Ecological Modelling

Non-compliance with regulations threatens the sustainability of fisheries worldwide. Understanding the interconnected feedbacks of this complex social-ecological problem is key for sustainability but rarely integrated into fisheries management. We provide an adaptive stochastic modelling framework that integrates economic, social behavior, and ecological aspects of the Chilean kelp fishery, which plays a critical economic and ecological role in coastal social-ecological ecosystem. High levels of non-compliance is threatening sustainability, fishers well-being, and ecosystem health. Our model considers inherent environmental uncertainties and enables the assessment of different harvesting and compliance scenarios and the role of market-based economic incentives in reducing non-compliance. Results show that, unlike the sustainability obtained under an idealized full-compliance scenario, under dynamic compliance the social, economic, and ecological feedbacks leads to system collapse. Importantly, price premiums can promote compliance and sustainability, but the probability of collapse, albeit small, still exist. Our generalizable stochastic modeling framework evidenced that accounting for inherent uncertainty in natural resource management is key to designing interventions for sustainability.

The ongoing decline in biodiversity highlights the need for understanding the causes of population changes. This study uses 25-year, large-scale monitoring dataset to investigate the influence of climate and landscape structure on the annual population growth rates of 84 bird species across Poland. Our methodological framework involves the spatiotemporal decomposition of these environmental drivers to decouple demographic effects of long-term carrying capacities from the short-term effects of environmental perturbations. Using species-specific demographic models followed by a community-wide meta-analysis, we evaluated how individual species responses scale up to shape community-level dynamics. The results reveal significant variation in species-specific responses to individual drivers. At the community level, our findings suggest that bird populations are mainly regulated by the long-term spatial constraints rather than short-term disturbances. Persistent environmental heterogeneity had the strongest positive demographic effect on birds, followed by temperature, forest dominance over croplands, and precipitation. In contrast, rapid temporal shifts in environmental heterogeneity and precipitation anomalies negatively affected population growth, whereas urbanisation consistently exerted a negative effect across both spatiotemporal dimensions. Our results highlight the significance of protecting existing heterogeneous and ecotonal habitats, as well as the need to incorporate features that enhance habitat heterogeneity into urban development. Article impact statementPreserving heterogeneous habitats is essential for the conservation of bird populations.

O_LIAnimal-mediated seed dispersal is key for the maintenance and functioning of tropical ecosystems. Specifically, in the Cerrado, the largest Neotropical savanna and a global biodiversity hotspot, nearly 60% of plant species rely on animals for dispersal. C_LIO_LIClimate change threatens these interactions by affecting species distributions, reshaping communities, and potentially decoupling plants from their dispersers. Anticipating how such disruptions may alter seed dispersal networks is particularly relevant for understanding the resilience of future tropical ecosystems. C_LIO_LIHere, we combined empirical data on 139 pairwise plant-frugivore interactions with species distribution forecasts to build probabilistic interaction matrices under present and future climate scenarios, which were then used to construct 6,221 local seed dispersal networks. Using ecological niche modelling, we tested how climate change influences species range size and centroid displacement. Then, we evaluated whether such changes translate into losses of pairwise plant-frugivore co-occurrence. Finally, we investigated how these changes in occurrence overlap may affect key structural properties of future local seed dispersal networks. C_LIO_LIWe forecast that by the 2070s, under a business-as-usual climate scenario, species are likely to contract their ranges by 56 {+/-} 33% and shift their distribution centroids by 88 {+/-} 57 km within the Cerrado, leading to a 27 {+/-} 29% loss in plant-frugivore co-occurrence mainly driven by reductions in plant species distributions. At the community level, these losses will lead to smaller and more nested networks and specialized, indicating a structural simplification of seed dispersal systems in the Cerrado. C_LIO_LISynthesis: By combining empirical data on animal-mediated seed dispersal with forecasts of species distributions, we found that climate change may simplify frugivore-plant interaction networks in the Cerrado by decreasing species ranges and co-occurrence of partners. Our study demonstrates that future climate may pose a threat not only to species distributions but also to ecological interactions, such as seed dispersal, that are key to enabling climate-tracking by plants. Thus, preventing the simplification of interaction networks will be essential to conserve biodiversity in species-rich regions. C_LI

