Ecology

Suding, K. et al. Committing to ecological restoration. Science 348, 638–640 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chazdon, R. L. Landscape restoration, natural regeneration, and the forests of the future. mobt 102, 251–257 (2017).
Google Scholar 
Crouzeilles, R., Lorini, M. L. & Grelle, C. Applying graph theory to design networks of protected areas: using inter-patch distance for regional conservation planning. Natureza Conservaçao Rev. Brasileira de Conservaçao da Natureza 9, 219–224 (2011).
Google Scholar 
Crouzeilles, R., Lorini, M. L. & Grelle, C. E. V. The importance of using sustainable use protected areas for functional connectivity. Biol. Cons. 159, 450–457 (2013).Article 

Google Scholar 
Arroyo-Rodríguez, V. et al. Designing optimal human-modified landscapes for forest biodiversity conservation. Ecol. Lett. 23, 1404–1420 (2020).PubMed 
Article 

Google Scholar 
O’Farrell, P. J. & Anderson, P. M. Sustainable multifunctional landscapes: a review to implementation. Curr Opin Environ. Sustain. 2, 59–65 (2010).Article 

Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

Google Scholar 
César, R. G. et al. It is not just about time: agricultural practices and surrounding forest cover affect secondary forest recovery in agricultural landscapes. Biotropica 53, 496–508 (2021).Article 

Google Scholar 
Crouzeilles, R. et al. A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes. J. Appl. Ecol. 56, 2675–2686 (2019).Article 

Google Scholar 
Villard, M.-A. & Metzger, J. P. Beyond the fragmentation debate: a conceptual model to predict when habitat configuration really matters. J. Appl. Ecol. 51, 309–318 (2014).Article 

Google Scholar 
Taylor, P. D., Fahrig, L. & With, K. A. Landscape connectivity: a return to the basics. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 29–43 (Cambridge University Press, 2006).Tischendorf, L. & Fahrig, L. On the usage and measurement of landscape connectivity. Oikos 90, 7–19 (2000).Article 

Google Scholar 
McRae, B. H., Hall, S. A., Beier, P. & Theobald, D. M. Where to restore ecological connectivity? Detecting barriers and quantifying restoration benefits. PLoS ONE 7, e52604 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Torrubia, S. et al. Getting the most connectivity per conservation dollar. Front. Ecol. Environ. 12, 491–497 (2014).Article 

Google Scholar 
Crouzeilles, R. et al. A global meta-analysis on the ecological drivers of forest restoration success. Nat. Commun. 7, 11666 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leal-Ramos, D. et al. Forest and connectivity loss drive changes in movement behavior of bird species. Ecography 43, 1203–1214 (2020).Article 

Google Scholar 
Pérez-Cárdenas, N. et al. Effects of landscape composition and site land-use intensity on secondary succession in a tropical dry forest. For. Ecol. Manage. 482, 118818 (2021).Article 

Google Scholar 
Holl, K. D., Reid, J. L., Chaves-Fallas, J. M., Oviedo-Brenes, F. & Zahawi, R. A. Local tropical forest restoration strategies affect tree recruitment more strongly than does landscape forest cover. J. Appl. Ecol. 54, 1091–1099 (2017).Article 

Google Scholar 
Holl, K. D., Zahawi, R. A., Cole, R. J., Ostertag, R. & Cordell, S. Planting seedlings in tree islands versus plantations as a large-scale tropical forest restoration strategy. Restor. Ecol. 19, 470–479 (2011).Article 

Google Scholar 
Cole, R. J., Holl, K. D. & Zahawi, R. A. Seed rain under tree islands planted to restore degraded lands in a tropical agricultural landscape. Ecol. Appl. 20, 1255–1269 (2010).CAS 
PubMed 
Article 

Google Scholar 
Zahawi, R. A., Holl, K. D., Cole, R. J. & Reid, J. L. Testing applied nucleation as a strategy to facilitate tropical forest recovery. J. Appl. Ecol. 50, 88–96 (2013).Article 

Google Scholar 
Reid, J. L., Kormann, U., Zarrate-Chary, D., Holl, K. D. & Zahawi, R. A. Predicting toucan-mediated seed dispersal in tropical forest restoration. Ecosphere (In press).Zahawi, R. A. et al. Proximity and abundance of mother trees affects recruitment patterns in a long-term tropical forest restoration study. Ecography 44,1826–1837 (2021).Lehouck, V. et al. Habitat disturbance reduces seed dispersal of a forest interior tree in a fragmented African cloud forest. Oikos 118, 1023–1034 (2009).Article 

Google Scholar 
Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).Article 

Google Scholar 
Fahrig, L. et al. Is habitat fragmentation bad for biodiversity?. Biol. Cons. 230, 179–186 (2019).Article 

Google Scholar 
Schupp, E. W., Jordano, P. & Gómez, J. M. Seed dispersal effectiveness revisited: a conceptual review. New Phytol. 188, 333–353 (2010).PubMed 
Article 

Google Scholar 
Rogers, H. S., Donoso, I., Traveset, A. & Fricke, E. C. Cascading impacts of seed disperser loss on plant communities and ecosystems. Annu. Rev. Ecol. Evol. Syst. 52, 641–666 (2021).Article 

Google Scholar 
Howe, H. F. & Smallwood, J. Ecology of seed dispersal. Annu. Rev. Ecol. Syst. 13, 201–228 (1982).Article 

Google Scholar 
Holdridge, L. R., Grenke, W. C., Hatheway, W. H., Liang, T. & Tosi, J. A. J. Forest environments in tropical life zones: a pilot study (Pergamon Press, 1971).
Google Scholar 
Zahawi, R. A., Duran, G. & Kormann, U. Sixty-seven years of land-use change in Southern Costa Rica. PLoS ONE 10, e0143554 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Holl, K. D. et al. Applied nucleation facilitates tropical forest recovery: Lessons learned from a 15-year study. J. Appl. Ecol. 57, 2316–2328 (2020).Article 

Google Scholar 
Reid, J. L., Mendenhall, C. D., Rosales, J. A., Zahawi, R. A. & Holl, K. D. Landscape context mediates avian habitat choice in tropical forest restoration. PLoS ONE 9, e90573 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Buchanan, G. M., Donald, P. F. & Butchart, S. H. M. Identifying priority areas for conservation: a global assessment for forest-dependent birds. PLoS ONE 6, e29080 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Carrara, E. et al. Impact of landscape composition and configuration on forest specialist and generalist bird species in the fragmented Lacandona rainforest, Mexico. Biol. Conser. 184, 117–126 (2015).Article 

Google Scholar 
Chao, A. & Shen, T. J. Program SPADE (Species Prediction and Diversity Estimation). Program and User’s Guide. (http://chao.stat.nthu.edu.tw, 2010).Chazdon, R. L. et al. A novel statistical method for classifying habitat generalists and specialists. Ecology 92, 1332–1343 (2011).PubMed 
Article 

Google Scholar 
de Souza, R. P. & Válio, I. F. M. Seed size, seed germination, and seedling survival of Brazilian tropical tree species differing in successional status. Biotropica 33, 447–457 (2001).Article 

Google Scholar 
Werden, L. K., Holl, K. D., Rosales, J. A., Sylvester, J. M. & Zahawi, R. A. Effects of dispersal- and niche-based factors on tree recruitment in tropical wet forest restoration. Ecol. Appl. 30, e02139 (2020).PubMed 

Google Scholar 
Mendenhall, C. D., Shields-Estrada, A., Krishnaswami, A. J. & Daily, G. C. Quantifying and sustaining biodiversity in tropical agricultural landscapes. PNAS 113, 14544–14551 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jesus, F. M., Pivello, V. R., Meirelles, S. T., Franco, G. A. D. C. & Metzger, J. P. The importance of landscape structure for seed dispersal in rain forest fragments. J. Veg. Sci. 23, 1126–1136 (2012).Article 

Google Scholar 
Galán-Acedo, C., Arroyo-Rodríguez, V., Estrada, A. & Ramos-Fernández, G. Drivers of the spatial scale that best predict primate responses to landscape structure. Ecography 41, 2027–2037 (2018).Article 

Google Scholar 
Pardini, R., de Souza, S. M., Braga-Neto, R. & Metzger, J. P. The role of forest structure, fragment size and corridors in maintaining small mammal abundance and diversity in an Atlantic forest landscape. Biol. Cons. 124, 253–266 (2005).Article 

Google Scholar 
Forman, R. T. T. & Godron, M. Landscape ecology. (Wiley, 1986).QGIS Development Team. QGIS Geographic Information System. (Open Source Geospatial Foundation, 2016).Gillies, C. S. & Clair, C. C. S. Riparian corridors enhance movement of a forest specialist bird in fragmented tropical forest. PNAS 105, 19774–19779 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Harvey, C. A., Tucker, N. I. & Estrada, A. Live fences, isolated trees, and windbreaks: tools for conserving biodiversity in fragmented tropical landscapes. in Agroforestry and biodiversity conservation in tropical landscapes 261–289 (2004).Harvey, C. A. et al. Contribution of live fences to the ecological integrity of agricultural landscapes. Agric. Ecosyst. Environ. 111, 200–230 (2005).Article 

Google Scholar 
Saura, S., Bodin, Ö. & Fortin, M.-J. EDITOR’S CHOICE: Stepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networks. J. Appl. Ecol. 51, 171–182 (2014).Article 

Google Scholar 
He, H. S., DeZonia, B. E. & Mladenoff, D. J. An aggregation index (AI) to quantify spatial patterns of landscapes. Landscape Ecol. 15, 591–601 (2000).Article 

Google Scholar 
Radford, J. Q., Bennett, A. F. & Cheers, G. J. Landscape-level thresholds of habitat cover for woodland-dependent birds. Biol. Cons. 124, 317–337 (2005).Article 

Google Scholar 
Pires, A. S., Lira, P. K., Fernandez, F. A. S., Schittini, G. M. & Oliveira, L. C. Frequency of movements of small mammals among Atlantic Coastal Forest fragments in Brazil. Biol. Conserv. 108, 229–237 (2002).Article 

Google Scholar 
Holbrook, K. M. Home range and movement patterns of toucans: implications for seed dispersal. Biotropica 43, 357–364 (2011).Article 

Google Scholar 
Şekercioğlu, Ç. H. et al. Tropical countryside riparian corridors provide critical habitat and connectivity for seed-dispersing forest birds in a fragmented landscape. J Ornithol 156, 343–353 (2015).Article 

Google Scholar 
Eigenbrod, F., Hecnar, S. J. & Fahrig, L. Sub-optimal study design has major impacts on landscape-scale inference. Biol. Conserv. 144, 298–305 (2011).Article 

