Philippa Kaur

All methods were performed in accordance with the local relevant guidelines, regulations and legislation.InstrumentsLI-6400 photosynthesis system (LI-6400 Inc., Lincoln, NE, USA) and PAM-2500 portable chlorophyll fluorescence apparatus (PAM-2500, Walz, Germany) were used in the study.MaterialsAbout 90 living E. brevicornu plants were collected from Taihang Mountains in October 2018. The E. brevicornu was not in endangered or protected. The collection of these E. brevicornu plants was permitted by local government. These plants were averagely planted in nine plots of 2 m2. The roots of E. pubescens were planted 6–8 cm below ground. These plots were placed on farmland near Taihang Mountains and covered with sunshade net (about 70% light transmittance). These plants were timely irrigated after planting to ensure that they grew well but not fertilized.Determination of photosynthetic characteristicsThe photosynthetic characteristics of mature leaves on the E. brevicornu plants were determined between June 6–8, 2019 with the Li-6400 photosynthesis system. The diurnal variation of photosynthesis in three leaves of three plants was determined. When the light response curve was determined, the temperature of the leaf chamber was set at 28 °C, and the concentration of CO2 in the leaf chamber was set at 400 µmol mol−1. When determining the CO2 response curve, the light intensity in the leaf chamber was set at 1000 µmol m−2 s−1, and the temperature of the leaf chamber was set at 28 °C. The light response curve and CO2 response curve were determined three times in three leaves of three different plants.Determination of chlorophyll fluorescence characteristicsThe fluorescence characteristics of chlorophyll in E. brevicornu leaves were determined with PAM-2500 portable chlorophyll fluorescence apparatus between June 8–9, 2019. The leaves underwent dark adaptation for 30 min before determining slow kinetics of chlorophyll fluorescence. Then the light curves of chlorophyll fluorescence were determined. All of these determinations were repeated three times on three mature leaves of three plants.The data was analysed with SPSS (Statistical Product and Service Solutions, International Business Machines Corporation, USA). The light response curves were fitted with following modified rectangular hyperbola model11,12.$${text{Photo}}, = ,{text{E}}cdotleft( {{1} – {text{M}}cdot{text{PAR}}} right)cdotleft( {{text{PAR}} – {text{LCP}}} right)/({1}, + ,{text{N}}cdot{text{PAR}})$$PAR is the value of light intensity in leaf chamber of Li-6400 photosynthesis system. Photo is net photosynthetic rate. LCP is the light compensation point. E is the apparent quantum yield. M and N are parameters. The dark respiration rate under the LCP is calculated according to E·LCP. The light saturation point (LSP) is calculated according to (((M + N) ·(1 + N·LCP)/M)½)/−1)/N.The net photosynthetic rate under the light saturation point (LSP) can be calculated according to the above model.The CO2 response curves were fitted with below modified rectangular hyperbola model11,12.$${text{Photo}}, = ,{text{E}}cdotleft( {{1} – {text{M}}cdot{text{PAR}}} right)cdotleft( {{text{PAR}} – {text{CCP}}} right)/({1}, + ,{text{N}}cdot{text{PAR}})$$PAR is the value of light intensity in leaf chamber of Li-6400 photosynthesis system. Photo is net photosynthetic rate. CCP is CO2 compensation point. E is also the apparent quantum yield. M and N are parameters. The dark respiration rate under the CO2 calculated according to E·CCP. The CO2 saturation point (CSP) is calculated according to (((M + N) ·(1 + N·CCP)/M)½)/−1)/N.The net photosynthetic rate under the CO2 saturation point (CSP) can be alternatively calculated according to the above model.The light curves of chlorophyll fluorescence were fitted according to the below model of Eilers and Peeters12,13.$${text{ETR}}, = ,{text{PAR}}/({text{a}}cdot{text{PAR}}^{{2}} , + ,{text{b}}cdot{text{PAR}}, + ,{text{c}})$$ETR is the electron transport rate of photosynthetic system II. PAR is fluorescence intensity. The letters a, b and c are parameters. More

Seguin, R. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-00981-x (2022).Article 

Google Scholar 
Roberts, C. M. & Polunin, N. V. C. Rev. Fish Biol. Fish. 1, 65–91 (1991).Article 

Google Scholar 
Cinner, J. E. et al. Soc. Nat. Resour. 27, 994–1005 (2014).Article 

Google Scholar 
MacNeil, M. A. et al. Nature 520, 341–344 (2015).Article 
CAS 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Coral Reefs 39, 1221–1231 (2020).Article 

Google Scholar 
Morais, R. A., Connolly, S. R. & Bellwood, D. R. Glob. Change Biol. 26, 1295–1305 (2020).Article 

Google Scholar 
Di Lorenzo, M. et al. Fish Fish. 21, 906–915 (2020).Article 

Google Scholar 
Ban, N. C. et al. Nat. Sustain. 2, 524–532 (2019).Article 

Google Scholar 
Rogers, A. et al. Ecology 99, 450–463 (2018).Article 

Google Scholar 
Robinson, J. P. W. et al. Nat. Ecol. Evol. 3, 183–190 (2019).Article 

