Ecology

At December’s United Nations Convention on Biological Diversity summit (COP15), an insidious threat emerged to national parks — even as scientists argued for expanding protected areas. The World Travel & Tourism Council wants commercial tourism to be allowed to build developments in national parks globally, without obligation to help finance park conservation (see go.nature.com/3x2fsi9). This would undermine existing private tourism developments that do support conservation.
Competing Interests
The authors declare no competing interests. More

The University of California, Los Angeles, suspended ecologist Priyanga Amarasekare without salary or benefits for one year, and will cut her salary by 20% for two more years.Credit: Al Seib/Los Angeles Times via Getty

In April of last year, the Ecological Society of America awarded Priyanga Amarasekare one of the highest honours in the field of ecology: the Robert H. MacArthur Award. A little over two months later, the University of California, Los Angeles (UCLA), placed Amarasekare on a one-year suspension without pay or benefits, and forbid her from accessing her laboratory, maintaining her insect colonies, managing her grants or contacting students. Now scientists from around the world, who call Amarasekare a “highly distinguished ecologist”, “a committed teacher and outstanding mentor” and a “tireless advocate for under-represented groups”, are calling for her reinstatement.
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The precise allegations that led to her suspension are unknown. UCLA has declined to release them, and barred Amarasekare from discussing the matter publicly. But long-standing tensions between Amarasekare and the university are no secret. A native of Sri Lanka and one of two women of colour who have tenure in the ecology and evolution department, she has previously accused the university of discrimination for repeatedly denying her promotions that were granted to colleagues. Former students and faculty members who are familiar with the situation think that Amarasekare’s suspension was retaliation for speaking out.Some 315 scientists raised concerns about her suspension in a petition that was delivered to the university on 23 January, arguing that Amarasekare “has long been denied significant advancement within her department, out of keeping with her contributions to the field”. Moreover, the sanctions levied against Amarasekare — including the one-year suspension and 20% salary reduction for an additional two years — represent “the kind of punishment normally applied only to the most egregious wrongdoings”, including scientific misconduct and sexual harassment violations, the petitioners write.In the absence of compelling evidence to the contrary, the scientists ask that UCLA rescind the disciplinary actions and fully compensate Amarasekare.Officials with UCLA say that the university “supports freedom of expression and does not condone retaliation of any sort”. They declined to discuss the accusations against or in support of Amarasekare, saying the university is “bound to respect the privacy of the numerous individuals involved in this matter”. Amarasekare also declined to comment.A confusing decisionColleagues told Nature that Amarasekare is the rare ecologist whose research spans the theoretical, computational and experimental realms. One project in her laboratory that touches on all of these areas focuses on the impact of climate change on insect communities. “She’s really several years ahead of everybody else,” says Andy Dobson, an ecologist at Princeton University in New Jersey who led the petition. Dobson has written letters to support Amarasekare’s various applications for promotion at UCLA and says he has been baffled by the university’s decisions. “She complained, and most of what’s happened seems to be a reaction against that,” he says.
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Nature spoke to several former students and faculty members who defended Amarasekare in administrative hearings in September 2021. Although none knew the specific details of the charges against her, they all thought she had been targeted for speaking out against what she saw as discrimination within the department. In particular, they said Amarasekare vented about her own experience at UCLA on a departmental e-mail listserve created to discuss issues of racism and discrimination in the aftermath of the killing of George Floyd, whose death in May 2020 sparked national protests.“That’s why she got into trouble. She ended up criticizing pretty much the entire department — with good reason,” says Marcel Vaz, an ecologist at Wilkes University in Wilkes-Barre, Pennsylvania, who was a graduate student in the department at the time. He and other students came forward to support her. “We demanded some explanation,” Vaz says, “but we never got any feedback.”Peter Kareiva, a former UCLA faculty member who spoke on Amarasekare’s behalf during the administrative proceedings, calls her a brilliant scientist as well as a terrific teacher and student mentor. Kareiva witnessed Amarasekare raise uncomfortable issues and challenge internal policies in faculty meetings. He says she might have made mistakes in terms of “facilitating harmony” among fellow faculty members, but that her goal was always to improve the department.