Google Scholar 
McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. (2012).Jackson, H. B. & Fahrig, L. Are ecologists conducting research at the optimal scale?. Global Ecol. Biogeography 24, 52–63 (2015).Article 

Google Scholar 
Jackson, H. B. & Fahrig, L. What size is a biologically relevant landscape?. Landscape Ecol 27, 929–941 (2012).Article 

Google Scholar 
McGarigal, K., Wan, H. Y., Zeller, K. A., Timm, B. C. & Cushman, S. A. Multi-scale habitat selection modeling: a review and outlook. Landscape Ecol 31, 1161–1175 (2016).Article 

Google Scholar 
Huais, P. Y. multifit: an R function for multi-scale analysis in landscape ecology. Landscape Ecol 33, 1023–1028 (2018).Article 

Google Scholar 
R Development Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).Crawley, M. J. Statistical modelling in the R book. (John Wiley & Sons Ltd., 2007).Leite, M. de S., Tambosi, L. R., Romitelli, I. & Metzger, J. P. Landscape ecology perspective in restoration projects for biodiversity conservation: a review. Natureza & Conservação 11, 108–118 (2013).Neter, J., Kutner, M. H., Nachtsheim, C. J. & Wasserman, W. Applied linear statistical models. (McGraw-Hill/Irwin, 1996).Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. (Springer, 2002).Calcagno, V. & Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Soft. 34, 1–29 (2010).Article 

Google Scholar 
Giam, X. & Olden, J. D. Quantifying variable importance in a multimodel inference framework. Methods Ecol. Evol. 7, 388–397 (2016).Article 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
Article 

Google Scholar 
Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355–366 (1994).Article 

Google Scholar 
Fagan, M. E., DeFries, R. S., Sesnie, S. E., Arroyo-Mora, J. P. & Chazdon, R. L. Targeted reforestation could reverse declines in connectivity for understory birds in a tropical habitat corridor. Ecol. Appl. 26, 1456–1474 (2016).PubMed 
Article 

Google Scholar 
Reid, J. L. & Holl, K. D. Arrival ≠ survival. Restor. Ecol. 21, 153–155 (2013).Article 

Google Scholar 
Pejchar, L. et al. Birds as agents of seed dispersal in a human-dominated landscape in southern Costa Rica. Biol. Cons. 141, 536–544 (2008).Article 

Google Scholar 
Norden, N. et al. Is temporal variation of seedling communities determined by environment or by seed arrival? A test in a neotropical forest. J. Ecol. 95, 507–516 (2007).Article 

Google Scholar 
Tabarelli, M., Lopes, A. V. & Peres, C. A. Edge-effects drive tropical forest fragments towards an early-successional system. Biotropica 40, 657–661 (2008).Article 

Google Scholar 
Lôbo, D., Leão, T., Melo, F. P. L., Santos, A. M. M. & Tabarelli, M. Forest fragmentation drives Atlantic forest of northeastern Brazil to biotic homogenization. Divers. Distrib. 17, 287–296 (2011).Article 

Google Scholar 
Costa, J. B. P., Melo, F. P. L., Santos, B. A. & Tabarelli, M. Reduced availability of large seeds constrains Atlantic forest regeneration. Acta Oecologica 39, 61–66 (2012).ADS 
Article 

Google Scholar 
Miguet, P., Jackson, H. B., Jackson, N. D., Martin, A. E. & Fahrig, L. What determines the spatial extent of landscape effects on species?. Landscape Ecol 31, 1177–1194 (2016).Article 

Google Scholar  More

Chaudhary, G. & Singh, S. K. Global status of genetically modified crops and its commercialization: environmental issues in logistics and manufacturing. (2019).Zwahlen, C., Hilbeck, A., Gugerli, P. & Nentwig, W. Degradation of the Cry1Ab protein within transgenic Bacillus thuringiensis corn tissue in the field. Mol. Ecol. 12, 765–775 (2010).Article 

Google Scholar 
Kamota, A., Muchaonyerwa, P. & Mnkeni, P. N. S. Decomposition of surface-applied and soil-incorporated Bt maize leaf litter and Cry1Ab protein during winter fallow in South Africa. Pedosphere 24, 251–257 (2014).CAS 
Article 

Google Scholar 
Xue, K., Diaz, B. R. & Thies, J. E. Stability of Cry3Bb1 protein in soils and its degradation in transgenic corn residues. Soil Biol. Biochem. 76, 119–126 (2014).CAS 
Article 

Google Scholar 
Griffiths, N. A. et al. Occurrence, leaching, and degradation of Cry1Ab protein from transgenic maize detritus in agricultural streams. Sci. Total Environ. 592, 97–105 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, B. F., Yin, J. Q., Wu, F. C., Jiang, Z. L. & Song, X. Y. Field decomposition of Bt-506 maize leaves and its effect on Collembola in the black soil region of Northeast China. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2021.e01480 (2021).Article 

Google Scholar 
Shu, Y. H., Zhang, Y. Y., Zeng, H., Zhang, Y. H. & Wang, J. W. Effects of Cry1Ab Bt maize straw return on bacterial community of earthworm Eisenia Fetida. Chemosphere 173, 1–13 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Čerevková, A., Miklisová, D., Szoboszlay, M. S., Tebbe, C. C. & Cagáň, L. The responses of soil nematode communities to Bt maize cultivation at four field sites across Europe. Soil Biol. Biochem. 119, 194–202 (2018).Article 
CAS 

Google Scholar 
Liu, T. et al. Root and detritus of transgenic Bt crop did not change nematode abundance and community composition but enhanced trophic connections. Sci. Total Environ. 644, 822–829 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Domínguez, M. T., Holthof, E., Smith, A. R., Koller, E. & Emmett, B. A. Contrasting response of summer soil respiration and enzyme activities to long-term warming and drought in a wet shrubland (NE Wales, UK). Appl. Soil Ecol. 110, 151–155 (2016).Article 

Google Scholar 
Zhang, Q. F. et al. Are the combined effects of warming and drought on foliar C:N:P:K stoichiometry in a subtropical forest greater than their individual effects?. Forest Ecol. Manag. 448, 256–266 (2019).Article 

Google Scholar 
Chen, Q., Niu, B., Hu, Y., Luo, T. & Zhang, G. Warming and increased precipitation indirectly affect the composition and turnover of labile-fraction soil organic matter by directly affecting vegetation and microorganisms. Sci. Total Environ. 714, 136787.1-136787.9 (2020).
Google Scholar 
Dai, A. Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Change 2, 45–65 (2011).Article 

Google Scholar 
Martin, J. T., Pederson, G. T., Woodhouse, C. A., Cook, E. R. & King, J. Increased drought severity tracks warming in the United States’ largest river basin. Proc. Natl. Acad. Sci. USA 117, 201916208 (2020).
Google Scholar 
Ma, S., Zhu, C. & Liu, J. Combined impacts of warm central equatorial pacific sea surface temperatures and anthropogenic warming on the 2019 severe drought in east China. Adv. Atmos. Sci. 37, 1149–1163 (2020).Article 

Google Scholar 
Peñuelas, J. et al. Nonintrusive field experiments show different plant responses to warming and drought among sites, seasons, and species in a north–south European gradient. Ecosystems 7, 598–612 (2004).Article 

Google Scholar 
Sardans, J., Peñuelas, J. & Estiarte, M. Warming and drought alter soil phosphatase activity and soil P availability in a Mediterranean shrubland. Plant Soil 289, 227–238 (2006).CAS 
Article 

Google Scholar 
Viciedo, D. O., Prado, R., Martinez, C. A., Habermann, H. & Piccolo, M. Short-term warming and water stress affect Panicum maximum Jacq. stoichiometric homeostasis and biomass production. Sci. Total Environ. 681, 267–274 (2019).ADS 
Article 
CAS 

Google Scholar 
Meeran, K. et al. Warming and elevated CO2 intensify drought and recovery responses of grassland carbon allocation to soil respiration. Glob. Change Biol. 27, 3230–3243 (2021).Article 

Google Scholar 
Lang, B., Rall, B. C., Scheu, S. & Brose, U. Effects of environmental warming and drought on size-structured soil food webs. Oikos 123, 1224–1233 (2014).Article 

Google Scholar 
Pold, G., Melillo, J. M. & Deangelis, K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front. Microbiol. 6, 480 (2010).
Google Scholar 
Séneca, J. et al. Composition and activity of nitrifier communities in soil are unresponsive to elevated temperature and CO2, but strongly affected by drought. ISME J. 14, 1–16 (2020).Article 
CAS 

Google Scholar 
Santos, A. et al. Water stress alters lignin content and related gene expression in two sugarcane genotypes. J. Agric. Food Chem. 63, 4708 (2015).CAS 
PubMed 
Article 

Google Scholar 
Albert, K. R. et al. Effects of elevated CO2, warming and drought episodes on plant carbon uptake in a temperate heath ecosystem are controlled by soil water status. Plant Cell Environ. 34, 1207–1222 (2011).CAS 
PubMed 
Article 

Google Scholar 
Peñuelas, J. et al. Nonintrusive field experiments show different plant responses to warming and drought among sites, seasons, and species in a north-south European gradient. Ecosystems 7, 598–612 (2004).Article 

Google Scholar 
Zhu, E., Cao, Z., Jia, J., Liu, C. & Feng, X. Inactive and inefficient: Warming and drought effect on microbial carbon processing in alpine grassland at depth. Glob. Change Biol. https://doi.org/10.1111/gcb.15541 (2021).Article 

Google Scholar 
Sardans, J., Peñuelas, J. & Estiarte, M. Changes in soil enzymes related to C and N cycle and in soil C and N content under prolonged warming and drought in a Mediterranean shrubland. Appl. Soil Ecol. 39, 223–235 (2008).Article 

Google Scholar 
Xu, G. L. et al. Seasonal exposure to drought and air warming affects soil Collembola and Mites. PLoS ONE 7, e43102 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chang, L. et al. Warming limits daytime but not nighttime activity of epigeic microarthropods in Songnen grasslands. Appl. Soil Ecol. 141, 79–83 (2019).Article 

Google Scholar 
Dai, A. G., Trenberth, K. E. & Qian, T. T. A global dataset of palmer drought severity index for 1870–2002: Relationship with soil moisture and effects of surface warming. J. Hydrometeorol. 5, 1117–1130 (2004).ADS 
Article 

Google Scholar 
Bongaarts, J. Intergovernmental panel on climate change special report on global warming of 1.5 °C Switzerland: IPCC, 2018. Popul. Dev. Rev. 45, 251–252 (2019).Article 

