Ecology

OverviewWe used data from the national forest inventory conducted by the US Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) program to quantify tree biomass and growth along forest edges and within the forest interior. We estimated the causal impact of the forest edge environment on patterns of tree biomass and growth, while accounting for potentially confounding variables. We then used the regression models to estimate the aggregate difference in growth attributable to forest edges throughout the northeastern U.S. Finally, to better understand the implications of our findings, we quantified the degree of forest fragmentation throughout temperate and tropical forest biomes world-wide, using a 30 m forest cover map.Study areaOur analyses of edge impacts on forest biomass and growth were conducted throughout twenty-states (1.7 million km2) in the northeastern and upper mid-west of the United States (Supplementary Fig. 1). This region contains 765,000 km2 of forest and encompasses gradients of dominant land-uses, climatic conditions, and forest composition while remaining within deciduous, coniferous, and mixed temperate forest ecosystems.Identifying edges in forest inventory dataThe FIA collects measurements of tree size, growth, and land-use within a nested plot design across the country19. Each FIA plot is composed of four individual subplots; within each subplot, the diameter at breast height (dbh) of every tree >12.7 cm is measured during each measurement period. The re-measurement frequency for FIA plots in our study area is between 5 and 7 years, but this can differ between Forest Service regions. In addition to tree measurements, the database details land-use condition data that includes the proportion of the area that is forested and, on some plots, the land-cover class of the non-forest area (FIA User’s Manual, Condition Table). FIA plots are considered forested if some portion of the plot includes a contiguous forest patch (including potentially outside of the plot area) of greater than 4047 m2 that has more than 10% canopy cover. With a memorandum of understanding between the USFS and Harvard University, we had access to the true, unfuzzed plot coordinates, which are not publicly available. Evaluating >48,000 plots in the USFS Northern Region sampled from 2010 to 2020 and selecting the most recent measurement cycle for each plot, we identified subplots that contained both a forest and a non-forest condition and categorized these as edges (Supplementary Table 1). Only subplots that included a forest condition in both the most recent and previous measurement were included. Subplots where the mapped condition changed from forest to non-forest were excluded. Changes in the amount of mapped forest condition were included and are incorporated into the calculation of response variables using the most recent condition area. We identified FIA plots where all four subplots were fully forested as interior plots to be used for comparison. Subplots located within the same plot as an edge subplot (i.e., edge-proximate subplots) were excluded from this study due to limitations in our ability to quantify their distance from an edge. The spatial configuration of subplots is such that a fully forested subplot may be up to ~65 m away from an identified forest edge within another subplot. Studies suggest that the distance of edge influence in temperate forest does not extend more than 30 m into the forest interior15,33. Since the FIA does not contain information about the geometry of non-forest conditions beyond the subplot boundary, we deemed that the large uncertainty in the relationship between these subplots to a non-forest edge precluded their inclusion in the study. The FIA plot configuration prevented quantification of the distance of edge influence in our analysis; the exclusion of subplots adjacent to edge-subplots may limit direct comparisons with other fragmentation studies.We used the FIA condition data to characterize the non-forest land use in edge subplots. Information on adjacent non-forest land cover is not collected on all FIA plots (4327 of 6607 edge subplots). We aggregated FIA land-cover classification to a binary anthropogenic or unknown edge type designation and present results from all edge subplots and the anthropogenic edge subset (FIA User’s Manual Condition Table, Section 2.4.50).For each subplot (168 m2 in area), we calculated two primary response variables of interest: total live tree BA and BAI. Notably, trees smaller than 12.7 cm dbh) in m2. BAI was calculated on a per-tree basis as the difference in radial growth of live adult trees between the most recent and previous measurements, and then divided by the number of years between measurements (m2 yr−1). In addition, we aggregated individual tree diameter measurements to calculate mean stem density (stems ha−1) and mean tree diameter for each subplot (Fig. 2).We accounted for variable subplot area by normalizing both BA and BAI to a per-hectare of forested area basis, resulting in units of m2 ha−1 and m2 ha−1 yr−1, respectively. To account for potential small-area bias, we performed a sensitivity analysis on the relationship between BA and subplot forested area (Supplementary Fig. 2). We subsequently excluded 1284 subplots under 30 m2 in area as the area to BA relationship asymptotes relationship above this threshold. Finally, we accounted for errors in field dbh measurements, sometimes resulting in negative BAI values, by excluding the 97.5% quantiles of both BA and BAI distributions.Given their spatial configuration, FIA subplots are not fully independent measurements, potentially introducing issues with pseudo-replication and spatial autocorrelation within our dataset. To test for spatial autocorrelation we examined the semivariance of model residuals36, and found that there was high correlation only at distances of less than 1 km. The spatial stratification of the FIA plot design minimizes issues of plot–plot proximity within our study. However, to account for autocorrelation between subplots, we filtered our pre-matched dataset to only including one subplot from each FIA plot. For plots containing multiple edge subplots, we selected the subplot with the largest forested area. For interior plots, we selected the central subplot and excluded all others.Isolating the effect of edges on growthAbiotic controlsTo account for environmental controls on forest growth we included the most critical abiotic predictors of terrestrial vegetation productivity (light, water, temperature, and nitrogen deposition) as covariates in the regression models (Supplementary Fig. 4, Supplementary Table 2). Light, water, and temperature data were drawn from spatial raster maps (0.5° resolution) as unit-less indices of relative limitation on vegetation productivity, ranging from 0 to 13. Nitrogen data were drawn from the 2018 NADP gridded inorganic wet nitrogen deposition product (4 km spatial resolution; kg of N ha−1)37. To interpolate across small gaps in the raster data (usually along water bodies), we used the Nibble tool from ArcGis Pro (ESRI Team). We then used FIA plot locations to extract values from each raster layer for all FIA subplots.Forest compositionTree species may vary in their responses to biogeochemical changes that occur on forest edges. Overall forest community response emerges from complex interactions between species. We used aggregations of tree species, termed forest composition groups (or forest types)38, to assess if species composition influenced the response to altered edge condition. Forest type classifications for each subplot are provided by the FIA (FIA User’s Manual, Condition Table) and are defined in Appendix D therein. We aggregated the FIA forest types into eight broader species groups, following Thompson et al.23, and defined in Supplementary Table 1.Matching, GLM regressions, and model selectionAll statistical analyses and most of the data processing were conducted in R, version 3.439. Using a causal inference framework, we created a quasi-experimental statistical design that included pre-matching followed by a GLM regression analysis40. Matching emulates an experimental design using observational data by identifying control groups of untreated (forest interior) plots that were as similar as possible to treated (forest edge) plots in terms of observable confounders. By capturing key differences in abiotic variables we control for the fundamental drivers of forest productivity, allowing for a direct estimation of the average treatment effect of edges. Similarity was defined by nearest-neighbor covariate matching determined by Malahanobis distance, implemented in the MatchIt library in R41, the simplest and best method when the dataset is robust enough to find a match for every treated plot20. This method excludes forest interior plots that are not matched with an edge plot. Given differences in sample size between the full edge dataset and the subset designated as anthropogenic edges, we performed matching separately on the two datasets. To assess the efficacy of matching on reducing the differences in covariate distributions, we used summary statistics calculated with the MatchIt library and report the pre- and post-matched covariate balance in Supplementary Table 4 and Supplementary Table 5 (sensu Schleicher et al.42). Matching was highly successful, largely eliminating differences in all covariate distributions in both datasets.Our primary response variables of interest, BA and BAI, were right-skewed, non-normally distributed and violated the assumptions of normality necessary for ordinary least squares regression43. We, therefore, used a GLM to better fit the structure of our data. GLMs are an extension of linear regression that allow more freedom in the choice of probability distribution function through the use of a link function to model relationships between predictors and response variables44. The gamma probability distribution is frequently chosen to model BA, given its assumptions of positive, continuous values and flexible model form23,45. We performed a series of GLM regressions on our post-matched datasets, using a gamma probability distribution with an inverse link function to model the relationship of BA and BA with a suite of predictor variables, using the glm function as implemented in the R Core stats package39. Due to differences in sample size between the all-edge dataset and the anthropogenic-edge subset, we modeled these two datasets separately for each of BA and BAI, resulting in four separate regression analyses. We used a model selection framework to identify the most parsimonious model within each of the model sets based on the Akaike Information Criterion (AIC) and residual deviance statistic46,47. We report the model-selection and model-fit results for each of our separate analyses, including model forms, AIC, Nagelkerke Pseudo-R2, and residual deviance in Supplementary Table 2. Across all four regression analyses, the best-performing model was one that included an interaction between the edge-status and forest type categorical variables, as well as the variables of temperature-limitation, light-limitation, water-limitation, and nitrogen deposition.We then used the best performing model from each analysis to compare the differences in BA and BAI between forest edge and interior across each forest type. We estimated the treatment effect of edge-state within each forest type using the ggeffects package48 to calculate marginal effects with the continuous predictors (temperature, light, water, and nitrogen deposition) held at their within-forest type regional means. The results of this analysis are displayed in Fig. 1 and Supplementary Table 3; primary error bars on the interior point show the 95% confidence interval of the marginal effect from the full edge model, while secondary error bars show the CI from the anthropogenic edge model. Due to the smaller sample size in the anthropogenic model, estimates of the mean marginal effect of the interior plots vary slightly (though non-significantly) from those from the full dataset. The main text description reports outputs from both models, calculated from separate interior mean estimates. For visual clarity, we only display one set of interior means in Fig. 1.Mortality and timber harvestIn tropical forests, large reductions in productivity along edges are associated with increased tree mortality.9 To assess differences in tree mortality across our study region, we applied a simplified GLM analysis, including edge-state as our only predictor variable. The FIA differentiates between mortality attributed to timber harvest and that attributed to other, non-harvest causes. The results of this analysis are presented as marginal effects of each edge category in Supplementary Fig. 3. There are no significant differences in biogenic mortality between edge groups and no difference in overall mortality (combined biogenic and anthropogenic); there is a small, but statistically significant (p  More

1.Wilson, E. O. Introduction. in Biodiversity II: understanding and protecting our biological resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 1–3 (Joseph Henry Press, 1997).2.Lovejoy, T. E. Biodiversity: what is it? in Biodiversity II: Understanding and protecting our biological resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 7–14 (Joseph Henry Press, 1997).3.Ehrlich, P. R. & Wilson, E. O. Biodiversity studies: Science and policy. Science 253, 758–762 (1991).ADS 
CAS 
Article 

