Loreto, F. & Delwiche, C. High carbon dioxide and sun/shade effects on isoprene emission from oak and aspen tree leaves. Plant, Cell Environ. 14, 333–338 (1991).Article 
CAS 

Google Scholar 
Eller, A. S. D. et al. Volatile organic compound emissions from switchgrass cultivars used as biofuel crops. Atmos. Environ. 45, 3333–3337 (2011).Article 
ADS 
CAS 

Google Scholar 
Morrison, E. C., Drewer, J. & Heal, M. R. A comparison of isoprene and monoterpene emission rates from the perennial bioenergy crops short-rotation coppice willow and Miscanthus and the annual arable crops wheat and oilseed rape. GCB Bioenergy 8, 211–225 (2016).Article 
CAS 

Google Scholar 
Sivy, T. L., Shirk, M. C. & Fall, R. Isoprene synthase activity parallels fluctuations of isoprene release during growth of Bacillus subtilis. Biochem. Biophys. Res. Commun. 294, 71–75 (2002).Article 
CAS 
PubMed 

Google Scholar 
Crombie, A. T. et al. Poplar phyllosphere harbors disparate isoprene-degrading bacteria. Proc. Natl Acad. Sci. USA. 115, 13081–13086 (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
El Khawand, M. et al. Isolation of isoprene degrading bacteria from soils, development of isoA gene probes and identification of the active isoprene-degrading soil community using DNA-stable isotope probing. Environ. Microbiol. 18, 2743–2753 (2016).Article 
CAS 
PubMed 

Google Scholar 
Nowicka, B. & Kruk, J. Occurrence, biosynthesis and function of isoprenoid quinones. Biochim. Biophys. Acta – Bioenerg. 1797, 1587–1605 (2010).Article 
CAS 

Google Scholar 
Kałużna, M. et al. Pseudomonas cerasi sp. nov. (non Griffin, 1911) isolated from diseased tissue of cherry. Syst. Appl. Microbiol. 39, 370–377 (2016).Article 
PubMed 

Google Scholar 
El-Tarabily, K. A., Nassar, A. H., Hardy, G. E. S. J. & Sivasithamparam, K. Plant growth promotion and biological control of Pythium aphanidermatum, a pathogen of cucumber, by endophytic actinomycetes. J. Appl. Microbiol 106, 13–26 (2009).Article 
CAS 
PubMed 

Google Scholar 
Javed, Z., Tripathi, G. D., Mishra, M. & Dashora, K. Actinomycetes – the microbial machinery for the organic-cycling, plant growth, and sustainable soil health. Biocatal. Agric. Biotechnol. 31, 101893 (2021).Article 
CAS 

Google Scholar 
Anwar, S., Ali, B. & Sajid, I. Screening of rhizospheric actinomycetes for various in-vitro and in-vivo plant growth promoting (PGP) traits and for agroactive compounds. Front. Microbiol. 7, 1–11 (2016).Article 

Google Scholar 
Bao, L. et al. Microbial community overlap between the phyllosphere and rhizosphere of three plants from Yongxing Island, South China Sea. Microbiologyopen 9, 1–18 (2020).Article 

Google Scholar 
Remus-Emsermann, M. N. P. & Schlechter, R. O. Phyllosphere microbiology: at the interface between microbial individuals and the plant host. N. Phytol. 218, 1327–1333 (2018).Article 

Google Scholar 
Beilsmith, K. et al. Genome-wide association studies on the phyllosphere microbiome: embracing complexity in host–microbe interactions. Plant J. 97, 164–181 (2019).Article 
CAS 
PubMed 

Google Scholar 
Levy, A., Conway, J. M., Dangl, J. L. & Woyke, T. Elucidating bacterial gene functions in the plant microbiome. Cell Host Microbe 24, 475–485 (2018).Article 
CAS 
PubMed 

Google Scholar 
Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).Article 
CAS 
PubMed 

Google Scholar 
Choi, H. et al. Identification of viruses and viroids infecting tomato and pepper plants in vietnam by metatranscriptomics. Int. J. Mol. Sci. 21, 1–16 (2020).Article 
ADS 

Google Scholar 
Marzano, S. Y. L. & Domier, L. L. Novel mycoviruses discovered from metatranscriptomics survey of soybean phyllosphere phytobiomes. Virus Res 213, 332–342 (2016).Article 
CAS 
PubMed 

Google Scholar 
Chao S, et al. Metatranscriptomic sequencing suggests the presence of novel RNA viruses in rice rransmitted by brown planthopper. Viruses. 13, 2464 (2021).Delmotte, N. et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc. Natl Acad. Sci. 106, 16428–16433 (2009).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Suzuki, Y., Makino, A. & Mae, T. An efficient method for extraction of RNA from rice leaves at different ages using benzyl chloride. J. Exp. Bot. 52, 1575–1579 (2001).Article 
CAS 
PubMed 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. 108, 4516–4522 (2011).Article 
ADS 
CAS 
PubMed 

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

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Druzhinina, I. S. et al. Massive lateral transfer of genes encoding plant cell wall-degrading enzymes to the mycoparasitic fungus Trichoderma from its plant-associated hosts. PLoS Genet. 14, e1007322 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Haridas, S. et al. 101 Dothideomycetes genomes: A test case for predicting lifestyles and emergence of pathogens. Stud. Mycol. 96, 141–153 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gostinčar, C. et al. Genome sequencing of four Aureobasidium pullulans varieties: Biotechnological potential, stress tolerance, and description of new species. BMC Genomics 15, 549 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Gill, U. S. et al. Draft genome sequence resource of switchgrass rust pathogen, puccinia novopanici isolate ard-01. Phytopathology 109, 1513–1515 (2019).Article 
CAS 
PubMed 

Google Scholar 
Bowsher, A. W., Benucci, G. M. N., Bonito, G. & Shade, A. Seasonal dynamics of core fungi in the switchgrass phyllosphere, and co-occurrence with leaf bacteria. Phytobiomes J. 5, 60–68 (2021).Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
CAS 
PubMed 

Google Scholar 
Kang, D. D. et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019, 1–13 (2019).
Google Scholar 
Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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 
PubMed 
PubMed Central 

Google Scholar 
Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl Acad. Sci. USA. 110, 11463–11468 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for’omics data. PeerJ. 2015, 1–29 (2015).
Google Scholar 
Lee, M. D. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996 (2018).Article 
CAS 
PubMed 

Google Scholar 
Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006).Article 
CAS 
PubMed 

Google Scholar 
Blin, K. et al. AntiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Res. 49, W29–W35 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Navarro-Muñoz, J. C. et al. A computational framework to explore large-scale biosynthetic diversity. Nat. Chem. Biol. 16, 60–68 (2020).Article 
PubMed 

Google Scholar 
Zimmermann, J., Kaleta, C. & Waschina, S. Gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 22, 1–35 (2021).Article 

Google Scholar 
Tseng, T. T., Tyler, B. M. & Setubal, J. C. Protein secretion systems in bacterial-host associations, and their description in the Gene Ontology. BMC Microbiol 9, 1–9 (2009).Article 

Google Scholar 
Lucke, M., Correa, M. G. & Levy, A. The role of secretion systems, effectors, and secondary metabolites of beneficial rhizobia in interactions with plants and microbes. Front. Plant Sci. 11, 589416 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Palmer, J. L., Hilton, S., Picot, E., Bending, G. D. & Schäfer, H. Tree phyllospheres are a habitat for diverse populations of CO-oxidizing bacteria. Environ. Microbiol. 23, 6309–6327 (2021).Article 
CAS 
PubMed 

Google Scholar 
Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nat. Microbiol. 6, 246–256 (2021).Article 
CAS 
PubMed 

Google Scholar  More

Moore, J. An overview of parasite-induced behavioral alterations – and some lessons from bats. J. Exp. Biol. 216, 11–17 (2012).Article 

Google Scholar 
Nunn, C. L. & Altizer, S. Infectious Diseases in Primates: Behavior, Ecology and Evolution (Oxford University Press, 2006).Book 

Google Scholar 
Hutchings, M. R., Athanasiadou, S., Kyriazakis, I. & Gordon, I. J. Nutrition and Behaviour Group Symposium on ‘Exploitation of medicinal properties of plants by animals and man through food intake and foraging behaviour’: Can animals use foraging behaviour to combat parasites?. Proc. Nutr. Soc. 62, 361–370 (2003).Article 

Google Scholar 
Hawley, D. M., Etienne, R. S., Ezenwa, V. O. & Jolles, A. E. Does animal behavior underlie covariation between hosts’ exposure to infectious agents and susceptibility to infection? Implications for disease dynamics. Integr. Comp. Biol. 51, 528–539 (2011).Article 

Google Scholar 
Rimbach, R. et al. Brown spider monkeys (Ateles hybridus): a model for differentiating the role of social networks and physical contact on parasite transmission dynamics. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140110 (2015).Article 

Google Scholar 
Friant, S., Ziegler, T. E. & Goldberg, T. L. Changes in physiological stress and behaviour in semi-free-ranging red-capped mangabeys (Cercocebus torquatus) following antiparasitic treatment. Proc. R. Soc. B Biol. Sci. 283, 20161201 (2016).Article 

