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Confusion about model validation is one of the main challenges in using ecological models for decision support, such as the regulation of pesticides. Decision makers need to know whether a model is a sufficiently good representation of its real counterpart and what criteria can be used to answer this question. Unclear terminology is one of the main obstacles to a good understanding of what model validation is, how it works, and what it can deliver. Therefore, we performed a literature review and derived a standard set of terms. 'Validation' was identified as a catch-all term, which is thus useless for any practical purpose. We introduce the term 'evaludation', a fusion of 'evaluation' and 'validation', to describe the entire process of assessing a model's quality and reliability. Considering the iterative nature of model development, the modelling cycle, we identified six essential elements of evaludation: (i) 'data evaluation' for scrutinising the quality of numerical and qualitative data used for model development and testing; (ii) 'conceptual model evaluation' for examining the simplifying assumptions underlying a model's design; (iii) 'implementation verification' for testing the model's implementation in equations and as a computer programme; (iv) 'model output verification' for comparing model output to data and patterns that guided model design and were possibly used for calibration; (v) 'model analysis' for exploring the model's sensitivity to changes in parameters and process formulations to make sure that the mechanistic basis of main behaviours of the model has been well understood; and (vi) 'model output corroboration' for comparing model output to new data and patterns that were not used for model development and parameterisation. Currently, most decision makers require 'validating' a model by testing its predictions with new experiments or data. Despite being desirable, this is neither sufficient nor necessary for a model to be useful for decision support. We believe that the proposed set of terms and its relation to the modelling cycle can help to make quality assessments and reality checks of ecological models more comprehensive and transparent. (C) 2013 Elsevier B.V. All rights reserved.
Terrestrial environmental systems are characterised by numerous feedback links between their different compartments. However, scientific research is organized into disciplines that focus on processes within the respective compartments rather than on interdisciplinary links. Major feedback mechanisms between compartments might therefore have been systematically overlooked so far. Without identifying these gaps, initiatives on future comprehensive environmental monitoring schemes and experimental platforms might fail. We performed a comprehensive overview of feedbacks between compartments currently represented in environmental sciences and explores to what degree missing links have already been acknowledged in the literature. We focused on process models as they can be regarded as repositories of scientific knowledge that compile findings of numerous single studies. In total, 118 simulation models from 23 model types were analysed. Missing processes linking different environmental compartments were identified based on a meta-review of 346 published reviews, model inter-comparison studies, and model descriptions. Eight disciplines of environmental sciences were considered and 396 linking processes were identified and ascribed to the physical, chemical or biological domain. There were significant differences between model types and scientific disciplines regarding implemented interdisciplinary links. The most wide-spread interdisciplinary links were between physical processes in meteorology, hydrology and soil science that drive or set the boundary conditions for other processes (e.g., ecological processes). In contrast, most chemical and biological processes were restricted to links within the same compartment. Integration of multiple environmental compartments and interdisciplinary knowledge was scarce in most model types. There was a strong bias of suggested future research foci and model extensions towards reinforcing existing interdisciplinary knowledge rather than to open up new interdisciplinary pathways. No clear pattern across disciplines exists with respect to suggested future research efforts. There is no evidence that environmental research would clearly converge towards more integrated approaches or towards an overarching environmental systems theory. (c) 2017 Elsevier B.V. All rights reserved.
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 distribution of poikilotherms is determined by the thermal structure of the marine environment that they are exposed to. Recent research has indicated that changes in migration phenology of beluga whales in the Arctic are triggered by changes in the thermal structure of the marine environment in their summering area. If sea temperatures reflect the spatial distribution of food resources, then changes in the thermal regime will affect how homogeneous or clumped food is distributed. We explore, by individual-based modelling, the hypothesis that changes in migration phenology are not necessarily or exclusively triggered by changes in food abundance, but also by changes in the spatial aggregation of food. We found that the level of food aggregation can significantly affect the relationship between the timing of the start of migration to the winter grounds and the total prey capture of individuals. Our approach strongly indicates that changes in the spatial distribution of food resources should be considered for understanding and quantitatively predicting changes in the phenology of animal migration.
BEEHAVE offers a valuable tool for researchers to design and focus field experiments, for regulators to explore the relative importance of stressors to devise management and policy advice and for beekeepers to understand and predict varroa dynamics and effects of management interventions. We expect that scientists and stakeholders will find a variety of applications for BEEHAVE, stimulating further model development and the possible inclusion of other stressors of potential importance to honeybee colony dynamics.
1. The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality. 2. However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management. 3. We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee - varroa mite - virus interactions. 4. We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. 5. Synthesis and applications. We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions.
Competition is a key process in plant populations and communities. We thus need, if we are to predict the responses of ecological systems to environmental change, a comprehensive and mechanistic understanding of plant competition. Considering competition, however, only at the population level is not sufficient because plant individuals usually are different, interact locally, and can adapt their behaviour to the current state of themselves and of their biotic and abiotic environment. Therefore, simulation models that are individual-based and spatially explicit are increasingly used for studying competition in plant systems. Many different individual-based modelling approaches exist to represent competition, but it is not clear how good they are in reflecting essential aspects of plant competition. We therefore first summarize current concepts and theories addressing plant competition. Then, we review individual-based approaches for modelling competition among plants. We distinguish between approaches that are used for more than 10 years and more recent ones. We identify three major gaps that need to be addressed more in the future: the effects of plants on their local environment, adaptive behaviour, and below-ground competition. To fill these gaps, the representation of plants and their interactions have to be more mechanistic than most existing approaches. Developing such new approaches is a challenge because they are likely to be more complex and to require more detailed knowledge and data on individual-level processes underlying competition. We thus need a more integrated research strategy for the future, where empirical and theoretical ecologists as well as computer scientists work together on formulating, implementing, parameterization, testing, comparing, and selecting the new approaches. (c) 2008 Rubel Foundation, ETH Zurich. Published by Elsevier GmbH. All rights reserved.