Google Scholar 
Bellinger, P.F., Christiansen, K. A. & Janssens, F. Checklist of the Collembola of the World. 1996–2019. http://www.collembola.org (Accessed 10 Sept 2021).Hopkin, S. P. Biology of the Springtails (Insecta:Collembola) 1–330 (Oxford University Press, 1997).
Google Scholar 
Rusek, J. Biodiversity of Collembola and their functional role in the ecosystem. Biodivers. Conserv. 7, 1207–1219 (1998).Article 

Google Scholar 
Filser, J. The role of Collembola in carbon and nitrogen cycling in soil. Pedobiologia 46, 234–245 (2002).
Google Scholar 
Endlweber, K. & Scheu, S. Effects of Collembola on root properties of two competing ruderal plant species. Soil Biol. Biochem. 38, 2025–2031 (2006).CAS 
Article 

Google Scholar 
Rebek, E. J., Hogg, D. B. & Young, D. K. Effect of four cropping systems on the abundance and diversity of epedaphic Springtails (Hexapoda: Parainsecta: Collembola) in southern Wisconsin. Environ. Entomol. 31, 37–46 (2002).Article 

Google Scholar 
Santorufo, L. et al. An assessment of the influence of the urban environment on collembolan communities in soils using taxonomy- and trait-based approaches. Appl. Soil Ecol. 78, 48–56 (2014).Article 

Google Scholar 
Rossetti, I. et al. Isolated cork oak trees affect soil properties and biodiversity in a Mediterranean wooded grassland. Agric. Ecosyst. Environ. 202, 203–216 (2015).Article 

Google Scholar 
Hönemann, L., Zurbrügg, C. & Nentwig, W. Effects of Bt-corn decomposition on the composition of the soil meso- and macrofauna. Appl. Soil Ecol. 40, 203–209 (2008).Article 

Google Scholar 
Arias-Martín, M. et al. Effects of three-year cultivation of Cry1Ab-expressing Bt maize on soil microarthropod communities. Agric. Ecosyst. Environ. 220, 125–134 (2016).Article 
CAS 

Google Scholar 
Song, X. Y. et al. Use of taxonomic and trait-based approaches to evaluate the effects of transgenic Cry1Ac corn on the community characteristics of soil Collembola. Environ. Entomol. 48, 263–269 (2019).PubMed 
Article 

Google Scholar 
Thibaud, J. M. Intermue ettemperatures lethales chez les insects collemboles arthropleones. II.—Isotomidae, Entomobryidae et Tomoceridae. Rev. Ecol. Biol. Sol. 14, 267–278 (1977).
Google Scholar 
Eisenbeis, G. & Wichard, W. Atlas on the Biology of Soil Arthropods 200–228 (Springer, 1987).Book 

Google Scholar 
Wang, B. F., Wu, F. C., Yin, J. Q., Jiang, Z. L. & Song, X. Y. Use of taxonomic and trait-based approaches to evaluate the effect of Bt maize expressing cry1Ie protein on non-target Collembola: A case study in Northeast China. Insects. https://doi.org/10.3390/insects12020088 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Chang, L., Song, X. Y., Wang, B. F., Wu, D. H. & Reddy, G. Effect of Bt corn (Bt 38) cultivation on community structure of Collembola. Ann. Entomol. Soc. Am. 113, 1–5 (2020).CAS 
Article 

Google Scholar 
Al-Deeb, M., Wilde, G. E., Blair, J. M. & Todd, T. C. Effect of Bt corn for corn rootworm control on nontarget soil microarthropods and nematodes. Environ. Entomol. 32, 859–865 (2003).Article 

Google Scholar 
Bitzer, R. J., Rice, M. E., Pilcher, C. D., Pilcher, C. L. & Lam, W. F. Biodiversity and community structure of epedaphic and euedaphic springtails (Collembola) in transgenic rootworm Bt maize. Environ. Entomol. 34, 1346–1376 (2005).Article 

Google Scholar 
Yang, Y. et al. Toxicological and biochemical analyses demonstrate no toxic effect of Cry1C and Cry2A to Folsomia candida. Sci. Rep. 5, 15619 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jiang, Z., Zhou, L., Wang, B. F., Wang, D. M. & Song, X. Y. Toxicological and biochemical analyses demonstrate no toxic effect of Bt maize on the Folsomia candida. PLoS ONE 15, e0232747 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Frouz, J., Elhottová, D., Helingerová, M. & Kocourek, F. The effect of bt corn on soil invertebrates, soil microbial community and decomposition rates of corn post-harvest residues under field and laboratory conditions. J. Sustain. Agric. 32, 645–655 (2008).Article 

Google Scholar 
Daghighi, E., Filser, J., Koehler, H. & Kesel, R. Long-term succession of Collembola communities in relation to climate change and vegetation. Pedobiologia 64, 25–38 (2017).Article 

Google Scholar 
Chang, L. et al. Green more than brown food resources drive the effect of simulated climate change on Collembola: A soil transplantation experiment in Northeast China. Geoderma 392, 115008 (2021).ADS 
CAS 
Article 

Google Scholar 
Convey, P., Block, W. & Peat, H. J. Soil arthropods as indicators of water stress in Antarctic terrestrial habitats. Glob. Change Biol. 9, 1718–1730 (2003).ADS 
Article 

Google Scholar 
Alvarez, T., Frampton, G. K. & Goulson, D. The effects of drought upon epigeal Collembola from arable soils. Agric. For. Entomol. 1, 243–248 (2015).Article 

Google Scholar 
Fountain, M. T. & Hopkin, S. P. Folsomia candida (collembola): A “standard” soil arthropod. Annu. Rev. Entomol. 50, 201–222 (2005).CAS 
PubMed 
Article 

Google Scholar 
Holmstrup, M. Water relations and drought sensitivity of Folsomia candida eggs (Collembola: Isotomidae). Eur. J. Entomol. 116, 229–234 (2019).Article 

Google Scholar 
Meehan, M. L., Barreto, C., Turnbull, M. S., Bradley, R. L. & Lindo, Z. Response of soil fauna to simulated global change factors depends on ambient climate conditions. Pedobiologia 83, 150672 (2020).Article 

Google Scholar 
Harte, J., Rawa, A. & Price, V. Effects of manipulated soil microclimate on mesofaunal biomass and diversity. Soil Biol. Biochem. 28, 313–322 (1996).CAS 
Article 

Google Scholar 
Lindberg, N. Soil fauna and global change: responses to experimental drought, irrigation, fertilisation and soil warming. Acta Universitatis Agriculturae Sueciae Silvestria 37, + Papers I-IV (2003).Bokhorst, S. et al. Extreme winter warming events more negatively impact small rather than large soil fauna: shift in community composition explained by traits not taxa. Global Change Biolo. 18, 1152–1162 (2012).Macfadyen, A. Improved funnel-type extractors for soil arthropods. J. Anim. Ecol. 30, 171–184 (1961).Article 

Google Scholar 
Christiansen, K. A. & Bellinge, P. F. The Collembola of North America, North of the Rio Grande: A Taxonomic Analysis 2nd edn. (Grinnell College, 1998).
Google Scholar 
Fjellberg, A. The Collembola of Fennoscandia and Denmark. Part II: Entomobryomorpha and Symphypleona. In Fauna Entomologica Scandinavica, Vol. 42, 1−264 (Koninklijke Brill, 2007).Potapov, M. Synopses on Palaearctic Collembola: Isotomidae. Abhandlungen und Berichte des Naturkundemuseums, Görlitz, Poland 73, 1–603 (2001).
Google Scholar 
Yin, W. Y. Pictorial Keys to Soil Animals of China. 282−292, 592−600 (Science Press, 1998).Grime, J. P. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).Article 

Google Scholar 
Cerabolini, B., Pierce, S., Luzzaro, A. & Ossola, A. Species evenness affects ecosystem processes in situ via diversity in the adaptive strategies of dominant species. Plant Ecol. 207, 333–345 (2010).Article 

Google Scholar  More

Simpson, G. G. The Major Features of Evolution (Columbia Univ. Press, 1953).Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism: American Society of Naturalists E. O. Wilson award address. Am. Nat. 175, 623–639 (2010).PubMed 
Article 

Google Scholar 
Osborn, H. F. The law of adaptive radiation. Am. Nat. 36, 353–363 (1902).Article 

Google Scholar 
Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596 (2010).CAS 
PubMed 
Article 

Google Scholar 
Robertson, G. P. et al. Soil resources, microbial activity, and primary production across an agricultural ecosystem. Ecol. Appl. 7, 158–170 (1997).Article 

Google Scholar 
Singer, D. et al. Protist taxonomic and functional diversity in soil, freshwater and marine ecosystems. Environ. Int. 146, 106262 (2021).CAS 
PubMed 
Article 

Google Scholar 
Miller, M. F. & Labandeira, C. C. Slow crawl across the salinity divide: delayed colonization of freshwater ecosystems by invertebrates. GSA Today 12, 4–10 (2002).Article 

Google Scholar 
Cnaani, A. & Hulata, G. Improving salinity tolerance in tilapias: past experience and future prospects. Isr. J. Aquac. 63, 20590 (2011).
Google Scholar 
Eiler, A. et al. Productivity and salinity structuring of the microplankton revealed by comparative freshwater metagenomics. Environ. Microbiol. 16, 2682–2698 (2014).CAS 
PubMed 
Article 

Google Scholar 
Cabello-Yeves, P. J. & Rodriguez-Valera, F. Marine-freshwater prokaryotic transitions require extensive changes in the predicted proteome. Microbiome 7, 117 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Hutchinson, G. E. A Treatise on Limnology (John Wiley & Sons, 1957).Vermeij, G. J. & Dudley, R. Why are there so few evolutionary transitions between aquatic and terrestrial ecosystems? Biol. J. Linn. Soc. 70, 541–554 (2000).Article 

Google Scholar 
Lee, C. E. & Bell, M. A. Causes and consequences of recent freshwater invasions by saltwater animals. Trends Ecol. Evol. 14, 284–288 (1999).CAS 
PubMed 
Article 

Google Scholar 
Logares, R. et al. Infrequent marine–freshwater transitions in the microbial world. Trends Microbiol. 17, 414–422 (2009).CAS 
PubMed 
Article 

Google Scholar 
Paver, S. F., Muratore, D., Newton, R. J. & Coleman, M. L. Reevaluating the salty divide: phylogenetic specificity of transitions between marine and freshwater systems. mSystems 3, e00232-18 (2018).Filker, S. et al. Transition boundaries for protistan species turnover in hypersaline waters of different biogeographic regions. Environ. Microbiol. 19, 3186–3200 (2017).CAS 
PubMed 
Article 

