Ecological Applications

O_LIUnderstanding the processes underlying successful eradication of invasive species is essential for achieving global island conservation goals. Despite the widespread availability of capture records from eradication programs, modeling frameworks that utilize these datasets to elucidate spatio-temporal population dynamics remain underdeveloped. C_LIO_LIIn this study, we reconstructed the spatio-temporal population dynamics of the small Indian mongoose on Amami-Oshima Island (712 km{superscript 2}), Japan, where the species was introduced in 1979 and officially declared eradicated in 2024 after more than 30 years of systematic removal. We integrated introduction records, capture data, and monitoring data using a hierarchical harvest-based model (HBM). To evaluate the models capacity to support management decisions and assess eradication success, we conducted retrospective analyses and compared estimated eradication probabilities with those obtained from a rapid eradication assessment (REA; Samaniego-Herrera et al., 2013). C_LIO_LIThe estimated population size (before reproduction) peaked at 5,449 individuals (95% CI: 4,703, 6,175) in 2000 and subsequently declined almost monotonically. The maximum invaded area was 547.78 km{superscript 2} (posterior median, 95% CI: 496.47, 566.04) in 2009, indicating that the removal program successfully prevented island-wide expansion. Retrospective analyses showed that population estimates remained within the 95% credible intervals of the full dataset estimates, demonstrating temporal consistency. Eradication probabilities estimated by the HBM were substantially higher than those from the REA, highlighting the sensitivity of estimates to fine-scale heterogeneity in detection processes. C_LIO_LISynthesis and applications: Hierarchical HBMs provide a powerful framework for reconstructing, predicting, and evaluating invasive species eradication dynamics. Being aware of the limitations for application to eradication evaluations, HBMs can support adaptive management in long-term eradication programs and improve our understanding of the mechanisms underlying successful eradication. C_LI

Agricultural intensification is a major cause of population declines in farmland birds and insects worldwide. While these declines are expected to disrupt trophic interactions through density-dependent cascading effects, examples outside of crop pest control or involving more than two trophic levels remain poorly documented. Here we quantify such cascading effects across three trophic levels involving a declining aerial insectivore, the Tree Swallow (Tachycineta bicolor), which breeding success decreases with agricultural intensity, Protocalliphora bird blowflies (Diptera: Calliphoridae) that parasitize its nestlings, and Nasonia parasitoid jewel wasps (Hymenoptera: Pteromalidae) that feed upon blowfly pupae within swallow nests. Agriculture intensity may affect each of these actors either directly via, for instance, toxicological effects from pesticide use, or indirectly through density-dependent trophic interactions, which can exacerbate or mitigate one another. Using Bayesian models within a causal inference framework, we quantified how nestling host density, meteorological conditions, and agricultural landscape context affect Nasonia parasitoid infestation of Protocalliphora pupae, and estimated Protocalliphora emergence success. Analyses were based on 3,340 Protocalliphora puparia collected from 248 Tree Swallow nests over a 16-year period (2004-2019) across a 10,200-km2 agricultural intensity gradient in Southern Quebec, Canada. Model predictions indicated that Protocalliphora emergence probability increased steeply with nestling host availability (i.e., nestling-days). Whereas Nasonia parasitoid infestation occurrence at the nest level increased with Protocalliphora host availability, the proportion of infested Protocalliphora pupae within nest decreased and led to a dilution effect at the individual level. Although Protocalliphora abundance within nests decreased with agricultural intensity, their emergence probability peaked in agrointensive landscapes. This increase was associated with reduced Nasonia infestation rates, suggesting that agricultural intensity indirectly mitigated parasitoid pressure on Protocalliphora host populations. Our results highlight how agricultural intensification can shape multitrophic interactions and life-history traits (emergence success and infestation rates) by simultaneously reducing host availability and weakening top-down control by parasitoids.