The relative contribution of personal and social information to explain individual and collective behavior in different species and contexts is an open question in animal ecology. In particular, there is a major lack of studies combining theoretical and empirical approaches to test the relative relevance of different hypothesized individual behaviors to predict empirical collective patterns. We used an individual-based model to confront three hypotheses about the information transfer between social scavengers (Griffon Vultures, Gyps fulvus) when searching for carrion: (1) Vultures only use personal information during foraging ("nonsocial" hypothesis); (2) they create long chains of vultures by following both other vultures that are flying towards carcasses and vultures that are following other vultures that are flying towards carcasses ("chains of vultures" hypothesis); and (3) vultures are only attracted by other vultures that are sinking vertically to a carcass ("local enhancement" hypothesis). The chains of vultures hypothesis has been used in existing models, but never been confronted with field data. Testing is important, though, because these hypotheses could have different management implications. The model was parameterized to mimic the behavior and the densities of both Griffon Vultures and carcasses in a 10 000-km(2) study area in northeastern Spain. We compared the number of vultures attending simulated carcasses with those attending 25 continuously monitored experimental carcasses in the field. Social hypotheses outperformed the nonsocial hypothesis. The chains of vultures hypothesis overestimated the number of vultures feeding on carcasses; the local enhancement hypothesis fitted closely to the empirical data. Supported by our results, we discuss mechanistic and adaptive considerations that reveal that local enhancement may be the key social mechanism behind collective foraging in this and likely other avian scavengers and/or social birds. It also highlights the current need for more studies confronting alternative models of key behaviors with empirical patterns in order to understand how collective behavior emerges in animal societies.
Intraspecific trait variation (ITV) is thought to play a significant role in community assembly, but the magnitude and direction of its influence are not well understood. Although it may be critical to better explain population persistence, species interactions, and therefore biodiversity patterns, manipulating ITV in experiments is challenging. We therefore incorporated ITV into a trait‐ and individual‐based model of grassland community assembly by adding variation to the plants’ functional traits, which then drive life‐history tradeoffs. Varying the amount of ITV in the simulation, we examine its influence on pairwise‐coexistence and then on the species diversity in communities of different initial sizes. We find that ITV increases the ability of the weakest species to invade most, but that this effect does not scale to the community level, where the primary effect of ITV is to increase the persistence and abundance of the competitively‐average species. Diversity of the initial community is also of critical importance in determining ITV's efficacy; above a threshold of interspecific diversity, ITV does not increase diversity further. For communities below this threshold, ITV mainly helps to increase diversity in those communities that would otherwise be low‐diversity. These findings suggest that ITV actively maintains diversity by helping the species on the margins of persistence, but mostly in habitats of relatively low alpha and beta diversity.
When data are limited it is difficult for conservation managers to assess alternative management scenarios and make decisions. The natterjack toad (Bufo calamita) is declining at the edges of its distribution range in Europe and little is known about its current distribution and abundance in Poland. Although different landscape management plans for central Poland exist, it is unclear to what extent they impact this species. Based on these plans, we investigated how four alternative landscape development scenarios would affect the total carrying capacity and population dynamics of the natterjack toad. To facilitate decision-making, we first ranked the scenarios according to their total carrying capacity. We used the software RAMAS GIS to determine the size and location of habitat patches in the landscape. The estimated carrying capacities were very similar for each scenario, and clear ranking was not possible. Only the reforestation scenario showed a marked loss in carrying capacity. We therefore simulated metapopulation dynamics with RAMAS taking into account dynamical processes such as reproduction and dispersal and ranked the scenarios according to the resulting species abundance. In this case, we could clearly rank the development scenarios. We identified road mortality of adults as a key process governing the dynamics and separating the different scenarios. The renaturalisation scenario clearly ranked highest due to its decreased road mortality. Taken together our results suggest that road infrastructure development might be much more important for natterjack toad conservation than changes in the amount of habitat in the semi-natural river valley. We gained these insights by considering both the resulting metapopulation structure and dynamics in the form of a PVA. We conclude that the consideration of dynamic processes in amphibian conservation management may be indispensable for ranking management scenarios.
Current environmental risk assessment (ERA) of chemicals for aquatic invertebrates relies on standardized laboratory tests in which toxicity effects on individual survival, growth and reproduction are measured. Such tests determine the threshold concentration of a chemical below which no population-level effects are expected. How well this procedure captures effects on individuals and populations, however, remains an open question. Here we used mechanistic effect models, combining individual-level reproduction and survival models with an individual-based population model (IBM), to understand the individuals' responses and extrapolate them to the population level. We used a toxicant (Dispersogen A) for which adverse effects on laboratory populations were detected at the determined threshold concentration and thus challenged the conservatism of the current risk assessment method. Multiple toxicity effects on reproduction and survival were reported, in addition to effects on the F1 generation. We extrapolated commonly tested individual toxicity endpoints, reproduction and survival, to the population level using the IBM. Effects on reproduction were described via regression models. To select the most appropriate survival model, the IBM was run assuming either stochastic death (SD) or individual tolerance (IT). Simulations were run for different scenarios regarding the toxicant's effects: survival toxicity, reproductive toxicity, or survival and reproductive toxicity. As population-level endpoints, we used population size and structure and extinction risk. We found that survival represented as SD explained population dynamics better than IT. Integrating toxicity effects on both reproduction and survival yielded more accurate predictions of population effects than considering isolated effects. To fully capture population effects observed at high toxicant concentrations, toxicity effects transmitted to the F1 generation had to be integrated. Predicted extinction risk was highly sensitive to the assumptions about individual-level effects. Our results demonstrate that the endpoints used in current standard tests may not be sufficient for assessing the risk of adverse effects on populations. A combination of laboratory population experiments with mechanistic effect models is a powerful tool to better understand and predict effects on both individuals and populations. Mechanistic effect modelling thus holds great potential to improve the accuracy of ERA of chemicals in the future. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved.
