Landscape Ecology

Identifying reliable indicators of dengue vector habitat is critical for proactive surveillance and spatially targeted vector control. Bird communities are sensitive to habitat quality and landscape condition, and their functional diversity may track ecological conditions that support Aedes mosquito populations. We tested whether bird functional diversity predicts dengue vector presence across two working landscapes in Costa Rica, comparing bird-based predictors against landscape composition, vegetation structure, and plant diversity. Bird functional dispersion (FDis), the spread of species in multidimensional trait space, was the top-ranked predictor for Ae. aegypti and Ae. albopictus larval presence and for adult Ae. aegypti, outperforming all other predictor classes. Probability of presence declined with increasing FDis, where functionally homogeneous bird communities, characteristic of disturbed landscapes, co-occur with higher vector presence, consistent with shared habitat filtering. Functional diversity estimations may offer a practical signal for identifying habitats at elevated dengue vector risk.

Protected areas are vital to the recovery of endangered species. Of the 24 remaining endemic passerines in Hawaii, 16 species are endangered or critically endangered. Yet protected areas in the archipelago are changing as a result of climate change and biological invasions. For example, the non-native southern house mosquito (Culex quinquefasciatus), the primary vector of avian malaria (Plasmodium relictum), has been encroaching upward as higher elevations warm. How the ranges of endemic birds have also shifted their range upward in the Ka[u] Rainforest, the largest native forest on the Island of Hawaii, is not well understood. We used hierarchical distance sampling to characterize how population density has changed from 2002 to 2024 for eight endemic bird species in the Ka[u] Rainforest. For five species, the most parsimonious model included an interaction between year and a quadratic elevation effect. Species that were most common at lower elevations in 2002, e.g., Apapane (Himatione sanguinea), tended to be most common at mid-elevations by 2024. Species that were already most common at higher elevations, such as Iiwi (Drepanis coccinea) and Hawaii [A]kepa (Loxops coccineus), tended to decline in density. For example, Iiwi density at 1,530 m declined from 3.31 birds per ha (95% confidence interval [CI]: 2.67-4.11) to 1.34 birds per ha (95% CI: 1.05-1.71), and Hawaii [A]kepa were completely extirpated from elevations below 1,530 m by 2024. Our results demonstrate how the elevational ranges of endemic species have shifted in response to climate change, biological invasions, and habitat degradation. Translating these results into conservation measures may require a more thorough investigation of the causal mechanisms of these range shifts with finer scale habitat data, under the same hierarchical modeling framework.

The tidal wetlands of Louisiana comprise about 25% of those found throughout the conterminous United States yet estimates of wetland loss rates in the region between 1932 and 2016 have exceeded 60 km2 yr-1. To mitigate further degradation and wetland loss in the region, a globally unprecedented $50B, 50-year plan for coastal Louisiana is driving restoration efforts, and demand exists from multiple stakeholders for regularly updated, regional-scale, accurate land cover information. We used machine learning (random forests; RF) and cloud computing to develop a new Landsat-based, marsh vegetation community geospatial dataset. The dataset depicts wetland vegetation community types defined in a previous study at annual (1985-2025) time steps at 30-m resolution. An RF algorithm was used to integrate training samples with feature variables derived from Landsat imagery, and the resulting geospatial data product achieved an overall correct classification rate of 78%. The approach for development of the land cover dataset presented here has potential for application in other coastal wetland habitats throughout the world.

Ecologists remotely track movement steps of animals (e.g., via global positioning systems) and use step selection functions to study the effect of environmental factors upon their movement decisions. Constructing such functions requires pairing each observed step with some number of unobserved but feasible comparison steps. Larger numbers of comparison steps generally yield better estimates but also incur potentially challenging computational demands. Thus, it is important to determine an appropriate number of comparison steps. No established guidance exists for this decision. Here, we use simulated tracks to assess how many comparison steps are needed, fitting each set of steps to a conditional logistic regression model. We monitor errors in estimated effects for several classes of tracks, identifying the number of comparison steps for which mean relative absolute error in estimated effects is consistently low. By this criterion, 32 comparison steps per observed step are needed for our primary class of simulated tracks. Tracks in more homogeneous landscapes, tracks with shorter mean step lengths, or shorter tracks generally require more comparison steps (ranging from 64 to 128 per observed step) to achieve the same level of accuracy. Longer tracks generally require fewer comparison steps (16 per observed step). These results clearly demonstrate that the number of comparison steps influences how well step selection functions estimate covariate effects and provides initial direction in a research area that currently lacks quantitative guidance. Movement ecologists should take care when selecting the number of comparison steps paired with each observed step because those decisions matter.

