Ecological Applications

Understanding the risks of pesticide exposure in agricultural environments is crucial for maintaining essential agroecosystem services. Pollinators behaviour can influence exposure routes, but their role in the formation of pesticide-induced ecological traps has received little attention. Using an individual-based modelling framework (BEE-STEWARD), I examined how three behavioural processes - preference for mass-flowering crops, inadvertent social information (ISI) use, and pesticide-induced foraging bias - affect nectar and pollen visitation rates of buff-tailed bumblebees (Bombus terrestris) to pesticide-treated crop patches located near or far from the colony. All behavioural processes increased visitation to treated patches, though their magnitude and context differed. Crop preference consistently elevated visitation rates, ISI use amplified nectar foraging on nearby crops when combined with preference, and pesticide-induced bias in foraging behaviour strongly increased pollen visits across distances. These results suggest that pesticides capable of altering patch quality assessment can create ecological traps for pollinators, even when untreated patches are available. Incorporating behavioural pathways into pesticide risk assessments could improve mitigation strategies in pesticide-treated agricultural landscapes.

Restoring lost and degraded ecosystems to enhance biodiversity and ecosystem services is a global priority, and animal responses to the restoration of habitats are a critical but undervalued component. Identifying the key drivers of animal colonization in restored habitats provides critical insights for restoration practitioners seeking to maximize ecological outcomes. When integrated into predictive frameworks and spatial decision- support tools, this knowledge becomes especially valuable for strategic planning, particularly in complex multi-habitat restoration projects where spatial configuration remains a crucial yet understudied dimension influencing ecosystem recovery trajectories. We collect and analyze animal data from one of the worlds largest multi- habitat coastal restoration systems in Denmark, comprising restored seagrass (Zostera marina), boulder reefs and mussel reefs. Using fine-scale spatial patterns in population abundances, we develop spatially explicit predictions across the seascape for various seagrass restoration scenarios and produce a series of optimizations, showing that it is practical to configure restoration to optimize biodiversity objectives, including those linked with fished species. Species-specific responses translated to variable outcomes across restoration scenarios and optimizations. While the optimal number and arrangement of restored patches varied depending on the target species or species group (e.g., fisheries species or seagrass specialists), one near-ubiquitous arrangement was patchy seagrass planting. This aligns with current practice, maximizes restoration efficiency, and highlights the importance of not homogenizing seascapes for biodiversity. Our approach provides a practical framework for incorporating animal monitoring data into restoration planning, helping practitioners design and optimize spatial planting configurations to achieve specific ecological objectives. Open Research StatementAll data and code/scripts (R language), including a README file, are freely available at: https://github.com/msievers100/DenmarkSpatial

Invasion of nonindigenous species is considered one of the most urgent problems affecting native ecosystems and agricultural systems. Mechanistic models that account for short-term population dynamics can improve prediction because they incorporate differing demographic processes that link the environmental conditions of a spatial location explicitly with the invasion process. Yet short-term population dynamics are rarely accounted for in spatial models of invasive species spread. Accounting for transient population dynamics, we predict the population growth rate and establishment probability of wild pigs following introduction into any location in North America. We compared predicted population growth rate with observed geographic rates of spread and found significant relationships between the annual rate of spread and population growth rates. We used geospatial data on the distribution of mast producing tree species (a principle forage resource of wild pigs) and agricultural crops that can replace mast in their diets to predict population dynamics using transient population simulations. We simulated populations under different initial population sizes (i.e. number of introduced individuals, often termed propagule size) and for different amounts of time following introduction. By varying the initial population size and simulation time, we were able to identify areas in North America with high probability for establishment of wild pigs if introduced. Our findings can be used to inform surveillance and removal efforts to reduce the potential for establishment and spread of wild pigs.