Google Scholar  More

Dataset acquisitionPlant-frugivore network data were obtained through different online sources and publications (Supplementary Table 1). Only networks that met the following criteria were retrieved: (i) the network contains quantitative data (a measure of interaction frequency) from a location, pooling through time if necessary; (ii) the network includes avian frugivores. Importantly, we removed non-avian frugivores from our analyses because only 28 out of 196 raw networks (before data cleaning) sampled non-avian frugivores, and not removing non-avian frugivores would generate spurious apparent turnover between networks that did vs. did not sample those taxa. In addition, the removal of non-avian frugivores did not strongly decrease the number of frugivores in our dataset (Supplementary Fig. 20a) or the total number of links in the global network of frugivory (Supplementary Fig. 20b). Furthermore, non-avian frugivores, as well as their interactions, were not shared across ecoregions and biomes (Supplementary Fig. 21), so their inclusion would only strengthen the results we found (though as noted above, we believe that this would be spurious because they are not as well sampled); (iii) the network (after removal of non-avian frugivores) contains greater than two species in each trophic level. Because this size threshold was somewhat arbitrary, we used a sensitivity analysis to assess the effect of our network size threshold on the reported patterns (see Sensitivity analysis section in the Supplementary Methods and Supplementary Figs. 22–24); and (iv) network sampling was not taxonomically restricted, that is, sampling was not focused on a specific taxonomic group, such as a given plant or bird family. Note, however, that authors often select focal plants or frugivorous birds to be sampled, but this was not considered as a taxonomic restriction if plants and birds were not selected based on their taxonomy (e.g., focal plants were selected based on the availability of fruits at the time of sampling, or focal birds were selected based on previous studies of bird diet in the study site). The first source for network data was the Web of Life database42, which contains 33 georeferenced plant-frugivore networks from 28 published studies, of which 12 networks met our criteria.We also accessed the Scopus database on 04 May 2020 using the following keyword combination: (“plant-frugivore*” OR “plant-bird*” OR “frugivorous bird*” OR “avian frugivore*” OR “seed dispers*”) AND (“network*” OR “web*”) to search for papers that include data on avian frugivory networks. The search returned a total of 532 studies, from which 62 networks that met the above criteria were retrieved. We also contacted authors to obtain plant-frugivore networks that were not publicly available, which provided us a further 110 networks. The remaining networks (N = 12) were obtained by checking the database from a recently published study12. In total, 196 quantitative avian frugivory networks were used in our analyses.Generating the distance matrices to serve as predictor and response variablesEcoregion and biome distancesWe used the most up-to-date (2017) map of ecoregions and biomes3, which divides the globe into 846 terrestrial ecoregions nested within 14 biomes, to generate our ecoregion and biome distance matrices. Of these, 67 ecoregions and 11 biomes are represented in our dataset (Supplementary Figs. 1 and 2). We constructed two alternative versions of both the ecoregion and biome distance matrices. In the first, binary version, if two ecological networks were from localities within the same ecoregion/biome, a dissimilarity of zero was given to this pair of networks, whereas a dissimilarity of one was given to a pair of networks from distinct ecoregions/biomes (this is the same as calculating the Euclidean distance on a presence–absence matrix with networks in rows and ecoregion/biomes in columns).In the second, quantitative version, we estimated the pairwise environmental dissimilarity between our ecoregions and biomes using six environmental variables recently demonstrated to be relevant in predicting ecoregion distinctness, namely mean annual temperature, temperature seasonality, mean annual rainfall, rainfall seasonality, slope and human footprint38. We obtained climatic and elevation data from WorldClim 2.143 at a spatial resolution of 1-km2. We transformed the elevation raster into a slope raster using the terrain function from the raster package44 in R45. As a measure of human disturbance, we used human footprint—a metric that combines eight variables associated with human disturbances of the environment: the extent of built environments, crop land, pasture land, human population density, night-time lights, railways, roads and navigable waterways26. The human footprint raster was downloaded at a 1-km2 resolution26. Because human footprint data were not available for one of our ecoregions (Galápagos Islands xeric scrub), we estimated human footprint for this ecoregion by converting visually interpreted scores into the human footprint index. We did this by analyzing satellite images of the region and following a visual score criterion26. Given the previously demonstrated strong agreement between visual score and human footprint values26, we fitted a linear model using the visual score and human footprint data from 676 validation plots located within the Deserts and xeric shrublands biome – the biome in which the Galápagos Islands xeric scrub ecoregion is located – and estimated the human footprint values for our own visual scores using the predict function in R45.We used 1-km2 resolution rasters and the extract function from the raster package44 to calculate the mean value of each of our six environmental variables for each ecoregion in our dataset. Because biomes are considerably larger than ecoregions (which makes obtaining environmental data for biomes more computationally expensive) we used a coarser spatial resolution of 5-km2 for calculating the mean values of environmental variables for each biome. Since a 5-km2 resolution raster was not available for human footprint, we transformed the 1-km2 resolution raster into a 5-km2 raster using the resample function from the same package.To combine these six environmental variables into quantitative matrices of ecoregion and biome environmental dissimilarity, we ran a Principal Component Analysis (PCA) on our scaled multivariate data matrix (where rows are ecoregions or biomes and columns are environmental variables). From this PCA, we selected the scores of the four and three principal components, which represented 89.6% and 88.7% of the variance for ecoregions and biomes, respectively, and converted it into a distance matrix by calculating the Euclidean distance between pairs of ecoregions/biomes using the vegdist function from the vegan package46. Finally, we transformed the ecoregion or biome distance matrix into a N × N matrix where N is the number of local networks. In this matrix, cell values represent the pairwise environmental dissimilarity between the ecoregions/biomes where the networks are located. The main advantage of using this quantitative approach is that, instead of simply evaluating whether avian frugivory networks located in distinct ecoregions or biomes are different from each other in terms of network composition and structure (as in our binary approach), we were also able to determine whether the extent of network dissimilarity depended on how environmentally different the ecoregions or biomes are from one another.Local-scale human disturbance distanceTo generate our local human disturbance distance matrix, we extracted human footprint data at a 1-km2 spatial resolution26 and calculated the mean human footprint values within a 5-km buffer zone around each network site. For the networks located within the Galápagos Islands xeric scrub ecoregion (N = 4), we estimated the human footprint index using the same method described in the previous section for ecoregion- or biome-scale human footprint. We then calculated the pairwise Euclidean distance between human footprint values from our network sites. Thus, low cell values in the local human disturbance distance matrix indicate pairs of network sites with a similar level of human disturbance, while high values represent pairs of network sites with very different levels of human disturbance.Spatial distanceThe spatial distance matrix was generated using the Haversine (i.e., great circle) distance between all pairwise combinations of network coordinates. In this matrix, cell values represent the geographical distance between network sites.Elevational differenceWe calculated the Euclidean distance between pairwise elevation values (estimated as meters above sea level) of network sites to generate our elevational difference matrix. Elevation values were obtained from the original sources when available or using Google Earth47. In the elevational difference matrix, low cell values represent pairs of network sites within similar elevations, whereas high values represent pairs of network sites within very different elevations.Network sampling dissimilarityWe used the metadata retrieved from each of our 196 local networks to generate our network sampling dissimilarity matrices, which aim to control statistically for differences in network sampling. There are many ways in which sampling effort could be quantified, so we began by calculating a variety of metrics, then narrowed our options by assessing which of these was most related to network metrics. We divided the sampling metrics into two categories: time span-related metrics (i.e., sampling hours and months) and empirical metrics of sampling completeness (i.e., sampling completeness and sampling intensity), which aim to account for how complete network sampling was in terms of species interactions (Supplementary Table 2).We selected the quantitative sampling metrics to be included in our models based on (i) the fit of generalized linear models evaluating the relationship between number of sampling hours and sampling months of the study and network-level metrics (i.e., bird richness, plant richness and number of links), and (ii) how well time span-related metrics, sampling completeness and sampling intensity predicted the proportion of known interactions that were sampled in each local network (hereafter, ratio of interactions) for a subset of the data. This latter metric, defined as the ratio between the number of interactions in the local network and the number of known possible interactions in the region involving the species in the local network, captures raw sampling completeness. Therefore, ratio of interactions estimates, for a given set of species, the proportion of all their interactions known for a region that are found to occur among those same species in the local network. To calculate this metric, we needed high-resolution information on the possible interactions, so we used a subset of 14 networks sampled in Aotearoa New Zealand, since there is an extensive compilation of frugivory events recorded for this country48. After this process, we selected number of sampling hours, number of sampling months and sampling intensity for inclusion in our statistical models (Supplementary Figs. 7 and 8; Supplementary Table 2). We generated the corresponding distance matrices by calculating the Euclidean distance between metric values. Similarly, we generated a Euclidean distance matrix for differences in sampling year between pairs of networks, which aims to account for long-term changes in the environment, species composition and network sampling methods. We obtained the sampling year of our local networks from the original sources and calculated the mean sampling year value for those networks sampled across multiple years.Because sampling methods, such as sampling design, focus (i.e., focal taxa, which determines whether a zoocentric or phytocentric method was used), interaction frequency type (i.e., how interaction frequency was measured) and coverage (total or partial) might also affect the observed plant-frugivore interactions49, we combined these variables into a single distance matrix to estimate the overall differences in sampling methods between networks. Because most of these variables were categorical with multiple levels (Supplementary Table 3), we generated our method’s dissimilarity matrix by using a generalization of Gower’s distance method50, which allows the treatment of different types of variables when calculating distances. For this, we used the dist.ktab function from the ade4 package51. We ran a Principal Coordinates Analysis (PCoA) on this distance matrix, selected the first four axes, which explained 81.2% of the variation in method’s dissimilarity, and calculated the Euclidean distance between pairs of networks using the vegdist function from the vegan package46 in R45.Network dissimilarityWe generated three network dissimilarity matrices to be our response variables in the statistical models. In the first, cell values represent the pairwise dissimilarity in species composition between networks (beta diversity of species; βS)27. Second, we measured interaction dissimilarity (beta diversity of interactions; βWN), which represents the pairwise dissimilarity in the identity of interactions between networks27. Importantly, we did not include interaction rewiring (βOS) in our main analysis because this metric can only be calculated for networks that share interaction partners (i.e., it estimates whether shared species interact differently)27, which limited the number and the spatial distribution of networks available for analysis (but see the Rewiring analysis section for an analysis on the subset of our dataset for which this was possible). Metrics were calculated using the network_betadiversity function from the betalink package52 in R45.Finally, we calculated a third dissimilarity matrix to capture overall differences in network structure. We recognize that there are many potential metrics of network structure, and that many of these are strongly correlated with one another53,54,55,56. We therefore chose a range of metrics that captured the number of links, their relative weightings (including across trophic levels), and their arrangement among species, then combined these into a single distance matrix. Specifically, we quantified network structural dissimilarity using the following metrics: weighted connectance, weighted nestedness, interaction evenness, PDI and modularity.Weighted connectance represents the number of links relative to the number of possible links, weighted by the frequency of each interaction55, and is therefore a measure of network-level specialization (higher values of weighted connectance indicate lower specialization). Importantly, it has been suggested that connectance affects persistence in mutualistic systems54. We measured nestedness (i.e., the pattern in which specialist species interact with proper subsets of the species that generalist species interact with) using the weighted version of nestedness based on overlap and decreasing fill (wNODF)57. Notably, nested structures have been commonly reported in plant-frugivore networks33. Interaction evenness is Shannon’s evenness index applied for species interactions and represents how evenly distributed the interactions are in the network21,58. This metric has been previously demonstrated to decline with habitat modification as a consequence of some interactions being favored over others in high-disturbance environments21. PDI (Paired Difference Index) is a measure of species-level specialization on resources and a reliable indicator not only of specialization, but also of absolute generalism59. Thus, this metric contributes to understanding of the ecological processes that drive the prevalence of specialists or generalists in ecological networks59. In order to obtain a network-level PDI, we calculated the weighted mean PDI for each local network. Finally, we calculated modularity (i.e., the level of compartmentalization within networks) using the DIRTPLAwb+ algorithm60. Modularity estimates the extent to which species within modules interact more with each other than with species from other modules61, and it has been demonstrated to affect the persistence and resilience of mutualistic networks54. All the selected network metrics are based on weighted (quantitative) interaction data, as these have been suggested to be less biased by sampling incompleteness62 and to better reflect environmental changes21. All network metrics were calculated using the bipartite package63 in R45.We ran a Principal Component Analysis (PCA) on our scaled multivariate data matrix (N × M where N is the number of local networks in our dataset and M is the number of network metrics), selected the scores of the three principal components, which represented 89.9% of the variance in network metrics, and converted it into a network structural dissimilarity matrix by calculating the Euclidean distance between networks. In this distance matrix, cell values represent differences in the overall architecture of networks (over all the network metrics calculated), and therefore provide a complementary approach for evaluating how species interaction patterns vary across large-scale environmental gradients.Statistical analysisWe employed a two-tailed statistical test that combines Generalized Additive Models (GAM)29 and Multiple Regression on distance Matrices (MRM)30 to evaluate the effect of each of our predictor distance matrices on our response matrix. With this approach, we were able to fit GAMs where the predictor and responsible variables are distance matrices, while accounting for the non-independence of distances from each local network by permuting the response matrix30. The main advantage of using GAMs is their flexibility in modeling non-linear relationships through smooth functions, which are represented by a sum of simpler, fixed basis functions that determine their complexity29. Using GAM-based MRM models allowed us to obtain F values for each of the smooth terms (i.e., smooth functions of the predictor variables in our model), and test statistical significance at the level of individual variables. The binary versions of ecoregion and biome distance matrices (with two levels, “same” or “distinct”) were treated as categorical variables in the models, and t values were used for determining statistical significance. We fitted GAMs with thin plate regression splines64 using the gam function from the mgcv package29 in R45. Smoothing parameters were estimated using restricted maximum likelihood (REML)29. Our GAM-based MRM models were calculated using a modified version of the MRM function from the ecodist package65, which allowed us to combine GAMs with the permutation approach from the original MRM function (see Code availability). All the models were performed with 1000 permutations (i.e., shuffling) of the response matrix.We explored the unique and shared contributions of our predictor variables to network dissimilarity using deviance partitioning analyses. These were performed by fitting reduced models (i.e., GAMs where one or more predictor variables of interest were removed) using the same smoothing parameters as in the full model and comparing the explained deviance. We fixed smoothing parameters for comparisons in this way because these parameters tend to vary substantially (to compensate) if one of two correlated predictors is dropped from a GAM.Assessing the influence of individual studies on the reported patternsBecause our dataset comprises 196 local frugivory networks obtained from 93 different studies, and some of these studies contained multiple networks, we needed to evaluate whether our results were strongly biased by individual studies. To do this, we followed the approach from a previous study66 and tested whether F values of smooth terms and t values of categorical variables (binary version of ecoregion and biome distances) changed significantly when jackknifing across studies. We did this by dropping one study from the dataset and re-fitting the models, and then repeating this same process for all the studies in our dataset.We found a number of consistent patterns within different subsets of the data (Supplementary Figs. 15 and 16); however, some of the patterns we observed appear to be driven by individual studies with multiple networks, and hence are less representative. For instance, the study with the greatest number of networks in our dataset (study ID = 76), which contains 35 plant-frugivore networks sampled across an elevation gradient in Mt. Kilimanjaro, Tanzania67, had an overall high influence on the results when compared with the other studies. By re-running our GAM-based MRM models after removing this study from our dataset, we found that the effect of biome boundaries on interaction dissimilarity is no longer significant, whereas the effects of ecoregion boundaries, human disturbance distance, spatial distance and elevational differences remained consistent with those from the full dataset (Supplementary Table 33). Nevertheless, all the results were qualitatively similar to those obtained for the entire dataset when using network structural dissimilarity as the response variable (Supplementary Table 34).Rewiring analysisInteraction rewiring (βOS) estimates the extent to which shared species interact differently27. Because this metric can only be calculated for networks that share species from both trophic levels, we selected a subset of network pairs that shared plants and frugivorous birds (N = 1314) to test whether interaction rewiring increases across large-scale environmental gradients. Importantly, since not all possible combinations of network pairs contained values of interaction rewiring (i.e., not all pairs of networks shared species), a pairwise distance matrix could not be generated for this metric. Thus, we were not able to use the same statistical approach used in our main analysis, which is based on distance matrices (see Statistical analysis section). Instead, we performed a Generalized Additive Mixed-effects Model (GAMM) using ecoregion, biome, human disturbance, spatial, elevational, and sampling-related distance metrics as fixed effects and network IDs as random effects (to account for the non-independence of distances) (Supplementary Table 35). We also performed a reduced model with only ecoregion and biome distance metrics as predictor variables (Supplementary Table 36). The binary version of ecoregion and biome distance metrics (with two levels, “same” or “distinct”) were used as categorical variables in both models. Interaction rewiring (βOS) was calculated using the network_betadiversity function from the betalink package52 in R45. Although it has been recently argued that this metric may overestimate the importance of rewiring for network dissimilarity68, our main focus was not the partitioning of network dissimilarity into species turnover and rewiring components, but rather simply detecting whether the sub-web of shared species interacted differently. In this case, βOS (as developed by ref. 27) is an adequate and useful metric68. We fitted our models using the gamm4 function from the gamm4 package69 in R45. Smoothing parameters were estimated using restricted maximum likelihood (REML)29.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Prasad, N. G. & Joshi, A. What have two decades of laboratory life-history evolution studies on Drosophila melanogaster taught us?. J. Genet. 82, 45–76 (2003).CAS 
PubMed 