Social wasps (family: Vespidae) are increasingly concerning invaders and have been subject to increased detections and a growing number of invasive populations in the last few decades. As established invasive populations are challenging to eradicate, preventing introductions and prioritizing early interventions are the most cost-effective management solutions to mitigate these effects. A current challenge to this approach is that species distribution data is limited for many social wasp species, hindering our ability to accurately predict novel habitats with high suitability. To address this gap, we used MAXENT to create species distribution models (SDM) for 299 species of social vespid. We identified existing invasive populations of social wasps and incorporated their current invasive ranges to improve the transferability of our models in predicting habitat suitability in new environments. Current range sizes and habitat suitability varied widely among species and genera. We identified new species of high invasive concern, particularly in the genus Vespa. We also identified previously unrecognized regions that may be at high risk of future invasion primarily in Central Africa and the Indo-Australian Archipelago. Combining current and suitable ranges, we calculated an "Invasion Risk Score" to compare the relative likelihood of each species establishing a new invasive population based upon habitat suitability. To assess invasion risk in the future, we projected habitat suitability under four Shared Socioeconomic Pathway (SSP) climate change scenarios. Under all scenarios, species faced significant changes in habitat suitability for current native ranges. Habitat suitability generally shrank and shifted towards the poles, leaving equatorial species at highest risk of habitat loss. Notably, Vespa was the only genus whose suitable habitat expanded under these climate scenarios. Our framework demonstrates how multi-species SDMs can be applied to risk management of invasive populations.

The global market concentrates extractive pressure on lands held by Indigenous peoples, including peoples living in isolation, for whom free, prior and informed consent cannot be obtained and protection must therefore rest on territorial instruments. Halmahera, Indonesia, holds some of the worlds largest lateritic nickel reserves beneath a lowland rainforest inhabited by the Hongana Manyawa, yet the trajectory of land use and cover change across the island has not been quantified. We characterised land use and cover change over the 17,437 km2 island between 2014 and 2024 using MapBiomas time series, and projected a business-as-usual scenario to 2054 with a stochastic cellular-automata model implemented in Dinamica EGO, calibrated with weights of evidence on eight variables describing mining and logging concessions, transport infrastructure, settlements and previous clearing. Forest covered 83.0% of the island in 2014 and 82.1% in 2024; under unchanged policy it falls to 73.7% by 2054, a net loss of 162 thousand ha, or 11.2% of the 2014 baseline, at gross rates of 47,000-51,000 ha per decade. Deforestation probability is highest within 500 m of previous clearing and declines with distance from settlements, cities and mining sites, while proximity to national parks carries a negative weight of evidence. The frontier is self-propagating and spatially predictable, and legally designated territory retains forest within it. Protecting the Hongana Manyawa consequently depends on excluding extractive licensing from the interior forest ahead of the frontier rather than behind it.

AimSpecies in mountain ecosystems often experience upslope distribution shifts in response to climate change. However, elevational range dynamics exhibit substantial complexity around this general trend, with varying magnitude and direction of shifts observed among studies. We tested whether factors other than temperature, such as habitat size and land use, are also responsible for observed elevational shifts in a cold adapted species. LocationHokkaido, Japan MethodsWe resurveyed 61 historical (1963-2007) occurrence sites covering a wide elevational range (60-2,210 m) within the distribution range of the northern pika (Ochotona hyperborea). We assessed the existence of elevational shifts using quantile regression and the relative importance of temperature, habitat size, and land use on the shift using occupancy analysis. ResultsWe detected presence of the northern pika at 41 sites, suggesting extirpations at 20 sites (32.8%) across a wide elevational range of 60-1,550 m. The concentration of extirpation sites at low to mid elevations resulted in a significant upslope shift of the distribution centroid, although the shift was nonsignificant at lower and upper portions of the range. The occupancy analysis revealed a negative effect of long-term mean of summer maximum temperature and a stronger positive effect of habitat size. ConclusionsThis highlights the susceptibility of the northern pika to heat stress and the importance of larger habitats and thus habitat heterogeneity for persistence of local populations. Given the possibility that the observed shift represents a precursor of elevational contraction, continuous monitoring of the local populations is highly needed to evaluate their long-term viability.

Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.

Wildflower plantings are an important conservation measure for supporting wild bee diversity. They aim to enhance floral resources for nutrition, but do not explicitly consider that bees also require suitable nesting and overwintering resources. Wildflower plantings may provide nesting habitat for ground-nesting bees, but the effects of soil management, e.g. ploughing, on ground-nesting bees are hardly known. To study how ploughing and age of wildflower plantings affect ground-nesting bees, we sampled bees on ploughed and unploughed wildflower plantings aged 0-4 years. We used emergence traps to sample bees directly after emergence from the ground, allowing inference on nest numbers. We found that ploughing and wildflower planting age negatively affected overwintering bee nest numbers. Nesting peaked in the year of wildflower planting establishment and declined thereafter, indicating lower habitat suitability at later successional stages. Annual ploughing caused the greatest reduction in nest numbers (-72 %), providing evidence for an ecological trap.

Conserving migratory birds effectively requires full annual cycle strategies that identify where on-the-ground efforts can have the greatest impact. Here, we present a hemispheric spatial framework to identify priority areas for 112 migratory bird species across the Americas. Building on full annual cycle prioritizations, we defined finer-scale spatial planning units that reflect differences in migratory and congregational behaviors between shorebirds and landbirds. We compiled population data for each planning unit and focal species and applied conservation planning tools to design area-efficient portfolios of sites and landscapes that secure 10% of each species population within the Americas flyways. The resulting minimum area portfolios include 175 shorebird sites and 80 landbird landscapes optimized to meet the species-specific 10% representation targets across breeding, non-breeding, and passage seasons. We also identified a broader set of complementary solutions, ranked by an importance score, to provide decision-makers with flexible options for strategic resource allocation. This framework provides the scientific foundation for the Americas Flyways Initiative (AFI), which aims to catalyze investment in nature-based solutions and bird-friendly infrastructure to enhance the conservation of migratory birds and strengthen the resilience of the Americas flyways by 2050.

Understanding the spatial patterns of biodiversity and their environmental determinants is fundamental to ecology and conservation, particularly in mountainous transitional zones where species assemblages can shift rapidly. Amphibians, as highly sensitive organisms, are excellent indicators of environmental change, yet quantitative assessments of their diversity in the Huangshan Mountains-a biodiversity hotspot at the junction of the Palaearctic and Oriental realms in southern Anhui, China-remain limited. We conducted systematic line-transect surveys across 200 grids (5 km x 5 km) during the spring and autumn of 2023 and 2024, recording a total of 10,342 individuals belonging to 23 species, 20 genera, 9 families, and 2 orders. Three species (Fejervarya multistriata, Microhyla fissipes, and Bufo gargarizans) were identified as dominant, and two nationally protected species (Hoplobatrachus chinensis and Andrias davidianus) were detected. Inter-annual alpha diversity did not differ significantly, but pronounced seasonal variation was observed, with spring supporting higher Shannon-Wiener and Pielou evenness than autumn in both years. Using generalized additive models and model averaging, we found that annual precipitation and the normalized difference vegetation index (NDVI) were consistently the strongest positive predictors of Shannon-Wiener diversity, Simpson dominance, and species richness, while species richness also declined significantly with increasing human footprint. Total beta diversity (Soerensen dissimilarity) was very high (0.981) and overwhelmingly driven by species turnover (97.86%) rather than nestedness. Partial Mantel tests and distance-based redundancy analysis further revealed that environmental distances-particularly annual precipitation-significantly shaped overall beta diversity and its turnover component after accounting for geographic distance. These results highlight the predominant role of climatic and vegetation gradients in structuring amphibian assemblages in this subtropical mountainous region, providing baseline data to inform local conservation strategies.