Google Scholar 
4.Myers, R. A. & Ottensmeyers, C. A. Extinction risk in marine species. in Marine Conservation Biology: The Science of Maintaining the Sea’s Biodiversity (eds. Norse, E. A. & Crowder, L. B.) 58–79 (Island Press, 2005).5.Reaka-Kudla, M. L. The global biodiversity of coral reefs: a comparison with rain forests. in Biodiversity II: understanding and protecting our biological resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 83–108 (Joseph Henry Press, 1997).6.Briggs, J. C. Marine extinctions and conservation. Mar. Biol. 158, 485–488 (2011).Article 

Google Scholar 
7.Harley, C. D. G. et al. The impacts of climate change in coastal marine systems: Climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).ADS 
Article 

Google Scholar 
8.Dupont, S., Dorey, N. & Thorndyke, M. What meta-analysis can tell us about vulnerability of marine biodiversity to ocean acidification?. Estuar. Coast. Shelf Sci. 89, 182–185 (2010).ADS 
Article 

Google Scholar 
9.Stork, N. E. Measuring global biodiversity and its decline. in Biodiversity II: understanding and protecting our biological resources (eds. Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 41–68 (Joseph Henry Press, 1997).10.Richards, Z. T. & Day, J. C. Biodiversity of the Great Barrier Reef—How adequately is it protected? PeerJ 6, e4747 (2018).11.Pyle, R. L. & Copus, J. M. Mesophotic Coral Ecosystems: introduction and overview. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12 3–27 (Springer International Publishing, 2019).12.Hinderstein, L. M. et al. Theme section on ‘Mesophotic coral ecosystems: Characterization, ecology, and management’. Coral Reefs 29, 247–251 (2010).ADS 
Article 

Google Scholar 
13.Bongaerts, P., Ridgway, T., Sampayo, E. M. & Hoegh-Guldberg, O. Assessing the ‘deep reef refugia’ hypothesis: Focus on Caribbean reefs. Coral Reefs 29, 309–327 (2010).Article 

Google Scholar 
14.Bongaerts, P. & Smith, T. B. Beyond the “Deep Reef Refuge” hypothesis: a conceptual framework to characterize persistence at depth. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 881–895 (Springer International Publishing, 2019).15.Vermeij, G. J. Survival during biotic crises: the properties and evolutionary significance of refuges. Dyn. Extinct. 231–246 (1986).16.Glynn, P. W. Coral reef bleaching: Facts, hypotheses and implications. Glob. Change Biol. 2, 495–509 (1996).ADS 
Article 

Google Scholar 
17.Riegl, B. & Piller, W. E. Possible refugia for reefs in times of environmental stress. Int. J. Earth Sci. 92, 520–531 (2003).Article 

Google Scholar 
18.Halfar, J., Godinez-Orta, L., Riegl, B., Valdez-Holguin, J. E. & Borges, J. M. Living on the edge: high-latitude Porites carbonate production under temperate eutrophic conditions. Coral Reefs 24, 582–592 (2005).ADS 
Article 

Google Scholar 
19.Loya, Y., Eyal, G., Treibitz, T., Lesser, M. P. & Appeldoorn, R. Theme section on mesophotic coral ecosystems: Advances in knowledge and future perspectives. Coral Reefs 35, 1–9 (2016).ADS 
Article 

Google Scholar 
20.Laverick, J. H. et al. To what extent do mesophotic coral ecosystems and shallow reefs share species of conservation interest? A systematic review. Environ. Evid. 7, 15 (2018).Article 

Google Scholar 
21.Smith, T. B., Glynn, P. W., Maté, J. L., Toth, L. T. & Gyory, J. A depth refugium from catastrophic coral bleaching prevents regional extinction. Ecology 95, 1663–1673 (2014).Article 

Google Scholar 
22.Smith, T. B. et al. Caribbean mesophotic coral ecosystems are unlikely climate change refugia. Glob. Change Biol. 22, 2756–2765 (2016).ADS 
Article 

Google Scholar 
23.Holstein, D. M., Smith, T. B., Gyory, J. & Paris, C. B. Fertile fathoms: Deep reproductive refugia for threatened shallow corals. Sci. Rep. 5 (2015).24.Holstein, D. M., Paris, C. B., Vaz, A. C. & Smith, T. B. Modeling vertical coral connectivity and mesophotic refugia. Coral Reefs 35, 23–37 (2016).ADS 
Article 

Google Scholar 
25.Holstein, D. M., Smith, T. B. & Paris, C. B. Depth-independent reproduction in the reef coral Porites astreoides from shallow to mesophotic zones. PLoS ONE 11, e0146068 (2016).26.Assis, J. et al. Deep reefs are climatic refugia for genetic diversity of marine forests. J. Biogeogr. 43, 833–844 (2016).Article 

Google Scholar 
27.Bongaerts, P. et al. Deep reefs are not universal refuges: Reseeding potential varies among coral species. Sci. Adv. 3, e1602373 (2017).28.Muir, P. R., Marshall, P. A., Abdulla, A. & Aguirre, J. D. Species identity and depth predict bleaching severity in reef-building corals: Shall the deep inherit the reef?. Proc. R. Soc. B. 284, 20171551 (2017).Article 

Google Scholar 
29.Semmler, R. F., Hoot, W. C. & Reaka, M. L. Are mesophotic coral ecosystems distinct communities and can they serve as refugia for shallow reefs?. Coral Reefs 36, 433–444 (2017).ADS 
Article 

Google Scholar 
30.Kavousi, J. & Keppel, G. Clarifying the concept of climate change refugia for coral reefs. ICES J. Mar. Sci. 75, 43–49 (2018).Article 

Google Scholar 
31.Morais, J. & Santos, B. A. Limited potential of deep reefs to serve as refuges for tropical Southwestern Atlantic corals. Ecosphere 9, e02281 (2018).32.Pereira, P. H. C., Macedo, C. H., Nunes, J. de A. C. C., Marangoni, L. F. de B. & Bianchini, A. Effects of depth on reef fish communities: Insights of a “deep refuge hypothesis” from Southwestern Atlantic reefs. PLoS ONE 13, e0203072 (2018).33.Rocha, L. A. et al. Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs. Science 361, 281–284 (2018).ADS 
CAS 
Article 

Google Scholar 
34.Slattery, M. et al. The Pulley Ridge deep reef is not a stable refugia through time. Coral Reefs 37, 391–396 (2018).ADS 
Article 

Google Scholar 
35.Kavousi, J. Biological interactions: The overlooked aspects of marine climate change refugia. Glob. Change Biol. 25, 3571–3573 (2019).ADS 
Article 

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

Google Scholar 
37.Fisher, R. et al. Species richness on coral reefs and the pursuit of convergent global estimates. Curr. Biol. 25, 500–505 (2015).CAS 
Article 

Google Scholar 
38.Montgomery, A. D., Fenner, D. & Toonen, R. J. Annotated checklist for stony corals of American Sāmoa with reference to mesophotic depth records. ZK 849, 1–170 (2019).39.Colwell, R. K. et al. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. J. Plant Ecol. 5, 3–21 (2012).Article 

Google Scholar 
40.Rooney, J. et al. Mesophotic coral ecosystems in the Hawaiian Archipelago. Coral Reefs 29, 361–367 (2010).ADS 
Article 

Google Scholar 
41.Bridge, T. C. L. et al. Diversity of Scleractinia and Octocorallia in the mesophotic zone of the Great Barrier Reef, Australia. Coral Reefs 31, 179–189 (2012).ADS 
Article 

Google Scholar 
42.Pyle, R. L. et al. A comprehensive investigation of mesophotic coral ecosystems in the Hawaiian Archipelago. PeerJ 4, e2475 (2016).43.Muir, P. R. & Pichon, M. Biodiversity of reef-building, Scleractinian corals. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 589–620 (Springer International Publishing, 2019).44.Spalding, H. L. et al. The Hawaiian Archipelago. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 445–464 (Springer International Publishing, 2019).45.Turak, E. & DeVantier, L. Reef-building corals of the upper mesophotic zone of the Central Indo-West Pacific. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 621–651 (Springer International Publishing, 2019).46.Vermeij, G. J. & Grosberg, R. K. Rarity and persistence. Ecol. Lett. 21, 3–8 (2018).Article 

Google Scholar 
47.Kammer, T. W., Baumiller, T. K. & Ausich, W. I. Evolutionary significance of differential species longevity in Osagean-Meramecian (Mississippian) crinoid clades. Paleobiology 24, 155–176 (1998).
Google Scholar 
48.Jones, G. P., Julian, C. M. & Munday, P. L. Rarity in coral reef fish communities. in Coral reef fishes: dynamics and diversity in a complex ecosystem (ed. Sale, P. F.) 81–102 (Academic Press, 2006).49.Yang, Q., Liu, G., Casazza, M., Gonella, F. & Yang, Z. Three dimensions of biodiversity: New perspectives and methods. Ecol. Indic. 130, 108099 (2021).50.Richards, Z. T. Rarity in the coral genus Acropora: Implications for biodiversity conservation. (James Cook University, 2009).51.Soares, M. de O. Marginal reef paradox: A possible refuge from environmental changes? Ocean Coast. Manag. 185, 105063 (2020).52.Soares, M. de O. et al. Why do mesophotic coral ecosystems have to be protected? Sci. Total Environ. 726, 138456 (2020).53.White, K. N. et al. Typhoon damage on a shallow mesophotic reef in Okinawa, Japan. PeerJ 1, e151 (2013).54.Smith, T. B., Holstein, D. M. & Ennis, R. S. Disturbance in mesophotic coral ecosystems and linkages to conservation and management. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 911–929 (Springer International Publishing, 2019).55.Pinheiro, H. T., Eyal, G., Shepherd, B. & Rocha, L. A. Ecological insights from environmental disturbances in mesophotic coral ecosystems. Ecosphere 10, e02666 (2019).56.Veron, J. E. N. Corals of the world. (Australian Institute of Marine Science, 2000).57.Luzon, K. S., Lin, M.-F., Ablan Lagman, Ma. C. A., Licuanan, W. R. Y. & Chen, C. A. Resurrecting a subgenus to genus: molecular phylogeny of Euphyllia and Fimbriaphyllia (order Scleractinia; Family Euphyllidae; clade V). PeerJ 5, e4074 (2017).58.Eyal, G. et al. Euphyllia paradivisa, a successful mesophotic coral in the northern Gulf of Eilat/Aqaba, Red Sea. Coral Reefs 35, 91–102 (2016).ADS 
Article 