1. Common dolphins (Delphinus delphis) are the most abundant cetaceans off mainland Portugal and a species of conservation and bycatch concern, so reliable abundance estimates are a management priority. Yet no single survey provides adequate spatiotemporal coverage: dedicated programmes offer sparse snapshots, while opportunistic programmes are spatially constrained by their routes. Integrating them is methodologically demanding, and the existing methods that do so in a single model estimate the density of animal groups, not of the individual animals that management often requires. 2. We develop a spatiotemporal marked log-Gaussian Cox process that integrates four structurally distinct surveys through a shared intensity surface, comprising environmental covariates, a latent spatial field, and survey-specific detection functions. Group size enters as a zero-truncated negative binomial mark with its own spatiotemporal field. We apply it to a dedicated aerial survey and three opportunistic ship-based programmes off mainland Portugal, comprising 162,514 km of on-effort transects and 1,756 sightings of 17,000 animals. We fit the model in a Bayesian framework with integrated nested Laplace approximations, yielding seasonally resolved group and animal density surfaces across the Portuguese Exclusive Economic Zone, and abundance estimates with fully propagated uncertainty. 3. Predicted animal density concentrated over the shelf and upper slope in every season, peaking northwest, and increased in shallower, cooler, more productive waters and over steeper seabed. Abundance nearly doubled from summer (77,000 [95% credible interval: 60,000, 98,000]) to winter (146,000 [99,000, 208,000]). Seasons shared 56% of the spatial field's variance, so effort from any programme in any season informed all others, and the seasonal contrast remained estimable despite uneven coverage. 4. Synthesis and applications. Our framework allows managers to combine dedicated surveys with existing platforms of opportunity to estimate animal density by season. Because group size is modelled explicitly, densities are estimated in animals rather than groups, as required for bycatch risk assessment and marine spatial planning. Because each survey enters through its own observation model, the approach accommodates programmes that differ in platform and protocol and transfers to any species or region where several surveys each cover part of a population.

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

1. Natal dispersal is a key process in population ecology because it links local demographic processes to broader-scale population dynamics by redistributing individuals. When using capture-recapture data, multistate capture-recapture models using discrete spatial units as states are the gold standard for estimating natal dispersal among spatial units while accounting for spatial variation in survival, recruitment and imperfect detection. However, because their computational cost increases rapidly with the number of spatial units, applications have been limited to a small number of units. Therefore, in practice, these models cannot provide spatially detailed inference on natal dispersal across large landscapes. 2. We develop a computationally efficient Bayesian capture-recapture model, called the efficient natal dispersal (END) model, to estimate natal dispersal among discrete spatial units jointly with spatial variation in demographic parameters and detection probabilities. The END model relies on two key structural features: juveniles and breeders are separated into two arrays, and resightings outside the natal spatial unit are aggregated over time for individuals released as juveniles. 3. Using simulations, we show that the END model is considerably (up to 30 times) more computationally efficient than a conventional multistate model, while maintaining comparable parameter accuracy. We then apply the END model to white stork (Ciconia ciconia) capture-recapture data from Germany across 101 hexagonal spatial units, a spatial resolution at which a conventional multistate model is computationally infeasible. We estimate natal dispersal among units jointly with spatial variation in survival and recruitment. This allows us to identify areas of lower or higher survival, earlier or delayed recruitment, and dispersal probabilities among all units. By combining estimated dispersal probabilities with existing data on the number of juveniles born in each spatial unit, we estimate natal dispersal in terms of numbers of individuals and identify units with positive or negative net migration, sources and sinks. 4. Overall, our approach moves capture-recapture analyses from estimating natal dispersal among a few spatial units to inferring dispersal networks and assessing their demographic consequences across large domains. Our approach is applicable to many spatially structured capture-recapture datasets, opening new opportunities for studying spatial population dynamics.

The persistence of pest populations during crop off-seasons is a major challenge for integrated pest management, yet the ecological processes underlying survival and re-infestation often remain unclear. We investigated the oriental fruit fly, Bactrocera dorsalis, a major mango pest, through a two-year demographic monitoring across 56 orchards in the two main mango production basins in Senegal: the Sahelian Niayes and the tropical Casamance. Using a framework combining mechanistic and machine learning models, we identified the main environmental drivers of two key demographic parameters: the local abundance during the mango off-season and the onset of population growth in orchards. In the Niayes, harsh dry-season conditions confined residual populations to micro-habitats providing humidity and shade, while alternative hosts such as citrus trees could act as catalysts for early outbreaks. In contrast, under tropical conditions in the Casamance basin, B.dorsalis populations were more abundant year-round, and higher off-season densities appear to delay outbreak onset, likely due to density-dependent processes such as competition and predation. These findings show that distinct ecological processes shape B. dorsalis off-season persistence across environments, thereby determining population carry-over and growth onset, and ultimately leading to contrasting population dynamics across environments. They support region-specific management strategies to effectively reduce pest pressure: targeting refuges and early host resources in the Niayes, and enhancing natural regulation in Casamance.