Spatial and spatiotemporal models are increasingly critical for understanding species distributions, tracking population change, and informing conservation decisions. As biological processes are influenced by increasing external pressures, including human disturbance or environmental change, accurate model predictions become essential for adaptive management. However, the reliability of spatial predictions depends on often-overlooked modelling choices, including the spatial resolution used to approximate underlying processes. Using long term monitoring data from a large-scale groundfish survey in the California Current ecosystem, we investigated how spatial model complexity affects the quality of ecological predictions and derived indices used for management. We fit spatial and spatiotemporal models of ocean temperature and fish biomass density for 27 commercially important species using varying levels of spatial resolution. We evaluated both in-sample and out-of-sample prediction, and effects on area-weighted biomass indices. Counter to common assumptions, increasing spatial approximation resolution did not universally improve predictions. Our case studies demonstrate that for many datasets, out-of-sample prediction quality peaked at intermediate spatial resolutions and declined at the finest scales. Through simulation testing, we found this pattern was strongest when spatial patterning had a small range and high spatial variance, and observation error was low. For most species, spatial resolution had a minimal effect on biomass trend estimates used in management, but for several commercially important rockfish species, resolution choices substantially affected both the scale and uncertainty of population indices. Our findings demonstrate that spatial model specification can substantially affect ecological inference, with direct implications for management and conservation planning. We provide practical guidance for ecologists on selecting appropriate spatial complexity through cross-validation. When out-of-sample prediction is a focus, appropriate approximation complexity should improve both parameter estimation accuracy and derived quantities.

Crop pests can significantly damage crops, cause economic loss, and reduce the sustainability of agroecosystems. New ecoinformatic approaches are needed to understand the drivers of pest population dynamics and improve pest management practices. Here we analyze spatiotemporal drivers of Colorado potato beetle (CPB, Leptinotarsa decemlineata) abundance across Wisconsin potato fields using multi-year scouting data (2014-2024) linked with climate and cropping histories. We develop statistical models that account for spatial and temporal correlations, and find these approaches substantially improve fit and prediction. After accounting for spatiotemporal random effects, we find that three predictors increased CPB abundance: cumulative growing degree days (GDD), recent potato intensity in the surrounding landscape, and winter coldest-day temperature. Cumulative GDD and potato intensity are positively associated with abundance, and warmer winter minima (higher coldest-day temperatures) are likewise associated with higher abundance, consistent with improved overwintering survival. Using the best-performing model, we generate a preliminary, statewide risk surface for Wisconsin in order to support regional decision-making. Our results highlight the value of integrating field-level history with landscape context and explicit spatial structure when forecasting pest pressures in agroecosystems.

Marine foundation species are critical for the structure and functioning of ecosystems and constitute the pillar of trophic chains while also providing a variety of ecosystem services. In recent decades many foundation species have declined in abundance, sometimes threatening their current geographical distribution. Kelps (Laminariales) are the primary foundation species in temperate coastal systems worldwide. Kelp ecosystems are notoriously variable and identifying the key factors that control the dynamics of kelp abundance is key to predicting the fate of kelp ecosystems under climatic change and informing management and conservation decisions such as forest restoration. Here, we used in situ data from long-term monitoring programs across 1,350 km of coast spanning multiple biogeographic regions in the state of California (USA) to identify the major regional drivers of density of two dominant canopy-forming kelp species and to elucidate the spatial and temporal scales over which they operate. We used generalized additive models to identify the key drivers of density of two dominant kelp species (Nereocystis luetkeana and Macrocystis pyrifera) across four ecological regions of the state of California (north, central, south-west and south-east) and for the past two decades (2004-2021). Our study identified that the dominant drivers of kelp density varied between regions and species but always included some combination of nitrate availability, wave energy and exposure, density of purple sea urchins, and temperature as the most important predictors explaining 63% of the variability of bull kelp in the north and central regions, and 45% and 51.4% of the variability in giant kelp for the central/south-west and south-east regions, respectively. These large-scale analyses infer that a combination of lower nutrient availability, changes in wave energy and exposure, and increases in temperature and purple sea urchin counts have contributed to the decline of kelp observed in the last decade. Understanding the drivers of kelp dynamics can be used to identify regions and periods of significant change and historical stability, ultimately informing resource management and conservation decisions such as site selection for kelp protection and restoration. Open research statementData (Giraldo-Ospina et al. 2023) are available in DataOne at doi:10.25494/P6/When_where_and_how_kelp_restoration_guidebook_2.