Google Scholar 
MacMillan, H. A., Walsh, J. P. & Sinclair, B. J. The effects of selection for cold tolerance on cross-tolerance to other environmental stressors in Drosophila melanogaster. Insect Sci. 16, 263–276 (2009).
Google Scholar 
Flatt, T. Life-history evolution and the genetics of fitness components in drosophila melanogaster. Genetics 214(1), 3–48. https://doi.org/10.1534/genetics.119.300160 (2020).Article 
CAS 
PubMed 

Google Scholar 
Hoffmann, A. A. & Parsons, P. A. Selection for increased desiccation resistance in Drosophila melanogaster: Additive genetic control and correlated responses for other stresses. Genetics 122, 837–845 (1989).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nghiem, D., Gibbs, A. G., Rose, M. R. & Bradley, T. J. Postponed aging and desiccation resistance in Drosophila melanogaster. Exp. Gerontol. 35, 957–969 (2000).CAS 
PubMed 

Google Scholar 
Hoffmann, A. A., Scott, M., Partridge, L. & Hallas, R. Overwintering in Drosophila melanogaster: Outdoor field cage experiments on clinal and laboratory selected populations help to elucidate traits under selection. J. Evol. Biol. 16, 614–623 (2003).CAS 
PubMed 

Google Scholar 
Bubliy, O. A. & Loeschcke, V. Correlated responses to selection for stress resistance and longevity in a laboratory population of Drosophila melanogaster. J. Evol. Biol. 18, 789–803 (2005).CAS 
PubMed 

Google Scholar 
Bourg, É. L. & Le Bourg, É. A cold stress applied at various ages can increase resistance to heat and fungal infection in aged Drosophila melanogaster flies. Biogerontology 12, 185–193 (2011).PubMed 

Google Scholar 
Sejerkilde, M., Sørensen, J. G. & Loeschcke, V. Effects of cold- and heat hardening on thermal resistance in Drosophila melanogaster. J. Insect Physiol. 49, 719–726 (2003).CAS 
PubMed 

Google Scholar 
Coulson, S. C. & Bale, J. S. Effect of rapid cold hardening on reproduction and survival of offspring in the housefly Musca domestica. J. Insect Physiol. 38, 421–424 (1992).
Google Scholar 
Bayley, M., Petersen, S. O., Knigge, T., Köhler, H.-R. & Holmstrup, M. Drought acclimation confers cold tolerance in the soil collembolan Folsomia candida. J. Insect Physiol. 47, 1197–1204 (2001).CAS 
PubMed 

Google Scholar 
Wu, B. S. et al. Anoxia induces thermotolerance in the locust flight system. J. Exp. Biol. 205, 815–827 (2002).CAS 
PubMed 

Google Scholar 
Phelan, J. P. et al. Breakdown in correlations during laboratory evolution. I. Comparative analyses of Drosophila populations. Evolution 57, 527–535 (2003).PubMed 

Google Scholar 
Hoffmann, A. A. & Harshman, L. G. Desiccation and starvation resistance in Drosophila: Patterns of variation at the species, population and intrapopulation levels. Heredity 83(Pt 6), 637–643 (1999).PubMed 

Google Scholar 
Sinclair, B. J., Nelson, S., Nilson, T. L., Roberts, S. P. & Gibbs, A. G. The effect of selection for desiccation resistance on cold tolerance of Drosophila melanogaster. Physiol. Entomol. 32, 322–327 (2007).
Google Scholar 
Anderson, A. R., Hoffmann, A. A. & McKechnie, S. W. Response to selection for rapid chill-coma recovery in Drosophila melanogaster: Physiology and life-history traits. Genet. Res. 85, 15–22 (2005).PubMed 

