Ecological Modelling

Agroecosystems are dynamic ecosystems, constituted by patches of vegetation and agricultural use, where biodiversity is shaped by spatial and temporal variability. While most studies have focused on spatial composition and configuration, the role of temporal variability remains poorly understood. Yet, temporal dynamics can strongly modify species composition, abundance, and persistence in ecological communities. Temporal variability is particularly relevant in agroecosystems with rainfed agriculture where environmental conditions shift dramatically between rainy and dry seasons. In this paper, we assess the role of temporal variability on biodiversity maintenance in an agricultural matrix using a metacommunity model that simulates an agricultural landscape under rainfed conditions, that is, with abrupt seasonal changes in the agricultural patches. This model couples a local community network dynamic with a migration dynamic and is based on empirically documented features of rainfed agricultural matrices. Our results show that temporal variability provides new opportunities for species to recover from low densities. However, the effect of temporal variability is not straightforward. It depends on the initial and final conditions, the migration and mortality rates and the intensity of temporal variability. Overall, our findings highlight the need to further investigate temporal variability to better understand its role in shaping biodiversity in agricultural landscapes.

Humans are efficient and deadly predators, yet they may also interact with wildlife in non-lethal ways. This study explores how interactions with lethal and non-lethal human "superpredators" alter predator-prey dynamics using an agent-based modelling approach. Our model incorporates both the consumptive (lethal) and non-consumptive (behavioural) effects of humans, as well as of predators on prey. We explored how the replacement of apex predators by humans affects mesopredator-prey dynamics, with particular emphasis on trophic targeting and differences between lethal and non-lethal interactions. We found that human superpredators have a greater effect on model outcomes than apex predators. When superpredators consume mesopredators alone or with prey, the probability of mesopredator-prey coexistence increases to a greater extent than when apex predators consume mesopredators. In contrast, superpredators consuming only prey slightly increases overall extinction risks and reduces coexistence. Non-lethal superpredators, despite eliciting anti-predator responses in mesopredators and prey, had a negligible effect on population dynamics. Our findings demonstrate that human superpredators may functionally replace apex predators when they are lethal. However, non-lethal interactions with humans may not be as ecologically significant as lethal interactions, even when humans induce anti-predator responses.

After introduction, many non-native marine species are dispersed planktonically. Secondary spread within the non-native range has been shown to prevent the establishment of the introduced species if the advection of larvae prevents sufficient return of larvae to maintain the population in the face of competition with native species. However, those studies have largely neglected the effects of spatial variation in alongshore larval transport. We examine the introduction of a novel species with planktonic dispersal into a more realistic coastal environment which includes spatial variation in larval transport estimated from the Mercator Ocean 1/12th degree global circulation model. The introduction may either be from a distant habitat, or through range expansion. We find that there are locations in the global coastal ocean where introduced species are more likely to persist because of spatial variation of coastal currents. These include regions where alongshore larval transport diverges, such as estuaries. The location where a non-native species is introduced may not be where it flourishes - it cannot be assumed that the region where invading species are first noticed to be abundant is the region where it was introduced. We extend closed-population theory to open coastal systems to estimate persistence as a function of local circulation, habitat extent, and the competitive advantage of the introduced species. Software is provided which allows the estimations of regions where introduced species are more likely to persist and flourish as a function of larval depth behavior, planktonic duration and release timing.

ContextThe movement of vertebrate hosts across urbanized landscapes can play a key role in the transmission of direct-contact diseases. Understanding how wildlife hosts move in urban landscapes, and how transmission is affected by their landscape-constrained and disease-altered movements, is imperative for better predicting the spread of disease. ObjectiveWe assess how the movement of red foxes (Vulpes vulpes) according to landcover type, and their infection status, affect the spread of mange (caused by Sarcoptes scabiei) in an urbanized landscape. MethodsWe developed a mange transmission model (MTM) using an agent-based model to compare two movement behaviours of foxes in Scarborough (Ontario, Canada): random and landcover-based. We further assessed the effects of movement on disease transmission by considering the foxs infection status and comparing a range of movement probability scenarios. We quantified the number of effective contact events and the effective reproduction number (Re) according to each scenario. ResultsWe found that both landcover-dependent movement and infection status influenced the spread of mange within fox populations. The number of effective contact events and effective reproduction number Re was greatest when landscape heterogeneity was included in the model and foxes moved through paths of least resistance to movement, and when susceptible and infected foxes had an equal probability of leaving a fragmented habitat patch. ConclusionsOur findings suggest that mange spread may be accelerated along movement corridors in fragmented, heterogenous landscapes. As urban areas expand and remnant habitat within these is further lost and animals are relegated to fewer movement pathways, disease transmission may increase.

