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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.
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.
Biodiversity loss is a result of interacting ecological and economic factors, and it must be addressed through an analysis of biodiversity conservation policies. Ecological-economic modelling is a helpful approach to this analysis, but it is also challenging since modellers often have a specific disciplinary background and tend to misrepresent either the ecological or economic aspects. Here, we introduce some of the most important concepts from both disciplines, and since the two modelling cultures also differ between the two disciplines, we present an integrated, consistent guide through all the steps of generic ecological-economic modelling, such as formulation of the research question, development of the conceptual model, model parametrisation and analysis, and interpretation of model results. Although we focus on generic models aimed at a general understanding of causes and remedies for biodiversity loss, the concepts and guidance provided here may also help in the modelling of more specific conservation problems. This guide is aimed at the intersection of three disciplines: ecology, economics and mathematical modelling, and addresses readers who have some knowledge in at least one of these disciplines and want to learn about the others to build and analyse generic ecological-economic models. Compared to textbooks, the guide focuses on the practice of modelling rather than lengthy explanations of theoretical concepts. We attempt to demonstrate that generic ecological-economic modelling does not require magical powers and instead is a manageable exercise.
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.
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