Ecological Modelling

In the framework of resource-competition models, it has been argued that the number of species stably coexisting in an ecosystem cannot exceed the number of shared resources. However, plankton seems to be an exception of this so-called "competitive-exclusion principle". In planktic ecosystems, a large number of different species stably coexist in an environment with limited resources. This contradiction between theoretical expectations and empirical observations is often referred to as "The Paradox of the Plankton". This project aims to investigate biophysical models that can account for the large biodiversity observed in real ecosystems in order to resolve this paradox. A model is proposed that combines classical resource competition models, metabolic trade-offs and stochastic ecosystem assembly. Simulations of the model match empirical observations, while relaxing some unrealistic assumptions from previous models. Paradox: from Greek para: "distinct from", and doxa: opinion. Sainsbury (1995) defines a paradox as "an apparently unacceptable conclusion derived by apparently acceptable reasoning from apparently acceptable premises". Paradoxes are useful research tools as they suggest logical inconsistencies. In order to spot the flaw, the validity of all the premises has to be carefully assessed. Plankton: refers to the collection of organisms that spend part or all of their lives in suspension in water (Reynolds 2006). Plankton, or plankters, are "organisms that have velocities significantly smaller than oceanic currents and thus are considered to travel with the water parcel they occupy" (Lombard et al. 2019). Phytoplankters refer to the members of the plankton that perform photosynthesis.

The shallow Antarctic continental shelf experiences strong physical disturbances in the form of ice-scour which play a key role in maintaining biodiversity. Due to climate change, both the extent and duration of sea ice cover is expected to rapidly decline, leading to complex shifts in disturbance regimes with unknown impacts on successional dynamics and biodiversity in benthic communities. We introduce a simulation model to study the assemblage dynamics of Bryozoa, which are sessile, suspension feeding animals and key pioneers in the shallows. The model captures colonisation events, colony growth as well as intra- and interspecific overgrowth competition for space between colonies. Mortality due to predation is modelled as the removal of zooids within small areas of the model world. Using the model, we simulate various disturbance regimes, e.g. by varying the timing of ice-scour events, the length of the growing season and the spatial distribution of predation events. We find that the timing of ice-scour events throughout the growing season has only minor short-term effects on successional dynamics in bryozoan assemblages, while an extended growing season substantially accelerates succession in the long term. We furthermore find that relatively rare but large predation events lead to a slower recovery, whereas relatively frequent but small events result in a faster succession with higher overall abundances. These results highlight that in order to understand how benthic biodiversity will be impacted by climate change, it is necessary to consider the interplay between biotic interactions and complex changes in physical disturbance regimes. HighlightsO_LIWe use an individual-based model to simulate climate change impacts on bryozoan assemblages. C_LIO_LIWarming leads to complex shifts in timing and spatial distribution of disturbances. C_LIO_LISuccessional dynamics are affected by changes in growing season and predation events. C_LIO_LIInterplay of biotic interactions and physical disturbances drives benthic biodiversity. C_LI

Natural Protected Areas (NPAs) are the main biodiversity conservation strategy in Mexico. Generally, NPAs are established on the territories of indigenous and rural groups driving important changes in their local resource management practices. In this paper we study the case of Otoch Maax Yetel Kooh, an NPA in the Yucatan Peninsula, Mexico, that has been studied in a multidisciplinary way for more than twenty years. This reserve and its buffer zone is homeland to Yucatec Mayan communities that until recently used to manage their resources following a multiple use strategy (MUS), which involves local agricultural practices and has been proposed as resilience-enhancing mechanism. However, due to the restrictions imposed by the decree of the reserve and the growth of tourism in the region, some of these communities have started to abandon the MUS and specialize on tourism-related activities. We build a dynamical computational model to explore the effects of some of these changes on the capacity of this NPA to conserve the biodiversity and on the resilience of households to some frequent disturbances in the region. The model, through the incorporation of agent-based and boolean network modelling, explores the interaction between the forest, the monkey population and some productive activities done by the households (milpa agriculture, ecotourism, agriculture, charcoal production). We calibrated the model, explored its sensibility, compared it with empirical data and simulated different management scenarios. Our results suggest that those management strategies that do not exclude traditional activities may be compatible with conservation objectives, supporting previous studies. Also, our results support the hypothesis that the MUS, throughout a balanced integration of traditional and alternative activities, is a mechanism to enhance household resilience in terms of income and food availability, as it reduces variability and increases the resistance to some disturbances. Our study, in addition to highlighting the importance of local management practices for resilience, also illustrates how computational modeling and systems perspective are effective means of integrating and synthesizing information from different sources.

Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements, and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta-modelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories (i.e., spatio-temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where the organisation of cultivated fields and semi-natural elements are known to play a crucial role for pest regulation. It aids to formulate guidelines for biological control strategies at global and local scale.

O_LISpecies interactions are fundamental to the stability and productivity of ecosystems. To improve our capacity to predict and understand how climate change shapes the distribution of species and the dynamics of biotic interactions, we need to develop spatially explicit multi-species models that are built upon species-specific responses to changing conditions. C_LIO_LIWe developed a two-dimensional diffusion-advection-reaction predator-prey model that integrates species-specific responses to heterogeneous landscapes, affecting species dispersal, reproduction and survival rates. We derived conditions for the stability and feasibility of the coexistence steady-state and observed how temperature variation stabilises or destabilises the system. C_LIO_LIWe conducted numerical simulations to explore the effect of predicted extreme temperatures on the spatial dynamics of a parasitoid-butterfly system and their interactions. Applied to four different climatic environments, the numerical approximations demonstrate the asymmetric impact of a warming climate on interacting species. The output density distribution maps highlight the capability of our model to produce interpretable multiscale predictions which can be used to identify and evaluate species vulnerability locally and across their range. C_LIO_LIBy building upon a solid mechanistic understanding of species-specific responses to environmental change, our model can be extended to other species and variables, including environments where the availability of empirical data is limited, and explore the dynamics and distribution of interacting species under different scenarios of environmental change. C_LI

The Northern Peru Current Ecosystem (NPCE) is the most productive ecosystem in terms of fish biomass and sustains the worlds largest small pelagic fishery, the Peruvian anchovy fishery. A cooling of this system has been observed during recent decades but the potential regional impacts of rising atmospheric CO2 concentrations on upwelling dynamics and productivity are still unknown. We used the ecosystem model OSMOSE to forecast the impacts of several scenarios of climate change on Peruvian anchovy in the NPCE. The OSMOSE model was forced by plankton production and climate drivers from the Earth System Models IPSL CM5A-LR and GFDL-ESM2M for the period 2009-2100. For each earth system model, representative concentration pathways (RCP) scenarios 2.6, 4.5, 6.0 and 8.5 were run. Our results showed that an optimistic trajectory for anchovy is a reduction of biomass at a rate of 14% per decade until mid-21st century, followed by a collapse and late recovery by the end of the 21st century, while no changes in the spatial distribution of the population were observed. The pessimistic trajectory for anchovy is a reduction of biomass of 22% per decade, with a collapse after 2020 and near extinction by 2060, with a spatial displacement of the population to the south and to more coastal areas. Further research is needed to include additional key environmental variables such as oxygen as well as more realistic fisheries management intervention scenarios to evaluate future options for the sustainability of anchovy resource and fisheries.

We developed a discrete and qualitative model of integrated socio-ecosystems, with the help of formal Petri nets. We illustrated such Petri nets in the case study of temporary marshes in the Mediterranean part of France, the Camargue delta, by integrating biotic, abiotic and human-related components along with their processes into the same interaction network. The model demonstrated that when marshes are exposed to extensive grazing the presence of marsh heritage species is facilitated by opening up the vegetation through various trajectories. This supports the commonly used management practice of extensive grazing to conserve certain protected habitats. With this Possibilistic approach, we identified all potential ecosystem trajectories and provided their differential (non-systematic) impacts on heritage species richness (number). Hence, we rigorously demonstrate with this new type of model that grazing benefits marsh species which are faced with competition from common grassland species. The detailed analysis of the explicit state space and trajectories allows exploring simultaneously the identification of a range of recommendations for management strategies.

