Philippa Kaur

Here, we analyzed the burrowing mechanisms of beetle larvae. Beetle larvae were placed on the soil surface to make sure they could burrow into the soil (Fig. 1a). In order to observe the burrowing behavior, a two-dimensional (2D) observation tank (130 × 210 ×  ~ 20 mm) was constructed (Fig. 1b); we succeeded in observing the dynamics of the larvae under a 2D soil condition (Fig. 1c, Supplementary Movie 1). The larvae burrowed by rotating themselves (Fig. 1d, Supplementary Movie 1). Rotation was observed regardless of sex. All observed individuals proceeded towards the bottom and stopped when rotating at the bottom layer (Fig. 1c).Figure 1Burrowing dynamics of scarab beetle (Trypoxylus dichotomus) larva. (a) Burrowing images. After beetle is put on the soil, they can burrow in a short time ( More

1.Spalding, M. D., Ravilious, C. & Green, E. P. World atlas of coral reefs, University of California Press, Berkeley, CA, USA (2001).2.Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).ADS 
CAS 
Article 

Google Scholar 
3.Hughes, T. P. et al. Phase shifts, herbivory, and the resilience of coral reefs to climate change. Curr. Biol. 17, 360–365 (2007).CAS 
Article 

Google Scholar 
4.Moritz, C., Vii, J., Lee Long, W., Tamelander, J., Thomassin, A. & Planes, S. Status and Trends of Coral Reefs of the Pacific. Global Coral Reef Monitoring Network, 114 pp (2018).5.Morrison, T. H., Hughes, T. P., Adger, W. N. & Brown, K. Save reefs to rescue all ecosystems. Nature 573, 334–336 (2019).ADS 
Article 

Google Scholar 
6.Bellwood, D. R., Hughes, T. P. & Hoey, A. S. Sleeping functional group drives coral-reef recovery. Curr. Biol. 16, 2434–2439 (2006).CAS 
Article 

Google Scholar 
7.Graham, N. A. et al. Managing resilience to reverse phase shifts in coral reefs. Front. Ecol. Environ. 11, 541–548 (2013).Article 

Google Scholar 
8.Cavender-Bares, J., Keen, A. & Miles, B. Phylogenetic structure of Floridian plant communities depends on taxonomic and spatial scale. Ecology 87, 109–122 (2006).Article 

Google Scholar 
9.Gajdzik, L. et al. Similar levels of trophic and functional diversity within damselfish assemblages across Indo- Pacific coral reefs. Function. Ecol. 32, 1358–1369 (2018).Article 

Google Scholar 
10.Holling, C. S. Resilience and stability of ecological systems. Ann. Rev. Ecol. Syst. 4, 1–23 (1973).Article 

Google Scholar 
11.Viviani, J. et al. Synchrony patterns reveal different degrees of trophic guild vulnerability after disturbances in a coral reef fish community. Divers. Distrib. 5, 1–12 (2019).
Google Scholar 
12.Paine, R. T. A note on trophic complexity and community stability. Am. Nat. 103, 91–93 (1969).Article 

Google Scholar 
13.Mills, L. C., Soule, M. E. & Doak, D. F. The keystone-species concept in ecology and conservation. Bioscience 4, 219–224 (1993).Article 

Google Scholar 
14.Bouchon, C. et al. Status of the coral reefs of the Lesser Antilles after 2005 coral bleaching event in Status of Caribbean coral reefs after bleaching and hurricanes in 2005 (eds. Wilkinson C et al.) 85–104 (Global Coral Reef Monitoring Network and Reef and Rainforest Research Center, Townsville, 2008).15.Jackson, J. B. C., Donovan, M. K., Cramer, K. L., Lam, V. V. Status and Trends of Caribbean Coral Reefs: 1970–2012. Global Coral Reef Monitoring Network, IUCN, Gland, Switzerland (2014)16.Cernohorsky, N. H., McClanahan, T. R., Babu, I. & Horsa, M. Small herbivores suppress algal accumulation on Agatti atoll, Indian Ocean. Coral Reefs 34, 1023–1035 (2015).ADS 
Article 

Google Scholar 
17.Altman-Kurosaki, N. T., Priest, M. A., Golbuu, Y., Mumby, P. J. & Marshell, J. Microherbivores are significant grazers on Palau’s forereefs. Mar. Biol. 165, 74–86 (2018).Article 

Google Scholar 
18.Ceccarelli, D. M., Jones, G. P. & McCook, L. J. Territorial damselfishes as determinants of the structure of benthic communities on coral reefs. Oceanog. Mar. Biol. Annu. Rev. 39, 355–389 (2001).
Google Scholar 
19.Hata, H. & Ceccarelli, D. M. Farming behaviour of territorial Damselfishes in Biology of Damselfishes (eds. Parmentier, E. & Frederich B.) 122–152 (CRC Press, 2016).20.Brooker, R. M. et al. Niche construction and the natural selection of domestication. Nat. Commun. 11, 6253 (2020).ADS 
CAS 
Article 

Google Scholar 
21.Hata, H. & Kato, M. Monoculture and mixed-species algal farms on a coral reef are maintained through intensive and extensive management by damselfishes. J. Exp. Mar. Biol. Ecol. 313, 285–296 (2004).Article 

Google Scholar 
22.Ceccarelli, D. M. Modification of benthic communities by territorial damselfish: a multi-species comparison. Coral Reefs 26, 853–866 (2007).ADS 
Article 

Google Scholar 
23.Emslie, M. J. et al. Regional-scale variation in the distribution and abundance of farming damselfishes on Australia’s Great Barrier Reef. Mar. Biol. 159, 1293–1304 (2012).Article 

Google Scholar 
24.Precht, W. F., Aronson, R. B. & Moody, R. M. Changing patterns of microhabitat utilization by the threespot damselfish, Stegastes planifrons, on Caribbean reefs. PloS ONE 5, e10835 (2010).25.Casey, J. M., Ainsworth, T. D., Choat, J. H. & Connolly, S. R. Farming behaviour of reef fishes increases the prevalence of coral disease associated microbes and black band disease. Proc. R. Soc. B. 281, 20141032 (2014).Article 

Google Scholar 
26.Randazzo-Eisemann, Á., Montero Muñoz, J. L., McField, M., Myton, J. & Arias-González, J. E. The effect of Algal-gardening damselfish on the resilience of the mesoamerican Reef. Front. Mar. Sci. 6, 414–421 (2019).27.Sandin, S. A. et al. Baselines and degradation of coral reefs in the Northern Line Islands. PLoS ONE 3, e1548 (2008).28.Naim, O. et al. Fringing reefs of Reunion Island and eutrophication effects: long term monitoring of primary producers. Atoll Res. Bull. 597, 1–14 (2013).ADS 
Article 

Google Scholar 
29.Figueira, W. F., Lyman, S. J., Crowder, L. B. & Rilov, G. Small-scale demographic variability of the biocolor damselfish, Stegastes partitus, in the Florida Keys USA. Environ. Biol. Fish 81, 297–311 (2008).Article 

Google Scholar 
30.Carpenter, R. C. Partitioning herbivory and its effects on coral reef algal communities. Ecol. Monogr. 56, 345 (1986).Article 

Google Scholar 
31.Bellwood, D. R. & Fulton, C. J. Sediment-mediated suppression of herbivory on coral reefs: decreasing resilience to rising sea-levels and climate change?. Limnol. Oceanogr. 53(6), 2695–2701 (2008).ADS 
Article 

Google Scholar 
32.Galzin, R. Biomasse ichtyologique dans les écosystèmes récifaux. Etude préliminaire de la dynamique d’une population de Stegastes nigricans dans le lagon de Moorea (Société, Polynésie française). Rev. Trav. Inst. Pêches Marit. 40, 575–578 (1977).33.Lison de Loma, T., Galzin, R. & Planes, S. A framework for assessing impacts of marine protected areas in Moorea (French Polynesia). Pacif. Sci. 62, 431–441(2008).34.Glaser, M. et al. Breaking resilience for a sustainable future: thoughts for the anthropocene. Front. Mar. Sci. 5, 34–40 (2018).Article 

Google Scholar 
35.Siu, G. et al. Shore fishes of French Polynesia. Cybium 41, 245–278 (2017).
Google Scholar 
36.Tebbett, S. B., Chase, T. J. & Bellwood, D. R. Farming damselfishes shape algal turf sediment dynamics on coral reefs. Mar. Envirron. Res. 160, 104988 (2020).37.Wilkes, A. A. et al. A comparison of damselfish densities on live staghorn coral (Acropora cervicornis) and coral rubble in Dry Tortugas National Park. Southeast. Nat. 7, 483–492 (2008).Article 

Google Scholar 
38.Blanchette, A. et al. Damselfish Stegastes nigricans increase algal growth within their territories on shallow coral reefs via enhanced nutrient supplies. J. Exp. Mar. Biol. Ecol. 513, 21–26 (2019).Article 