1. As city populations expand globally, understanding how urbanization shapes ecosystem services is increasingly important. Urban agriculture plays a key role in food access, particularly in low-income areas with limited access to fresh produce. Arthropods play important roles in these systems through pollination, herbivory, pest control, and nutrient cycling. Urban development of natural areas often reduces biodiversity, including that of arthropods. Additionally, urban biodiversity frequently correlates positively with socioeconomic conditions, a pattern known as the luxury effect. 2. In this study, we examined how gradients in urbanization and socioeconomic context relate to local and landscape habitat quality, arthropod abundance, and pollination services across 21 community gardens in the Denver, CO Metro Area. To quantify ecosystem services, we employed a sentinel plant, Cucumis sativus (cucumber), and collected arthropods (insects, myriapods, arachnids, isopods), assigning them to functional groups based on their known feeding behavior. We evaluated direct and indirect pathways linking urban matrix characteristics to arthropod-mediated ecosystem services. 3. The abundance of all arthropod groups (including pollinators, herbivores, and natural enemies) declined steeply with increasing impervious surface. Neighborhood wealth was associated only with bee species richness and Shannon diversity and displayed a hump-shaped pattern that peaked at wealth levels 35-46% above the national average, then declined. Multivariate GLMs revealed that Western honeybees (Apis mellifera) were the only species driving differences in bee community composition, with their abundance positively related to neighborhood wealth. 4. We also observed pollen limitation in our cucumber plants, and as bee Shannon diversity increased, cucumber production increased. Environmental covariates alone showed no direct relationship with cucumber production, indicating that insect pollination mediated this effect. This work identifies the biophysical and social-ecological drivers of urban arthropod biodiversity and ecosystem services, revealing where crop production may be most vulnerable to decline in cities.

The ecological and economic threat posed by invasive pests demands proactive mitigation. Species distribution models (SDMs) are widely used in efforts to predict where invasive species might spread after introduction, though such models face several limitations. Among these is the unrealistic assumption of niche uniformity throughout a species' range. This has led to interest in developing SDMs that explicitly account for local adaptation, though few methods have achieved this in a way that confidently separates local adaptation from population structure. Here we develop and implement a sequential SDM approach that incorporates experimentally verified associations between genotype, phenotype, and environment to forecast establishment risk in a major agricultural pest. We leverage genomic data from 738 individuals to characterize the geographic distribution of alleles at a major-effect locus for cold tolerance (tret1) in Helicoverpa armigera, an invasive crop pest of major economic concern in North America. We demonstrate that a recently detected North American population carries a cold-adapted tret1 allele, which has likely contributed to its persistence. We quantify the contribution of cold-adapted tret1 to the potential invasive range of H. armigera in North America under current and future climate scenarios. We find that cold-adapted tret1 may dramatically expand the potential range of H. armigera, and that potential future range expansion is likely to be driven primarily by cold-adapted individuals. Our results highlight the importance of accounting for intraspecific variation in invasive species risk assessments and management strategies.

Recent work on the design of spatial capture-recapture (SCR) studies has largely focused on settings with a single set of movement and detection parameters. In many applied contexts, however, populations are naturally stratified-for example, when different sexes exhibit markedly different movement scales and encounter processes, or when more than one focal species is monitored. Designs that perform well for one group may be inefficient or suboptimal for others, despite the substantial logistical effort typically required to implement an SCR survey. We describe several potential SCR design strategies for surveying multiple groups, and compare these using simulation. Designs considered include regular and optimised grids, clustered and lacework designs, and designs generated using genetic algorithms, including new approaches that optimise information from both groups and a recently published two-stage design. Using proximity detectors such as camera traps, we evaluate design performance in terms of precision, bias, and coverage, and examine how performance changes with the number of available resources. Our results show that multi-group SCR surveys can perform well even when focal groups differ greatly in movement scale and encounter rate, provided that some simple guidelines are followed. Systematic layouts accommodating more than one characteristic spacing e.g. cluster designs, performed consistently well and are a reliable default option. Regular grid designs with a single spacing between detectors are feasible when differences between groups are moderate but performed poorly as differences increased. Establishing when these designs become problematic adds unnecessary risk to the design process. Algorithmic designs did not outperform systematic designs even in highly-skewed scenarios expected to favour them. In practice, when there is more than one focal group we recommend identifying the smallest and largest plausible movement scales and rejecting potentially pathological designs, primarily those that cannot generate recaptures at the smaller scale or cover a substantial area at the larger scale.