Squid abundance has been reported to increase globally between 1970 and 2010. This increase has been hypothesized to result from two primary factors: the loss of top predators due to overfishing and rising ocean temperatures. The decline in apex predators may lead to the expansion of squid populations either through reduced predation pressure or diminished competition with juvenile predators. Concurrently, increased temperatures could enhance the somatic growth rates of squid, thereby accelerating their population growth. However, empirically disentangling the impacts of predator loss and temperature on squid biomass remains challenging, especially in a food-web context. In this study, we used a size- and trait-based model of upper trophic levels that resolves the ecosystem structure -- biomass and trophic interactions of fish and squid -- for varying depth, temperature, and secondary production, to investigate two hypotheses of the historical expansion of squid, i.e., the effects of predator depletion from fishing and rising temperatures on squid biomass. Our model reveals that intensified fishing of squid predators -- specifically large demersal fish in shelf systems and large pelagic fish in open oceans -- leads to a slight increase in squid biomass. Conversely, elevated temperatures are associated with a decline in squid biomass. This temperature-driven reduction in biomass is attributed to an increased metabolism of squids beyond the available food supply. If historic overfishing on large marine predators continues to be curtailed, we expect a corresponding reduction in global squid biomass and fisheries potential, which could be further exacerbated by rising temperatures.

In order to feed a growing global population without silencing nature, conceiving agricultural management strategies reconciling yield and conservation goals is key. Using numerical simulations of a metacommunity model, we explore the possibilities for compromise offered by spatial management strategies of farmed areas. Each strategy is characterized by its farming intensity, the proportion of farmed lands and their spatial aggregation. We show that achieving equitable yield-biodiversity compromise is difficult. While conciliatory strategies offering top yield and biodiversity are typically not possible, accepting slightly lower yields (ie, "Pretty Good Yield strategies") allows to recover substantial biodiversity. Such reconciliation possibilities are limited for species with small dispersal. Yield increases mainly through farmland expansion, whereas farming intensity strongly influences biodiversity, increasing it at low intensity before decreasing with further intensification. Finally, we demonstrate that reconciliation is easier if agricultural production relies on biodiversity through ecosystem services.

An outbreak of New World screwworm has recently been spreading across Mexico, after more than 30 years of absence. The sterile insect technique, which consists of the massive release of sterilized males, has proven to be one of the most efficient methods for controlling the screwworm pest. However, given the limited number of sterile males available, improving the release strategy is critical. We propose a mathematical model of population dynamics adapted to the biology of Cochliomyia hominivorax and derive a feedback control function to determine the number of sterile males to release. We further construct a Luenberger observer to estimate wild fly populations from infected animal counts--the variable monitored by Mexican sanitary authorities--enabling field implementation of the control function. We show that eradication is achievable within approximately 60-100 weeks and that eradication time is governed primarily by the intrinsic biology of the system rather than by infestation magnitude. We then extend the model to a spatially explicit framework and show that when sterile male releases are applied at the outbreak focus and within a 120 km radius, eradication of the pest is attainable.

Existing literature on ecological networks provides valuable insights into the structure-stability relation of antagonistic, mutualistic or competitive systems, but it remains unclear whether these insights also apply to networks that contain a mix of different interaction types. Here, we study the effect of modularity on stability in systems that contain not only antagonistic interactions between two guilds, but also competition, facilitation or even antagonism within each guild, inspired by Ghanaian tree-liana interaction networks. We represent these systems as structured community matrices with random interaction strengths, in which we vary both the modularity within the antagonistic subnetwork and the type of intraguild interactions. Using the eigenvalues of the community matrix to assess stability, we find that modularity in antagonistic interactions generally has a stabilising effect, in line with results on single-interaction type networks. We furthermore find that the magnitude of this effect is modulated by the type of intra-guild interaction under consideration and is largest when these interactions describe facilitation. We explain these findings via a shift in the balance between self-reinforcing and self-damping feedback loops. Our results highlight the need to study how patterns in inter- and intraguild interactions jointly affect ecosystem stability.

