Pernille Thorbek

Ecological models supporting environmental decision making: a strategy for the future

Ecological models are important for environmental decision support because they allow the consequences of alternative policies and management scenarios to be explored. However, current modeling practice is unsatisfactory. A literature review shows that the elements of good modeling practice have long been identified but are widely ignored. The reasons for this might include lack of involvement of decision makers, lack of incentives for modelers to follow good practice, and the use of inconsistent terminologies. As a strategy for the future, we propose a standard format for documenting models and their analyses: transparent and comprehensive ecological modeling (TRACE) documentation. This standard format will disclose all parts of the modeling process to scrutiny and make modeling itself more efficient and coherent.

Ecological models in support of regulatory risk assessments of pesticides: developing a strategy for the future.

Abstract This brief communication reports on the main findings of the LEMTOX workshop, held from 9 to 12 September 2007, at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. The workshop brought together a diverse group of stakeholders from academia, regulatory authorities, contract research organizations, and industry, representing Europe, the United States, and Asia, to discuss the role of ecological modeling in risk assessments of pesticides, particularly under the European regulatory framework. The following questions were addressed: What are the potential benefits of using ecological models in pesticide registration and risk assessment? What obstacles prevent ecological modeling from being used routinely in regulatory submissions? What actions are needed to overcome the identified obstacles? What recommendations should be made to ensure good modeling practice in this context? The workshop focused exclusively on population models, and discussion was focused on those categories of population models that link effects on individuals (e.g., survival, growth, reproduction, behavior) to effects on population dynamics. The workshop participants concluded that the overall benefits of ecological modeling are that it could bring more ecology into ecological risk assessment, and it could provide an excellent tool for exploring the importance of, and interactions among, ecological complexities. However, there are a number of challenges that need to be overcome before such models will receive wide acceptance for pesticide risk assessment, despite having been used extensively in other contexts (e.g., conservation biology). The need for guidance on Good Modeling Practice (on model development, analysis, interpretation, evaluation, documentation, and communication), as well as the need for case studies that can be used to explore the added value of ecological models for risk assessment, were identified as top priorities. Assessing recovery potential of exposed nontarget species and clarifying the ecological relevance of standard laboratory test results are two areas for which ecological modeling may be able to provide considerable benefits.

REVIEW: Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models

Summary 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. 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. 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. 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. 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.

Ecological models and pesticide risk assessment: Current modeling practice

Ecological risk assessments of pesticides usually focus on risk at the level of individuals, and are carried out by comparing exposure and toxicological endpoints. However, in most cases the protection goal is populations rather than individuals. On the population level, effects of pesticides depend not only on exposure and toxicity, but also on factors such as life history characteristics, population structure, timing of application, presence of refuges in time and space, and landscape structure. Ecological models can integrate such factors and have the potential to become important tools for the prediction of population-level effects of exposure to pesticides, thus allowing extrapolations, for example, from laboratory to field. Indeed, a broad range of ecological models have been applied to chemical risk assessment in the scientific literature, but so far such models have only rarely been used to support regulatory risk assessments of pesticides. To better understand the reasons for this situation, the current modeling practice in this field was assessed in the present study. The scientific literature was searched for relevant models and assessed according to nine characteristics: model type, model complexity, toxicity measure, exposure pattern, other factors, taxonomic group, risk assessment endpoint, parameterization, and model evaluation. The present study found that, although most models were of a high scientific standard, many of them would need modification before they are suitable for regulatory risk assessments. The main shortcomings of currently available models in the context of regulatory pesticide risk assessments were identified. When ecological models are applied to regulatory risk assessments, we recommend reviewing these models according to the nine characteristics evaluated here.

BEEHAVE: a systems model of honeybee colony dynamics and foraging to explore multifactorial causes of colony failure

Summary A notable increase in failure of managed European honeybee Apis mellifera L. colonies has been reported in various regions in recent years. Although the underlying causes remain unclear, it is likely that a combination of stressors act together, particularly varroa mites and other pathogens, forage availability and potentially pesticides. It is experimentally challenging to address causality at the colony scale when multiple factors interact. In silico experiments offer a fast and cost‐effective way to begin to address these challenges and inform experiments. However, none of the published bee models combine colony dynamics with foraging patterns and varroa dynamics. We have developed a honeybee model, BEEHAVE, which integrates colony dynamics, population dynamics of the varroa mite, epidemiology of varroa‐transmitted viruses and allows foragers in an agent‐based foraging model to collect food from a representation of a spatially explicit landscape. We describe the model, which is freely available online (www.beehave-model.net). Extensive sensitivity analyses and tests illustrate the models robustness and realism. Simulation experiments with various combinations of stressors demonstrate, in simplified landscape settings, the models potential: predicting colony dynamics and potential losses with and without varroa mites under different foraging conditions and under pesticide application. We also show how mitigation measures can be tested. Synthesis and applications. 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.

CREAM: a European project on mechanistic effect models for ecological risk assessment of chemicals.