Google Scholar 
Cavalier-Smith, T. Megaphylogeny, cell body plans, adaptive zones: causes and timing of eukaryote basal radiations. J. Eukaryot. Microbiol. 56, 26–33 (2009).PubMed 
Article 

Google Scholar 
Carr, M. et al. A six-gene phylogeny provides new insights into choanoflagellate evolution. Mol. Phylogenet. Evol. 107, 166–178 (2017).PubMed 
Article 

Google Scholar 
Simon, M., López-García, P., Moreira, D. & Jardillier, L. New haptophyte lineages and multiple independent colonizations of freshwater ecosystems. Environ. Microbiol. Rep. 5, 322–332 (2013).CAS 
PubMed 
Article 

Google Scholar 
Bråte, J., Klaveness, D., Rygh, T., Jakobsen, K. S. & Shalchian-Tabrizi, K. Telonemia-specific environmental 18S rDNA PCR reveals unknown diversity and multiple marine–freshwater colonizations. BMC Microbiol. 10, 168 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Shalchian-Tabrizi, K. et al. Diversification of unicellular eukaryotes: cryptomonad colonizations of marine and fresh waters inferred from revised 18S rRNA phylogeny. Environ. Microbiol. 10, 2635–2644 (2008).CAS 
PubMed 
Article 

Google Scholar 
Von Der Heyden, S., Chao, E. E. & Cavalier-Smith, T. Genetic diversity of goniomonads: an ancient divergence between marine and freshwater species. Eur. J. Phycol. 39, 343–350 (2004).Article 
CAS 

Google Scholar 
Žerdoner Čalasan, A., Kretschmann, J. & Gottschling, M. They are young, and they are many: dating freshwater lineages in unicellular dinophytes. Environ. Microbiol. 21, 4125–4135 (2019).PubMed 
Article 

Google Scholar 
Annenkova, N. V., Giner, C. R. & Logares, R. Tracing the origin of planktonic protists in an ancient lake. Microorganisms 8, 543 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Annenkova, N. V., Hansen, G., Moestrup, Ø. & Rengefors, K. Recent radiation in a marine and freshwater dinoflagellate species flock. ISME J. 9, 1821–1834 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Annenkova, N. V., Hansen, G. & Rengefors, K. Closely related dinoflagellate species in vastly different habitats—an example of a marine–freshwater transition. Eur. J. Phycol. 55, 478–489 (2020).CAS 
Article 

Google Scholar 
Obiol, A. et al. A metagenomic assessment of microbial eukaryotic diversity in the global ocean. Mol. Ecol. Resour. 20, 718–731 (2020).CAS 
Article 

Google Scholar 
Jamy, M. et al. Long-read metabarcoding of the eukaryotic rDNA operon to phylogenetically and taxonomically resolve environmental diversity. Mol. Ecol. Resour. 20, 429–443 (2020).CAS 
PubMed 
Article 

Google Scholar 
Burki, F., Roger, A. J., Brown, M. W. & Simpson, A. G. B. The new tree of eukaryotes. Trends Ecol. Evol. 35, 43–55 (2020).PubMed 
Article 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D597–D604 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jamy, M. et al. Data for ‘Global patterns and rates of habitat transitions across the eukaryotic tree of life’. figshare https://doi.org/10.6084/m9.figshare.15164772.v3 (2022).Dunthorn, M. et al. Placing environmental next-generation sequencing amplicons from microbial eukaryotes into a phylogenetic context. Mol. Biol. Evol. 31, 993–1009 (2014).CAS 
PubMed 
Article 

Google Scholar 
Barbera, P. et al. EPA-ng: massively parallel evolutionary placement of genetic sequences. Syst. Biol. 68, 365–369 (2019).PubMed 
Article 

Google Scholar 
Pagel, M. Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters. Proc. R. Soc. B 255, 37–45 (1994).Article 

Google Scholar 
Ishikawa, S. A., Zhukova, A., Iwasaki, W., Gascuel, O. & Pupko, T. A fast likelihood method to reconstruct and visualize ancestral scenarios. Mol. Biol. Evol. 36, 2069–2085 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gottschling, M., Czech, L., Mahé, F., Adl, S. & Dunthorn, M. The windblown: possible explanations for dinophyte DNA in forest soils. J. Eukaryot. Microbiol. 68, e12833 (2021).Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).PubMed 
Article 

Google Scholar 
Strassert, J. F. H., Irisarri, I., Williams, T. A. & Burki, F. A molecular timescale for eukaryote evolution with implications for the origin of red algal-derived plastids. Nat. Commun. 12, 1879 (2021).Parfrey, L. W., Lahr, D. J. G., Knoll, A. H. & Katz, L. A. Estimating the timing of early eukaryotic diversification with multigene molecular clocks. Proc. Natl Acad. Sci. USA 108, 13624–13629 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Loron, C. C. et al. Early fungi from the Proterozoic era in Arctic Canada. Nature 570, 232–235 (2019).CAS 
PubMed 
Article 

Google Scholar 
Al Jewari, C. & Baldauf, S. L. Conflict over the eukaryote root resides in strong outliers, mosaics and missing data sensitivity of site-specific (CAT) mixture models. Syst. Biol. syac029 (2022).He, D. et al. An alternative root for the eukaryote tree of life. Curr. Biol. 24, 465–470 (2014).CAS 
PubMed 
Article 

Google Scholar 
Derelle, R. & Lang, B. F. Rooting the eukaryotic tree with mitochondrial and bacterial proteins. Mol. Biol. Evol. 29, 1277–1289 (2012).CAS 
PubMed 
Article 

Google Scholar 
Del Campo, J. et al. The others: our biased perspective of eukaryotic genomes. Trends Ecol. Evol. 29, 252–259 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Boaden, P. J. S. Meiofauna and the origins of the Metazoa. Zool. J. Linn. Soc. 96, 217–227 (1989).Article 

Google Scholar 
Wiens, J. J. Faster diversification on land than sea helps explain global biodiversity patterns among habitats and animal phyla. Ecol. Lett. 18, 1234–1241 (2015).PubMed 
Article 

Google Scholar 
Oliverio, A. M. et al. The global-scale distributions of soil protists and their contributions to belowground systems. Sci. Adv. 6, eaax8787 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martijn, J. et al. Confident phylogenetic identification of uncultured prokaryotes through long read amplicon sequencing of the 16S‐ITS‐23S rRNA operon. Environ. Microbiol. 21, 2485–2498 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krehenwinkel, H. et al. Nanopore sequencing of long ribosomal DNA amplicons enables portable and simple biodiversity assessments with high phylogenetic resolution across broad taxonomic scale. Gigascience 8, giz006 (2019).Furneaux, B., Bahram, M., Rosling, A., Yorou, N. S. & Ryberg, M. Long‐ and short‐read metabarcoding technologies reveal similar spatiotemporal structures in fungal communities. Mol. Ecol. Resour. 21, 1833–1849 (2021).CAS 
PubMed 
Article 

Google Scholar 
Logares, R. et al. Phenotypically different microalgal morphospecies with identical ribosomal DNA: a case of rapid adaptive evolution? Microb. Ecol. 53, 549–561 (2007).CAS 
PubMed 
Article 

Google Scholar 
Logares, R. et al. Recent evolutionary diversification of a protist lineage. Environ. Microbiol. 10, 1231–1243 (2008).CAS 
PubMed 
Article 

Google Scholar 
Nee, S., Holmes, E. C., May, R. M. & Harvey, P. H. Extinction rates can be estimated from molecular phylogenies. Philos. Trans. R. Soc. Lond. B 344, 77–82 (1994).CAS 
Article 

Google Scholar 
Knoll, A. H. Paleobiological perspectives on early eukaryotic evolution. Cold Spring Harb. Perspect. Biol. 6, a016121 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Strother, P. K., Battison, L., Brasier, M. D. & Wellman, C. H. Earth’s earliest non-marine eukaryotes. Nature 473, 505–509 (2011).CAS 
PubMed 
Article 

Google Scholar 
Knoll, A. H., Javaux, E. J., Hewitt, D. & Cohen, P. Eukaryotic organisms in Proterozoic oceans. Philos. Trans. R. Soc. B 361, 1023–1038 (2006).CAS 
Article 

Google Scholar 
Sánchez-Baracaldo, P., Raven, J. A., Pisani, D. & Knoll, A. H. Early photosynthetic eukaryotes inhabited low-salinity habitats. Proc. Natl Acad. Sci. USA 114, E7737–E7745 (2017).PubMed 
PubMed Central 

Google Scholar 
Blank, C. E. & SÁnchez-Baracaldo, P. Timing of morphological and ecological innovations in the cyanobacteria—a key to understanding the rise in atmospheric oxygen. Geobiology 8, 1–23 (2010).CAS 
PubMed 
Article 

Google Scholar 
Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).Article 

Google Scholar 
Richards, T. A., Jones, M. D. M., Leonard, G. & Bass, D. Marine fungi: their ecology and molecular diversity. Ann. Rev. Mar. Sci. 4, 495–522 (2012).PubMed 
Article 

Google Scholar 
Amend, A. From dandruff to deep-sea vents: Malassezia-like fungi are ecologically hyper-diverse. PLoS Pathog. 10, e1004277 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Orsi, W., Biddle, J. F. & Edgcomb, V. Deep sequencing of subseafloor eukaryotic rRNA reveals active fungi across marine subsurface provinces. PLoS ONE 8, e56335 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Klein, M., Swinnen, S., Thevelein, J. M. & Nevoigt, E. Glycerol metabolism and transport in yeast and fungi: established knowledge and ambiguities. Environ. Microbiol. 19, 878–893 (2017).CAS 
PubMed 
Article 

Google Scholar 
Kaserer, A. O., Andi, B., Cook, P. F. & West, A. H. in Methods in Enzymology Vol. 471 (eds Simon M. I. et al.) 59–75 (Academic Press, 2010).Nakov, T., Beaulieu, J. M. & Alverson, A. J. Diatoms diversify and turn over faster in freshwater than marine environments. Evolution 73, 2497–2511 (2019).PubMed 
Article 

Google Scholar 
Maddison, W. P., Midford, P. E. & Otto, S. P. Estimating a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007).PubMed 
Article 

Google Scholar 
Nelson, D. R. et al. Large-scale genome sequencing reveals the driving forces of viruses in microalgal evolution. Cell Host Microbe 29, 250–266.e8 (2021).CAS 
PubMed 
Article 

