Population Ecology

A high proportion of the world's population relies on marine fisheries as a source of food and employment, highlighting the need for sustainable exploitation strategies. However, fisheries management commonly relies on single-species models that overlook ecological interactions and economic trade-offs, which may lead to stocks being exploited above sustainable levels. To address this gap, we developed a novel size-structured, multispecies bioeconomic module integrated within the spatially explicit, individual-based OSMOSE ecosystem model. OSMOSE represents exploited fish communities in which individuals interact through opportunistic, size-dependent predator-prey interactions which are explicitly incorporated into the bioeconomic framework. The bioeconomic model accounts for multiple species and size classes, allowing for size-dependent market prices. Fishing costs and profits are represented using an extension of the Gordon-Schaefer model to multiple interacting species, while consumer demand is modelled using a nested Dixit-Stiglitz utility function with three levels of constant elasticity of substitution for fish commodity, species and size classes. We present a method for estimating parameters for the bioeconomic module when empirical estimates are unavailable, using the North Sea OSMOSE configuration, which comprises 15 species. Our method successfully estimated the six bioeconomic parameters required to operationalise the model. Results indicate that differences in cost parameters were primarily associated with variability in species biomass rather than fishing gear, while fish prices were more strongly influenced by consumer's demand than by availability. This framework provides a basis for assessing the economic consequences of alternative climate change scenarios and supporting sustainable fisheries management.

The Peruvian anchovy (Engraulis ringens) is a small pelagic fish. It is the largest singlespecies fishery in the world and it is used mainly for the production of fishmeal and fish oil. The Peruvian hake (Merluccius gayi) is a predatory demersal fish that is valued for direct human consumption, mainly as frozen products. In this study, I compared the response of both species to certain fishing mortality scenarios using a climatological set-up of the multispecies model OSMOSE for the northern Humboldt Current System. I observed that hake landings benefit from a decreased fishing pressure. In addition, the resilience of the anchovy fishery may increase if a higher threshold for their minimum catch size was implemented. The results of this study provide insights into better management strategies that could be beneficial for the two species based on their life-strategies.

Managers must make decisions in the face of uncertainty, especially when available resources are limiting. Identifying thresholds when certain conditions are met or exceeded enable the potential to mitigate risks. In 2005, sustainable harvest levels of willow ptarmigan were identified to avoid harvest efforts exceeding three hunter days km2. Here we evaluate these recommendations by analyzing line transect counts and harvest data from six areas forming three open/closed pairs in a region of state managed willow ptarmigan harvest. We developed three sets of Bayesian hierarchical models, one static distance model, and two dynamics models. One mechanistic hazard model and a Gompertz phenomenological model. Adult and juvenile density showed pronounced year-to-year variation that was largely synchronous across all six sites regardless of hunting status. The harvest effort parameter shows a striking difference between the two models. In the Hazard model, is positive, and excludes zero with near certainty, but in the Gompertz model, the parameter is highly uncertain. However, the two models do not contradict each other but answer complementary questions with different sensitivity to the harvest signal, harvest mortality is additive at the individual level, but this additive mortality is masked at the level of population abundance. The demographic cost of harvest is therefore real and quantifiable through the survival chain, but bounded in the long run by the stabilizing dynamics. A fixed limit anchored to monitored effort and bag is not a crude substitute for adaptive management but the appropriate design under the information commonly at hand. It will be a precautionary instrument grounded in the one relationship this study establishes firmly, the translation of hunter effort into harvest mortality.

O_LIUnderstanding the processes underlying successful eradication of invasive species is essential for achieving global island conservation goals. Despite the widespread availability of capture records from eradication programs, modeling frameworks that utilize these datasets to elucidate spatio-temporal population dynamics remain underdeveloped. C_LIO_LIIn this study, we reconstructed the spatio-temporal population dynamics of the small Indian mongoose on Amami-Oshima Island (712 km{superscript 2}), Japan, where the species was introduced in 1979 and officially declared eradicated in 2024 after more than 30 years of systematic removal. We integrated introduction records, capture data, and monitoring data using a hierarchical harvest-based model (HBM). To evaluate the models capacity to support management decisions and assess eradication success, we conducted retrospective analyses and compared estimated eradication probabilities with those obtained from a rapid eradication assessment (REA; Samaniego-Herrera et al., 2013). C_LIO_LIThe estimated population size (before reproduction) peaked at 5,449 individuals (95% CI: 4,703, 6,175) in 2000 and subsequently declined almost monotonically. The maximum invaded area was 547.78 km{superscript 2} (posterior median, 95% CI: 496.47, 566.04) in 2009, indicating that the removal program successfully prevented island-wide expansion. Retrospective analyses showed that population estimates remained within the 95% credible intervals of the full dataset estimates, demonstrating temporal consistency. Eradication probabilities estimated by the HBM were substantially higher than those from the REA, highlighting the sensitivity of estimates to fine-scale heterogeneity in detection processes. C_LIO_LISynthesis and applications: Hierarchical HBMs provide a powerful framework for reconstructing, predicting, and evaluating invasive species eradication dynamics. Being aware of the limitations for application to eradication evaluations, HBMs can support adaptive management in long-term eradication programs and improve our understanding of the mechanisms underlying successful eradication. C_LI