Despite the impacts of within-host disease dynamics on disease outcomes in individual hosts and disease spread among-hosts, generic models of within-host population dynamics have received far less attention than their among-host counterparts. While a number of models have been proposed to explore theoretical eco-evolutionary dynamics, they have yet to be evaluated for estimability, raising questions about their ability to provide reliable inference when confronted with data. We evaluated the estimability of two generic within-host population dynamics models by assessing: (1) parameter estimation, our ability to recover correct values of model parameters from data, (2) the consequences of mis-assigning the underlying mechanistic model on parameter estimation, and (3) the reproduction of qualitative dynamics, or, our ability to use parameter estimates to reproduce observed dynamical behaviours. In some cases, fitting a mis-matched mechanistic model to time series data produced reasonable parameter estimates that were able to reproduce system dynamics, and that when provided the data-generating model, parameter uncertainty can produce substantial behavioural uncertainty. Our findings highlight the impacts of structural, parametric, and behavioural uncertainty on inference, and demonstrate the value of improving system-specific knowledge to prevent the use of incorrect functional forms and of measuring consequential parameters to improve estimability.

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

Environmental variability can strongly alter coexistence among competing species and their extinction risk, particularly when population dynamics are shaped by behavioral interactions, such as fear. In this work, we develop a novel stochastic differential equation competition model that incorporates both non-consumptive fear effects and environmental variability to investigate how behavioral interactions influence species coexistence under random fluctuations. Our result reveals that environmental stochasticity can drive species to extinction even when the corresponding deterministic system admits coexistence. In particular, under an explicit stability condition on the fear and competition parameters and sufficiently strong averaged noise intensities, we prove that both competing species become extinct exponentially almost surely. Conversely, we derive a stochastic persistence criterion in terms of fear, competition, and noise-induced suppression parameters for the fearful species. We further demonstrate that environmental noise may reverse classical competition-exclusion outcomes, leading to qualitatively different long-term dynamics from those predicted deterministically. These results provide rigorous thresholds separating stochastic extinction from persistence and highlight the critical role of environmental variability in fear-mediated competitive ecosystems. From an applied perspective, these results provide insight into how behavioral interactions and environmental variability influence species survival, with potential applications in ecological management and conservation.

Offshore wind farms are rapidly expanding to meet growing demands for renewable energy, with development expected to extend further offshore into deeper waters. This expansion requires a robust understanding of the long-term ecological consequences of offshore wind farms (OWFs) and how these may interact with ongoing climate change. We used the coupled hydrodynamic-ecosystem-biogeochemical water-column model (GOTM-ERSEM-BFM) to investigate ecosystem-wide responses to environmental changes associated with OWFs and climate warming. Specifically, we examined OWF-related scenarios of reduced benthic suspension-feeding activity, representing potential effects of contaminant emissions from OWFs, and reduced wind forcing, together with increased sea surface temperature. The scenarios were simulated individually and in combination to explore potential interactive effects. These scenarios were simulated at two contrasting locations in the North Sea, representing a well-mixed coastal site and a seasonally stratified offshore site. The coastal site exhibited comparatively modest ecosystem responses across the scenarios, whereas responses were generally stronger at the deeper offshore site. At the offshore site, changes in stratification altered vertical nutrient dynamics and contributed to pronounced differences in ecosystem responses between the surface and bottom layers. Our results demonstrate that ecosystem responses to OWF-related and climate-driven environmental changes are strongly dependent on local environmental conditions, suggesting that ecological consequences may differ substantially as wind farm development expands into deeper offshore environments.

