Ecological Applications

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.

Accelerating global urbanization necessitates a better understanding of how to manage cities that promote biodiversity. However, we currently lack multi-year, multi-city studies, which limits a generalizable understanding of how both within and between city differences impact the spatial and temporal dynamics of urban biodiversity. Here, we tested hypotheses about the drivers of butterfly diversity within and across urban parks by applying Bayesian occupancy models to five years of iNaturalist community science data from 2,550 parks in 22 major U.S. cities. We found that cities with bigger parks supported more species per park, including more disturbance- and edge-avoidant species. This was driven by a positive effect of park size on butterfly species colonization rates. We also found that attributes of habitat quality (plant diversity within parks and tree cover surrounding parks) contributed to butterfly species occupancy. Park connectivity increased species persistence, but the overall effects on butterfly species occupancy varied across cities. Finally, we found that the total area of tree cover throughout a city, rather than the size or connectivity of individual parks, was the primary determinant of city-wide diversity: Increasing total tree canopy cover from below-average (~6%) to above-average (~22%) increased city-wide species richness by ~10%. These findings highlight the need for cities to maintain large parks while also increasing city-wide tree cover to support biodiversity across local to regional scales. By integrating high-resolution community science data across the continental U.S., this study provides mechanistic insight into how cross-scale processes shape urban biodiversity dynamics and identifies generalizable recommendations for improving urban conservation management.

Invasive species pose a significant threat to native ecosystems. Effective management of invasive species requires accurate abundance estimates through time, particularly at large spatial scales. Traditional methods of abundance estimation from management data, such as camera trap studies and harvest data, face challenges due to variations in sampling protocols and effort standardization. We propose an extension to a previously calibrated removal model by integrating into an Integrated Population Model (IPM) and incorporating multiple removal techniques. The model utilizes management and land cover data, both of which are readily available in many invasive species systems. We evaluate the model with a simulation study, then apply it to management data on wild pigs (Sus scrofa). Simulations showed that the model performs well across a wide range of densities and population trajectories. Density estimates were the most biased when effort and density were low and sharpshooting or fixed wing aircraft were used to conduct removals. We discuss the advantages and limitations of using removal models for invasive species management, how the model performs with respect to previous removal modeling efforts, how performance might be improved through changes to data management practices, and the models potential to inform decision-making processes for invasive species management. Data availabilityThe data for this manuscript is available from Dryad (http://datadryad.org/share/gFz3ClanB-8yPB5lQ1x-dl1JqI07CP3SX92P-9UyfaU). The code for this manuscript will is available from Zenodo (10.5281/zenodo.15784366).

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.

Culling is frequently used to control animal diseases. Intensive culling can alter the movement behavior of surviving animals, especially in socially-structured wildlife species. These behavioral responses could have unexpected consequences on the spread of a disease. Thus, planning effective culling responses to diseases in wildlife hosts requires a thorough understanding of the potential impacts of culling on the target wildlife host species. We conducted a BACI design study of behavioral response to culling in wild pigs. We examined movement and contact responses in the populations using 122 GPS-collared wild pigs and three different culling methods (aerial operations, trapping, and an experimental toxic bait). Movement and contact metrics included home range area, net-squared displacement (i.e., home range shift), movement speed, distance, contact degree and contact duration. We observed increased movement distances during and after trapping treatments, and home range shifts and reduced area size after the toxicant treatment. We also observed increases in contact duration and number of unique contacts during trapping removals. Movement and contact responses varied by sex. Our results suggest that continued, intensive culling as with extensive trapping can substantially alter wild pig space use and contact. These behavioral responses could have important consequences for disease spread when managing an introduction of transboundary animal diseases or endemic diseases.