Google Scholar 
Kellett, M., Hoffmann, A. A. & Mckechnie, S. W. Hardening capacity in the Drosophila melanogaster species group is constrained by basal thermotolerance. Funct. Ecol. 19, 853–858 (2005).
Google Scholar 
Overgaard, J., Sørensen, J. G., Petersen, S. O., Loeschcke, V. & Holmstrup, M. Reorganization of membrane lipids during fast and slow cold hardening in Drosophila melanogaster. Physiol. Entomol. 31, 328–335 (2006).CAS 

Google Scholar 
Hoffmann, A. A., Hallas, R., Anderson, A. R. & Telonis-Scott, M. Evidence for a robust sex-specific trade-off between cold resistance and starvation resistance in Drosophila melanogaster. J. Evol. Biol. 18, 804–810 (2005).CAS 
PubMed 

Google Scholar 
Singh, K., Kochar, E. & Prasad, N. G. Egg Viability, Mating Frequency and Male Mating Ability Evolve in Populations of Drosophila melanogaster Selected for Resistance to Cold Shock. PLoS ONE 10, e0129992 (2015).PubMed 
PubMed Central 

Google Scholar 
Singh, K., Kochar, E., Gahlot, P., Bhatt, K. & Prasad, N. G. Evolution of reproductive traits have no apparent life-history associated cost in populations of Drosophila melanogaster selected for cold shock resistance. BMC Ecol. Evol. 21, 1–4 (2021).
Google Scholar 
Salehipour-Shirazi, G., Ferguson, L. V. & Sinclair, B. J. Does cold activate the Drosophila melanogaster immune system?. J. Insect Physiol. 96, 29–34 (2017).CAS 
PubMed 

Google Scholar 
Singh, K., Zulkifli, M. & Prasad, N. G. Identification and characterization of novel natural pathogen of Drosophila melanogaster isolated from wild captured Drosophila spp. Microbes Infect. 18, 813–821 (2016).PubMed 

Google Scholar 
Singh, K., Samant, M. A., Tom, M. T. & Prasad, N. G. Evolution of Pre- and Post-Copulatory Traits in Male Drosophila melanogaster as a Correlated Response to Selection for Resistance to Cold Stress. PLoS ONE 11, e0153629 (2016).PubMed 
PubMed Central 

Google Scholar 
Lefevre, G. J. & Jonsson, U. B. The effect of cold shock on D. melanogaster sperm. Drosophila Inf. Serv. 1962(36), 86–876 (1962).
Google Scholar 
Novitski, E. & Rush, G. Viability and fertility of Drosophila exposed to sub-zero temperatures. Biol. Bull. 97, 150–157 (1949).CAS 
PubMed 

Google Scholar 
Arbogast, R. T. Mortality and Reproduction of Ephestia cautella and Plodia interpunctella 1 Exposed as Pupae to High Temperatures. Environ. Entomol. 10, 708–711 (1981).
Google Scholar 
Saxena, B. P., Sharma, P. R., Thappa, R. K. & Tikku, K. Temperature induced sterilization for control of three stored grain beetles. J. Stored Prod. Res. 28, 67–70 (1992).
Google Scholar 
Collett, J. I. & Jarman, M. G. Adult female Drosophila pseudoobscura survive and carry fertile sperm through long periods in the cold: Populations are unlikely to suffer substantial bottlenecks in overwintering. Evolution 55, 840–845 (2001).CAS 
PubMed 

Google Scholar 
Schnebel, E. M. & Grossfield, J. Mating-temperature range in drosophila. Evolution 38, 1296–1307 (1984).PubMed 

Google Scholar 
Chakir, M., Chafik, A., Moreteau, B., Gibert, P. & David, J. R. Male sterility thermal thresholds in Drosophila: D. simulans appears more cold-adapted than its sibling D. melanogaster. Genetica 114, 195–205 (2002).PubMed 

Google Scholar 
David, J. R. et al. Male sterility at extreme temperatures: A significant but neglected phenomenon for understanding Drosophila climatic adaptations. J. Evol. Biol. 18, 838–846 (2005).CAS 
PubMed 

Google Scholar 
Dolgin, E. S., Whitlock, M. C. & Agrawal, A. F. Male Drosophila melanogaster have higher mating success when adapted to their thermal environment. J. Evol. Biol. 19, 1894–1900 (2006).CAS 
PubMed 

Google Scholar 
David, J. R. Male sterility at high and low temperatures in Drosophila. J. Soc. Biol. 202, 113–117 (2008).PubMed 

Google Scholar 
Zhang, W., Zhao, F., Hoffmann, A. A. & Ma, C.-S. A single hot event that does not affect survival but decreases reproduction in the diamondback moth, plutella xylostella. PLoS ONE 8, e75923 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tucić, N. Genetic capacity for adaptation to cold resistance at different developmental stages of Drosophila melanogaster. Evolution 33, 350–358 (1979).PubMed 

Google Scholar 
Chen, C.-P. & Walker, V. K. Increase in cold-shock tolerance by selection of cold resistant lines in Drosophila melanogaster. Ecol. Entomol. 18, 184–190 (1993).
Google Scholar 
Ring, R. A. & Danks, H. V. Desiccation and cryoprotection: Overlapping adaptations. Cryo Lett. 15, 181–190 (1994).
Google Scholar 
Ring, R. A. & Danks, H. The role of trehalose in cold-hardiness and desiccation. Cryo Lett. 19, 275–282 (1998).CAS 

Google Scholar 
Singh, K. & Prasad, N. G. Cold stress upregulates the expression of heat shock proteins and Frost genes, but evolution of cold stress resistance is apparently not mediated through either heat shock proteins or Frost genes in the cold stress selected population. bioRxiv https://doi.org/10.1101/2022.03.07.483305 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Bubliy, O. A., Kristensen, T. N., Kellermann, V. & Loeschcke, V. Plastic responses to four environmental stresses and cross-resistance in a laboratory population of Drosophila melanogaster. Funct. Ecol. 26, 245–253 (2012).
Google Scholar 
Kristensen, T. N., Loeschcke, V. & Hoffmann, A. A. Can artificially selected phenotypes influence a component of field fitness? Thermal selection and fly performance under thermal extremes. Proc. Biol. Sci. 274, 771–778 (2007).PubMed 

Google Scholar 
Hoffmann, A. A., Anderson, A. & Hallas, R. Opposing clines for high and low temperature resistance in Drosophila melanogaster. Ecol. Lett. 5, 614–618 (2002).
Google Scholar 
Yi, S.-X. & Lee, R. E. Jr. Detecting freeze injury and seasonal cold-hardening of cells and tissues in the gall fly larvae, Eurosta solidaginis (Diptera: Tephritidae) using fluorescent vital dyes. J. Insect Physiol. 49, 999–1004 (2003).CAS 
PubMed 

Google Scholar 
Macmillan, H. A. & Sinclair, B. J. Mechanisms underlying insect chill-coma. J. Insect Physiol. 57, 12–20 (2011).CAS 
PubMed 

Google Scholar 
Marshall, K. E. & Sinclair, B. J. The sub-lethal effects of repeated freezing in the woolly bear caterpillar Pyrrharctia isabella. J. Exp. Biol. 214, 1205–1212 (2011).PubMed 

Google Scholar 
Sinclair, B. J., Ferguson, L. V., Salehipour-shirazi, G. & MacMillan, H. A. Cross-tolerance and cross-talk in the cold: Relating low temperatures to desiccation and immune stress in insects. Integr. Comp. Biol. 53, 545–556 (2013).PubMed 

Google Scholar 
Roxström-Lindquist, K., Terenius, O. & Faye, I. Parasite-specific immune response in adult Drosophila melanogaster: A genomic study. EMBO Rep. 5, 207–212 (2004).PubMed 
PubMed Central 

Google Scholar 
Pham, L. N., Dionne, M. S., Shirasu-Hiza, M. & Schneider, D. S. A specific primed immune response in Drosophila is dependent on phagocytes. PLoS Pathog. 3, e26 (2007).PubMed 
PubMed Central 

Google Scholar 
Mikonranta, L., Mappes, J., Kaukoniitty, M. & Freitak, D. Insect immunity: Oral exposure to a bacterial pathogen elicits free radical response and protects from a recurring infection. Front. Zool. 11, 23 (2014).PubMed 
PubMed Central 

Google Scholar 
Ramløv, H. & Lee, R. E. Jr. Extreme resistance to desiccation in overwintering larvae of the gall fly Eurosta solidaginis (Diptera, tephritidae). J. Exp. Biol. 203, 783–789 (2000).PubMed 

Google Scholar 
Holmstrup, M., Bayley, M. & Ramløv, H. Supercool or dehydrate? An experimental analysis of overwintering strategies in small permeable arctic invertebrates. Proc. Natl. Acad. Sci. 99, 5716–5720 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chippindale, A. K. et al. Resource acquisition and the evolution of stress resistance in drosophila melanogaster. Evolution 52, 1342 (1998).PubMed 