Google Scholar 
Hudson, P. J. & Dobson, A. P. Macroparasites: Observed patterns in naturally fluctuating animal populations. In Ecology of infectious diseases in natural populations (eds Grenfell, B. T. & Dobson, A. P.) 144–176 (Cambridge University Press, 1995). https://doi.org/10.1017/CBO9780511629396.006.Chapter 

Google Scholar 
Murray, D. L., Lloyd, B. K. & Cary, J. R. Do parasitism and nutritional status interact to affect production in snowshoe hares?. Ecology 79, 1209–1222 (1998).Article 

Google Scholar 
Coop, R. L. & Holmes, P. H. Nutrition and parasite interaction. Int. J. Parasitol. 26, 951–962 (1996).Article 
CAS 

Google Scholar 
Møller, A. P., de Lope, F., Moreno, J., González, G. & Pérez, J. J. Ectoparasites and host energetics: House martin bugs and house martin nestlings. Oecologia 98, 263–268 (1994).Article 
ADS 

Google Scholar 
Munger, J. C. & Karasov, W. H. Sublethal parasites and host energy budgets: Tapeworm infection in white-footed mice. Ecology 70, 904–921 (1989).Article 

Google Scholar 
Hicks, O. et al. The energetic cost of parasitism in a wild population. Proc. R. Soc. B Biol. Sci. 285, 20180489 (2018).Article 

Google Scholar 
Sánchez, C. A. et al. On the relationship between body condition and parasite infection in wildlife: A review and meta-analysis. Ecol. Lett. 21, 1869–1884 (2018).Article 

Google Scholar 
Kyriazakis, I., Tolkamp, B. J. & Hutchings, M. R. Towards a functional explanation for the occurrence of anorexia during parasitic infections. Anim. Behav. 56, 265–274 (1998).Article 
CAS 

Google Scholar 
Hart, B. L. Behavioral adaptations to pathogens and parasites: Five strategies. Neurosci. Biobehav. Rev. 14, 273–294 (1990).Article 
CAS 

Google Scholar 
Lopes, P. C., Block, P. & König, B. Infection-induced behavioural changes reduce connectivity and the potential for disease spread in wild mice contact networks. Sci. Rep. 6, 1–10 (2016).Article 

Google Scholar 
Pelletier, F. & Festa-Bianchet, M. Effects of body mass, age, dominance and parasite load on foraging time of bighorn rams. Ovis canadensis. Behav. Ecol. Sociobiol. 56, 546–551 (2004).Article 

Google Scholar 
Bonneaud, C. et al. Assessing the cost of mounting an immune response. Am. Nat. 161, 367–379 (2003).Article 

Google Scholar 
Hart, B. L. The behavior of sick animals. Vet. Clin. North Am. Small Anim. Pract. 21, 225–237 (1991).Article 
CAS 

Google Scholar 
Poulin, R. Meta-analysis of parasite-induced behavioural changes. Anim. Behav. 48, 137–146 (1994).Article 

Google Scholar 
Janson, C. H. Toward an experiemental socioecology of primates. Examples from Argentine brown capuchin monkeys (Cebus apella nigritus). In Adaptive Radiations of Neotropical Primates (eds Janson, C. H. et al.) 309–325 (Plenum Press, 1996).Chapter 

Google Scholar 
Robinson, J. G. Seasonal variation in use of time and space by the wedge-capped capuchin monkey, Cebus olivaceus: Implications for foraging theory. Smithson. Contrib. Zool. https://doi.org/10.5479/si.00810282.431 (1986).Article 

Google Scholar 
Saj, T., Sicotte, P. & Paterson, J. D. Influence of human food consumption on the time budget of vervets. Int. J. Primatol. 20, 977–994 (1999).Article 

Google Scholar 
Ghai, R. R., Fugère, V., Chapman, C. A., Goldberg, T. L. & Davies, T. J. Sickness behaviour associated with non-lethal infections in wild primates. Proc. Biol. Sci. 282, 20151436 (2015).
Google Scholar 
Blersch, R. et al. Sick and tired: Sickness behaviour, polyparasitism and food stress in a gregarious mammal. Behav. Ecol. Sociobiol. 75, 169 (2021).Article 

Google Scholar 
Müller-Klein, N. et al. Physiological and social consequences of gastrointestinal nematode infection in a nonhuman primate. Behav. Ecol. 30, 322–335 (2019).Article 

Google Scholar 
Chapman, C. A. et al. Social behaviours and networks of vervet monkeys are influenced by gastrointestinal parasites. PLoS ONE 11, e0161113 (2016).Article 

Google Scholar 
Owen-Ashley, N. T. & Wingfield, J. C. Acute phase responses of passerine birds: characterization and seasonal variation. J. Ornithol. 148, 583–591 (2007).Article 

Google Scholar 
Owen-Ashley, N. T. & Wingfield, J. C. Seasonal modulation of sickness behavior in free-living northwestern song sparrows (Melospiza melodia morphna). J. Exp. Biol. 209, 3062–3070 (2006).Article 

Google Scholar 
Janson, C. H. & Di Bitetti, M. S. Experimental analysis of food detection in capuchin monkeys: Effects of distance, travel speed, and resource size. Behav. Ecol. Sociobiol. 41, 17–24 (1997).Article 

Google Scholar 
Di Bitetti, M. S. Food-associated calls in the tufted capuchin monkey (Cebus apella). PhD Thesis. (Stony Brook University, New York, 2001).Di Bitetti, M. S. & Janson, C. H. Reproductive socioecology of tufted capuchins (Cebus apella nigritus) in Norteastern Argentina. Int. J. Primatol. 22, 127–142 (2001).Article 

Google Scholar 
Janson, C., Baldovino, M. C. & Di Bitetti, M. The group life cycle and demography of brown capuchin monkeys (Cebus [apella] nigritus) in Iguazú National Park, Argentina. In Long-Term Field Studies of Primates (eds Kappeler, P. M. & Watts, D. P.) 185–212 (Springer, Berlin, 2012). https://doi.org/10.1007/978-3-642-22514-7_9.Chapter 

Google Scholar 
Robinson, J. C. & Galán Saúco, V. Bananas and plantains. (Crop production science in horticulture series N. 19, CAB International, 2010). https://doi.org/10.1079/9781845936587.0000Tiddi, B., Pfoh, R. & Agostini, I. The impact of food provisioning on parasite infection in wild black capuchin monkeys: A network approach. Primates 60, 297–306 (2019).Article 

Google Scholar 
Agostini, I., Vanderhoeven, E., Di Bitetti, M. S. & Beldomenico, P. M. Experimental testing of reciprocal effects of nutrition and parasitism in wild black capuchin monkeys. Sci. Rep. 7, 1–11 (2017).Article 

Google Scholar 
de Vries, H., Netto, W. J. & Hanegraaf, P. L. H. Matman: a program for the analysis of sociometric matrices and behavioural transition matrices. Behaviour 125, 157–175 (1993).Article 

Google Scholar 
Martin, P. & Bateson, P. Measuring Behaviour (Cambridge University Press, 1993). https://doi.org/10.1017/cbo9780511810893.Book 

Google Scholar 
Cox, D. D. & Todd, A. C. Survey of gastrointestinal parasitism in Wisconsin dairy cattle. J. Am. Vet. Med. Assoc. 141, 706–709 (1962).CAS 

Google Scholar 
Ballweber, L. R., Beugnet, F., Marchiondo, A. A. & Payne, P. A. American association of veterinary parasitologists’ review of veterinary fecal flotation methods and factors influencing their accuracy and use—Is there really one best technique?. Vet. Parasitol. 204, 73–80 (2014).Article 
CAS 

Google Scholar 
Godfrey, S. S. Networks and the ecology of parasite transmission: a framework for wildlife parasitology. Int. J. Parasitol. Parasites Wildl. 2, 235–245 (2013).Article 

Google Scholar 
Sosa, S., Sueur, C. & Puga-Gonzalez, I. Network measures in animal social network analysis: Their strengths, limits, interpretations and uses. Methods Ecol. Evol. 2020, 1–12 (2020).
Google Scholar 
Sosa, S. et al. A multilevel statistical toolkit to study animal social networks: The Animal Network Toolkit Software (ANTs) R package. Sci. Rep. 10, 12507 (2020).Article 
ADS 
CAS 

Google Scholar 
Croft, D. P., Madden, J. R., Franks, D. W. & James, R. Hypothesis testing in animal social networks. Trends Ecol. Evol. 26, 502–507 (2011).Article 

Google Scholar 
Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed). Ecological Modelling (Springer, 2002). https://doi.org/10.1016/j.ecolmodel.2003.11.004Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Barton, K. MuMIn: Multi-model inference. R package version 1.15.6. 63 (2016). citeulike:11961261Carlton, E. D., Demas, G. E. & French, S. S. Leptin, a neuroendocrine mediator of immune responses, inflammation, and sickness behaviors. Horm. Behav. 62, 272–279 (2012).Article 
CAS 

Google Scholar 
Tizard, I. Sickness behavior, its mechanisms and significance. Anim. Health Res. Rev. 9, 87–99 (2008).Article 

Google Scholar 
Inoue, W. & Luheshi, G. N. Acute starvation alters lipopolysaccharide-induced fever in leptin-dependent and -independent mechanisms in rats. Am. J. Physiol. Regul. Integr. Comp. Physiol. 299, R1709-19 (2010).Article 