The rapid expansion of oil palm plantations in Southeast Asia has caused extensive deforestation and landscape fragmentation. Riparian buffers (vegetated strips along the edges of rivers) have been shown to enhance biodiversity, water quality, and erosion control. However, plantation managers have raised concerns that these buffers may harbour pests such as nettle caterpillars, bagworms, and rhinoceros beetles (Oryctes rhinoceros). These pests damage the palms and facilitate the spread Ganoderma boninense (a fungal pathogen). Using causal inference modelling we examined how riparian buffer characteristics (width and habitat quality), oil palm age, and surrounding landscape features influence pest and disease incidence in oil palms adjacent to riparian areas in Sabah, Malaysian Borneo. We surveyed 47,500 palms for pest and disease damage and used mark-release-recapture techniques to track O. rhinoceros movements in oil palms adjacent to riparian buffers. Most O. rhinoceros activity (66.30%) occurred within the plantations, and only 6.10% occurred within riparian buffers, with limited movement between habitats. Oil palm age was a dominant driver of pest attacks: young palms were more susceptible to lepidopteran caterpillars and O. rhinoceros, whereas G. boninense was more prevalent in mature palms. Neither the surrounding forest cover nor the quality of the riparian buffer affected the incidence of pest attacks. Riparian buffer width increased O. rhinoceros attacks, reduced G. boninense infection, and had no effect on lepidopteran caterpillars, highlighting that surrounding forest cover and riparian buffers do not drive pest attacks in oil palm plantations. Instead, management of oil palms within the buffers s is likely to be more important in managing pests; increases in invasive oil palms within the buffers increased the incidence of caterpillar damage, and higher numbers of remnant old oil palms increased O. rhinoceros attacks in adjacent oil palms. Overall, riparian buffers were found to contribute little to pest spillover, suggesting that their biodiversity and connectivity benefits outweigh minor pest risks, especially if invasive young and remnant old oil palms within the buffers are effectively managed and native vegetation restored.

O_LIMany conservation interventions prove ineffective because they lack a rigorous evidence-base. Multifactorial drivers and data limitations often hinder anticipatory planning and are difficult to tackle with standard methods. Predictive modelling approaches that integrate diverse data sources with biological hypotheses can bridge this gap by clarifying the drivers of decline and evaluating management options under uncertainty. C_LIO_LI We developed a Bayesian integrated population model (IPM) applicable to lekking species, combining incomplete data from different life stages and seasons while accounting for observation error. IPMs are widely recognised for reducing bias relative to single-data-stream analyses, yet their application in conservation planning remains limited. C_LIO_LIWe applied the model to the threatened Western Capercaillie population in Scotland, which has been declining since at least the 1980s despite decades of conservation efforts, a typical case requiring urgent, evidence-based management. By fitting the model to 30 years of biased and incomplete monitoring data, we investigated associations between demographic processes and their potential drivers, including weather variables, fence collision mortality and a proxy of predation pressure. We used model predictions to evaluate the joint effectiveness of proposed management actions aimed at improving vital rates, including fence management and diversionary feeding. C_LIO_LIData integration improved population estimate precision by 17-49% relative to standalone national survey estimates, confirming that the decline continued from 1990-2023. Reproductive rate was related to the pattern of April warming, negatively affected by pre-breeding precipitation and positively by vole abundance, the latter consistent with the alternative prey hypothesis. Model predictions indicated that combining diversionary feeding with fence removal produced the most favourable conservation outcomes, but growth remained limited and uncertainty included possible continued decline. C_LIO_LI5. This modelling approach forms a key component of Scotlands Capercaillie Emergency Plan 2025-2030, guiding management by assessing population-level responses and predicting conservation intervention outcomes. In future, the model could support formal adaptive management through iterative evaluation of interventions as new data become available. Beyond this case study, our integrated approach offers a transferable framework for managing multifactorial declines in other threatened species under uncertainty. C_LI