Google Scholar 
Czech, L. & Bremer, E. With a pinch of extra salt—did predatory protists steal genes from their food? PLoS Biol. 16, e2005163 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sibbald, S. J., Eme, L., Archibald, J. M. & Roger, A. J. Lateral gene transfer mechanisms and pan-genomes in eukaryotes. Trends Parasitol. 36, 927–941 (2020).CAS 
PubMed 
Article 

Google Scholar 
Stairs, C. W. et al. Microbial eukaryotes have adapted to hypoxia by horizontal acquisitions of a gene involved in rhodoquinone biosynthesis. eLife 7, e34292 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Savory, F. R., Milner, D. S., Miles, D. C. & Richards, T. A. Ancestral function and diversification of a horizontally acquired oomycete carboxylic acid transporter. Mol. Biol. Evol. 35, 1887–1900 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McDonald, S. M., Plant, J. N. & Worden, A. Z. The mixed lineage nature of nitrogen transport and assimilation in marine eukaryotic phytoplankton: a case study of Micromonas. Mol. Biol. Evol. 27, 2268–2283 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Walsh, D. A., Lafontaine, J. & Grossart, H. P. in Lateral Gene Transfer in Evolution (ed. Gophna, U.) 55–77 (Springer, 2013).Dorrell, R. G. et al. Phylogenomic fingerprinting of tempo and functions of horizontal gene transfer within ochrophytes. Proc. Natl Acad. Sci. USA 118, e2009974118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gluck-Thaler, E. et al. Giant Starship elements mobilize accessory genes in fungal genomes. Mol. Biol. Evol. 39, msac109 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eiler, A. et al. Tuning fresh: radiation through rewiring of central metabolism in streamlined bacteria. ISME J. 10, 1902–1914 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Urbina, H., Scofield, D. G., Cafaro, M. & Rosling, A. DNA-metabarcoding uncovers the diversity of soil-inhabiting fungi in the tropical island of Puerto Rico. Mycoscience 57, 217–227 (2016).CAS 
Article 

Google Scholar 
Kalsoom Khan, F. et al. Naming the untouchable—environmental sequences and niche partitioning as taxonomical evidence in fungi. IMA Fungus 11, 23 (2020).Peura, S. et al. Ontogenic succession of thermokarst thaw ponds is linked to dissolved organic matter quality and microbial degradation potential. Limnol. Oceanogr. 65, S248–S263 (2020).CAS 
Article 

Google Scholar 
Giner, C. R. et al. Marked changes in diversity and relative activity of picoeukaryotes with depth in the world ocean. ISME J. 14, 437–449 (2020).PubMed 
Article 

Google Scholar 
Jing, H., Zhang, Y., Li, Y., Zhu, W. & Liu, H. Spatial variability of picoeukaryotic communities in the Mariana Trench. Sci Rep. 8, 15357 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Santos, S. S. et al. Soil DNA extraction procedure influences protist 18S rRNA gene community profiling outcome. Protist 168, 283–293 (2017).CAS 
PubMed 
Article 

Google Scholar 
Derelle, E. et al. Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features. Proc. Natl Acad. Sci. USA 103, 11647–11652 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).PubMed 
Article 
CAS 

Google Scholar 
Cavalier-Smith, T., Lewis, R., Chao, E. E., Oates, B. & Bass, D. Helkesimastix marina n. sp. (Cercozoa: Sainouroidea superfam. n.) a gliding zooflagellate of novel ultrastructure and unusual ciliary behaviour. Protist 160, 452–479 (2009).PubMed 
Article 

Google Scholar 
Schwelm, A., Berney, C., Dixelius, C., Bass, D. & Neuhauser, S. The large subunit rDNA sequence of Plasmodiophora brassicae does not contain intra-species polymorphism. Protist 167, 544–554 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Heeger, F. et al. Long-read DNA metabarcoding of ribosomal RNA in the analysis of fungi from aquatic environments. Mol. Ecol. Resour. 18, 1500–1514 (2018).CAS 
PubMed 
Article 

Google Scholar 
Jamy, M. Code for ‘Global patterns and rates of habitat transitions across the eukaryotic tree of life’ v1.0.0. Zenodo https://doi.org/10.5281/zenodo.6656264 (2022).Article 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
PubMed Central 
Article 

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

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA Genes. PLoS ONE 4, e6372 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10 (2011).Article 

Google Scholar 
Adl, S. M. et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J. Eukaryot. Microbiol. 66, 4–119 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 
PubMed 
Article 

Google Scholar 
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 
Stamatakis, A. Phylogenetic models of rate heterogeneity: a high performance computing perspective. In Proc. 20th IEEE International Parallel & Distributed Processing Symposium (IEEE Computer Society, 2006); https://ieeexplore.ieee.org/document/1639535Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Czech, L., Barbera, P. & Stamatakis, A. Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data. Bioinformatics 36, 3263–3265 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mai, U. & Mirarab, S. TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees. BMC Genomics 19, 23–40 (2018).Article 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R. P., Moret, B. M. E. & Stamatakis, A. How many bootstrap replicates are necessary? J. Comput. Biol. 17, 337–354 (2010).CAS 
PubMed 
Article 

Google Scholar 
Mahé, F. et al. Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests. Nat. Ecol. Evol. 1, 0091 (2017).Article 

Google Scholar 
Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Vaulot, D. et al. metaPR2: a database of eukaryotic 18S rRNA metabarcodes with an emphasis on protists. Preprint at bioRxiv https://doi.org/10.1101/2022.02.04.479133 (2022).Sieber, G., Beisser, D., Bock, C. & Boenigk, J. Protistan and fungal diversity in soils and freshwater lakes are substantially different. Sci Rep. 10, 20025 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaulot, D., Geisen, S., Mahé, F. & Bass, D. pr2-primers: an 18S rRNA primer database for protists. Mol. Ecol. Resour. 22, 168–179 (2022).CAS 
PubMed 
Article 

Google Scholar 
Berger, S. A., Krompass, D. & Stamatakis, A. Performance, accuracy, and Web server for evolutionary placement of short sequence reads under maximum likelihood. Syst. Biol. 60, 291–302 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meade, A. & Pagel, M. BayesTraits v.3.0.2. Reading Evolutionary Biology Group (2019); http://www.evolution.reading.ac.uk/BayesTraitsV3.0.2/Files/BayesTraitsV3.0.2Manual.pdfPagel, M., Meade, A. & Barker, D. Bayesian estimation of ancestral character states on phylogenies. Syst. Biol. 53, 673–684 (2004).PubMed 
Article 

Google Scholar 
Pagel, M. & Meade, A. Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo. Am. Nat. 167, 808–825 (2006).PubMed 
Article 

Google Scholar 
Varga, T. et al. Megaphylogeny resolves global patterns of mushroom evolution. Nat. Ecol. Evol. 3, 668–678 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Xie, W., Lewis, P. O., Fan, Y., Kuo, L. & Chen, M. H. Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Syst. Biol. 60, 150–160 (2011).PubMed 
Article 

Google Scholar 
Pagel, M. & Meade, A. The deep history of the number words. Philos. Trans. R. Soc. B 373, 20160517 (2018).Article 

Google Scholar 
Baker, J. & Venditti, C. Rapid change in mammalian eye shape is explained by activity pattern. Curr. Biol. 29, 1082–1088 (2019).CAS 
PubMed 
Article 

Google Scholar 
Smith, S. A. & O’Meara, B. C. TreePL: divergence time estimation using penalized likelihood for large phylogenies. Bioinformatics 28, 2689–2690 (2012).CAS 
PubMed 
Article 

Google Scholar 
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016). More

Effect of different organic fertilizers on the growth of Pear-jujubeEffect of different organic fertilizers on the bearing branch length of Pear-jujubeJujube-bearing branch has the dual role of fruiting and photosynthesis32,33. It can be seen from Fig. 1 that different organic fertilizer treatments have a significant impact on the growth of jujube-bearing branches. Among them, the longest jujube-bearing branch in the SC treatment is 20.17 cm, which is significantly higher than that in CK and CF; the jujube-bearing branch length in the SC, SM and BM treatment are increased by 34%, 23% and 25% compared with that in CK, and the difference is significant (P  SM  > SC  > CK. Among them, the density of light of BM is the largest. It reaches 38.06 mol/(m2 d). CF, SC, SM and BM respectively increase by 11.54%, 8.09%, 7.96% and 15.13% compared with CK, and the difference is significant. The canopy transmittance of jujube is BM  CF  > SM  > SC. The highest Tr of BM reaches 8.66 µmol/moL. It may be related to higher LAI, and the instantaneous water use efficiency of SC is highest, which reaches 3.30%. The WUEp of CF, SC, SM and BM treatments increase by 22.4%, 64.2%, 44.3% and 30.8%, respectively, compared with that of CK. It reaches a significant difference level (P  SM  > BM  > CF  > CK. Compared with CK (9.37%), the SC, SM, BM, and CF increased by 3.69, 3.18, 1.11 and 0.40% points, respectively. Organic fertilizer is beneficial to increase the water content of the soil. Among them, soybean cake fertilizer (SC) has the largest increase, which is significantly different from CK (P  SM  > SC  > CF  > CK. The RWC of BM reaches 94.20%, which is significantly different from CK (P  SM  > BM  > CK. The total flavonoid content of SC reaches 14.35 mg/kg, which is 24.57% higher than that of CK. The total flavonoid content of SM and BM increase by 17.01% and 9.2%, respectively, compared with that of CK. Moreover, each treatment is significantly different from CK (P  More

Daskalova, G. N. et al. Science 368, 1341–1347 (2020).CAS 
Article 

Google Scholar 
Cardinale, B. J. et al. Nature 486, 59–67 (2012).CAS 
Article 

Google Scholar 
Hector, A. & Bagchi, R. Nature 448, 188–190 (2007).CAS 
Article 

Google Scholar 
Fanin, N. et al. Nat. Ecol. Evol. 2, 269–278 (2018).Article 

Google Scholar 
Duffy, J. E. Front. Ecol. Environ. 7, 437–444 (2009).Article 

Google Scholar 
Manning, P. et al. Adv. Ecol. Res. 61, 323–356 (2019).Article 

Google Scholar 
Lefcheck, J. S. et al. Nat. Commun. 6, 6936 (2015).CAS 
Article 

Google Scholar 
Soliveres, S. et al. Nature 536, 456–459 (2016).CAS 
Article 

Google Scholar 
Moi, D. A. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01827-7 (2022).Article 

Google Scholar 
Venter, O. et al. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
Allan, E. et al. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).CAS 
Article 

Google Scholar 
Manning, P. et al. Nat. Ecol. Evol. 2, 427–436 (2018).Article 

Google Scholar 
Gamfeldt, L. et al. Nat. Commun. 4, 1340 (2013).Article 

Google Scholar 
Schuldt, A. et al. Nat. Commun. 9, 2989 (2018).Article 

Google Scholar 
Jochum, M. et al. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 