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“I am still incredulous by the punishment levied,” says Kareiva, who now serves as president of the Aquarium of the Pacific in Long Beach, California.It is unclear what happens next, but scientists and former students and faculty members contacted by Nature are concerned about the impact on Amarasekare’s current students, the disruption of federally funded research and the potentially irretrievable loss of time-sensitive experiments that could provide insights regarding the ecological impacts of climate change.As the recipient of the MacArthur award, Amarasekare is expected to discuss this research when she delivers her keynote address at the Ecological Society of America’s annual meeting in Portland, Oregon, in August. More

There is a growing impetus to leverage our fundamental understanding of microbial community assembly towards applied problems. With microbes contributing to diverse physiological, biogeochemical, and agricultural processes, the potential to control and optimise microbial communities holds promise for interventions ranging from industrial and environmental remediation to human medicine and biofuel production [1, 2]. Realising this goal is contingent on high fidelity between theory, experiments, and the natural dynamics of target systems.Theoretical and experimental research in microbial community optimisation has largely proceeded along two parallel paths. Theoretical approaches leverage mathematical models and metabolic networks to predict which species combinations are stable and how they can optimise a given function (e.g., maximum biomass, waste degradation or host health) [3,4,5,6,7]. Experimental studies often take a combinatorial approach, iteratively assembling different species combinations in vitro and evaluating their stability and functional attributes [8,9,10,11]. Both theory and experiments are valuable but they are also susceptible to their own modus operandi that may limit their correspondence and their translation to real-world systems. On the one hand, theoretical approaches typically adopt the analytical tractability of steady state dynamics, where microbial consumers and the resources on which they depend are assumed to establish a stable equilibrium. On the other hand, experimental approaches almost exclusively embrace the high-throughput efficiency of serial-batch culture, where consumers and resources are made to fluctuate over several orders of magnitude with each serial passage. This raises an important question: should we expect unity in the composition of optimised communities emerging under continuous resource supply (e.g., chemostat) versus the discrete pulsed resource supply of, for example, serial-batch culture?To explore how microbial community composition varies under contrasting resource supply dynamics, we performed simulations of a classical resource-competition model:$$frac{{dN_i}}{{dt}} = N_ileft( {mathop {sum}limits_{j = 1}^n {mu _{ij}left( {R_j} right) – m} } right)$$
(1)
$$frac{{dR_j}}{{dt}} = {Psi}_jleft( {R_j} right) – mathop {sum }limits_{i = 1}^n Q_{ij}mu _{ij}left( {R_j} right)N_i,$$
(2)
where Ni is the population density of consumer i, Rj is the concentration of resource j, μij(Rj) is the per capita functional response of consumer i, m is the per capita mortality rate due to dilution, Ψj(Rj) is the resource supply function, and Qij is the resource quota of consumer i on resource j (amount of resource per unit consumer). The consumer functional response is given by the Monod function, (mu _{ij}(R_j) = mu _{max_{ij}}frac{{R_j}}{{K_{s_{ij}} + R_j}}) , where (mu _{max_{ij}}) is the maximum growth rate and (K_{s_{ij}}) is the half saturation constant for consumer i on resource j.To set up the simulations, we randomly sampled the parameters of the Monod growth functions, (μmax and Ks) for five species competing for five substitutable resources (essential resources are treated separately in the supplementary information, with similar findings). In one set of parametrisations (n = 100 unique competitor combinations) we used both random μmax and Ks, and in another set (n = 100) we imposed a trade-off in maximum growth rate and substrate affinity (( {frac{{mu _{max}}}{{K_s}}} )) (Fig. 1a). The rationale for imposing a trade-off is that metabolic theory predicts that organisms that invest energy into a high maximum growth rate will have lower substrate affinities and vice versa [12, 13]. To ensure reasonable growth rates relative to the time-scale of resource pulsing, we sampled μmax such that minimum doubling times spanned from 21 to 52 min (when all resources are non-limiting). For each of the random competitor combinations, we simulated resources under continuous or pulsed resource supply with resource replenishment every 1/2, 1, 2, 4, 12, or 24 h. Under pulsed resource supply, Ψj(Rj) and m are removed from Eq. (1) and (2) and replaced by discontinuous resource pulsing