Google Scholar 
39.Galzin, R. Structure of fish communities of French Polynesian coral reefs. I. Spatial scales. Mar. Ecol. Prog. Ser. 41, 129–136 (1987a).40.Galzin, R. Structure of fish communities of French Polynesian coral reefs. II. Temporal scales. Mar. Ecol. Prog. Ser. 41, 137–145 (1987b).41.Lecchini, D. & Galzin, R. Spatial repartition and ontogenetic shifts in habitat use by coral reef fishes (Moorea, French Polynesia). Mar. Biol. 147, 47–58 (2005).Article 

Google Scholar 
42.Galzin, R., Marfin, J. P. & Salvat, B. Long term coral reef monitoring program: heterogeneity of the Tiahura barrier reef (Moorea, French Polynesia). Galaxea 11, 73–91 (1993).
Google Scholar 
43.Galzin, R., Lecchini, D., Lison de Loma, T., Moritz, C. & Siu, G. Long term monitoring of coral and fish assemblages (1983–2014) in Tiahura reefs, Moorea, French Polynesia. Cybium 40, 31–41 (2016).44.Froese, R. & Pauly, D. Eds. FishBase. World Wide Web electronic publication. www.fishbase.org (2018).45.Hamed, K. H. & Rao, A. R. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 204, 182–196 (1998).ADS 
Article 

Google Scholar 
46.Patakamuri, S.K. & O’Brien, N. Modifiedmk: Modified versions of Mann Kendall and Spearman’s Rho trend tests. R package version 1.5.0 (2020).47.R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna (2017).48.RStudio Team. R Studio: Integrated Development for R. RStudio, PBC, Boston, MA (2020). More

1.McInerney, J. O., McNally, A. & O’Connell, M. J. Why prokaryotes have pangenomes. Nat. Microbiol. 2, 17040 (2017).2.Tettelin, H., Riley, D., Cattuto, C. & Medini, D. Comparative genomics: the bacterial pan-genome. Curr. Opin. Microbiol. 11, 472–477 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Bentley, S. Sequencing the species pan-genome. Nat. Rev. Microbiol. 7, 258–259 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Bromham, L. & Penny, D. The modern molecular clock. Nat. Rev. Genet. 4, 216–224 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Otto, S. P. & Whitlock, M. C. The probability of fixation in populations of changing size. Genetics 146, 723–733 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Moura, A. et al. Whole genome-based population biology and epidemiological surveillance of Listeria monocytogenes. Nat. Microbiol. 2, 16185 (2016).7.Linke, K. et al. Reservoirs of Listeria species in three environmental ecosystems. Appl. Environ. Microbiol. 80, 5583–5592 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Liao, J., Wiedmann, M. & Kovac, J. Genetic stability and evolution of the sigB allele, used for Listeria sensu stricto subtyping and phylogenetic inference. Appl. Environ. Microbiol. 83, e00306–e00317 (2017).PubMed 
PubMed Central 

Google Scholar 
9.Duché, O., Trémoulet, F., Glaser, P. & Labadie, J. Salt stress proteins induced in Listeria monocytogenes. Appl. Environ. Microbiol. 68, 1491–1498 (2002).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Mcclure, P. J., Roberts, T. A. & Oguru, P. O. Comparison of the effects of sodium chloride, pH and temperature on the growth of Listeria monocytogenes on gradient plates and in liquid medium. Lett. Appl. Microbiol. 9, 95–99 (1989).CAS 
Article 

Google Scholar 
11.Schwarz, G., Mendel, R. R. & Ribbe, M. W. Molybdenum cofactors, enzymes and pathways. Nature 460, 839–847 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Cordero, O. X. & Polz, M. F. Explaining microbial genomic diversity in light of evolutionary ecology. Nat. Rev. Microbiol. 12, 263–273 (2014).CAS 
PubMed 
Article 

Google Scholar 
13.Iranzo, J., Wolf, Y. I., Koonin, E. V. & Sela, I. Gene gain and loss push prokaryotes beyond the homologous recombination barrier and accelerate genome sequence divergence. Nat. Commun. 10, 5376 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Thomas, C. M. & Nielsen, K. M. Mechanisms of, and barriers to, horizontal gene transfer between bacteria. Nat. Rev. Microbiol. 3, 711–721 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Smith, J. M., Feil, E. J. & Smith, N. H. Population structure and evolutionary dynamics of pathogenic bacteria. BioEssays 22, 1115–1122 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Crits-Christoph, A., Olm, M. R., Diamond, S., Bouma-Gregson, K. & Banfield, J. F. Soil bacterial populations are shaped by recombination and gene-specific selection across a grassland meadow. ISME J. 14, 1834–1846 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Murrell, B. et al. Gene-wide identification of episodic selection. Mol. Biol. Evol. 32, 1365–1371 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Angelastro, A. Chemoenzymatic synthesis of isotopically labelled folates. J. Am. Chem. Soc. 139, 13047–13054 (2017).CAS 
PubMed 
Article 

Google Scholar 
19.Shapiro, B. J. et al. Population genomics of early events in the ecological differentiation of bacteria. Science 336, 48–51 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Choudoir, M. J., Doroghazi, J. R. & Buckley, D. H. Latitude delineates patterns of biogeography in terrestrial Streptomyces. Environ. Microbiol. 18, 4931–4945 (2016).PubMed 
Article 

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

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

Google Scholar 
23.Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Black, C. A., Evans, D. D., Ensminger, L. E., White, J. L. & Clark, F. E. Methods of Soil Analysis Part 1: Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling 128–151 (American Society of Agronomy, 1965).27.Weller, D., Belias, A., Green, H., Roof, S. & Wiedmann, M. Landscape, water quality, and weather factors associated with an increased likelihood of foodborne pathogen contamination of New York streams used to source water for produce production. Food Sustain. Food Syst. 3, 124 (2020).Article 

Google Scholar 
28.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Google Scholar 
29.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, 309–314 (2018).Article 
CAS 

Google Scholar 
31.Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Pritchard, L., Glover, R. H., Humphris, S., Elphinstone, J. G. & Toth, I. K. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal. Methods 8, 12–24 (2016).Article 

Google Scholar 
36.Carlin, C. R. et al. Listeria cossartiae sp. nov., Listeria immobilis sp. nov., Listeria portnoyi sp. nov. and Listeria rustica sp. nov. isolated from agricultural water and natural environments. Int J. Syst. Evol. Microbiol. 71, 004795 (2021).CAS 

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

Google Scholar 
38.Méric, G. et al. A reference pan-genome approach to comparative bacterial genomics: identification of novel epidemiological markers in pathogenic Campylobacter. PLoS ONE 9, e92798 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Gardner, S. N., Slezak, T. & Hall, B. G. kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome. Bioinformatics 31, 2877–2878 (2015).CAS 
PubMed 
Article 

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

Google Scholar 
41.Kelly, J. K. A test of neutrality based on interlocus associations. Genetics 146, 1197–1206 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).CAS 
PubMed 
Article 

Google Scholar 
43.Martin, D. P., Murrell, B., Golden, M., Khoosal, A. & Muhire, B. RDP4: detection and analysis of recombination patterns in virus genomes. Virus Evol. 1, vev003 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Liao, J. et al. Serotype-specific evolutionary patterns of antimicrobial-resistant Salmonella enterica. BMC Evol. Biol. 19, 132 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.Pond, S. L. K., Posada, D., Gravenor, M. B., Woelk, C. H. & Frost, S. D. W. Automated phylogenetic detection of recombination using a genetic algorithm. Mol. Biol. Evol. 23, 1891–1901 (2006).CAS 
Article 

Google Scholar  More

1.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).Article 

Google Scholar 
2.Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLOS Biol. 14, e2001104 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Anadón, J. D., Sala, O. E. & Maestre, F. T. Climate change will increase savannas at the expense of forests and treeless vegetation in tropical and subtropical Americas. J. Ecol. 102, 1363–1373 (2014).Article 

Google Scholar 
5.ECLAC et al. Climate Change in Central America: Potential Impacts and Public Policy Options (United Nations, 2015).6.Khatun, K., Imbach, P. & Zamora, J. An assessment of climate change impacts on the tropical forests of Central America using the Holdridge Life Zone (HLZ) land classification system. iForest—Biogeosciences Forestry 6, 183 (2013).Article 

Google Scholar 
7.TEEB. The Economics of Ecosystems and Biodiversity: Mainstreaming the Economics of Nature: A synthesis of the approach, conclusions and recommendations of TEEB (Progress Press, 2010).8.Diffenbaugh, N. S. & Giorgi, F. Climate change hotspots in the CMIP5 global climate model ensemble. Climatic Change 114, 813–822 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Myers, N. Biodiversity hotspots revisited. BioScience 53, 916–917 (2003).Article 