Google Scholar 
Macdonald, L., Radler, M., Paolini, A. G. & Kent, S. Calorie restriction attenuates LPS-induced sickness behavior and shifts hypothalamic signaling pathways to an antiinflammatory bias. Am. J. Physiol. Regul. Integr. Comp. Physiol. 301, 172–184 (2011).Article 

Google Scholar 
Wisse, B. E. et al. Physiological regulation of hypothalamic IL-1β gene expression by leptin and glucocorticoids: implications for energy homeostasis. Am. J. Physiol. Endocrinol. Metab. 287, R1107–R1113 (2004).Article 

Google Scholar 
Pohl, J., Woodside, B. & Luheshi, G. N. Changes in hypothalamically mediated acute-phase inflammatory responses to lipopolysaccharide in diet-induced obese rats. Endocrinology 150, 4901–4910 (2009).Article 
CAS 

Google Scholar 
Bretscher, P. On analyzing how the Th1/Th2 phenotype of an immune response is determined: classical observations must not be ignored. Front. Immunol. 10, 1–7 (2019).Article 

Google Scholar 
Poppi, D. P., Sykes, A. R. & Dynes, R. A. The effect of endoparasitism on host nutrition – the implications for nutrient manipulation. Proc. New Zeal. Soc. Anim. Prod. 50, 237–243 (1990).
Google Scholar 
Coulson, G., Cripps, J. K., Garnick, S., Bristow, V. & Beveridge, I. Parasite insight: assessing fitness costs, infection risks and foraging benefits relating to gastrointestinal nematodes in wild mammalian herbivores. Philos. Trans. R. Soc. B Biol. Sci. 373, 197 (2018).Article 

Google Scholar 
Worsley-Tonks, K. E. L. & Ezenwa, V. O. Anthelmintic treatment affects behavioural time allocation in a free-ranging ungulate. Anim. Behav. 108, 47–54 (2015).Article 

Google Scholar 
Jones, O. R., Anderson, R. M. & Pilkington, J. G. Parasite-induced anorexia in a free-ranging mammalian herbivore: An experimental test using Soay sheep. Can. J. Zool. 84, 685–692 (2006).Article 

Google Scholar 
Cripps, J. K., Martin, J. K. & Coulson, G. Anthelmintic treatment does not change foraging strategies of female eastern grey kangaroos, Macropus giganteus. PLoS ONE 11, e0147384 (2016).Article 

Google Scholar 
Giles, N. Predation risk and reduced foraging activity in fish: experiments with parasitized and non-parasitized three-spined sticklebacks, Gasterosteus aculeatus L.. J. Fish Biol. 31, 37–44 (1987).Article 

Google Scholar 
Knutie, S. A., Wilkinson, C. L., Wu, Q. C., Ortega, C. N. & Rohr, J. R. Host resistance and tolerance of parasitic gut worms depend on resource availability. Oecologia 183, 1031–1040 (2017).Article 
ADS 

Google Scholar 
Lopes, P. C., French, S. S., Woodhams, D. C. & Binning, S. A. Metabolic response of dolphins to short-term fasting reveals physiological changes that differ from the traditional fasting model. J. Exp. Biol. 224, jeb225847 (2021).Article 

Google Scholar 
Behringer, D. C., Butler, M. J. & Shields, J. D. Ecology: Avoidance of disease by social lobsters. Nature 441, 421 (2006).Article 
ADS 
CAS 

Google Scholar 
Poirotte, C. et al. Mandrills use olfaction to socially avoid parasitized conspecifics. Sci. Adv. 3, e1601721 (2017).Article 
ADS 

Google Scholar  More

Asner, G. P., Elmore, A. J., Olander, L. P., Martin, R. E. & Harris, A. T. Grazing systems, ecosystem responses, and global change. Annu. Rev. Environ. Resour. 29, 261–299 (2004).Article 

Google Scholar 
Millenium Ecosystem Assessment Board. Ecosystems and Human Well-Being: Wetlands and Water: Synthesis (Island Press, Washington, DC, 2005).Lind, J., Sabates-Wheeler, R., Caravani, M., Kuol, L. B. D. & Nightingale, D. M. Newly evolving pastoral and post-pastoral rangelands of Eastern Africa. Pastoralism 10, 24 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hoffman, T. & Vogel, C. Climate change impacts on African rangelands. Rangelands 30, 12–17 (2008).Article 

Google Scholar 
Joyce, L. A. et al. Climate change and North American rangelands: Assessment of mitigation and adaptation strategies. Rangeland Ecol. Manage. 66, 512–528 (2013).Article 

Google Scholar 
Stringer, L. C., Reed, M. S., Dougill, A. J., Seely, M. K. & Rokitzki, M. Implementing the UNCCD: Participatory challenges. Nat. Resour. Forum 31, 198–211 (2007).Article 

Google Scholar 
Vågen, T.-G., Winowiecki, L. A., Tondoh, J. E., Desta, L. T. & Gumbricht, T. Mapping of soil properties and land degradation risk in Africa using MODIS reflectance. Geoderma 263, 216–225 (2016).Article 
ADS 

Google Scholar 
Stevens, N., Lehmann, C. E. R., Murphy, B. P. & Durigan, G. Savanna woody encroachment is widespread across three continents. Glob. Chang. Biol. 23, 235–244 (2017).Article 
ADS 
PubMed 

Google Scholar 
Muñoz, P. et al. Land degradation, poverty and inequality (2019).Bond, W. & Keeley, J. Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends Ecol. Evol. 20, 387–394 (2005).Article 
PubMed 

Google Scholar 
Lehmann, C. E. R., Archibald, S. A., Hoffmann, W. A. & Bond, W. J. Deciphering the distribution of the savanna biome. New Phytol. 191, 197–209 (2011).Article 
PubMed 

Google Scholar 
Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).Article 
ADS 
CAS 
MATH 
PubMed 

Google Scholar 
Fuhlendorf, S. D., Fynn, R. W. S., McGranahan, D. A. & Twidwell, D. Heterogeneity as the basis for rangeland management in Rangeland Systems: Processes, Management and Challenges, Springer Series on Environmental Management (ed. Briske, D. D.), 169–196 (Springer International Publishing, 2017).Liao, C., Agrawal, A., Clark, P. E., Levin, S. A. & Rubenstein, D. I. Landscape sustainability science in the drylands: mobility, rangelands and livelihoods. Landsc. Ecol. 35, 2433–2447 (2020).Article 

Google Scholar 
Galvin, K. A. Transitions: pastoralists living with change. Annu. Rev. Anthropol. 38, 185–198 (2009).Article 

Google Scholar 
López-i Gelats, F., Fraser, E. D. G., Morton, J. F. & Rivera-Ferre, M. G. What drives the vulnerability of pastoralists to global environmental change? A qualitative meta-analysis. Glob. Environ. Change 39, 258–274 (2016).Obiri, J. F. Invasive plant species and their disaster-effects in dry tropical forests and rangelands of Kenya and Tanzania. Jàmbá: Journal of Disaster Risk Studies 3, 417–428 (2011).Kioko, J., Kiringe, J. W. & Seno, S. O. Impacts of livestock grazing on a savanna grassland in Kenya. J. Arid Land 4, 29–35 (2012).Article 

Google Scholar 
Kotiaho, J. S. et al. The IPBES assessment report on land degradation and restoration. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem (2018).Western, D., Mose, V. N., Worden, J. & Maitumo, D. Predicting extreme droughts in savannah Africa: A comparison of proxy and direct measures in detecting biomass fluctuations, trends and their causes. PLoS One 10, e0136516 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Dai, A. Drought under global warming: a review. WIREs Climate Change 2, 45–65 (2011).Article 

Google Scholar 
Holechek, J. L., Cibils, A. F., Bengaly, K. & Kinyamario, J. I. Human population growth, African pastoralism, and rangelands: A perspective. Rangeland Ecol. Manage. 70, 273–280 (2017).Article 

Google Scholar 
Midgley, G. F. & Bond, W. J. Future of African terrestrial biodiversity and ecosystems under anthropogenic climate change. Nat. Clim. Chang. 5, 823–829 (2015).Article 
ADS 

Google Scholar 
Hill, M. J. & Guerschman, J. P. The MODIS global vegetation fractional cover product 2001–2018: Characteristics of vegetation fractional cover in grasslands and savanna woodlands. Remote Sensing 12, 406 (2020).Article 
ADS 

Google Scholar 
Lake, P. S. Resistance, resilience and restoration. Ecol. Manage. Restor. 14, 20–24 (2013).Article 

Google Scholar 
Hodgson, D., McDonald, J. L. & Hosken, D. J. What do you mean, ‘resilient’?. Trends Ecol. Evol. 30, 503–506 (2015).Article 
PubMed 

Google Scholar 
Tilman, D. & Downing, J. A. Biodiversity and stability in grasslands. Nature 367, 363–365 (1994).Article 
ADS 

Google Scholar 
Fedrigo, J. K. et al. Temporary grazing exclusion promotes rapid recovery of species richness and productivity in a long-term overgrazed Campos grassland. Restor. Ecol. 26, 677–685 (2018).Article 

Google Scholar 
Ruppert, J. C. et al. Quantifying drylands’ drought resistance and recovery: the importance of drought intensity, dominant life history and grazing regime. Glob. Chang. Biol. 21, 1258–1270 (2015).Article 
ADS 
PubMed 