and cell transfer at fixed intervals. The total resource flux (and mortality) was held constant under all frequencies of resource supply i.e., less frequent replenishment corresponds to larger resource pulses (see Supplementary Information for full model/simulation specifications).Fig. 1: Quantifying compositional overlap between communities assembled under continuous vs. pulsed resource supply.a Per capita growth responses (Monod functions) from a single iteration of the model assuming a trade-off between maximum growth rate and resource affinity (colours correspond to individual consumers). b Time series of consumers in a under different resource supply regimes. Numbers above individual panels reflect pulsing intervals in hours. The amplitude of population fluctuations increases with longer intervals between pulses, with distinct phases of growth, saturation, and instantaneous mortality visible at a finer temporal resolution (Fig. S10). c Example measure of compositional overlap (Jaccard similarity index) between communities assembled under continuous resource supply (far left panel in b) vs. pulsing every two hours (centre panel in b).Full size imageAfter allowing the competitors to reach a steady state (time-averaged over 24 h under pulsed treatments), we quantified the correspondence between the continuous supply treatment and the pulsed treatments using the Jaccard similarity index, (Jleft( {A,B} right) = frac{{left| {A cap B} right|}}{{left| {A cup B} right|}}) (0 ≤ J(A,B) ≤ 1), where the numerator gives the number of species (max = 5) that persist under continuous (A) and pulsed (B) resource supply, and the denominator gives the number of species (max = 5) that persist under continuous or pulsed resource supply (Fig. 1b, c).Under both sets of simulations (with and without enforcing a trade-off between maximal growth rate and resource affinity), we observe that the similarity in final community composition between continuous and pulsed resource supply decays with increasingly large intervals between resource replenishment (Fig. 2a). When no trade-off is imposed between maximum growth rate and resource affinity (orange line in Fig. 2a) the mean compositional similarity is only 0.68 when resources are pulsed every 2 h and down to 0.41 when resources are pulsed every 24 h (typical of serial-batch culture). The rate of decay in the Jaccard index is more severe when a trade-off is imposed between maximum growth rate and substrate affinity, to the extent that once pulsing intervals reach four hours there is almost zero overlap in community composition (blue line in Fig. 2a).Fig. 2: Impact of resource supply regime on community composition and abundance weighted mean trait values.a Compositional overlap (Jaccard similarity) between communities under continuous versus pulsed resource supply. Orange lines, points and circles denote model parametrisations with random sampling of both μmax and Ks; blue lines, points and circles denote model parametrisations with a trade-off imposed between μmax and resource affinity (( {frac{{mu _{max}}}{{K_s}}} )). Simulation parameters provided in the Supplementary Information. b Mean trait values for affinity and μmax averaged for each consumer across the five resources and weighted by their final abundance at the end of a simulation (cont. = continuous). In both a and b, small points (jittered for clarity) give the result of an individual simulation; large circles indicate the corresponding mean.Full size imageEcological theory provides an intuitive explanation for these observations. When resources are more continuously supplied, the better competitor is the one that can sustain a positive growth rate at the lowest concentrations of a limiting resource (i.e., has a higher resource affinity or lower R* in the language of resource competition theory [14]). In contrast, under increasingly pulsed resource supply, the better competitor is the one that can grow rapidly at higher resource concentrations. Having a high resource affinity (low R*) is of little benefit if resource concentrations fluctuate over large amplitudes because it only confers an ephemeral competitive advantage in the brief period before the resource is completely depleted (ahead of the next resource pulse). Instead, a high maximum growth rate is optimal because it allows the consumer to grow rapidly and quickly deplete a shared limiting resource. This high maximum growth strategy is, however, sub-optimal under continuous resource supply because a low R* strategist can draw the resource down and hold it at a concentration at which the maximum growth strategist is unable to maintain a positive growth rate.Looking at the mean trait values for resource affinity and μmax weighted by each consumer’s final abundance, it is indeed apparent that consumers with a higher affinity (averaged across the five resources) are favoured under continuous resource supply, while consumers with high maximum