Google Scholar 
Rose, M. R. Laboratory evolution of postponed senescence in Drosophila melanogaster. Evolution 38, 1004–1010 (1984).ADS 
PubMed 

Google Scholar 
Crill, W. D., Huey, R. B. & Gilchrist, G. W. Within- and between-generation effects of temperature on the morphology and physiology of Drosophila melanogaster. Evolution 50, 1205–1218 (1996).PubMed 

Google Scholar 
Kwan, L., Bedhomme, S., Prasad, N. G. & Chippindale, A. K. Sexual conflict and environmental change: Trade-offs within and between the sexes during the evolution of desiccation resistance. J. Genet. 87, 383–394 (2008).PubMed 

Google Scholar  More

McCauley, D. J. et al. Marine defaunation: animal loss in the global ocean. Science 347, 1255641 (2015).Article 

Google Scholar 
Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. 4, 158 (2017).Article 

Google Scholar 
Ceballos, G., Ehrlich, P. R. & Raven, P. H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. Proc. Natl Acad. Sci. USA 117, 13596–13602 (2020).Article 
CAS 

Google Scholar 
Brandl, S. J. et al. Extreme environmental conditions reduce coral reef fish biodiversity and productivity. Nat. Commun. 11, 3832 (2020).Article 
CAS 

Google Scholar 
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).Article 
CAS 

Google Scholar 
Woodhead, A. J., Hicks, C. C., Norström, A. V., Williams, G. J. & Graham, N. A. J. Coral reef ecosystem services in the Anthropocene. Funct. Ecol. https://doi.org/10.1111/1365-2435.13331 (2019).Pereira, P. H. C. et al. Effectiveness of management zones for recovering parrotfish species within the largest coastal marine protected area in Brazil. Sci. Rep. 12, 12232 (2022).Article 
CAS 

Google Scholar 
Campbell, S. J. et al. Fishing restrictions and remoteness deliver conservation outcomes for Indonesia’s coral reef fisheries. Conserv. Lett 13, e12698 (2020).Article 

Google Scholar 
Cinner, J. E. et al. Gravity of human impacts mediates coral reef conservation gains. Proc. Natl Acad. Sci. USA 115, E6116–E6125 (2018).Article 
CAS 

Google Scholar 
Edgar, G. J. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216–220 (2014).Article 
CAS 

Google Scholar 
Mumby, P. J., Steneck, R. S., Roff, G. & Paul, V. J. Marine reserves, fisheries ban, and 20 years of positive change in a coral reef ecosystem. Conserv. Biol. 35, 1473–1483 (2021).Article 

Google Scholar 
Harrison, H. B. et al. Larval export from marine reserves and the recruitment benefit for fish and fisheries. Curr. Biol. 22, 1023–1028 (2012).Article 
CAS 

Google Scholar 
Kerwath, S. E., Winker, H., Götz, A. & Attwood, C. G. Marine protected area improves yield without disadvantaging fishers. Nat. Commun. 4, 2347 (2013).Article 

Google Scholar 
Di Lorenzo, M., Guidetti, P., Di Franco, A., Calò, A. & Claudet, J. Assessing spillover from marine protected areas and its drivers: a meta‐analytical approach. Fish Fish. 21, 906–915 (2020).Article 

Google Scholar 
Ban, N. C. et al. Well-being outcomes of marine protected areas. Nat. Sustain. 2, 524–532 (2019).Article 

Google Scholar 
Cinner, J. E. et al. Winners and losers in marine conservation: fishers’ displacement and livelihood benefits from marine reserves. Soc. Nat. Resour. 27, 994–1005 (2014).Article 

Google Scholar 
Gurney, G. G. et al. Biodiversity needs every tool in the box: use OECMs. Nature 595, 646–649 (2021).Article 
CAS 

Google Scholar 
Smallhorn-West, P. F. et al. Hidden benefits and risks of partial protection for coral reef fisheries. Ecol. Soc. 27, art26 (2022).Article 

Google Scholar 
Turnbull, J. W., Johnston, E. L. & Clark, G. F. Evaluating the social and ecological effectiveness of partially protected marine areas. Conserv. Biol. 35, 921–932 (2021).Article 

Google Scholar 
Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).Article 
CAS 

Google Scholar 
Cinner, J. E. et al. Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world. Science 368, 307–311 (2020).Article 
CAS 

Google Scholar 
McShane, T. O. et al. Hard choices: making trade-offs between biodiversity conservation and human well-being. Biol. Conserv. 144, 966–972 (2011).Article 

Google Scholar 
MacNeil, M. A. et al. Recovery potential of the world’s coral reef fishes. Nature 520, 341–344 (2015).Article 
CAS 

Google Scholar 
McClanahan, T. R. et al. Critical thresholds and tangible targets for ecosystem-based management of coral reef fisheries. Proc. Natl Acad. Sci. USA 108, 17230–17233 (2011).Article 
CAS 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Principles for estimating fish productivity on coral reefs. Coral Reefs 39, 1221–1231 (2020).Article 

Google Scholar 
Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).Article 

Google Scholar 
Pauly, D. & Froese, R. MSY needs no epitaph—but it was abused. ICES J. Mar. Sci. 78, 2204–2210 (2021).Article 

Google Scholar 
Rindorf, A. et al. Strength and consistency of density dependence in marine fish productivity. Fish Fish. 23, 812–828 (2022).Article 

Google Scholar 
Morais, R. A., Connolly, S. R. & Bellwood, D. R. Human exploitation shapes productivity–biomass relationships on coral reefs. Glob. Change Biol. 26, 1295–1305 (2020).Article 

Google Scholar 
Kolding, J., Bundy, A., van Zwieten, P. A. M. & Plank, M. J. Fisheries, the inverted food pyramid. ICES J. Mar. Sci. 73, 1697–1713 (2016).Article 

Google Scholar 
Morais, R. A. et al. Severe coral loss shifts energetic dynamics on a coral reef. Funct. Ecol. 34, 1507–1518 (2020).Article 

Google Scholar 
Sala, E. & Giakoumi, S. No-take marine reserves are the most effective protected areas in the ocean. ICES J. Mar. Sci. 75, 1166–1168 (2018).Article 

Google Scholar 
Edgar, G. J. & Stuart-Smith, R. D. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1, 140007 (2014).Article 

Google Scholar 
Parravicini, V. et al. Global patterns and predictors of tropical reef fish species richness. Ecography 36, 1254–1262 (2013).Article 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Global drivers of reef fish growth. Fish Fish. 19, 874–889 (2018).Article 

Google Scholar 
Gislason, H., Daan, N., Rice, J. C. & Pope, J. G. Size, growth, temperature and the natural mortality of marine fish: natural mortality and size. Fish Fish. 11, 149–158 (2010).Article 

Google Scholar 
Graham, N. A. J. et al. Human disruption of coral reef trophic structure. Curr. Biol. 27, 231–236 (2017).Article 
CAS 

Google Scholar 
Froese, R. & Pauly, D. (eds.). FishBase. Version 06/2022. https://www.fishbase.org (2022).Cochrane, K. L. Reconciling sustainability, economic efficiency and equity in marine fisheries: has there been progress in the last 20 years? Fish Fish. 22, 298–323 (2021).Article 

Google Scholar 
Morais, R. A., Siqueira, A. C., Smallhorn-West, P. F. & Bellwood, D. R. Spatial subsidies drive sweet spots of tropical marine biomass production. PLoS Biol. 19, e3001435 (2021).Article 
CAS 

Google Scholar 
Hamilton, M. et al. Climate impacts alter fisheries productivity and turnover on coral reefs. Coral Reefs https://doi.org/10.1007/s00338-022-02265-4 (2022).Cooke, R. et al. Anthropogenic disruptions to longstanding patterns of trophic-size structure in vertebrates. Nat Ecol Evol. 6, 684–692 (2022).Article 

Google Scholar 
Eddy, T. D. et al. Energy flow through marine ecosystems: confronting transfer efficiency. Trends Ecol. Evol. 36, 76–86 (2021).Article 

Google Scholar 
Devillers, R. et al. Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection? Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 480–504 (2015).Article 

Google Scholar 
Fontoura, L. et al. Protecting connectivity promotes successful biodiversity and fisheries conservation. Science 375, 336–340 (2022).Article 
CAS 

Google Scholar 
Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).Article 
CAS 

Google Scholar 
Agardy, T., di Sciara, G. N. & Christie, P. Mind the gap: addressing the shortcomings of marine protected areas through large scale marine spatial planning. Mar. Policy 35, 226–232 (2011).Article 

Google Scholar 
Robinson, J. P. W. et al. Habitat and fishing control grazing potential on coral reefs. Funct. Ecol. 34, 240–251 (2020).Article 

Google Scholar 
Robinson, J. P. W. et al. Productive instability of coral reef fisheries after climate-driven regime shifts. Nat. Ecol. Evol. 3, 183–190 (2019).Article 