Google Scholar 
Homewood, K. M. Policy, environment and development in African rangelands. Environ. Sci. Policy 7, 125–143 (2004).Article 

Google Scholar 
Caro, T. & Davenport, T. R. B. Wildlife and wildlife management in Tanzania. Conserv. Biol. 30, 716–723 (2016).Article 
PubMed 

Google Scholar 
Bollig, M. & Schulte, A. Environmental change and pastoral perceptions: degradation and indigenous knowledge in two African pastoral communities. Hum. Ecol. 27, 493–514 (1999).Article 

Google Scholar 
Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science 363, 1424–1428 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Nicholson, S. E. Climate and climatic variability of rainfall over Eastern Africa. Rev. Geophys. 55, 590–635 (2017).Article 
ADS 

Google Scholar 
2012 Population and Housing Census (National Bureau of Statistics, Ministry of Finance, 2013).Kiffner, C., Nagar, S., Kollmar, C. & Kioko, J. Wildlife species richness and densities in wildlife corridors of Northern Tanzania. J. Nat. Conserv. 31, 29–37 (2016).Article 

Google Scholar 
Foley, C. A. H. & Faust, L. J. Rapid population growth in an elephant Loxodonta africana population recovering from poaching in Tarangire National Park, Tanzania. Oryx 44, 205–212 (2010).Article 

Google Scholar 
Kebacho, L. L. Large-scale circulations associated with recent interannual variability of the short rains over East Africa. Meteorol. Atmos. Phys. 134, 10 (2021).Article 
ADS 

Google Scholar 
Wainwright, C. M., Finney, D. L., Kilavi, M., Black, E. & Marsham, J. H. Extreme rainfall in East Africa, October 2019-January 2020 and context under future climate change. Weather 76, 26–31 (2021).Article 
ADS 

Google Scholar 
Abukari, H. & Mwalyosi, R. B. Comparing pressures on national parks in Ghana and Tanzania: The case of mole and Tarangire National Parks. Global Ecol. Conserv. 15, e00405 (2018).Article 

Google Scholar 
Kaswamila, A. An analysis of the contribution of community wildlife management areas on livelihood in Tanzania. Sustain. Natl. Res. Manag. 139–54 (2012).NTRI. Maps | NTRI – Northern Tanzania Rangelands Initiative. https://www.ntri.co.tz/maps/ (2016). Accessed: 2021-3-29.Mworia, J., Kinyamario, J. & John, E. Impact of the invader Ipomoea hildebrandtii on grass biomass, nitrogen mineralisation and determinants of its seedling establishment in Kajiado, Kenya. Afr. J. Range Forage Sci. 25, 11–16 (2008).Article 

Google Scholar 
Manyanza, N. M. & Ojija, F. Invasion, impact and control techniques for invasive Ipomoea hildebrandtii on Maasai steppe rangelands. NATO Adv. Sci. Inst. Ser. E Appl. Sci. 17, 12 (2021).Thaiyah, A. G. et al. Acute, sub-chronic and chronic toxicity of Solanum incanum L in sheep in Kenya. Kenya Veterinarian 35, 1–8 (2011).
Google Scholar 
Roques, K. G., O’Connor, T. G. & Watkinson, A. R. Dynamics of shrub encroachment in an African savanna: relative influences of fire, herbivory, rainfall and density dependence. J. Appl. Ecol. 38, 268–280 (2001).Article 

Google Scholar 
Riginos, C. & Herrick, J. E. Monitoring rangeland health: a guide for pastoralists and other land managers in Eastern Africa. Version II (2010).Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).QGIS Development Team. QGIS Geographic Information System. QGIS Association (2022).Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
ADS 

Google Scholar 
Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 [Data set] (NASA EOSDIS Land Processes DAAC, 2015).Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+ Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2019).Vermote, E. MOD09A1 MODIS/Terra Surface Reflectance 8-day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC 10 (2015).Funk, C. et al. The climate hazards infrared precipitation with stations–a new environmental record for monitoring extremes. Scientific Data 2, 1–21 (2015).Article 

Google Scholar 
Zeileis, A. & Grothendieck, G. zoo: S3 infrastructure for regular and irregular time series. arXiv:math/0505527 (2005).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2016).Scaramuzza, P. & Barsi, J. Landsat 7 scan line corrector-off gap-filled product development in Proceeding of Pecora 16, 23–27 (2005).
Google Scholar 
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).Article 
ADS 

Google Scholar 
Rikimaru, A., Roy, P. S. & Miyatake, S. Tropical forest cover density mapping. Trop. Ecol. 39–47 (2002).Diek, S., Fornallaz, F., Schaepman, M. E. & De Jong, R. Barest pixel composite for agricultural areas using landsat time series. Remote Sensing 9, 1245 (2017).Article 
ADS 

Google Scholar 
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. & Sorooshian, S. A modified soil adjusted vegetation index. Remote Sens. Environ. 48, 119–126 (1994).Article 
ADS 

Google Scholar 
Adams, B. et al. Mapping forest composition with Landsat time series: An evaluation of seasonal composites and harmonic regression. Remote Sensing 12, 610 (2020).Article 
ADS 

Google Scholar 
Nwanganga, F. & Chapple, M. Practical machine learning in R (John Wiley and Sons, Indianapolis, 2020).Adam, E., Mutanga, O., Odindi, J. & Abdel-Rahman, E. M. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. Int. J. Remote Sens. 35, 3440–3458 (2014).Article 

Google Scholar 
Mansour, K., Mutanga, O., Adam, E. & Abdel-Rahman, E. M. Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors. Geocarto Int. 31, 477–491 (2016).Article 

Google Scholar 
Hunter, F. D. L., Mitchard, E. T. A., Tyrrell, P. & Russell, S. Inter-Seasonal time series imagery enhances classification accuracy of grazing resource and land degradation maps in a savanna ecosystem. Remote Sensing 12, 198 (2020).Article 
ADS 

Google Scholar 
Yang, L. et al. Estimating surface downward shortwave radiation over china based on the gradient boosting decision tree method. Remote Sensing 10, 185 (2018).Article 
ADS 

Google Scholar 
Pham, T. D. et al. Estimating mangrove Above-Ground biomass using extreme gradient boosting decision trees algorithm with fused Sentinel-2 and ALOS-2 PALSAR-2 data in Can Gio biosphere reserve, Vietnam. Remote Sensing 12, 777 (2020).Article 
ADS 

Google Scholar 
Adobe Inc. Adobe illustrator.Lenth, R. V. emmeans: Estimated marginal means, aka Least-Squares means. R package version 1.5.4 (2021).Royall, R. M. The effect of sample size on the meaning of significance tests. Am. Stat. 40, 313–315 (1986).MATH 

Google Scholar 
Rue, H., Martino, S. & Chopin, N. Approximate bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Series B Stat. Methodol. 71, 319–392 (2009).Lindgren, F. & Rue, H. Bayesian spatial modelling with R-INLA. J. Stat. Softw. 63, 1–25 (2015).Article 

Google Scholar 
Bakka, H. et al. Spatial modelling with R-INLA: A review. arXiv:1802.06350 [stat] (2018).Lobora, A. L. et al. Modelling habitat conversion in Miombo woodlands: Insights from Tanzania. J. Land Use Sci. 1747423X.2017.1331271 (2017).Bright, E. A., Rose, A. N., Urban, M. L. & McKee, J. LandScan 2017 High-Resolution global population data set. Tech. Rep., Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States) (2018).Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci Data 5, 180227 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Yang, Y., Fang, J., Ma, W. & Wang, W. Relationship between variability in aboveground net primary production and precipitation in global grasslands. Geophys. Res. Lett. 35 (2008).Guo, Q. et al. Spatial variations in aboveground net primary productivity along a climate gradient in Eurasian temperate grassland: effects of mean annual precipitation and its seasonal distribution. Glob. Chang. Biol. 18, 3624–3631 (2012).Article 
ADS 

Google Scholar 
Wang, X., Yue, Y. & Faraway, J. J. Bayesian Regression Modeling with INLA (Chapman and Hall/CRC, 2018).Côté, I. M. & Darling, E. S. Rethinking ecosystem resilience in the face of climate change. PLoS Biol. 8, e1000438 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
O’Loughlin, J. et al. Climate variability and conflict risk in East Africa, 1990–2009. Proc. Natl. Acad. Sci. 109, 18344–18349 (2012).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Ongoma, V., Chen, H., Gao, C., Nyongesa, A. M. & Polong, F. Future changes in climate extremes over Equatorial East Africa based on CMIP5 multimodel ensemble. Nat. Hazards 90, 901–920 (2018).Article 

Google Scholar 
Homewood, K. & Rodgers, W. A. Pastoralism, conservation and the overgrazing controversy. Conservation in Africa: People, policies and practice 111–128 (1987).Scoones, I. Exploiting heterogeneity: habitat use by cattle in dryland Zimbabwe. J. Arid Environ. 29, 221–237 (1995).Article 
ADS 

Google Scholar 
Goldman, M. J. & Riosmena, F. Adaptive capacity in Tanzanian Maasailand: Changing strategies to cope with drought in fragmented landscapes. Glob. Environ. Change 23, 588–597 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Selemani, I. S. & Others. Communal rangelands management and challenges underpinning pastoral mobility in Tanzania: a review. Livestock Res. Rural Dev. 26, 1–12 (2014).Middleton, N. Rangeland management and climate hazards in drylands: dust storms, desertification and the overgrazing debate. Nat. Hazards 92, 57–70 (2018).Article 