Guinea worm disease eradication efforts may benefit from environmental surveillance methods capable of detecting infected copepod intermediate hosts in aquatic habitats. We developed a three-dimensional, spatially explicit agent-based model to examine how ecological processes influence detection probability for a hypothetical water sampling method. The results show that surveillance sensitivity is shaped by the combined effects of larval diffusion, copepod density, and pond size, with interactions among these factors producing nonlinear relationships. Detection, in our model, was concentrated within a relatively restricted period after larvae matured to the infective stage and before dispersal and mortality reduced presence, indicating a limited spatiotemporal window for effective sampling. Surveillance performance peaked under intermediate dispersal regimes that generated sufficient spatial overlap between larvae and intermediate hosts, while both limited dispersal and excessive diffusion reduced detection by constraining encounters or diluting larval concentrations. Increasing habitat size reduced detection by diluting larval concentrations, but the magnitude of this effect depended on copepod density and dispersal dynamics, producing nonlinear and threshold responses rather than simple scaling with pond volume. Spatial and temporal patterns of detection shifted as larvae dispersed, with the most favorable detection periods occurring when both larval abundance and intermediate host encounters were elevated. These findings indicate that surveillance can be guided by local ecological conditions. When the timing of larval introduction is uncertain, effective surveillance requires repeated sampling over time to capture transient windows of detectability and the sampling will be less effective in very stagnant and highly mixed waterbodies. Overall, this study demonstrates how mechanistic modeling can support the design and interpretation of environmental surveillance strategies for Guinea worm eradication programs. Author summaryGuinea worm disease is close to eradication but confirming that transmission has fully stopped remains difficult because detecting infectious larvae in water is challenging. Transmission depends on freshwater copepods that become infected after ingesting Guinea worm larvae. These copepods are short-lived and unevenly distributed within ponds, and infected individuals may die before larvae reach the infective stage. As a result, environmental detection is inherently uncertain. We developed a three-dimensional agent-based model to simulate larval dispersal, copepod infection, and water sampling in a pond environment. The model shows that detection is constrained to a brief period when mature larvae and copepods overlap in space and time, and that this window depends strongly on local ecological conditions such as larval dispersal, copepod density, and pond size. Because infected copepods can be present outside these narrow detection windows, negative water samples do not necessarily indicate absence of transmission, highlighting the need for repeated, spatially targeted surveillance during the final stages of eradication.

Mark-recapture analyses on the delineation of natural populations between areas often assume random sampling, with a between/within (B/W) area resighting ratio that declines towards zero as the population components of two areas become more-and-more isolated from one another, with fewer-and-fewer individuals mixing between areas. I use an individual based population model split in two areas to simulate this result, analysing also for the potential effects of the space-time fidelity of the mark-recapture sampling in the areas. I find that small B/W resighting ratios--that traditionally is taken as evidence of population isolation--can easily be observed within a completely mixing population if a random sampling scheme is restricted in space and/or time. Random sampling within restricted areas and time windows is not sufficient to estimate mixing rates and population isolation between areas, unless the resighting rates are analysed by a method that accounts both for the space-time fidelity of the scientific sampling scheme and the space-time fidelity of the distributional behaviour of the individuals in the population.

