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The reintroduction of a species into its historic range is a critical component of conservation programmes designed to restore extirpated metapopulations. However, many reintroduction efforts fail, and the lack of rigorous monitoring programmes and statistical models have prevented a general understanding of the factors affecting metapopulation viability following reintroduction. Spatially explicit metapopulation theory provides the basis for understanding the dynamics of fragmented populations linked by dispersal, but the theory has rarely been used to guide reintroduction programmes because most spatial metapopulation models require presence–absence data from every site in the network, and they do not allow for observation error such as imperfect detection. We develop a spatial occupancy model that relaxes these restrictive assumptions and allows for inference about metapopulation extinction risk and connectivity. We demonstrate the utility of the model using six years of data on the Chiricahua leopard frog Lithobates chiricahuensis, a threatened desert‐breeding amphibian that was reintroduced to a network of sites in Arizona USA in 2003. Our results indicate that the model can generate precise predictions of extinction risk and produce connectivity maps that can guide conservation efforts following reintroduction. In the case of L. chiricahuensis, many sites were functionally isolated, and 82% of sites were characterized by intermittent water availability and high local extinction probabilities (0·84, 95% CI: 0·64–0·99). However, under the current hydrological conditions and spatial arrangement of sites, the risk of metapopulation extinction is estimated to be <3% over a 50‐year time horizon. Low metapopulation extinction risk appears to result from the high dispersal capability of the species, the high density of sites in the region and the existence of predator‐free permanent wetlands with low local extinction probabilities. Should management be required, extinction risk can be reduced by either increasing the hydroperiod of existing sites or by creating new sites to increase connectivity. Synthesis and applications. This work demonstrates how spatio‐temporal statistical models based on ecological theory can be applied to forecast the outcomes of conservation actions such as reintroduction. Our spatial occupancy model should be particularly useful when management agencies lack the funds to collect intensive individual‐level data.

There is little consensus on how agriculture will meet future food demands sustainably. Soils and their biota play a crucial role by mediating ecosystem services that support agricultural productivity. However, a multitude of site‐specific environmental factors and management practices interact to affect the ability of soil biota to perform vital functions, confounding the interpretation of results from experimental approaches. Insights can be gained through models, which integrate the physiological, biological and ecological mechanisms underpinning soil functions. We present a powerful modelling approach for predicting how agricultural management practices (pesticide applications and tillage) affect soil functioning through earthworm populations. By combining energy budgets and individual‐based simulation models, and integrating key behavioural and ecological drivers, we accurately predict population responses to pesticide applications in different climatic conditions. We use the model to analyse the ecological consequences of different weed management practices. Our results demonstrate that an important link between agricultural management (herbicide applications and zero, reduced and conventional tillage) and earthworms is the maintenance of soil organic matter (SOM). We show how zero and reduced tillage practices can increase crop yields while preserving natural ecosystem functions. This demonstrates how management practices which aim to sustain agricultural productivity should account for their effects on earthworm populations, as their proliferation stimulates agricultural productivity. Synthesis and applications. Our results indicate that conventional tillage practices have longer term effects on soil biota than pesticide control, if the pesticide has a short dissipation time. The risk of earthworm populations becoming exposed to toxic pesticides will be reduced under dry soil conditions. Similarly, an increase in soil organic matter could increase the recovery rate of earthworm populations. However, effects are not necessarily additive and the impact of different management practices on earthworms depends on their timing and the prevailing environmental conditions. Our model can be used to determine which combinations of crop management practices and climatic conditions pose least overall risk to earthworm populations. Linking our model mechanistically to crop yield models would aid the optimization of crop management systems by exploring the trade‐off between different ecosystem services.

Survival is a critical component of individual fitness, population dynamics and the landscape ecology of organisms. Survival in animal populations is frequently estimated from capture–mark–recapture studies, yet these estimates are biased low when the permanent emigration of individuals is confounded with mortality. This systematic bias can limit the value of demographic information available for conservation and management efforts. We developed a novel multistate mark–recapture model for survival estimation in fossorial organisms that incorporates auxiliary passive integrated transponder (PIT‐tag) detection data to account for the possibility of permanent emigration from our study area as well as the imperfect detection of individuals. Our study provides a direct comparison of mortality, emigration and reduced ground surface activity as explanations for declines in terrestrial salamander counts which are commonly reported following timber harvest. Reduced ground surface activity was not supported as a likely cause for reduced counts of plethodontid salamanders after timber harvest. Instead, ground surface activity was predicted to be considerably higher after timber harvest, suggesting that surface counts would under‐represent the extent of population losses relative to control areas. Controlling for multiple causes for non‐detection of salamanders, we found evidence that survival probability was reduced while permanent emigration rates may also be elevated in the initial months after timber harvest. However, a substantial majority of salamanders were known to survive the process of initial forest stand entry and timber removal. Synthesis and applications. Our analysis of passive integrated transponder (PIT‐tag) detection data with a novel multistate mark–recapture model indicated that mortality and emigration are both potential causes for short‐term reductions in salamander abundance following timber harvest. We suggest that salamander mortality is likely tied to habitat or microclimate conditions in early successional timber cuts, rather than the physical process of timber removal.

The leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade-off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of plant functional types (PFTs). We investigated whether global leaf economic relationships are also present within the scale of plant functional types (PFTs) commonly used by Earth System models, and the extent to which this global-PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFTs and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (specific leaf area [SLA], N, P) were the most robust within and across PFTs, suggesting that covariation in these traits is driven by universal leaf construction trade-offs and stoichiometry. Relationships among metabolic traits (dark respiration [Rd], maximum RuBisCo carboxylation rate [Vc,max], maximum electron transport rate [Jmax]) were slightly less consistent, reflecting in part their much sparser sampling (especially for high-latitude PFTs), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across-PFT differences and that within-PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, this covariance was an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.

Distributional data are essential for understanding species and community responses to environmental and anthropogenic change. Large biodiversity databases provide key information on distributional patterns, but their temporal coverage can be limited. Facilitating access to untapped occurrence data is a pressing need. We present the Historical Occurrence Georeferencing System (HOGS), a Python protocol that isolates occurrence markers from distribution maps, assigns them to taxonomic groups and georeferences each image relative to a baseline coordinate system, producing latitude–longitude coordinates for all mapped marker occurrences. We tested our georeferencing protocol on two historical atlases of over 1000 sub‐Saharan bird species, georeferencing 153,052 markers from 751 maps. Our new georeferencing protocol enables rapid generation of occurrence data from historical maps, providing reference or enhanced distribution data for species and inputs for ecological niche modelling in biogeographical, evolutionary and conservation studies.

Addressing the challenge of predicting biodiversity requires models that fully capture the complex dynamics of species and communities. While species distribution models (SDMs) predict individual species' distributions based on environmental variables, they often fail to reconstruct realistic communities. Macroecological models (MEMs), on the other hand, predict community indices but lack species‐level resolution. To bridge this gap, we introduce CAMELIA (Community Assemblage Modelling macroEcoLogically Informed with AI), a neural network framework integrating species distribution data with community constraints to improve biodiversity predictions. Approach and Methods. CAMELIA uses a multitask neural network to jointly optimize species occurrence probabilities and community indices. It recalibrates species probabilities from stacked SDMs using observed or predicted community indices. We tested CAMELIA on plant communities in the French Alps, using MEM predictions (CAMELIA‐MEM), high‐quality field data (CAMELIA‐OBS) and degraded field data (CAMELIA‐PROXY) for various community indices: species richness, community mean and community standard deviation for plant height, specific leaf area and leaf nitrogen content. CAMELIA substantially improved community predictions over stacked SDMs. With observed indices, it reconstructed communities with functional indices highly correlated with observations (for community mean plant height: R2 = 0.98 vs. 0.74 with S‐SDMs), reduced the mean errors in predicting species richness by 38%, and increased the Sørensen coefficient by an average of 33%. Even with lower‐quality data, CAMELIA‐MEM outperformed stacked SDMs in community standard deviation (R2 +6.9%) and species composition (+10.6% for Sørensen). With noisy community indices (CAMELIA‐PROXY), CAMELIA remained robust, with R2 values for all indices falling between CAMELIA‐MEM and CAMELIA‐OBS, often closer to the latter (e.g. R2 for community mean plant height = 0.92), demonstrating resilience to imperfect data. CAMELIA provides a flexible and effective framework for improving community structure predictions. By optimizing species probabilities to align with community constraints, it produces more ecologically realistic assemblages than stacked SDMs. Its adaptability to different data qualities makes it a valuable tool for biodiversity modelling. Future developments should explore the integration of remotely sensed biodiversity indices to further enhance predictions, particularly for conservation applications.