O_LITrophic rewilding, the practice of restoring animal species to recover lost ecosystem functions, has been proposed as a nature-based climate change solution (NbCS) due to animal biogeochemical influences potentially extending to ecosystem carbon capture. C_LIO_LIWe examined this potential using a case study of puma (Puma concolor) and guanaco (Lama guanicoe) restoration in a grassland ecosystem in Patagonia National Park, Chile. We parameterized a model of animal-driven ecosystem carbon dynamics with published measurements from Patagonian grasslands and compared three scenarios: (1) a no-rewilding baseline; (2) rewilding only guanaco; and (3) rewilding guanaco and puma. We estimated net primary productivity (NPP), net ecosystem carbon balance (NECB), and plant and soil carbon stocks. Using differences among scenarios, we estimated ecosystem carbon gains attributable to rewilding guanacos and pumas, and validated baseline estimates using published carbon capture data for Patagonian grasslands. C_LIO_LIPatagonian grasslands with pumas and guanacos could capture (NPP and NECB) 1.27-2.5 times more carbon, and increase plant carbon by 1.76-3.25 times, above the no-rewilding baseline. Large uncertainties in parameter values make estimating soil carbon challenging, but the rewilded ecosystem could store up to 1.16 times more soil carbon, or up to 0.57 times less. C_LIO_LIThe model estimates that NECB in the rewilded ecosystem could amount to 94.41 t C km-2 y-1 (94.38 t C km-2 y-1-94.44 t C km-2 y-1) of which 23%-43% attributable to animal effects. Plant carbon storage estimates were [~]290 t C km-2 (280-300 t C km-2), of which 43%-67% attributable to animal effects. Finally, the model estimated soil C stock gains up to 178 t C km-2, or losses up to 4300 t C km-2. C_LIO_LIPractical implications. We illustrate how to develop first approximation estimates of carbon capture and storage to help assess the feasibility of trophic rewilding as a NbCS. Our modelling revealed that restoring a puma-guanaco trophic cascade could be a feasible NbCS, and identified looming uncertainties about the fate of carbon that need further empirical exploration. More generally, the modelling identifies key measurements that can inform whether restoring trophic cascades can contribute to NbCS. C_LI

Virus-induced mortality influences plankton biogeography, community structure, and ocean elemental cycles. However, quantification of virus-induced impacts remains challenging and often limited in scope. An alternative to explicit inclusion of viral dynamics in biogeochemical models is to represent viral effects implicitly, by assuming that mortality increases quadratically with cell or biomass density. Using 1D and 3D configurations of a nutrient-phytoplankton-zooplankton-virus-detritus (NPZVD) model, we ask whether the implicit quadratic mortality assumption captures patterns of virus-induced mortality, and its impact on biomass and primary production. The 1D water-column configuration shows that, at the onset of the spring bloom, the quadratic, implicit representation imposes viral losses on phytoplankton density instantaneously, which limits spring bloom formation. This is in contrast to the explicit representation, which allows initial bloom formation to proceed unhampered initially, but imposes a far stronger viral mortality later in the year driven by high rates of host-virus contact due to high phytoplankton and viral densities that take time to accumulate. By comparison to the implicit model, explicit resolution of viruses within the 3D global model shows strong potential for viruses to prematurely terminate phytoplankton blooms. Biogeochemical models would therefore benefit from explicit representation of viral infection insofar as models can be developed that adequately recapitulate in situ observations. Key PointsO_LIGlobal model reveals significant spatial heterogeneity arising from explicit, rather than implicit, representation of viruses C_LIO_LIImplicit representation of viral dynamics fails to capture the potential for viruses to terminate phytoplankton blooms C_LIO_LIBiogeochemical models require explicit representation of viruses to adequately simulate their effect on marine systems C_LI

Diaphorina citri is the primary vector of Huanglongbing (HLB), a devastating disease that affects global citrus production. Effective biological control using the parasitoid Tamarixia radiata represents a sustainable alternative to chemical insecticides, but its efficacy depends heavily on environmental variables and resource availability. In this work, we develop a four-population mathematical model to analyze the dynamics of D. citri in the presence of T. radiata, a known parasitoid. Our model incorporates the cyclical and periodic behavior of citrus flushing (new shoots), providing a realistic representation of resource-limited dynamics as observed in field conditions. Through comparative simulations of three scenarios, without parasitoid, a single initial introduction, and periodic augmentative releases, we characterize and compare the impact of T. radiata as a biological control agent. Our results show that periodic releases maintain pest suppression, whereas a single introduction only delays pest recovery. By aligning theoretical modeling with ecological reality, this framework supports the role of T. radiata in pest suppression and provides a baseline for future work on optimal and cost-effective release strategies.

Ecologists have long investigated how community complexity affects ecological stability, yet how community complexity influences multistability, defined as the presence of alternative stable states, remains poorly understood. We developed a novel framework integrating stochastic community assembly with stability landscape analysis to quantify multistability from species interaction matrices. Using this framework, we systematically explored how species interaction properties shape the relationship between species diversity (species pool size) and the number of alternative stable states. Mean interaction strength was the primary determinant: competitive interactions amplified the positive relationship between species diversity and the number of alternative stable states. In competitive communities, a greater number of alternative stable states was associated with lower community uncertainty, a measure of the long-term unpredictability of community assembly dynamics. These results highlight the importance of characterizing the entire stability landscape. Our framework provides a general approach for understanding and quantifying multistability in complex ecological communities.