In addition to natural stressors, populations are increasingly exposed to chemical pollutants released into the environment. We experimentally demonstrate the loss of resilience for Daphnia magna populations that are exposed to a combination of natural and chemical stressors even though effects on population size of a single stressor were cryptic, i.e. hard to detect statistically. Data on Daphnia population demography and along with model-based exploration of our predator-prey system revealed that direct trophic interactions changed the population size-structure and thereby increased population vulnerability to the toxicant which acts in a size selective manner. Moreover, population vulnerability to the toxicant increases with predator size and predation intensity whereas indirect trait-mediated interactions via predator kairomones may buffer chemical effects to a certain extent. Our study demonstrates that population size can be a poor endpoint for risk assessments of chemicals and that ignoring disturbance interactions can lead to severe underestimation of extinction risk.
The potential of ecological models for supporting environmental decision making is increasingly acknowledged. However, it often remains unclear whether a model is realistic and reliable enough. Good practice for developing and testing ecological models has not yet been established. Therefore, TRACE, a general framework for documenting a model's rationale, design, and testing was recently suggested. Originally TRACE was aimed at documenting good modelling practice. However, the word 'documentation' does not convey TRACE's urgency. Therefore, we re-define TRACE as a tool for planning, performing, and documenting good modelling practice. TRACE documents should provide convincing evidence that a model was thoughtfully designed, correctly implemented, thoroughly tested, well understood, and appropriately used for its intended purpose. TRACE documents link the science underlying a model to its application, thereby also linking modellers and model users, for example stakeholders, decision makers, and developers of policies. We report on first experiences in producing TRACE documents. We found that the original idea underlying TRACE was valid, but to make its use more coherent and efficient, an update of its structure and more specific guidance for its use are needed. The updated TRACE format follows the recently developed framework of model 'evaludation': the entire process of establishing model quality and credibility throughout all stages of model development, analysis, and application. TRACE thus becomes a tool for planning, documenting, and assessing model evaludation, which includes understanding the rationale behind a model and its envisaged use. We introduce the new structure and revised terminology of TRACE and provide examples. (C) 2014 Elsevier B.V. All rights reserved.
Robustness analysis: Deconstructing computational models for ecological theory and applications
(2016)
The design of computational models is path-dependent: the choices made in each step during model development constrain the choices that are available in the subsequent steps. The actual path of model development can be extremely different, even for the same system, because the path depends on the question addressed, the availability of data, and the consideration of specific expert knowledge, in addition to the experience, background, and modelling preferences of the modellers. Thus, insights from different models are practically impossible to integrate, which hinders the development of general theory. We therefore suggest augmenting the current culture of communicating models as working just fine with a culture of presenting analyses in which we try to break models, i.e., model mechanisms explaining certain observations break down. We refer to the systematic attempts to break a model as “robustness analysis” (RA). RA is the systematic deconstruction of a model by forcefully changing the model's parameters, structure, and representation of processes. We discuss the nature and elements of RA and provide brief examples. RA cannot be completely formalized into specific techniques and instead corresponds to detective work that is driven by general questions and specific hypotheses, with strong attention focused on unusual behaviours. Both individual modellers and ecological modelling in general will benefit from RA because RA helps with understanding models and identifying “robust theories”, which are general principles that are independent of the idiosyncrasies of specific models. Integrating the results of RAs from different models to address certain systems or questions will then provide a comprehensive overview of when certain mechanisms control system behaviour and when and why this control ceases. This approach can provide insights into the mechanisms that lead to regime shifts in actual ecological systems.
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.
Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity
The Southern Ocean ecosystem is characterized by extreme seasonal changes in environmental factors such as day length, sea ice extent and food availability. The key species Antarctic krill (Euphausia superba) has evolved metabolic and behavioural seasonal rhythms to cope with these seasonal changes. We investigate the switch between a physiological less active and active period for adult krill, a rhythm which seems to be controlled by internal biological clocks. These biological clocks can be synchronized by environmental triggers such as day length and food availability. They have evolved for particular environmental regimes to synchronize predictable seasonal environmental changes with important life cycle functions of the species. In a changing environment the time when krill is metabolically active and the time of peak food availability may not overlap if krill's seasonal activity is solely determined by photoperiod (day length). This is especially true for the Atlantic sector of the Southern Ocean where the spatio-temporal ice cover dynamics are changing substantially with rising average temperatures. We developed an individual-based model for krill to explore the impact of photoperiod and food availability on the growth and demographics of krill. We simulated dynamics of local krill populations (with no movement of krill assumed) along a south-north gradient for different triggers of metabolic activity and different levels of food availability below the ice. We also observed the fate of larval krill which cannot switch to low metabolism and therefore are likely to overwinter under ice. Krill could only occupy the southern end of the gradient, where algae bloom only lasts for a short time, when alternative food supply under the ice was high and metabolic activity was triggered by photoperiod. The northern distribution was limited by lack of overwintering habitat for krill larvae due to short duration of sea ice cover even for high food content under the ice. The variability of the krill's length-frequency distributions varied for different triggers of metabolic activity, but did not depend on the sea ice extent. Our findings suggest a southward shift of krill populations due to reduction in the spatial sea ice extent, which is consistent with field observations. Overall, our results highlight the importance of the explicit consideration of spatio-temporal sea ice dynamics especially for larval krill together with temporal synchronization through internal clocks, triggered by environmental factors (photoperiod and food) in adult krill for the population modelling of krill. (C) 2015 Elsevier B.V. All rights reserved.