Mechanistic models are essential for projecting ecosystem responses to novel conditions, yet the application and biological realism of agent-based models (ABMs) has often been limited by computational constraints. This paper introduces SwimmingIndividuals, an open-source agent-based model (ABM) framework that advances our ability to simulate complex biological processes at large ecological scales. The softwares main contribution is its suite of mechanistic sub-models that govern the full life cycle of each agent, including physics-based visual predation, adaptive behaviors such as diel vertical migration, and a flexible bioenergetics engine with multiple taxa-specific equations. These detailed biological processes are made computationally tractable for large populations through a hybrid CPU/GPU architecture written in the Julia language. We demonstrate the frameworks capabilities through three targeted simulations. The model successfully reproduced complex ecological phenomena as emergent properties, including diel vertical migration, cetacean diving patterns, size-based trophic dynamics, population-level growth curves, and stock-recruitment relationships. Furthermore, long-term simulations generated realistic population dynamics and quantified the impacts of different fishery harvest control rules. By coupling high-resolution biological realism with technical scalability, SwimmingIndividuals provides a powerful and flexible tool for a wide range of ecological inquiries. It can be used to conduct in-silico experiments into the ecosystem-scale impacts of environmental disturbances, test fundamental ecological theory, and evaluate the efficacy of complex, ecosystem-based management strategies. This framework advances our ability to build a more mechanistic understanding of marine ecosystem resilience in a changing world. Author SummaryTo understand and predict how marine ecosystems function, we need to account for the decisions and life histories of the individual animals within them. We have developed a new open-source software tool, SwimmingIndividuals, that acts as a "virtual laboratory" for marine ecology and fisheries science. This software allows us to create large, realistic simulations with millions of virtual organisms, from multiple taxa and species, each with its own unique set of biological traits. These modeled animals make decisions based on their internal state (e.g., hunger) and their perception of the surrounding environment, such as light and temperature. By simulating these individual actions at scale, our software allows us to see emergent, complex, ecosystem-level patterns, like population dynamics and food web structure. SwimmingIndividuals provides the scientific community with a powerful tool to investigate fundamental biological questions, explore "what-if" scenarios for fisheries management, and forecast how marine life might respond to future environmental changes.

Basin morphometry can strongly affect lake-internal processes relevant for productivity, such as turbulent mixing, photosynthetic energy acquisition, sedimentation, and nutrient recycling. Yet, in both empirical and theoretical studies of whole-lake primary production, lake morphometry is often simplified to a single 1-dimensional measure - lake mean depth. Using the conceptual, process-based model Lake2D, we addressed the question: To what extent can pelagic and benthic producer dynamics, integrated over a lake basin, be captured by approaches that use mean depth as the only morphometrical variable? We created two models of algal biomass dynamics in a radially symmetric, cone-shaped lake - one preserving the lakes vertical and radial dimensions and one preserving only the lakes mean depth - and compared model predictions of algal biomass dynamics across a wide range of lake sizes, mixing conditions, water transparency, and nutrient content. Our analyses reveal that model predictions differ substantially but predictably in much of the investigated parameter space, and identifies the light environment set by lake depth, water clarity and pelagic nutrients, but also lake area, as main drivers of the differences. Most commonly, the model based on mean depth underestimates benthic algal biomass and overestimates pelagic algal biomass, the net effect on total biomass being a 5-50% underestimate in shallow lakes and a 5-20% overestimate in many deeper lakes. Since gross primary production (GPP) in our model scales with algal biomass, we believe that global estimates of lake GPP should be corrected for the systematic errors inflicted by the prevailing 1-dimensional approaches.

Measuring resilience has been a longstanding challenge in social-ecological research. While there are established resilience metrics, they often do not account for agency, which is crucial in social-ecological systems. Pathway diversity is a recent approach to measuring resilience that integrates systems thinking with individual-based elements, such as agency. According to this approach, resilience is larger if actors have more decision pathways and can maintain them over time. Here we measure resilience using pathway diversity within a lake eutrophication model to analyse (a) the extent to which it is sensitive to changes in agency and (b) its compatibility with more established resilience metrics. Our findings reveal that pathway diversity is sensitive to regime shifts and can provide early warnings of them. Through five policy decision-making scenarios, we then show how pathway diversity can capture constraints on decision makers capability and agency that affect the systems resilience. We find that higher pathway diversity is associated with greater capability of decision-makers to influence the system. Pathway diversity addresses a critical gap by incorporating agency into a resilience metric while remaining compatible with established metrics. This work shows the potential of pathway diversity to identify resilience-based policy implications.