Explainable machine learning (ML) methods are gaining increasing attention in environmental and ecological research for their ability to reveal relationships between environmental drivers and population dynamics. However, there remain questions on the reliability of these tools, especially given recent research shows that these explanations can be highly sensitive to model architecture. In ecology, it is typical to use a single ML model, and a comparative evaluation of sensitivity of explainability for different ML approaches is overlooked. In this paper, we develop a novel framework that quantifies explanation consistency between multiple ML model architectures. This framework provides a discrepancy measure for each model prediction, with high discrepancy indicating substantive explanation disagreement across models and low discrepancy indicating strong consensus in explanations across models. We then demonstrate that low explanation discrepancy aligns well with ground truth mechanism. Furthermore, high explanation discrepancy provide a mechanism to identify areas for model refinement and further investigation by domain experts. We do this by using a simulation study based on synthetic coral cover data that incorporate spatio-temporal variability driven by known disturbance effects. Our method provides a quantitative approach to assess the sensitivity of explainable ML in the absence of ground truth. As a result, this enhances the utility of ML approaches in conservation and ecological management. While we focus primarily on ecological modelling for coral reefs, our methods are generally applicable to other ecological and environmental modelling settings.

A major goal of community ecology lies in the deciphering of the processes underlying species distribution. A widespread approach to this question is to identify patterns in species community data and relate them to possible processes. Joint Species Distribution Models (JS-DMs) offer one way to do so through the infernece of association networks that describe patterns of statistical correlations and dependencies between species, but it is unclear what processes can explain the presence of such correlations. While it has now been established that there is no equivalence between JSDM-inferred associations and biotic interactions, the later remain one possible explanation, among others, for the former. However, to our knowledge, there is no specific study of the statistical patterns induced by different types of interactions or of the conditions under which they may or may not appear as statistical correlations / dependencies in species communities. To explore these questions, we propose a "virtual ecologist" approach that consists in simulating community data based on abiotic and biotic processes with the VirtualCom model that emulates the effects of environmental processes and of competition and facilitation interactions. Then, we study to what extent JSDMs retrieve correlations between species that match the simulated interactions. We show that these interactions are better identified when using JSDMs that model partial correlations between species rather than marginal ones. We further demonstrate how critical it is to correctly model abiotic effects in order to identify biotic ones and that the "correct modelling" of these effects depend on the type of interactions at stake.

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.

O_LIPlants take up nutrients from the soil while investing in absorptive root surface or mycorrhizal partners. Root hairs - a major structure for nutrient uptake and cheap to build - increase the absorptive root surface. As such they are an important component of plant resource economics but largely neglected in root economic concepts so far. C_LIO_LIThis is mainly due to data scarcity, which we set out to overcome by measuring root-hair traits on 82 European grassland species in a greenhouse experiment. Using fluorescence and light microscopy, root-hair length and incidence was measured along with mycorrhizal colonization. C_LIO_LIWe found a phylogenetically conserved trade-off between plant investment into root hairs and mycorrhiza. A similar trade-off between root-hair incidence and mycorrhiza occurred at the intraspecific level, while patterns were heterogeneous among species. Plant species with high colonization rates showed the highest variability in root-hair incidence. C_LIO_LIWe conclude that plants vary along a gradient ranging from investment into root hairs as part of a "do-it-yourself" strategy to collaboration with mycorrhizal fungi while showing intraspecific variation in root-hair incidence. These findings demonstrate that root hairs play a fundamental role in fine-root trait variation and need to be considered when studying belowground plant economic strategies. C_LI

Elucidating how habitat degradation facilitates extinction is critical for effective conservation efforts. Here, we propose integrating physiologically-structured population models into stochastic population viability analyses to assess how differing consequences of habitat degradation interact to drive extinction dynamics in a focal population. Using the isolated spectacled caiman Caiman crocodilus population/ecomorph from the Apaporis River as a case study, we find that threatening the resource base, which individuals increasingly rely upon, to outgrow vulnerable size ranges and mature accelerates extinction. We also found that when habitat degradation impacts both the primary adult and juvenile resource bases, this can have marked synergistic effects on threatening population viability. By contrast, destroying nesting sites has only a small effect on accelerating the impact of deteriorating prey availability. Through integrating community-level feedback between habitat degradation/change and population dynamics/structure, our approach provides a comparative framework for assessing the relative importance of distinct mechanisms through which habitat degradation ultimately drives extinction risk.