Forage availability has been suggested as one driver of the observed decline in honey bees. However, little is known about the effects of its spatiotemporal variation on colony success. We present a modeling framework for assessing honey bee colony viability in cropping systems. Based on two real farmland structures, we developed a landscape generator to design cropping systems varying in crop species identity, diversity, and relative abundance. The landscape scenarios generated were evaluated using the existing honey bee colony model BEEHAVE, which links foraging to in-hive dynamics. We thereby explored how different cropping systems determine spatiotemporal forage availability and, in turn, honey bee colony viability (e.g., time to extinction, TTE) and resilience (indicated by, e.g., brood mortality). To assess overall colony viability, we developed metrics,P(H)andP(P,)which quantified how much nectar and pollen provided by a cropping system per year was converted into a colony's adult worker population. Both crop species identity and diversity determined the temporal continuity in nectar and pollen supply and thus colony viability. Overall farmland structure and relative crop abundance were less important, but details mattered. For monocultures and for four-crop species systems composed of cereals, oilseed rape, maize, and sunflower,P(H)andP(P)were below the viability threshold. Such cropping systems showed frequent, badly timed, and prolonged forage gaps leading to detrimental cascading effects on life stages and in-hive work force, which critically reduced colony resilience. Four-crop systems composed of rye-grass-dandelion pasture, trefoil-grass pasture, sunflower, and phacelia ensured continuous nectar and pollen supply resulting in TTE > 5 yr, andP(H)(269.5 kg) andP(P)(108 kg) being above viability thresholds for 5 yr. Overall, trefoil-grass pasture, oilseed rape, buckwheat, and phacelia improved the temporal continuity in forage supply and colony's viability. Our results are hypothetical as they are obtained from simplified landscape settings, but they nevertheless match empirical observations, in particular the viability threshold. Our framework can be used to assess the effects of cropping systems on honey bee viability and to develop land-use strategies that help maintain pollination services by avoiding prolonged and badly timed forage gaps.
Forage availability has been suggested as one driver of the observed decline in honey bees. However, little is known about the effects of its spatiotemporal variation on colony success. We present a modeling framework for assessing honey bee colony viability in cropping systems. Based on two real farmland structures, we developed a landscape generator to design cropping systems varying in crop species identity, diversity, and relative abundance. The landscape scenarios generated were evaluated using the existing honey bee colony model BEEHAVE, which links foraging to in-hive dynamics. We thereby explored how different cropping systems determine spatiotemporal forage availability and, in turn, honey bee colony viability (e.g., time to extinction, TTE) and resilience (indicated by, e.g., brood mortality). To assess overall colony viability, we developed metrics,P(H)andP(P,)which quantified how much nectar and pollen provided by a cropping system per year was converted into a colony's adult worker population. Both crop species identity and diversity determined the temporal continuity in nectar and pollen supply and thus colony viability. Overall farmland structure and relative crop abundance were less important, but details mattered. For monocultures and for four-crop species systems composed of cereals, oilseed rape, maize, and sunflower,P(H)andP(P)were below the viability threshold. Such cropping systems showed frequent, badly timed, and prolonged forage gaps leading to detrimental cascading effects on life stages and in-hive work force, which critically reduced colony resilience. Four-crop systems composed of rye-grass-dandelion pasture, trefoil-grass pasture, sunflower, and phacelia ensured continuous nectar and pollen supply resulting in TTE > 5 yr, andP(H)(269.5 kg) andP(P)(108 kg) being above viability thresholds for 5 yr. Overall, trefoil-grass pasture, oilseed rape, buckwheat, and phacelia improved the temporal continuity in forage supply and colony's viability. Our results are hypothetical as they are obtained from simplified landscape settings, but they nevertheless match empirical observations, in particular the viability threshold. Our framework can be used to assess the effects of cropping systems on honey bee viability and to develop land-use strategies that help maintain pollination services by avoiding prolonged and badly timed forage gaps.
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.
Editorial
(2020)
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.
In a selected literature survey we reviewed studies on the habitat heterogeneity-animal species diversity relationship and evaluated whether there are uncertainties and biases in its empirical support. We reviewed 85 publications for the period 1960-2003. We screened each publication for terms that were used to define habitat heterogeneity, the animal species group and ecosystem studied, the definition of the structural variable, the measurement of vegetation structure and the temporal and spatial scale of the study. The majority of studies found a positive correlation between habitat heterogeneity/diversity and animal species diversity. However, empirical support for this relationship is drastically biased towards studies of vertebrates and habitats under anthropogenic influence. In this paper we show that ecological effects of habitat heterogeneity may vary considerably between species groups depending on whether structural attributes are perceived as heterogeneity or fragmentation. Possible effects may also vary relative to the structural variable measured. Based upon this, we introduce a classification framework that may be used for across-studies comparisons. Moreover, the effect of habitat heterogeneity for one species group may differ in relation to the spatial scale. In several studies, however, different species groups are closely linked to 'keystone structures' that determine animal species diversity by their presence. Detecting crucial keystone structures of the vegetation has profound implications for nature conservation and biodiversity management.