growth rates are favoured under pulsing intervals of increasing length (Fig. 2b). Enforcing this trade-off, therefore, leads to the rapid decline in compositional similarity we observe under resource pulsing. Notably, it also leads to a richness peak at intermediate pulsing intervals, where these alternative strategies have a higher probability of coexisting [15] (Fig. S1). At the same time, we still observe a decline in compositional similarity when μmax and Ks are randomly sampled independently of each other simply because the trade-off between maximum growth and resource affinity will emerge occasionally by chance. Two experimental tests of microbial community composition under continuous versus pulsed resource supply are consistent with these observations [16, 17].To evaluate the sensitivity of these observations to different assumptions, we ran additional simulations under various alternative model parameterisations and formulations. In brief, comparable trends to those described above are observed when: i) maximum growth rates are faster or slower than those presented in the main text (Figs. S2, S3); ii) all resources are assumed to be essential to growth (following Liebig’s law of the minimum) (Fig. S4); iii) a weaker trade-off is imposed between maximum growth and affinity (Figs. S5, S6); or iv) mortality is continuous rather than intermittent (Figs. S7, S8). We also investigated the relationship between observed compositional overlap and the dynamical stability under continuous resource supply, anticipating that more stable communities would tend to be more resistant to compositional shifts under resource pulsing. The reality appears more nuanced, namely that weaker dynamical stability at the limit of constant resource supply is associated with higher variance in compositional overlap under continuous vs. pulsed conditions (Fig. S9). In other words, systems with weaker stability are less predictable. A wide range of other microbial traits and trade-offs may interact unpredictably with the relationship between resource supply and community composition. The potential modulating role of system instabilities generated by cross-feeding interactions, non-convex trade-off functions, and the evolution of specialist versus generalist strategies present several especially valuable lines of enquiry [18,19,20].Although these observations are germane to any consumer-resource system, our emphasis here is on the emerging field of microbial community optimisation, where the practical implications are especially timely and important; namely, the resource supply regime must be tailored to the community being optimised. For example, wastewater treatment might be more appropriately modelled under continuous resource supply [21], whereas fermented food and beverage production may be more closely allied to the pulsed resource dynamics observed in batch culture [22]. Resource supply might also be manipulated to favourably modify the competitive hierarchy in an existing community (e.g., by regulating the rate of nutrient supply to the gut through meal timing). Indeed, there is emerging evidence that feeding frequency can drive significant changes in gut microbiota composition [23, 24]. Thus, resource supply dynamics should be considered both a constraint in the design of novel microbial communities and as a tuning mechanism for the optimisation of preexisting communities like those found in the human gut. More

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Our study is focussed on population trends of large carnivores; a culturally important group32, essential for regulating ecosystem function33. Large carnivores represent an important study group as their population status is unclear, with reports of devastating declines33 contrasted with remarkable recoveries23. Further, as a well-studied taxa with abundant trend and trait datasets, large carnivores present a good system to evaluate important drivers of trends without being impacted by poor inference from missing data34. Finally, as large carnivores are considered indicator species of the overall status of biodiversity within an area35, our inference may provide insight beyond our focal taxa.Population trendsWe sourced population (defined by the authors of the original studies, who reported on population trends for one or more studied groups of individuals) trend information for species in the families Canidae, Felidae, Hyaenidae, and Ursidae of the order Carnivora from two large trend datasets: CaPTrends12 and the Living Planet Database13. The CaPTrends database is the product of a semi-systematic literature search for population trends of large carnivore species (from the families listed above); the dataset possesses trend information for 50 species from locations around the world, and trends are reported in a variety of ways. The Living Planet Database contains population abundance time-series for vertebrates from thousands of sites