Cities have been acclaimed as hotspots for bee biodiversity and potential conservation targets, leading to continued investment in urban pollinator plantings. However, newly created habitats are rarely assessed for their efficacy in supporting bee fitness or the extent to which bees use seeded wildflowers. We compared urban bee nesting in targeted "pocket prairie" pollinator plantings versus urban farms that were intended to support multiple ecosystem services in Cleveland, Ohio, USA. We used trap nests to evaluate nesting success of cavity nesting bees and pollen metabarcoding to determine whether bees collected pollen from seeded plantings during nest provisioning. Pollen DNA revealed most bee-collected pollen was from urban spontaneous vegetation (or "weeds") in Fabaceae, especially Trifolium spp. We also found that urban farms harbored more native bee larvae than targeted pollinator plantings. Finally, when bee nests were situated in a landscape with greater greenspace connectivity, we observed more native bee larvae and greater plant diversity in bees nesting provisions. Collectively, these findings suggest that multi-service greenspaces like urban farms can provide important urban pollinator habitat, and greenspace value for bees is driven by resident weeds and greenspace configuration. Open Research StatementData are not yet provided. Upon acceptance data will be archived in Dryad Digital Repository. Each applicable R package is cited in the text; no novel code is presented. Larval DNA sequences will be available through BOLD, Barcode of Life Data Systems; Pollen DNA reads will be available through Dryad; Raw ecological data (larvae abundance, bloom characteristics, landscape structure) will be available through Dryad. HighlightsO_LIExotic weeds are cavity nesting bees dominant pollen source in cities C_LIO_LIPollen DNA improves foraging observations across a diverse urban matrix C_LIO_LINative bee reproduction was greater in urban farms than pollinator plantings C_LIO_LIMulti-service urban farms can promote win-win conservation scenarios C_LIO_LIGreenspace connectivity modulates bee foraging and nesting success C_LI

O_LIChanges in habitat structure across species distributions may contribute to the generation and maintenance of range limits, but few studies have evaluated this by directly measuring habitat availability across relevant spatial scales. C_LIO_LIHere, we test the predictions that coarse-scale and patch-level habitat availability decline towards and beyond the northern range limit of Pacific coastal dune endemic Camissoniopsis cheiranthifolia. We used aerial imagery and geographic information system (GIS) tools to measure the coarse-scale availability of coastal dune habitat in California and Oregon. The availability of finer-scale habitat patches specifically suitable for C. cheiranthifolia was measured in a 2-generation field survey of > 4,200 5m x 5m plots randomly distributed across 1100 km of coastal dune habitat transcending the species northern range limit. At each plot, we estimated the proportion of area that contained suitable habitat as well as recorded occupancy by C. cheiranthifolia. As an alternative approach to visually estimating habitat suitability, we recorded plant community composition at each plot to predict beyond-range habitat suitability using a random forest model. C_LIO_LIContrary to our predictions, we found that coastal dune habitat, measured coarsely from aerial imagery, was more abundant and continuous towards and beyond the northern range limit. At the fine scale, however, the proportion of plots with suitable habitat (patch suitability) and the proportion of habitat within plots that was suitable (patch size) declined across the range limit. Moreover, patches were more isolated from one another and, in one survey year, less temporally stable towards and beyond the range limit. Finally, occupancy by C. cheiranthifolia was less likely in smaller, more isolated, and temporally unstable patches, providing mechanistic insight to the previously observed decline in occupancy towards the range limit. C_LIO_LISynthesis: Taken together, our results suggest that fine-scale habitat patch configuration changes in ways that likely impede patch colonization, thereby reducing occupancy and limiting the species northern distribution. Thus, consideration of geographic variation in patch and landscape structure, rather than only coarse-scale habitat availability, may be essential for understanding the processes that limit species ranges. C_LI