Minimum viable populations (MVPs) are population levels large enough to surmount risk from demographic, environmental, and genetic stochasticity. MVPs are estimated by biologists to guide conservation practices. However, MVPs are generally estimated for a target population without regard for how they interact with intra- and inter-species population dynamics in the broader ecological community. Thus, how and why population dynamics interact with MVPs imposed by conservation biologists remain unclear. When MVPs are imposed on a continuous population model, traditional analyses fail to capture the range of possible outcomes those MVPs create. Here, we describe viability space decomposition (VSD) as a mathematical tool to systematically analyze the potential crossing of MVPs during population dynamics. We demonstrate that different extinction and survival outcomes can be recovered from a model with imposed MVPs using three VSD concepts in junction with a traditional phase portrait: mortality manifolds which separate conditions that lead to different existential outcomes, ordering manifolds which determine the order of extinction events for multiple populations, and collapse manifolds which determine the survival or extinction of one species given the loss of another. We employ these methods with a standard consumer-resource model, and the methods can be scaled to systems with more species. VSD is a useful tool for conservation biologists and community ecologists concerned with boundary crossing problems in any dynamical system.

Despite substantial global commitments to expand protected-area networks, the strategic allocation of limited resources remains challenging. Spatial conservation planning helps identify priority regions that maximise conservation benefits per unit area. Yet, they also tend to neglect two fundamental aspects of conservation: climate-driven range shifts and the representation of environmentally distinct populations within species. Here, we propose a continental-scale conservation planning framework that explicitly accounts for both processes through novel routines implemented in the conservation planning software CAPTAIN. We apply this framework to European crop wild relatives (CWR), for which niche coverage is a focal priority, as it underpins their potential to support agricultural adaptation to future environmental stressors through breeding programs. Comparative analyses on a subset of 186 CWR associated with five focal crops show that accounting for range shifts and niche coverage leads to substantially different conservation priorities from those obtained with a baseline model based on current distributions only. These additions reduced the number of non-protected species by 64%, increased the average protected distribution range by 43%, increased mean niche coverage from 75.8% to 84.5% and reduced the number of species with less than half of their niche protected from 35 to 10. Applied to a more comprehensive checklist of 1,140 European CWRs, the final framework identifies continental-scale priority areas representing 93.5% of these taxa and includes 94.4% of its critically endangered species. Our results highlight the importance of incorporating both temporal dynamics and within-species environmental representation when designing conservation strategies under climate change. RepositoryThe repository will be made publicly accessible after publication at doi: https://10.5281/zenodo.19855597