Google Scholar 
10.Corrales, L., Bouroncle, C. & Zamora, J. C. In Climate Change Impacts on Tropical Forests in Central America (ed. Chiabai, A.) 17–38 (Routledge, 2015).11.Gunter, U., Ceddia, M. G. & Tröster, B. International ecotourism and economic development in Central America and the Caribbean. J. Sustain. Tour. 25, 43–60 (2017).Article 

Google Scholar 
12.Hernández-Blanco, M., Costanza, R., Anderson, S., Kubiszewski, I. & Sutton, P. Future scenarios for the value of ecosystem services in Latin America and the Caribbean to 2050. Curr. Res. Environ. Sustainability 2, 100008 (2020).Article 

Google Scholar 
13.Hecht, S. B. Forests lost and found in tropical Latin America: the woodland ‘green revolution’. J. Peasant Stud. 41, 877–909 (2014).Article 

Google Scholar 
14.Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Imbach, P. et al. Modeling potential equilibrium states of vegetation and terrestrial water cycle of Mesoamerica under climate change scenarios. J. Hydrometeor 13, 665–680 (2012).Article 

Google Scholar 
16.Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 1–9. https://doi.org/10.1038/s41559-020-01303-0 (2020).17.Freeman, B. G., Lee-Yaw, J. A., Sunday, J. M. & Hargreaves, A. L. Expanding, shifting and shrinking: the impact of global warming on species’ elevational distributions. Glob. Ecol. Biogeogr. 27, 1268–1276 (2018).Article 

Google Scholar 
18.Booth, T. H. Species distribution modelling tools and databases to assist managing forests under climate change. For. Ecol. Manag. 430, 196–203 (2018).Article 

Google Scholar 
19.Urbina-Cardona, N. et al. Species distribution modeling in Latin America: a 25-year retrospective review. Trop. Conserv. Sci. 12, 1940082919854058 (2019).Article 

Google Scholar 
20.Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
21.BIOMARCC-SINAC-GIZ. Estimación de los posibles cambios en la distribución de especies de flora arbórea en el Pacífico Norte y Sur de Costa Rica en respuesta a los efectos del Cambio Climático (2013).22.de Sousa, K. et al. Suitability of Key Central American Agroforestry Species Under Future Climates: an Atlas (World Agroforestry Centre, 2017).23.Calabrese, J. M., Certain, G., Kraan, C. & Dormann, C. F. Stacking species distribution models and adjusting bias by linking them to macroecological models. Glob. Ecol. Biogeogr. 23, 99–112 (2014).Article 

Google Scholar 
24.Biber, M. F., Voskamp, A., Niamir, A., Hickler, T. & Hof, C. A comparison of macroecological and stacked species distribution models to predict future global terrestrial vertebrate richness. J. Biogeogr. 47, 114–129 (2020).Article 

Google Scholar 
25.Zakharova, L., Meyer, K. M. & Seifan, M. Trait-based modelling in ecology: a review of two decades of research. Ecol. Model. 407, 108703 (2019).Article 

Google Scholar 
26.Lyra, A. et al. Projections of climate change impacts on central America tropical rainforest. Climatic Change 141, 93–105 (2017).CAS 
Article 

Google Scholar 
27.Boukili, V. K. & Chazdon, R. L. Environmental filtering, local site factors and landscape context drive changes in functional trait composition during tropical forest succession. Perspect. Plant Ecol. Evol. Syst. 24, 37–47 (2017).Article 

Google Scholar 
28.Imbach, P. A., Locatelli, B., Molina, L. G., Ciais, P. & Leadley, P. W. Climate change and plant dispersal along corridors in fragmented landscapes of Mesoamerica. Ecol. Evol. 3, 2917–2932 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Meyer, N. F. V., Moreno, R., Reyna-Hurtado, R., Signer, J. & Balkenhol, N. Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models. Mov. Ecol. 8, 3 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Cabrera-Guzmán, E. & Reynoso, V. H. Amphibian and reptile communities of rainforest fragments: minimum patch size to support high richness and abundance. Biodivers. Conserv 21, 3243–3265 (2012).Article 

Google Scholar 
31.Crespin, S. J. & García-Villalta, J. E. Integration of land-sharing and land-sparing conservation strategies through regional networking: The Mesoamerican Biological Corridor as a Lifeline for Carnivores in El Salvador. AMBIO 43, 820–824 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Rehm, E. & Feeley, K. J. Many species risk mountain top extinction long before they reach the top. Front. Biogeogr. 8, (2016).33.Fung, E. et al. Mapping conservation priorities and connectivity pathways under climate change for tropical ecosystems. Climatic Change 141, 77–92 (2017).Article 

Google Scholar 
34.Ojea, E., Zamora, J. C., Martin-Ortega, J. & Imbach, P. In Climate Change Impacts on Tropical Forests in Central America: an Ecosystem Service Perspective (ed. Chiabai, A.) 113–151 (Routledge, 2015).35.Bernal, B., Murray, L. T. & Pearson, T. R. H. Global carbon dioxide removal rates from forest landscape restoration activities. Carbon Balance Manag. 13, 22 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Toledo, M. et al. Climate is a stronger driver of tree and forest growth rates than soil and disturbance. J. Ecol. 99, 254–264 (2011).Article 

Google Scholar 
37.Rojas, M. R., Locatelli, B. & Billings, R. Climate change and outbreaks of Southern Pine Beetle in Honduras. For. Syst. 19, 70–76 (2010).
Google Scholar 
38.Estrada‐Villegas, S., Hall, J. S., Breugel, Mvan & Schnitzer, S. A. Lianas reduce biomass accumulation in early successional tropical forests. Ecology 101, e02989 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Balslev, H. et al. Species diversity and growth forms in Tropical American Palm Communities. Bot. Rev. 77, 381–425 (2011).Article 

Google Scholar 
40.Ratajczak, Z., D’Odorico, P. & Yu, K. The Enemy of My Enemy Hypothesis: Why Coexisting with Grasses May Be an Adaptive Strategy for Savanna Trees. Ecosystems 20, 1278–1295 (2017).Article 

Google Scholar 
41.Heijden, G. M. F., van der, Powers, J. S. & Schnitzer, S. A. Lianas reduce carbon accumulation and storage in tropical forests. PNAS 112, 13267–13271 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.da Cunha Vargas, B., Grombone-Guaratini, M. T. & Morellato, L. P. C. Lianas research in the Neotropics: overview, interaction with trees, and future perspectives. Trees https://doi.org/10.1007/s00468-020-02056-w. (2020).43.Nanni, A. S. et al. The neotropical reforestation hotspots: a biophysical and socioeconomic typology of contemporary forest expansion. Glob. Environ. Change 54, 148–159 (2019).Article 

Google Scholar 
44.Stan, K. et al. Climate change scenarios and projected impacts for forest productivity in Guanacaste Province (Costa Rica): lessons for tropical forest regions. Reg. Environ. Change 20, 14 (2020).Article 

Google Scholar 
45.Olson, D. M. et al. Terrestrial ecoregions of the World: a New Map of Life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

Google Scholar 
46.Poorter, L. et al. Wet and dry tropical forests show opposite successional pathways in wood density but converge over time. Nat. Ecol. Evol. 3, 928–934 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Condit, R., Pérez, R. & Daguerre, N. Trees of Panama and Costa Rica (Princeton University Press, 2010).48.CATIE. Árboles de Centroamérica: un Manual Para Extensionistas (CATIE, 2003).49.Flores-Vindas, E. & Obando-Vargas, G. Árboles del Trópico Húmedo: Importancia Socioeconómica (Editorial Tecnológica de Costa Rica, 2014).50.Hammel, B. E., Grayum, M. H., Herrera, C. & Zamora Villalobos, N. Manual de plantas de Costa Rica vols 1–6 (Missouri Botanical Garden, 2003).51.Boukili, V. Functional trait data for La Selva, database (2014).52.Burns, R. M., Mosquera, M. S. & Whitmore, J. L. Useful Trees of the Tropical Region of North America (North American Forestry Commission, 1998).53.CATIE. Rasgos funcionales, base de datos del Programa Producción y Conservación en Bosques del CATIE (colleción de resultados de tesis). (2019).54.Delgado, D. et al. Análisis de la Vulnerabilidad al Cambio Climático de Bosques de Montaña en Latinoamérica (Centro Agronómico Tropical de Investigación y Enseñanza (CATIE), 2016).55.FAO. Crop Ecological Requirements Database (ECOCROP). http://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1027491/ (2020).56.Finegan, B., Camacho, M. & Zamora, N. Diameter increment patterns among 106 tree species in a logged and silviculturally treated Costa Rican rain forest. For. Ecol. Manag. 121, 159–176 (1999).Article 