Google Scholar 
59.Eyal, G., Tamir, R., Kramer, N., Eyal-Shaham, L. & Loya, Y. The Red Sea: Israel. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12, 199–214 (Springer International Publishing, 2019).60.Tamir, R., Eyal, G., Kramer, N., Laverick, J. H. & Loya, Y. Light environment drives the shallow‐to‐mesophotic coral community transition. Ecosphere 10 (2019).61.Fujii, T., Kitano, Y. F. & Tachikawa, H. New distributional records of three species of Euphylliidae (Cnidaria, Anthozoa, Hexacorallia, Scleractinia) from the Ryukyu Islands, Japan. Spec. Div. 25, 275–282 (2020).Article 

Google Scholar 
62.Longenecker, K., Roberts, T. E. & Colin, P. L. Papua New Guinea. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12 321–336 (Springer International Publishing, 2019).63.NOAA, [National Oceanic and Atmospheric Administration]. Endangered and threatened species; Critical habitat for the threatened Indo-Pacific corals. 85 FR 76262 (50 CFR Part 223 and 226) 76262–76299 (2020).64.Maragos, J. E., Hunter, C. L. & Meier, K. Z. Reefs and corals observed during the 1991–92 American Samoa coastal resources inventory. 50 (1994).65.Coles, S. et al. Introduced marine species in Pago Pago Harbor, Fagatele Bay and the National Park Coast, American Samoa. 182 (2003).66.Montgomery, A. D. et al. American Samoa. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) vol. 12 387–407 (Springer International Publishing, 2019).67.Wallace, C. C. Staghorn corals of the world: A revision of the coral genus Acropora (Scleractinia; Astrocoeniina; Acroporidae) worldwide, with emphasis on morphology, phylogeny and biogeography. (Csiro Publishing, 1999).68.Hoeksema, B. W. Taxonomy, phylogeny and biogeography of mushroom corals (Scleractinina: Fungiidae). Zoologische Verhandelingen 254, 1–295 (1989).
Google Scholar 
69.World Register of Marine Species: WoRMS. Available online: http://www.marinespecies.org/. Accessed on 9/9/2020 (2020). https://doi.org/10.14284/170.70.Hsieh, T. C., Ma, K. H. & Chao, A. Interpolation and extrapolation for species diversity. (2020).71.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
72.Baselga, A. et al. Partitioning beta diversity into turnover and nestedness components ver. 1.5.2. (2020).73.Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for Primer: Guide to software and statistical methods. 218 (2008).74.Clarke, K. R. & Gorley, R. N. Getting started with PRIMER 7. 18 http://updates.primer-e.com/primer7/manuals/Getting_started_with_PRIMER_7.pdf (2015).75.Gaston, K. What is rarity? in Rarity 1–21 (Chapman & Hall, 1994). More

1.Hou, S. et al. A microbiota–root–shoot circuit favours Arabidopsis growth over defence under suboptimal light. Nat. Plants 7, 1078–1092 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Durán, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983.e14 (2018).PubMed 
PubMed Central 

Google Scholar 
3.van der Heijden, M. G., Bruin, S., de, Luckerhoff, L., van Logtestijn, R. S. & Schlaeppi, K. A widespread plant-fungal-bacterial symbiosis promotes plant biodiversity, plant nutrition and seedling recruitment. ISME J. 10, 389–399 (2015).PubMed 
PubMed Central 

Google Scholar 
4.Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).ADS 
PubMed 

Google Scholar 
5.Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E. E. & van der Heijden, M. G. A. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
6.Martin, F. M., Uroz, S. & Barker, D. G. Ancestral alliances: Plant mutualistic symbioses with fungi and bacteria. Science 356 (2017).7.Nagy, L. G. et al. in The Fungal Kingdom 35–56 (ASM Press, 2017). https://doi.org/10.1128/9781555819583.ch2.8.Brundrett, M. C. & Tedersoo, L. Evolutionary history of mycorrhizal symbioses and global host plant diversity. N. Phytol. 220, 1108–1115 (2018).
Google Scholar 
9.Delavaux, C. S. et al. Mycorrhizal fungi influence global plant biogeography. Nat. Ecol. Evol. 3, 424–429 (2019).PubMed 

Google Scholar 
10.Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 1–10 (2019).CAS 

Google Scholar 
11.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).ADS 
CAS 
PubMed 

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

Google Scholar 
13.Lugtenberg, B. J. J., Caradus, J. R. & Johnson, L. J. Fungal endophytes for sustainable crop production. FEMS Microbiol. Ecol. 92, fiw194 (2016).PubMed 

Google Scholar 
14.Glynou, K. et al. The local environment determines the assembly of root endophytic fungi at a continental scale. Environ. Microbiol. 18, 2418–2434 (2016).CAS 
PubMed 

Google Scholar 
15.Glynou, K., Nam, B., Thines, M. & Maciá-Vicente, J. G. Facultative root-colonizing fungi dominate endophytic assemblages in roots of nonmycorrhizal Microthlaspi species. N. Phytol. 217, 1190–1202 (2018).
Google Scholar 
16.U’Ren, J. M. et al. Host availability drives distributions of fungal endophytes in the imperilled boreal realm. Nat. Ecol. Evol. 3, 1430–1437 (2019).PubMed 

Google Scholar 
17.Maciá-Vicente, J. G., Piepenbring, M. & Koukol, O. Brassicaceous roots as an unexpected diversity hot-spot of helotialean endophytes. IMA Fungus 11, 1–23 (2020).
Google Scholar 
18.Thiergart, T. et al. Root microbiota assembly and adaptive differentiation among European Arabidopsis populations. Nat. Ecol. Evol. 4, 122–131 (2020).PubMed 

Google Scholar 
19.Oita, S. et al. Climate and seasonality drive the richness and composition of tropical fungal endophytes at a landscape scale. Commun. Biol. 4, 1–11 (2021).
Google Scholar 
20.Vannier, N., Bittebiere, A. K., Mony, C. & Vandenkoornhuyse, P. Root endophytic fungi impact host plant biomass and respond to plant composition at varying spatio-temporal scales. Fungal Ecol. 44, 100907 (2020).
Google Scholar 
21.Jumpponen, A., Herrera, J., Porras-Alfaro, A. & Rudgers, J. Biogeography of root-associated fungal endophytes. Biogeography of Mycorrhizal Symbiosis 195–222. https://doi.org/10.1007/978-3-319-56363-3_10 (2017).22.Bokati, D., Herrera, J. & Poudel, R. Soil influences colonization of root-associated fungal endophyte communities of maize, wheat, and their progenitors. J. Mycol. 2016, 1–9 (2016).
Google Scholar 
23.Card, S. D. et al. Beneficial endophytic microorganisms of Brassica – A review. Biol. Control 90, 102–112 (2015).
Google Scholar 
24.Junker, C., Draeger, S. & Schulz, B. A fine line – endophytes or pathogens in Arabidopsis thaliana. Fungal Ecol. 5, 657–662 (2012).
Google Scholar 
25.Fesel, P. H. & Zuccaro, A. Dissecting endophytic lifestyle along the parasitism/mutualism continuum in Arabidopsis. Curr. Opin. Microbiol. 32, 103–112 (2016).PubMed 

Google Scholar 
26.Kia, S. H. et al. Influence of phylogenetic conservatism and trait convergence on the interactions between fungal root endophytes and plants. ISME J. 11, 777–790 (2017).PubMed 

Google Scholar 
27.Lahrmann, U. et al. Mutualistic root endophytism is not associated with the reduction of saprotrophic traits and requires a noncompromised plant innate immunity. N. Phytol. 207, 841–857 (2015).CAS 

Google Scholar 
28.Hacquard, S. et al. Survival trade-offs in plant roots during colonization by closely related beneficial and pathogenic fungi. Nat. Commun. 7, 1–13 (2016).
Google Scholar 
29.Hiruma, K. et al. Root endophyte Colletotrichum tofieldiae confers plant fitness benefits that are phosphate status dependent. Cell 165, 464–474 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Almario, J. et al. Root-associated fungal microbiota of nonmycorrhizal Arabis alpina and its contribution to plant phosphorus nutrition. Proc. Natl Acad. Sci. USA 114, E9403–E9412 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Kohler, A. et al. Convergent losses of decay mechanisms and rapid turnover of symbiosis genes in mycorrhizal mutualists. Nat. Genet. 47, 410–415 (2015).CAS 
PubMed 

Google Scholar 
32.Miyauchi, S. et al. Large-scale genome sequencing of mycorrhizal fungi provides insights into the early evolution of symbiotic traits. Nat. Commun. 11, 1–17 (2020).
Google Scholar 
33.Spatafora, J. W., Sung, G. H. J. M. S., Hywel-Jones, N. L. & White, J. F. Phylogenetic evidence for an animal pathogen origin of ergot and the grass endophytes. Mol. Ecol. 16, 1701–1711 (2007).CAS 
PubMed 

Google Scholar 
34.Xu, X. H. et al. The rice endophyte Harpophora oryzae genome reveals evolution from a pathogen to a mutualistic endophyte. Sci. Rep. 4, 1–9 (2014).CAS 

Google Scholar 
35.Weiß, M., Waller, F., Zuccaro, A. & Selosse, M. Sebacinales – one thousand and one interactions with land plants. N. Phytol. 211, 20–40 (2016).
Google Scholar 
36.Knapp, D. G. et al. Comparative genomics provides insights into the lifestyle and reveals functional heterogeneity of dark septate endophytic fungi. Sci. Rep. 8, 6321 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
37.Hettiarachchige, I. K. et al. Global changes in asexual Epichloë transcriptomes during the early stages, from seed to seedling, of symbiotum establishment. Microorg 9, 991 (2021).
Google Scholar 
38.Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 1–14 (2020).
Google Scholar 
39.Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).PubMed 
PubMed Central 

Google Scholar 
40.Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Google Scholar 
41.Selosse, M.-A., Schneider-Maunoury, L. & Martos, F. Time to re-think fungal ecology? Fungal ecological niches are often prejudged. N. Phytol. 217, 968–972 (2018).
Google Scholar 
42.Zuccaro, A. et al. Endophytic life strategies decoded by genome and transcriptome analyses of the mutualistic root symbiont Piriformospora indica. PLoS Pathog. 7, e1002290 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
43.David, A. S. et al. Draft genome sequence of Microdochium bolleyi, a dark septate fungal endophyte of beach grass. Genome Announc. 4, e00270-16 (2016).44.Walker, A. K. et al. Full genome of Phialocephala scopiformis DAOMC 229536, a fungal endophyte of spruce producing the potent anti-insectan compound rugulosin. Genome Announc. 4, e01768-15 (2016).45.Wu, W. et al. Characterization of four endophytic fungi as potential consolidated bioprocessing hosts for conversion of lignocellulose into advanced biofuels. Appl. Microbiol. Biotechnol. 101.6, 2603–2618 (2017).
Google Scholar 
46.Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 1–14 (2019).
Google Scholar 
47.Csűös, M. Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood. Bioinformatics 26, 1910–1912 (2010).
Google Scholar 
48.Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. N. Phytol. 209, 1705–1719 (2016).CAS 