Marine protected areas (MPAs) are widely implemented to protect and/or rebuild exploited populations, yet their population-level effectiveness remains highly variable. Key unresolved knowledge gaps include how the size and the spatial placement of no-take zones interacts with larval dispersal directionality, particularly in linear coastal systems where connectivity is asymmetric. For sedentary species with planktonic larvae, such as European lobster, it is unclear under which dispersal regimes spatial configuration of protection critically determines positive demographic outcomes. Here, we address this gap using a spatially explicit individual-based model parameterized for the European lobster. We ask (i) whether no-take zones consistently enhance abundance and size structure relative to fished areas, and (ii) whether the positioning of no-take and open areas affects spatial protection while holding total protected area constant and (iii) how the alignment between larval dispersal direction and the positioning of no-take areas influences protection outcome. We contrast local, symmetric long-distance, and strongly unidirectional larval dispersal across alternative MPA layouts with equal total protected area but with different spacing. We show that no-take zones reliably increase abundance and the prevalence of large individuals. However, when larval dispersal is strongly unidirectional, population recovery depends on reserve placement: downstream no-take zones benefit from both larval import and local retention, whereas upstream reserves primarily export reproductive output and show limited local recovery. These results indicate that reserve performance cannot be evaluated independently of connectivity structure and identify dispersal directionality as a key determinant of when and where spatial configuration matters for MPA effectiveness in linear coastal systems.

Ocean warming interacts with local stressors to negatively affect coral reefs. The adaptive capacity of reefs to survive these stressors is driven by ecological and evolutionary processes occurring at multiple spatial scales. Marine protected area (MPA) networks are one solution that can address both local and regional threats, yet the impacts of MPA network design on adaptive processes remains unclear. In this paper, we used an eco-evolutionary model to simulate hypothetical MPA configurations in the Caribbean, Southwest Pacific and Coral Triangle under projected warming. We found that protecting thermal refugia (i.e., cooler reefs) largely benefited corals inside the refugia while other reefs declined. In contrast, protecting a diverse habitat portfolio led to increased coral cover both inside and outside of the MPA network. We then quantified the thermal habitat and connectivity representations of reefs both inside and outside existing MPA networks across each region. Most strikingly, reefs in current MPA networks in the Southwest Pacific and Coral Triangle are approximately 2 {degrees}C cooler than reefs outside the MPA networks, while the Caribbeans MPA network is approximately 1 {degrees}C warmer than reefs outside the network, based on mean temperatures from 2008-2018. These results suggest that the Caribbean MPA network is poised to protect sources of warm-adapted larvae but not destinations, and the opposite is true of the Southwest Pacific and Coral Triangle. Our results suggest that 1) by protecting sites with particular temperature and connectivity characteristics, marine spatial planning may alter eco-evolutionary processes to enhance or inhibit the adaptive capacity of a reef network and 2) the distribution, extent, and effectiveness of local interventions have the potential to affect regional distributions of coral cover beyond what would be expected from local benefits alone, due to the potentially wide-reaching effects of larval dispersal and gene flow.