around the world and is one of the larger population trend datasets. Combined, these datasets produce a cumulative 1123 trends (after removing duplicates and records we deemed unreliable or unsuitable), derived from >10,000 individual population estimates. In the Living Planet Database, and for most records in CaPTrends, trends are reported as a time-series of abundance (or density) estimates. We modelled these time-series with log-linear regressions, where abundance (the response) was loge transformed, and year of abundance estimates was selected as the predictor. We included a continuous Ornstein-Uhlenbeck (OU) autoregressive process to control for temporal autocorrelation in these models. The OU process estimates covariance between abundance values, under the assumption that abundances in time point 1 will be more similar to abundances in time point 2, than time-point 3, 4, 5, etc. Accounting for covariance resolves non-independence within time-series. We extracted the slope coefficient which represents the annual instantaneous rate of change, sometimes called the population growth rate (rt). Alongside the abundance time-series, CaPTrends also has three other quantitative datatypes, all of which we converted into an annual instantaneous rate of change (rt): (1) a mean finite rate of change; (2) estimates of percentage abundance change between two points in time; and (3) time-series’ of population change estimates (e.g. in year 1 the population doubled and in year 2 it halved). We converted all annual instantaneous rates of change into an annual rate of change percentage to improve interpretability. These annual rates of change ranged from −75 to 68%, but the majority of values fell within −10 to 10% (Supplementary Fig. 1a).Alongside the quantitative records, 138 populations in the CaPTrends dataset were only described qualitatively with categories: Increase, Stable, and Decrease. These qualitative records were more common for populations located in traditionally poorer-sampled countries (e.g. with lower human development), so whilst they are less informative (only describing the direction and not the magnitude), we deem them important to reduce bias (Fig. 1). As a result, we used a combination of percentage annual rates of change (N = 985) and qualitative categories (N = 138) as our response in our model (see below), representing 50 large carnivore species.CovariatesFor each population, we extracted sixteen covariates (each z-transformed) that fell into four categories: land-use, climate, governance, and traits. Our covariates were designed to cover a diverse array of factors that could influence population trends in large carnivores (Supplementary Table 1). Each covariate is described briefly in Fig. 2 with full descriptions of how variables were derived in the Supplementary material: Covariates.One of the challenges in identifying how covariates—which can vary in space and time—impact population trends is matching the spatial and temporal scale of the covariate with the population i.e. how much of the population is affected by the covariate at a given point in time. To tackle the spatial element of this problem, we used data on the area of extent of each population (e.g. how large is the spatial extent of the population or monitoring zone) to generate a circular distribution zone around the population’s coordinate centroid. We refer to this as the ‘population area’ hereafter. We then sampled covariate values within each population area, with more sampling points in larger areas (range: 13–295 sampling points, Supplementary Fig. 2b). For covariates which varied over time, we extracted the covariates across the ‘population monitoring period’, which refers to the period (from start to end year) the population was monitored for. However, as evidence suggests there can be a lag period between impact or change and any detectable changes in population abundance3, we tested how 0-, 5-, and 10-year lags in covariates changed model fits and effect sizes. We implemented these lags by extending the start of the population monitoring period backwards for each given lag e.g. for a 10-year lag, a normal population monitoring period of 1990–2000, would then capture covariates between 1980–2000. Sensitivity analysis showed a 10-year lag had the greatest balance of improved model fit, with high taxonomic and spatial coverage (see Supplementary: Sensitivity analysis).ModellingAt its core, our model is a linear mixed effects model, regressing annual rates of change against a combined 23 covariates and interactions, using random intercepts to account for phylogenetic and spatial nesting. The model was written in BUGS language and implemented in JAGS 4.3.036 via R 4.0.337. The model structure is summarised below, with a full description in Supplementary: Modelling.ResponseWe modelled our annual rate of change response with a normal error prior. However, to allow the two different types of population