The classical concept of Critical Community Size (CCS) as formulated by Bartlett defines the minimum host population required for a pathogen to persist endemically without stochastic extinction. While this framework successfully described directly transmitted childhood infections in relatively isolated populations, it is increasingly inadequate for modern urban systems characterized by strong connectivity between cities. Pathogens circulating in highly connected urban networks can repeatedly re-emerge through spatial reintroduction even when local transmission temporarily fades out. In such systems, persistence is inherently probabilistic and influenced simultaneously by population size, environmental suitability, and network connectivity. In this study, we develop a generalization of the CCS concept, the Empirical Persistence Threshold (EPT), and apply it to three of the main arboviruses circulating in Brazil--dengue, chikungunya, and Zika--over the period 2017-2024. The Empirical Persistence Threshold generalizes the classical notion of critical community size by replacing a single deterministic threshold with a probabilistic, datadriven measure. Instead of asking for the minimum population at which persistence is guaranteed, EPT characterizes the lower tail of the population distribution among municipalities that empirically sustain transmission. Using weekly incidence data from thousands of municipalities, we transform temporal incidence series into binary sequences describing the presence or absence of reported transmission. For each municipality, we characterize persistence through the empirical distribution of run lengths of consecutive weeks with reported cases. Distances between run-length distributions are computed using the Wasserstein-1 metric, allowing a geometrically meaningful comparison between persistence profiles, and municipalities are grouped into epidemiological regimes using hierarchical clustering methods. Across all three arboviruses, we identify two robust regimes: one exhibiting sporadic and recurrent epidemic transmission, and the other exhibiting sustained persistent transmission. We then estimate the population scales associated with each persistence regime. The analysis is further extended to evaluate how persistence thresholds vary across climate regimes (Koppen classification) and urban hierarchy levels (REGIC). This framework allows the estimation of probabilistic persistence thresholds analogous to CCS, but adapted to connected urban systems. We define the Empirical Persistence Threshold as lower quantiles of the population distribution among municipalities in the persistent regime, and additionally estimate persistence thresholds based on regime membership probabilities. Results reveal strong interactions between population size, climate, and urban connectivity. Dengue exhibits the lowest persistence thresholds, Zika intermediate thresholds, and chikungunya the highest thresholds. These findings demonstrate that pathogen persistence in modern urban systems cannot be described by a single deterministic population threshold. Instead, persistence emerges from the joint effects of demographic scale, environmental suitability, and network position within metapopulation systems. Author SummaryInfectious diseases often require a minimum population size to persist locally, a concept known as the critical community size (CCS). This idea was developed for relatively isolated populations, but modern cities form highly connected networks where diseases can repeatedly reappear even after local transmission disappears. In this study, we introduce the Empirical Persistence Threshold (EPT), a data-driven approach that replaces the idea of a single fixed threshold with a probabilistic description of persistence. Instead of focusing on case counts, we analyze how long transmission persists over time in each municipality. Using weekly data for dengue, chikungunya, and Zika across Brazil from 2017 to 2024, we identify distinct patterns of transmission persistence and estimate the population levels associated with sustained transmission. We also examine how these thresholds vary with climate and urban structure. Our results show that persistence depends not only on population size, but also on environmental conditions and the position of cities within the urban network.

Philippine freshwater ecosystems are considered one of the most diverse ecosystems harboring numerous fish species. However, in the Philippines, these ecosystems are threatened by invasive species that potentially disrupt ecological balance. In this study, we focused on the vermiculated sailfin catfish Pterygoplichthys disjunctivus, an invasive aquarium species reported in several Philippine aquatic ecosystems. Despite its documented spread, its potential range under a rapidly changing climate remains poorly understood for the country. Hence, in this study, we utilized the MaxEnt model to predict its near-current and future habitat suitability in the Philippines. Using 11 reported occurrences, our model showed high predictive accuracy (AUC = 0.882{+/-} .034, TSS = 0.7394 {+/-} 0.154, SEDI = 0.971 {+/-} 0.019). Across the current and future scenarios, slope was the primary contributor (78.7% - 81.3%), followed by BIO 10 or the mean temperature of the warmest quarter(18% - 27.8%), and flow accumulation (0% - 5.2%). However, for the SSP126 scenario, BIO10 is projected to triple by 2050 (18 - 48%). Current projections identify high-risk regions, particularly central Luzon (Laguna de Bay and Lake Taal), the Cagayan River Valley, and portions of eastern Mindanao (Agusan Marsh and Lake Mainit). Sankey transition analysis confirms a high habitat stability rate (>73%) for high-suitability pixels in both SSPs, indicating persistent invasion risk. Overall, our study provides a framework for invasive species management and contributes to the conservation of Philippine aquatic ecosystems.

