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

West Nile virus (WNv) in eastern North America is transmitted primarily by Culex pipiens and Culex restuans, two cryptic vector species whose relative abundance shifts seasonally and across urbanization gradients - gradients that are themselves now shifting under continuing climate change and urban expansion. Because the two species are difficult to separate morphologically and are typically pooled in routine surveillance, this turnover is seldom tracked directly, and agencies lack species-resolved tools to anticipate when, where, and how vector communities will reorganize. Yet the timing of this turnover has been linked to the seasonal timing and intensity of human WNv cases, making it a potentially forecastable correlate of risk. We molecularly identified 9,789 Culex collected over six years, of which 5,808 came from a designed multi-year study in the Baltimore-Washington metropolitan region, and the remainder from Philadelphia and Chicago for cross-regional comparison. We combined negative-binomial generalized linear mixed models of species-specific abundance in Baltimore-Washington with Gradient Forest models of compositional turnover across all three regions. The two species diverged along both the urbanization and thermal gradients: Cx. pipiens abundance increased with impervious surface and with temperature, whereas Cx. restuans declined along both. Consequently, highly urbanized areas remained Cx. pipiens-rich across the season, whereas suburban, low-to-moderate-development landscapes exhibited the largest seasonal shifts in community composition. Accumulated degree-days (ADD) and weekly mean temperature were the most consistent drivers of turnover across regions, with the shift from Cx. restuans to Cx. pipiens fastest between 594 and 610 ADD. These patterns translate into three operational tools for WNv management: a climate-based degree-day window that lets agencies forecast the community shift from temperature data before it is detectable in trap composition, identification of suburban landscapes as priority targets for intensified surveillance and early intervention, and species-resolved environmental responses that help anticipate how community composition may shift within these regions as local climate and land-use conditions change. Together they offer a path from reactive surveillance to anticipatory, spatially targeted, and forward-looking management of WNv risk under environmental change. Open Research StatementAll datasets and R scripts used to generate the results and figures in this study will be made publicly available on DRYAD (DOI pending) upon acceptance of the manuscript. Climate data are publicly available from the PRISM Climate Group (PRISM Group, Oregon State University). Landscape predictor datasets are publicly available from the sources cited in Methods (Dewitz 2023; WorldPop 2018; U.S. Census Bureau 2020; Zell & Sanford 2020; Gesch et al., 2018; Didan 2015).

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 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.

Effective conservation depends on demographic metrics that reliably reflect species status, particularly population abundance. For elusive species occurring at low densities, however, such metrics remain difficult to obtain. Spatial capture-recapture (SCR) models are the standardized approach for estimating density in marked populations, but their data requirements, especially the need for multiple spatial recaptures across individuals, often limit applicability in small or data-poor populations. This constraint has resulted in knowledge gaps for some of the most vulnerable species, undermining evidence-based conservation planning and management. Using camera-trap data and SCR-derived density estimates from data-rich populations, we evaluated alternative, less data-demanding metrics and tested the hypothesis: Space to Event (STE), Mean Local Abundance (MLA), and Relative Abundance Index (RAI) exhibit predictable relationships with SCR-derived density; if supported, these metrics can reliably estimate density in populations where SCR models cannot be implemented. We applied this framework to the European wildcat (Felis silvestris), an elusive small felid with highly fragmented populations across Europe, for which density estimates are largely lacking despite growing conservation concern. Across 21 study areas spanning most of the species' range, our results indicate that European wildcats generally occur at lower densities than previously reported. SCR-derived estimates (n=10) averaged 10.32 {+/-} 11.56 inds/100km2, while STE enabled density estimation in five additional data-poor areas (mean 5.52 {+/-} 5.33 inds/100km2). STE showed a strong linear relationship with SCR-derived density (R2=0.98), supporting its use as a viable alternative when SCR is infeasible, although it tended to underestimate compared to SCR, especially at higher densities. In contrast, MLA and RAI showed weaker and non-linear relationships with SCR-derived density (R2=0.65), indicating substantially lower explanatory power and suggesting their estimates are more strongly influenced by confounding processes. By explicitly calibrating alternative metrics across a wide density gradient throughout most of the species' distribution, this study provides a transferable methodological framework for estimating density in low-density wildlife populations and the first continent-wide, standardized density assessment of a carnivore species. From a management perspective, our findings identify populations that may be most vulnerable, particularly those with the lowest densities, and highlight the need to prioritize absolute abundance monitoring.