Fishery-dependent indices of abundance are often used to inform stock assessments, but fishing effort is generally not randomly distributed. Fishers concentrate sampling in locations expected to be profitable, leading to a form of preferential sampling that can bias model-based indices of abundance. We show that when the strength of preferential sampling is constant over time, it biases only the magnitude of an index, which is absorbed by estimated catchability parameters in most stock assessments. A more interesting case arises when the strength of preferential sampling changes over time; in these cases the index trend can be biased in a way that conventional catchability estimation cannot correct. Using simulations structured on long running trawl survey data from the west coast of the USA for shortspine thornyhead (Sebastolobus alascanus), we evaluate two structurally different methods for correcting time-varying trend bias: a joint spatiotemporal model of the density and sampling processes, and a discrete-choice model of fisher location choice. Our simulations include two mechanisms generating preferential biases: effort tracking local density, and effort responding to a spatially structured, but unobserved anomaly that also inflates catch. We show that the ability of the estimation methods to correct bias depends on the mechanism responsible for concentrating effort; while the joint model is robust to both data-generating mechanisms, the discrete choice model is only able to correct biases when the mechanism is anomaly-driven. As a real-world case study, we apply these corrections to simulated preferentially sampled data and include indices in the shortspine thornyhead stock assessment model. We found that uncorrected time-varying preferential sampling positively biased estimates of a major recruitment event and, to a lesser extent, unfished recruitment. This bias propagated through to bias in overfishing limits that grew as the population was projected forward. While bias-corrected indices still produced some positive biases in overfishing limits, these were substantially reduced compared to the naive approach. As the mechanism of preferential sampling is rarely known in advance, our results suggest that the joint model represents a robust default for correcting fishery-dependent indices of abundance.

As anthropogenic threats have intensified over the past 500 years, we find ourselves in the midst of a sixth mass extinction, with continued losses of biodiversity threatening ecosystem stability. This biodiversity loss has caused species extinctions across taxa, and placed several others at high risk of functional extinction. These disturbance-driven impacts represent one of the most acute biodiversity crises facing global marine systems. Species exhibiting slow life histories characteristically have low resilience to disturbance. Here, we assess the risk of functional extinction and identify policy pathways for population recovery of the slow-living, Critically Endangered elasmobranch, the spinetail devil ray (Mobula mobular). We develop a stochastic, state-structured Integral Projection Model (IPM) parameterised with demographic data collected from fishery landings data in India, the world's largest mobulid fishery, and supplemented with data on vital rates from published literature. Using the IPM, we estimate that the population is declining at approximately 12% annually, experiencing substantial limiting pressure from fisheries overexploitation and failing to approach its biological maximum growth potential. Our results indicate that populations of M. mobular will be at high risk of functional extinction if 'business as usual' harvest scenario persists for another decade. We further show that long-term population recovery is only possible if survival increases significantly across all size classes, especially among large reproductive females, alongside a concurrent increase in fecundity. We conclude that no single policy measure is sufficient to recover population of M. mobular along the southeastern coast of India. Instead, combined protection through maximum bycatch mitigation and protection of nursery areas in no-take zones will be required for population recovery. This research demonstrates that recovery of overexploited populations often requires integrated resource management across life stages, and that the Critically Endangered M. mobular warrants urgent conservation action to avoid functional extinction.