Invasive mosquito species pose substantial risks to human and animal health. Since 2004, Culex coronator, a mosquito vector species of public health concern, has shown rapid range expansion within the United States, spreading from a historically limited distribution in southern Texas to across the Gulf Coast region and into eastern and mid-Atlantic states. However, changes in environmental suitability associated with this expansion across historical, contemporary, and future climate conditions have not been evaluated. Here, we used species distribution models (SDMs) to compare predictions of abiotic suitability for Cx. coronator under historic (1960-1989) and recent (2000-2024) climate conditions calibrated on the historical range in the United States. We also created a contemporary SDM based on occurrence records prior to and following species range expansion (1960-2024), and further, to predict potential distributions under current and future climate conditions. Models calibrated on the historical range predicted only modest changes in suitability along the Gulf Coast region and failed to identify large areas of the humid subtropical eastern United States that are now occupied. In contrast, the contemporary model predicted widespread suitability across much of the southern and eastern United States. Future projections under the mid-range SSP3 scenario predicted increasing suitability at higher latitudes and elevations. Across all models, suitability was consistently low in arid and semi-arid regions, including along the historical western range limit, suggesting that moisture availability may constrain Cx. coronator distributions. Together, these results highlight the need to incorporate updated occurrence records when modeling invasive mosquito species to strengthen surveillance and control strategies.

Nutrient availability in many temperate ecosystems is shaped by soil properties and historical land use. Especially in otherwise nutrient-poor landscapes human-induced, local fertilisation can generate fine-scale mosaics of nutrient hotspots. Whether and how large herbivores respond to such heterogeneity remains poorly understood. We tested whether spatial variation in soil-derived nutrient availability structures habitat selection by large herbivores, using full-year GPS tracking data from 7 red deer (Cervus elaphus) in the Veluwe, the Netherlands. We used soil types as a proxy for nutrient availability and assigned nutrient scores based on soil pH, cation exchange capacity and soil structure. We then evaluated habitat selection across multiple components of space use: (i) home range size; (ii) use of relatively nutrient-rich parts within home ranges; (iii) selection among soil types; and (iv) selection of locally enriched former agricultural patches. Red deer used relatively nutrient-rich within their home range more than expected based on availability, including local patches enriched by former agricultural use. However, site selection did not consistently follow nutrient scores among soil types. These results show that nutrient availability does shape habitat selection, but primarily through fine-scale, localised nutrient enrichment rather than broad-scale variation in soil properties. Our findings demonstrate that nutrient-related foraging contributes to habitat selection in a large wild herbivore, while also revealing that this process is scale- and context-dependent. By repeatedly concentrating their foraging in nutrient-rich patches, large herbivores may contribute to nutrient redistribution across the landscape, with the potential to reinforce or modify existing spatial heterogeneity in resource availability and ecosystem functioning.

AimHere we aim to disentangle species-specific bioclimatic drivers of forest site productivity and project their future dynamics, providing a spatially explicit basis for anticipating climate-driven shifts in productivity and their implications for forest carbon sequestration. LocationIberian Peninsula. Time period1985-2014 (calibration); 2071-2100 (projected under CMIP6 scenarios). Major taxa studied21 Iberian tree species. MethodsWe used Site Form (SF) maps derived from the Third Spanish National Forest Inventory, spatially interpolating plot-level SF estimates as a continuous productivity index and relating them to 25 bioclimatic variables. Multiple linear regression models were selected via complementary stepwise and subset regression and validated on independent hold-out data (80%/20% split). ResultsValidated [Formula] ranged from 0.46 (Quercus faginea) to 0.97 (Pinus pinaster); 17 of 21 species reached [Formula]. BI013 precipitation of the wettest month), not BI014, was the most frequently retained predictor (15/17); BI014 was retained in only (11/17 models with a near-even sign split. Combining projected changes in mean productivity and habitat extent under SSP5-8.5, fifteen of sixteen applicable species lose total productivity by 2071-2100, six -- including Fagus sylvatica and Betula alba -- collapsing to below 1% of their reference-period value; only Pinus pinaster gains, and only under the lowest-emission pathway (up to 175%) -- under SSP5-8.5 it too loses productivity, albeit less than any other species (35% of its reference-period value retained). Limiting warming to SSP1-2.6 spares Mediterranean pine and oak species but not Euro-Siberian and montane ones. Main conclusionsThese validated, extrapolation-aware models reveal a near-universal, climate-driven collapse in Iberian forest site productivity, with direct implications for the carbon-sink potential currently attributed to these forest types, and provide a route to dynamic, climate-aware carbon-uptake estimates for the region.