Ecological responses to environmental stressors are increasingly shaped by interactions among anthropogenic and climatic drivers, yet the influence of prior exposure history on subsequent stress responses remains poorly integrated into multiple stressor ecology. Using the blue mussel (Mytilus edulis) as a model system, we experimentally tested whether prior exposure to environmental nanoplastics modifies responses to subsequent stressors, including copper contamination, nutrient enrichment, and salinity fluctuations, individually and in combination. Nanoplastic pre-exposure reduced survival across subsequent treatments and altered stressor interaction outcomes at the community level, shifting responses toward synergistic effects that amplified metabolic costs. These results demonstrate that exposure history can modify both the magnitude and direction of multi-stressor effects, with consequences extending beyond individual performance to ecosystem functioning. By incorporating temporal exposure dynamics into multiple stressor research, our study highlights how ecological responses to global change depend not only on the identity and intensity of stressors, but also on prior environmental conditions shaping subsequent organismal and ecosystem health status and responses.

Field ecologists often rely on observational data to understand the impact of environmental stressors and management interventions on natural systems. Natural and anthropogenic events (e.g. wildfires, protected areas, nutrient deposition) do not occur randomly in space, however, which can introduce bias into observational studies--which we refer to as causal selection bias. Field study designs that ignore the non-random occurrence of stressors may yield biased estimates of stressor effects on ecosystems. Matching methods commonly used in economics, political science and epidemiology offer a powerful framework for controlling for causal selection bias by identifying more comparable treatment and control sites. Although these methods are increasingly used in conservation, they are rarely used in ecological field-based studies. Here we review how Propensity Score Matching (PSM) can improve field sampling designs in ecology and strengthen causal identification of stressor effects. Then we apply this approach to a case study examining wildfire effects on forest recovery in California. We conclude with practical recommendations for implementing PSM to improve causal identification of ecological change, which is particularly important for developing effective management interventions.

Citizen science observations are abundant, but conservation requires turning uneven records into reliable predictions and directing new surveys to where information is missing. We developed a biodiversity platform for Japan that is updated monthly and integrates 2.32 million records to predict 8,297 species across seven taxonomic groups. Shared representation models outperformed species-specific models in four groups and extended predictions to species with few records. Five independent datasets, including structured monitoring, environmental DNA and complete forest inventories, confirmed that the models ranked observed species and occupied sites above alternatives, with median AUCs of 0.724 to 0.894 across sites and 0.650 to 0.841 across species. For any user-selected area, the platform returns candidate species, distribution predictions, a biodiversity map corrected for uneven observation effort, a conservation priority map for native species and a map recommending where to survey next. This map highlights places where species with few records are predicted to occur despite limited sampling. Independent observations showed that areas ranked highly by this predicted potential contained many such species, indicating that model predictions can help direct surveys toward knowledge gaps. New observations are incorporated into monthly updates, creating a national feedback system connecting citizen science, local conservation decisions and future surveys.

Global biodiversity recovery demands restoration at landscape scales, where ecological outcomes are shaped not only by habitat amount but also spatial arrangement. Alternative landscape restoration strategies can increase habitat amount, improve quality, and alter configuration in fundamentally different ways and may therefore vary in their efficiency. Here, we conceptualise five landscape restoration strategies and use a spatially explicit metapopulation framework to compare their ability to increase predicted species persistence across heterogeneous landscapes and trait space defined by dispersal ability and specialisation. We find that restoration outcomes are strongly trait-dependent, with restoration of existing degraded habitats yielding the highest benefits - particularly for specialists. Improvement of the landscape matrix to create low-quality habitat primarily benefits generalists, while strategies creating new habitat patches or enlarging patches provide more consistent but modest gains. These results reveal fundamental trade-offs among restoration strategies and highlight the utility of trait-explicit approaches to inform their implementation and evaluation.