Google Scholar 
57.Hall, J. S. & Ashton, M. S. Guide to Early Growth and Survival in Plantations of 64 Tree Species Native to Panama and the Neotropics. (Smithsonian Tropical Research Institute, 2016).58.MARENA/INAFOR. Guía de Especies Forestales (Editora de Arte, S.A, 2002).59.Runes Vargas, V. Base de rasgos funcionales y usos de las especies más abundantes en los sistemas agroforestales de Centroamérica (Agroforestry Tree Functional Traits). in Diversidad en sistemas agroforestales de Centroamérica una aproximación desde el enfoque functional. Master thesis, CATIE, Costa Rica (2016).60.Vázquez-Yanes, C., Batis Muñoz, A. I., Alcocer Silva, M. I., Gual Díaz, M. & Sánchez Dirzo, C. Árboles y arbustos potencialmente valiosos para la restauración ecológica y la reforestación. Reporte técnico del proyecto J084. (1999).61.Vozzo, J. A. Tropical Tree Seed Manual (U.S. Department of Agriculture, Forest Service, 2002).62.Webb, D. B., Wood, P. J., Smith, J. P. & Sian Henman, G. A Guide to Species Selection for Tropical and Sub-tropical Plantations (Unit of Tropical Silviculture, Commonwealth Forestry Institute, University of Oxford, 1984).63.Soultan, A. & Safi, K. The interplay of various sources of noise on reliability of species distribution models hinges on ecological specialisation. PLoS ONE 12, e0187906 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.GBIF. GBIF Occurrence Download. Accessed from R via rgbif 2020-05-18. Darwin Core Archive. https://doi.org/10.15468/dl.pstza2. (2020).65.Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015).Article 

Google Scholar 
66.CRIA. SpeciesLink (CRIA, 2012).67.Peet, R. K., Lee, M. T., Jennings, M. D. & Faber-Langendoen, D. VegBank: a permanent, open-access archive for vegetation plot data. Biodivers. Ecol. 4, 233–241 (2012).Article 

Google Scholar 
68.Šímová, I. et al. Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species. J. Biogeogr. 45, 895–916 (2018).Article 

Google Scholar 
69.US Forest Service. Forest Inventory and Analysis National Program (US Forest Service, 2013).70.de Sousa, K., van Zonneveld, M., Holmgren, M., Kindt, R. & Ordoñez, J. C. Replication data for: ‘The future of coffee and cocoa agroforestry in a warmer Mesoamerica’. Harvard Dataverse https://doi.org/10.7910/DVN/0O1GW1. (2019).71.Chamberlain, S. rgbif: Interface to the Global ‘Biodiversity’ Information Facility API. R package version 2.3. (2020).72.Maitner, B. S. et al. The BIEN R package: A tool to access the botanical information and ecology network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2018).Article 

Google Scholar 
73.Morales, J. F. Sinopsis of the genus Weinmannia (Cunoniaceae) in Mexico and Central America. An. Jard.ín Bot.ánico Madr. 67, 137–155 (2010).Article 

Google Scholar 
74.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
75.Danielson, J. J. & Gesch, D. B. Global multi-resolution terrain elevation data 2010 (GMTED2010). http://pubs.er.usgs.gov/publication/ofr20111073 (2011).76.Hengl, T. et al. SoilGrids1km—Global Soil Information Based on Automated Mapping. PLoS ONE 9, e105992 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Schmitt, S., Pouteau, R., Justeau, D., de Boissieu, F. & Birnbaum, P. SSDM—an R package to predict distribution of species richness and composition based on stacked species distribution models. Methods. Ecol. Evol. 8, 1795–1803 (2017).
Google Scholar 
78.Beaumont, L. J. et al. Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges? Ecol. Model. 342, 135–146 (2016).Article 

Google Scholar 
79.Lay, G. L., Engler, R., Franc, E. & Guisan, A. Prospective sampling based on model ensembles improves the detection of rare species. Ecography 33, 1015–1027 (2010).Article 

Google Scholar 
80.Guo, C. et al. Uncertainty in ensemble modelling of large-scale species distribution: effects from species characteristics and model techniques. Ecol. Model. 306, 67–75 (2015).Article 

Google Scholar 
81.Naimi, B. On uncertainty in species distribution modelling https://doi.org/10.3990/1.9789036538404 (University of Twente, 2015).82.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many?: How to use pseudo-absences in niche modelling? Methods Ecol. Evol. 3, 327–338 (2012).Article 

Google Scholar 
83.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 
84.Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).Article 

Google Scholar 
85.Diniz‐Filho, J. A. F. et al. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32, 897–906 (2009).Article 

Google Scholar 
86.Guillera‐Arroita, G. et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292 (2015).Article 

Google Scholar 
87.Phillips, S. J. & Elith, J. POC plots: calibrating species distribution models with presence-only data. Ecology 91, 2476–2484 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
88.Schwarz, J. & Heider, D. GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making. Bioinformatics 35, 2458–2465 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.D’Amen, M., Rahbek, C., Zimmermann, N. E. & Guisan, A. Spatial predictions at the community level: from current approaches to future frameworks: Methods for community-level spatial predictions. Biol. Rev. 92, 169–187 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Lobo, J. M., Jiménez‐Valverde, A. & Real, R. AUC: a misleading measure of the performance of predictive distribution models. Glob. Ecol. Biogeogr. 17, 145–151 (2008).Article 

Google Scholar 
91.Lewis, O. T. Climate change, species–area curves and the extinction crisis. Philos. Trans. R. Soc. B: Biol. Sci. 361, 163–171 (2006).Article 

Google Scholar 
92.Griscom, H. P. & Ashton, M. S. Restoration of dry tropical forests in Central America: a review of pattern and process. For. Ecol. Manag. 261, 1564–1579 (2011).Article 

Google Scholar 
93.Rahman, M., Islam, M., Gebrekirstos, A. & Bräuning, A. Trends in tree growth and intrinsic water-use efficiency in the tropics under elevated CO2 and climate change. Trees 33, 623–640 (2019).Article 

Google Scholar 
94.Riitters, K., Wickham, J., O’Neill, R., Jones, K. B. & Smith, E. Global-scale patterns of forest fragmentation. Conservation Ecol. 4, 3 (2000).95.Morelli, T. L. et al. The fate of Madagascar’s rainforest habitat. Nat. Clim. Change 10, 89–96 (2020).Article 

Google Scholar 
96.Baumbach, L., Warren, D. L., Yousefpour, R. & Hanewinkel, M. Replication data for ‘Climate change may induce connectivity loss and mountaintop extinction in Central American forests’. https://doi.org/10.5281/zenodo.4835834 (2021).97.Baumbach, L., Warren, D. L., Yousefpour, R. & Hanewinkel, M. Supplementary data for ‘Climate change may induce connectivity loss and mountaintop extinction in Central American forests’. https://doi.org/10.5281/zenodo.4836270. (2021).98.Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).CAS 
Article 

Google Scholar  More

In fig gardens, trees and wasps have been locked in a delicate, 90-million-year-old eco-evolutionary dance1. Fig wasps use the fruit of the fig tree as a sweet incubator for their eggs, while fig trees rely on wasps to pollinate their flowers. Neither can live without the other. This is an example of an obligate mutualism — a bi-directional interdependency that is essential for each partner’s survival. Given how intertwined the two partners are, it’s easy to assume that obligate mutualisms are limiting; that is, one partner can live only where the other thrives, thus constraining the range of environments that support the growth of the pair. Writing in Nature Ecology & Evolution, Oña, et al.2 use synthetic microbial communities to demonstrate that quite the opposite can occur: obligate mutualists facilitate the growth of their partners and expand their range of habitable environments, including environments in which neither could survive alone. Such examples of ‘niche expansion’, as the authors define it, may provide clues as to how vast swaths of species diversity are maintained in nature. More

1.Grinnell, J. The niche-relationships of the California thrasher. Auk 34, 427–433 (1917).Article 

Google Scholar 
2.Elton, C. S. Animal Ecology (Univ. Chicago Press, 2001).3.Hutchinson, G. E. Concluding remarks Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).4.Hutchinson, G. E. An Introduction to Population Ecology (Yale Univ. Press, 1978).5.Colwell, R. K. & Rangel, T. F. Hutchinson’s duality: the once and future niche. Proc. Natl Acad. Sci. USA 106, 19651–19658 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Polechová, J. & Storch, D. in Encyclopedia of Ecology 2nd edn, Vol. 3 (ed Fath, B.) 72–80 (Elsevier, 2018).7.Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Hutchinson, G. E. Population studies: animal ecology and demography. Bull. Math. Biol. 53, 193–213 (1991).Article 