Wildlife crossing infrastructure is promoted to restore connectivity for fragmented populations, but its effectiveness at enabling natural recolonisation remains untested. We tested this using a spatially explicit agent-based model parameterised with GPS telemetry data from bobcats (Lynx rufus) in New Jersey, USA. By integrating movement behaviour, stochastic demography, habitat suitability, and traffic-dependent mortality risk, we simulated 50-year recolonisation dynamics across a highly urbanised landscape. Despite extensive unoccupied suitable habitat, natural recolonisation completely failed across all scenarios, with vehicle-induced mortality during dispersal acting as the primary limiting factor and turning the matrix into a demographic sink. Even an idealised mitigation scenario in which mortality at high-mortality crossings was reduced to zero failed to produce a self-sustaining population. Although dispersal increased, individuals at the recolonisation front remained too sparse to overcome the mate-finding Allee effect. Sensitivity analysis confirmed that the recolonisation-failure result is robust to {+/-}50% variation in per-crossing mortality and {+/-}25% variation in disperser survival. Restoring structural connectivity is not, in itself, a sufficient intervention for recovering low-density carnivore populations facing a high-mortality matrix. Instead disperser survival and local density at the recolonisation front are the rate-limiting determinants. In such systems translocation rather than crossing-structure investment is more likely to result in recolonisation success.

Many insects are in clear decline, with monarch butterflies (Danaus plexippus) drawing particular attention as a flagship species. Falling numbers of overwintering monarchs are well documented, but there has been debate regarding population trends of summer breeding populations. Here, we compile a series of long-term monarch monitoring datasets, some which are analyzed here for the first time, that reveal highly variable responses across the migratory geographic range, but no broad net decline in numbers of breeding monarchs. We also did not find evidence that sampling biased towards natural sites was masking declines at disturbed sites. Overall, our results suggest a robust resiliency in summer populations that thus far has allowed recovery from losses during the winter. Thus, monarchs may not require as much breeding habitat restoration as once thought, and focus should be on conserving the fall and spring migration.

All populations experience density dependence, lest they grow infinitely. However, elucidating what forms of density dependence act most strongly for a given species remains incredibly challenging. Identifying the mechanisms that regulate population density is particularly relevant to pest control, which often causes high mortality, reducing population density but enabling recovery when survivors are freed from intraspecific competition and density dependent vital rates (e.g., birth, death, and maturation rates). Understanding these demographic responses is critical for effective pest management, especially when specific life stages of pests differ in the harm they cause. Guinea worm disease (GWD) is a neglected tropical disease with an obligate copepod intermediate host, and importantly, only large-bodied copepods can be infected with GW parasites. GW disease has been the target of control for several decades, with main management strategies including the use of a chemical larvicide, Abate, which is applied to water bodies to cull copepod populations. Despite the wide application of Abate in GW endemic countries, little is known about long-term copepod population dynamics in response to Abate. Thus, we evaluated this mortality-based management of intermediate copepod hosts to control GW transmission, combining mathematical models with a population dynamics experiment designed to mimic Abate pesticide control methods across a range of intensities. Despite initial reductions in both total and stage specific population densities, copepod populations recovered so rapidly that control appeared to fail, enabled by extremely fast maturation rates in low-density populations. Much to our surprise, model simulations of GW transmission showed that although total and stage-specific densities rebounded, infectious adult copepods --the proximate cause of infections--were strongly suppressed, indicating cryptic success. This effect was enabled by the 15-day developmental period of GW larvae within copepods, which blocks the accumulation of infectious copepods between interventions. Ultimately, this study highlights the importance of understanding mechanisms of density dependence when designing and optimizing pest control interventions, as well as interpreting counterintuitive consequences of interventions.

Non-invasive genetic sampling and spatially explicit capture-recapture (SCR) models are used increasingly to estimate abundance of wildlife populations, but have not been adequately tested on gregarious animals such as elk (Cervus canadensis), for which correlated space use and movements violate model assumptions of independence. To evaluate the robustness and accuracy of SCR, and to assess the utility of an alternative non-invasive method for estimating density of gregarious ungulates, we utilized a tule elk (Cervus canadensis nannodes) population of known size within a fenced enclosure on the San Luis National Wildlife Refuge in central California. We evaluated fecal genetic SCR to camera trap-based random encounter model (REM) approaches to density estimation based on comparison to the true abundance. We also subsampled the dataset to explore the effects of varying search effort and elk density on the precision and accuracy of results. We found that SCR outperformed REM methods in the full datasets, and reliably provided accurate (relative bias <10%) and reasonably precise (relative standard error [&le;]20%) estimates of density at moderately low to high densities (6-17 elk/km2), when the subsampling scenarios yielded a minimum average of 20 recaptures. We also found that the number of samples used to construct detection histories was a reliable predictor of precision, and could be used to establish minimum sampling requirements in future population surveys of elk. Although field-testing in free-ranging populations is needed, our results suggest that non-invasive genetic SCR is a promising tool for future population studies and monitoring of elk and potentially other gregarious ungulates. In contrast, the REM estimate of density was highly inaccurate, imprecise, and highly sensitive to camera parameters.