1. This study analyzed 21 years (2000-2025) of butterfly count data from Central Alberta, integrated with intensive 5-year (2021-2025) high-resolution intra-seasonal sampling. 2. Long-term macro-scale analysis revealed a significant decline in Shannon Diversity, a change that remained obscured when relying solely on traditional metrics of species richness and evenness. 3. This diversity decline was primarily driven by the severe, long-term collapse of the native Common Ringlet (Coenonympha tullia). 4. Four other dominant species--Cabbage White (Pieris rapae), Clouded Sulphur (Colias eriphyle), European Skipper (Thymelicus lineola), and Common Wood Nymph (Cercyonis pegala)--maintained long-term population stability, though their abundances were significantly constrained by extreme winter minimum temperatures and rapid spring warming. 5. High-resolution intra-seasonal analysis (2021-2025) demonstrated that community indices and species-specific abundances were strongly limited by daily weather, particularly wind velocity and temperature. 6. These findings illustrate that while traditional metrics like richness and evenness are fundamental to community ecology, they provide incomplete insights when applied in isolation; they are most effective when utilized as part of a complementary, multi-scalar framework. 7. This study highlights the necessity of coupling multi-decadal historical datasets with high-frequency, fine-scale sampling to accurately identify the mechanisms of community turnover that simpler metrics may overlook. 8. The results underscore the critical importance of standardized citizen science monitoring in quantifying environmental impacts and establishing conservation priorities for terrestrial insect groups.

While plant communities that exist at the edges of agricultural fields are typically composed of 'weedy' species, they provide essential resources for insect pollinators and other wildlife. However, little is known about how sublethal exposures to herbicides via drift (off-target chemical movement by air) affects the fitness of weedy plant species across varying agro-ecological environments. If weed species vary in sensitivity to herbicide drift, then this could result in plant community shifts in favor of more tolerant species. Consequently, these changes in species composition could alter resource availability in agroecosystems. Here, we replicated a common garden experiment in three US states where agriculture is a major form of land use (Michigan, Pennsylvania, and Tennessee). We exposed nine weed species to a sublethal, drift-level rate of the herbicide dicamba and examined plant fitness. We found that fitness in the presence of dicamba drift depended on both the weed species and the environmental context. For some species, dicamba drift significantly reduced fitness. For others, it had no effect, indicating greater tolerance to the herbicide. However, more variation in tolerance to dicamba drift among species was observed in Michigan compared to Pennsylvania or Tennessee, a result that was partially explained by the severity of damage from insect herbivory that occurred at each location. In a follow-up study, we explored the relationships between plant developmental traits and fitness to determine the functional pathways that underlie tolerance to dicamba drift using structural equation modeling. This approach revealed that weedy plants that are better able to buffer the effects of dicamba drift on short-term growth are then better equipped to minimize effects on flower production and ultimately fitness (seed set). Overall, our work advances knowledge about how wild plant populations in different contexts are responding to unintentional, yet increasingly common occurrences of anthropogenic stress in the form of off-target herbicide exposures.

Assessing the availability of protected habitats for at-risk species is needed to determine how the spatial distribution of land cover and land use shapes conservation outcomes. The Loammi skipper (Atrytonopsis loammi) is a nonmigratory, prairie-associated butterfly that has experienced a significant reduction in its formerly widespread southeastern United States distribution over the past several decades. Observations in recent years have been limited to a small number of isolated Florida populations, and it is unclear how much suitable habitat remains and what proportion of that habitat is protected. Here we used publicly available community science data and collected specimens to identify the predominant land cover types occupied by A. loammi. We then quantified the extent to which each of these land cover types overlap with protected areas to estimate the proportion of the butterflys current distribution that occurs on managed conservation lands. Our findings show that A. loammi relies heavily on protected lands, with over half of the total area of the most strongly associated habitats located within protected areas. Given the continued expansion of human-modified landscapes in Florida and the reliance of A. loammi on protected lands, the long-term persistence of A. loammi will likely depend on the conservation and effective management of its remaining suitable habitat. Implications for insect conservationOur results highlight the critical role of protected areas in sustaining at-risk insect populations, particularly for species with limited dispersal and shrinking ranges. More broadly, they suggest that conserving and restoring protected habitats while maintaining connectivity are essential strategies for effective insect conservation in rapidly developing regions.