Land-use change, such as the transformation of woody ecosystems into open pastures, acts as a strong ecological filter, favouring some species while excluding others according to differences in ecological niche breadth. Understanding how differences in niche breadth influence species responses under anthropogenic filters is crucial to anticipate their persistence or displacement. In this study, we quantified realized niche breadth in two sympatric ecosystem engineers, the Neotropical leaf-cutter ants Atta cephalotes and Atta laevigata, to test whether breadth differences are consistent with specialist and generalist ecological strategies. We characterized realized niche breadth across fine-scale environmental gradients by integrating hemispherical photography, microclimatic data, mound architecture, and edaphic profiles from 114 colonies across a regional transect in the Colombian Andes, alongside macroclimatic data from Copernicus. Principal Component Analysis (PCA) and PERMANOVA identified canopy openness and bushes- and tree-type vegetation density as the principal axes of interspecific niche partitioning. The observed differences in realized niche breadth were consistent with specialist and generalist ecological strategies. A. laevigata was predominantly associated with open-canopy areas, warmer micro- and macroclimatic conditions, and narrower edaphic dispersion. In contrast, A. cephalotes occupied a wider range of microhabitat conditions. This broader realized niche breadth is compatible with previous reports of A. cephalotes occurring in urban areas. Together, these findings suggest that niche breadth may influence how Neotropical leaf-cutter ants respond to habitat transformation, helping to understand the ecological consequences of land-use change.

Abstract The gayal (Bos frontalis) is an endangered semi-domesticated bovine species renowned for its high-quality beef. However, its semi-feral lifestyle, ongoing habitat fragmentation, and extensive genetic introgression from sympatric local cattle have led to dramatic population decline and severe erosion of purebred genetic integrity, posing substantial challenges to its conservation and utilization. To address the urgent demand for rapid, non-invasive, and field-compatible germplasm identification, we developed an integrated artificial intelligence (AI) framework that predicts genomic admixture composition from external morphological images. We constructed a comprehensive dataset comprising 6,245 morphological images and matched genomic sequences from 52 gayals maintained at the Yunnan Provincial Gayal Conservation Farms. Following a preliminary evaluation of nine deep learning models, five were incorporated into a anatomical segment-based multi-modal pipeline, among which Inception_V3 delivered the optimal overall performance. To enhance simultaneous extraction of local fine-grained features and global structural information, we further designed an innovative HybridInceptionViT model by integrating the multi-scale Inception module with the Vision Transformer (ViT) framework. This hybrid model significantly outperformed the baseline Inception_V3, boosting the accuracy of phenotype-derived prediction against genomic admixture estimate from 69.69% to 87.87% (absolute error <15%). This study establishes a practical, low-cost "phenotype-to-genotype" tool for rapid on-site gayal germplasm screening, offering a scalable strategy for the conservation and breeding management of endangered livestock, and holds broad application prospects for agricultural and livestock production systems.

Infectious-disease risk models often rely on occurrence records that are incomplete and spatially biased by surveillance effort, diagnostic access, and outbreak history. Ecological niche modelling (ENM) can identify areas where disease occurrence is environmentally plausible, yet most approaches represent locations using only pointwise covariate values and therefore overlook the surrounding spatial context and rely on presence-only data. Here, we present a deep-learning framework for presence-only data that estimates relative disease suitability by comparing the environmental conditions surrounding reported occurrences with those sampled across the wider study area. The model processes gridded environmental patches at local, neighbourhood, and broader landscape scales, learns the contribution of each scale, and accommodates missing raster values. Using dengue virus as a global case study, we evaluate whether multiscale spatial representation improves upon point-based ENM baselines including random forest and maximum entropy (MaxEnt) under a spatially disjoint train-test design. The model achieved a Boyce index of 0.971 and an AUC of 0.976 on the held-out test set. Learned scale weights and ablation experiments indicated that neighbourhood context contributed most strongly, while local and broader-scale information provided complementary predictive signals. Compared with point-based baselines, the model identified 6-18% more environmentally suitable area across South Asia, Southeast Asia, and South America, encompassing tens of millions of residents. These findings demonstrate that multiscale spatial context can improve estimates of relative dengue suitability. More broadly, mask-aware convolutional density-ratio estimation provides a flexible framework for mapping environmentally structured pathogens from incomplete, presence-only occurrence data.