Google Scholar 
Dudley, N. et al. The essential role of other effective area-based conservation measures in achieving big bold conservation targets. Glob. Ecol. Conserv. 15, e00424 (2018).Article 

Google Scholar 
Zupan, M. et al. How good is your marine protected area at curbing threats? Biol. Conserv. 221, 237–245 (2018).Article 

Google Scholar 
Pollnac, R. et al. Marine reserves as linked social–ecological systems. Proc. Natl Acad. Sci. USA 107, 18262–18265 (2010).Article 
CAS 

Google Scholar 
McClanahan, T. R., Marnane, M. J., Cinner, J. E. & Kiene, W. E. A comparison of marine protected areas and alternative approaches to coral-reef management. Curr. Biol. 16, 1408–1413 (2006).Article 
CAS 

Google Scholar 
Smallhorn-West, P. F., Weeks, R., Gurney, G. & Pressey, R. L. Ecological and socioeconomic impacts of marine protected areas in the South Pacific: assessing the evidence base. Biodivers. Conserv. 29, 349–380 (2020).Article 

Google Scholar 
Cinner, J. E. et al. Sixteen years of social and ecological dynamics reveal challenges and opportunities for adaptive management in sustaining the commons. Proc. Natl Acad. Sci. USA 116, 26474–26483 (2019).Article 
CAS 

Google Scholar 
Wilson, S. K. et al. Habitat degradation and fishing effects on the size structure of coral reef fish communities. Ecol. Appl. 20, 442–451 (2010).Article 
CAS 

Google Scholar 
Nash, K. L. & Graham, N. A. J. Ecological indicators for coral reef fisheries management. Fish Fish. 17, 1029–1054 (2016).Article 

Google Scholar 
Brandl, S. J., Goatley, C. H. R., Bellwood, D. R. & Tornabene, L. The hidden half: ecology and evolution of cryptobenthic fishes on coral reefs. Biol. Rev. 93, 1846–1873 (2018).Article 

Google Scholar 
Willis, T. J. Visual census methods underestimate density and diversity of cryptic reef fishes. J. Fish. Biol. 59, 1408–1411 (2001).Article 

Google Scholar 
Allen, K. R. Relation between production and biomass. J. Fish. Res. Board Can. 28, 1573–1581 (1971).Article 

Google Scholar 
Leigh, E. G. On the relation between the productivity, biomass, diversity, and stability of a community. Proc. Natl Acad. Sci. USA 53, 777–783 (1965).Article 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Cinner, J. E., Daw, T. & McClanahan, T. R. Socioeconomic factors that affect artisanal fishers’ readiness to exit a declining fishery. Conserv. Biol. 23, 124–130 (2009).Article 
CAS 

Google Scholar 
Cinner, J. E. et al. Linking social and ecological systems to sustain coral reef fisheries. Curr. Biol. 19, 206–212 (2009).Article 
CAS 

Google Scholar 
Hicks, C. C., Crowder, L. B., Graham, N. A., Kittinger, J. N. & Cornu, E. L. Social drivers forewarn of marine regime shifts. Front. Ecol. Environ. 14, 252–260 (2016).Article 

Google Scholar 
Espinosa-Romero, M. J., Rodriguez, L. F., Weaver, A. H., Villanueva-Aznar, C. & Torre, J. The changing role of NGOs in Mexican small-scale fisheries: from environmental conservation to multi-scale governance. Mar. Policy 50, 290–299 (2014).Article 

Google Scholar 
Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).Article 

Google Scholar 
Edgar, G. J. et al. Establishing the ecological basis for conservation of shallow marine life using Reef Life Survey. Biol. Conserv. 252, 108855 (2020).Article 

Google Scholar 
Selig, E. R. et al. Mapping global human dependence on marine ecosystems. Conserv. Lett. 12, e12617 (2019).Article 

Google Scholar  More

Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).Article 
PubMed 
CAS 

Google Scholar 
Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).Article 
PubMed 
CAS 

Google Scholar 
Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).Article 
PubMed 
CAS 

Google Scholar 
Tully, B. J. & Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data 5, 170203 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat. Commun. 9, 870 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography and lifestyle. Cell 176, 649–662 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509, https://doi.org/10.1038/s41587-020-0718-6 (2021).Article 
PubMed 
CAS 

Google Scholar 
Gilbert, J. A., Jansson, J. K. & Knight, R. The Earth Microbiome project: successes and aspirations. BMC Biol 12, 69 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Saheb Kashaf, S., Almeida, A., Segre, J. A. & Finn, R. D. Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data. Nat. Protoc. 16, 2520–2541 (2021).Article 
PubMed 
CAS 

Google Scholar 
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).Article 
PubMed 
CAS 

Google Scholar 
Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 49, D10–D17 (2021).Article 
PubMed 
CAS 

Google Scholar 
Kluyver, T., et al. Jupyter Notebooks – a publishing format for reproducible computational workflows. In: Loizides F, Schmidt B, editors. Positioning and Power in Academic Publishing: Players, Agents and Agendas. p. 87–90 (2016).Banfield, J. Development of a Knowledgebase to Integrate, Analyze, Distribute, and Visualize Microbial Community Systems Biology Data. (2015). Report number: DOE-UCB-4918, OSTI ID: 1167269.Chen, I.-M. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res 47, D666–D677 (2019).Article 
PubMed 
CAS 

Google Scholar 
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 44, W3–W10 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Devisetty, U. K., Kennedy, K., Sarando, P., Merchant, N. & Lyons, E. Bringing your tools to CyVerse discovery environment using Docker. F1000Res. 5, 1442 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L., Lu, Z., Van Buren, P. & Ware, D. SciApps: a bioinformatics workflow platform powered by XSEDE and CyVerse. in Proceedings of the Practice and Experience on Advanced Research Computing 1–5 (Association for Computing Machinery, 2018).Eren, A. M. et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat. Microbiol. 6, 3–6 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Wattam, A. R. et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res 45, D535–D542 (2017).Article 
PubMed 
CAS 

Google Scholar 
Mitchell, A. L. et al. MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48, D570–D578 (2020).PubMed 
CAS 

Google Scholar 
Wu, Y.-W. et al. Ionic liquids impact the bioenergy feedstock-degrading microbiome and transcription of enzymes relevant to polysaccharide hydrolysis. mSystems 1, e00120–16 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Rajeev, L. et al. Dynamic cyanobacterial response to hydration and dehydration in a desert biological soil crust. ISME J 7, 2178–2191 (2013).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Foster, I. Globus Online: accelerating and democratizing science through cloud-based services. IEEE Internet Comput 15, 70–73 (2011).Article 

Google Scholar 
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46, W95–W101 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).PubMed Central 

Google Scholar 
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinforma 10, 421 (2009).Article 

Google Scholar 
Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res 42, D26–D31 (2014).Article 
PubMed 
CAS 

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

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Freitas, T. A. K., Li, P.-E., Scholz, M. B. & Chain, P. S. G. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res 43, e69 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).Article 
PubMed 
CAS 

Google Scholar 
Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 2014 (2019).Article 

Google Scholar 
Youngblut, N. D. & Ley, R. E. Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets. Peer J 9, e12198 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a Web browser. BMC Bioinform 12, 385 (2011).Article 

Google Scholar 
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
PubMed 
CAS 

Google Scholar 
Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).Article 
PubMed 
CAS 

Google Scholar 
Orakov, A. et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol 22, 178 (2021).Article 
PubMed 
PubMed Central 
CAS 

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

Google Scholar 
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).Article 
PubMed 
CAS 

Google Scholar 
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).Article 
PubMed 
CAS 

Google Scholar 
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25, 1043–1055 (2015).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Delcher, A. L., Salzberg, S. L. & Phillippy, A. M. Using MUMmer to identify similar regions in large sequence sets. Curr. Protoc. Bioinform. Chapter 10, Unit 10.3 (2003).
Google Scholar 
Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res 14, 1394–1403 (2004).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res 50, D785–D794 (2022).Article 
PubMed 
CAS 

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

Google Scholar 
Brettin, T. et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci. Rep. 5, 8365 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Overbeek, R. et al. The SEED and the rapid annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res 42, D206–D214 (2014).Article 
PubMed 
CAS 

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

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform 11, 119 (2010).Article 

Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 
PubMed 
CAS 

Google Scholar 
Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6, 946–959 (2021).Article 
PubMed 
CAS 

Google Scholar 
Haft, D. H. et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res 46, D851–D860 (2018).Article 
PubMed 
CAS 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).Article 
PubMed 
PubMed Central 

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

Google Scholar 
Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res 43, D261–D269 (2015). (Database Issue).Article 
PubMed 
CAS 

Google Scholar 
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res 47, D427–D432 (2019).Article 
PubMed 
CAS 

Google Scholar 
Haft, D. H. et al. TIGRFAMs and Genome Properties in 2013. Nucleic Acids Res 41, D387–D395 (2013). (Database issue).Article 
PubMed 
CAS 