Google Scholar 
49.Pellegrin, C., Morin, E., Martin, F. M. & Veneault-Fourrey, C. Comparative analysis of secretomes from ectomycorrhizal fungi with an emphasis on small-secreted proteins. Front. Microbiol. 6, 1278 (2015).PubMed 
PubMed Central 

Google Scholar 
50.Tung Ho, L. S. & Ané, C. A linear-time algorithm for gaussian and non-gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).
Google Scholar 
51.Klopfenstein, D. V. et al. GOATOOLS: A Python library for Gene Ontology analyses. Sci. Rep. 8, 1–17 (2018).CAS 

Google Scholar 
52.Szklarczyk, D. et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Schulz, B. & Boyle, C. The endophytic continuum. Mycol. Res. 109, 661–686 (2005).PubMed 

Google Scholar 
54.Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
56.Curran, D. M., Gilleard, J. S. & Wasmuth, J. D. MIPhy: identify and quantify rapidly evolving members of large gene fam. PeerJ 2018, e4873 (2018).
Google Scholar 
57.Atanasova, L. et al. Evolution and functional characterization of pectate lyase PEL12, a member of a highly expanded Clonostachys rosea polysaccharide lyase 1 family. BMC Microbiol. 18, 1–19 (2018).
Google Scholar 
58.Keim, J., Mishra, B., Sharma, R., Ploch, S. & Thines, M. Root-associated fungi of Arabidopsis thaliana and Microthlaspi perfoliatum. Fungal Divers 66, 99–111 (2014).
Google Scholar 
59.Vannier, N., Agler, M. & Hacquard, S. Microbiota-mediated disease resistance in plants. PLoS Pathog. 15, e1007740 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Hassani, M. A., Durán, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6, 1–17 (2018).
Google Scholar 
61.Getzke, F., Thiergart, T. & Hacquard, S. Contribution of bacterial-fungal balance to plant and animal health. Curr. Opin. Microbiol. 49, 66–72 (2019).CAS 
PubMed 

Google Scholar 
62.Wolinska, K. W. et al. Tryptophan metabolism and bacterial commensals prevent fungal dysbiosis in Arabidopsis roots. Proc. Natl Acad Sci USA. 118, e2111521118 (2021).PubMed 

Google Scholar 
63.Lofgren, L. A. et al. Genome-based estimates of fungal rDNA copy number variation across phylogenetic scales and ecological lifestyles. Mol. Ecol. 28, 721–730 (2019).PubMed 

Google Scholar 
64.Karasov, T. L. et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe 24, 168–179.e4 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Karasov, T. L. et al. The relationship between microbial population size and disease in the Arabidopsis thaliana phyllosphere. Preprint at https://doi.org/10.1101/828814 (2020).66.Benen, J. A. E., Kester, H. C. M., Pařenicová, L. & Visser, J. Characterization of Aspergillus niger pectate lyase A. Biochemistry 39, 15563–15569 (2000).CAS 
PubMed 

Google Scholar 
67.Bauer, S., Vasu, P., Persson, S., Mort, A. J. & Somerville, C. R. Development and application of a suite of polysaccharide-degrading enzymes for analyzing plant cell walls. Proc. Natl Acad. Sci. USA 103, 11417–11422 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Bacic, A. Breaking an impasse in pectin biosynthesis. Proc. Natl Acad. Sci. USA 103, 5639–5640 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Vogel, J. Unique aspects of the grass cell wall. Curr. Opin. Plant Biol. 11, 301–307 (2008).CAS 
PubMed 

Google Scholar 
70.Bacete, L. et al. Arabidopsis response reGUlator 6 (ARR6) modulates plant cell-wall composition and disease resistance. Mol. Plant-Microbe Interact. 33, 767–780 (2020).CAS 
PubMed 

Google Scholar 
71.Molina, A. et al. Arabidopsis cell wall composition determines disease resistance specificity and fitness. Proc. Natl Acad. Sci. USA 118, 2021 (2021).
Google Scholar 
72.Sun, Z.-B. et al. Biology and applications of Clonostachys rosea. J. Appl. Microbiol. 129, 486–495 (2020).PubMed 

Google Scholar 
73.Broberg, M. et al. Comparative genomics highlights the importance of drug efflux transporters during evolution of mycoparasitism in Clonostachys subgenus Bionectria (Fungi, Ascomycota, Hypocreales). Evol. Appl. 14, 476–497 (2021).CAS 
PubMed 

Google Scholar 
74.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Grabherr, M. G. et al. Trinity: Reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat. Biotechnol. 29, 644 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Grigoriev, I. V. et al. MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res. 42, D699–D704 (2014).CAS 
PubMed 

Google Scholar 
78.Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 

Google Scholar 
79.Solovyev, V., Kosarev, P., Seledsov, I. & Vorobyev, D. Automatic annotation of eukaryotic genes, pseudogenes and promoters. Genome Biol. 7, S10 (2006).PubMed 
PubMed Central 

Google Scholar 
80.Cohen, O., Ashkenazy, H., Belinky, F., Huchon, D. & Pupko, T. GLOOME: gain-loss mapping engine. Bioinformatics 26, 2914–2915 (2010).CAS 
PubMed 

Google Scholar 
81.Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Machine Learning Res. http://scikit-learn.sourceforge.net. (2011).82.Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness. In Methods in Molecular Biology vol. 1962, 227–245 (Humana Press Inc., 2019).83.Morin, E. et al. Comparative genomics of Rhizophagus irregularis, R. cerebriforme, R. diaphanus and Gigaspora rosea highlights specific genetic features in Glomeromycotina. N. Phytol. 222, 1584–1598 (2019).CAS 

Google Scholar 
84.Cantarel, B. I. et al. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 37, 233–238 (2009).
Google Scholar 
85.Rawlings, N. D., Barrett, A. J. & Finn, R. Twenty years of the MEROPS database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 44, D343–D350 (2016).CAS 
PubMed 

Google Scholar 
86.Fischer, M. & Pleiss, J. The Lipase Engineering Database: a navigation and analysis tool for protein families. Nucleic Acids Res. 31, 319–321 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
88.Deorowicz, S., Debudaj-Grabysz, A. & Gudys, A. FAMSA: Fast and accurate multiple sequence alignment of huge protein families. Sci. Rep. 6, 1–13 (2016).
Google Scholar 
89.Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).90.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 

Google Scholar 
91.Morris, J. H. et al. ClusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinforma. 12, 436 (2011).CAS 

Google Scholar 
92.Gruber, B. D., Giehl, R. F. H., Friedel, S. & von Wirén, N. Plasticity of the Arabidopsis root system under nutrient deficiencies. Plant Physiol. 163, 161–179 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Hedges, L. V. Distribution Theory for Glass’s estimator of effect size and related estimators. J. Educ. Stat. 6, 107–128 (1981).
Google Scholar 
94.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).CAS 

Google Scholar 
96.Zhu, A., Ibrahim, J. G. & Love, M. I. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 35, 2084–2092 (2019).CAS 
PubMed 

Google Scholar 
97.Mesny, F. Genomic determinants of endophytism in the Arabidopsis root mycobiome. GitHub https://doi.org/10.5281/zenodo.5642698 (2021). More

1.Pernthaler J. Predation on prokaryotes in the water column and its ecological implications. Nat Rev Microbiol. 2005;3:537–46.CAS 
PubMed 

Google Scholar 
2.Gast RJ, Sanders RW, Caron DA. Ecological strategies of protists and their symbiotic relationships with prokaryotic microbes. Trends Microbiol. 2009;17:563–9.CAS 
PubMed 

Google Scholar 
3.Wein T, Romero Picazo D, Blow F, Woehle C, Jami E, Reusch TBH, et al. Currency, exchange, and inheritance in the evolution of symbiosis. Trends Microbiol. 2019;27:836–49.CAS 
PubMed 

Google Scholar 
4.Ushida K, Newbold CJ, Jouany J-P. Interspecies hydrogen transfer between the rumen ciliate Polyplastron multivesiculatum and Methanosarcina barkeri. J Gen Appl Microbiol. 1997;43:129–31.CAS 
PubMed 

Google Scholar 
5.D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep. 2018;35:455–88.PubMed 

Google Scholar 
6.Graf JS, Schorn S, Kitzinger K, Ahmerkamp S, Woehle C, Huettel B, et al. Anaerobic endosymbiont generates energy for ciliate host by denitrification. Nature. 2021;591:445–50.CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Bell T, Bonsall MB, Buckling A, Whiteley AS, Goodall T, Griffiths RI. Protists have divergent effects on bacterial diversity along a productivity gradient. Biol Lett. 2010;6:639–42.PubMed 
PubMed Central 

Google Scholar 
8.Johnke J, Baron M, de Leeuw M, Kushmaro A, Jurkevitch E, Harms H, et al. A generalist protist predator enables coexistence in multitrophic predator-prey systems containing a phage and the bacterial predator bdellovibriot. Front Ecol Evol. 2017;5:536.
Google Scholar 
9.Leibold MA. A graphical model of keystone predators in food webs: trophic regulation of abundance, incidence, and diversity patterns in communities. Am Nat. 1996;147:784–812.
Google Scholar 
10.Glücksman E, Bell T, Griffiths RI, Bass D. Closely related protist strains have different grazing impacts on natural bacterial communities. Environ Microbiol. 2010;12:3105–13.PubMed 

Google Scholar 
11.Espinoza-Vergara G, Hoque MM, McDougald D, Noorian P. The impact of protozoan predation on the pathogenicity of Vibrio cholerae. Front Microbiol. 2020;11:17.PubMed 
PubMed Central 

Google Scholar 
12.Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–76.CAS 
PubMed 

Google Scholar 
13.Rosenberg K, Bertaux J, Krome K, Hartmann A, Scheu S, Bonkowski M. Soil amoebae rapidly change bacterial community composition in the rhizosphere of Arabidopsis thaliana. ISME J. 2009;3:675–84.CAS 
PubMed 

Google Scholar 
14.Chudnovskiy A, Mortha A, Kana V, Kennard A, Ramirez JD, Rahman A, et al. Host-protozoan interactions protect from mucosal infections through activation of the inflammasome. Cell. 2016;167:444–.e14.CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Nieves-Ramírez ME, Partida-Rodríguez O, Laforest-Lapointe I, Reynolds LA, Brown EM, Valdez-Salazar A, et al. Asymptomatic intestinal colonization with protist blastocystis is strongly associated with distinct microbiome ecological patterns. mSystems. 2018;3:e00007–18.PubMed 
PubMed Central 