To investigate the sensitivity of model choice on the results, I used a Bayesian approach to sample the posterior distributions of competition (αij), growth rate (λi) and treatment effect parameters for seven different alternative competition models of similar complexity. Because coexistence is predicted using invasion analysis at these equilibria, it is important to acknowledge the potential trade-off between models' predictive performance on observed data (affecting estimates ofA and a), and realism when these are used to extrapolate carrying capacities. [...]of the original ten species pairs that were predicted to have switched coexistence outcomes between treatments, only four such switches are now predicted at probabilities greater than 0.5 (Fig. 1), including for two species pairs that were scored as not having switched in the original analysis. Carrying the posterior means of model 7's niche and fitness differences forward through the remaining analyses results in the loss of statistically significant differences between competition and demographic differences between treatments (Extended Data Fig. 1).

We present a series of competing path models relating interspecific patterns between specific leaf area, leaf nitrogen content, net photosynthesis and stomatal conductance and test these against data from 22 species of herbaceous plants grown under controlled conditions with contrasting irradiance and nutrient supply rates. We then compare these results with two previous data sets, one based on field measures and one based on glasshouse measures, to determine the robustness of the results. Only one model was able to account for the patterns of direct and indirect effects between the four variables to all data sets. In this model specific leaf area is the forcing variable that directly affects both leaf nitrogen levels and net photosynthetic rates. Leaf nitrogen then directly affects net photosynthetic rates which in turn then affect stomatal conductance to water.

Aim: It is hypothesized that the ecological niches of polyploids should be both distinct and broader than those of diploids — characteristics that might have allowed the successful colonization of open habitats by polyploids during the Pleistocene glacial cycles. Here, we test these hypotheses by quantifying and comparing the ecological niches and niche breadths of a group of European primroses. Location: Europe. Methods: We gathered georeferenced data of four related species in Primula sect. Aleuritia at different ploidy levels (diploid, tetraploid, hexaploid and octoploid) and used seven bioclimatic variables to quantify niche overlap between species by applying a series of univariate and multivariate analyses combined with modelling techniques. We also employed permutation-based tests to evaluate niche similarity between the four species. Niche breadth for each species was evaluated both in the multivariate environmental space and in geographical space. Results: The four species differed significantly from each other in mono-dimensional comparisons of climatological variables and occupied distinct habitats in the multi-dimensional environmental space. The majority of the permutation-based tests either indicated that the four species differed significantly in their habitat preferences and ecological niches or did not support significant niche similarity. Furthermore, our results revealed narrower niche breadths and geographical ranges in species of P. sect. Aleuritia at higher ploidy levels. Main conclusions: The detected ecological differentiation between the four species of P. sect. Aleuritia at different ploidy levels is consistent with the hypothesis that polyploids occupy distinct ecological niches that differ from those of their diploid relative. Contrary to expectations, we find that polyploid species of P. sect. Aleuritia occupy narrower environmental and geographical spaces than their diploid relative. These results on the ecological niches of closely related polyploid and diploid species highlight factors that potentially contribute to the evolution and distribution of polyploid species.

Aim: As data accumulate, a multi-taxon biogeographical synthesis of the Mojave Desert is beginning to emerge. The initial synthesis, which we call the 'Mojave Assembly Model', was predominantly based on comparisons of phylogeographical patterns from vertebrate taxa. We tested the predictions of this model by examining the phylogeographical history of Hadrurus arizonensis, a large scorpion from the Mojave and Sonoran deserts. Location: Mojave and Sonoran deserts, United States and Mexico. Methods: We sequenced mitochondrial cytochrome c oxidase subunit I (COI) data from 256 samples collected throughout the range of H. arizonensis. We analysed sequence data using a network analysis, spatial analysis of molecular variance (SAMOVA), and a Mantel test. We then used a molecular clock to place the genetic patterns in a temporal framework. We tested for signals of expansion using neutrality tests, mismatch distributions and Bayesian skyline plots. We used Maxent to develop current and late-glacial species distribution models from occurrence records and bioclimatic variables. Results: Phylogenetic and structure analyses split the maternal genealogy basally into a southern clade along the coast of Sonora and a northern clade that includes six lineages distributed in the Mojave Desert and northern Sonoran Desert. Molecular dating suggested that the main clades diverged between the late Pliocene and early Pleistocene, whereas subsequent divergences between lineages occurred in the middle and late Pleistocene. Species distribution models predicted that the distribution of suitable climate was reduced and fragmented during the Last Glacial Maximum. Main conclusions: Genetic analyses and species distribution modelling suggest that the genetic diversity within H. arizonensis was predominantly structured by Pleistocene climate cycles. These results are generally consistent with the predictions of Pleistocene refugia for arid-adapted taxa described in the Mojave Assembly Model, but suggest that a northern area of the Lower Colorado River Valley may have acted as an additional refugium during Pleistocene glacial cycles.