Ecological buffering mechanisms in savannas : a unifying theory of long-term tree-grass coexistence
(2000)
Animal personality may affect an animal’s mobility in a given landscape, influencing its propensity to take risks in an unknown environment. We investigated the mobility of translocated common voles in two corridor systems 60 m in length and differing in width (1 m and 3 m). Voles were behaviorally phenotyped in repeated open field and barrier tests. Observed behavioral traits were highly repeatable and described by a continuous personality score. Subsequently, animals were tracked via an automated very high frequency (VHF) telemetry radio tracking system to monitor their movement patterns in the corridor system. Although personality did not explain movement patterns, corridor width determined the amount of time spent in the habitat corridor. Voles in the narrow corridor system entered the corridor faster and spent less time in the corridor than animals in the wide corridor. Thus, landscape features seem to affect movement patterns more strongly than personality. Meanwhile, site characteristics, such as corridor width, could prove to be highly important when designing corridors for conservation, with narrow corridors facilitating faster movement through landscapes than wider corridors.
Animal personality may affect an animal’s mobility in a given landscape, influencing its propensity to take risks in an unknown environment. We investigated the mobility of translocated common voles in two corridor systems 60 m in length and differing in width (1 m and 3 m). Voles were behaviorally phenotyped in repeated open field and barrier tests. Observed behavioral traits were highly repeatable and described by a continuous personality score. Subsequently, animals were tracked via an automated very high frequency (VHF) telemetry radio tracking system to monitor their movement patterns in the corridor system. Although personality did not explain movement patterns, corridor width determined the amount of time spent in the habitat corridor. Voles in the narrow corridor system entered the corridor faster and spent less time in the corridor than animals in the wide corridor. Thus, landscape features seem to affect movement patterns more strongly than personality. Meanwhile, site characteristics, such as corridor width, could prove to be highly important when designing corridors for conservation, with narrow corridors facilitating faster movement through landscapes than wider corridors.
The cultivation of energy crops leads to direct and indirect land use changes that impair the biodiversity of the agricultural landscape. In our study, we analyse the effects of mitigation measures on the European brown hare (Lepus europaeus), which is directly affected by ongoing land use change and has experienced widespread decline throughout Europe since the 1960s. Therefore, we developed a spatially explicit and individual-based ecological model to study the effects of different landscape configurations and compositions on hare population development. As an input, we used two 4 x 4 km large model landscapes, which were generated by a landscape generator based on real field sizes and crop proportions and differed in average field size and crop composition. The crops grown annually are evaluated in terms of forage suitability, breeding suitability and crop richness for the hare. In six mitigation scenarios, we investigated the effects of a 10 % increase in the following measures: (1) mixed silphie, (2) miscanthus, (3) grass-clover ley, (4) alfalfa, (5) set-aside, and (6) general crop richness. All mitigation measures had significant effects on hare population development. Compared to the base scenario, the relative change in hare abundance ranged from a factor of 0.56 in the grass-clover ley scenario to-0.16 in the miscanthus scenario. The mitigation measures of mixed silphie, grass-clover ley and increased crop richness led to distinct increases in hare abundance in both landscapes ( > 0.3). The results show that both landscape configuration and composition have a significant effect on hare population development, which responds particularly strongly to compositional changes. The increase in crop diversity, e.g., through the cultivation of alternative energy crops such as mixed silphie and grass-clover ley, proves to be beneficial for the brown hare.
There is an increasing need for an assessment of the impacts of land use and land use change (LUCC). In this context, simulation models are valuable tools for investigating the impacts of stakeholder actions or policy decisions. Agricultural landscape generators (ALGs), which systematically and automatically generate realistic but simplified representations of land cover in agricultural landscapes, can provide the input for LUCC models. We reviewed existing ALGs in terms of their objectives, design and scope. We found eight ALGs that met our definition. They were based either on generic mathematical algorithms (pattern-based) or on representations of ecological or land use processes (process-based). Most ALGs integrate only a few landscape metrics, which limits the design of the landscape pattern and thus the range of applications. For example, only a few specific farming systems have been implemented. We conclude that existing ALGs contain useful approaches that can be used for specific purposes, but ideally generic modular ALGs are developed that can be used for a wide range of scenarios, regions and model types. We have compiled features of such generic ALGs and propose a possible software architecture. Considerable joint efforts are required to develop such generic ALGs, but the benefits in terms of a better understanding and development of more efficient agricultural policies would be high.
Both climate change and land use regimes affect the viability of populations, but they are often studied separately. Moreover, population viability analyses (PVAs) often ignore the effects of large environmental gradients and use temporal resolutions that are too coarse to take into account that different stages of a population's life cycle may be affected differently by climate change. Here, we present the High-resolution Large Environmental Gradient (HiLEG) model and apply it in a PVA with daily resolution based on daily climate projections for Northwest Germany. We used the large marsh grasshopper (LMG) as the target species and investigated (1) the effects of climate change on the viability and spatial distribution of the species, (2) the influence of the timing of grassland mowing on the species and (3) the interaction between the effects of climate change and grassland mowing. The stageand cohort-based model was run for the spatially differentiated environmental conditions temperature and soil moisture across the whole study region. We implemented three climate change scenarios and analyzed the population dynamics for four consecutive 20-year periods. Climate change alone would lead to an expansion of the regions suitable for the LMG, as warming accelerates development and due to reduced drought stress. However, in combination with land use, the timing of mowing was crucial, as this disturbance causes a high mortality rate in the aboveground life stages. Assuming the same date of mowing throughout the region, the impact on viability varied greatly between regions due to the different climate conditions. The regional negative effects of the mowing date can be divided into five phases: (1) In early spring, the populations were largely unaffected in all the regions; (2) between late spring and early summer, they were severely affected only in warm regions; (3) in summer, all the populations were severely affected so that they could hardly survive; (4) between late summer and early autumn, they were severely affected in cold regions; and (5) in autumn, the populations were equally affected across all regions. The duration and start of each phase differed slightly depending on the climate change scenario and simulation period, but overall, they showed the same pattern. Our model can be used to identify regions of concern and devise management recommendations. The model can be adapted to the life cycle of different target species, climate projections and disturbance regimes. We show with our adaption of the HiLEG model that high-resolution PVAs and applications on large environmental gradients can be reconciled to develop conservation strategies capable of dealing with multiple stressors.