Google Scholar 
Sallu, S. M., Twyman, C. & Stringer, L. C. Resilient or vulnerable livelihoods? Assessing livelihood dynamics and trajectories in rural Botswana. Ecology and Society 15 (2010).Oba, G. & Lusigi, W. J. An overview of drought strategies and land use in African pastoral systems (Agricultural Administration Unit, Overseas Development Institute, 1987).Russell, S., Tyrrell, P. & Western, D. Seasonal interactions of pastoralists and wildlife in relation to pasture in an African savanna ecosystem. J. Arid Environ. 154, 70–81 (2018).Article 
ADS 

Google Scholar 
Girvetz, E. et al. Future climate projections in Africa: Where are we headed? In The Climate-Smart Agriculture Papers: Investigating the Business of a Productive, Resilient and Low Emission Future 15–27 (Springer International Publishing, 2019).Lyon, B. & DeWitt, D. G. A recent and abrupt decline in the East African long rains. Geophys. Res. Lett. 39 (2012).Liebmann, B. et al. Climatology and interannual variability of boreal spring wet season precipitation in the Eastern Horn of Africa and implications for its recent decline. J. Clim. 30, 3867–3886 (2017).Article 
ADS 

Google Scholar 
Shongwe, M. E., van Oldenborgh, G. J., van den Hurk, B. & van Aalst, M. Projected changes in mean and extreme precipitation in Africa under global warming. part II: East Africa. J. Clim. 24, 3718–3733 (2011).Dunning, C. M., Black, E. & Allan, R. P. Later wet seasons with more intense rainfall over Africa under future climate change. J. Clim. 31, 9719–9738 (2018).Article 
ADS 

Google Scholar 
Rowell, D. P., Booth, B. B. B., Nicholson, S. E. & Good, P. Reconciling past and future rainfall trends over East Africa. J. Clim. 28, 9768–9788 (2015).Article 
ADS 

Google Scholar 
Vizy, E. K. & Cook, K. H. Mid-Twenty-First-Century changes in extreme events over Northern and Tropical Africa. J. Clim. 25, 5748–5767 (2012).Article 
ADS 

Google Scholar 
Gebremeskel Haile, G. et al. Droughts in East Africa: Causes, impacts and resilience. Earth-Sci. Rev. 193, 146–161 (2019).Kendon, E. J. et al. Enhanced future changes in wet and dry extremes over Africa at convection-permitting scale. Nat. Commun. 10, 1794 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Finney, D. L. et al. Effects of explicit convection on future projections of mesoscale circulations, rainfall, and rainfall extremes over Eastern Africa. J. Clim. 33, 2701–2718 (2020).Article 
ADS 

Google Scholar 
Prins, H. H. T. & Loth, P. E. Rainfall patterns as background to plant phenology in Northern Tanzania. J. Biogeogr. 15, 451–463 (1988).Article 

Google Scholar 
Ngondya, I. B., Treydte, A. C., Ndakidemi, P. A. & Munishi, L. K. Invasive plants: ecological effects, status, management challenges in Tanzania and the way forward. J. Biodivers. Environ. Sci. (JBES) 10, 204–217 (2017).
Google Scholar 
Drusch, M. et al. Sentinel-2: ESA’s optical High-Resolution mission for GMES operational services. Remote Sens. Environ. 120, 25–36 (2012).Article 
ADS 

Google Scholar 
Rapinel, S. et al. Evaluation of Sentinel-2 time-series for mapping floodplain grassland plant communities. Remote Sens. Environ. 223, 115–129 (2019).Article 
ADS 

Google Scholar 
Li, W. et al. Accelerating savanna degradation threatens the Maasai Mara socio-ecological system. Glob. Environ. Change 60, 102030 (2020).Article 

Google Scholar 
Wonkka, C. L., Twidwell, D., Franz, T. E., Taylor, C. A. & Rogers, W. E. Persistence of a severe drought increases desertification but not woody dieback in semiarid savanna. Rangeland Ecol. Manage. 69, 491–498 (2016).Article 

Google Scholar 
Vierich, H. I. D. & Stoop, W. A. Changes in West African savanna agriculture in response to growing population and continuing low rainfall. Agric. Ecosyst. Environ. 31, 115–132 (1990).Article 

Google Scholar 
Fynn, R. W. S. & O’Connor, T. G. Effect of stocking rate and rainfall on rangeland dynamics and cattle performance in a semi-arid savanna, South Africa. J. Appl. Ecol. 37, 491–507 (2000).Article 

Google Scholar 
Wang, S., Chen, W., Xie, S. M., Azzari, G. & Lobell, D. B. Weakly supervised deep learning for segmentation of remote sensing imagery. Remote Sensing 12, 207 (2020).Article 
ADS 

Google Scholar 
Alananga, S., Makupa, E. R., Moyo, K. J., Matotola, U. C. & Mrema, E. F. Land administration practices in Tanzania: A replica of past mistakes. Journal of Property, Planning and Environmental Law (2019).Huggins, C. Village land use planning and commercialization of land in Tanzania. LANDac Research Brief 1 (2016).Stein, H., Maganga, F. P., Odgaard, R., Askew, K. & Cunningham, S. The formal divide: Customary rights and the allocation of credit to agriculture in Tanzania. J. Dev. Stud. 52, 1306–1319 (2016).Article 

Google Scholar 
Hall, D. G. M., Reeve, M. J., Thomasson, A. J. & Wright, V. F. Water retention, porosity and density of field soils (No. Tech. Monograph N9, 1977).Moore, D. C. & Singer, M. J. Crust formation effects on soil erosion processes. Soil Sci. Soc. Am. J. 54, 1117–1123 (1990).Article 
ADS 

Google Scholar 
Cotler, H. & Ortega-Larrocea, M. P. Effects of land use on soil erosion in a tropical dry forest ecosystem, Chamela watershed, Mexico. Catena 65, 107–117 (2006).Article 

Google Scholar 
Bach, E. M., Baer, S. G., Meyer, C. K. & Six, J. Soil texture affects soil microbial and structural recovery during grassland restoration. Soil Biol. Biochem. 42, 2182–2191 (2010).Article 
CAS 

Google Scholar 
Butz, R. J. Traditional fire management: historical fire regimes and land use change in pastoral East Africa. Int. J. Wildland Fire 18, 442–450 (2009).Article 

Google Scholar  More

Fernández-Martínez, M. et al. Global trends in carbon sinks and their relationships with CO2 and temperature. Nat. Clim. Change 9, 73–79 (2019).Article 
ADS 

Google Scholar 
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. Proc. Natl Acad. Sci. USA 105, 14308–14312 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gasser, T. et al. Path-dependent reductions in CO2 emission budgets caused by permafrost carbon release. Nat. Geosci. 11, 830–835 (2018).Article 
ADS 
CAS 

Google Scholar 
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).Article 
ADS 
CAS 

Google Scholar 
Bastos, A. et al. Contrasting effects of CO2 fertilization, land-use change and warming on seasonal amplitude of Northern Hemisphere CO2 exchange. Atmos. Chem. Phys. 19, 12361–12375 (2019).Article 
ADS 
CAS 

Google Scholar 
Pugh, T. A. M. et al. Role of forest regrowth in global carbon sink dynamics. Proc. Natl Acad. Sci. USA 116, 4382–4387 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Peñuelas, J. et al. Assessment of the impacts of climate change on Mediterranean terrestrial ecosystems based on data from field experiments and long-term monitored field gradients in Catalonia. Environ. Exp. Bot. 152, 49–59 (2018).Article 

Google Scholar 
Terrer, C. et al. Nitrogen and phosphorus constrain the CO2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).Article 
ADS 
CAS 

Google Scholar 
Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Carpenter, S. R. & Brock, W. A. Rising variance: a leading indicator of ecological transition. Ecol. Lett. 9, 311–318 (2006).Article 
CAS 
PubMed 

Google Scholar 
Dakos, V., Nes, E. H. & Scheffer, M. Flickering as an early warning signal. Theor. Ecol. 6, 309–317 (2013).Article 

Google Scholar 
Sillmann, J., Daloz, A. S., Schaller, N. & Schwingshackl, C. in Climate Change 3rd edn (ed. Letcher, T. M.) 359–372 (Elsevier, 2021).Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Wang, X. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Buermann, W. et al. Climate-driven shifts in continental net primary production implicated as a driver of a recent abrupt increase in the land carbon sink. Biogeosciences 13, 1597–1607 (2016).Article 
ADS 
CAS 

Google Scholar 
Luyssaert, S. et al. CO2 balance of boreal, temperate, and tropical forests derived from a global database. Glob. Change Biol. 13, 2509–2537 (2007).Article 
ADS 

Google Scholar 
Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).Article 
PubMed 

Google Scholar 
Fernández-Martínez, M. et al. Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Change 4, 471–476 (2014).Article 
ADS 

Google Scholar 
Fernández-Martínez, M. et al. Spatial variability and controls over biomass stocks, carbon fluxes and resource-use efficiencies in forest ecosystems. Trees Struct. Funct. 28, 597–611 (2014).Article 