Google Scholar 
9.Odum, E. P. Fundamentals of Ecology (Saunders, 1959).10.Begon, M., Townsend, C. R. & JL., H. Ecology: From Individuals to Ecosystems (Wiley, 2006).11.Levin, S. & Carpenter, S. The Princeton Guide to Ecology (Princeton Univ. Press, 2009).12.Bruno, J. F., Stachowicz, J. J. & Bertness, M. D. Inclusion of facilitation into ecological theory. Trends Ecol. Evol. 18, 119–125 (2003).Article 

Google Scholar 
13.Bulleri, F., Bruno, J. F., Silliman, B. R. & Stachowicz, J. J. Facilitation and the niche: implications for coexistence, range shifts and ecosystem functioning. Funct. Ecol. 30, 70–78 (2016).Article 

Google Scholar 
14.Austin, M. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol. Modell. 157, 101–118 (2002).Article 

Google Scholar 
15.Soberon, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inform. 2, 1–10 (2005).Article 

Google Scholar 
16.Pires, M. M. & Guimarães, P. R. Interaction intimacy organizes networks of antagonistic interactions in different ways. J. R. Soc. Interface 10, 20120649 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Ashby, B., Watkins, E., Lourenço, J., Gupta, S. & Foster, K. R. Competing species leave many potential niches unfilled. Nat. Ecol. Evol. 1, 1495–1501 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Pérez-Gutiérrez, R. A. et al. Antagonism influences assembly of a Bacillus guild in a local community and is depicted as a food-chain network. ISME J. 7, 487–497 (2013).PubMed 
Article 
CAS 

Google Scholar 
19.Russel, J., Røder, H. L., Madsen, J. S., Burmølle, M. & Sørensen, S. J. Antagonism correlates with metabolic similarity in diverse bacteria. Proc. Natl Acad. Sci. USA 114, 10684–10688 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Ricklefs, R. E. Evolutionary diversification, coevolution between populations and their antagonists, and the filling of niche space. Proc. Natl Acad. Sci. USA 107, 1265–1272 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Stadler, B. & AFG, D. Ecology and evolution of aphid–ant interactions. Annu. Rev. Ecol. Evol. Syst. 107, 345–372 (2005).Article 

Google Scholar 
22.Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).PubMed 
Article 
CAS 

Google Scholar 
23.Rohr, R. P., Saavedra, S. & Bascompte, J. On the structural stability of mutualistic systems. Science 345, 1253497 (2014).PubMed 
Article 
CAS 

Google Scholar 
24.Hom, E. & Murray, A. Niche engineering demonstrates a latent capacity for fungal–algal mutualism. Science 345, 94–95 (2014).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
26.Yurtsev, E. A., Conwill, A. & Gore, J. Oscillatory dynamics in a bacterial cross-protection mutualism. Proc. Natl Acad. Sci. USA 113, 6236–6241 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Pereira, F. C. & Berry, D. Microbial nutrient niches in the gut. Environ. Microbiol. 19, 1366–1378 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).PubMed 
Article 

Google Scholar 
29.Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Schink, B. Energetics of syntrophic cooperation in methanogenic degradation. Microbiol. Mol. Biol. Rev. 61, 262–280 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Matthews, B., Aebischer, T., Sullam, K. E., Lundsgaard-Hansen, B. & Seehausen, O. Experimental evidence of an eco-evolutionary feedback during adaptive divergence. Curr. Biol. 26, 483–489 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Hendry, A. Eco-evolutionary Dynamics (Princeton Univ. Press, 2017).34.Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Giri, S. et al. Metabolic dissimilarity determines the establishment of cross- feeding interactions in bacteria. Preprint at bioRxiv https://doi.org/10.1101/2020.10.09.333336 (2020).36.Preussger, D., Giri, S., Muhsal, L. K., Oña, L. & Kost, C. Reciprocal fitness feedbacks promote the evolution of mutualistic cooperation. Curr. Biol. 30, 3580–3590.e7 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Stearns, S. Trade-offs in life-history evolution. Funct. Ecol. 3, 259–268 (1989).Article 

Google Scholar 
38.Agrawal, A. A., Conner, J. K. & Rasmann, S. in Evolution Since Darwin (eds Bell, M. A. et al.) Ch. 10 (Sinauer Associates, 2010).39.González-Cabaleiro, R., Ofiţeru, I. D., Lema, J. M. & Rodríguez, J. Microbial catabolic activities are naturally selected by metabolic energy harvest rate. ISME J. 9, 2630–2641 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity. J. Evol. Biol. 15, 173–190 (2002).Article 

Google Scholar 
41.Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Annu. Rev. Ecol. Evol. Syst. 48, 183–206 (2017).Article 

Google Scholar 
42.May, R. & Arthur, R. Niche overlap as a function of environmental variability. Proc. Natl Acad. Sci. USA 69, 1109–1113 (1972).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Bono, L. M., Draghi, J. A. & Turner, P. E. Evolvability costs of niche expansion. Trends Genet. 36, 14–23 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Treves, D. S., Manning, S. & Adams, J. Repeated evolution of an acetate-cross-feeding polymorphism in long-term populations of Escherichia coli. Mol. Biol. Evol. 15, 789–797 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Rozen, D. E., Schneider, D. & Lenski, R. E. Long-term experimental evolution in Escherichia coli. XIII. Phylogenetic history of a balanced polymorphism. J. Mol. Evol. 61, 171–180 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Rakoff-Nahoum, S., Coyne, M. J. & Comstock, L. E. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr. Biol. 24, 40–49 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Enke, T. N. et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr. Biol. 29, 1528–1535.e6 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Gentile, C. L. & Weir, T. L. The gut microbiota at the intersection of diet and human health. Science 362, 776–780 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Ruff, W. E., Greiling, T. M. & Kriegel, M. A. Host–microbiota interactions in immune-mediated diseases. Nat. Rev. Microbiol. 18, 521–538 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Morris, B. E. L., Henneberger, R., Huber, H. & Moissl-Eichinger, C. Microbial syntrophy: interaction for the common good. FEMS Microbiol. Rev. 37, 384–406 (2013).CAS 
PubMed 
Article 

Google Scholar 
52.D’Souza, G. et al. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat. Prod. Rep. 35, 455–488 (2018).PubMed 
Article 

Google Scholar 
53.Johnson, W. M. et al. Auxotrophic interactions: a stabilizing attribute of aquatic microbial communities? FEMS Microbiol. Ecol. 96, 1–14 (2020).Article 
CAS 

Google Scholar 
54.Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Bernhardsson, S., Gerlee, P. & Lizana, L. Structural correlations in bacterial metabolic networks. BMC Evol. Biol. 11, 20 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Hester, E. R., Jetten, M. S. M., Welte, C. U. & Lücker, S. Metabolic overlap in environmentally diverse microbial communities. Front. Genet. https://doi.org/10.3389/fgene.2019.00989 (2019).58.Mitri, S. & Richard Foster, K. The genotypic view of social interactions in microbial communities. Annu. Rev. Genet. 47, 247–273 (2013).CAS 
PubMed 
Article 

Google Scholar 
59.Levine, J. M., Bascompte, J., Adler, P. B. & Allesina, S. Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546, 56–64 (2017).CAS 
PubMed 
Article 

Google Scholar 
60.Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 
Article 

Google Scholar 
61.Vanstockem, M., Michiels, K., Vanderleyden, J. & van Gool, A. P. Transposon mutagenesis of Azospirillum brasilense and Azospirillum lipoferum: physical analysis of Tn5 and Tn5-Mob insertion mutants. Appl. Environ. Microbiol. 53, 410–415 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Thomason, L. C., Costantino, N. & Court, D. L. E. coli genome manipulation by P1 transduction. Curr. Protoc. Mol. Biol. 79, 1.17.1–1.17.8 (2007).63.Pande, S. et al. Metabolic cross-feeding via intercellular nanotubes among bacteria. Nat. Commun. 6, 6238 (2015).CAS 
PubMed 
Article 

Google Scholar 
64.Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl Acad. Sci. USA 97, 6640–6645 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Konkol, M. A., Blair, K. M. & Kearns, D. B. Plasmid-encoded comi inhibits competence in the ancestral 3610 strain of Bacillus subtilis. J. Bacteriol. 195, 4085–4093 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Koo, B. M. et al. Construction and analysis of two genome-scale deletion libraries for Bacillus subtilis. Cell Syst. 4, 291–305.e7 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Thompson, I., Lilley, A., Ellis, R., Bramwell, P. & Bailey, M. Survival, colonization and dispersal of genetically modified Pseudomonas fluorescens SBW25 in the phytosphere of field grown sugar beet. Nat. Biotechnol. 13, 1493–1497 (1995).CAS 
Article 

Google Scholar 
68.Rainey, P. B. Adaptation of Pseudomonas fluorescens to the plant rhizosphere. Environ. Microbiol. 1, 243–257 (1999).CAS 
PubMed 
Article 