Google Scholar 
Dudgeon, D. et al. Biol. Rev. 81, 163–182 (2005).Article 

Google Scholar 
Blois, J. L. et al. Proc. Natl Acad. Sci. USA 110, 9374–9379 (2013).CAS 
Article 

Google Scholar 
França, F. et al. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

Google Scholar 
Ewers, R. M. et al. Nat. Commun. 6, 6836 (2015).CAS 
Article 

Google Scholar 
Reich, P. B. et al. Science 336, 589–592 (2012).CAS 
Article 

Google Scholar  More

Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
PubMed 

Google Scholar 
Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinctions risk. Nat. Commun. 9, 4621 (2018).PubMed 
PubMed Central 

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

Google Scholar 
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936 (2015).CAS 
PubMed 

Google Scholar 
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).CAS 
PubMed 

Google Scholar 
Schuldt, A. et al. Biodiversity across trophic levels drive multifunctionality in highly diverse forests. Nat. Commun. 9, 2989 (2018).PubMed 
PubMed Central 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 211–220 (2020).
Google Scholar 
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).CAS 
PubMed 

Google Scholar 
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).PubMed 
PubMed Central 

Google Scholar 
Jing, X. et al. The links between ecosystem multifunctionality and above- and belowground biodiversity are mediated by climate. Nat. Commun. 6, 8159 (2015).PubMed 

Google Scholar 
Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 

Google Scholar 
Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).PubMed 

Google Scholar 
Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
PubMed Central 

Google Scholar 
Allan, E. et al. Interannual variation in land-use intensity enhances grassland multidiversity. Proc. Natl Acad. Sci. USA 111, 308–313 (2014).CAS 
PubMed 

Google Scholar 
Moi, D. A. et al. Regime shifts in a shallow lake over 12 years: consequences for taxonomic and functional diversities, and ecosystem multifunctionality. J. Anim. Ecol. 91, 551–565 (2022).PubMed 

Google Scholar 
Moi, D. A. et al. Multitrophic richness enhances ecosystem multifunctionality of tropical shallow lakes. Funct. Ecol. 35, 942–954 (2021).CAS 

Google Scholar 
Byrnes, J. E. K. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).
Google Scholar 
Li, F. et al. Human activitiesʼ fingerprint on multitrophic biodiversity and ecosystem functions across a major river catchment in China. Glob. Change Biol. 26, 6867–6879 (2020).
Google Scholar 
Enquist, B. J. et al. The megabiota are disproportionately importante for biosphere functioning. Nat. Commun. 11, 699 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Eisenhauer, N. et al. A multitrophic perspective on biodiversity–ecosystem functioning research. Adv. Ecol. Res. 61, 1–54 (2019).PubMed 
PubMed Central 

Google Scholar 
Agostinho, A. A., Thomaz, S. M. & Gomes, L. C. Threats for biodiversity in the floodplain of the Upper Paraná River: effects of hydrological regulation by dams. Ecohydrol. Hydrobiol. 4, 255–268 (2004).
Google Scholar 
Chiaravalloti, R. M., Homewood, K. & Erikson, K. Sustainability and land tenure: who owns the floodplain in the Pantanal, Brazil? Land Use Policy 64, 511–524 (2017).
Google Scholar 
Pelicice, F. M. et al. Large-scale degradation of the Tocantins–Araguaia River Basin. Environ. Manag. 68, 445–452 (2021).
Google Scholar 
Malekmohammadi, B. & Jahanishakib, F. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state-impact-response (DPSIR) model. Ecol. Indic. 82, 293–303 (2017).
Google Scholar 
McIntyre, P. B. et al. Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl Acad. Sci. USA 104, 4461–4466 (2006).
Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).CAS 
PubMed 

Google Scholar 
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Google Scholar 
Heino, J. et al. Lakes in the era of global change: moving beyond single-lake thinking in maintaining biodiversity and ecosystem services. Biol. Rev. 96, 89–106 (2020).PubMed 

Google Scholar 
Bridgewater, P. & Kim, R. E. The Ramsar conservation on wetlands at 50. Nat. Ecol. Evol. 5, 268–270 (2020).
Google Scholar 
Romero, G. Q. et al. Pervasive decline of subtropical aquatic insects over 20 years driven by water transparency, non-native fish and stoichiometric imbalance. Biol. Lett. 17, 20210137 (2021).PubMed 
PubMed Central 

Google Scholar 
Lansac-Tôha, F. M. et al. Scale-depedent patterns of metacommunity structuring in aquatic organisms across floodplain systems. J. Biogeogr. 48, 872–885 (2021).
Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar 
Weiss, K. C. B. & Ray, C. A. Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides. Ecography 42, 2012–2020 (2019).
Google Scholar 
Laliberté, E. & Legendre, R. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 

Google Scholar 
Mackereth, F. J. H, Heron, J & Talling, J. F. Water Analysis: Some Revised Methods for Limnologists. Publication No. 36 (Freshwater Biological Association, 1978).Golterman, H. L., Clymo, R. S. & Ohnstad, M. A. M. Methods for Physical and Chemical Analysis of Freshwaters (Blackwell Scientific Publications, 1978).Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, S99–S118 (2018).
Google Scholar 
Sun, J. & Liu, D. Geometric models for calculating cell biovolume and surface area for phytoplankton. J. Plankt. Res. 25, 1331–1346 (2003).
Google Scholar 
Froese, R. & Pauly, D. FishBase (2018); www.fishbase.orgPorter, K. G. & Feig, Y. S. The use of DAPI for identifying and counting aquatic microflora1. Limnol. Oceanogr. 25, 943–948 (1980).
Google Scholar 
Manning, P. et al. Redifining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).PubMed 