Sub-Saharan savanna ecosystems are undergoing transitions such as bush encroachment, desertification or agricultural expansion. Such shifts and persistence of land cover are increasingly well understood, especially bush encroachment which is of major concern in pastoral systems. Although dominant factors can explain such transformations, they often result from intertwined causes in which human activities play a significant role. Therefore, in this latter case, these issues may require integrated solutions, involving many interacting components. Ecosystem modelling has proved appropriate to support decision-makers in such complex situations. However, ecosystem models often require lots of quantitative information for estimating parameters and the precise functional form of interactions is often unknown. Alternatively, in rangeland management, States-and-Transitions Models (STMs) have been developed to organize knowledge about system transitions and to help decision-makers. However, these conceptual diagrams often lack mathematical analyzing tools, which strongly constrains their complexity. In this paper, we introduce the Ecological Discrete-Event Network (EDEN) modelling approach for representing the qualitative dynamics of an East-African savanna as a set of discrete states and transitions generated from empirical rules. These rules are derived from local knowledge, field observations and scientific literature. In contrast with STMs, EDEN generates automatically every possible states and transitions, thus enabling the prediction of novel ecosystem structures. Our results show that the savanna is potentially resilient to the disturbances considered. Moreover, the model highlights all transitions between vegetation types and socio-economic profiles under various climatic scenarios. The model also suggests that wildlife diversity may increase socio-economic resistance to seasonal drought. Tree-grass coexistence and agropastoralism have the widest ranges of conditions of existence of all vegetation types and socio-economic profiles, respectively. As this is a preliminary use of EDEN for applied purpose, analysis tools should be improved to enable finer investigation of desirable trajectories. By translating local knowledge into ecosystem dynamics, the EDEN approach seems promising to build a new bridge between managers and modellers.

1Forest encroachment over savannas has been recurrently reported in the tropics over the last decades, especially in northern tropical Africa. However, process-based, spatially-explicit modelling of the phenomenon is still trailing broad scale empirical observations. In this paper, we used remotely-sensed diachronic data from Central Cameroon to calibrate a simple reaction-diffusion model, embodying dynamical interactions between grass and woody biomasses in the savanna biome. Landsat satellite image series over the Mpem and Djim National Park witnessed a dramatic extension of forest over the last five decades and our estimates of forest front speeds based on randomly sampled transects indeed yielded higher values (5-7 meters per year) than in the existing literature. We used simulations of the model to provide the first hitherto estimates of woody biomass dispersal coefficients. Since the region under study did not provide examples of savanna progression, estimates of grass dispersal proved inconsistent and we reverted to literature-based historical data to reach rough estimates. This paper demonstrates that broad scale remote sensing data allows for calibrating simple reaction-diffusion models of vegetation dynamics in the savanna biome. Once calibrated, such models become a general baseline of expected changes and a valuable tool to understand how spatial environmental factors (e.g., soil substrate) may locally modulate the overall dynamics.

Functional diversity uses response and effect traits to understand how communities are affected by changes in the environment and their consequences on the structure and functioning of ecosystems. However, most studies focus on a single taxonomic or functional group, ignoring that many ecological processes result from trophic interactions. Here we established a multi-trophic trait-based framework to evaluate the consequences of community change for ecological processes resulting from trophic interactions. Specifically, we estimated the potential effect of each species considering the consumer and resource communities involved on the trophic interaction. The functional space of consumer and resource communities were incorporated into a single analysis by using resource traits to estimate consumers functional space. Our framework included a parameter that establishes different weights to unique interactions when estimating a species potential effect. We presented two modifications for application using abundance and species richness data and two modifications to allow incorporating absent species into the analysis. Our framework can be used to investigate consequences of community changes in different situations, such as species extinctions, invasions and refaunation. To demonstrate the insights derived from our framework we used an exemplary study case of refaunation of an impacted tropical forest. Our framework informs on a species contribution to an ecological process according to its originality, i.e., the uniqueness or redundancy of its interactions, and the magnitude of the effect, indicated by the frequency of the resources community trait values with which it interacts. Thus, it helps to increase the understanding of the effects of changes in community composition on ecological processes resulting from trophic interactions. It assists practitioners and researches with predictions and evaluations on potential loss and reestablishment of ecological functions resulted from changes in community functional composition.

Matrix models are widely used in population ecology studies and are valuable for analysing population dynamics. Nonetheless, this approach is somewhat rigid in terms of generating complex scenarios. Starting from the values of the transition matrix, we can build a dynamic model to incorporate more biological-based reality into the model (e.g. polyp stage) and provide a higher flexibility in generating scenarios. As an example, we used the transition matrix calculated for a time series data of a population of the box jellyfish Carybdea marsupialis (L. 1758) in the Western Mediterranean in a previously published study. Dynamic models can help us to better understand the complex relationships that drive populations, test different hypotheses and compare scenarios. The dynamic model was developed in STELLA Architect, calibrated and optimised and it has been used to simulate various scenarios of ecological interest, including a decline in food supply, jellyfish removal strategies, changes in drift currents and changes in substrate availability for planulae to settle. A sensitivity analysis showed that polyp strobilation rate and strobilation pattern were two of the most sensitive variables. This matrix-to-dynamic model approach could be useful to integrate more biological complexity into population models and, in turn, obtain a better fit to the field data.