Google Scholar 
69.Horton, R., Hunt, H., Ho, S., Pullen, J. & Pease, L. Engineering hybrid genes without the use of restriction enzymes: gene splicing by overlap extension. Gene 77, 61–68 (1989).CAS 
PubMed 
Article 

Google Scholar 
70.Ditta, G., Stanfield, S., Corbin, D. & Helinski, D. R. Broad host range DNA cloning system for Gram-negative bacteria: construction of a gene bank of Rhizobium meliloti. Proc. Natl Acad. Sci. USA 77, 7347–7351 (1980).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Zhang, X. X. & Rainey, P. B. Genetic analysis of the histidine utilization (hut) genes in Pseudomonas fluorescens SBW25. Genetics 176, 2165–2176 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Lassak, J., Henche, A. L., Binnenkade, L. & Thormann, K. M. ArcS, the cognate sensor kinase in an atypical arc system of Shewanella oneidensis MR-1. Appl. Environ. Microbiol. 76, 3263–3274 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).CAS 
Article 
PubMed 

Google Scholar 
74.Stecher, G., Tamura, K. & Kumar, S. Molecular evolutionary genetics analysis (MEGA) for macOS. Mol. Biol. Evol. 37, 1237–1239 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Bochner, B. R. Global phenotypic characterization of bacteria. FEMS Microbiol. Rev. 33, 191–205 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B 285, 20180792 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Ellis, E. C., Klein Goldewijk, K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).
Google Scholar 
4.Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
6.Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
7.Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239–1252 (2012).Article 

Google Scholar 
8.Falaschi, M., Manenti, R., Thuiller, W. & Ficetola, G. F. Continental‐scale determinants of population trends in European amphibians and reptiles. Glob. Change Biol. 25, 3504–3515 (2019).Article 

Google Scholar 
9.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
11.Jarzyna, M. A. & Jetz, W. Detecting the multiple facets of biodiversity. Trends Ecol. Evol. 31, 527–538 (2016).PubMed 
Article 

Google Scholar 
12.Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).CAS 
PubMed 
Article 

Google Scholar 
13.Bell, J. R. et al. Spatial and habitat variation in aphid, butterfly, moth and bird phenologies over the last half century. Glob. Change Biol. 25, 1982–1994 (2019).Article 

Google Scholar 
14.Finderup Nielsen, T., Sand‐Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).PubMed 
Article 

Google Scholar 
15.van Strien, A. J., van Swaay, C. A., van Strien-van Liempt, W. T., Poot, M. J. & WallisDeVries, M. F. Over a century of data reveal more than 80% decline in butterflies in the Netherlands. Biol. Conserv. 234, 116–122 (2019).Article 

Google Scholar 
16.Jarzyna, M. A. & Jetz, W. Taxonomic and functional diversity change is scale dependent. Nat. Commun. 9, 2565 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Magurran, A. E., Dornelas, M., Moyes, F. & Henderson, P. A. Temporal β diversity—a macroecological perspective. Glob. Ecol. Biogeogr. 28, 1949–1960 (2019).Article 

Google Scholar 
18.Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).CAS 
PubMed 
Article 

Google Scholar 
19.Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).Article 

Google Scholar 
20.Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).Article 

Google Scholar 
21.Kondratyeva, A., Grandcolas, P. & Pavoine, S. Reconciling the concepts and measures of diversity, rarity and originality in ecology and evolution. Biol. Rev. 94, 1317–1337 (2019).PubMed 
Article 

Google Scholar 
22.Auffret, A. G. & Thomas, C. D. Synergistic and antagonistic effects of land use and non‐native species on community responses to climate change. Glob. Change Biol. 25, 4303–4314 (2019).Article 

Google Scholar 
23.WallisDeVries, M. F. & van Swaay, C. A. A nitrogen index to track changes in butterfly species assemblages under nitrogen deposition. Biol. Conserv. 212, 448–453 (2017).Article 

Google Scholar 
24.Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).CAS 
PubMed 
Article 

Google Scholar 
25.Sgardeli, V., Zografou, K. & Halley, J. M. Climate change versus ecological drift: assessing 13 years of turnover in a butterfly community. Basic Appl. Ecol. 17, 283–290 (2016).Article 

Google Scholar 
26.van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2019).Article 
CAS 

Google Scholar 
27.Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384–392 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).CAS 
PubMed 
Article 

Google Scholar 
30.Marta, S. et al. ClimCKmap, a spatially, temporally and climatically explicit distribution database for the Italian fauna. Sci. Data 6, 195 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
31.Koleff, P., Gaston, K. J. & Lennon, J. T. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).Article 

Google Scholar 
32.Legendre, P. A temporal beta‐diversity index to identify sites that have changed in exceptional ways in space–time surveys. Ecol. Evol. 9, 3500–3514 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Suggit, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).Article 

Google Scholar 
34.Baselga, A., Bonthoux, S. & Balent, G. Temporal beta diversity of bird assemblages in agricultural landscapes: land cover change vs. stochastic processes. PLoS ONE 10, e0127913 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
35.Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).
Google Scholar 
36.Mason, N. W., de Bello, F., Mouillot, D., Pavoine, S. & Dray, S. A guide for using functional diversity indices to reveal changes in assembly processes along ecological gradients. J. Veg. Sci. 24, 794–806 (2013).Article 

Google Scholar 
37.Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).38.Giorgi, F. & Lionello, P. Climate change projections for the Mediterranean region. Glob. Planet. Change 63, 90–104 (2008).Article 

Google Scholar 
39.Brunetti, M., Maugeri, M., Monti, F. & Nanni, T. Temperature and precipitation variability in Italy in the last two centuries from homogenised instrumental time series. Int. J. Climatol. 26, 345–381 (2006).Article 

Google Scholar 
40.Terzago, S., von Hardenberg, J., Palazzi, E. & Provenzale, A. Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models. Cryosphere 11, 1625–1645 (2017).Article 

Google Scholar 
41.Beniston, M. et al. The European mountain cryosphere: a review of its current state, trends and future challenges. Cryosphere 12, 759–794 (2018).Article 

Google Scholar 
42.Wang, J. et al. Anthropogenically-driven increases in the risks of summertime compound hot extremes. Nat. Commun. 11, 528 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Turco, M. et al. Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with nonstationary climate–fire models. Nat. Commun. 9, 3821 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Jacobson, A. R., Provenzale, A., von Hardenberg, A., Bassano, B. & Festa-Bianchet, M. Climate forcing and density dependence in a mountain ungulate population. Ecology 85, 1598–1610 (2004).Article 

Google Scholar 
45.Imperio, S., Bionda, R., Viterbi, R. & Provenzale, A. Climate change and human disturbance can lead to local extinction of Alpine rock ptarmigan: new insight from the Western Italian Alps. PLoS ONE 8, e81598 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Hoffmann, S., Beierkuhnlein, C., Field, R., Provenzale, A. & Chiarucci, A. Uniqueness of protected areas for conservation strategies in the European Union. Sci. Rep. 8, 6445 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
47.Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene—HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).Article 

Google Scholar 
48.Queiroz, C., Beilin, R., Folke, C. & Lindborg, R. Farmland abandonment: threat or opportunity for biodiversity conservation? A global review. Front. Ecol. Environ. 12, 288–296 (2014).Article 

Google Scholar 
49.Falcucci, A., Maiorano, L. & Boitani, L. Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc. Ecol. 22, 617–631 (2007).Article 

Google Scholar 
50.Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Ranganathan, S., Swain, R. B. & Sumpter, D. J. T. The demographic transition and economic growth: implications for development policy. Palgrave Commun. 1, 15033 (2015).Article 

Google Scholar 
52.Weltzin, J. F. et al. Assessing the response of terrestrial ecosystems to potential changes in precipitation. BioScience 53, 941–952 (2003).Article 

Google Scholar 
53.Lacasella, F. et al. From pest data to abundance-based risk maps combining eco-physiological knowledge, weather, and habitat variability. Ecol. Appl. 27, 575–588 (2017).PubMed 
Article 

Google Scholar 
54.Ficetola, G. F. & Maiorano, L. Contrasting effects of temperature and precipitation change on amphibian phenology, abundance and performance. Oecologia 181, 683–693 (2016).PubMed 
Article 

Google Scholar 
55.Crimmins, S. M., Dobrowski, S. Z., Greenberg, J. A., Abatzoglou, J. T. & Mynsberge, A. R. Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331, 324–327 (2011).CAS 
PubMed 
Article 

Google Scholar 
56.Adams, H. D. et al. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc. Natl Acad. Sci. USA 106, 7063–7066 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
58.Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).PubMed 
Article 

Google Scholar 
59.Poff, N. L. et al. Sustainable water management under future uncertainty with eco-engineering decision scaling. Nat. Clim. Change 6, 25–34 (2017).Article 

Google Scholar 
60.Corlett, R. T. Restoration, reintroduction, and rewilding in a changing world. Trends Ecol. Evol. 31, 453–462 (2016).PubMed 
Article 

