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Climate change and land use management practices are major drivers of biodiversity in terrestrial ecosystems. To understand and predict resulting changes in community structures, individual-based and spatially explicit population models are a useful tool but require detailed data sets for each species. More generic approaches are thus needed. Here we present a trait-based functional type approach to model savanna birds. The aim of our model is to explore the response of different bird functional types to modifications in habitat structure. The functional types are characterized by different traits, in particular body mass, which is related to life-history traits (reproduction and mortality) and spatial scales (home range area and dispersal ability), as well as the use of vegetation structures for foraging and nesting, which is related to habitat quality and suitability. We tested the performance of the functional types in artificial landscapes varying in shrub:grass ratio and clumping intensity of shrub patches. We found that an increase in shrub encroachment and a decrease in habitat quality caused by land use mismanagement and climate change endangered all simulated bird functional types. The strength of this effect was related to the preferred habitat. Furthermore, larger-bodied insectivores and omnivores were more prone to extinction due to shrub encroachment compared to small-bodied species. Insectivorous and omnivorous birds were more sensitive to clumping intensity of shrubs whereas herbivorous and carnivorous birds were most affected by a decreasing amount of grass cover. From an applied point of view, our findings emphasize that policies such as woody plant removal and a reduction in livestock stocking rates to prevent shrub encroachment should prioritize the enlargement of existing grassland patches. Overall, our results show that the combination of an individual-based modelling approach with carefully defined functional types can provide a powerful tool for exploring biodiversity responses to environmental changes. Furthermore, the increasing accumulation of worldwide data sets on species’ core and soft traits (surrogates to determine core traits indirectly) on one side and the refinement of conceptual frameworks for animal functional types on the other side will further improve functional type approaches which consider the sensitivities of multiple species to climate change, habitat loss, and fragmentation.
Simulation models that describe autonomous individual organisms (individual based models, IBM) or agents (agent-based models, ABM) have become a widely used tool, not only in ecology, but also in many other disciplines dealing with complex systems made up of autonomous entities. However, there is no standard protocol for describing such simulation models, which can make them difficult to understand and to duplicate. This paper presents a proposed standard protocol, ODD, for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology. This protocol consists of three blocks (Overview, Design concepts, and Details), which are subdivided into seven elements: Purpose, State variables and scales, Process overview and scheduling, Design concepts, Initialization, Input, and Submodels. We explain which aspects of a model should be described in each element, and we present an example to illustrate the protocol in use. In addition, 19 examples are available in an Online Appendix. We consider ODD as a first step for establishing a more detailed common format of the description of IBMs and ABMs. Once initiated, the protocol will hopefully evolve as it becomes used by a sufficiently large proportion of modellers. (c) 2006 Elsevier B.V. All rights reserved.
Anthropogenic pressures increasingly alter natural systems. Therefore, understanding the resilience of agent-based complex systems such as ecosystems, i.e. their ability to absorb these pressures and sustain their functioning and services, is a major challenge. However, the mechanisms underlying resilience are still poorly understood. A main reason for this is the multidimensionality of both resilience, embracing the three fundamental stability properties recovery, resistance and persistence, and of the specific situations for which stability properties can be assessed. Agent-based models (ABM) complement empirical research which is, for logistic reasons, limited in coping with these multiple dimensions. Besides their ability to integrate multidimensionality through extensive manipulation in a fully controlled system, ABMs can capture the emergence of system resilience from individual interactions and feedbacks across different levels of organization. To assess the extent to which this potential of ABMs has already been exploited, we reviewed the state of the art in exploring resilience and its multidimensionality in ecological and socio-ecological systems with ABMs. We found that the potential of ABMs is not utilized in most models, as they typically focus on a single dimension of resilience by using variability as a proxy for persistence, and are limited to one reference state, disturbance type and scale. Moreover, only few studies explicitly test the ability of different mechanisms to support resilience. To overcome these limitations, we recommend to simultaneously assess multiple stability properties for different situations and under consideration of the mechanisms that are hypothesised to render a system resilient. This will help us to better exploit the potential of ABMs to understand and quantify resilience mechanisms, and hence support solving real-world problems related to the resilience of agent-based complex systems.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.