Estimating the extent and speed of an invasive species spread is crucial for optimizing surveys and management, but this estimation is challenging due to the scale-dependent nature of spread. For instance, when discretizing occurrence data to grids to estimate the extent of invaded ranges, larger cells can decrease boundary precision, while smaller cells can increase the risk of missing invaded areas. Moreover, on shorter timescales such as year-to-year comparisons, spread rates can exhibit lags, accelerations, and slowdowns. We present a multiscale spread optimization methodology developed for the spotted lanternfly (Lycorma delicatula), a pest impacting grapes that is spreading westward across the continental United States. We analyzed a dataset of >900,000 occurrence records covering spread from this pests initial detection in eastern Pennsylvania in 2014 to its invasion front in Chicago, IL, in 2023. First, we delineated the annual U.S. invasion front using square grids with varying cell sizes and -convex hulls with different boundary resolutions. For each method and scale, we regressed invaded range area against year using both simple linear and logistic nonlinear models. Coarser spatial scales yielded faster estimated spread rates, and logistic models outperformed linear regressions, indicating a lag, acceleration, and slowdown in annual spread rate. Next, to determine the spatial scale that best captured the invasion front boundary, we used cross-validation, optimizing the F{beta} metric, which balances recall and precision. Emphasizing recall generally favored coarser spatial scales, and - convex hulls outperformed grid-based methods in delineating invasion fronts. Finally, L. delicatula has dispersed long distances from its primary epicenter, establishing satellite populations. We delineated each satellite population each year since its inception using optimized -hull values and measured their year-to-year boundary expansion. Like the primary epicenter, these satellite populations showed accelerating expansion, at least until merging with the primary invasion. Overall, the expansion rate of the primary invasion has slowed since 2017, decreasing from a maximum of 21 km/year, possibly due in part to control efforts, although the exact cause remains unresolved. Considering spatial scale and temporal variation can optimize the analysis of invasive spread and motivate early interventions to manage nascent epicenters before their spread accelerates.

Spatial protections that exclude fisheries have long been used to manage and conserve marine species. Detecting the benefits of protected areas for fish within them and for adjacent fisheries (via spillover effects) is key to informing fisheries management yet also difficult. We leveraged an extensive dataset collected over 18 years by an alliance of four Indigenous Nations in Pacific Canada. Our analyses of those data quantified the benefits of spatial fishery closures, known as Rockfish Conservation Areas (RCAs), on 28 species of groundfish occupying a wide range of distances from RCAs (inside RCAs to {approx}150 km away). On average, body sizes and relative abundance increased by 1.1% and 11.0%, respectively, per 5 km-increase in RCA proximity. Variation around these average responses depended on the species life history characteristics, RCA location, and historical impacts from commercial fishing. Additionally, the benefits of spatial protection increased with time since RCA implementation.

Accurate and robust population trend assessments are key to successful biodiversity conservation. Citizen science surveys have provided good evidence of biodiversity declines whilst engaging people with them. Citizen scientists are also collecting opportunistic biodiversity records at unprecedented scales, vastly outnumbering records gathered through structured surveys. Opportunistic records exhibit spatio-temporal biases and heterogeneity in observer effort and skill, but their quantity offers a rich source of information. Data integration, the combination of multiple information sources in a common analytical framework, can potentially improve inferences about populations compared to analysing either in isolation. We combine count data from a structured citizen science survey and detection-nondetection data from an opportunistic citizen science programme. Population trends were modelled using dynamic N-mixture models to integrate both data sources. We applied this approach to two different inferential challenges arising from sparse data: (i) the estimation of population trends for an area smaller than a structured survey stratum, and (ii) the estimation of national population trends for a rare but widespread species. In both cases, data integration yielded population trajectories similar to those estimated from structured survey data alone but had higher precision when the density of opportunistic records was high. In some cases this allowed inferences about population trends where indices derived from single data sources were too uncertain to assess change. However, there were differences in the trend magnitude between the integrated and the standard survey model. We show that data integration of large-scale structured and unstructured data is feasible and offers potential to improve national and regional wildlife trend estimates, although a need to independently validate trends remains. Smaller gains are achieved in areas where uptake of opportunistic recording is low. The integration of opportunistic records from volunteer-selected locations alone may therefore not adequately address monitoring gaps for management and policy applications. To achieve the latter, scheme organisers should consider providing incentives for achieving representative coverage of target areas in both structured and unstructured recording schemes.