Patterns of land-use can affect the transmission of many infectious diseases with human health implications; yet, applied ecosystem service models have rarely accounted for disease transmission risk. A mechanistic understanding of how land-use changes alter infectious disease transmission would help to target public health interventions and to minimize human risk of disease with either ecosystem degradation or restoration. Here, we present a spatially explicit model of disease transmission on heterogeneous landscapes that is designed to serve as a road map for modeling the multifaceted impacts of land-use on disease transmission. We model the transmission of three vector-borne diseases with distinct transmission dynamics (parameterized using published literature to represent dengue, yellow fever, and malaria) on simulated landscapes of varying spatial heterogeneity in tree cover and urban area. Overall, we find that these three diseases depend on the biophysical landscape in different nonlinear ways, leading to tradeoffs in disease risk across the landscape; rarely do we predict disease risk to be high for all three diseases in a local setting. We predict that dengue risk peaks in areas of high urban intensity and human population density, yellow fever risk peaks in areas with low to moderate human population density and high tree cover, and malaria risk peaks where patchy tree cover abuts urban area. To examine how this approach can inform land use planning, we applied the model to a small landscape to the northwest of Bogota, Colombia under multiple restoration scenarios. We predict that in an area inhabited by both Aedes aegypti and Ae. albopictus, any increase in overall tree cover would increase dengue and yellow fever risk, but that risk can be minimized by pursuing a large contiguous reforestation project as opposed to many small, patchy projects. A large contiguous reforestation project is also able to both reduce overall malaria risk and the number of malaria hotspots. As sustainable development goals make ecosystem restoration and biodiversity conservation top priorities, it is imperative that land use planning account for potential impacts on both disease transmission and other ecosystem services. Open Research statementAll data and code used in this study are available in the online supplemental material. Code and data are also hosted at: https://github.com/morgankain/Land-Use_Disease_Model.

Intensive agricultural practices directly affect farmland bird and non-target insect populations by modifying their habitats, but may also act indirectly by altering their interactions. Notably, the breeding success of insectivorous birds has been shown to suffer from reduced prey availability. Yet little is known about how agriculture influences host-parasite relationships in wild birds. How agricultural intensity affects parasites, and whether this alleviates or exacerbates the trophic stress imposed on birds therefore remains to be determined. We estimated the number of obligate hematophagous Protocalliphora blowfly larva (Diptera: Calliphoridae) that parasitized nestlings in 2,560 Tree Swallow (Tachycineta bicolor) broods along a 10,200-km{superscript 2} gradient of agricultural intensity between 2004 and 2019 in Quebec, Canada. We first modeled two key variables along the causal paths expected to affect Protocalliphora prevalence and load (abundance) within infested broods: nestling hatching date and nestling host availability. Hatching phenology varied by several days with early-spring meteorological conditions and parental age, as for nestling availability (nestling-days), which also decreased along the agriculture intensity gradient as pastures and hay fields were replaced by large-scale, cereal row crops. Nestling availability peaked under low precipitation rates when temperatures reached 18 to 25 {degrees}C. Prevalence and load of blowfly larvae directly increased with nestling availability as well as with the temperature and precipitation that occurred during the larval development and pupation stages. Controlling for nestling availability, Protocalliphora prevalence and load peaked in forested landscapes interspersed by pastures and hay fields and reached their lowest in landscapes dominated by corn and soybean monocultures with minimal tree cover. Agricultural intensity thus reduced infestation likelihood and severity both directly and indirectly, by limiting nestling host availability. This finding is notable given the documented negative effects of agricultural intensity on fledgling number and body condition in farmland birds, even after controlling for insect prey reduction. If agricultural intensity indeed reduces the parasitic pressure exerted by bird blowflies and its consequences for fledgling condition and recruitment, this suggests that other agricultural impacts (e.g., toxicological effects from pesticides) may play a larger role than previously recognized in the severe declines of farmland bird populations observed across the Holarctic. Open research statementThe data supporting this study are not yet publicly available, as they require final harmonization, documentation and anonymization prior to archiving. Upon acceptance of the manuscript, all underlying data and associated code will be permanently deposited in the Zenodo repository and made fully accessible with a DOI.

Natural control of crop pests has the potential to complement or replace intensive agricultural practices, but its mainstream application requires reliable predictions in diverse socioecological settings. In lack of a widely accepted model of natural pest control, we review existing modelling approaches and critically examine their potential to provide understanding and predictions across agricultural landscapes. Models that explicitly represent the underlying mechanisms are better positioned to represent the diversity and context sensitivity of natural pest control than correlative models. Such mechanistic models have used diverse techniques to represent crop-pest-enemy combinations at various spatiotemporal scales. However, certain regions of the world and socioeconomic aspects of natural pest control are underrepresented, while modelling approaches are restricted by a fundamental trade-off between generality and realism. We propose that modelling natural pest control across agroecosystems requires a framework of context-specific generalizations, based on empirical evidence and theoretical expectations. Reviewed models of natural pest control indicate potential attributes of such a general predictive framework.