Google Scholar 
Ciais, P. et al. Five decades of northern land carbon uptake revealed by the interhemispheric CO2 gradient. Nature 568, 221–225 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Tilman, D., Lehman, C. L. & Thomson, K. T. Plant diversity and ecosystem productivity: theoretical considerations. Proc. Natl Acad. Sci. USA 94, 1857–1861 (1997).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
de Mazancourt, C. et al. Predicting ecosystem stability from community composition and biodiversity. Ecol. Lett. 16, 617–625 (2013).Article 
PubMed 

Google Scholar 
Sakschewski, B. et al. Resilience of Amazon forests emerges from plant trait diversity. Nat. Clim. Change 6, 1032–1036 (2016).Article 
ADS 

Google Scholar 
Fernández‐Martínez, M. et al. The role of climate, foliar stoichiometry and plant diversity on ecosystem carbon balance. Glob. Change Biol. 26, 7067–7078 (2020).Article 
ADS 

Google Scholar 
Musavi, T. et al. Stand age and species richness dampen interannual variation of ecosystem-level photosynthetic capacity. Nat. Ecol. Evol. 1, 0048 (2017).Article 

Google Scholar 
Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
IPBES: Summary for Policymakers. In The Global Assessment Report on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) 1–56 (IPBES, 2019).Heath, J. P. Quantifying temporal variability in population abundances. Oikos 115, 573–581 (2006).Article 

Google Scholar 
Fernández-Martínez, M., Vicca, S., Janssens, I. A., Martín-Vide, J. & Peñuelas, J. The consecutive disparity index, D, as measure of temporal variability in ecological studies. Ecosphere 9, e02527 (2018).Article 

Google Scholar 
Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc Natl Acad Sci USA 104, 5925–5930 (2007).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ackerman, D. E., Chen, X. & Millet, D. B. Global nitrogen deposition (2° × 2.5° grid resolution) simulated with GEOS-Chem for 1984–1986, 1994–1996, 2004–2006, and 2014–2016 (University of Minnesota, 2018); https://conservancy.umn.edu/handle/11299/197613.Harris, I., Jones, P. D. D., Osborn, T. J. J. & Lister, D. H. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2013).Article 

Google Scholar 
Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Wang, K. et al. Causes of slowing-down seasonal CO2 amplitude at Mauna Loa. Glob. Change Biol. 26, 4462–4477 (2020).Article 
ADS 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957–aaf8957 (2016).Article 
PubMed 

Google Scholar 
Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).Article 
PubMed 

Google Scholar 
Peguero, G. et al. Fast attrition of springtail communities by experimental drought and richness–decomposition relationships across Europe. Glob. Change Biol. 25, 2727–2738 (2019).Article 
ADS 

Google Scholar 
Díaz, S. & Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

Google Scholar 
Cardinale, B. J. Biodiversity improves water quality through niche partitioning. Nature 472, 86–91 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Scheffer, M. Critical Transitions in Nature and Society (Princeton University Press, 2009).Ostfeld, R. & Keesing, F. Pulsed resources and community dynamics of consumers in terrestrial ecosystems. Trends Ecol. Evol. 15, 232–237 (2000).Article 
CAS 
PubMed 

Google Scholar 
Chevallier, F. et al. CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements. J. Geophys. Res. 115, D21307 (2010).Article 
ADS 

Google Scholar 
Chevallier, F. et al. Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne measurements of atmospheric CO2. Geophys. Res. Lett. 41, 1065–1070 (2014).Article 
ADS 
CAS 

Google Scholar 
Rödenbeck, C., Houweling, S., Gloor, M. & Heimann, M. CO2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport. Atmos. Chem. Phys. 3, 1919–1964 (2003).Article 
ADS 

Google Scholar 
Rödenbeck, C., Zaehle, S., Keeling, R. & Heimann, M. How does the terrestrial carbon exchange respond to interannual climatic variations? A quantification based on atmospheric CO2 data. Biogeosciences 15, 2481–2498 (2018).Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).Article 
ADS 

Google Scholar 
Fernández‐Martínez, M. & Peñuelas, J. Measuring temporal patterns in ecology: the case of mast seeding. Ecol. Evol. 11, 2990–2996 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood, S. N. Generalized Additive Models: An introduction with R 2nd edn (Chapman and Hall/CRC, 2017).Ohlson, J. A. & Kim, S. Linear Valuation Without OLS: The Theil–Sen Estimation Approach (SSRN, 2015); https://ssrn.com/abstract=2276927.Komsta, L. Package mblm, 0.12.1: Median-based linear models (2013).Keeling, C. D. et al. in A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems (eds Ehleringer, J. R. et al.) 83–113 (Springer Verlag, 2005).Leroux, B. G., Lei, X. & Breslow, N. in Statistical Models in Epidemiology, the Environment and Clinical Trials (eds Halloran, M. & Berry, D.) 179–191 (Springer-Verlag, 2000).Lee, D. CARBayes: an R package for Bayesian spatial modeling with conditional autoregressive priors. J. Stat. Softw. 55, 1–24 (2013).Article 

Google Scholar 
Gonzalez, A. et al. Scaling‐up biodiversity–ecosystem functioning research. Ecol. Lett. 15, ele.13456 (2020).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Benson, E. S. Sci. Context 29, 107–128 (2016).Article 
PubMed 

Google Scholar 
Buckmaster, C. A. Lab Anim. 44, 237 (2015).Article 

Google Scholar 
Kelly, M. J. et al. J. Zool. 244, 473–488 (1998).Article 

Google Scholar 
Spagnuolo, O. S. B., Lemerle, M. A., Holekamp, K. E. & Wiesel, I. Mamm. Biol. https://doi.org/10.1007/s42991-022-00309-4 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
California Department of Fish and Wildlife. Mountain lion P-22 compassionately euthanized following complete health evaluation results. wildlife.ca.gov, https://wildlife.ca.gov/News/mountain-lion-p-22-compassionately-euthanized-following-complete-health-evaluation-results (17 December 2022).Road Ecology Center, UC Davis. California roadkill observation system, https://www.wildlifecrossing.net/california/ (accessed 19 December 2022).Wong-Parodi, G. & Feygina, I. Environ. Commun. 15, 571–593 (2021).Article 

Google Scholar 
Carmi, N., Arnon, S. & Orion, N. J. Environ. Educ. 46, 183–201 (2015).Article 

Google Scholar 
Manfredo, M. J., Urquiza-Haas, E. G., Don Carlos, A. W., Bruskotter, J. T. & Dietsch, A. M. Biol. Conserv. 241, 108297 (2020).Article 

Google Scholar 
Schueler, D. S. & Newberry, M. G. III Appl. Environ. Educ. Commun. 19, 259–273 (2020).Article 

Google Scholar 
Jennings, L. Public gets to name Dallas Zoo’s baby giraffe. Dallas Zoo https://zoohoo.dallaszoo.com/2014/11/05/public-gets-to-name-dallas-zoos-baby-giraffe/ (5 November 2014).Verma, A., van der Wal, R. & Fischer, A. Ambio 44(Suppl 4), 648–660 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Macdonald, D. W., Jacobsen, K. S., Burnham, D., Johnson, P. J. & Loveridge, A. J. Animals 6, 26 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Jones, M. D., Shanahan, E. A. & McBeth, M. K. The Science of Stories: Applications of the Narrative Policy Framework in Public Policy Analysis (Palgrave MacMillan, 2014). More