In many species, dispersal is decisive for survival in a changing climate. Simulation models for population dynamics under climate change thus need to account for this factor. Moreover, large numbers of species inhabiting agricultural landscapes are subject to disturbances induced by human land use. We included dispersal in the HiLEG model that we previously developed to study the interaction between climate change and agricultural land use in single populations. Here, the model was parameterized for the large marsh grasshopper (LMG) in cultivated grasslands of North Germany to analyze (1) the species development and dispersal success depending on the severity of climate change in subregions, (2) the additional effect of grassland cover on dispersal success, and (3) the role of dispersal in compensating for detrimental grassland mowing. Our model simulated population dynamics in 60-year periods (2020-2079) on a fine temporal (daily) and high spatial (250 x 250 m(2)) scale in 107 subregions, altogether encompassing a range of different grassland cover, climate change projections, and mowing schedules. We show that climate change alone would allow the LMG to thrive and expand, while grassland cover played a minor role. Some mowing schedules that were harmful to the LMG nevertheless allowed the species to moderately expand its range. Especially under minor climate change, in many subregions dispersal allowed for mowing early in the year, which is economically beneficial for farmers. More severe climate change could facilitate LMG expansion to uninhabited regions but would require suitable mowing schedules along the path. These insights can be transferred to other species, given that the LMG is considered a representative of grassland communities. For more specific predictions on the dynamics of other species affected by climate change and land use, the publicly available HiLEG model can be easily adapted to the characteristics of their life cycle.
Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning). Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.
Facilitation (positive interaction) has received increasing attention in plant ecology over the last decade. Just as for competition, distinguishing different modes of facilitation (mutualistic, commensal or even antagonistic) may be crucial. We therefore introduce the new concept of symmetric versus asymmetric facilitation and present a generic individual-based zone-of-influence model. The model simultaneously implements different modes of both facilitation and competition among individual plants via their overlapping zone of influence. Because we consider facilitation modes as a continuum related to environmental context, we integrated this concept with the stress-gradient hypothesis (SGH) by exploring differences in spatial pattern formation in self-thinning plants along a stress gradient in our model. The interplay among modes of interaction creates distinctly varied spatial patterns along stress gradients. When competition was symmetric, symmetric facilitation (mutualism) consistently led to plant aggregation along stress gradients. However, asymmetric facilitation (commensalism) produces plant aggregation only under more benign conditions but tends to intensify local competition and spatial segregation when conditions are harsh. When competition was completely asymmetric, different modes of facilitation contributed little to spatial aggregation. Symmetric facilitation significantly increased survival at the severe end of the stress gradient, which supports the claim of the SGH that facilitation should have generally positive net effects on plants under high stress levels. Asymmetric facilitation, however, was found to increase survival only under intermediate stress conditions, which contradicts the current predictions of the SGH. Synthesis. Our modelling study demonstrates that the interplay between modes of facilitation and competition affects different aspects of plant populations and communities, implying context-dependent outcomes and consequences. The explicit consideration of the modes and mechanisms of interactions (both facilitation and competition) and the nature of stress factors will help to extend the framework of the SGH and foster research on facilitation in plant ecology.
The wood mouse is a common and abundant species in agricultural landscape and is a focal species in pesticide risk assessment. Empirical studies on the ecology of the wood mouse have provided sufficient information for the species to be modelled mechanistically. An individual-based model was constructed to explicitly represent the locations and movement patterns of individual mice. This together with the schedule of pesticide application allows prediction of the risk to the population from pesticide exposure. The model included life-history traits of wood mice as well as typical landscape dynamics in agricultural farmland in the UK. The model obtains a good fit to the available population data and is fit for risk assessment purposes. It can help identify spatio-temporal situations with the largest potential risk of exposure and enables extrapolation from individual-level endpoints to population-level effects. Largest risk of exposure to pesticides was found when good crop growth in the "sink" fields coincided with high "source" population densities in the hedgerows.
For the ecological risk assessment of toxic chemicals, standardized tests on individuals are often used as proxies for population-level effects. Here, we address the utility of one commonly used metric, reproductive output, as a proxy for population-level effects. Because reproduction integrates the outcome of many interacting processes (e.g., feeding, growth, allocation of energy to reproduction), the observed toxic effects in a reproduction test could be due to stress on one of many processes. Although this makes reproduction a robust endpoint for detecting stress, it may mask important population-level consequences if the different physiological processes stress affects are associated with different feedback mechanisms at the population level. We therefore evaluated how an observed reduction in reproduction found in a standard reproduction test translates to effects at the population level if it is caused by hypothetical toxicants affecting different physiological processes (physiological modes of action; PMoA). For this we used two consumer-resource models: the Yodzis-Innes (YI) model, which is mathematically tractable, but requires strong assumptions of energetic equivalence among individuals as they progress through ontogeny, and an individual-based implementation of dynamic energy budget theory (DEB-IBM), which relaxes these assumptions at the expense of tractability. We identified two important feedback mechanisms controlling the link between individual- and population-level stress in the YI model. These mechanisms turned out to also be important for interpreting some of the individual-based model results; for two PMoAs, they determined the population response to stress in both models. In contrast, others stress types involved more complex feedbacks, because they asymmetrically stressed the production efficiency of reproduction and somatic growth. The feedbacks associated with different PMoAs drastically altered the link between individual- and population-level effects. For example, hypothetical stressors with different PMoAs that had equal effects on reproduction had effects ranging from a negligible decline in biomass to population extinction. Thus, reproduction tests alone are of little use for extrapolating toxicity to the population level, but we showed that the ecological relevance of standard tests could easily be improved if growth is measured along with reproduction.