Google Scholar 
16.Mizrahi I. Rumen symbioses. In: Eugene Rosenberg, Edward F. DeLong, Stephen Lory, Erko Stackebrandt, Thompson F, editors. The Prokaryotes. Springer Berlin Heidelberg; 2013. p. 533–44.17.Sylvester JT, Karnati SKR, Yu Z, Morrison M, Firkins JL. Development of an assay to quantify rumen ciliate protozoal biomass in cows using real-time PCR. J Nutr. 2004;134:3378–84.CAS 
PubMed 

Google Scholar 
18.Newbold CJ, de la Fuente G, Belanche A, Ramos-Morales E, McEwan NR. The role of ciliate protozoa in the rumen. Front Microbiol. 2015;6:1313.PubMed 
PubMed Central 

Google Scholar 
19.Firkins JL, Yu Z, Park T, Plank JE. Extending Burk Dehority’s perspectives on the role of ciliate protozoa in the rumen. Front Microbiol. 2020;11:123.PubMed 
PubMed Central 

Google Scholar 
20.Williams AG, Coleman GS. The rumen protozoa. New York, NY: Springer Science & Business Media; 2012.21.Solomon R, Jami E. Rumen protozoa: from background actors to featured role in microbiome research. Environ Microbiol Rep. 2021;13:45–49.PubMed 

Google Scholar 
22.Shabat SKB, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Berg Miller ME, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 2016;10:2958.CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Lima J, Auffret MD, Stewart RD, Dewhurst RJ, Duthie C-A, Snelling TJ, et al. Identification of rumen microbial genes involved in pathways linked to appetite, growth, and feed conversion efficiency in cattle. Front Genet. 2019;10:701.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Jami E, White BA, Mizrahi I. Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS ONE. 2014;9:e85423.PubMed 
PubMed Central 

Google Scholar 
25.Delgado B, Bach A, Guasch I, González C, Elcoso G, Pryce JE, et al. Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle. Sci Rep. 2019;9:11.PubMed 
PubMed Central 

Google Scholar 
26.Wallace RJ, Sasson G, Garnsworthy PC, Tapio I, Gregson E, Bani P, et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci Adv. 2019;5:eaav8391.CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Belanche A, de la Fuente G, Pinloche E, Newbold CJ, Balcells J. Effect of diet and absence of protozoa on the rumen microbial community and on the representativeness of bacterial fractions used in the determination of microbial protein synthesis. J Anim Sci. 2012;90:3924–36.CAS 
PubMed 

Google Scholar 
28.Belanche A, de la Fuente G, Moorby JM, Newbold CJ. Bacterial protein degradation by different rumen protozoal groups. J Anim Sci. 2012;90:4495–504.CAS 
PubMed 

Google Scholar 
29.Belanche A, de la Fuente G, Newbold CJ. Effect of progressive inoculation of fauna-free sheep with holotrich protozoa and total-fauna on rumen fermentation, microbial diversity and methane emissions. FEMS Microbiol Ecol. 2015;91:fiu026.PubMed 

Google Scholar 
30.Hackmann TJ, Firkins JL. Maximizing efficiency of rumen microbial protein production. Front Microbiol. 2015;6:465.PubMed 
PubMed Central 

Google Scholar 
31.Popova M, Martin C, Rochette Y, Graviou D, Morgavi DP. Methanogenesis kinetics and fermentation patterns in the rumen of sheep with or without protozoa. In: Ruminant physiology: digestion, metabolism and effects of nutrition on reproduction and welfare. Netherlands: Wageningen Academic publishers; 2009. 320.32.Levy B, Jami E. Exploring the prokaryotic community associated within the rumen ciliate protozoa population. Front Microbiol. 2018;9:2526.PubMed 
PubMed Central 

Google Scholar 
33.Borrel G, Brugère J-F, Gribaldo S, Schmitz RA, Moissl-Eichinger C. The host-associated archaeome. Nat Rev Microbiol. 2020;18:622–36.CAS 
PubMed 

Google Scholar 
34.Lloyd D, Williams AG, Amann R, Hayes AJ, Durrant L, Ralphs JR. Intracellular prokaryotes in rumen ciliate protozoa: Detection by confocal laser scanning microscopy after in situ hybridization with fluorescent 16S rRNA probes. Eur J Protistol. 1996;32:523–31.
Google Scholar 
35.Jouany JP. Effect of rumen protozoa on nitrogen utilization by ruminants. J Nutr. 1996;126:1335S–46S.CAS 
PubMed 

Google Scholar 
36.Coleman GS, Sandford DC. The engulfment and digestion of mixed rumen bacteria and individual bacterial species by single and mixed species of rumen ciliate protozoa grown in-vivo. J Agric Sci. 1979;92:729–42.
Google Scholar 
37.Zachut M, Honig H, Striem S, Zick Y, Boura-Halfon S, Moallem U. Periparturient dairy cows do not exhibit hepatic insulin resistance, yet adipose-specific insulin resistance occurs in cows prone to high weight loss. J Dairy Sci. 2013;96:5656–69.CAS 
PubMed 

Google Scholar 
38.National Research Council. 2001. Nutrient Requirements of Dairy Cattle: Seventh Revised Edition. Washington, DC: The National Academies Press; 2001.39.Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem. 1976;72:248–54.CAS 
PubMed 

Google Scholar 
40.Stevenson DM, Weimer PJ. Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR. Appl Microbiol Biotechnol. 2007;75:165–74.CAS 
PubMed 

Google Scholar 
41.NIH HMP Working Group, Peterson J, Garges S, Giovanni M, McInnes P, Wang L, et al. The NIH human microbiome project. Genome Res. 2009;19:2317–23.
Google Scholar 
42.Tapio I, Shingfield KJ, McKain N, Bonin A, Fischer D, Bayat AR, et al. Oral samples as non-invasive proxies for assessing the composition of the rumen microbial community. PLoS ONE. 2016;11:e0151220.PubMed 
PubMed Central 

Google Scholar 
43.Wobbrock JO, Findlater L, Gergle D, Higgins JJ. The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery; 2011. p. 143–6.44.Elkin LA, Kay M, Higgins JJ, Wobbrock JO. An aligned rank transform procedure for multifactor contrast tests. https://arxiv.org/abs/2102.11824.45.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
48.Hammer Ø, Harper DAT, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:9.
Google Scholar 
49.Oksanen J. vegan: community ecology package. R package version 2.5-7. 2011. http://cran.r-project.org/package=vegan.50.van den Boogaart KG, Tolosana-Delgado R. ‘compositions’: a unified R package to analyze compositional data. Comput Geosci. 2008;34:320–38.
Google Scholar 
51.Krzywinski M, Altman N, Blainey P. Points of significance: nested designs. For studies with hierarchical noise sources, use a nested analysis of variance approach. Nat Methods. 2014;11:977–8.CAS 
PubMed 

Google Scholar 
52.R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.53.Chernomor O, von Haeseler A, Minh BQ. Terrace aware data structure for phylogenomic inference from supermatrices. Syst Biol. 2016;65:997–1008.PubMed 
PubMed Central 

Google Scholar 
54.Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:256–9.
Google Scholar 
55.Belanche A, de la Fuente G, Newbold CJ. Study of methanogen communities associated with different rumen protozoal populations. FEMS Microbiol Ecol. 2014;90:663–77.CAS 
PubMed 

Google Scholar 
56.Ungerfeld EM. Metabolic hydrogen flows in rumen fermentation: principles and possibilities of interventions. Front Microbiol. 2020;11:589.PubMed 
PubMed Central 

Google Scholar 
57.Bonder MJ, Kurilshikov A, Tigchelaar EF, Mujagic Z, Imhann F, Vila AV, et al. The effect of host genetics on the gut microbiome. Nat Genet. 2016;48:1407–12.CAS 
PubMed 

Google Scholar 
58.Henderson G, Cox F, Ganesh S, Jonker A, Young W, Global Rumen Census Collaborators, et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci Rep.2015;5:1–15.
Google Scholar 
59.Fukami T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu Rev Ecol Evol Syst. 2015;46:1–23.
Google Scholar 
60.Shaani Y, Zehavi T, Eyal S, Miron J, Mizrahi I. Microbiome niche modification drives diurnal rumen community assembly, overpowering individual variability and diet effects. ISME J. 2018;12:2446–57.CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Paul RG, Williams AG, Butler RD. Hydrogenosomes in the rumen entodiniomorphid ciliate Polyplastron multivesiculatum. J Gen Microbiol. 1990;136:1981–9.CAS 
PubMed 

Google Scholar 
62.Greening C, Geier R, Wang C, Woods LC, Morales SE, McDonald MJ, et al. Diverse hydrogen production and consumption pathways influence methane production in ruminants. ISME J. 2019;13:2617–32.CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Gong J, Qing Y, Zou S, Fu R, Su L, Zhang X, et al. Protist-bacteria associations: gammaproteobacteria and alphaproteobacteria are prevalent as digestion-resistant bacteria in ciliated protozoa. Front Microbiol. 2016;7:498.PubMed 
PubMed Central 

Google Scholar 
64.Park T, Yu Z. Do ruminal ciliates select their preys and prokaryotic symbionts? Front Microbiol. 2018;9:1710.PubMed 
PubMed Central 

Google Scholar 
65.Matz C, Nouri B, McCarter L, Martinez-Urtaza J. Acquired type III secretion system determines environmental fitness of epidemic Vibrio parahaemolyticus in the interaction with bacterivorous protists. PLoS ONE. 2011;6:e20275.CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Kamke J, Soni P, Li Y, Ganesh S, Kelly WJ, Leahy SC, et al. Gene and transcript abundances of bacterial type III secretion systems from the rumen microbiome are correlated with methane yield in sheep. BMC Res Notes. 2017;10:367.PubMed 
PubMed Central 

Google Scholar 
67.Jami E, Mizrahi I. Composition and similarity of bovine rumen microbiota across individual animals. PLoS ONE. 2012;7:e33306.CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Brulc JM, Antonopoulos DA, Miller ME, Wilson MK, Yannarell AC, Dinsdale EA, et al. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci USA. 2009;106:1948–53.CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Indugu N, Vecchiarelli B, Baker LD, Ferguson JD, Vanamala JKP, Pitta DW. Comparison of rumen bacterial communities in dairy herds of different production. BMC Microbiol. 2017;17:190.PubMed 
PubMed Central 

Google Scholar 
70.Pope PB, Smith W, Denman SE, Tringe SG, Barry K, Hugenholtz P, et al. Isolation of Succinivibrionaceae implicated in low methane emissions from Tammar wallabies. Science. 2011;333:646–8.CAS 
PubMed 