Understanding the conditions that favor the persistence of metapopulations inhabiting riverine landscapes is a critical challenge in guiding conservation and restoration efforts aimed at preserving the biodiversity and ecological integrity of freshwater environments. In this work, we propose a modeling framework to investigate the transient persistence of fluvial metapopulations, i.e., the temporary occupation of riverscape patches by a metapopulation that is expected to go extinct in the long run. The theoretical foundation of our approach is rooted in the concept of ecological reactivity, which provides an effective complement to asymptotic stability analysis for studying the short-term response of ecological systems to impulsive perturbations to otherwise stable equilibria. Our results indicate that, under ecohydrological conditions conducive to a reactive metapopulation extinction equilibrium, even a metapopulation that is asymptotically bound to extinction can colonize parts of the riverscape for non-negligible periods of time. We find that the temporal scales associated with these transient phenomena, in the presence of repeated positive perturbations of the extinction equilibirum, may allow for reactive pseudo-persistence, i.e., an arbitrarily long delay in the eventual extinction of the metapopulation that may occur well below the deterministic extinction threshold. By identifying the ecohydrological drivers of reactive metapopulation persistence, as well as the riverscape patches that contributes the most to transient metapopulation dynamics over different temporal scales, our analysis may provide valuable suggestions for the spatial prioritization of conservation and restoration efforts.

In temperate lakes plankton dynamics are closely linked to seasonal shifts in water stratification, following air temperature. The onset of summer stratification typically coincides with spring bloom for-mation of algae in the well mixed surface layer (epilimnion). However, extended stratification periods lead to nutrient depletion and increases the risk of algae to sink out of the sunlit epilimnion. Planktonic primary producers have evolved different traits to counteract sinking, such as specific morphological shapes, but also adaptive mechanisms like active buoyancy regulation. The latter is very common for cyanobacteria and gives them a competitive advantage over sinking taxa specifically during extended stratified periods. Existing conceptual models on plankton phenology neglect the vertical dimension of plankton bloom formation, focusing mainly on epilimnion blooms. This limits projections of how changes in stratification through global warming will affect plankton composition, productivity, and water quality. Here we develop a theoretical framework to investigate resource competition between a passively sinking (S) and a buoyancy-regulating (BR) phytoplankton species along a one-dimensional water column. The BR species adaptively moves towards optimal light and nutrient availability along the water column. Our results indicate that coexistence between BR and S algae is critically dependent on differences in resource-use efficiencies, which can lead to situations of competitive exclusion but also coexistence in overlapping or vertically separated depths. Our results highlight the importance of vertical movement strategies in structuring phytoplankton communities and its consideration for projections on plankton phenology, com-position and lake primary production under changing stratification regimes.

In the last decade, marine ecosystem models have been increasingly used to project interspecific biodiversity under various global change and management scenarios, considering ecological dynamics only. However, fish populations may also adapt to climate and fishing pressures, via evolutionary changes, leading to modifications in their life-history that could either mitigate or worsen, or even make irreversible, the impacts of these pressures. Building on the multispecies individual-based model Bioen-OSMOSE, an eco-evolutionary fish community model, Ev-Osmose, has been developed to account for evolutionary dynamics together with physiological and ecological dynamics in fish diversity projections. A gametic inheritance module describing the individuals genetic structure has been implemented. The genetic structure is defined by finite numbers of loci and alleles per locus that determine the genetic variability of growth, maturation and reproductive effort. Climate change and fishing activities will generate selection pressures on fish life-history traits that will respond through microevolution. This paper is an overview of the Ev-OSMOSE model. To illustrate the ability of the Ev-OSMOSE model to represent realistic fish community dynamics, genotypic and phenotypic traits mean and variance and consistent evolutionary patterns, we applied the model to the North Sea ecosystem. The simulated outputs are confronted to observed data of commercial catch, maturity ogives and length at age and to estimates of biomass for each modeled species. In addition to the evaluation of their mean value, the emerging traits variability is confronted to length-at-age and maturity data. To ensure the consistency of genetic inheritance and the resulting evolutionary patterns, we assessed the transmission of traits genotypic value across cohorts. Overall, the state of the modelled ecosystem was convincing at all these different biological levels. These results open perspectives for using Ev-OSMOSE in different marine regions to project the eco-evolutionary impact of various global change and management scenarios on different biological levels.