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.
Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individual-based model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small-and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology.
Individual-based models (IBMs) predict how dynamics at higher levels of biological organization emerge from individual-level processes. This makes them a particularly useful tool for ecotoxicology, where the effects of toxicants are measured at the individual level but protection goals are often aimed at the population level or higher. However, one drawback of IBMs is that they require significant effort and data to design for each species. A solution would be to develop IBMs for chemical risk assessment that are based on generic individual-level models and theory. Here we show how one generic theory, Dynamic Energy Budget (DEB) theory, can be used to extrapolate the effect of toxicants measured at the individual level to effects on population dynamics. DEB is based on first principles in bioenergetics and uses a common model structure to model all species. Parameterization for a certain species is done at the individual level and allows to predict population-level effects of toxicants for a wide range of environmental conditions and toxicant concentrations. We present the general approach, which in principle can be used for all animal species, and give an example using Daphnia magna exposed to 3,4-dichloroaniline. We conclude that our generic approach holds great potential for standardized ecological risk assessment based on ecological models. Currently, available data from standard tests can directly be used for parameterization under certain circumstances, but with limited extra effort standard tests at the individual would deliver data that could considerably improve the applicability and precision of extrapolation to the population level. Specifically, the measurement of a toxicant's effect on growth in addition to reproduction, and presenting data over time as opposed to reporting a single EC50 or dose response curve at one time point.
Grazing is known as one of the key factors for diversity and community composition in grassland ecosystems, but the response of plant communities towards grazing varies remarkably between sites with different environmental conditions. It is generally accepted that grazing increases plant diversity in productive environments, while it tends to reduce diversity in unproductive habitats (grazing reversal hypothesis). Despite empirical evidence for this pattern the mechanistic link between modes of plant-plant competition and grazing response at the community level still remains poorly understood. Root-competition in particular has rarely been included in theoretical studies, although it has been hypothesized that variations in productivity and grazing regime can alter the relative importance of shoot- and root-competition. We therefore developed an individual-based model based on plant functional traits to investigate the response of a grassland community towards grazing. Models of different complexity, either incorporating only shoot competition or with distinct shoot- and root-competition, were used to study the interactive effects of grazing, resource availability, and the mode of competition (size-symmetric or asymmetric). The pattern predicted by the grazing reversal hypothesis (GRH) can only be explained by our model if shoot- and root-competition are explicitly considered and if size asymmetry of above- and symmetry of below-ground competition is assumed. For this scenario, the model additionally reproduced empirically observed plant trait responses: erect and large plant functional types (PFTs) dominated without grazing, while frequent grazing favoured small PFTs with a rosette growth form. We conclude that interactions between shoot- and root-competition and size symmetry/asymmetry of plant-plant interactions are crucial in order to understand grazing response under different habitat productivities. Our results suggest that future empirical trait surveys in grassland communities should include root traits, which have been largely ignored in previous studies, in order to improve predictions of plants" responses to grazing.
Females may select a mate based on signalling traits that are believed to accurately correlate with heritable aspects of male quality. Anthropogenic actions, in particular chemicals released into the environment, are now disrupting the accuracy of mating signals to convey information about male quality. The long-term prediction for disrupted mating signals is most commonly loss of female preference. Yet, this prediction has rarely been tested using quantitative models. We use agent-based models to explore the effects of rapid disruption of mating signals. In our model, a gene determines survival. Males signal their level of genetic quality via a signal trait, which females use to select a mate. We allowed this system of sexual selection to become established, before introducing a disruption between the male signal trait and quality, which was similar in nature to that induced by exogenous chemicals. Finally, we assessed the capacity of the system to recover from this disruption. We found that within a relatively short time frame, disruption of mating signals led to a lasting loss of female preference. Decreases in mean viability at the population-level were also observed, because sexual-selection acting against newly arising deleterious mutations was relaxed. The ability of the population to recover from disrupted mating signals was strongly influenced by the mechanisms that promoted or maintained genetic diversity in traits under sexual selection. Our simple model demonstrates that environmental perturbations to the accuracy of male mating signals can result in a long-term loss of female preference for those signals within a few generations. What is more, the loss of this preference can have knock-on consequences for mean population fitness.
Contamination of soil with toxic heavy metals poses a major threat to the environment and human health. Anthropogenic sources include smelting of ores, municipal wastes, fertilizers, and pesticides. In assessing soil quality and the environmental and ecological risk of contamination with heavy metals, often homogeneous contamination of the soil is assumed. However, soils are very heterogeneous environments. Consequently, both contamination and the response of soil organisms can be assumed to be heterogeneous. This might have consequences for the exposure of soil organisms and for the extrapolation of risk from the individual to the population level. Therefore, to explore how soil contamination of different spatial heterogeneity affects population dynamics of soil invertebrates, we developed a spatially explicit individual-based model of the springtail, Folsomia candida, a standard test species for ecotoxicological risk assessment. In the model, individuals were assumed to sense and avoid contaminated habitat with a certain probability that depends on contamination level. Avoidance of contaminated areas thus influenced the individuals' movement and feeding, their exposure, and in turn all other biological processes underlying population dynamics. Model rules and parameters were based on data from the literature, or were determined via pattern-oriented modelling. The model correctly predicted several patterns that were not used for model design and calibration. Simulation results showed that the ability of the individuals to detect and avoid the toxicant, combined with the presence of clean habitat patches which act as "refuges", made equilibrium population size due to toxic effects less sensitive to increases in toxicant concentration. Additionally, the level of heterogeneity among patches of soil (i.e. the difference in concentration) was important: at the same average concentration, a homogeneously contaminated scenario was the least favourable habitat, while higher levels of heterogeneity corresponded to higher population growth rate and equilibrium size. Our model can thus be used as a tool for extrapolating from short-term effects at the individual level to long-term effects at the population level under more realistic conditions. It can thus be used to develop and extrapolate from standard ecotoxicological tests in the laboratory to ecological risk assessments.