trend data (quantitative rates of change and qualitative descriptions of change) to be included in the same model, we treated the qualitative records as partially known. Specifically, we censored the qualitative records to indicate that the true value is unknown, but it occurs within a specified range, with annual rate changes ranging from −50 to 0%, −5 to 5%, and 0 to 50% within the decrease, stable and increase categories, respectively. The overlapping nature of these thresholds is by design, as we wanted to acknowledge that there is likely a grey area between the different categories. For instance, in one study, a 2% trend could be called stable, whilst a different study would consider this as increasing, our overlapping thresholds address this grey area. Admittedly, our category thresholds were arbitrarily selected—this is as a consequence of there being no strict rules on what population change is needed to be assigned a given category. However, despite being arbitrary, they were still carefully selected. For instance, our censoring range thresholds are similar to the range of the observed change (−75 to 68%). Further, whilst we don’t have a clear definition for what an increasing or decreasing population looks like (is it 1% or 10%), we can be confident that increasing and decreasing populations will fall above and below 0%, respectively. The stable category is most vulnerable to subjectivity, and so without clear definitions, we set a large range e.g. the maximum and minimum value we considered could be plausibly called stable was 5% and −5%, respectively.Many of the qualitative and short-term (brief monitoring period) quantitative records address known data biases as they occur in less-well represented regions, species, and time-periods (Fig. 1). However, these lower quality records can be more prone to error. As a result, we developed a weighting term within the model to inflate uncertainty around trends derived over a short timeframe, with few abundance observations, and less robust methods—see Supplementary: Modelling—Weighted error.CovariatesPrior to modelling, we identified missing values in some covariates (e.g. some species were missing Maximum longevity values), which can be problematic for inference if ignored34. We used imputation approaches38,39 to predict these missing values and recorded the associated imputation uncertainty alongside these predictions. Within our model, we accounted for uncertainty in the imputed estimates by treating imputed values of the covariates as distributions instead of point estimates. Specifically, for each imputed value we assigned a normal distribution defined by the mean and standard deviation of the imputed estimates. This approach allowed us to capture imputation uncertainty and improve inference robustness.With 16 covariates and a further seven interactive effects (23 effects in total), we were conscious of overparameterizing the model. As a result, we split these parameters into three groups: (1) core parameters—which included main effects that were considered likely drivers of population change; (2) optional parameters—which included main effects we considered interesting but with little evidence to-date of any influence on trends; and (3) interaction parameters—which includes the seven proposed interaction terms. We included our core parameters (Change in human density, Primary land loss, Population area, Body mass, Change in extreme heat, Governance, and Protected area coverage) in every model, but used Kuo and Mallick variable selection40 to identify parameters from the optional and interaction groups that improve model fit whilst balancing the risk of overfitting.Random interceptsWe used a hierarchical model structure to account for phylogenetic and spatial non-independence in the data, including species as a random intercept nested with genus, and country as a random intercept nested within sub-regions, as defined by the United Nations (https://www.un.org/about-us/member-states).Model runningWe ran the full model through three chains, each with 150,000 iterations. The first 50,000 iterations in each chain were discarded, and we only stored every 10th iteration along the chain (thinning factor of 10). We opted for a large chain and burn-in due to the model complexity, and to allow a broad selection of parameter combinations to be tested under variable selection. We assessed convergence of the full model on all parameters monitored in the sensitivity analysis, as well as the model intercept, and all 23 main and interactive effect slope coefficients. We checked the standard assumptions of a mixed effect linear model (normal residuals and heterogeneity of variance), and tested the residuals to ensure there was no spatial (Moran’s test) or phylogenetic (Pagel’s lambda) autocorrelation. We also conducted posterior predictive checks to ensure independently simulated values were broadly reminiscent of model predicted values.We