Google Scholar 
61.Kremen, C. & Merenlender, A. M. Landscapes that work for biodiversity and people. Science 362, eaau6020 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
62.Galland, T. et al. Colonization resistance and establishment success along gradients of functional and phylogenetic diversity in experimental plant communities. J. Ecol. 107, 2090–2104 (2019).Article 

Google Scholar 
63.Lister, A. M. et al. Natural history collections as sources of long-term datasets. Trends Ecol. Evol. 26, 153–154 (2011).PubMed 
Article 

Google Scholar 
64.Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Phil. Trans. R. Soc. Lond. B 345, 101–118 (1994).CAS 
Article 

Google Scholar 
65.Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).Article 

Google Scholar 
66.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6 (2019).67.Chazdon, R. L., Colwell, R. K., Denslow, J. S. & Guariguata, M.R. in Forest Biodiversity Research, Monitoring and Modeling: Conceptual Background and Old World Case Studies (eds. Dallmeir, F. & Cominsky, J. A.) 285–309 (Parthenon, 1998).68.Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Funct. Ecol. 31, 558–567 (2017).Article 

Google Scholar 
69.van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
70.Osborn, T. J. & Jones, P. The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth Syst. Sci. Data 6, 61–68 (2014).Article 

Google Scholar 
71.New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13, 2217–2238 (2000).Article 

Google Scholar 
72.Brunetti, M. et al. Projecting north eastern Italy temperature and precipitation secular records onto a high resolution grid. Phys. Chem. Earth. 40, 9–22 (2012).Article 

Google Scholar 
73.Brunetti, M., Maugeri, M., Nanni, T., Simolo, C. & Spinoni, J. High-resolution temperature climatology for Italy: interpolation method intercomparison. Int. J. Climatol. 34, 1278–1296 (2014).Article 

Google Scholar 
74.Crespi, A., Brunetti, M., Lentini, G. & Maugeri, M. 1961–1990 high-resolution monthly precipitation climatologies for Italy. Int. J. Climatol. 38, 878–895 (2018).Article 

Google Scholar 
75.Peterson, T. C. et al. Homogeneity adjustments of in situ atmospheric climate data: a review. Int. J. Climatol. 18, 1493–1517 (1998).Article 

Google Scholar 
76.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
77.Burnham, K. & Anderson, D. Model Selection and Multi-model Inference (Springer, 2002).78.Blonder, B & Harris, D. J. hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.12 (2019).79.Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n‐dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).Article 

Google Scholar 
80.Barros, C., Thuiller, W., Georges, D., Boulangeat, I. & Münkemüller, T. N‐dimensional hypervolumes to study stability of complex ecosystems. Ecol. Lett. 19, 729–742 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
Article 

Google Scholar 
82.Botta-Dukát, Z. Cautionary note on calculating standardized effect size (SES) in randomization test. Community Ecol. 19, 77–83 (2018).Article 

Google Scholar 
83.Signorell, A. et al. DescTools: Tools for descriptive statistics. R package version 0.99.40 (2021).84.Maclean, I. M. D., Suggitt, A. J., Wilson, R. J., Duffy, J. P. & Bennie, J. J. Fine-scale climate change: modelling fine-scale spatial variation in biologically meaningful rates of warming. Glob. Change Biol. 23, 256–268 (2017).Article 

Google Scholar 
85.Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).Article 

Google Scholar 
86.Besag, J., York, J. & Mollié, A. Bayesian image restoration, with two applications in spatial statistics. Ann. Inst. Stat. Math. 43, 1–59 (1991).Article 

Google Scholar 
87.Bivand, R. S. & Wong, D. W. Comparing implementations of global and local indicators of spatial association. Test 27, 716–748 (2018).Article 

Google Scholar 
88.Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71, 319–392 (2009).Article 

Google Scholar 
89.Bivand, R. S., Gómez-Rubio, V. & Rue, H. Spatial data analysis with R-INLA with some extensions. J. Stat. Softw. 63, 1–31 (2015).
Google Scholar 
90.Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).PubMed 
Article 

Google Scholar 
91.R Core Team R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).92.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed‐effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar  More