Forecasting the population dynamics of disease vectors is critical for mitigating the risks of vector-borne diseases under a changing climate. We evaluate an iterative Bayesian forecast model of black-legged tick (Ixodes scapularis) phenology and population dynamics at near-term to interannual (e.g. 12 month) scales. The black-legged tick is the vector of Borrelia burgdorferi, the causative agent of human Lyme disease. Our forecasts consistently outperformed seasonal null models based on historical day-of-year averages, particularly during peak questing periods when disease risk is highest. Iterative data assimilation improved forecast performance over time, demonstrating the ability to adaptively learn about climate-driven shifts in demographic parameters, and reinforcing the value of long-term data to support management. Weather and climate variables emerged as the dominant predictors of nymph survival, with daily maximum temperature displacing humidity as the strongest predictor as the iterative forecast cycle evolved over time. Short-term forecasts driven by local weather observations were more accurate than those relying on seasonal climate forecasts, highlighting the importance of fine-scale weather dynamics and data for subannual predictions. At interannual scales, seasonal climate forecasts and vertebrate host (mouse) abundance were important for maintaining strong predictive skill in forecasting nymphal tick abundance, which is often used as a proxy for risk of human exposure to tick-borne disease, but forecasts were largely unaffected by larval abundance. Investment in monitoring efforts should prioritize observations of the nymphal stage to reduce forecast uncertainty. These results offer a path forward for operationalizing ecological forecasts of tick populations under environmental change and underscore the importance of adaptive, process-based models for managing tick-borne disease risk in a changing climate. Data availability statementThe data that support the findings of this study are openly available. The data from the Cary Institute of Ecosystem Studies are located on Figshare. Specifically, the tick data (Ostfeld and Oggenfuss 2023) is at doi.org/10.25390/caryinstitute.23611374.v1, the mouse data (Ostfeld et al. 2024) is at doi.org/10.25390/caryinstitute.25742778.v1, and the meteorological data (Kelly, 2020) is at doi.org/10.25390/caryinstitute.11553219.v6. The NMME system data are openly available at https://www.earthsystemgrid.org/search.html?Project=NMME. Code for this manuscript (Foster, 2025) is available from Zenodo at doi.org/10.5281/zenodo.17161317.

Wild bee communities persist in cities despite major disruption of nesting and food resources by urban development. Bee diversity and abundance is key for urban agriculture and maintenance of plant diversity, and assessing what aspects of cities enhance bee populations will promote our capacity to retain and provision bee habitat. Here, we assessed how variation in land cover and neighborhood development history affected bee communities in the midwestern US urban landscape of Madison, Wisconsin. We sampled bee communities across 38 sites with relatively high (> 55%) or low (< 30%) levels of impervious surface, and assessed effects of land use and neighborhood development history on bee abundance and species richness. We show abundance and richness of bees was lower in recently developed neighborhoods, with particularly strong negative effects on soil nesting bees. Soil nesting bees and bee community richness decreased as cover of impervious surface increased, but above ground nesting bees were minimally impacted. Bee community similarity varied spatially and based on dissimilar local land cover, only for soil nesting bees, and the overall bee community. Impervious surface limited bee abundance and diversity, but new neighborhoods were associated with greater negative effects. We suggest that enhancing the structural diversity of new neighborhoods in urban ecosystems may imitate the structural benefits of older neighborhoods for bee populations.