O_LIPopulation dynamic models can forecast changes in the abundances of multiple interconnected species, which makes them potentially powerful tools for managing ecological communities, yet they remain largely under-utilised in applied settings. High data requirements and the ability to only model a narrow range of ecological interactions and/or trophic levels together limits their usefulness when faced with complex and data-poor systems, where beneficial (e.g. mutualism) and harmful (e.g. competition) interactions may operate simultaneously within and between species. C_LIO_LIWe present a model of population dynamics that can describe a wide range of ecological interaction outcomes with a simple, unified structure. Species growth rates are constrained by a maximum growth rate parameter which prevents the risk of population explosions even in the case of mutualism. Species interactions are defined by two, not mutually-exclusive interactions matrices that describe the effects of beneficial and harmful interactions respectively, together providing the potential for the net effect of interactions between one species and another to switch from beneficial to harmful as population density increases. C_LIO_LIThis model recreates classic dynamics in two-species mutualistic, competitive, and predator-prey scenarios, allowing us to model a wide range of trophic levels and interaction types together within the same equation. The maximum growth rate parameter, theoretically based in intrinsic constraints on reproduction, can be parameterised from a wide range of sources including natural history, historical data, and breeding programs. We illustrate the potential of this model with a data-poor case study of a threatened species and two interacting predators. C_LIO_LIThis new model is generaliseable to a wide range of natural ecological communities. Its model structure lowers data requirements whilst remaining intuitive and biologically realistic, making it an accessible option for predicting community-wide population changes in applied contexts where data is sparse and/or uncertain. C_LI

Model predictions of animal and human space-use patterns stemming from individual-level movement behaviors have not only contributed significantly to our understanding of population and community dynamics, but they could also inform the development of conservation, natural resource management, and disease control policies. The recent proliferation of high-resolution movement data has ushered in a paradigm shift in how space use is considered: instead of being defined narrowly as the stationary, long-term distribution of individual locations, there is a growing recognition of its transient dynamics, e.g.., how space-use pattern varies before it eventually stabilizes. However, movement models are slow to follow due to longstanding technical challenges in solving transient space-use dynamics. Here, we introduce a numerical framework that enables transient analysis of mechanistic movement models based on partial differential (Fokker-Planck) equations. We demonstrate its potential applications in the context of general research questions in movement ecology using classical and new case studies as illustrations. We demonstrate the frameworks applications and versatility in classical home range models, but also show how it may be extended to address new ecological questions.

O_LIMaking informed conservation decisions under climate change is a challenging task for practitioners, since decisions depend on changing environmental conditions and uncertain ecosystem responses to climate change. Given such uncertainties, the best practice to manage natural systems is adaptive management, where decisions dynamically adapt to the response of the ecosystem to previous conservation actions. Although adaptive approaches are optimal, they are also difficult to implement, have high computational costs, and recommend strategies that can be complex to interpret. These factors can hinder their on-ground application. On the other hand, simpler but suboptimal decision models can result in more interpretable recommendations, and might still yield good outcomes for ecosystems. Exploring trade-offs between complex optimal solutions and simpler sub-optimal solutions is essential for maximising conservation impact. C_LIO_LIIn this manuscript, we use value of information theory to help managers simplify their decision-models, while balancing optimality of strategies. Our approach provides modelling recommendations by determining the benefits of modelling non-stationary ecosystem dynamics and the uncertain ecosystem response to climate change. We illustrate our approach on four scenarios inspired from the management of the Great Barrier Reef, Australia, under different climate change trajectories. C_LIO_LIWe find that the two main drivers of the recommended reduction in model complexity are the strength of non-stationarity (e.g. climate change trajectory) and the degree of uncertainty in ecosystem responses to climate change (e.g. uncertainty in the thermal resistance of a coral reef). When non-stationarity is weak, the decision problem can be reduced from a non-stationary to a stationary formulation. Similarly, when uncertainty in the response to climate change is low, this uncertainty can be safely ignored in the decision-making process. Conversely, when non-stationarity is strong and/or uncertainty is high, our approach justifies the need to account for these complexities when making decisions, as simpler approaches would yield poor outcomes. C_LIO_LIThis manuscript guides managers in simplifying a modelling approach to manage ecosystems in the face of climate change. Our protocol can help simplify complex decision problems, allowing to reduce computational costs and enhance interpretability. By finding the balance between simplicity and optimality of models, this work contributes to bridging the gap between complex modelling and on-ground applications. C_LI