Assumptions for the space-for-time substitutionThe main methodological concept in this study is the notion of a space-for-time substitution. Such approach has previously been used in various studies to estimate the effect of land cover change on temperature20,22 or on the surface energy balance22,72. The overarching assumption behind the method is that the difference in properties of neighbouring patches of land can serve as a surrogate for changes in time. While this main assumption largely holds for land surface properties, such as skin temperature, it requires a more detailed articulation into several underlying assumptions in order to apply the approach to atmospheric properties such as cloud cover. This is because atmospheric properties are prone to lateral movements, partially decoupling them from the land cover directly below them, and thus adding considerable complexity to the analysis.The first underlying assumption is that the method will be mostly sensitive to low-level convective clouds generated in the boundary layer (i.e. cumulus clouds). These are typically formed under stable conditions of high pressure and low wind, and are thus expected to have a higher spatial correlation with the underlying landscape elements. Other types of low-level clouds, such as stratus clouds, are typically much more uniformly spread across the landscape, which would result in no difference in CFrC when comparing two distinct and neighbouring vegetation classes. For medium- or high-level clouds, their position will be determined mostly by the state of the atmosphere rather than by the land surface, resulting in a very low correlation with vegetation spatial patterns. The space-for-time substitution approach would thus similarly result in white noise.The second assumption is that the boundary layer cumulus clouds will see very limited lateral advection between the moment of their formation and the satellite observation. Cumulus clouds over land show a very stable climatology where the peak formation is largely confined to the early afternoon (around 14:00), timing which remains very stable across space and season43. Therefore this assumption should largely hold if the observations are made at this time.The third assumption is that if we consider topographically flat terrain that is away from a coastline, general weather conditions are essentially the same at a local scale (i.e. a region of radius circa 25 km around a given point). Within such an area, we then assume that variations in low cloud cover are mostly determined by local differences in surface properties, themselves determined by the type and condition of the present land cover.Preparation of input datasetsThis study requires gridded geospatial datasets for two variables: cloud fractional cover and land fractional cover. Both datasets used here have been prepared in the frame of the European Space Agency’s (ESA) Climate Change Initiative (CCI)73.The Cloud CCI55 provides a series of cloud properties derived from distinct satellite Earth observation platforms in a harmonized way. Here we use their cloud fractional cover variable (henceforth CFrC), which describes the fraction of a 0.05° × 0.05° pixel covered by clouds based on observations made at a finer spatial resolution at the given time of the satellite overpass. We chose to use Cloud CCI dataset based on the MODIS instrument on-board of the Aqua platform for two reasons. First, the timing of overpass of the Aqua platform (circa 13:30 local time at the Equator) coincides very well with the timing of peak of cumulus cloud formation43, thus greatly limiting the extent of possible cloud advection between the moment of cloud formation and observation. Second, native spatial resolution of the MODIS instrument is superior to the alternative (AVHRR), and should result in a better sensitivity to the presence of small cumulus clouds. More specifically, out of the 5 spectral bands of the MODIS instrument used by the Cloud CCI to characterize cloud properties (bands 1, 2, 20, 31 and 32), two of them (bands 1 and 2) have a native spatial resolution of 250 m. While these are aggregated to 1 km (the spatial resolution of the other MODIS bands) prior to their ingestion in the cloud retrieval algorithm, their finer native granularity and quality should prove to be an asset for small cumulus cloud detection. The CCI MODIS-AQUA CFrC data is available for the period 2004–2014. The values are first averaged from daily to monthly scale, and then a single monthly value is calculated for every pixel over the period 2004–2014. The results are 12 layers each representing the multi-annual average CFrC for a given month.The second type of data needed for the analysis is the fraction of the 0.05° × 0.05° pixels that are covered by distinct vegetation types (essentially trees and grasses) and by other land cover classes (urban areas, bare soil, etc.). These are derived from the Land Cover CCI54, a set of consistent annual maps describing, with a spatial resolution of 300 m, how the terrestrial surface is covered based on The United Nations Land Cover Classification Scheme74. This information is aggregated both spatially and thematically using a specifically designed framework75 to produce maps of general land fractional cover with a spatial resolution of 0.05° to match that of the cloud fractional cover data. The procedure is very similar to that done in a previous study22. For the context of this study, which has a focus on afforestation, the interest lies on transitions among three main vegetated classes, namely: deciduous forest, evergreen forests and herbaceous vegetation. Herbaceous vegetation is composed of both grasses and crops, irrespective of management practice such as irrigation. While irrigation has a clear biophysical effect of its own60, we deemed the land cover product was not consistent enough for this specific class. For reasons that are explained in the respective methodological section below, the full compositional description of the landscape is necessary (i.e. beyond the classes of interest), and therefore land cover fractions of the following classes are also generated: shrublands, savannas, wetlands, water, bare or sparsely vegetated, snow or ice, and urban.Retrieving potential cloud fractional cover changeUnder the above-mentioned assumptions, we apply a space-for-time substitution algorithm developed in a previous study22 to the cloud fractional cover and land fractional cover datasets. We summarize the main aspects of the methodology, along with the few necessary adaptations, but the reader requiring more detail is redirected to the original papers22,76. The approach consists in applying an un-mixing operation over a spatially moving window containing n pixels. Over each window we apply a linear regression based on a matrix X containing the explanatory variables, in which each column of X represents the fractional cover of a given land cover type for each of the n pixels. The response variable is a vector y containing the n values of CFrC for the n pixels, while the vector β represents the regression coefficients:$${bf{y}}={bf{X}}beta$$
(1)
This is equivalent to solving the following system of equations:$$left{begin{array}{ll}{y}_{1}=&{beta }_{1}{x}_{11}+{beta }_{2}{x}_{12}+…+{beta }_{m}{x}_{1m}\ {y}_{2}=&{beta }_{1}{x}_{21}+{beta }_{2}{x}_{22}+…+{beta }_{m}{x}_{2m}\ vdots &\ {y}_{n}=&{beta }_{1}{x}_{n1}+{beta }_{2}{x}_{n2}+…+{beta }_{m}{x}_{nm}end{array}right.$$
(2)
in which the digits of the subscript of x, e.g. xij, represent the land cover fraction j in pixel i, for the n pixels in the moving window and the m classes that are considered. Once identified, we can use the β coefficients to predict the local y value corresponding to a given composition x, including that composed of a single land cover j by setting xj = 1 and all other x values to zero. However, applying a regression directly on X carries a risk due to the compositional nature of the data (i.e. the sum of each row adds up to one), as the analysis of any given subset of compositional components can lead to very different patterns, results and conclusions77. To avoid this, we reduce the dimensionality of X through singular value decomposition (SVD) after removing the mean of each column:$$({bf{X}}-{bf{M}})={bf{U}}{bf{D}}{{bf{V}}}^{t}$$
(3)
where M is the appropriate matrix of column means, U and V are the matrices containing, respectively, the left-hand and right-hand singular vectors, and D is a diagonal matrix containing the singular values representing the standard deviations of the ensuing dimensions. The squared values of D represent the variance explained by each dimension, and can thus serve to define z, a reduced subset of dimensions that conserves 100% of the original matrix’s variation. The corresponding z right-hand singular vectors, Vz, can then be used to find the appropriately transformed predictor matrix of reduced dimension Z as follows:$${bf{Z}}=({bf{X}}-{bf{M}}){{bf{V}}}_{z}$$
(4)
which can now be regressed onto the CFrC y:$$y={bf{Z}}{beta }_{z}+varepsilon$$
(5)
where Z has been augmented with a leading column of 1s to accommodate an intercept term in the regression. We then use the standard method to obtain an estimate of βz:$${beta }_{z}={left({{bf{Z}}}^{t}{bf{Z}}right)}^{-1}{{bf{Z}}}^{t}y$$
(6)
However, because of the matrix transformation from X to Z, the regression coefficients βz do not provide direct information on the relationship between land fractional cover and cloud fractional cover (as in a normal regression). To identify the z values associated with a particular vegetation or land cover type (within the local analysis defined by the moving window), we define a ‘dummy pixel’ whose composition contains only a single class, with all other classes in its composition set to zero. This pixel’s composition is then transformed, and its y value predicted. This is the y associated with that vegetation type. To generalize this for all compositional components of interest, we define a matrix P with as many rows as these compositional components that we wish to predict. P is centred on the same column means as above (M, specific to each local analysis), and then multiplied by the correct number of transposed right-hand singular vectors (Vz, again, specific to each local analysis).$${{bf{Z}}}_{{rm{p}}}=({bf{P}}-{bf{M}}){{bf{V}}}_{z}$$
(7)
Predicted yp values for each vegetation or land cover type (identified by predicting the appropriately transformed ‘dummy pixels’) are then calculated as:$${y}_{{rm{p}}}={{bf{Z}}}_{{rm{p}}}{beta }_{z}$$
(8)
The expected change in variable y associated with a transition from vegetation type A (e.g. herbaceous vegetation) to vegetation type B (e.g. deciduous forest) at the centre of the local window is then the difference between the yp predicted for each ‘pure’ vegetation type:$${{Delta }}{y}_{{rm{A}}to {rm{B}}}={y}_{rm{B}}-{y}_{rm{A}}$$
(9)
The uncertainty in the estimation of ΔyA→B can be expressed as a standard deviation using the following expression:$${sigma }_{{rm{A}}to {rm{B}}}=sqrt{{sigma }_{rm{A}}^{2}+{sigma }_{rm{B}}^{2}-2{sigma }_{rm{AB}}}$$
(10)
where ({sigma }_{rm{A}}^{2}) and ({sigma }_{rm{B}}^{2}) are the variances in the estimates of yA and yB, and σAB is their covariance. These variances and covariances are in turn obtained from the covariance matrix, defined from the regression as:$${mathbf{Sigma }}={{bf{Z}}}_{{rm{p}}}{rm{Var}}[beta ]{{bf{Z}}}_{{rm{p}}}^{t}$$
(11)
The diagonal terms in Σ are the variances of individual predictions of (individual) classes. The off-diagonal parts of Σ hold the covariances between these predictions. As a reminder, the uncertainty σA→B calculated in this way is related to the methodological uncertainty and does not include the uncertainty in the input variables of land cover or cloud fractional cover.In the default set-up for this study, we concentrate on two transitions: herbaceous vegetation to deciduous forest and herbaceous vegetation to evergreen forest. These are calculated using a spatial window of 7 × 7 pixels, each pixel being of 0.05°, resulting in a squared spatial window of circa 35 km in size. To ensure there are enough values to do the un-mixing over each window, we established that there must be a minimum of 60% of valid values in each window, and that at least 40% must have distinct compositions. The operation is applied to all 12 monthly layers of CFrC, resulting in 12 maps of Δy with a 0.05° spatial resolution for each of the two vegetation cover transitions.Post-processingA series of post-processing steps are required to ensure the results of the Δy maps can be used to evaluate the effect of land on cloud cover. The first step is to mask all pixels in which there is insufficient co-occurrence of the two vegetation classes involved in the transition. This co-occurrence is quantified by an index of vegetation co-occurrence76, Ic, calculated from the land fractional cover layers using the same spatial moving window of 7 × 7 pixels as used before. This index is calculated pairwise, i.e. for 2 vegetation classes of interest A and B, using two vectors pA and pB, describing the presence of these two vegetation classes in each of the i pixels in the moving window. It also requires the definition of another i point evenly distributed along a hypothetical line B = 1 − A in the two-dimensional space describing the presences of vegetation class A and vegetation class B. These points, whose position in the 2-D space are labelled qA and qB, represent an ideal situation of maximum co-occurrence that serves as a reference to establish the index. The formal definition of the index is thus:$${I}_{{rm{c}}}=1-frac{{sum }_{i}min {sqrt{{left({q}_{A}-{p}_{A}right)}^{2}+{left({q}_{B}-{p}_{B}right)}^{2}}}}{{sum }_{i}sqrt{{q}_{A}^{2}+{q}_{B}^{2}}}$$
(12)
The minimum operator in the numerator selects the smallest distance that a given point p can have to any of the q points. The sum relates to the sum of this distance for all i points in the spatial moving window. The denominator characterizes the maximum distance that the point p can encounter. Ic will range from 0 to 1 corresponding to a gradient of ‘no presence of either class’ to ‘full and evenly balanced presence of both classes’. As in76, we retain only pixels with Ic ≥ 0.5 where we consider that there is sufficient information at local scale concerning both vegetation types to derive meaningful information about the target land cover transition.The second step is to remove the potential orographical effects, which can be especially problematic given that forests are more likely to be located over mountainous areas due to human action56. Here we mask the areas where considerable topographical variation occurs within the 7 × 7 pixel moving window of interest using the same implementation described in76. This involves using 3 different indicators, v1, v2 and v3, calculated over the moving window based on μh and σh, which are, respectively, the mean and the standard deviation of elevation over each grid cell of the input cloud dataset. These are defined as follows:$${v}_{1}=frac{1}{n}mathop{sum }limits_{i=1}^{n}{sigma }_{h,i}$$
(13)
$${v}_{2}=| {mu }_{h}-frac{1}{n}mathop{sum }limits_{i=1}^{n}{mu }_{h,i}|$$
(14)
$${v}_{3}=| {sigma }_{h}-{v}_{1}|$$
(15)
For an interpretation of these metrics, readers are invited to read76. These three indicators are combined together in a single layer depicting all pixels satisfying all of the following conditions: v1  More