Marine protected areas (MPAs) are increasingly promoted as climate mitigation tools, yet guidance on their placement to maximize resilience against climate stressors like marine heatwaves remains limited. Here, we develop MPA placement guidelines that explicitly consider a mechanistic pathway through which MPAs could enhance kelp forest resilience to heatwaves: protecting fishery-targeted urchin predators to prevent kelp overgrazing. Using a spatially explicit, tri-trophic model of California kelp forests, we evaluate alternative MPA configurations across a hypothetical coastline where half the habitat experiences an increased probability of experiencing heatwaves. We found that effective MPA placement depends on whether MPAs are being newly established or reconfigured within an existing network, and that among-patch connectivity and spillover played vital roles in the relative effectiveness of different MPA configurations. Changes in resilience occurred primarily at the patch scale, with trade-offs between increased within-MPA resilience and decreased resilience in some fished areas, resulting in minimal coastwide population effects. For example, for new MPAs, large single MPAs within heatwave-prone areas maximized within-MPA resilience gains, while multiple small MPAs in heatwave refugia best supported whole-coast resilience. When reconfiguring established networks, expanding existing MPAs in refugia areas was most effective. We also demonstrate the importance of considering MPA recovery timescales: for example, relocating old MPAs to heatwave refugia yielded minimal short-term benefits due to the loss of rebuilt, previously fished, predator biomass. Our findings demonstrate that climate-adaptive marine planning should explicitly consider the spatiotemporal implications of trophic cascades, connectivity, and transient population dynamics to support ecosystem resilience.

Determining where environmental management is best applied, either through regulating single sectors of human activities or across sectors, is complicated by interactions between human impacts and the environment. In this article, we show how an explicit representation of human-environment interactions can help, via "impact networks" including activities (e.g. shipping), stressors (e.g. ship strikes), species (e.g. humpback whales) or ecosystem services (e.g. marine recreation). Impact networks can enable the identification of "leverage nodes", which, if present, can direct managers to the activities and stressors crucial for reducing risk to important ecosystem components. Exploring an impact network for a coastal ecosystem in British Columbia, Canada, we seek to identify these leverage nodes using a new approach employing Bayesian Belief Networks of risks to ecosystems. In so doing, we address three key questions: (1) Do leverage nodes exist? (2) Do management plans for species correctly identify leverage nodes? (3) Does the management of leverage nodes for certain species realize benefits for other species and ecosystem services? We show that there are several leverage nodes across all species investigated, and show that preconceptions about the regulation of risk to species can misidentify leverage nodes, potentially leading to ineffective management. Notably, we show that managing fisheries does not reduce overall risk to herring whereas managing diverse cumulative impacts including nutrient runoff, oil spills, and marine debris can reduce risk to herring, additional species, and related ecosystem services. Thus, by targeting leverage nodes, managers can efficiently mitigate risks for whole communities, ecosystems, and ecosystem services.

In response to biodiversity loss and biotic community homogenization in urbanized landscapes, City managers around the world are increasingly working to conserve and increase urban biodiversity. Accordingly, around the world, previously extirpated species are (re)colonizing and otherwise infiltrating urban landscapes, while once abundant species are in various states of decline. Tracking the occurrence of traditionally urban intolerant species and loss of traditionally urban tolerant species should be a management goal of urban areas, but we generally lack tools to study this phenomenon. To address this, we first used species occurrences from iNaturalist, a large collaborative dataset of species observations, to measure an urban association index (UAI) for 967 native animal species that occur in the city of Los Angeles. On average, the occurrence of native species was negatively associated with our composite measure of urban intensity, with the exception of snails and slugs, which instead occur more frequently in areas of increased urban intensity. Next, we assessed 8,348 0.25 x 0.25 mile grids across the City of Los Angeles to determine the average grid-level UAI scores (i.e., a summary of the UAIs present in a grid cell, which we term Community Urban Tolerance Index or CUTI). We found that areas of higher urban intensity host more urban tolerant species, but also that taxonomic groups differ in their aggregate tolerance of urban areas, and that spatial patterns of tolerance vary between groups (e.g., mammals are not the same as birds). The framework established here was designed to be iteratively reevaluated by city managers of Los Angeles in order to track the progress of initiatives to preserve and encourage urban biodiversity, but can be rescaled to sample different regions within the city or different cities altogether to provide a valuable tool for city managers globally.