report the median slope coefficient and associated credible intervals for each of the main and interactive effects, and produce marginal effect plots for a selection of important parameters. These marginal effects hold all other covariates at zero (which is the equivalent of the mean, as covariates were z-transformed).LimitationsDeveloping macro-scale models of population change is challenging as response data are biased41 and hard to summarise42, and response-covariate relationships are likely complex and numerous2. Within our workflow, we attempted to address these challenges, and overall, this allowed us to achieve a moderate model fit (conditional R2 ~ 0.4). We minimised biases in the trend data by integrating qualitative trends with quantitative estimates, which allowed us to increase the taxonomic and spatial scale of the work. However, biases are likely still present to some extent. For instance, whilst we have population trend data covering the full parameter space of our most influential variable (change in human development), we have more population trends in high human development countries (Supplementary Fig. 20)—given these biases, caution should be used when interpreting results. While we could not avoid some biases, we found inference was similar across different fragments of the data and model structures (Supplementary results: Sensitivity analysis). We also attempted to capture complexity by covering a more comprehensive array of covariates than many previous analyses, but we still lack data on likely important aspects that are cryptic and difficult to measure (e.g. poaching, persecution, culling, and the conservation benefits of being flagship species). Further, there are temporal lags between disturbance-events and observable changes in the population10 and we tested several to incorporate the lag that maximised model fit. However, it is possible that responses to different types of disturbance (e.g. habitat loss and climate change) have different lags, although this has not been quantified. Long lags (the maximum lag we explored was 10-years) may also occur and be associated with slow recoveries, but an absence of longer temporal extents in the response and covariate data largely prohibits this analysis at global scales (long temporal extent data is less available outside of the global north).Counterfactual scenariosTo explore how observed changes in land-use, climate and human development have influenced population trends, we developed three counterfactual scenarios, where we compared observed population change to predicted population change if habitat, climate, and human development remained static. For instance, in the climate change counterfactual scenario, we predicted each population trend using the global model (all covariate parameters) with available covariate data (e.g. land-use, governance and trait covariates), as well as taxa and location data (to provide sensitivity to the models varying random intercepts), but set the climate change covariate data to zero (in this case, change in extreme heat and change in drought). We then subtracted these counterfactual predictions from the observed trends to define ‘Difference in annual rate of change (%)’, whereby a positive value indicates carnivore populations would be in better shape (fewer declines) under the counterfactual scenario, and vice-versa. We summarise counterfactual scenarios by reporting the median Difference in annual rate of change and 95% quantiles across the observed 1123 populations.Socioeconomic development and non-linearity in carnivore trendsGiven the large effect of human development change on carnivore population trends within our counterfactual scenarios, we further explore the potential impacts of human development change (i.e. changes in the socioeconomic standards of society) on the dynamics of potential carnivore abundance change. Specifically, we test how changing the rate of human development growth of a hypothetical low human development country could impact carnivore abundances. We test this by simulating time series of human development change between the years 1960 and 2020 along three common development pathways for low human development countries, each given: (1) a mean rate of change in human development (%) defined as Slow (1.25%), Moderate (1.5%) and Fast (1.75%); (2) a shared deceleration rate set to −0.02% per year—a key feature of the human development data is that as human development grows, its growth rate decreases; and (3) a shared initial human development value which we set as 0.2 (a hypothetical low human development country) at year 1960 (Fig. 4a). All our selected parameter values are representative of the human development data (Supplementary Fig. 2), with the Moderate pathway being largely typical for a country with an initial human development value of 0.2, while Slow and Fast represent plausible extremes.We then used our fitted model (Fig. 2) to evaluate