Animal collection and holding for this project was conducted under Marine Research Permit RE-19–28 issued by the Ministry of Natural Resources, Environment, and Tourism of the Republic of Palau (10.03.2019), Marine Research/Collection Permit and Agreement 62 issued by the Koror State Government (08.10.2019), Queensland Government GBRMPA Marine Parks Permit G14/36689.1, Queensland Government DNPRSR Marine Parks Permits QS2014/MAN247 and QS2014/MAN247a, Queensland Government General Fisheries Permit 168991, Queensland Government DAFF Animal Ethics approval CA2013/11/733, approval by The Bahamas Department of Marine Resources, approval by the Animal Care Officer of both the University of Bremen and the Leibniz Centre for Tropical Marine Research (ZMT), and in accordance with UK and Germany animal care guidelines.Sample collectionWe collected fish carbonate samples at four study locations across three tropical and subtropical regions: Eleuthera (24°50’N, 76°20’W), The Bahamas, between 2009 and 201127,37; Heron Reef (23°27’S, 151°55’E) and Moreton Bay (27°29’S, 153°24’E) in Queensland, Australia, in 2014 and 201528; and Koror (7°20’N, 134°28’E), Palau, during November and December 2019. These are located within four distinct marine biogeographic provinces and three realms (Tropical Atlantic, Central Indo-Pacific, and Temperate Australasia)43. At each location fish were collected using barrier nets, dip nets, clove oil or hook and line, and immediately transferred to aquaria facilities at the Cape Eleuthera Institute, Heron Island and Moreton Bay Research Stations, and the Palau International Coral Reef Center. Fish were held in a range of tanks (60, 400, or 1400 L in the Bahamas, 10, 60, 100, 120, or 400 L in Heron Island and Moreton Bay, and 8, 80, 280, or 400 L in Palau) of suitable dimensions for different fish sizes ( 5). Each sample was titrated with 0.01–0.5 N HCl (with continuous aeration with CO2-free air) until the end point (grey-lavender; pH~4.80) was reached and stable for at least 10 min. If the sample was over-titrated (pink), 0.01–0.1 N NaOH was added to titrate back to the end point and the amount of base used was subtracted from the amount of acid. Acid and base were added using an electronic multi-dispenser pipette (Eppendorf Repeater ®E3X, Eppendorf, Hamburg, Germany) with a precision of  ± 1 ({{{{{rm{mu }}}}}})L. Additionally, the pH of several samples was monitored using a pH microelectrode (Mettler Toledo InLab Micro) to ascertain the correctness of the colorimetric end point. The amount of carbonate in the sample was then calculated using Eq. (1). The method was validated using certified reference material (Alkalinity Standard Solution, 25,000 mg/L as CaCO3, HACH) and the accuracy in the determination of solid samples was verified using certified CaCO3 powder (Suprapur, ≥ 99.95% purity, Merck) samples (60–500 ({{{{{rm{mu }}}}}})g) and resulted in 96.53 ± 1.94% accuracy (mean ± SE; n = 8).To compare values obtained with the two titration methods we further analysed 12 samples collected at Lizard Island, Australia, in February 2016. Samples were collected at 24 h intervals from one individual of Lethrinus atkinsoni (f. Lethrinidae, body mass: 245 g), a group of five Lutjanus fulvus (f. Lutjanidae, mean body mass: 21 g), and an individual of Cephalopholis cyanostigma (f. Serranidae, body mass: 295 g), following the procedures described above. During sample collection water temperature ranged from 29.1 °C during the night to 32.6 °C during the day, with an average of ~31 °C, mean salinity was 35.4, and pHNBS ranged from 8.13 to 8.21. To compare the amount of carbonate measured by the two methods we added carbonate samples to 20 ml ultrapure water and disaggregated crystals via sonication. We then used a Metrohm Titrando autotitrator and Metrohm Aquatrode pH electrode to measure initial pH of the suspension of carbonates, then titrated each sample of carbonate in two stages. Firstly, they were titrated down to pH 4.80 using 0.1 M HCl, adding 20 µl increments of acid until this was sufficient to keep pH below 4.80 for 10 min whilst bubbling with CO2-free air. This first stage was comparable to the single end point titration used for samples collected in Palau. Secondly, whilst continuing to bubble with CO2-free air, further acid was added to the sample until it reached pH 3.89 and was stable for 1 min. Then 0.1 M NaOH was added to the samples to return them to the initial pH. For all samples the first end point titration (to pH 4.80) yielded slightly higher values for carbonate content than the second double titration. The ratio between the two methods (single end point/double titration) was 1.08 ± 0.01 (mean ± SE; range: 1.04–1.14; Supplementary Table 2). As we found a small but consistent difference between the two methods, all following analyses were initially performed on the actual data obtained with the double titration for samples from Australia and The Bahamas, and the single end point titration for samples from Palau. Then, to assess the robustness of the results, we repeated the analyses after applying a correction factor of 1.08 to the excretion rates of Palauan fishes (that used the single end point titration method). All results were consistent and robust to the measured difference between the titration methods (Supplementary Figs. 8, 9).Finally, measurements of multiple samples from each individual collected over periods of 18–169 h (median: 64 h) were combined to produce an average individual excretion rate in ({{{{{rm{mu }}}}}})mol h−1. For fish held in groups, carbonate excretion rates per individual (of average biomass) were obtained by averaging the total excretion rate of the group across the sampling period and dividing it by the number of individuals in the tank. Excretion rates obtained from fish groups thus evened the intraspecific variability within tanks, and are therefore more robust than those directly obtained from fish held individually. This aspect was considered in our models by fitting weighted regressions (see the “Statistical modelling” section). In total, we measured the carbonate excretion rates of 382 individual fishes arranged in 192 groups (i.e., independent observations), representing 85 species from 35 families across three tropical regions (180 individuals from 29 species in Australia, 90 individuals from 10 species in the Bahamas, and 112 individuals from 46 species in Palau; Supplementary Table 1).We assume that during the sampling of carbonates fishes were close to their resting metabolic rate and that their carbonate excretion rates are representative of fish at rest. Although the ratio of tank volume to fish volume in our study (median ~660; inter-quartile range ~180–1700) typically greatly exceeds the guideline ideal range for measuring resting metabolic rate (20–50)85, fishes were fasted prior to and throughout sampling, and in most instances their movement was somewhat constrained by tank volume. Fasting reduces metabolic rate in all animals, including fish, as they do not undergo energy-intensive digestive processes and use energy reserves to support vital processes, triggering metabolic changes in many tissues and reducing activity levels86,87. Additionally, other than the carbonate syphoning ( More

Mooney, H. et al. Biodiversity, climate change, and ecosystem services. Curr. Opin. Environ. Sustain. 1, 46–54 (2009).Article 

Google Scholar 
Perrings, C., Duraiappah, A., Larigauderie, A. & Mooney, H. The biodiversity and ecosystem services science-policy interface. Science 331, 1139–1140 (2011).Article 
ADS 
CAS 

Google Scholar 
Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis. Proc. Natl. Acad. Sci. USA 96, 1463–1468 (1999).Article 
ADS 
CAS 

Google Scholar 
Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
Gonzalez, A. & Loreau, M. The causes and consequences of compensatory dynamics in ecological communities. Annu. Rev. Ecol. Evol. Syst. 40, 393–414 (2009).Article 

Google Scholar 
Blüthgen, N. & Klein, A.-M. Functional complementarity and specialisation: The role of biodiversity in plant–pollinator interactions. Basic Appl. Ecol. 12, 282–291 (2011).Article 

Google Scholar 
Brittain, C., Kremen, C. & Klein, A. M. Biodiversity buffers pollination from changes in environmental conditions. Glob. Change Biol. 19, 540–547 (2013).Article 
ADS 

Google Scholar 
Rader, R., Reilly, J., Bartomeus, I. & Winfree, R. Native bees buffer the negative impact of climate warming on honey bee pollination of watermelon crops. Glob. Chang. Biol. 19, 3103–3110 (2013).Article 
ADS 

Google Scholar 
Rogers, S. R., Tarpy, D. R. & Burrack, H. J. Bee species diversity enhances productivity and stability in a perennial crop. PLoS ONE 9, e97307 (2014).Article 
ADS 

Google Scholar 
Kühsel, S. & Blüthgen, N. High diversity stabilizes the thermal resilience of pollinator communities in intensively managed grasslands. Nat. Commun. 6, 1–10 (2015).Article 

Google Scholar 
Knop, E. et al. Rush hours in flower visitors over a day-night cycle. Insect Conserv. Divers. 11, 267–275 (2018).Article 

Google Scholar 
Goodwin, E. K., Rader, R., Encinas-Viso, F. & Saunders, M. E. Weather conditions affect the visitation frequency, richness and detectability of insect flower visitors in the Australian Alpine zone. Environ. Entomol. 50, 348–358 (2021).Article 

Google Scholar 
Feit, B. et al. Landscape complexity promotes resilience of biological pest control to climate change. Proc. Biol. Sci. 288, 20210547 (2021).
Google Scholar 
Tomas, F., Martínez-Crego, B., Hernán, G. & Santos, R. Responses of seagrass to anthropogenic and natural disturbances do not equally translate to its consumers. Glob. Chang. Biol. 21, 4021–4030 (2015).Article 
ADS 

Google Scholar 
Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364 (2013).Article 

Google Scholar 
Cariveau, D. P., Williams, N. M., Benjamin, F. E. & Winfree, R. Response diversity to land use occurs but does not consistently stabilise ecosystem services provided by native pollinators. Ecol. Lett. 16, 903–911 (2013).Article 

Google Scholar 
Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608. https://doi.org/10.1126/science.1230200 (2013).Article 
ADS 
CAS 

Google Scholar 
Kennedy, C. M. et al. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol. Lett. 16, 584–599 (2013).Article 

Google Scholar 
Rader, R. et al. Non-bee insects are important contributors to global crop pollination. Proc. Natl. Acad. Sci. 113, 146–151 (2016).Article 
ADS 
CAS 

Google Scholar 
Klein, A.-M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).Article 

Google Scholar 
Smith, M. R., Singh, G. M., Mozaffarian, D. & Myers, S. S. Effects of decreases of animal pollinators on human nutrition and global health: A modelling analysis. Lancet 386, 1964–1972 (2015).Article 

Google Scholar 
González-Varo, J. P. et al. Combined effects of global change pressures on animal-mediated pollination. Trends Ecol. Evol. 28, 524–530 (2013).Article 

Google Scholar 
Marshall, L. et al. The interplay of climate and land use change affects the distribution of EU bumblebees. Glob. Change Biol. 24, 101–116 (2018).Article 
ADS 

Google Scholar 
Millard, J. et al. Global effects of land-use intensity on local pollinator biodiversity. Nat. Commun. 12, 1–11 (2021).Article 
ADS 

Google Scholar 
Vasiliev, D. & Greenwood, S. The role of climate change in pollinator decline across the Northern Hemisphere is underestimated. Sci. Total Environ. 775, 145788 (2021).Article 
ADS 
CAS 

Google Scholar 
Steffan-Dewenter, I., Münzenberg, U., Bürger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432 (2002).Article 

Google Scholar 
Hass, A. L. et al. Landscape configurational heterogeneity by small-scale agriculture, not crop diversity, maintains pollinators and plant reproduction in western Europe. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2017.2242 (2018).Article 