As climate changes, habitat suitability for multiple taxa are also expected to change. In recent years, mangrove latitudinal range expansion has been linked to increasing temperatures and reduced freezing in temperate regions, happening mainly through encroachment into saltmarshes. The range limit of mangrove forests in Eastern South America has not seen drastic changes in the last four decades, despite trends of increased temperature and the seemingly favorable direction of the Brazilian Current. Here, we investigate if and how the distribution of South-Atlantic mangrove forests may respond to different scenarios of climate change. To do this, we combine ecological niche modelling with propagule dispersal simulations to understand the roles of climate and ocean currents in defining the austral limits of South-Atlantic American mangroves. Our results indicate that minimum sea surface temperature strongly constrains habitat suitability beyond the current distribution of mangroves (28{degrees}2868" S), while dispersal processes heavily limit propagule stranding beyond 35{degrees} S. The Brazil-Malvinas currents confluence zone creates steep temperature gradients and an oceanographic barrier that makes the latitudinal expansion of mangroves unlikely in this region, even in future scenarios of heating climate. We found no evidence of current nor future poleward expansion of mangroves, but total mangrove area has increased in Brazil over the last decades, likely due to landward migration, but anthropogenic interference and urban expansion may restrict this process, leading to coastal squeeze. Under scenarios where both landward and poleward migration are limited, South American mangroves may face increasing vulnerability, with potential impacts on the several ecological, biogeochemical and social cycles they support. Our results contribute to leading hypotheses of climate restriction and shed light on the role of ocean currents in South America, helping to explain why the poleward expansion reported in other regions has not yet been observed in the South-Atlantic mangrove range limit.

Under complex perturbation regimes, biodiversity dynamics show temporal variability in species and community abundance around long-term population trends. Many species indeed show long-term declines while other species increase, putting natural communities far from stationary regimes, while variability is often studied near equilibrium. We contribute to bridging this gap by investigating population and community variability during long-term trends caused by press perturbations in stochastic models of population dynamics. By estimating the deterministic changes in mean and variance during the transient regime, we show that population variability deviates from stationary expectations. Moreover, the deviation strongly depends on the sign of the population trends: increases generate excesses of variability while declines generate deficits. Scaling up to community variability, we propose a decomposition of community variability deviation, allowing to highlight that community variability in the transient regime depends on how the press perturbation is distributed within species relative abundances and growth rates. These results challenge the equilibrium assumption and open new perspectives for the study of the variability of ecological systems under multiple perturbation types.

Biological invasions are a major driver of global change, reshaping ecosystems and threatening biodiversity worldwide. Anticipating where invaders will establish and where they will exert the strongest ecological impacts are key challenges for early detection and targeted management. Although Species Distribution Models (SDMs) are widely used to forecast biological invasions, they often provide uncertain estimates of establishment ranges and limited insight into invader performance, making it difficult to anticipate ecological impacts. Here, we address these limitations by integrating physiological information on invader performance with SDMs to identify regions of high invasion risk. Using the brown alga Rugulopteryx okamurae, one of the most prominent marine invaders in Europe, we first test alternative hypotheses of northern establishment limits: (i) a cold-survival constraint driven by winter temperatures and (ii) a growth constraint derived from the species' thermal performance. To identify the more likely scenario, we combine cold-tolerance experiments with seasonal growth comparisons between the invader and a native macroalga Dictyota dichotoma, whose established distribution allows physiological performance to be directly related to realised presence. Finally, we project seasonal growth of the invader across the predicted establishment range as a proxy for biomass accumulation and potential ecological impacts. Our results indicate that northern limit in Europe will be more likely constrained by winter survival rather than growth, extending the potential establishment range of Rugulopteryx to mid-Norway. In contrast, the highest impacts are likely to remain concentrated in southern Europe, where thermal conditions sustain high year-round growth. Overall, our approach illustrates how understanding the physiological response of invaders to their environment can improve the interpretation of SDM outputs and help identify areas at greatest risk of impact within their potential establishment range.

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.