Current chemical risk assessment procedures may result in imprecise estimates of risk due to sometimes arbitrary simplifying assumptions. As a way to incorporate ecological complexity and improve risk estimates, mechanistic effect models have been recommended. However, effect modeling has not yet been extensively used for regulatory purposes, one of the main reasons being uncertainty about which model type to use to answer specific regulatory questions. We took an individual-based model (IBM), which was developed for risk assessment of soil invertebrates and includes avoidance of highly contaminated areas, and contrasted it with a simpler, more standardized model, based on the generic metapopulation matrix model RAMAS. In the latter the individuals within a sub-population are not treated as separate entities anymore and the spatial resolution is lower. We explored consequences of model aggregation in terms of assessing population-level effects for different spatial distributions of a toxic chemical. For homogeneous contamination of the soil, we found good agreement between the two models, whereas for heterogeneous contamination, at different concentrations and percentages of contaminated area, RAMAS results were alternatively similar to IBM results with and without avoidance, and different food levels. This inconsistency is explained on the basis of behavioral responses that are included in the IBM but not in RAMAS. Overall, RAMAS was less sensitive than the IBM in detecting population-level effects of different spatial patterns of exposure. We conclude that choosing the right model type for risk assessment of chemicals depends on whether or not population-level effects of small-scale heterogeneity in exposure need to be detected. We recommend that if in doubt, both model types should be used and compared. Describing both models following the same standard format, the ODD protocol, makes them equally transparent and understandable. The simpler model helps to build up trust for the more complex model and can be used for more homogeneous exposure patterns. The more complex model helps detecting and understanding the limitations of the simpler model and is needed to ensure ecological realism for more complex exposure scenarios. (C) 2013 Elsevier B.V. All rights reserved.
The pace-of-life syndrome (POLS) hypothesis posits that suites of traits are correlated along a slow-fast continuum owing to life history trade-offs. Despite widespread adoption, environmental conditions driving the emergence of POLS remain unclear. A recently proposed conceptual framework of POLS suggests that a slow-fast continuum should align to fluctuations in density-dependent selection. We tested three key predictions made by this framework with an ecoevolutionary agent-based population model. Selection acted on responsiveness (behavioral trait) to interpatch resource differences and the reproductive investment threshold (life history trait). Across environments with density fluctuations of different magnitudes, we observed the emergence of a common axis of trait covariation between and within populations (i.e., the evolution of a POLS). Slow-type (fast-type) populations with high (low) responsiveness and low (high) reproductive investment threshold were selected at high (low) population densities and less (more) intense and frequent density fluctuations. In support of the predictions, fast-type populations contained a higher degree of variation in traits and were associated with higher intrinsic reproductive rate (r(0)) and higher sensitivity to intraspecific competition (gamma), pointing to a universal trade-off. While our findings support that POLS aligns with density-dependent selection, we discuss possible mechanisms that may lead to alternative evolutionary pathways.
Polychlorinated biphenyls (PCBs) can cause endocrine disruption, cancer, immunosuppression, or reproductive failure in animals. We used an individual-based model to explore whether and how PCB-associated reproductive failure could affect the dynamics of a hypothetical polar bear (Ursus maritimus) population exposed to PCBs to the same degree as the East Greenland subpopulation. Dose-response data from experimental studies on a surrogate species, the mink (Mustela vision), were used in the absence of similar data for polar bears. Two alternative types of reproductive failure in relation to maternal sum-PCB concentrations were considered: increased abortion rate and increased cub mortality. We found that the quantitative impact of PCB-induced reproductive failure on population growth rate depended largely on the actual type of reproductive failure involved. Critical potencies of the dose-response relationship for decreasing the population growth rate were established for both modeled types of reproductive failure. Comparing the model predictions of the age-dependent trend of sum-PCBs concentrations in females with actual field measurements from East Greenland indicated that it was unlikely that PCB exposure caused a high incidence of abortions in the subpopulation. However, on the basis of this analysis, it could not be excluded that PCB exposure contributes to higher cub mortality. Our results highlight the necessity for further research on the possible influence of PCBs on polar bear reproduction regarding their physiological pathway. This includes determining the exact cause of reproductive failure, i.e., in utero exposure versus lactational exposure of offspring; the timing of offspring death; and establishing the most relevant reference metrics for the dose-response relationship.
Climate change will increasingly affect the natural habitat and diet of polar bears (Ursus maritimus). Understanding the energetic needs of polar bears is therefore important. We developed a theoretical method for estimating polar bear food consumption based on using the highly recalcitrant polychlorinated biphenyl (PCB) congener, 2,2',4,4',55-hexaCB (CB153) in bear adipose tissue as an indicator of food intake. By comparing the CB153 tissue concentrations in wild polar bears with estimates from a purposely designed individual-based model, we identified the possible combinations of field metabolic rates (FMR) and CB153 deposition efficiencies in East Greenland polar bears. Our simulations indicate that if 30% of the CB153 consumed by polar bear individuals were deposited into their adipose tissue, the corresponding FMR would be only two times the basal metabolic rate. In contrast, if the modelled CB153 deposition efficiency were 10%, adult polar bears would require six times more energy than that needed to cover basal metabolism. This is considerably higher than what has been assumed for polar bears in previous studies though it is similar to FMRs found in other marine mammals. An implication of this result is that even relatively small reductions in future feeding opportunities could impact the survival of East Greenland polar bears.