Google Scholar 
71.Saleem M, Fetzer I, Dormann CF, Harms H, Chatzinotas A. Predator richness increases the effect of prey diversity on prey yield. Nat Commun. 2012;3:1305.PubMed 

Google Scholar 
72.Simek K, Vrba J, Pernthaler J, Posch T, Hartman P, Nedoma J, et al. Morphological and compositional shifts in an experimental bacterial community influenced by protists with contrasting feeding modes. Appl Environ Microbiol. 1997;63:587–95.CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Socolar J, Washburne A. Prey carrying capacity modulates the effect of predation on prey diversity. Am Nat. 2015;186:333–47.PubMed 

Google Scholar 
74.Gutierrez J. Observations on bacterial feeding by the rumen ciliate Isotricha prostoma. J Protozool. 1958;5:122–6.
Google Scholar 
75.Coleman GS. The metabolism of Escherichia coli and other bacteria by Entodinium caudatum. J Gen Microbiol. 1964;37:209–23.CAS 
PubMed 

Google Scholar 
76.Canter EJ, Cuellar-Gempeler C, Pastore AI, Miller TE, Mason OU. Predator identity more than predator richness structures aquatic microbial assemblages in Sarracenia purpurea leaves. Ecology. 2018;99:652–60.PubMed 

Google Scholar 
77.Paine RT. Food web complexity and species diversity. Am Nat. 1966;100:65–75.
Google Scholar 
78.Audebert C, Even G, Cian A, Loywick A, Merlin S, Blastocystis Investigation Group,et al. Colonization with the enteric protozoa Blastocystis is associated with increased diversity of human gut bacterial microbiota. Sci Rep. 2016;6:25255.CAS 
PubMed 
PubMed Central 

Google Scholar 
79.Chabé M, Lokmer A, Ségurel L. Gut protozoa: friends or foes of the human gut microbiota? Trends Parasitol. 2017;33:925–34.PubMed 

Google Scholar 
80.Asgari M, Steiner CF. Interactive effects of productivity and predation on zooplankton diversity. Oikos. 2017;126:1617–24.CAS 

Google Scholar 
81.Tokura M, Ushida K, Miyazaki K, Kojima Y. Methanogens associated with rumen ciliates. FEMS Microbiol Ecol. 1997;22:137–43.CAS 

Google Scholar 
82.Irbis C, Ushida K. Detection of methanogens and proteobacteria from a single cell of rumen ciliate protozoa. J Gen Appl Microbiol. 2004;50:203–12.CAS 
PubMed 

Google Scholar 
83.Karakoç C, Radchuk V, Harms H, Chatzinotas A. Interactions between predation and disturbances shape prey communities. Sci Rep. 2018;8:2968.PubMed 
PubMed Central 

Google Scholar  More

1.Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582 (2010).CAS 

Google Scholar 
2.Raich, J. W., Potter, C. S. & Bhagawati, D. Interannual variability in global soil respiration, 1980–94. Glob. Change Biol. 8, 800–812 (2002).
Google Scholar 
3.Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition feedbacks to climate change. Nature 440, 165–173 (2006).CAS 

Google Scholar 
4.Feng, X., Simpson, A. J., Wilson, K. P., Williams, D. D. & Simpson, M. J. Increased cuticular carbon sequestration and lignin oxidation in response to soil warming. Nat. Geosci. 1, 836–839 (2008).CAS 

Google Scholar 
5.Heimann, H. & Reichstein, R. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).CAS 

Google Scholar 
6.Fang, C. et al. Impacts of warming and nitrogen addition on soil autotrophic and heterotrophic respiration in a semi-arid environment. Agr. Forest Meteorol. 248, 449–457 (2018).
Google Scholar 
7.Wang, Q., Liu, S., Wang, Y., Tian, P. & Sun, T. Influences of N deposition on soil microbial respiration and its temperature sensitivity depend on N type in a temperate forest. Agr. Forest Meteorol. 260–261, 240–246 (2018).
Google Scholar 
8.Zhong, Y. Q. W., Yan, W. M., Zong, Y. Z. & Shangguan, Z. P. The effects of nitrogen enrichment on soil CO2 fluxes depending on temperature and soil properties. Global Ecol. Biogeogr. 25, 475–488 (2016).
Google Scholar 
9.Yu, G. R. et al. Stabilization of atmospheric nitrogen deposition in China over the past decade. Nat. Geosci. 12, 424–429 (2019).CAS 

Google Scholar 
10.Coucheney, E., Strömgren, M., Lerch, T. Z. & Herrmann, A. M. Long-term fertilization of a boreal Norway spruce forest increases the temperature sensitivity of soil organic carbon mineralization. Ecol. Evol. 3, 5177–5188 (2013).
Google Scholar 
11.Jiang, J. S., Guo, S. L., Wang, R., Liu, Q. F. & Sun, Q. Q. Effects of nitrogen fertilization on soil respiration and temperature sensitivity in spring maize field in semi-arid regions on loess plateau. Environ. Sci. 36, 1802–1809 (2015).
Google Scholar 
12.Wang, Q., Zhao, X., Tian, P., Liu, S. & Sun, Z. Bioclimate and arbuscular mycorrhizal fungi regulate continental biogeographic variations in effect of nitrogen deposition on the temperature sensitivity of soil organic carbon decomposition. Land Degrad. Dev. 32, 936–945 (2021).
Google Scholar 
13.Schindlbacher, A., Zechmeister-Boltenstern, S. & Jandl, R. Carbon losses due to soil warming: do autotrophic and heterotrophic soil respiration respond equally? Glob. Change Biol. 15, 901–903 (2009).
Google Scholar 
14.Carey, J. C. et al. Temperature response of soil respiration largely unaltered with experimental warming. Proc. Natl Acad. Sci. 113, 13797–13802 (2016).CAS 

Google Scholar 
15.Lyu, M., Giardina, C. P. & Litton, C. M. Interannual variation in rainfall modulates temperature sensitivity of carbon allocation and flux in a tropical montane wet forest. Glob. Change Biol. 27, 3824–3836 (2021).
Google Scholar 
16.Wang, Q. et al. Global synthesis of temperature sensitivity of soil organic carbon decomposition: latitudinal patterns and mechanisms. Funct. Ecol. 33, 514–523 (2019).
Google Scholar 
17.Li, J. et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Glob. Change Biol. 26, 1873–1885 (2020).
Google Scholar 
18.Delgado-Baquerizo, M. et al. Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities. Nat. Ecol. Evol. 1, 1339–1347 (2017).
Google Scholar 
19.Delgado-Baquerizo, M. et al. Climate legacies drive global soil carbon stocks in terrestrial ecosystems. Sci. Adv. 3, e1602008 (2017).
Google Scholar 
20.Delgado-Baquerizo, M. et al. Ecological drivers of soil microbial diversity and soil biological networks in the southern hemisphere. Ecology 99, 583–596 (2018).
Google Scholar 
21.Ding, J. Y. & Eldridge, D. J. Contrasting global effects of woody plant removal on ecosystem structure, function and composition. Perspect. Plant Ecol. 39, 125460 (2019).
Google Scholar 
22.Eldridge, D. J. & Delgado-Baquerizo, M. The influence of climatic legacies on the distribution of dryland biocrust communities. Glob. Change Biol. 25, 327–336 (2019).
Google Scholar 
23.Pärtel, M., Chiarucci, A., Chytrý, M. & Pillar, V. D. Mapping plant community ecology. J. Veg. Sci. 26, 1–3 (2017).
Google Scholar 
24.Richter, D. D. & Yaalon, D. H. “The changing model of soil” revisited. Soil Sci. Soc. Am. J. 76, 766–778 (2012).CAS 

Google Scholar 
25.Lyons, S. K. et al. Holocene shifts in the assembly of plant and animal communities implicate human impacts. Nature 529, 80–83 (2016).
Google Scholar 
26.Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).CAS 

Google Scholar 
27.Delgado-Baquerizo, M. et al. Carbon content and climate variability drive global soil bacterial diversity patterns. Ecol. Monogr. 86, 373–390 (2016).
Google Scholar 
28.Maestre, F. T., Delgado-Baquerizo, M., Jeffries, T. C., Eldridge, D. J. & Singh, B. K. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc. Natl Acad. Sci. 112, 15684–15689 (2015).CAS 

Google Scholar 
29.Monger, C. et al. Legacy effects in linked ecological–soil–geomorphic systems of drylands. Front. Ecol. Environ. 13, 13–19 (2016).
Google Scholar 
30.Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184–187 (2000).CAS 

Google Scholar 
31.Fierer, N., Colman, B. P., Schimel, J. P. & Jackson, R. B. Predicting the temperature dependence of microbial respiration in soil: a continental-scale analysis. Glob. Biogeochem. Cy. 20, GB3026 (2006).
Google Scholar 
32.Peng, S., Piao, S., Wang, T., Sun, J. & Shen, Z. Temperature sensitivity of soil respiration in different ecosystems in China. Soil Biol. Biochem. 41, 1008–1014 (2009).CAS 

Google Scholar 
33.Xu, Z. et al. Temperature sensitivity of soil respiration in China’s forest ecosystems: patterns and controls. Appl. Soil Ecol. 93, 105–110 (2015).
Google Scholar 
34.Niu, B. et al. Warming homogenizes apparent temperature sensitivity of ecosystem respiration. Sci. Adv. 7, eabc7358 (2021).
Google Scholar 
35.Janssens, I. A. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).CAS 

Google Scholar 
36.Yan, G. Y. et al. Sequestration of atmospheric CO2 in boreal forest carbon pools in northeastern China: Effects of nitrogen deposition. Agr. Forest Meteorol. 248, 70–81 (2018).
Google Scholar 
37.Du, E. Z. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).CAS 

Google Scholar 
38.Chen, Z. M. et al. Nitrogen fertilization stimulated soil heterotrophic but not autotrophic respiration in cropland soils: A greater role of organic over inorganic fertilizer. Soil Biol. Biochem. 116, 253–264 (2018).CAS 

Google Scholar 
39.Chen, F. et al. Effects of N addition and precipitation reduction on soil respiration and its components in a temperate forest. Agr. Forest Meteorol. 271, 336–345 (2019).
Google Scholar 
40.Zhang, C. et al. Effects of simulated nitrogen deposition on soil respiration components and their temperature sensitivities in a semiarid grassland. Soil Biol. Biochem. 75, 113–123 (2014).CAS 

Google Scholar 
41.Moinet, G. Y. K. et al. The temperature sensitivity of soil organic matter decomposition is constrained by microbial access to substrates. Soil Biol. Biochem. 116, 333–339 (2018).CAS 