Recovery debt (RD) quantifies the interim deficit of biodiversity and function during the recovery process after disturbance. Unlike typical recovery indices derived from data on experimental-control comparisons, RD further considers the target (reference) biodiversity level, modelling the rate at which it is approached over time. However, the application of the RD approach to active restoration has not been explicitly implemented to date. Here, we extend the RD framework to evaluate active ecological restoration in agricultural systems, defining the onset of recovery as the shift from intensive to wildlife-friendly management. We applied this approach to assess short-term pollinator recovery in 14 olive groves across a gradient of farming intensification and landscape complexity in southern Spain. Restoration actions included adopting low-intensity ground cover management and actively restoring field margins. At one, three, and five years post-restoration, we assessed community responses by quantifying bee abundance, species richness, plant-bee network properties, and flower visitation rates. Reference systems were defined by olive groves in complex landscapes with low-intensity herb cover management and organic farming practices. Following restoration, the RD of bee abundance decreased from 71% to 55%. We found no significant effects of pre-intervention agricultural management on RD. Instead, across sites, the reduction of the RD (i.e., recovery) of bee abundance, richness, network connectance and flower visitation rate was strongly mediated by the availability of high-quality semi-natural areas in the surrounding landscape and by the ecological contrast created by restoration interventions at both the farm and floral patch levels. RD for other network metrics showed no significant pattern of variation. Our study demonstrates that wildlife-friendly management and targeted habitat restoration can rapidly reduce recovery debt for bee abundance and function in permanent agroecosystems. However, the recovery of more complex interaction-network properties likely requires longer timescales.

The use of marine space by human activities is globally increasing, resulting in a competition with spatial management measures for marine conservation. Within the European Union (EU) these measures are currently implemented by the union member states to achieve the UN sustainable development goal (SDG) of protecting at least 10 % of the national marine waters. Further, the EU Marine Strategy Framework Directive (MSFD) and the Nature Restoration Regulation (NRL) are the two main legal means for the implementation of ambitious spatial conservation targets for benthic habitat types, which can range from 10 - 90 %. This study analysis how the targets of the MSFD and NRL are currently met in the German waters of the North Sea and which areas the full implementation of both legislations might require. A spatial optimisation tool ("prioritizr" in R) was used to identify optimised solutions for the conservation of up to 75 % of NRL benthic habitats. The current spatial conservation measures (which ban demersal trawling within certain zones of designated marine protected areas, MPA) are not sufficient to reach the targets of the MSFD and NRL. Extending the exclusion of demersal trawling to the entire area of the MPAs would achieve a sufficient coverage for all habitats except for offshore sand and mud habitats. These could be further protected, when including areas for offshore wind farms, where trawling is also banned. However, to date it is unclear, if and how these (or other human use) areas could be included into spatial conservation regimes, a debate that needs to be resolved to allow for the achievement of the ambitious MSFD and NRL targets.

O_LICalluna vulgaris is often managed in the UK by rotational burning, but this practice has recently been banned on peat with depth greater than 30-40 cm. It is unclear how then to manage the large areas of Calluna on blanket bogs used for sport shooting because without managed burning, fuel loads and wildfire risk will increase as the Calluna ages within the artificially narrow age distributions created by burn management. C_LIO_LIWe developed a model of Calluna mortality and management to understand duration and persistence of post-management effects. This allows us to assess how long it will take to reach a more natural age structure which would allow increased diversity if management ceases. C_LIO_LIOur results show that management effects persist for around 50 years depending on site-specific mortality rates. Active management may therefore be needed either to mitigate the elevated risk of severe wildfire or to speed up this transition. C_LIO_LISome studies have employed, as unmanaged analogues, Calluna stands that were last managed <50 years ago, but such studies may have unintentionally biased their results by observing Calluna still in post-management recovery leading to an over-estimation of wildfire risk associated with more natural blanket bogs. C_LIO_LISynthesis and applications: with the banning of burning as a management tool for Calluna on deep peat, alternative management is now likely needed as our model shows it could take around 50 years for the Calluna to reach a more natural age distribution. Mowing can replicate some of the effects of managed burning but requires repeated intervention and may compress the peat surface from repeated machine tracking. Rewetting and Sphagnum reintroduction may offer a more sustainable management approach to lowering Calluna fuel loads and reducing severe wildfire risk by creating wetter sub-optimal conditions for Calluna growth and thereby altering the competitive balance between Sphagnum and Calluna. Further work is needed to assess the efficacy of rewetting in controlling fuel loads and how this varies with climate and local pressures. More broadly, this work highlights the need to quantify the persistence of past management regimes to understand ecological trajectories. C_LI