ContextArtificial light at night (ALAN) has profound impacts on individual organisms and entire communities. Still, humans tend to underestimate the true biological (spatial) footprint of ALAN, in part because of our limited sensitivity to light compared to other organisms. ObjectivesWe sought to demonstrate how far ALAN can reach into dark spaces at levels that can impact organismal behavior and physiology using a fundamental physical law, the inverse square law. MethodsWe created a spatially explicit model of light spread on real landscapes, parameterized using increasingly available landscape-scale vegetation data to account for attenuation through forests and blocking by topographic relief. ResultsLighting types common in rural areas can produce biologically important effects more than 1 kilometer from the source, and effects of large lights might stretch 3 kilometers or more. The footprint of a light is determined by the complex and multidimensional interaction between characteristics of the light itself and the environment. For example, attenuation through a dense forest might decrease the footprint of a light more than 90% compared to the same light on a grassland. In complex environments, even small changes in light placement and characteristics can lead to large changes in the biological footprint of the light. ConclusionsDesigners and land stewards must account for lighting type, brightness, directionality, and reflected light to create ecologically responsible lighting. Vertical vegetation and topography strongly influence the propagation of biologically detrimental light, and environmental context is vital when planning and installing lights to minimize the biological impacts.

O_LIWhen eradication is unfeasible, invasive predator control should evaluate how removal affects ecological responses by native species. Assessments often use total invasive predator abundance to evaluate prey responses, yet intraspecific variation in diet and space use means that some subgroups cause disproportionate impacts. Identifying these problem individuals, and the spatial scales over which their impacts operate, can enable targeted spatially explicit removal to maximise impact reduction. However, despite individual-level information is often already collected during trapping operations it is seldom included when analysing predator impacts, potentially biasing the conservation outcomes expected under blanket removal. C_LIO_LIWe use a novel framework and two decades of invasive predator control data to estimate how individual variation in residency status influences the distance-dependent impacts of invasive American mink Neogale vison on water vole Arvicola amphibius occupancy across two prey surveys. We also develop a sub-model to predict mink residency status for individuals with missing age data. C_LIO_LIThe probability of capturing adult mink decreased with elevation and years of control, indicating that long-term control altered the resident population and demographic composition of mink around water vole sites. C_LIO_LIDistance-dependent negative impacts of mink varied by residency status, becoming negligible at approximately 20 km from water vole sites for resident mink and 2 km for transient. The spatial scale of mink impacts was largest during the first vole survey when resident mink were more abundant, and declined rapidly for the second survey, when mink were less abundant and spatially clustered. Our results suggest that water voles have benefited mostly from reducing resident mink rather than the total population, especially in early control phases. C_LIO_LIManagers can use our framework to develop spatially explicit and impact-based strategies, not restricted to invasive species control, to construct empirically informed management buffers around populations of conservation concern. Long-term efforts will change the landscape and invasive predator contexts, and thus we recommend iteratively updating and re-evaluating management outcome evaluations. We argue that incorporating individual heterogeneity improves our understanding of ecological mechanisms influencing management success but that the suitability of targeted strategies should be evaluated for target socioecological contexts. C_LI

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.

Protected areas cannot halt biodiversity loss in isolation; integrating them with surrounding human-dominated landscapes is critical. However, this integration is challenged by substantial landscape heterogeneity at their borders, hindering our understanding of cross-border changes in ecosystem service provision. We introduce a novel framework for characterizing these dynamics by analyzing ecosystem service gradients along protected area borders. For 16 protected areas in the French Alps, we assessed 12 ecosystem services using a mix of established biophysical models and novel connectivity-based models for mobile species. These were aggregated into three stakeholder-driven domains reflecting respectively rural, cultural, and urban management priorities. Automated polynomial regression analysis classified borders into five gradient types. The most common were 'Decreasing Gradients', representing a decline in ecosystem services outside the protected area, and 'Increasing Gradients', with the opposite pattern. Our analysis reveals these patterns are driven by specific landscape configurations, uncovering frequent trade-offs between the three management priorities, where, for instance, landscapes supporting rural priorities often degrade cultural and urban ones. We also identify key opportunities for synergies, by identifying areas where ecosystem services for all three priority domains increase simultaneously outside the protected area. This spatially explicit typology provides a powerful diagnostic tool for designing targeted interventions, such as prioritizing habitat restoration where ecosystem services decline or managing agricultural landscapes to mitigate conflicts across management priorities, supporting a more effective integration of protected areas into the wider landscape.