SummarySporobolus anglicus (C.E. Hubb) P.M. Peterson & Saarela (synonym Spartina anglica) is a highly invasive coastal weed that forms dense monocultures in intertidal mudflats and estuaries, displacing native reedbeds and associated fauna. Introduced to Aotearoa New Zealand from Britain in the early 1900s to aid coastal land reclamation, it became a conservation concern by the 1960s, prompting control efforts in the South Island from the 1970s. This study presents a detailed S. anglicus eradication case study from the Marlborough Sounds, where detections are nearing zero. We model changes in detection probability over time in Te Hoiere / Pelorus Sound, a complex estuarine catchment. We aim to inform control efforts and assess the effectiveness of ongoing management, by evaluating the probability of non-detection as a proxy for functional eradication. Using data from the Department of Conservation (DOC), we analysed detections across 79 search blocks between 2013 and 2024. A generalised linear mixed model was used to generate predicted detections through to 2040, using a modified dataset with pseudo-zero values for probable absences. The data were modelled as presence/absence with a binomial distribution, to identify the first year with a <0.01 probability of a positive detection (upper 99% confidence limit). Results suggest that by 2032, the likelihood of further detections under current management practices is remote, and that functional eradication may have occurred. We interpret this decline in detection probability as indicative of management success. Model outputs can support decision-making as to when active surveillance might reasonably be ceased. To accelerate the tail-end of eradication efforts, we recommend intensifying search effort and widening delimitations within the catchment over the next five years, to ensure removal of any remaining individuals. We also propose the use of environmental DNA as a cost-effective backstop for after operational wind-down. Implications for ManagersO_LIOur modelling predicts that S. anglicus detections in Te Hoiere / Pelorus Sound should decline to levels consistent with functional eradication by 2032, and may render continued manual surveillance uneconomical after this date. C_LIO_LIFull, regular and repeated surveillance of suitable habitats is needed within a five-year intensive monitoring period at the tail-end phase of eradication, as detections approach zero and managers consider withdrawal. C_LIO_LIWhile statistical analyses support eradication management decisions, absolute certainty of absence is unattainable. Decisions must balance technical feasibility with practical risk tolerance. C_LIO_LIEnvironmental DNA could provide an effective post-withdrawal monitoring tool, to allay the risk of re-invasion. C_LI

O_LIEstimating the impacts of anthropogenic disturbances requires an understanding of the habitat use patterns of individuals within a population. This is especially the case when disturbances are localized within a populations spatial range, as variation in habitat-use within a population can drastically alter the distribution of impacts. C_LIO_LIHere, we illustrate the potential for multilevel multinomial models to generate spatial networks from capture-recapture data, a common data source use in wildlife studies to monitor population dynamics and habitat use. These spatial networks capture which regions of a populations spatial distribution share similar/dissimilar individual usage patterns, and can be especially useful for detecting structured habitat use within the populations spatial range. C_LIO_LIUsing simulations and 18 years of capture-recapture data from St. Lawrence Estuary (SLE) beluga, we show that this approach can successfully estimate the magnitude of similarities/dissimilarities in individual usage patterns across sectors, and identify sectors that share similar individual usage patterns that differ from other sectors, i.e., structured habitat use. In the case of SLE beluga, this method identified multiple clusters of individuals, each preferentially using restricted areas within their summer range of the SLE. C_LIO_LISynthesis and applications. Multilevel multinomial models can be effective at estimating spatial structure in habitat use within wildlife populations sampled by capture-recapture of individuals. Our finding of a structured habitat use within the SLE beluga summer range has direct implications for estimating individual exposures to localized stressors, such as underwater noise from shipping or other activities. C_LI