O_LIIdentifying spatial origins of biological invasions, disease outbreaks, or environmental contaminants is critical for timely intervention. However, existing methods struggle to resolve overlapping signals from multiple sources or account for extreme zero/one inflation in bounded data. C_LIO_LIWe developed HiBASIL (Hierarchical BAyesian Source Inference and Localization), a Bayesian framework that jointly infers source coordinates and mechanistic dispersal kernels from zero-one-inflated spatial observations. We systematically tested HiBASIL across 2,500 simulations spanning localized to long-distance dispersal regimes, various foci weight mixtures (0.5/0.5 to 0.95/0.05), and varying sample sizes (N = 50 to 500) to evaluate geometric sensitivity, spatial robustness, parameter recovery capability, and multi-focal resolution. We applied HiBASIL in tracking cucurbit downy mildew dispersal in field experiments including non-inoculated control, single-focus, and two-foci treatments arranged in a randomized complete block design. To demonstrate broad applicability, we validated HiBASIL with historical London cholera epidemic data to determine whether it could accurately localize the epicenter. C_LIO_LIHiBASIL achieved 100% accuracy in kernel identification under correct model specification, 100% and 98% (2% predictive equivalence) in under- and over-parameterization scenarios, and maintained appropriate parsimony in null scenarios. Source localization was highly precise, with a median bias of 0.11 m, successfully resolving minority sources contributing only 5% of the observations. Importantly, the framework provides intrinsic self-diagnostics, signaling model complexity mismatch through posterior bimodality (under-parameterization) or parameter collapse (over-parameterization). HiBASIL is applicable in various epidemic scenarios, including diffused long-distance dispersal. Under an isotropic process, HiBASIL achieved sub-meter mean errors on two-source localization in cucurbit downy mildew field tests, effectively isolating transmission signals from landscape noise despite sparse sampling. Despite confounding influence of underlying network processes in the historical cholera data, the posterior localized the epicenter to within [~] 33m, demonstrating portability beyond plant disease epidemiology. C_LIO_LIIn summary, HiBASIL can accurately localize multiple sources across various epidemic scenarios, extremely unbalanced foci mixtures, and sparse sampling conditions. HiBASIL also demonstrates wide applicability across agricultural and human disease epidemic systems. By providing an open-source Python implementation, HiBASIL enables rigorous inverse spatial inference for ecology, epidemiology, and environmental monitoring, transforming how discrete transmission sources are identified in complex landscapes. C_LI

1- Biological control of mosquitoes using aquatic predators offers a sustainable alternative to chemical insecticides, yet the specific predator and prey functional traits that govern consumption efficacy remain poorly quantified at a global scale. 2- We conducted a global meta-analysis of 755 effect sizes across 59 studies to evaluate how predator identity (fish vs. odonate naiads), body size, dietary guilds, and prey characteristics influence consumption rates. Using multilevel models and robust publication-bias corrections, we quantified predation efficiency, expressed throughout as the consumption rate (CR, larvae predator -1 h -1 ), while accounting for methodological variations across experimental designs. The primary literature itself proved geographically skewed towards Asia (chiefly India), with Africa, the Americas, and Europe markedly under-represented. 3- Grouping predators solely by broad taxonomic identity concealed the central pattern in our data. Although naiads outperformed fish when compared directly within the same studies, this taxon-level difference was driven almost entirely by non-mosquitofish species, the least efficient predator group overall. Mosquitofish (\textit{Gambusia} spp.) and dragonfly naiads were statistically indistinguishable from one another, indicating that dietary specialisation, not taxonomic identity, is the stronger predictor of predation efficacy. Predator body size strongly and positively predicted consumption rates in naiads---driven primarily by dragonflies---but showed no significant or negative relationship in fish. 4- Methodological traits heavily structured the extreme heterogeneity observed across studies; notably, exposure time acted as a severe rate-suppressor, where prolonged assays drastically underestimated per-hour consumption rates due to satiety or handling constraints. Nevertheless, a combined model incorporating all significant ecological moderators simultaneously explained a substantial share of the between-study variance, confirming that predator-prey dynamics in these systems are highly predictable from functional traits. 5- Effective biological control cannot rely on broad taxonomic assumptions but requires evidence-based trait-matching. Management programs should prioritise body size when deploying insect predators, selecting the largest individuals within species known to consume mosquito larvae and favour insectivorous fish species over generalists. Crucially, because short-term laboratory assays artificially inflate efficacy, multi-duration assessments are essential to accurately scale up biocontrol predictions from experimental arenas to complex, real-world ecosystems.