Google Scholar 
Hijmans, R. J. & van Etten, J. raster: Geographic analysis and modeling with raster data. R version 2.0–12 https://rspatial.org/raster (2012).World Urbanization Prospects: The 2020 Revision: Highlights (United Nations, 2020).Junk, W. J. et al. Brazilian wetlands: their definition, delineation, and classification for research, sustainable management, and protection. Aquat. Conserv. Mar. Freshwater Ecosyst. 24, 5–22 (2013).
Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effects models. R version 3.1.137 https://CRAN.Rproject.org/package=nlme (2018).K. Barton, MuMIn: Model selection and model averaging based on information criteria (AICc and alike). R version 1–1 https://CRAN.R-project.org/package=MuMIn (2014).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).Schielzeth, H. Simple means to improve the interpretability ofregression coefficients. Meth. Ecol. Evol. 1, 103–113 (2010).
Google Scholar 
Aiken, L. S. & West, S. G. Multiple Regression: Testing and Interpreting Interactions (Sage Publications, 1991).Rosseel, Y. lavaan: an R package for structural equation modeling. J. Stat. Softw. 48, 1–36 (2015).
Google Scholar 
Grace, J. B. & Bollen, K. A. Representing general theoretical concepts in structural equation models: the role of composite variables. Environ. Ecol. Stat. 15, 191–213 (2008).
Google Scholar 
R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Study designWe used a food systems framing to conceptually position our research to investigate how small-scale fisheries shape two key aspects of food environments – physical access to food via living in proximity to small-scale fisheries (fish as food pathway), and economic access to food via small-scale fisheries livelihoods (fish as income pathway).We examined food system components of supply chains (small-scale fisheries livelihoods related to harvesting, processing and trade), food environments (proximity to small-scale fisheries and livelihoods), income poverty status, and household diets (fish consumption and annual food security) (Supplementary Fig 7)40,41. Small-scale fisheries are notably recognised for their safety net function during times of shocks and extreme events, increasing the ability of households to recover, exit poverty and afford food over the longer-term42.Country selection and household survey dataWe selected Malawi, Tanzania and Uganda, given these countries represent a region where small-scale fisheries provides the main supply of fish and are important for rural inland and coastal livelihoods24,43, and yet substantial data gaps remain in valuing small-scale fisheries in the regional food system. Small-scale fisheries, particularly inland fisheries, in this region are known to be highly productive with a linear increasing trend in catches over the last three decades25,35. On average 70% of the total catches consist of small pelagic species, which are largely driven by climate, and are highly productive, resilient, and under-exploited34. However, challenges do exist in fisheries governance and signs of over-exploitation of some few fish stocks44, as well as high post-harvest fish waste and loss across value-chains undermine the potential benefits from the sector23. We analysed the World Bank’s Living Standards Measurement Surveys and its Integrated Surveys on Agriculture (LSMS-ISA) from Malawi, Tanzania and Uganda. The LSMS-ISA surveys conducted in these countries collected georeferenced household-level data and had been designed and implemented with a dedicated fishery module39 which contained questions on household fish consumption (frequency, quantity, and form of fresh or dried fish) and small-scale fisheries livelihoods across value chains (harvesting, processing and trading). The fishery module was collected across different years in Malawi (2016–17), Tanzania (2014–15) and Uganda (2010–11), and accordingly these are the years analysed in this study. The LSMS-ISA surveys collects consumption data over a period of 12 months so that the indicator captures the intrinsic variability due to seasonality, such as low and high periods of food consumption.Geospatial data and distance to fishing groundsGeoreferenced household data from LSMS-ISA surveys were matched with geospatial data on the location of inland water bodies and coastlines (Supplementary Table 11) to investigate geographic correlates (e.g., distance to fishing grounds – water bodies where fisheries occur) of poverty and food security. Data on inland water bodies were from the Global Lakes and Wetlands Database (GLWD)45, and the European Space Agency GlobCover databases for coastlines46. Inland water bodies from the GLWD database include permanent, open water bodies (e.g., lakes, reservoirs, rivers) with a surface area ≥0.1 km2 for each country, including cross-border water bodies. We selected water bodies to represent types of water bodies known to support fisheries, based on catch data24,43. We assume the entire coastline of Tanzania was accessible and used for marine small-scale fisheries. We use the term ‘water body’ to mean either freshwater or marine waters.Distance between water bodies and households was calculated as the shortest, straight line, distance from the household location (identified through the GPS coordinates of the households) to any point of the nearest water body. The distance was expressed in km.In our descriptive statistics, a cut-off threshold of 5 km from fishing grounds was used to compare the key indicators presented in this study (e.g., percent of poor and food insecure households, frequency and quantity of fish consumption, etc), for households proximate and distant (≤5 km was considered close and >5 km was considered far) from fished water bodies, as well as between fishing and non-fishing households. The choice of the cut-off threshold used for our descriptive statistics was guided by other studies16,17, in addition to reflecting the distribution of households by quintile of distance to water bodies. Concerning the latter, we found that the average distance from fishing ground of the first quintile was always lower than 5 km in all countries.In the regression analyses, the distance to water bodies was included as a continuous variable (in km). This choice reflects the need to better understand dynamics for households that tend to live more distant from fishing grounds. These dynamics were captured by measuring the marginal increase in the probability of being poor or food insecure for a one-unit increase (1 km) from the mean distance to fishing grounds.We acknowledge two limitations behind the calculation of the straight-line distance to water bodies. First, using the straight-line distance to water bodies may introduce biases in the statistical analyses presented, especially for households located in any particular landscapes within the country. The walking or travel time distance over a road network would provide a better alternative, however there is lack of data on road networks. Despite the straight-line distance to water bodies encompasses some limitations, we still believe that this method of calculation provides a good proxy to categorize household in relation to their distance to water bodies, and the results from the analyses should not deviate substantially from other method of calculation. For example, a study51 found that the straight-line distance tends to be highly correlated (R  > 0.91) with both walking and travel time distance.Second, an additional bias in the presented analyses may be introduced due to the modification strategy of the households GPS coordinates. This strategy was implemented before dissemination of household level data to avoid the risk of disclosure of sampled households. In its essence, the modification strategy relies on random offset of cluster center-point within a specified range. For urban areas a range of 0–2 km is used. In rural areas, where risk of disclosure may be higher due to more dispersed communities, a range of 0–5 km offset was used. While we had no control over this modification strategy, we believe that the modification of the GPS coordinates does not affect the way households are classified in relation to their distance to fishing grounds: considering that the modification strategy was applied to both distant and proximate households, we expect that the distribution between households close and distant to water bodies has remained unchanged and, hence, the presented statistics are still valid for the analysis.Variable constructionWe used a range of socio-economic indicators across food system components (Supplementary Table 11). As a measure of physical and economic access to food we used two indicators of small-scale fisheries: proximity to fishing grounds and fishing households. Household livelihoods were assigned according to whether households primarily, but not exclusively, engaged in small-scale fisheries (fishing, harvesting, processing and/or trading which varied by survey), agriculture (e.g., crop or livestock), or neither fisheries or agriculture. For each country survey, households were categorised according to their engagement in fishing and/or agriculture activities in the prior 12 months. Households in which one or more member engaged in fish-related activities were defined as ‘fishing households’. Fish-related livelihood activities were defined as fish harvesting, processing, and trading in Malawi and Tanzania, whilst in Uganda they were defined only as fishing. Households with one or more member engaged in agriculture, but not in fish-related activities, were defined as ‘agriculture households’. Through data exploration of livelihood categories, we found that 96% of all fishing households in our study combine fish-related and agricultural activities, with only 4% engaging exclusively in small-scale fisheries. Examination of diverse livelihood typologies within fishing household category (e.g., fisher-farmer, which is common in the region or exclusive fisher) was deemed out of the scope of this study and not feasible due to the small number of observations of exclusive fishers.Household poverty was measured using the per-capita monthly expenditure (equivalized using the adult equivalent scale). Poor households were defined as those households with a per-capita monthly expenditure below the national poverty line. The national poverty line –which was defined by national authorities in the three countries analysed–is a country-specific monetary threshold below which a household (and its members) cannot meet their basic needs. The poverty metric, as defined above, was used across physical, natural and human capital: asset wealth, distance to markets, access to land and education level of head of household. Since the asset wealth captures the typologies and number of assets owned by the household (durable goods – radio, bicycle, TV; utilities and infrastructure – access to protected water source and electricity), we developed an index for assets using the principal component analysis. This technique reduced the multi-dimensionality of the asset’s variables, and it allowed the data to identify the linear combinations of the assets components that explain the greatest share of the variation in wealth. As the final wealth index was standardised across households, this index allowed providing a ranking of households which reflected their ownership of assets.Food security was measured using two indicators; household-level food consumption profile – using the Food Consumption Score (FCS) index20, and subjective food insecurity defined as the number of months during a year that a household reported not having enough food to feed the household. Together, these indicators provide a more comprehensive understanding of household food security over a longer period than other surveys (e.g. Demographic and Health)47,48,49. The LSMS-ISA surveys collects food consumption data over a 7-day recall period. To capture seasonal variation in the food consumption indicators, sampled households were interviewed over a 12-month period: for each month of the year, a different portion of sampled households was interviewed so that the derived indicators reflect the intrinsic variability in food consumption, which may be due to seasonality. We used the FCS index as a food security indicator as it is akin to the data collected via the LSMS-ISA surveys, and that there was a need for comparison across select countries. The FCS index measures the frequency (number of days) and diversity of food groups consumed over a 7-day recall period, with weights given to groups based on nutritional value. The FCS index is validated as a proxy for energy sufficiency (quantity of food) and food access, and is associated with other household-level diet diversity measures (e.g. household dietary diversity score (HDDS))20,48. The difference between FCS and other indicators such as HDDS is the recall period (7-days versus 24 h), diversity of groups, weights assigned based on nutrition, and use of frequency together with diversity of groups consumed. The FCS with a longer recall period can show more habitual consumption but can also have limitations with people’s recall reliability. Although it has not been validated yet as an indicator for micronutrient intake, it does provide weights to nutrient-rich food groups and accounts for frequency of consumption, which other indicators do not. Fish consumption was described in terms of the (i) quantity (kg of wet weight equivalent per household per week), (ii) form (fresh, dried, smoked, other) and (iii) source (purchased, own consumption, gift) of fish consumed. The share of households reporting consumption of other animal source foods was also calculated to examine the relative role of fish in overall diets.We also examined the prices of foods consumed to investigate the accessibility of fish as food in terms of affordability compared to animal source foods. The LSMS-ISA survey collects data on the value and volume of food that were purchased and consumed. Those two variables were further used to construct the average price for each food item. To control for price level differences between countries, food prices data calculated from the survey were converted from local currency unit to international USD, using the Purchase power parity conversion factor corresponding to the year of the survey (Source: World Development Indicators database, World Bank). Moreover, since the surveys were conducted in different years, nominal prices corresponding to the years of the surveys were converted into real, inflation-adjusted prices using the Consumer Price Index (CPI, base year: 2010). This allowed to control for potential inflation patterns within countries and provide a better comparison of food prices per Kg. across the three countries analyzed (Source: World Development Indicators database, World Bank).Finally, we drew upon nutritional databases (food composition tables, FishBase and Illuminating Hidden Harvest Initiative) to understand the relative nutritional value of fish; by species, size (small or large) and form (e.g., fresh or dried), compared to other animal source foods (Supplementary Table 12). This enables us to contextualise the nutritional importance of consumption patterns.Descriptive statisticsWe created a harmonized multi-country dataset for Malawi, Tanzania and Uganda with 18,715 nationally representative household-level observations. The sample included in this study represents more than 19 million households corresponding to a population of 93.8 million people across the three countries (Supplementary Information).Descriptive statistics were calculated to compare poverty and food security indicators among households proximate and distant from fished water bodies, and between fishing and non-fishing households (see full details in Supplementary Information). For this analysis, households distant and proximate from fished water bodies were clustered into two groups based on a cut-off threshold of 5 km (distant  > 5 km; proximate ≤5 km). The Welch’s t-test was then used throughout to assess the statistical significance of mean statistics between these two groups.Econometric modelThe estimated probabilities of being poor (household living below the national poverty line) and food insecure (household with a poor food consumption profile) were modelled through two separate probit regression models, where the outcome variable was equal to 1 for poor and food insecure households and 0 otherwise. The independent variables in both models included the household’s distance to water bodies and the distance to food market. Both variables are expressed as continuous variables (in km), reflecting the need to measure the marginal increase in the probability of being poor or food insecure (i.e., the estimated β coefficients) given a one-unit change (1 km) in the distance to fishing ground (or food markets) from its mean. Both models also included an interaction variable which measured the household’s distance to water bodies but restricted to only those households who were unable to reach the food market. We tested this interaction as we expected that living in proximity to water bodies could mitigate the negative effects on poverty and food insecurity when households are unable to access food markets. In order to measure the conditional difference in the average probability to be poor and food insecure between households who engaged in fisheries and households who engaged in other non-fishing activities, we constructed a categorical variable that classified households according to their main livelihood activity, namely (1) neither fishing, nor agriculture households (i.e., the reference baseline household category), (2) fishing households and (3) agriculture households. This categorical variable was further restricted to only households living in proximity to water bodies to better measure for which typology of household the proximity to fishing grounds is most beneficial. Both models were controlled for the age, sex and the highest level of education attained by the head of the household, as well as the total number of household members employed (over total household members) and the wealth index of the household.For each model (poverty and food insecurity), we examined associations at the cross-country, national and rural levels (Tables 1 and 2, also available as Supplementary Data 1 and 2). Stata 15 was used for all statistical analyses. Both descriptive statistics and the regression coefficients were estimated using the household probability weight, the latter instrumental to make the derived indicators from the surveys representative of the population of interest thus allowing general inference for the three countries.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 4, 599–610 (2018).Article 

Google Scholar 
Verdone, M. & Seidl, A. Time, space, place, and the Bonn Challenge global forest restoration target. Restor. Ecol. 25, 903–911 (2017).Article 

Google Scholar 
Bastin, J. F. et al. The global tree restoration potential. Science 365, 76–79 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Stanturf, J. A., Palik, B. J. & Dumroese, R. K. Contemporary forest restoration: A review emphasizing function. For. Ecol. Manag. 331, 292–323 (2014).Article 

Google Scholar 
Wang, J. et al. Use of direct seeding and seedling planting to restore Korean pine (Pinus koraiensis Sieb. Et Zucc.) in secondary forests of Northeast China. For. Ecol. Manag. 493, 119243 (2021).Article 

Google Scholar 
Han, A. R., Kim, H. J., Jung, J. B. & Park, P. S. Seed germination and initial seedling survival of the subalpine tree species, Picea jezoensis, on different forest floor substrates under elevated temperature. For. Ecol. Manag. 429, 579–588 (2018).Article 

Google Scholar 
Thomas, E. et al. Genetic considerations in ecosystem restoration using native tree species. For. Ecol. Manag. 333, 66–75 (2014).Article 

Google Scholar 
Hawkins, B. J., Jones, M. D. & Kranabetter, J. M. Ectomycorrhizae and tree seedling nitrogen nutrition in forest restoration. New For. 46, 747–771 (2015).Article 

Google Scholar 
Perry, D. A., Molina, R. & Amaranthus, M. P. Mycorrhizae, mycorrhizospheres, and reforestation: Current knowledge and research needs. Can. J. For. Res. 17, 929–940 (1987).Article 