Demographic stochasticity is generally expected to contribute little to population variability when census abundance is large, because random demographic events are increasingly averaged across many individuals. This expectation, however, depends on how evenly individuals contribute to reproduction. In many natural populations, reproductive success is highly skewed, so that a large census population can coexist with a much smaller effective number of breeders (Nb). Although Nb is commonly used as a genetic measure of contemporary effective size, it also summarizes variation in reproductive contribution and may therefore contain information about demographic variability. Here, we develop a female-based age-structured theoretical framework linking Nb to temporal variability in mature population abundance, with N denoting the census number of mature females and Nb the effective number of female breeders. Recruitment variability arises from non-Poisson reproductive variation and environmental stochasticity, and its transmission to mature abundance is described by a life-history coefficient, {gamma}, determined by survival, maturation, and the contribution of overlapping cohorts. When the Poisson component of recruitment variance is negligible and Nb/N is sufficiently small, the coefficient of variation in mature abundance, CV[N], can be approximated from Nb, environmental variability, and {gamma}, with little additional information from census abundance. This approximation does not require specification of the functional form of the relationship between adult abundance and recruitment. Stochastic age-structured simulations closely reproduced the analytical prediction, even when additional demographic stochasticity in survival was included. Substantial fluctuations in mature abundance occurred at census sizes on the order of 105 when Nb was small, whereas overlapping cohorts buffered recruitment fluctuations. Because Nb can be estimated genetically from samples of a single cohort, the framework provides a direct link between genetic monitoring and population dynamics. More broadly, these results show that large census abundance alone does not guarantee weak demographic variability and highlight the potential of genetic data to inform expected population fluctuations when detailed demographic information is limited.

Many Sockeye salmon (Oncorhynchus nerka) populations have declined over recent decades, and climate change is likely to exacerbate these declines through direct and indirect ecological effects. The response to the associated environmental changes is likely to vary among life stages, populations, and regions. Quantitative estimates of climate change driven impacts that account for this variability could fill a critical gap and provide forward-looking insights into how sockeye are expected to respond to future climate-driven change across their lifecycle. To address this need we developed a hierarchical population dynamics model parameterized with juvenile, adult return and spawner abundance data from 13 sockeye salmon populations from Washington State to northern British Columbia. We used a formal causal inference framework that paired salmon abundance data with a suite of environmental covariates hypothesized to represent ecological conditions across the lifecycle. We used the model to estimate population-specific responses to each environmental driver, then combined parameter estimates with projections from down-scaled climate change models to estimate productivity responses to anticipated environmental change. We found that historical sockeye productivity was strongly associated with environmental covariates, which explained more interannual variability in return abundance than spawner abundance in most populations. However, the life stages and specific environmental covariates with the largest impacts differed among populations and regions, often displaying a latitudinal gradient. Increases in coastal ocean temperatures and mixed layer depth generally had negative effects though they varied among regions. Increased freshwater summer rearing and return migration temperatures had weaker but consistently negative effects. Under future climate conditions, projected changes in these environmental covariates are expected to result in substantial declines in productivity across most populations. Sockeye salmon display varying degrees of sensitivity to climate change across life stages, populations, and regions. Effective future management will require explicitly accounting for these life stage and population-specific responses.

Inbreeding depression (the reduction in fitness associated with inbreeding) has been demonstrated in a wide range of animals, but despite its ubiquity, is not an inevitable consequence of inbreeding. As a result, there is uncertainty about the extent to which inbreeding depression poses an ongoing risk to endangered species currently experiencing significant demographic recovery. Quantifying inbreeding depression will be critical if we want to understand these risks. A comprehensive quantification of the fitness costs of inbreeding requires detailed individual-based longitudinal data so lifetime impacts can be assessed. Here, we use an extraordinarily detailed long-term dataset on Mauritius kestrels (Falco punctatus) to explore inbreeding depression in a population currently experiencing significant demographic recovery. To do so, we constructed a social pedigree of 1,758 individuals and combined this with 1,240 nest records and 1,411 individual resighting histories to explore lifetime fitness effects over a 30-year period. Inbreeding increased significantly over time as the population recovered before stabilising. Inbred eggs were less likely to survive to fledging. Inbred adult male and female birds had significantly lower annual reproductive success than outbred individuals because of a lower annual egg-to-fledgling survival probability. This resulted in significantly lower lifetime reproductive success in inbred females but not males, which showed a negative trend. Population growth was negative and extinction risk increased slightly at current levels of inbreeding. These impacts will become more severe should inbreeding levels increase in the future, which is highly likely given ongoing genomic erosion. Taken together, our results demonstrate significant fitness costs associated with inbreeding in Mauritius kestrels, which pose an ongoing risk to population viability. This suggests that monitoring and managing inbreeding risks in endangered species will likely be required even in populations that are showing significant demographic recovery in response to conservation interventions.