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

Frugivorous insect larvae are dependent on fruiting plants for development, leading to complex host-parasite interactions that may be influenced by many factors at various scales. We compared the relative effects of factors at the individual, neighborhood, and landscape scales in forest patches. Our results suggest that in areas like upstate New York, where agricultural land uses are dominant, individual scale factors are the most influential. Specifically, parasitism increased with host fruit crop size, but was not associated with host species richness or proximity to forest edge. Notably, the most parasitized hosts were non-native species, including Frangula alnus Mill. (Glossy Buckthorn), indicating a potential role of invasive species to shape host-parasite interactions in our system. Our results underscore the importance of host-specific traits in structuring parasitism patterns and suggest management could consider both the ecological context of host traits and the influence of invasive species at multiple scales.

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.

Veterinary ectoparasiticides widely used on companion animals often contain synthetic insecticides whose agricultural applications have been restricted in the European Union because of environmental concerns. These neurotoxic compounds can persist on animal fur and enter surrounding environments, potentially exposing non-target organisms. The Great Tit (Parus major), a common passerine in both urban and forest habitats, frequently incorporates animal fur in nest linings, thereby creating a potential exposure route for breeding adults and nestlings. We analysed fur-containing nest material collected from 63 Great Tit nests in artificial nest boxes at an urban site and a nearby protected forest in Hungary during the 2025 breeding season, sampling at mid-nestling stage and also after fledging. Using HPLC-MS/MS and GC-MS, we detected several veterinary ectoparasiticides in nest materials, including fipronil, fipronil sulfone, imidacloprid, and permethrin. Acetamiprid was found only in urban nests, indicating additional, non-veterinary environmental sources. Multiple insecticides were present in nest material, with higher contamination levels and greater compound diversity in urban compared to forest nests. Residues were present at both sampling times but declined over the course of the breeding cycle. Although contamination was not associated with the measured reproductive parameters of Great Tits, our findings show that veterinary ectoparasiticides can contaminate wild bird nests, including those in protected forest ecosystems. This highlights a previously under-recognised pathway linking companion animal treatments to wildlife exposure and underscores the need to assess the ecological risks and trade-offs associated with widespread veterinary insecticide use. HighlightsO_LIVeterinary ectoparasiticides were detected in urban and forest Great Tit nests C_LIO_LIUrban nests contained higher contamination levels and greater compound diversity C_LIO_LIAcetamiprid occurred only in urban nests, indicating additional environmental inputs C_LIO_LIProtected forests also exposed to fipronil, fipronil sulfone and permethrin C_LIO_LIBird nests reveal an overlooked pathway linking pet treatments to wildlife exposure C_LI

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.

Understanding the ecological determinants of species ranges is a central goal of ecology. Novel tools like global datasets and machine learning models allow us to describe species ranges with increasing scope and accuracy, and to develop and test ecological hypotheses about their determinants. Here, I focus on the widespread nuisance mosquito and dog heartworm vector Aedes sierrensis, a tree hole-breeding mosquito that is widespread and abundance within its native range in western North America, and develop species distribution models (SDMs) to characterize the species range and its environmental determinants. I find that the species range is highly predictable from long-term average bioclimatic variables. Temperature and precipitation were the primary determinants: suitability was highest at wet-season average temperatures of 0 - 10{degrees}C, minimum temperatures of -5 - 5{degrees}C, and summer temperatures of 8 - 22{degrees}C in environments with adequate seasonal rainfall concentrated in the winter. After accounting for climate, land cover variables showed minimal importance for prediction, but suitability was higher in forests and outside of urban areas. The results are consistent with ecological knowledge of the Ae. sierrensis life cycle from field observations and previous laboratory experiments, suggesting that individual physiological constraints scale up to determine species distributional limits.

Various hypotheses have been proposed to explain why some species persist or even flourish in urban areas. Yet, despite its central role in determining when and where animals encounter resources, disturbance, and risk, movement behaviour remains an overlooked mechanism of urban success. In urban areas, human activities are strongly periodic, i.e. predictable in space and time. This may favour species able to adjust their behaviour to predictable cycles of resources and risks in space and time. Here, we tested this hypothesis and tracked movement behaviour along an urbanisation gradient in three mammal species with different urban success: red fox (Vulpes vulpes), an urban dweller; raccoon (Procyon lotor), an invasive urban dweller; and wild boar (Sus scrofa), an urban utiliser. We analysed periodicity in movement behaviour and investigated whether increasing urbanisation is associated with periodic reorganisation of activity timing, space use, and further analysed alterations in habitat selection along the urbanisation gradient. Our results show that foxes aligned their movement behaviour with human activity, having stronger day-night contrasts and more repeatable space use than their rural counterparts. Urban raccoons showed a contrasting strategy; they were more active during the day, without changes in their movement routines under increasing urbanisation, suggesting a flexible strategy that explains their urban success. In contrast, wild boars reduced routine movement behaviours with increasing urbanisation, consistent with their occurrence in less predictable suburban environments and avoidance of city centres. In summary, our results suggest that movement behaviour may be a key mechanism enabling animals to persist in cities, revealing distinct behavioural strategies for coping with urban environments.