Google Scholar 
Duñabeitia, M. K. et al. Differential responses of three fungal species to environmental factors and their role in the mycorrhization of Pinus radiata D. Don. Mycorrhiza 14, 11–18 (2004).PubMed 
Article 

Google Scholar 
Rincón, A., De Felipe, M. R. & Fernández-Pascual, M. Inoculation of Pinus halepensis Mill. with selected ectomycorrhizal fungi improves seedling establishment 2 years after planting in a degraded gypsum soil. Mycorrhiza 18, 23–32 (2007).PubMed 
Article 

Google Scholar 
Sanchez-Zabala, J. et al. Physiological aspects underlying the improved outplanting performance of Pinus pinaster Ait. seedlings associated with ectomycorrhizal inoculation. Mycorrhiza 23, 627–640 (2013).CAS 
PubMed 
Article 

Google Scholar 
Sousa, N. R., Franco, A. R., Oliveira, R. S. & Castro, P. M. Reclamation of an abandoned burned forest using ectomycorrhizal inoculated Quercus rubra. For. Ecol. Manag. 320, 50–55 (2014).Article 

Google Scholar 
Policelli, N., Horton, T. R., Hudon, A. T., Patterson, T. & Bhatnagar, J. M. Back to roots: The role of ectomycorrhizal fungi in boreal and temperate forest restoration. Front. For. Glob. Change 3, 97 (2020).Article 

Google Scholar 
Jones, M. D., Durall, D. M. & Cairney, J. W. G. Ectomycorrhizal fungal communities in young forest stands regenerating after clearcut logging. New Phytol. 157, 399–422 (2003).PubMed 
Article 

Google Scholar 
Policelli, N., Bruns, T. D., Vilgalys, R. & Nuñez, M. A. Suilloid fungi as global drivers of pine invasions. New Phytol. 222, 714–725 (2019).PubMed 
Article 

Google Scholar 
Visser, S. Ectomycorrhizal fungal succession in jack pine stands following wildfire. New Phytol. 129, 389–401 (1995).Article 

Google Scholar 
Nuñez, M. A., Horton, T. R. & Simberloff, D. Lack of belowground mutualisms hinders pinaceae invasions. Ecology 90, 2352–2359 (2009).PubMed 
Article 

Google Scholar 
Pec, G. J., Simard, S. W., Cahill, J. F. & Karst, J. The effects of ectomycorrhizal fungal networks on seedling establishment are contingent on species and severity of overstorey mortality. Mycorrhiza 130, 173–183 (2020).Article 

Google Scholar 
Grossnickle, S. C. & Reid, C. P. P. The use of ectomycorrhizal conifer seedlings in the revegetation of a high-elevation mine site. Can. J. For. Res. 12, 354–361 (1982).Article 

Google Scholar 
Teste, F. P., Schmidt, M. G., Berch, S. M., Bulmer, C. & Egger, K. N. Effects of ectomycorrhizal inoculants on survival and growth of interior Douglas-fir seedlings on reforestation sites and partially rehabilitated landings. Can. J. For. Res. 34, 2074–2088 (2004).Article 

Google Scholar 
Trappe, J. M. Selection of fungi for ectomycorrhizal inoculation in nurseries. Annu. Rev. Phytopathol. 15, 203–222 (1977).Article 

Google Scholar 
van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Finlay, R. D., Frostegård, Å. & Sonnerfeldt, A. M. Utilization of organic and inorganic nitrogen sources by ectomycorrhizal fungi in pure culture and in symbiosis with Pinus contorta Dougl ex. Loud. New Phytol. 120, 105–115 (1992).Article 

Google Scholar 
Keller, G. Utilization of inorganic and organic nitrogen sources by high-subalpine ectomycorrhizal fungi of Pinus cembra in pure culture. Mycol. Res. 100, 989–998 (1996).ADS 
CAS 
Article 

Google Scholar 
Hatakeyama, T. & Ohmasa, M. Mycelial growth of strains of the genera Suillus and Boletinus in media with a wide range of concentrations of carbon and nitrogen sources. Mycoscience 45, 169–176 (2004).CAS 
Article 

Google Scholar 
Itoo, Z. A. & Reshi, Z. A. Effect of different nitrogen and carbon sources and concentrations on the mycelial growth of ectomycorrhizal fungi under in-vitro conditions. Scand. J. For. Res. 29, 619–628 (2014).Article 

Google Scholar 
Lazarević, J., Stojičić, D. & Keča, N. Effects of temperature, pH and carbon and nitrogen sources on growth of in vitro cultures of ectomycorrhizal isolates from Pinus heldreichii forest. For. Syst. 25, 3 (2016).
Google Scholar 
Valdés, R. C., Villarreal, R. M., García, F. G., Morales, S. G. & Peña, S. S. Improved parameters of Pinus greggii seedling growth and health after inoculation with ectomycorrhizal fungi. South. For. 81, 23–30 (2019).Article 

Google Scholar 
Daza, A. et al. Effect of carbon and nitrogen sources, pH and temperature on in vitro culture of several isolates of Amanita caesarea (Scop.: Fr.) Pers. Mycorrhiza 16, 133–136 (2006).CAS 
PubMed 
Article 

Google Scholar 
Wani, A. A., Joshi, P. K., Singh, O. & Shafi, S. Multi-temporal forest cover dynamics in Kashmir Himalayan region for assessing deforestation and forest degradation in the context of REDD+ policy. J. Mt. Sci. 13, 1431–1441 (2016).Article 

Google Scholar 
Chung, H. C., Kim, D. H. & Lee, S. S. Mycorrhizal formations and seedling growth of Pinus desiflora by in vitro synthesis with the inoculation of ectomycorrhizal fungi. Mycobiology 30, 70–75 (2002).Article 

Google Scholar 
Barroetaveña, C., Cázares, E. & Rajchenberg, M. Ectomycorrhizal fungi associated with ponderosa pine and Douglas-fir: A comparison of species richness in native western North American forests and Patagonian plantations from Argentina. Mycorrhiza 17, 355–373 (2007).PubMed 
Article 

Google Scholar 
Ekwebelam, S. A. Effect of mycorrhizal fungi on the growth and yield of Pinus oocarpa and Pinus caribaea var. bahamensis seedlings. E. Afr. Agric. For. J. 45, 290–295 (1980).
Google Scholar 
Kasuya, M. C. M. & Igarashi, T. In vitro ectomycorrhizal formation in Picea glehnii seedlings. Mycorrhiza 6, 451–454 (1996).Article 

Google Scholar 
Wang, E. J., Jeon, S. M., Jang, Y. & Ka, K. H. Mycelial growth of edible ectomycorrhizal fungi according to nitrogen sources. Korean J. Mycol. 44, 166–170 (2016).CAS 

Google Scholar 
Dar, A. R. & Dar, G. H. Taxonomic appraisal of conifers of Kashmir Himalaya. Pak. J. Biol. Sci. 9, 859–867 (2006).Article 

Google Scholar 
Adeleke, R. A., Nunthkumar, B., Roopnarain, A. & Obi, L. Applications of plant-microbe interactions in agro-ecosystems. In Microbiome in Plant Health and Disease 1–34 (Springer, 2019).
Google Scholar 
Yamanaka, T. Utilization of inorganic and organic nitrogen in pure cultures by saprotrophic and ectomycorrhizal fungi producing sporophores on urea-treated forest floor. Mycol. Res. 103, 811–816 (1999).CAS 
Article 

Google Scholar 
Berredjem, A., Garnier, A., Putra, D. P. & Botton, B. Effect of nitrogen and carbon sources on growth and activities of NAD and NADP dependent isocitrate dehydrogenases of Laccaria bicolor. Mycol. Res. 102, 427–434 (1998).CAS 
Article 

Google Scholar 
Cairney, J. W. G. Intra-specific physiological variation: implications for understanding functional diversity in ectomycorrhizal fungi. Mycorrhiza 9, 125–135 (1999).Article 

Google Scholar 
France, R. C. & Reid, C. P. P. Pure culture growth of ectomycorrhizal fungi on inorganic nitrogen sources. Microb. Ecol. 10, 187–195 (1984).CAS 
PubMed 
Article 

Google Scholar 
Kibar, B. & Peksen, A. Nutritional and environmental requirements for vegetative growth of edible ectomycorrhizal mushroom Tricholoma terreum. Zemdirb. Agric. 4, 409–414 (2011).
Google Scholar 
Nygren, C. M. R. et al. Growth on nitrate and occurrence of nitrate reductase encoding genes in a phylogenetically diverse range of ectomycorrhizal fungi. New Phytol. 180, 875–889 (2008).CAS 
PubMed 
Article 

Google Scholar 
Rangel-Castro, I. J., Danell, E. & Taylor, A. F. Use of different nitrogen sources by the edible ectomycorrhizal mushroom Cantharellus cibarius. Mycorrhiza 12, 131–137 (2002).CAS 
PubMed 
Article 

Google Scholar 
Jenkins, M. L., Cripps, C. L. & Gains-Germain, L. Scorched Earth: Suillus colonization of Pinus albicaulis seedlings planted in wildfire-impacted soil affects seedling biomass, foliar nutrient content, and isotope signatures. Plant Soil 425, 113–131 (2018).CAS 
Article 

Google Scholar 
Taudière, A., Richard, F. & Carcaillet, C. Review on fire effects on ectomycorrhizal symbiosis, an unachieved work for a scalding topic. For. Ecol. Manag. 391, 446–457 (2017).Article 

Google Scholar 
Bigelow, H. E. & Smith, A. H. The status of Lepista: A new section of Clitocybe. Brittonia 21, 144–177 (1969).Article 

Google Scholar 
Kuo, M. Clitocybe Nuda. Retrieved from MushroomExpert.Com. http://www.mushroomexpert.com/clitocybe_nuda.html (2010).Mycobank. www.mycobank.org. Accessed on Jan 28, 2020. (2020).Peck, C. H. Report of the Botanist 1869. Annu. Rep. N.Y. State Mus. Nat. Hist. 23, 27–135 (1873).
Google Scholar 
Kuo, M. Cortinarius Distans. Retrieved from MushroomExpert.Com. http://www.mushroomexpert.com/cortinarius_distans.html (2011).Losinger, W. C. Germination and Growth of Some Ectomycorrhizal Basidiomycetes in Culture. Doctoral dissertation, Kalamazoo College (1980).Norvell, L. L. & Exeter, R. L. Ectomycorrhizal epigeous basidiomycete diversity in Oregon Coast Range Pseudotsuga menziesii forests-preliminary observations. Memoirs 89, 159–190 (2004).
Google Scholar  More