Google Scholar 
42.Li, Y. et al. Soil acid cations induced reduction in soil respiration under nitrogen enrichment and soil acidification. Sci. Total Environ. 615, 1535–1546 (2018).CAS 

Google Scholar 
43.Sanderman, J. Comment on “Climate legacies drive global soil carbon stocks in terrestrial ecosystems”. Sci. Adv. 4, e1701482 (2018).
Google Scholar 
44.Ding, J. et al. The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region. Nat. Commun. 10, 4195 (2019).
Google Scholar 
45.Gershenson, A., Bader, N. E. & Cheng, W. X. Effects of substrate availability on the temperature sensitivity of soil organic matter decomposition. Glob. Change Biol. 15, 176–183 (2009).
Google Scholar 
46.Doetterl, S. et al. Soil carbon storage controlled by interactions between geochemistry and climate. Nat. Geosci. 8, 780–783 (2015).CAS 

Google Scholar 
47.Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).
Google Scholar 
48.Li, J., Ziegler, S. E., Lane, C. S. & Billings, S. A. Legacies of native climate regime govern responses of boreal soil microbes to litter stoichiometry and temperature. Soil Biol. Biochem. 66, 204–213 (2013).CAS 

Google Scholar 
49.Xu, M. et al. High microbial diversity stabilizes the responses of soil organic carbon decomposition to warming in the subsoil on the Tibetan Plateau. Glob. Chang. Biol. 27, 2061–2075 (2021).
Google Scholar 
50.Du, Y. et al. The response of soil respiration to precipitation change is asymmetric and differs between grasslands and forests. Glob. Chang. Biol. 26, 6015–6024 (2020).
Google Scholar 
51.Meier, I. C. & Leuschner, C. Leaf size and leaf area index in Fagus sylvatica forests: competing effects of precipitation, temperature, and nitrogen availability. Ecosystems 11, 655–669 (2008).CAS 

Google Scholar 
52.Li, J., Pei, J., Pendall, E., Fang, C. & Nie, M. Spatial heterogeneity of temperature sensitivity of soil respiration: A global analysis of field observations. Soil Biol. Biochem. 141, 107675 (2020).CAS 

Google Scholar 
53.Katz, M. H. Multivariable Analysis: A Practical Guide for Clinicians and Public Health Researchers (Cambridge Univ. Press, Cambridge, 2006).54.Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. 112, 10967–10972 (2015).CAS 

Google Scholar 
55.Grace, J. B. Structural Equation Modeling Natural Systems (Cambridge Univ. Press, Cambridge, 2006).56.Lefcheck, J. S. PiecewiseSEM: piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol 7, 573–579 (2016).
Google Scholar 
57.Bates, D. et al. lme4: Linear mixed-effects models using Eigen and S4. R package version 1, 1–13 (2017).
Google Scholar  More

Due to contrasts in oceanographic properties along the NAP24, the sampling grid was split in two subregions: north and south (Fig. 1; see “Methods”). The north and south subregions showed from the satellite data a much higher spring/summer (November–February) mean chlorophyll-a (Chl-a) in 2015/2016 than the decadal average time series (2010–2019; Table 1). In agreement with the El Niño effects10,16, the sea surface temperature (SST) and the air temperature showed substantially lower mean values during the spring/summer of 2015/2016 along the subregions (Table 1). However, there was an evident spatial/temporal variability in sea ice concentration/duration between the subregions, with a northward (southward) lower (higher) mean value during 2015/2016 in relation to the decadal average (Table 1). Along the south subregion during the spring/summer of 2015/2016, the increased Chl-a during January followed the decline in the sea ice concentration over the spring and early summer, concurrent with increased SST, which was markedly colder throughout the seasonal phytoplankton succession (Fig. 2a). These results to the south subregion are consistent with previous studies along the WAP, in which years characterized by longer sea ice cover in winter have led to higher phytoplankton biomass in the following summer associated with a more stable water column11,16,26. To the north subregion, however, although there was a similar pattern between Chl-a and SST, the increased Chl-a during January was not related with the sea ice retreat (Fig. 2b). Moreover, there was a clear difference between the Chl-a peaks (the highest Chl-a value reached) along the subregions from the satellite data. The Chl-a peak in the south subregion occurred in early January (10 January 2016, reaching 1.73 mg m–3), whereas in the north subregion the Chl-a peak was observed in late January (29 January 2016, reaching 2.23 mg m–3).Fig. 1: Study area.Location of hydrographic stations is marked by open circles (November), stars (January), and blue circles (February). The black dashed lines indicate the subregions (north and south) along the NAP and delimit the areas used to estimate average remote sensing measurements. The decadal-mean (2010–2019) remote sensing chlorophyll-a (Chl-a) is exhibited in the background, indicating the biomass (Chl-a) distribution of phytoplankton along the NAP in the last decade. An inset map in the lower right corner shows the location of the NAP within the Atlantic sector of the Southern Ocean.Full size imageTable 1 Biological production and ocean/atmosphere parameters by measurements of remote sensing and local meteorological stations during spring/summer in the NAP subregions.Full size tableFig. 2: Biological production and sea ice dynamics in the NAP seasonal phytoplankton succession of 2015/2016.Continuum remote sensing measurements of chlorophyll-a (Chl-a; solid green line), sea surface temperature (SST; solid blue line), and sea ice concentration (gray area) along the NAP, in south (a) and north (b) subregions during spring/summer of 2015/2016. The dashed green, blue and gray lines indicate the decadal average (2010–2019) of Chl-a, SST, and sea ice concentration, respectively. The solid light green lines represent the Chl-a interpolated values. The background shades show the in situ data sampling periods. It is important to note that Chl-a remote sensing data in Antarctic coastal waters are typically underestimated in respect to in situ Chl-a data (see Supplementary Fig. 1)12,29.Full size imageIt has been estimated that drifters entrained in the Gerlache Strait Current and the Bransfield Strait Current exit the Bransfield Strait in 10–20 days17, which is consistent with the interval of 19 days between both Chl-a peaks when considering the extreme distance between the subregions (see Fig. 1). These authors also estimated that drifters deployed in the Gerlache Strait Current were quickly advected out of the Gerlache Strait in less than 1 week (i.e., low residence time)17, which supports the similar diatom species assemblages identified in our microscopic analysis between stations of the Gerlache Strait and southwestern Bransfield Strait24. Therefore, it is plausible that phytoplankton growth in the north of the Gerlache Strait may be laterally advected northward into the Bransfield Strait, explaining the observed concomitant increase of satellite Chl-a data in both subregions from spring, associated with sea ice retreat southward (Fig. 2). In addition, as phytoplankton biomass tends to accumulate northward17,27,28, the advection processes could also explain the temporal and intensity differences of the Chl-a peaks along the subregions (see Fig. 2). This suggests that there was a link between the sea ice dynamics, phytoplankton biomass (Chl-a) and advection processes along the NAP during the spring/summer of 2015/2016, in which the sea ice melting first triggered an increase in phytoplankton biomass through water column stratification along the south subregion, and the advection processes led to a subsequent increase northward.The satellite Chl-a data require extensive validation with in situ data, especially in polar regions, where cloud cover is ubiquitous and performance is typically poor, due to not properly accurate Chl-a algorithms12,29. For that, although the mean Chl-a in 2015/2016 from the satellite data was approximately twice as large as the decadal average, there was a severe discrepancy in the mean Chl-a values observed between the in situ and remote sensing data (see Table 1 and Supplementary Table 1). This highlights the importance of the in situ dataset reported here, especially evident during February 2016, when the signal of an intense diatom bloom ( > 40 mg m–3 Chl-a)24 was not captured in the satellite data (Supplementary Fig. 1), supporting that phytoplankton biomass accumulation during this summer was much higher than recorded by remote sensing observations (see Table 1). In general, the in situ Chl-a achieved its maximum (40 mg m–3) and higher mean value (17.4 mg m–3) during February comparing to November and January (Supplementary Table 1).Phytoplankton community structure during the spring/summer of 2015/2016 was assessed through Chemical taxonomy (CHEMTAX) software, using accessory pigments versus in situ Chl-a concentrations measured via high-performance liquid chromatography (HPLC; see “Methods”). The main phytoplankton group over the season were diatoms, followed by haptophytes (Phaeocystis antarctica), cryptophytes, and dinoflagellates, according to the succession stage (Fig. 3a). Diatoms dominated the phytoplankton community composition in relation to the other groups along the whole in situ sampling period, although their relative biomass (to the total in situ Chl-a) was lower in some stations compared to others in different moments during spring/summer (Fig. 3a). To assess the degree to which the water column structure was a primary driver for development and intensity of diatom growth3,24, the mixed layer depth (MLD) and water column stability were calculated as a function of seawater potential density (see “Methods”). There was an inverse polynomial relationship between MLD and mean upper ocean stability (averaged over 5−150 m depth; hereafter referred to as upper ocean stability) (Fig. 3b). The significant positive exponential relationship between the upper ocean stability and diatom absolute concentrations (in situ Chl-a) demonstrates that stability, associated with MLD, was an important driver of diatom dynamics (Fig. 3b). This elucidates the increase in biological production during summer months of 2016, when upper ocean physical structures (MLD and stability) were sufficiently shallow and stable to produce the high phytoplankton biomass (in situ Chl-a) registered here. However, as MLD and stability showed similar values between summer months (Supplementary Table 1), only the upper ocean physical structures cannot be accounted for the high differences of in situ Chl-a values observed between diatom blooms in January (maximum of 12 mg m–3) and February (maximum of 40 mg m–3). Likewise, also not explaining these differences of in situ Chl-a values between summer months, macronutrients were highly abundant throughout the seasonal phytoplankton succession (Supplementary Table 1). Furthermore, although no measurements of dissolved iron, which can be considered as a limiting factor to primary productivity30, were carried out here, the Antarctic Peninsula continental shelves have been depicted as a substantial source of this micronutrient to the upper ocean, not limiting phytoplankton growth even during intense blooms31,32.Fig. 3: Phytoplankton community composition and upper ocean physical structures along the NAP seasonal phytoplankton succession of 2015/2016.a Relative biomass (to the total in situ chlorophyll-a; Chl-a) distribution of phytoplankton groups on surface, via HPLC/CHEMTAX analysis, during spring/summer of 2015/2016 along the NAP subregions. The black open circles indicate diatoms, the blue squares indicate Phaeocystis antarctica, the gray diamonds with crosses indicate cryptophytes, the green triangles indicate dinoflagellates, and the light gray open circles indicate green flagellates. b Exponential curve (R2 = 0.57; p 40% the community composition proportion in respect to the total Chl-a (considering the three fractionated size classes). Symbol color indicates the sampling month in respect to November (brown), January (gray), and February (black). The inset shows the polynomial inverse relationship (R2 = 0.51; p  70 µm in length; ref. 24), during January a large number ( > 2.5 × 106 cells L–1) of small ( More