how the three pathways of Change in Human development would affect annual abundance of a hypothetical carnivore. This involved predicting the annual rate of change in abundance using the Change in human development pathways and the marginal effect of the Change in human development parameter from the fitted model—setting all other covariates in the model to zero, which in our z-transformed variables represents the mean. We then used the predicted annual rates of change in abundance to project carnivore abundance up to the year 2020, from an arbitrary baseline abundance of 100 in the year 1960 (Fig. 4c). These projections capture the 95% credible intervals around the human development change model coefficient, and assume constant and average values for all other effects (e.g. primary habitat loss or climate change).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Bt rice led to the redistribution of soil nitrogenTo characterize the influence of Bt rice on soil environmental biochemistry, samples were first separated into two portions including soils and surface waters. Bt proteins were not detected in surface waters from all cultivars (Supplemental Table S2). However, Bt protein contents for rhizospheres from all three cultivars and bulk soils ranged between 64.14 and 126.68 pg/g soil (Supplemental Table S3). Bt protein contents in samples from Bt rice grown in IRRI rice nutrient solution reached 850 pg/ml (Supplementary Table S1). We speculated that the vast majority of Bt protein released from Bt plants was bound tightly to soil particles and was thus difficult to isolate, purify, and detect. Total N, NH4+-N, NO3−-N, and NO2−-N contents in T1C-1 rhizospheres were significantly higher than in the Minghui 63 rhizospheres, although the soil pH of T1C-1 rhizospheres was also significantly lower than for Minghui 63 soils (Supplemental Table S3). Interestingly, the total N, NH4+-N, and NO3−-N contents in the Zhonghua11 rhizospheres were significantly higher than in the Minghui 63 rhizospheres, pointing to an apparent impact of genotypic differences from different conventional cultivars on soil nitrogen. No differences in organic matter and total P contents were identified among all soil samples (Supplemental Table S3). In addition, the surface waters of T1C-1 exhibited higher NO3-N contents than Minghui 63 soils, but lower pH values than Minghui 63 (Supplemental Table 2), consistent with soil results.
Bt rice altered soil microbial communities, but not surface water communitiesSoil and surface water samples were collected and analyzed to characterize metagenomic profiles associated with different cultivars. A total of 11,529,157 and 2,880,919 genes were obtained for soil and surface water samples, respectively (Supplementary Table S4). The α diversity indices, Shannon–Wiener index (H’), Simpson index (D), and Evenness (E) were significantly higher in soils than in surface waters, but significant differences were not observed for Richness (R) (Fig. 2A). Except for R, the α diversity indices E, H′, and D were significantly higher in the T1C-1 rhizosphere than in the other samples, suggesting that Bt rice increased soil microbial diversity rather than altering taxonomic compositions. Differences in α diversity indices were not observed among all of the surface water samples (Supplementary Table S5). Principal coordinates analysis (PCoA) (Fig. 2B) based on microbial taxonomic level (genera) and functional classifications (clusters of orthologous groups of proteins, COG) indicated that soil samples from different rice cultivars and bulk soils formed distinct clusters in ordination space. These distinct groupings were not observed for surface water samples, suggesting that Bt rice cultivation altered soil microbial community composition and functions, but these changes did not occur in surface waters. The rhizospheres of T1C-1, Minghui 63, and Zhonghua 11 shared substantial overlap in total genera (Supplementary Fig. S2A). In addition, 40 genera specifically inhabited T1C-1 rhizospheres (Supplementary Fig. S2B). To further identify taxa that were differential between T1C-1 and Minghui 63 soils, the 50 most abundant genera that were differentially abundant for T1C-1 or Minghui 63 were specifically analyzed using a T-test. Among these, 33 were elevated in T1C-1 soils compared with Minghui 63 soils (Supplementary Fig. S3). Thus, the strongest enrichment was observed for taxa in T1C-1 soils, which is consistent with the general increased α diversity indices for T1C-1 communities (Supplementary Table S5).Fig. 2: Comparison of soil and surface water shotgun metagenomic sequencing data.A Differences in α-diversity metrics, Shannon–Wiener index (H′), Simpson index (D), Richness (R), and Evenness (E) between soil and surface water communities. Black asterisks indicate that the α-diversity index was significantly higher in soils (***, p  More

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