Sky islands are high-elevation ecosystems surrounded by lowland habitats that create isolated environments with distinct climatic conditions. These factors have driven the evolution of many endemic species, separated from their larger, contiguous populations. An Individual-Based Model (IBM) was used to simulate population dynamics by modeling the behaviors and interactions of Tamiasciurus hudsonicus grahamensis (Mount Graham Red Squirrel) a subspecies of the American red squirrel (Tamiasciurus hudsonicus) that is endemic to the Pinaleno Mountains in southeastern Arizona. This approach can help predict future population trends based on historical species data leading to better conservation decisions. Using species-specific ecological preferences--including temperature, precipitation, and vegetation indices (NDVI)--an IBM was developed to simulate population dynamics and spatial distribution projections through 2100. Climate change projections, based on the best- and worst-case scenarios outlined in the 2014 National Climate Assessment, were incorporated to assess potential future population trends under changing environmental conditions. The population faces a 45-62% probability of extinction by 2100, with a significant risk of extinction within the next 50 years. A translocation experiment was conducted to evaluate the viability of relocating individuals to the Chiricahua Mountains, another sky island with a larger habitable area. However, the risk of extinction remains even higher (87-89%) due to environmental disturbances affecting both the Chiricahua and Pinaleno regions. This highlights the challenges of conservation efforts in the face of climate change and emphasizes the need for targeted management strategies to preserve this critically endangered subspecies.

Gene drives, genetic constructs that can spread deleterious alleles in wild populations, have the potential to address some of the major pressing challenges of the Anthropocene such as invasive species, spread of disease vectors, and agricultural pests. However, responsible and effective deployment of gene drive requires taking into account the complex nature of real-world population connectivity networks. In particular, it is unclear how the topological position of the deployment site affects the spread process and its final outcome. Here we develop a framework for modeling gene drive spread in population connectivity networks, and study the eco-evolutionary dynamics of gene drive spread under complex population structures. We investigated the relationship between the position of the deployment site in the topology of the network and whether the gene drive is eventually lost, fixed, or maintained at an intermediate frequency. We identified network centrality measures of deployment sites that are highly correlated with the outcome of deployment for different gene drive designs and across diverse network topologies. We also show that there is a trade-off between the time-to-fixation and the final outcome, implying that multiple centrality measures of the deployment site would need to be considered when aiming to achieve rapid and successful population control using gene drives.

Nature-Based Solutions are increasingly promoted to address current urban challenges. While their potential effects on vector-borne disease risks have been documented, data on Aedes albopictus, a known arbovirus vector, remain limited in France. A previous study showed that urban vegetation moderately increases the abundance of adult mosquitoes of this species, but the monitoring period lasted only six months. Using ovitraps, we monitored Ae. albopictus egg density dynamics over multiple years (2022 to 2024) and analysed its environmental predictors in various urban environments. We included lagged meteorological variables, land cover metrics, and the cumulated egg densities recorded in the previous weeks as environmental predictors. Both parametric (GLMM) and non-parametric (Random Forest) models were fitted to weekly egg counts per trap. Our findings highlight that (i) egg density dynamics were related to how vegetation classes structured the landscape, (ii) growing degree days and cumulated number of eggs recorded in specific lagged time windows were the main contributors to egg density, and (iii) the non-parametric and parametric models performed similarly in terms of prediction accuracy.