Data sourcesEighteen rice-producing countries were selected for our analysis (Supplementary Table 1). Those countries account for 88 and 86% of global rice production and harvested rice area2, respectively (FAOSTAT, 2015–2017). We followed two steps to select the dominant cropping systems in each country. Within each country, our study focused on the main rice-producing area(s) (Supplementary Tables 2 and 3). For example, in the case of Brazil, we selected the southern and northern regions, which together account for nearly all rice production in this country. In the case of Vietnam, we selected the Mekong Delta region, which accounts for nearly 60% of national rice production57. While we tried to cover all major rice cropping systems in each country, this was not possible in the case of rainfed lowland rice cropping systems in northeastern Thailand and eastern India because of lack of reliable estimates of yield potential and access to farmer yield and management data. Once the main rice-producing region(s) in each country was (were) identified, we then determined the dominant rice cropping system(s) for each of them (Supplementary Table 3). We note that “cropping system” refers to a unique combination of a number of rice crops planted on the same piece of land within a 12-month period (and their temporal arrangement), water regime (rainfed or irrigated), and ecosystem (upland or lowland) (Supplementary Fig. 1 and Supplementary Table 2). In our study, rice cropping systems are single-, double-, or triple-season rice; none of the cropping systems are ratoon rice. Following the previous examples, two cropping systems were selected for Brazil (rainfed upland single rice and lowland irrigated single rice in the northern and southern regions, respectively) and two systems (double and triple) were selected for the Mekong Delta region in Vietnam. These systems account for nearly all rice harvested areas in these regions. We distinguished between rice-based cropping systems sowing hybrid versus inbred cultivars in the southern USA. Across the 18 countries, this study included a total of 32 rice cropping systems, which, in turn, covered 51% of the global rice harvested area (Supplementary Tables 1 and 3). Note that the area coverage reported here corresponds to that accounted by 32 cropping systems (and not by the countries where the cropping systems were located). These systems portrayed a wide range of biophysical and socio-economic backgrounds (Supplementary Figs. 1 and 2 and Supplementary Tables 1 and 2), leading to average rice yields ranging from 2–10.4 Mg ha−1 (Supplementary Fig. 3). For data analysis purposes, rice cropping systems were classified into tropical and non-tropical9,58,59 and also based upon water regime and crop season.Agronomic information was collected via structure questionnaires completed by agricultural specialists in each country or region (Supplementary Table 6). The collected data included field size, tillage method, crop establishment method, degree of mechanization for each field operation, seeding rate, crop establishment, and harvest dates, nutrient fertilizer rates, manure type, and rate, pesticides (number of applications, products, and rates), irrigation amount (in irrigated systems), energy source for irrigation pumping, labor input, and straw management (Supplementary Tables 4 and 5). Average values for each cropping system reported by country experts were retrieved from survey data available from previous projects (Supplementary Table 7). Rice grain yield was reported at a standard moisture content of 140 g H2O kg−1 grain, separately for each crop cycle, using data from, at least, three recent cropping seasons in each cropping system. In the case of irrigated rice cropping system in Nigeria and Mali, data were only available for one crop cycle in double-season rice. In this case, we assumed management and actual yield to be identical in the two crop cycles.In all cases, and wherever possible, data were cross-validated with other independent datasets (e.g., FAOSTAT, World Bank, IFA, and published journal papers), which gives confidence about the representativeness and accuracy of the survey data. For example, we estimated area-weighted national yield according to actual yield provided for each cropping system and annual rice harvested area in each system for each of the 18 countries. Comparison of these yields against those reported by FAOSTAT2 showed a strong association and agreement between data sources (Supplementary Fig. 10). We also cross-validated actual yield, N fertilizer, labor, and irrigation from our database with those reported by previous studies (published after the year 2000) based on on-farm data collected in ten selected countries. Due to the lack of on-farm data on irrigation, we used published data collected from experiments that follow typical farmer irrigation practices. In the case of irrigation, our cross-validation differentiated between crop seasons (wet versus dry) in the case of irrigated double-season rice cropping systems. In all cases, average yield, N fertilizer, labor, and irrigation from our database fell within (or very close) the range of values reported in previously published studies for those same cropping systems (Supplementary Table 8). Measured daily weather data, including daily solar radiation, minimum and maximum temperatures, and precipitation, were derived from representative weather stations in each region (Supplementary Fig. 2 and Supplementary Table 9). Data on per-capita gross domestic product (GDP) during 2015–2017 were retrieved for each country to explore relationships between yield gap and economic development60 (Supplementary Fig. 9 and Supplementary Table 1).Estimation of yield gapsThe yield gap is defined as the difference between yield potential and average farmer yield. Estimates of yield potential for irrigated rice or water-limited yield potential for rainfed rice were adopted from Global Yield Gap Atlas (GYGA)61 (Supplementary Table 7). Yield potential simulation in GYGA was performed using crop growth and development model ORYZA2000 or ORYZA (v3) (except for APSIM in the case of India) and based on actual data on crop management, soil data, measured daily weather data, and representative rice varieties planted in each region (see details for yield potential simulation in Supplementary Information Text Section 1). Data on yield potential were not available for Australia (AUIS) in GYGA; hence, we used estimates of yield potential from Lacy et al.62. Yield potential (or water-limited yield potential for rainfed rice) and average yields were computed separately for each rice crop in each rice cropping system (Supplementary Fig. 3). The coefficient of variation (CV) of yield potential (or water-limited yield potential) was estimated for each cropping system (Supplementary Fig. 4). In this study, average rice yield was expressed as percentage of the yield potential (or water-limited yield potential for rainfed rice) for each cropping system (Fig. 1 and Supplementary Fig. 5). In those cropping systems where more than one rice crop is grown within a 12-month period, we estimated yield potential and average yield on both per-crop and annual basis by averaging and summing up the estimates for each rice crop, respectively. In the case of per-crop averages, for those cropping systems in which the harvested rice area changed between crop cycles, we weighted the values for each cycles based on the associated harvested rice area. However, for simplicity, the main text reports only the values on a per-crop basis; annual estimates are provided in the Supplementary Information. Normalizing average yield by the yield potential at each site provides a direct comparison of yield gap closure across systems with diverse biophysical backgrounds (e.g., variation in solar radiation, temperature, and water supply). Without this normalization, one might make biased assessment in relation to the available room for improving yield. For example, an actual yield of 8 Mg ha−1 is equivalent to 80% of yield potential in the cropping system of central China, whereas a yield of 8 Mg ha−1 achieved by irrigated rice farmers in Brazil only represents 55% of yield potential (Supplementary Fig. 3).Quantifying resource-use efficiencyWe assessed the performance of rice production by calculating the following metrics: global warming potential (GWP), fossil-fuel energy inputs, water supply (irrigation plus in-season precipitation), number of pesticide applications, nitrogen (N) balance, and labor input, each expressed on an area and yield-scaled basis (Figs. 2, 3 and 4 and Supplementary Figs. 6, 7 and 11). We estimated metrics on both per-crop and annual basis and report the values on a per-crop basis in the main text while the annual estimates are provided in the Supplementary Information. In the case of GWP, it includes CO2, CH4, and N2O emissions (expressed as CO2-eq) from (i) production, packaging, and transportation of agricultural inputs (seed, fertilizer, pesticides, machinery, etc.), (ii) fossil-fuel energy directly used for farm operations (including irrigation pumping), and (iii) CH4 and N2O emission during rice cultivation63. Emissions from agricultural inputs were calculated on application rates and associated GHG emissions factors (see details in Supplementary Information Text Section 2, Supplementary Table 10). In the case of fossil fuel used for field operations, it was calculated based on the number and type of farm operations and associated fuel requirements (Supplementary Table 11). Total N2O emissions were calculated as the sum of direct and indirect N2O emissions. A previous meta-analysis including rice showed that direct soil N2O emissions can be estimated from the magnitude of N-surplus, which was calculated as applied N inputs minus accumulated N in aboveground biomass at physiological maturity21. Therefore, direct soil N2O emissions for a given rice crop cycle were estimated following van Groenigen et al. N-balance approach21. Indirect N2O emissions were estimated based on the Intergovernmental Panel on Climate Change (IPCC) methodology64, assuming indirect N2O emissions represent 20% of direct N2O emissions. The CH4 emissions from rice paddy field were calculated following IPCC65. Following this approach, CH4 emissions are estimated considering the duration of the rice cultivation period, water regime during the cultivation period and during the pre-season before the cultivation period, and type and amount of organic amendment applied (e.g., straw, manure, compost) based on a baseline emission factor. We assumed no net change in soil carbon stocks as soil organic matter is typically at steady state in lowland rice66. We did not attempt to estimate changes on soil C in the upland rice system in Brazil. All emissions were converted to CO2-eq, with GWP for CH4 set at 25 relatives to CO2 and 298 for N2O on a per mass basis over a 100-year time horizon67. For each rice crop cycle in each of the 32 rice systems, GWP was calculated as the sum of CO2, CH4, and N2O emissions expressed as CO2-eq. (Details on N2O and CH4 emissions estimates and GWP calculations are provided in Supplementary Information Text Section 2).Calculation of energy inputs was similar to that of GWP and was based on the reported rates of agricultural inputs and field operations and associated embodied energy (see details for energy input estimates in Supplementary Information Text Section 2, Supplementary Table 12). Human labor was also included in the calculation of energy inputs. There was a strong positive relationship between energy input and GWP on both per-crop (r = 0.81; p  More

.readcube-buybox { display: none !important;}

Two of the three native reptiles on to Gran Canaria have nearly vanished from some parts of the Spanish island — eaten by an invasive snake species originally imported as a pet1.

Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:block;padding-right:20px;padding-left:20px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label{color:#069}
/* style specs end */Subscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Rent or Buy articleGet time limited or full article access on ReadCube.from$8.99Rent or BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

doi: https://doi.org/10.1038/d41586-021-03647-4

References1.Piquet, J. C. & López-Darias, M. Proc. R. Soc. B https://doi.org/10.1098/rspb.2021.1939 (2021).Article 

Google Scholar 
Download references

Subjects

Conservation biology

Jobs

Research Associate (m/f/x)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

PhD Position in Cybersecurity

Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg
Luxembourg, Luxembourg

Doctoral (PhD) position in Structural Proteomics with EU Training network ALLODD

Karolinska Institutet, doctoral positions
Solna, Sweden

Post-doctoral Fellow

Luxembourg Institute of Health (LIH)
Esch Sur Alzette, Luxembourg More