Littoral mysids facilitate benthic-pelagic coupling through horizontal migration, yet quantitative monitoring in structurally complex habitats remains methodologically challenged where traditional active gears fail. We evaluated the efficacy of standardized light traps for monitoring littoral mysids (Neomysis integer, Praunus flexuosus) and mesopredatory three-spined sticklebacks (Gasterosteus aculeatus) in the northern Baltic proper, Baltic Sea. Using a paired experimental design with predator-exclusion and unmodified traps, alongside concurrent passive benthic trapping, we assessed abiotic drivers affecting catchability, biotic interactions, and statistical power to monitor changes in population size over time. Results indicated significant biotic interference: unmodified traps attracted high densities of sticklebacks, which reduced mysid catches by approximately 85% through predation or behavioural avoidance. Consequently, physical predator exclusion is mandatory for accurate mysid sampling. Generalized Linear Mixed Models (GLMMs) confirmed that catch rates for all taxa were primarily driven by night duration rather than water temperature. While passive benthic trap catches tracked metabolic activity (peaking in warm summer months), light trap efficiency peaked in spring and collapsed during summer, confirming that sampling efficiency was strictly limited by the short duration of the night. Simulation-based power analysis revealed a stark contrast in monitoring utility based on spatial aggregation. For highly aggregated mysids, the method demonstrated low precision (Power < 0.25 to detect a 50% decline), rendering it suitable primarily for detecting substantial population collapses (>90%). In contrast, for less aggregated sticklebacks, the method achieved a more robust statistical power (>0.80 for a 60% decline), validating light traps as a precise tool for monitoring these abundant mesopredators. We conclude that light traps fill a critical methodological gap for winter and early spring monitoring when traditional passive gears underperform. Appropriate abundance indices should be based on statistical models accounting for night duration and strictly employ physical exclusion barriers when targeting mysids.

Citizen science data are increasingly used to infer biodiversity change, but causal claims based on such data are credible only if sampling effort and its temporal shifts are explicitly modeled. Zhang et al. (1) used citizen science data to conclude that greater photovoltaic policy stringency, measured using the photovoltaic policy stringency index (PSI), reduced county-level bird diversity in China. We reproduced their fixed effects and instrumental variable estimates. However, the observed Shannon diversity derived from pooled citizen science records reflects both bird communities and sampling effort, which the authors' controls do not adequately capture. Accounting for observer count changed the reported statistically significant 2.10% decline in Shannon index to a nonsignificant 0.58% increase (P = 0.288) per one-standard-deviation increase in PSI, and rendered the instrumental variable estimate statistically indistinguishable from zero (P = 0.912). Yet observer count is only one of many sources of sampling bias. PSI was also associated with multiple dimensions of sampling effort, consistent with sampling effort acting as a potential mediator in the PSI-diversity chain. The sampling domain also shifted markedly from 2014 to 2023: recorded county-months increased almost 24-fold, median observer count rose from one to three, and zero-duration records declined from 57.2% to 0.17%. Without adequate adjustment, these shifts confound estimates of temporal change in observed bird diversity. Beyond its inadequate treatment of sampling effort, the original study also misinterpreted its statistical results. Although the reported R{superscript 2} values are high, they are dominated by county and year-month fixed effects, with PSI contributing a partial R{superscript 2} of only 0.048% on observed Shannon index. The PSI-photovoltaic-area correlation is also weak (r = 0.0414) and vanishes after accounting for fixed effects (P = 0.977). Furthermore, the released bird observation data contain many erroneous outliers, raising significant concerns about insufficiently rigorous data preprocessing and quality control. These results show that the released data cannot properly distinguish ecological change from sampling effort change. Robust inference from citizen science data requires checklist-level effort metadata, explicit correction for spatiotemporal sampling shifts, and close collaboration among researchers with complementary methodological and ecological expertise.