1. Soil invertebrate biomass is closely linked to ecosystem processes, yet its routine quantification remains constrained by the effort required to weigh large numbers of small individuals. Length-mass relationships provide an indirect alternative, but body length can be difficult to standardise and extract automatically from images of curved, coiled or contracted organisms. Projected body area may provide a more tractable image-derived predictor, but its performance across diverse soil-faunal taxa and at the community scale remains insufficiently evaluated. 2. Here, we developed a framework using projected body area and contour length to estimate fresh and dry mass from measurements of 1,792 soil invertebrates representing 15 higher-level taxa. We fitted taxon-specific models for six well-represented groups and general models across taxa, and evaluated predictive performance using leave-one-out cross-validation. We then assessed cross-dataset transferability by comparing our data with those of Lemoine et al. (2026) and pooled the two datasets to derive combined models. Finally, we tested whether individual predictions could be scaled to independently measured community biomass. 3. Taxon-specific models generally reduced prediction error relative to general models, with mean absolute percentage error (MAPE) values of 6.9-37.3% for fresh mass and 7.6-30.2% for dry mass. General models retained MAPEs of 27.2% and 22.4%, respectively. Comparisons of our dataset with that of Lemoine et al. (2026) showed no detectable differences in six of eight taxon-by-mass slopes. Although intercepts frequently differed, no overall dataset effect was detected after accounting for taxa. Pooling both datasets broadened model development, while taxon-specific models generally retained lower error than the combined general model. Finally, community-level predictions tracked measured biomass across all model sets (R2 = 0.87-0.99), but absolute error varied among models and faunal groups (MAPE = 9.6-52.5%). 4. Body area and contour length extracted from images therefore provide a practical basis for scaling soil invertebrate biomass estimation from individuals to communities. Integration with automated imaging and segmentation tools such as flatbug, BIODISCOVER and CollembolAI could extend soil invertebrate monitoring at larger scales beyond abundance and taxonomic composition to biomass, while providing scalable inputs for analyses of food webs and general ecosystem ecology.

Managers must make decisions in the face of uncertainty, especially when available resources are limiting. Identifying thresholds when certain conditions are met or exceeded enable the potential to mitigate risks. In 2005, sustainable harvest levels of willow ptarmigan were identified to avoid harvest efforts exceeding three hunter days km2. Here we evaluate these recommendations by analyzing line transect counts and harvest data from six areas forming three open/closed pairs in a region of state managed willow ptarmigan harvest. We developed three sets of Bayesian hierarchical models, one static distance model, and two dynamics models. One mechanistic hazard model and a Gompertz phenomenological model. Adult and juvenile density showed pronounced year-to-year variation that was largely synchronous across all six sites regardless of hunting status. The harvest effort parameter shows a striking difference between the two models. In the Hazard model, is positive, and excludes zero with near certainty, but in the Gompertz model, the parameter is highly uncertain. However, the two models do not contradict each other but answer complementary questions with different sensitivity to the harvest signal, harvest mortality is additive at the individual level, but this additive mortality is masked at the level of population abundance. The demographic cost of harvest is therefore real and quantifiable through the survival chain, but bounded in the long run by the stabilizing dynamics. A fixed limit anchored to monitored effort and bag is not a crude substitute for adaptive management but the appropriate design under the information commonly at hand. It will be a precautionary instrument grounded in the one relationship this study establishes firmly, the translation of hunter effort into harvest mortality.