Population connectivity is essential for long-term species persistence, and a major challenge to the conservation of isolated wildlife populations globally. Understanding how individual variation in movement behavior scales up to affect population connectivity across human-dominated landscapes is essential for the development and implementation of effective conservation and management plans, particularly for wide-ranging species with potential for human-wildlife conflict. Based on the observed movement behavior of 84 pumas (Puma concolor) in the Santa Cruz Mountains, we characterized individual variation in tolerance towards anthropogenic presence and used EcoScape, a novel functional habitat connectivity algorithm, to predict how individual differences in behavior affect the connectivity and dispersal capacity of an at-risk carnivore population. We found that individual differences in human tolerance have a greater effect than sex-based differences on modeled ability to disperse through anthropogenic landscapes, and strongly influence connectivity. This work highlights how individual variation in animal responses to human disturbance affects dispersal capacity, and offers a novel approach to predicting the persistence of metapopulations in the context of rapid global change.

Decision making in conservation science often relies on the best available information. This may include using models that were not designed for purpose and are not accompanied by an assessment of limitations. To begin addressing these issues, we sought to reproduce, and evaluate the suitability of, the best available models for predicting impacts of proposed mining on boreal woodland caribou (Rangifer tarandus caribou) resource selection and demography in northern Ontario. We then evaluated their suitability for projecting the impacts of development in the Ring of Fire region. To aid in accessibility, we developed an R package for data preparation, analyses of resource selection, and demographic parameters. We found existing models were either ill suited, or lacking, for ongoing regional planning. The specificity of the regional resource selection model limited its usefulness for predicting impacts of development, and the high variability across caribou ranges limited the usefulness of a national aspatial demographic model for predicting range-specific impacts. Variability in model coefficients across caribou ranges suggests selection responses vary with habitat availability (i.e. a functional response) while demographic responses continue to decline with increasing disturbance. Models designed for forecasting that are continuously updated by range-specific demographic and habitat information, are required to better inform conservation decisions and ongoing policy and planning practices in the Ring of Fire region.

Species distribution models (SDMs) for marine megafauna, such as cetaceans, traditionally use occurrence data from scientific surveys. These surveys follow standardised protocols and provide spatially structured data, making them a reliable source of information on distributions. However, they are costly and temporally limited, offering only a snapshot of distributions in time. Because cetaceans track dynamic oceanographic features, models built on survey data alone often lack temporal transferability. In contrast, platforms of opportunity (such as whale-watching vessels) allow obtaining occurrence data with high temporal resolution and near-daily sampling. These data are restricted to small coastal areas and subject to spatial bias, yet remain informative through time. Given the complementary nature of these sources and the absence of a general spatiotemporal framework for combining designed line-transect detections with opportunistic presence-only data, here we present an integrated modelling approach that leverages their strengths: broad spatial structure from surveys and high temporal resolution from whale watching. We combine these sources in a joint-likelihood log-Gaussian Cox process (LGCP), fitted in a Bayesian hierarchical framework using integrated nested Laplace approximations (INLA). The survey contributes a line-transect distance-sampling likelihood, and whale watching contributes an availability-restricted presence-only likelihood. Simulations show that integration lets the model absorb the temporal signal from whale watching without losing spatial structure from the survey. We apply the approach to short-beaked common dolphins (Delphinus delphis) off mainland Portugal. The integrated model improves spatial fit and combines heterogeneous data within a coherent framework, though dynamic covariate effects were weak, limiting temporal variation in spatial predictions. The results demonstrate potential and limitations of spatiotemporal integration. They clarify data requirements when combining surveys with opportunistic data and provide a general, extensible framework for integrating marine datasets that differ in design, extent, and temporal resolution.