Space-for-time Cormack-Jolly-Seber (CJS) models have been developed to estimate survival of migrating animals that are imperfectly detected at spatially discrete sampling stations. However, space-for-time CJS models typically ignore survival over time and age-specific probability of movement, missing the diversity of life history strategies. To address these limitations, we extended space-for-time Cormack-Jolly-Seber models to explicitly incorporate time, age, and individual-level covariates to account for diverse life history strategies. We incorporate a sub-model that includes uncertainty in individuals age using a finite-mixture model. The detection and transition probabilities were parameterized using generalized linear models, facilitating flexible model specification and inclusion of spatiotemporal and individual covariates. We apply the model to detections of juvenile steelhead (Oncorhynchus mykiss) from two populations in the Snake River Basin in Idaho, USA, to estimate trends in survival with respect to population, age, time, and the length of individuals. Additionally, we generalized the model so that it can be applied to other systems and populations with different life history strategies and monitoring infrastructure.

Habitat destruction leads to a severe decline in biodiversity. Ascertaining effects of habitat loss afterwards is regularly done, whereas predicting the effects of different habitat loss scenarios on biodiversity beforehand is much preferred but as of yet rarely possible. Such predictions would help inform management decisions to prevent biodiversity loss in the first place, or at least mitigate effects if necessary, by providing handles to set priorities in making trade-offs. To this end, we developed and applied an adapted version of the information-theoretical framework of Maximum Entropy Theory in Ecology (METE) to predict species-area relationships in tropical forest landscapes undergoing habitat loss and fragmentation. Species-area relationships provide information about biodiversity and can, in undisturbed systems, be predicted using METE, based on state variables such as the total number of individuals, the total number of species, and the total area size. In disturbed systems, however, it is established that METEs predictions are off. Using an individual-based model, we explore how METEs state variables should be adjusted, based on the landscape characteristics habitat cover and degree of habitat fragmentation, to restore its accuracy of species-area relationship predictions in disturbed environments. We validate our adjusted METE predictions with empirical data. Our adjusted METE predictions demonstrate the severity of fragmentations impact on biodiversity, relative to what could be expected in a pristine, continuous environment. With this new method, we can estimate the immediate effects of habitat loss on biodiversity, as well as its long-term effects.

Movement models often assume that animal decisions depend on perceived environmental conditions. However, optimal foraging and cognition theory predict that accumulated experiences should drive changes in animal motivation and decision-making. Here, we propose a mechanistic framework that models state-switching habitat selection as a history-dependent process, where internal states, such as fear or energy, emerge as latent variables accumulating through environmental exposure. Simulations showed that our model accurately recovers memory timescales and cumulative effects of environmental exposure on behavioural switching that would be undetected by existing state-switching habitat selection models. Applied to woodland caribou, it reveals that an individual integrates predation risk experienced over the previous 15 days, but food intake over only 2 days, when deciding to remain or leave an area. Our model advances perspectives on movement ecology by quantifying how accumulated experiences shape changes in motivation driving animal movement decisions.

The density-yield relationship of wild plant populations, which in agricultural systems is typically consistent with the constant final yield hypothesis, has remained largely unexplored in a restoration context despite its potential importance. In degraded California shrublands, non-native annual grasses are associated with reduced native shrub recruitment. By studying their density-yield relationship, we can potentially optimize current restoration practices by identifying the level of grass control needed to reduce the competitive pressure they create for woody plants. This in turn could enhance woody plant establishment. Here, we conducted field experiments at a degraded chaparral site in Santa Barbara County, California. We manipulated the density of Bromus diandrus, a dominant invasive annual grass, through 0-100% removal treatments to test its density-yield relationship. Simultaneously, we outplanted Ceanothus megacarpus seedlings as phytometers while also monitoring soil moisture and light availability throughout the growing season. Our results demonstrate that the Bromus diandrus density-yield relationship follows the constant final yield hypothesis, with strong intraspecific competition observed from moderate to high grass density contributing to a constant population biomass and a density dependence allometric exponent comparable to previous agricultural studies. A reduction in grass biomass (yield) was observed at 75% grass removal, yet an increase in resource availability (i.e., light and soil moisture) was only observed at 100% grass removal, as indicated by both direct measurements and enhanced phytometer performance. Applied to restoration in California Chaparral and other Mediterranean ecosystems, where the reduction of "weed" biomass is a common management goal, our findings suggest full removal of non-native annual grasses is likely needed to aid in the restoration of native woody seedlings.