Google Scholar 
Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res 42, D490–D495 (2014).Article 
PubMed 
CAS 

Google Scholar 
Chivian, D., Dehal, P. S., Keller, K. & Arkin, A. P. MetaMicrobesOnline: phylogenomic analysis of microbial communities. Nucleic Acids Res 41, D648–D654 (2013).Article 
PubMed 
CAS 

Google Scholar 
Karaoz, U. & Brodie, E. L. microTrait: a toolset for a trait-based representation of microbial genomes. Front. Bioinform. https://doi.org/10.3389/fbinf.2022.918853 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Wood-Charlson, E. M. et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat. Rev. Microbiol. 18, 313–314 (2020).Article 
PubMed 
CAS 

Google Scholar 
Hofmeyr, S. et al. Terabase-scale metagenome coassembly with MetaHipMer. Sci. Rep. 10, 10689 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Kolmogorov, M. et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat. Methods 17, 1103–1110 (2020).Article 
PubMed 
CAS 

Google Scholar 
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27, 722–736 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Bertrand, D. et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat. Biotechnol. 37, 937–944 (2019).Article 
PubMed 
CAS 

Google Scholar 
Chen, L.-X. et al. Accurate and complete genomes from metagenomes. Genome Res 30, 315–333 (2020).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Lui, L. M., Nielsen, T. N. & Arkin, A. P. A method for achieving complete microbial genomes and improving bins from metagenomics data. PLoS Comput Biol 17, e1008972 (2021).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Miller, C. S., Baker, B. J., Thomas, B. C., Singer, S. W. & Banfield, J. F. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol 12, R44 (2011).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Chivian, D. et al. Genome extraction from shotgun metagenome sequence data. KBase n/33233/628 https://doi.org/10.25982/33233.606/1831502 (2022).Article 

Google Scholar 
Chivian, D., et al. Moab desert crust – sample 4E. KBase n/62384/334 (2022). https://doi.org/10.25982/62384.253/1831503Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform 11, 538 (2010).Article 

Google Scholar 
Benson, D. A. et al. GenBank. Nucleic Acids Res 46, D41–D47 (2018).Article 
PubMed 
CAS 

Google Scholar 
Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998).Article 
PubMed 
CAS 

Google Scholar 
Teiling, C. BaseSpace: Simplifying metagenomic analysis. 26th European Congress of Clinical Microbiology and Infectious Diseases (2016) 10.26226/morressier.56d5ba2ed462b80296c9509dReich, M. et al. The GenePattern notebook environment. Cell Syst 5, 149–151.e1 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Karp, P. D. et al. A comparison of microbial genome web portals. Front. Microbiol. 10, 208 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Yue, Y. et al. Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets. BMC Bioinform 21, 334 (2020).Article 
CAS 

Google Scholar 
Nelson, W. C., Tully, B. J. & Mobberley, J. M. Biases in genome reconstruction from metagenomic data. PeerJ 8, e10119 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J 11, 2864–2868 (2017).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Li, L., Stoeckert, C. J. Jr & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 13, 2178–2189 (2003).Article 
PubMed 
PubMed Central 
CAS 

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

Google Scholar 
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).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).Article 
PubMed 
PubMed Central 
CAS 

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

Google Scholar 
Kumari, S. et al. A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in sorghum. Curr. Plant Biol. 28, 100229 (2021).Article 
CAS 

Google Scholar 
Seaver, S. M. D. et al. The ModelSEED biochemistry database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 49, D575–D588 (2021).Article 
PubMed 
CAS 

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

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).Article 
PubMed 
PubMed Central 
CAS 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Zhang, S. et al. Sustained productivity and agronomic potential of perennial rice. Nat. Sustain. https://doi.org/10.1038/s41893-022-00997-3 (2022). More

Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

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

Google Scholar 
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
Hector, A. et al. Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127 (1999).Article 
CAS 
PubMed 

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

Google Scholar 
Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
van der Plas, F. et al. Towards the development of general rules describing landscape heterogeneity–multifunctionality relationships. J. Appl. Ecol. 56, 168–179 (2019).Article 

Google Scholar 
Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 
PubMed 

Google Scholar 
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).Article 
CAS 
PubMed 

Google Scholar 
van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proc. Natl Acad. Sci. USA 113, E2549–E2549 (2016).
Google Scholar 
Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).Article 
CAS 
PubMed 

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

Google Scholar 
Srivastava, D. S. & Vellend, M. Biodiversity–ecosystem function research: is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294 (2005).Article 

Google Scholar 
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Chase, J. M. & Knight, T. M. Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecol. Lett. 16, 17–26 (2013).Article 
PubMed 

Google Scholar 
Chase, J. M. et al. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities. Ecol. Lett. 21, 1737–1751 (2018).Article 
PubMed 

Google Scholar 
Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).Article 
PubMed 

Google Scholar 
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).Article 
CAS 
PubMed 

Google Scholar 
Hagan, J. G., Vanschoenwinkel, B. & Gamfeldt, L. We should not necessarily expect positive relationships between biodiversity and ecosystem functioning in observational field data. Ecol. Lett. 24, 2537–2548 (2021).Article 
PubMed 

Google Scholar 
Brose, U. & Hillebrand, H. Biodiversity and ecosystem functioning in dynamic landscapes. Philos. Trans. R. Soc. B 371, 20150267 (2016).Article 

Google Scholar 
Isbell, F. et al. Benefits of increasing plant diversity in sustainable agroecosystems. J. Ecol. 105, 871–879 (2017).Article 

Google Scholar 
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes-eight hypotheses. Biol. Rev. 87, 661–685 (2012).Article 
PubMed 

Google Scholar 
Ricotta, C. On beta diversity decomposition: trouble shared is not trouble halved. Ecology 91, 1981–1983 (2010).Article 
PubMed 

Google Scholar 
Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).Article 
CAS 
PubMed 

Google Scholar 
Gonthier, D. J. et al. Biodiversity conservation in agriculture requires a multi-scale approach. Proc. R. Soc. Lond. B 281, 20141358 (2014).
Google Scholar 
Flynn, D. F. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).Article 
PubMed 

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

Google Scholar 
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).Article 
CAS 
PubMed 

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

Google Scholar 
Le Provost, G. et al. Land-use history impacts functional diversity across multiple trophic groups. Proc. Natl Acad. Sci. USA 117, 1573–1579 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Adl, S. M., Coleman, D. C. & Read, F. Slow recovery of soil biodiversity in sandy loam soils of Georgia after 25 years of no-tillage management. Agric. Ecosyst. Environ. 114, 323–334 (2006).Article 

Google Scholar 
Le Provost, G. et al. Contrasting responses of above- and belowground diversity to multiple components of land-use intensity. Nat. Commun. 12, 3918 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
James, L. A. Legacy effects. Oxford Bibliographies in Environmental Science https://doi.org/10.1093/OBO/9780199363445-0019 (2015).Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11, 124017 (2016).Article 

Google Scholar 
Alsterberg, C. et al. Habitat diversity and ecosystem multifunctionality—the importance of direct and indirect effects. Sci. Adv. 3, e1601475 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity—ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

Google Scholar 
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).Article 
PubMed 

Google Scholar 
Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

Google Scholar 
Bullock, J. M., Aronson, J., Newton, A. C., Pywell, R. F. & Rey-Benayas, J. M. Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends Ecol. Evol. 26, 541–549 (2011).Article 
PubMed 

Google Scholar 
Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mitchell, M. G. E., Bennett, E. M. & Gonzalez, A. Linking landscape connectivity and ecosystem service provision: current knowledge and research gaps. Ecosystems 16, 894–908 (2013).Article 

Google Scholar 
Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories. Basic Appl. Ecol. 11, 473–485 (2010).Article 

Google Scholar 
Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).Article 

Google Scholar 
Vogt, J. et al. Eleven years’ data of grassland management in Germany. Biodivers. Data J. 7, e36387 (2019).Article 
PubMed 
PubMed Central 

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

Google Scholar 
Linders, T. E. W. et al. Stakeholder priorities determine the impact of an alien tree invasion on ecosystem multifunctionality. People Nat. 3, 658–672 (2021).Article 

Google Scholar 
Nathan, R. Long-distance dispersal of plants. Science 313, 786–788 (2006).Article 
CAS 
PubMed 

Google Scholar 
Manning, P. et al. Grassland management intensification weakens the associations among the diversities of multiple plant and animal taxa. Ecology 96, 1492–1501 (2015).Article 

Google Scholar 
Clough, Y. et al. Density of insect-pollinated grassland plants decreases with increasing surrounding land-use intensity. Ecol. Lett. 17, 1168–1177 (2014).Article 
PubMed 

Google Scholar 
Vickery, J. A. et al. The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. J. Appl. Ecol. 38, 647–664 (2001).Article 