Current risk assessments are mainly based on ecotoxicological endpoints at the level of individual organisms, but according to the EU directives, the protection goal aims at achieving sustainable populations (European Commission 2002a, b; Forbes et al. 2009; Preuss et al. 2009a; Thorbek et al. 2009). Population-level effects depend not only on exposure and toxicity, but also on important ecological factors that are impossible to fully address empirically. At present, a number of testing approaches exist that provide endpoints on the community and the population level, respectively (nontarget arthropod and earthworm field tests, aquatic and terrestrial model ecosystem tests). However, not all fields and regulatory questions can be covered by these approaches. To fill these gaps and to enhance the scientific quality of ecological risk assessments, we suggest implementing mechanistic effect models (MEMs), as these also

A METHOD TO PREDICT AND UNDERSTAND FISH SURVIVAL UNDER DYNAMIC CHEMICAL STRESS USING STANDARD ECOTOXICITY DATA

The authors present a method to predict fish survival under exposure to fluctuating concentrations and repeated pulses of a chemical stressor. The method is based on toxicokinetic-toxicodynamic modeling using the general unified threshold model of survival (GUTS) and calibrated using raw data from standard fish acute toxicity tests. The model was validated by predicting fry survival in a fish early life stage test. Application of the model was demonstrated by using Forum for Co-ordination of Pesticide Fate Models and Their Use surface water (FOCUS-SW) exposure patterns as model input and predicting the survival of fish over 485 d. Exposure patterns were also multiplied by factors of five and 10 to achieve higher exposure concentrations for fish survival predictions. Furthermore, the authors quantified how far the exposure profiles were below the onset of mortality by finding the corresponding exposure multiplication factor for each scenario. The authors calculated organism recovery times as additional characteristic of toxicity as well as number of peaks, interval length between peaks, and mean duration as additional characteristics of the exposure pattern. The authors also calculated which of the exposure patterns had the smallest and largest inherent potential toxicity. Sensitivity of the model to parameter changes depends on the exposure pattern and differs between GUTS individual tolerance and GUTS stochastic death. Possible uses of the additional information gained from modeling to inform risk assessment are discussed. Environ. Toxicol. Chem. 2013;32:954–965.

Density-Dependent Processes in the Life History of Fishes: Evidence from Laboratory Populations of Zebrafish Danio rerio

Population regulation is fundamental to the long-term persistence of populations and their responses to harvesting, habitat modification, and exposure to toxic chemicals. In fish and other organisms with complex life histories, regulation may involve density dependence in different life-stages and vital rates. We studied density dependence in body growth and mortality through the life-cycle of laboratory populations of zebrafish Danio rerio. When feed input was held constant at population-level (leading to resource limitation), body growth was strongly density-dependent in the late juvenile and adult phases of the life-cycle. Density dependence in mortality was strong during the early juvenile phase but declined thereafter and virtually ceased prior to maturation. Provision of feed in proportion to individual requirements (easing resource limitation) removed density dependence in growth and substantially reduced density dependence in mortality, thus indicating that ‘bottom-up’ effects act on growth as well as mortality, but most strongly on growth. Both growth and mortality played an important role in population regulation, with density-dependent growth having the greater impact on population biomass while mortality had the greatest impact on numbers. We demonstrate a clear ontogenic pattern of change in density-dependent processes within populations of a very small (maximum length 5 mm) fish, maintained in constant homogeneous laboratory conditions. The patterns are consistent with those distilled from studies on wild fish populations, indicating the presence of broad ontogenic patterns in density-dependent processes that are invariant to maximum body size and hold in homogeneous laboratory, as well as complex natural environments.

VALIDATION OF A SIMPLE METHOD FOR MONITORING AERIAL ACTIVITY OF SPIDERS

Abstract Many species of spider disperse by ballooning (aerial dispersal), and indices of aerial activity are required in studies of population dynamics and biological control in field crops where spider immigrants are needed for pest suppression. Current methods (e.g., suction traps, sticky traps, deposition traps) of monitoring aerial activity are very labor-intensive, expensive, or require a power supply. We tested Ballooning Index (BI), an alternative, simple method utilizing inexpensive equipment. This method involved the monitoring of spiders climbing an array of 30 cm tall wooden sticks placed vertically in short turf. During a two-year study in arable land in the UK, the incidence of spiders (mainly Linyphiidae) on sticks was correlated with the numbers caught at 1.4 m and 12.2 m above ground in suction traps. Climbing activity on sticks was greater during the morning than in the afternoon, and this activity started progressively earlier in summer than in winter. There was no seasonal change in the proportion of spiders caught at the two heights in suction traps. The pattern of catches (on sticks and in suction traps) suggested strongly that the majority of ballooning spiders dispersed by a number of short flights, rather than by a single long flight, and that segregation of immigrants and emigrants is not possible by any current method. The BI method appears to be, however, a simple and reliable technique for monitoring the overall aerial activity of ballooning spiders.

Persistence of aquatic insects across managed landscapes: effects of landscape permeability on re-colonization and population recovery

Human practices in managed landscapes may often adversely affect aquatic biota, such as aquatic insects. Dispersal is often the limiting factor for successful re-colonization and recovery of stressed habitats. Therefore, in this study, we evaluated the effects of landscape permeability, assuming a combination of riparian vegetation (edge permeability) and other vegetation (landscape matrix permeability), and distance between waterbodies on the colonization and recovery potential of weakly flying insects. For this purpose, we developed two models, a movement and a population model of the non-biting midge, Chironomus riparius, an aquatic insect with weak flying abilities. With the movement model we predicted the outcome of dispersal in a landscape with several linear water bodies (ditches) under different assumptions regarding landscape-dependent movement. Output from the movement model constituted the probabilities of encountering another ditch and of staying in the natal ditch or perishing in the landscape matrix, and was used in the second model. With this individual-based model of midge populations, we assessed the implications for population persistence and for recovery potential after an extreme stress event. We showed that a combination of landscape attributes from the movement model determines the fate of dispersing individuals and, once extrapolated to the population level, has a big impact on the persistence and recovery of populations. Population persistence benefited from low edge permeability as it reduced the dispersal mortality which was the main factor determining population persistence and viability. However, population recovery benefited from higher edge permeability, but this was conditional on the low effective distance that ensured fewer losses in the landscape matrix. We discuss these findings with respect to possible landscape management scenarios.