Current rates of environmental change are exceeding the capacity of many populations to adapt to new conditions and thus avoid demographic collapse and ultimate extinction. In particular, cold-water freshwater fish species are predicted to experience strong selective pressure from climate change and a wide range of interacting anthropogenic stressors in the near future. To implement effective management and conservation measures, it is crucial to quantify the maximum rate of change that cold-water freshwater fish populations can withstand. Here, we present a spatially explicit eco-genetic individual-based model, inSTREAM-Gen, to predict the eco-evolutionary dynamics of stream-dwelling trout under anthropogenic environmental change. The model builds on a well-tested demographic model, which includes submodels of river dynamics, bioenergetics, and adaptive habitat selection, with a new genetic module that allows exploration of genetic and life-history adaptations to new environments. The genetic module models the transmission of two key traits, size at emergence and maturity size threshold. We parameterized the model for a brown trout (Salmo trutta L.) population at the warmest edge of its range to validate it and analyze its sensitivity to parameters under contrasting thermal profiles. To illustrate potential applications of the model, we analyzed the population's demographic and evolutionary dynamics under scenarios of (1) climate change-induced warming, and (2) warming plus flow reduction resulting from climate and land use change, compared to (3) a baseline of no environmental change. The model predicted severe declines in density and biomass under climate warming. These declines were lower than expected at range margins because of evolution towards smaller size at both emergence and maturation compared to the natural evolution under the baseline conditions. Despite stronger evolutionary responses, declining rates were substantially larger under the combined warming and flow reduction scenario, leading to a high probability of population extinction over contemporary time frames. Therefore, adaptive responses could not prevent extinction under high rates of environmental change. Our model demonstrates critical elements of next generation ecological modelling aiming at predictions in a changing world as it accounts for spatial and temporal resource heterogeneity, while merging individual behaviour and bioenergetics with microevolutionary adaptations.
The causes underlying the increased mortality of honeybee Apis mellifera colonies observed over the past decade remain unclear. Since so far the evidence for monocausal explanations is equivocal, involvement of multiple stressors is generally assumed. We here focus on various aspects of forage availability, which have received less attention than other stressors because it is virtually impossible to explore them empirically. We applied the colony model BEEHAVE, which links within-hive dynamics and foraging, to stylized landscape settings to explore how foraging distance, forage supply, and “forage gaps”, i.e. periods in which honeybees cannot find any nectar and pollen, affect colony resilience and the mechanisms behind. We found that colony extinction was mainly driven by foraging distance, but the timing of forage gaps had strongest effects on time to extinction. Sensitivity to forage gaps of 15 days was highest in June or July even if otherwise forage availability was sufficient to survive. Forage availability affected colonies via cascading effects on queen's egg-laying rate, reduction of new-emerging brood stages developing into adult workers, pollen debt, lack of workforce for nursing, and reduced foraging activity. Forage gaps in July led to reduction in egg-laying and increased mortality of brood stages at a time when the queen's seasonal egg-laying rate is at its maximum, leading to colony failure over time. Our results demonstrate that badly timed forage gaps interacting with poor overall forage supply reduce honeybee colony resilience. Existing regulation mechanisms which in principle enable colonies to cope with varying forage supply in a given landscape and year, such as a reduction in egg-laying, have only a certain capacity. Our results are hypothetical, as they are obtained from simplified landscape settings, but they are consistent with existing empirical knowledge. They offer ample opportunities for testing the predicted effects of forage stress in controlled experiments.
Both dispersal and local demographic processes determine a population's distribution among habitats of varying quality, yet most theory, experiments, and field studies have focused on the former. We use a generic model to show how both processes contribute to a population's distribution, and how the relative importance of each mechanism depends on scale. In contrast to studies only considering habitat-dependent dispersal, we show that predictions of ideal free distribution (IFD) theory are relevant even at landscape scales, where the assumptions of IFD theory are violated. This is because scales that inhibit one process, promote the other's ability to drive populations to the IFD. Furthermore, because multiple processes can generate IFDs, the pattern alone does not specify a causal mechanism. This is important because populations with IFDs generated by dispersal or demography respond much differently to shifts in resource distributions.
Environmental factors shape the spatial distribution and dynamics of populations. Understanding how these factors interact with movement behavior is critical for efficient conservation, in particular for migratory species. Adult female green sea turtles, Chelonia mydas, migrate between foraging and nesting sites that are generally separated by thousands of kilometers. As an emblematic endangered species, green turtles have been intensively studied, with a focus on nesting, migration, and foraging. Nevertheless, few attempts integrated these behaviors and their trade‐offs by considering the spatial configurations of foraging and nesting grounds as well as environmental heterogeneity like oceanic currents and food distribution. We developed an individual‐based model to investigate the impact of local environmental conditions on emerging migratory corridors and reproductive output and to thereby identify conservation priority sites. The model integrates movement, nesting, and foraging behavior. Despite being largely conceptual, the model captured realistic movement patterns which confirm field studies. The spatial distribution of migratory corridors and foraging hot spots was mostly constrained by features of the regional landscape, such as nesting site locations, distribution of feeding patches, and oceanic currents. These constraints also explained the mixing patterns in regional forager communities. By implementing alternative decision strategies of the turtles, we found that foraging site fidelity and nesting investment, two characteristics of green turtles' biology, are favorable strategies under unpredictable environmental conditions affecting their habitats. Based on our results, we propose specific guidelines for the regional conservation of green turtles as well as future research suggestions advancing spatial ecology of sea turtles. Being implemented in an easy to learn open‐source software, our model can coevolve with the collection and analysis of new data on energy budget and movement into a generic tool for sea turtle research and conservation. Our modeling approach could also be useful for supporting the conservation of other migratory marine animals.
Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species-specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi-dimensional assessments are still to be recommended.