Google Scholar 
López-Jamar, J., Casas, F., Díaz, M. & Morales, M. B. Local differences in habitat selection by Great Bustards Otis tarda in changing agricultural landscapes: implications for farmland bird conservation. Bird. Conserv. Int. 21, 328–341 (2011).Article 

Google Scholar 
Wells, K., Böhm, S. M., Boch, S., Fischer, M. & Kalko, E. K. Local and landscape-scale forest attributes differ in their impact on bird assemblages across years in forest production landscapes. Basic Appl. Ecol. 12, 97–106 (2011).Article 

Google Scholar 
Bommarco, R., Lindborg, R., Marini, L. & Öckinger, E. Extinction debt for plants and flower-visiting insects in landscapes with contrasting land use history. Divers. Distrib. 20, 591–599 (2014).Article 

Google Scholar 
Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 
PubMed 

Google Scholar 
Lee, M., Manning, P., Rist, J., Power, S. A. & Marsh, C. A global comparison of grassland biomass responses to CO2 and nitrogen enrichment. Philos. Trans. R. Soc. B 365, 2047–2056 (2010).Article 
CAS 

Google Scholar 
Smith, P. Do grasslands act as a perpetual sink for carbon? Glob. Change Biol. 20, 2708–2711 (2014).Article 

Google Scholar 
Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. A. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, 5266–5270 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bradford, M. A. et al. Discontinuity in the responses of ecosystem processes and multifunctionality to altered soil community composition. Proc. Natl Acad. Sci. USA 111, 14478–14483 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schaub, S. et al. Plant diversity effects on forage quality, yield and revenues of semi-natural grasslands. Nat. Commun. 11, 768 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).Article 
PubMed 

Google Scholar 
Peter, S., Le Provost, G., Mehring, M., Müller, T. & Manning, P. Cultural worldviews consistently explain bundles of ecosystem service prioritisation across rural Germany. People Nat. 4, 218–230 (2022).Article 

Google Scholar 
Emmerson, M. et al. How agricultural intensification affects biodiversity and ecosystem services. Adv. Ecol. Res. 55, 43–97 (2016).Article 

Google Scholar 
Gonzalez, A. et al. Scaling-up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl Acad. Sci. USA 100, 12765–12770 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, B. J. et al. Spatial covariance between biodiversity and other ecosystem service priorities. J. Appl. Ecol. 46, 888–896 (2009).Article 

Google Scholar 
Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).Article 

Google Scholar 
Metzger, J. P. et al. Considering landscape-level processes in ecosystem service assessments. Sci. Total Environ. 796, 149028 (2021).Article 
CAS 
PubMed 

Google Scholar 
Costanza, R. et al. Twenty years of ecosystem services: how far have we come and how far do we still need to go? Ecosyst. Serv. 28, 1–16 (2017).Article 

Google Scholar 
DeFries, R. & Nagendra, H. Ecosystem management as a wicked problem. Science 356, 265–270 (2017).Article 
CAS 
PubMed 

Google Scholar 
Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).Article 
PubMed 

Google Scholar 
Schenk, N. et al. Assembled ecosystem measures from grassland EPs (2008–2018) for multifunctionality synthesis—June 2020. Version 40. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27087 (2022).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, HAI, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27568 (2020).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, Alb, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27569 (2020).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, SCH, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27570 (2020).Penone, C. et al. Assembled RAW diversity from grassland EPs (2008–2020) for multidiversity synthesis—November 2020. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27707 (2021).Penone, C. et al. Assembled species information from grassland EPs (2008–2020) for multidiversity synthesis—November 2020. Version 3. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27706 (2021).Junge, X., Schüpbach, B., Walter, T., Schmid, B. & Lindemann-Matthies, P. Aesthetic quality of agricultural landscape elements in different seasonal stages in Switzerland. Landsc. Urban Plan. 133, 67–77 (2015).Article 

Google Scholar 
Lindemann-Matthies, P., Junge, X. & Matthies, D. The influence of plant diversity on people’s perception and aesthetic appreciation of grassland vegetation. Biol. Conserv. 143, 195–202 (2010).Article 

Google Scholar 
Haines-Young, R. & Potschin, M. B. Common International Classification of Ecosystem Services (CICES) V5.1 and Guidance on the Application of the Revised Structure. https://cices.eu/content/uploads/sites/8/2018/01/Guidance-V51-01012018.pdf (2018)Byrnes, J. E. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).Article 

Google Scholar 
Neyret, M. et al. Assessing the impact of grassland management on landscape multifunctionality. Ecosyst. Serv. 52, 101366 (2021).Article 

Google Scholar 
Ferraro, D. M. et al. The phantom chorus: birdsong boosts human well-being in protected areas. Proc. R. Soc. B 287, 20201811 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Graves, R. A., Pearson, S. M. & Turner, M. G. Species richness alone does not predict cultural ecosystem service value. Proc. Natl Acad. Sci. USA 114, 3774–3779 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chan, K. M. A., Satterfield, T. & Goldstein, J. Rethinking ecosystem services to better address and navigate cultural values. Ecol. Econ. 74, 8–18 (2012).Article 

Google Scholar 
Villamagna, A. M., Angermeier, P. L. & Bennett, E. M. Capacity, pressure, demand, and flow: a conceptual framework for analyzing ecosystem service provision and delivery. Ecol. Complex. 15, 114–121 (2013).Article 

Google Scholar 
Bolliger, R., Prati, D., Fischer, M., Hoelzel, N. & Busch, V. Vegetation Records for Grassland EPs, 2008–2018. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/24247 (2020).Le Provost, G. & Manning, P. Cover of all vascular plant species in representative 2×2 quadrats of the major surrounding homogeneous vegetation zones in a 75-m radius of the 150 grassland EPs, 2017–2018. Version 4. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27846 (2021).Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).Article 

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

Google Scholar 
Ostrowski, A., Lorenzen, K., Petzold, E. & Schindler, S. Land use intensity index (LUI) calculation tool of the Biodiversity Exploratories project for grassland survey data from three different regions in Germany since 2006, BEXIS 2 module. Zenodo https://doi.org/10.5281/zenodo.3865579 (2020).Thiele, J., Weisser, W. & Scherreiks, P. Historical land use and landscape metrics of grassland EP. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/25747 (2020).Steckel, J. et al. Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists. Biol. Conserv. 172, 56–64 (2014).Article 

Google Scholar 
Westphal, C., Steckel, J. & Rothenwöhrer, C. InsectScale / LANDSCAPES – Landscape heterogeneity metrics (grassland EPs, radii 500 m–2000 m, 2009) – shape files. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/24046 (2019).Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).Article 
PubMed 

Google Scholar 
Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gessler, P. E., Moore, I. D., Mckenzie, N. J. & Ryan, P. J. Soil–landscape modelling and spatial prediction of soil attributes. Int. J. Geogr. Inf. Syst. 9, 421–432 (1995).Article 

Google Scholar 
Zinko, U., Seibert, J., Dynesius, M. & Nilsson, C. Plant species numbers predicted by a topography-based groundwater flow index. Ecosystems 8, 430–441 (2005).Article 
CAS 

Google Scholar 
Moeslund, J. E. et al. Topographically controlled soil moisture drives plant diversity patterns within grasslands. Biodivers. Conserv. 22, 2151–2166 (2013).Article 

Google Scholar 
Keddy, P. A. Assembly and response rules: two goals for predictive community ecology. J. Veg. Sci. 3, 157–164 (1992).Article 

Google Scholar 
Myers, M. C., Mason, J. T., Hoksch, B. J., Cambardella, C. A. & Pfrimmer, J. D. Birds and butterflies respond to soil-induced habitat heterogeneity in experimental plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. J. Appl. Ecol. 52, 1176–1187 (2015).Article 

Google Scholar 
Carvalheiro, L. G. et al. Soil eutrophication shaped the composition of pollinator assemblages during the past century. Ecography 43, 209–221 (2020).Article 

Google Scholar 
Schöning, I., Klötzing, T., Schrumpf, M., Solly, E. & Trumbore, S. Mineral soil pH values of all experimental plots (EP) of the Biodiversity Exploratories project from 2011, Soil (core project). Version 8. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/14447 (2021).Sørensen, R., Zinko, U. & Seibert, J. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol. Earth Syst. Sci. 10, 101–112 (2006).Article 

Google Scholar 
Le Provost, G. et al. Aggregated environmental and land-use covariates of the 150 grassland EPs used in ‘Contrasting responses of above- and belowground diversity to multiple components of land-use intensity’. Version 5. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/31018 (2021).R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Grace, J. B. Structural equation modeling for observational studies. J. Wildl. Manag. 72, 14–22 (2008).Article 

Google Scholar 
Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, 2006).Rosseel, Y. Lavaan: an R package for structural equation modeling and more. Version 0.5–12 (BETA). J. Stat. Softw. 48, 1–36 (2012).Article 

Google Scholar 
Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).Article 
PubMed 
PubMed Central 

Google Scholar  More