Once central challenge in adopting ecosystem-based fisheries management (EBFM) is understanding and predicting the interconnected processes, feedbacks, and temporal dependencies that characterize ecosystem dynamics. Structural equation models and structural causal models (SEM and SCM) quantify interconnected relationships and evaluate causality from observational data but cannot readily accommodate the incomplete and irregularly sampled data typical of complex systems. Dynamic structural equation models (DSEM) estimate SEMs but accommodate lagged effects, missing data, and non-linear and non-stationary effects. However, there is little guidance regarding how DSEM can be used to support EBFM, particularly within governance and decision-making contexts. Here, we present three case studies that highlight distinct inferential goals for understanding ecosystem processes in the context of EBFM. First, we quantify ecosystem-level indicators to support monitoring of climate-driven ecosystem changes. Second, we examine whether hypothesized direct and indirect ecosystem linkages improve predictions of recruitment. Finally, we compare support for hypotheses of causal drivers of fish productivity. To provide practical advice, we synthesize lessons learned into a workflow to for others navigating DSEM. We suggest that the first step is identifying the inferential objective, which establishes the foundation for the remainder of the workflow, guiding the development of graphical models, model specification, and the selection of statistical and scientific validation steps (e.g., testing conditional independencies) needed to evaluate hypothesized causal relationships. By integrating observational data, expert knowledge, and mechanistic hypotheses within a unified framework, DSEM is a powerful tool for advancing EBFM, and more broadly, understanding the processes that govern complex ecosystems.

Long-lived plants facing anthropogenic disturbance often exhibit recruitment failure despite persistent reproductive adults, generating extinction debt masked by longevity. However, whether adult presence reliably reflects environmental suitability for recruitment remains unclear. Here, we examine ontogenetic niche differentiation and its consequences for recruitment in Jubaea chilensis, an endangered long-lived Mediterranean palm with an aging population. We assigned individuals within the largest known population to four ontogenetic stages and characterized their environmental niches using climatic, edaphic, topographic, and vegetation variables. We then applied spatial and multivariate analyses, including Random Forest models to evaluate environmental segregation and identify predictors of seedling establishment. Age classes occupied significantly different environmental niches, with the greatest differentiation between seedlings and reproductive adults. Saplings and adult differentiation reflected mainly topographic variables at landscape scale, whereas seedling establishment was primarily predicted by microhabitat conditions (vegetation cover heterogeneity, east-facing slope orientation, and soil texture). This pattern is consistent with niche reconfiguring throughout the life cycle, suggesting that adult occurrence and recruitment suitability respond to distinct environmental conditions. Over one-fifth of sampled individuals occupied high-suitability sites without recruitment, suggesting that ontogenetic niche shifts are associated with a spatial decoupling between adult persistence and recruitment, consistent with demographic collapse independent of habitat degradation. This failure is likely mediated by insufficient effective seed dispersal, as the sole disperser (Octodon degus) preys on most seeds before dispersal. Conservation strategies based solely on adult distribution may therefore overestimate effective habitat and underestimate extinction risk in long-lived species.

Spatial expansion is a common feature of many ecological processes, including biological invasions, range shifts, population recovery, and the spread of pathogens and human activities. Although such expansion often arises from diffusive processes, there remain few general statistical methods to quantify these dynamics. Here, we present a diffusion-enhanced spatio-temporal model (DESTM) that approximates diffusive process using Gaussian Markov random fields (GMRF). The model works by incorporating the statistical interaction between spatial diffusion and temporal lags into a joint precision structure of the latent field. To demonstrate, we simulate data from a diffusion movement process using a continuous-time Markov chain (CTMC), and then approximate this mechanistic process using the DESTM. We show that separable spatio-temporal models can lead to large bias in estimates of the local density. In contrast, the DESTM did not appear to bias inferences, and reverts to the separable model when diffusion is not apparent. Finally, we apply the model to two large-scale examples of spatial expansion: (1) the historical spread of the Japanese distant-water longline fleet across the Pacific, representing anthropogenic spatial expansion in the ocean, and (2) the recovery and geographic expansion of Bald Eagle populations across North America. Estimates from both case studies, supported by model comparisons and ecological plausibility, suggest that the DESTM provides a better description of the underlying expansion dynamics, although improvements in forecast performance were more apparent for the Japanese longline fleet. We highlight that the model provides a fast and efficient framework for modeling spatial expansion dynamics, with the potential to represent increasingly complex ecological dynamics through future extensions. We propose that future research explore this framework as a quantitative tool to explain how spatial expansion occurs.