Developing accurate and cost-effective methods to detect and predict the spread of invasive species remains an ongoing challenge. Species distribution models (SDMs) are used to predict invasion under current and future climates. However, occurrence datasets are often spatially and temporally biased because they are collected in an unstructured and opportunistic manner. Remote sensing individual species over broad spatial and temporal scales has become increasingly feasible with the accumulation of satellite images and the development of convolutional neural networks. In this study, we used a 21-year archival Landsat satellite imagery (2000-2020) to train a temporal convolutional neural network model to predict the probability of occurrence of the invasive species, leafy spurge (Euphorbia virgata), across Minnesota. We validated our predictions with an independent dataset. First, we show that that leafy spurge has expanded from 1,067 km{superscript 2} occupied in 2000-2002 to 7,156 km{superscript 2} in 2018-2020, a 570% increase. Surprisingly, drought severity modulated the predicted area invaded and was associated with fluctuations over the study period. Second, we tracked changes in probability over time for individual pixels and showed that invasion has been concentrated in two largely disjunct regions of Minnesota. Third, our remotely-sensed occurrence dataset and community science dataset were biased to roadsides, although the latter was more severely biased. Last, we showed that SDMs built using remotely-sensed occurrences had higher discrimination, were less overfit, and had higher performance outside of urban areas. Overall, twenty one years of archival satellite imagery provided valuable insight into the spatial and temporal population dynamics of leafy spurge invasion and improved forecasts of future invasion by reducing spatial bias.

As urban populations grow, cities are increasingly seen not only as drivers of climate change but also as critical arenas for implementing mitigation and adaptation strategies. Urban forests and green infrastructure play a vital role in shaping environmental quality and human health, yet access to these benefits remains inequitable. The Montreal Urban Observatory was designed to investigate the complex relationships among biodiversity, ecosystem functioning, and human health. Twenty-five permanent plots were established across the Island of Montreal, strategically located along gradients in vegetation cover, population density, and mean household income. Interdisciplinary research within the Observatory focuses on themes including urban forest structure and function, abiotic conditions, biodiversity, ecological interactions, natures contributions to people, related human health outcomes, and developing novel methodological approaches. The Observatory supports long-term, cross-scale monitoring and fosters collaborative research on urban social-ecological systems, thus contributing to global initiatives to enhance urban sustainability and equity through nature-based solutions. Early results confirm the relevance of the main gradients for several organisms and response variables, from microbes to trees and abiotic factors. For example, sampling all trees, public and private, within a radius of 200m centered on each plot revealed significant differences in the diversity and structure of private and public trees, including an overwhelming dominance of Thuya occidentalis not captured in commonly used public tree inventories.

Animal population control is widely used to mitigate conflicts between wildlife and agriculture worldwide. Structured, monitored removals are rare in South America, however, and their consequences for wildlife populations as well as their effectiveness in reducing crop damage are little understood. Using eight years of data from an experimental white-lipped peccary management program in an agricultural mosaic in the Brazilian Cerrado biome, we assess how structured, non-lethal removals affect both peccary demography and second-crop corn damage. Leslie removal models based on 6,619 captured individuals indicated that cumulative removals to approximately 85% of the initial population strongly reduced peccary abundance, with limited demographic compensation despite fluctuations in reproductive output. Corn crop damage, quantified with satellite imagery, declined over time and was correlated with peccary population size. Interannual variation in population growth and juvenile recruitment was poorly explained by climate, fire, or landscape composition. Source-sink dynamics likely play a role in maintaining healthy populations at the regional scale. Together, these results demonstrate that sustained and monitored ungulate removals can reliably reduce population size and agricultural damage, supporting coexistence between wildlife and food crop production in human-dominated tropical landscapes.