Google Scholar 
Winfree, R. & Kremen, C. Are ecosystem services stabilized by differences among species? A test using crop pollination. Proc. R. Soc. B Biol. Sci. 276, 229–237 (2009).Article 

Google Scholar 
Jauker, F., Diekoetter, T., Schwarzbach, F. & Wolters, V. Pollinator dispersal in an agricultural matrix: Opposing responses of wild bees and hoverflies to landscape structure and distance from main habitat. Landsc. Ecol. 24, 547–555 (2009).Article 

Google Scholar 
Weiner, C. N., Werner, M., Linsenmair, K. E. & Blüthgen, N. Land-use impacts on plant–pollinator networks: Interaction strength and specialization predict pollinator declines. Ecology 95, 466–474 (2014).Article 

Google Scholar 
Chain-Guadarrama, A., Martínez-Salinas, A., Aristizábal, N. & Ricketts, T. H. Ecosystem services by birds and bees to coffee in a changing climate: A review of coffee berry borer control and pollination. Agric. Ecosyst. Environ. 280, 53–67 (2019).Article 

Google Scholar 
Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A. L. & Totland, Ø. How does climate warming affect plant–pollinator interactions?. Ecol. Lett. 12, 184–195 (2009).Article 

Google Scholar 
Bartomeus, I. et al. Contribution of insect pollinators to crop yield and quality varies with agricultural intensification. PeerJ 2, e328 (2014).Article 

Google Scholar 
Albrecht, M., Schmid, B., Hautier, Y. & Müller, C. B. Diverse pollinator communities enhance plant reproductive success. Proc. R. Soc. B Biol. Sci. 279, 4845–4852 (2012).Article 

Google Scholar 
Ellis, C. R., Feltham, H., Park, K., Hanley, N. & Goulson, D. Seasonal complementary in pollinators of soft-fruit crops. Basic Appl. Ecol. 19, 45–55 (2017).Article 

Google Scholar 
Brittain, C., Williams, N., Kremen, C. & Klein, A.-M. Synergistic effects of non-Apis bees and honey bees for pollination services. Proc. R. Soc. B Biol. Sci. 280, 20122767 (2013).Article 

Google Scholar 
Miñarro, M. & Twizell, K. W. Pollination services provided by wild insects to kiwifruit (Actinidia deliciosa). Apidologie 46, 276–285 (2015).Article 

Google Scholar 
Senapathi, D., Goddard, M. A., Kunin, W. E. & Baldock, K. C. Landscape impacts on pollinator communities in temperate systems: Evidence and knowledge gaps. Funct. Ecol. 31, 26–37 (2017).Article 

Google Scholar 
Papanikolaou, A. D., Kuehn, I., Frenzel, M. & Schweiger, O. Landscape heterogeneity enhances stability of wild bee abundance under highly varying temperature, but not under highly varying precipitation. Landsc. Ecol. 32, 581–593 (2017).Article 

Google Scholar 
Papanikolaou, A. D., Kühn, I., Frenzel, M. & Schweiger, O. Semi-natural habitats mitigate the effects of temperature rise on wild bees. J. Appl. Ecol. 54, 527–536 (2017).Article 

Google Scholar 
Orford, K. A., Vaughan, I. P. & Memmott, J. The forgotten flies: The importance of non-syrphid Diptera as pollinators. Proc. R. Soc. B Biol. Sci. 282, 20142934 (2015).Article 

Google Scholar 
Settele, J., Bishop, J. & Potts, S. G. Climate change impacts on pollination. Nat. Plants 2, 1–3 (2016).Article 

Google Scholar 
Taki, H., Okabe, K., Makino, S. I., Yamaura, Y. & Sueyoshi, M. Contribution of small insects to pollination of common buckwheat, a distylous crop. Ann. Appl. Biol. 155, 121–129 (2009).Article 

Google Scholar 
Krkošková, B. & Mrazova, Z. Prophylactic components of buckwheat. Food Res. Int. 38, 561–568 (2005).Article 

Google Scholar 
Campbell, J. W., Irvin, A., Irvin, H., Stanley-Stahr, C. & Ellis, J. D. Insect visitors to flowering buckwheat, Fagopyrum esculentum (Polygonales: Polygonaceae), in north-central Florida. Fla. Entomol. 99, 264–268 (2016).Article 

Google Scholar 
Hadley, N. F. Water Relations of Terrestrial Arthropods (CUP Archive, 1994).
Google Scholar 
Sgolastra, F. et al. Temporal activity patterns in a flower visitor community of Dictamnus albus in relation to some biotic and abiotic factors. Bull. Insectol. 69, 291–300 (2016).
Google Scholar 
Vicens, N. & Bosch, J. Weather-dependent pollinator activity in an apple orchard, with special reference to Osmia cornuta and Apis mellifera (Hymenoptera: Megachilidae and Apidae). Environ. Entomol. 29, 413–420 (2000).Article 

Google Scholar 
Carlucci, M. B., Brancalion, P. H., Rodrigues, R. R., Loyola, R. & Cianciaruso, M. V. Functional traits and ecosystem services in ecological restoration. Restor. Ecol. 28, 1372–1383 (2020).Article 

Google Scholar 
Lavorel, S. Plant functional effects on ecosystem services. (2013).Defra. (ed Food and Rural Affairs Department for Environment) (2019).Agency, J. M. Amedas, https://tenki.jp/past/2019/09/amedas/ (2019).Jacquemart, A.-L., Gillet, C. & Cawoy, V. Floral visitors and the importance of honey bee on buckwheat (Fagopyrum esculentum Moench) in central Belgium. J. Hortic. Sci. Biotechnol. 82, 104–108 (2007).Article 

Google Scholar 
Taki, H. et al. Effects of landscape metrics on Apis and non-Apis pollinators and seed set in common buckwheat. Basic Appl. Ecol. 11, 594–602 (2010).Article 

Google Scholar 
Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006).Article 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, 2012).MATH 

Google Scholar 
Dray S, et al. adespatial: Multivariate Multiscale Spatial Analysis. R package version 0.3-20, https://CRAN.R-project.org/package=adespatial. (2022).Benjamin, F. E., Reilly, J. R. & Winfree, R. Pollinator body size mediates the scale at which land use drives crop pollination services. J. Appl. Ecol. 51, 440–449 (2014).Article 

Google Scholar 
Földesi, R. et al. Relationships between wild bees, hoverflies and pollination success in apple orchards with different landscape contexts. Agric. For. Entomol. 18, 68–75 (2016).Article 

Google Scholar 
Oksanen J, et al. vegan: Community Ecology Package. R package version 2.6-4. https://CRAN.R-project.org/package=vegan. (2022)Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis (Chapman and Hall/CRC, 1995).Book 
MATH 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing (2019).Sasaki, H. & Wagatsuma, T. Bumblebees (Apidae: Hymenoptera) are the main pollinators of common buckwheat, Fogopyrum esculentum, in Hokkaido, Japan. Appl. Entomol. Zool. 42, 659–661 (2007).Article 

Google Scholar 
Nagano, Y., Miyashita, T., Taki, H. & Yokoi, T. Diversity of co-flowering plants at field margins potentially sustains an abundance of insects visiting buckwheat, Fagopyrum esculentum, in an agricultural landscape. Ecol. Res. 36, 882–891 (2021).Article 

Google Scholar 
Samra, S., Samocha, Y., Eisikowitch, D. & Vaknin, Y. Can ants equal honeybees as effective pollinators of the energy crop Jatropha curcas L. under Mediterranean conditions?. Gcb Bioenergy 6, 756–767 (2014).Article 

Google Scholar 
Sugiura, N., Miyazaki, S. & Nagaishi, S. A supplementary contribution of ants in the pollination of an orchid, Epipactis thunbergii, usually pollinated by hover flies. Plant Syst. Evol. 258, 17–26 (2006).Article 

Google Scholar 
Natsume, K., Hayashi, S. & Miyashita, T. Ants are effective pollinators of common buckwheat Fagopyrum esculentum. Agric. For. Entomol. 24, 446–452 (2022).Article 

Google Scholar 
Carvalheiro, L. G., Seymour, C. L., Nicolson, S. W. & Veldtman, R. Creating patches of native flowers facilitates crop pollination in large agricultural fields: Mango as a case study. J. Appl. Ecol. 49, 1373–1383 (2012).Article 

Google Scholar 
Michiyama, H., Arikuni, M. & Hirano, T. Effect of air temperature on the growth, flowering and ripening in common buckwheat. In The Procceeding of the 8th ISB (2001)Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199-U196. https://doi.org/10.1038/nature10282 (2011).Article 
ADS 
CAS 

Google Scholar 
McCain, C. M. & Colwell, R. K. Assessing the threat to montane biodiversity from discordant shifts in temperature and precipitation in a changing climate. Ecol. Lett. 14, 1236–1245 (2011).Article 

Google Scholar 
Choi, S.-W. Effects of weather factors on the abundance and diversity of moths in a temperate deciduous mixed forest of Korea. Zool. Sci. 25, 53–58 (2008).Article 

Google Scholar 
Feldmeier, S. et al. Climate versus weather extremes: Temporal predictor resolution matters for future rather than current regional species distribution models. Divers. Distrib. 24, 1047–1060 (2018).Article 

Google Scholar  More