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.

Human-wildlife conflicts over shared resources are often interpreted as direct competition, whereby resources consumed by wildlife are unavailable to people. Yet, individuals of wild populations can differ in their propensity to exploit resources, and the demographic, ecological, and economic consequences of such behavioural heterogeneity remain poorly understood. We developed an integrated demographic-bioeconomic model of Atlantic cod (Gadus morhua), grey seals (Halichoerus grypus), and a coastal gillnet fishery. Stage-structured population models incorporated density dependence, predator-prey interactions, adaptive harvesting, and transitions of adult seals into a bold, depredating state. We compared four scenarios of increasing complexity over 50 years across 100 coupled demographic realisations and examined sensitivity to the energetic contribution of depredated catch, harvest levels, and social amplification of depredation behaviour. Cod and seals persisted in all demographic realisations, supporting our ecological-persistence hypothesis. Contrary to our predictions, however, adding bold seals neither reduced long-term fishery performance nor destabilised the system. Median net present value was approximately three-fold greater in the full system than without depredation, and was greater in 76% of paired realisations. The full system was also the most temporally stable: median cod variability declined by 74% relative to the otherwise equivalent fishery without depredation, while seal variability declined by 45%. Behavioural depredation therefore altered predator population structure and imposed direct catch losses, but these effects were counterbalanced by indirect demographic feedbacks. Synthesis and applications. Depredating predators should not necessarily be viewed simply as competitors that reduce contested resources. Behavioural heterogeneity can introduce compensatory pathways that alter population stability and long-term economic outcomes. Management should therefore consider not only predator abundance and immediate catch losses, but also how harvest creates opportunities for behavioural change and how those changes propagate through coupled ecological-economic systems.

1. Invasive species pose significant ecological, economic, and public health challenges. Their management requires repeated decisions under uncertainty about when, where and how intensively to intervene. Although structured decision-making and adaptive management provide conceptual frameworks for addressing these challenges, their practical implementation remains limited because optimization methods such as stochastic dynamic programming (SDP) are often perceived as mathematically complex, challenting to apply and difficult to interpret. 2. Here, we provide a practical guide to using SDP and its extensions for invasive species management. Using coypu (*Myocastor coypus*) management as a running case study, we introduce the framework through a minimal working example before progressively extending it to spatial dynamics, imperfect detection using partially observable Markov decision processes (POMDPs), adaptive management under model uncertainty, and value-of-information analyses that quantify the benefits of learning before acting. 3. All examples are accompanied by reproducible implementations in R, together with recommendations on model formulation, parameterization and interpretation. We also discuss the strengths and limitations of SDP. 4. By lowering the technical barrier to SDP, this guide aims to help ecologists and wildlife managers integrate decision theory into invasive species management, enabling transparent, reproducible and objective-driven management strategies under uncertainty. More broadly, the methods presented here are applicable to a wide range of ecological decision problems beyond invasive species, wherever management requires balancing immediate actions against uncertain future outcomes.

BACKGROUND: The Argentine ant (Linepithema humile) is among the world's worst invasive species. In the Donana Biological Reserve (Spain), it is associated with human structures that provide the mild, moist conditions it needs within the hot, dry scrubland. We evaluated a habitat-modification treatment, predicting that removing this suitable habitat would eradicate the invader locally. Over an 18-year period, we monitored ant communities at a treated and an untreated site, at different distances from the human structures, before and after treatment, and analysed how temperature and worker numbers affected encounters at baits between the Argentine ant and native ants. RESULTS: Distance to the human structures shaped ant communities at both sites, but only the treated site changed after treatment. There, the Argentine ant decreased close to the human structure and almost disappeared farther from it, whereas the endemic Cataglyphis tartessica and C. floricola recovered, especially close to the structure. At baits, the outcome of encounters depended mainly on how temperature changes favoured each species relative to its maximum activity temperature: Cataglyphis spp. displaced the Argentine ant in the morning, as temperature increased, and the Argentine ant recovered the baits in the late afternoon. Numerical advantage favoured only the Argentine ant. CONCLUSION: Habitat modification reduced the Argentine ant and allowed endemic ants to recover, but did not eradicate the invader, which persisted at low abundance 13 years after treatment. In protected areas, habitat management should be combined with eradication, monitoring and prevention of spread.