The accelerated loss of biodiversity in sub-Saharan Africa threatens the functioning of tropical ecosystems. In Cote dIvoire, the Comoe National Park (PNCOMOE), a Sudano-Guinean savannah, and the Tai National Park (PNTAI), a dense rainforest, both UNESCO World Heritage Sites, are home to fauna assemblages of global importance, whose long-term resilience remains insufficiently quantified. This study assesses and compares, over a decade (2014-2025), the functional stability of vertebrate communities in these two contrasting ecosystems, using nine metrics covering resistance, invariance, persistence, interspecific synchrony, Tilmans stability, Jacobian resilience and a Composite Stability Index (CSI). Abundance data for 107 vertebrate species were collected via foot transects at PNTAI and aerial surveys at PNCOMOE. The stability metrics were calculated using the R package estar, integrated with an alpha diversity analysis (Shannon H', species richness S, Pielous evenness J') and a Jacobian spectral analysis within a multidimensional ecological assessment. PNTAI (0.708) exhibits significantly higher alpha diversity (H' = 2.82; S = 55.7 taxa) and community resilience 4.6 times higher than in the PNCOMOE (0.153). Its interspecific asynchrony index (0.504) reveals a strong portfolio effect, absent in the PNCOMOE (0.232). In contrast, PNCOMOE exhibits higher temporal invariance (0.382 versus 0.116) and Tilman stability (0.276 versus 0.152), reflecting more predictable dynamics. The overall ICS favours the PNTAI (0.484) and (0.370). The Jacobian analysis detects local instability in both parks (Re({lambda}max) = 5.58 at the PNTAI; 3.73 at the PNCOMOE). The two parks exhibit distinct yet complementary stability architectures: PNTAI relies on dynamic stability based on resilience and interspecific compensation, whilst PNCOMOE demonstrates conservative stability through temporal regularity. The absence of calculable resilience at PNCOMOE suggests a potential crossing of a functional degradation threshold, arguing for urgent restoration interventions and differentiated conservation strategies, tailored to the resilience mechanisms specific to each ecosystem.

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.

IntroductionChanges in vector control strategies alter the selection pressures exerted on natural populations of Anopheles gambiae s.l. and may influence the dynamics of insecticide resistance mechanisms. However, the consequences of discontinuing indoor residual spraying (IRS) campaigns on the evolution of Ace-1-mediated resistance remain poorly documented under natural conditions. This study aimed to assess the spatiotemporal evolution of resistance to pirimiphos-methyl following the cessation of IRS and to investigate evidence consistent with the existence of a biological cost associated with Ace-1-mediated resistance. MethodsNatural populations of An. gambiae s.l. were collected between 2017 and 2023 in three districts in Burkina Faso that had undergone IRS campaigns (Kampti, Solenzo, and Kongoussi). Susceptibility tests with pirimiphos-methyl (0.25%) were conducted out following WHO protocols, and a subsample of exposed mosquitoes was genotyped to detect the Ace-1 G119S mutation. Spatiotemporal trends in mortality, allele frequencies and genotypes were analyzed according to the pre-IRS, IRS, and post-IRS periods. The association between the Ace-1 genotype and survival following exposure to pirimiphos-methyl was assessed using logistic regression, while the concordance between phenotypic and molecular indicators of resistance was examined using Spearmans correlation. ResultsThe susceptibility of An. gambiae s.l. populations to pirimiphos-methyl was gradually restored after the discontinuation of IRS at all sites. At the same time, the frequencies of the Ace-1 119S resistance allele declined sharply, particularly in Kampti and Solenzo, while they remained low in Kongoussi throughout the study period. Mosquitoes carrying resistant genotypes had a significantly higher probability of survival after exposure to pirimiphos-methyl than susceptible homozygotes, with resistant homozygotes (RR) exhibiting the greatest survival advantage (OR = 27.62; 95% CI: 6.91-110.45; p < 0.001). A significant negative correlation was observed between the frequency of the Ace-1 119S allele and phenotypic mortality ({rho} = -0.48; p = 0.033), indicating a concordance between the two indicators of resistance. The progressive decline in allele frequencies, the decreasing prevalence of resistant genotypes, and the concomitant restoration of susceptibility are field observations consistent with the existence of biological costs associated with Ace-1-mediated resistance. ConclusionThis study provides field evidence consistent with the existence of biological costs associated with Ace-1-mediated resistance in natural populations of An. gambiae s.l. These results underscore the value of an adaptive resistance management strategy based on alternating selection pressures and could guide future vector control strategies, particularly if indoor residual spraying campaigns or other interventions relying on organophosphates were reintroduced.