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The acceptance and usefulness of simulation models are often limited by the efficiency, transparency, reproducibility, and reliability of the modelling process. We address these issues by suggesting that modellers (1) "trace" the iterative modelling process by keeping a modelling notebook corresponding to the laboratory notebooks used by empirical researchers, (2) use a standardized notebook structure and terminology based on the existing TRACE documentation framework, and (3) use their notebooks to compile TRACE documents that supplement publications and reports. These practices have benefits for model developers, users, and stakeholders: improved and efficient model design, analysis, testing, and application; increased model acceptance and reuse; and replicability and reproducibility of the model and the simulation experiments. Using TRACE terminology and structure in modelling notebooks facilitates production of TRACE documents. We explain the

Quantifying and understanding movement is critical for a wide range of questions in basic and applied ecology. Movement ecology is also fostered by technological advances that allow automated tracking for a wide range of animal species. However, for aquatic macroinvertebrates, such detailed methods do not yet exist. We developed a video tracking method for two different species of benthic macroinvertebrates, the crawling isopod Asellus aquaticus and the swimming fresh water amphipod Gammarus pulex. We tested the effects of different light sources and marking techniques on their movement behavior to establish the possibilities and limitations of the experimental protocol and to ensure that the basic handling of test specimens would not bias conclusions drawn from movement path analyses. To demonstrate the versatility of our method, we studied the influence of varying population densities on different movement parameters related to resting behavior, directionality, and step lengths. We found that our method allows studying species with different modes of dispersal and under different conditions. For example, we found that gammarids spend more time moving at higher population densities, while asellids rest more under similar conditions. At the same time, in response to higher densities, gammarids mostly decreased average step lengths, whereas asellids did not. Gammarids, however, were also more sensitive to general handling and marking than asellids. Our protocol for marking and video tracking can be easily adopted for other species of aquatic macroinvertebrates or testing conditions, for example, presence or absence of food sources, shelter, or predator cues. Nevertheless, limitations with regard to the marking protocol, material, and a species’ physical build need to be considered and tested before a wider application, particularly for swimming species. Data obtained with this approach can deepen the understanding of population dynamics on larger spatial scales and of the effects of different management strategies on a species’ dispersal potential.

Behaviour links physiological function with ecological processes and can be very sensitive towards environmental stimuli and chemical exposure. As such, behavioural indicators of toxicity are well suited for assessing impacts of pesticides at sublethal concentrations found in the environment. Recent developments in video-tracking technologies offer the possibility of quantifying behavioural patterns, particularly locomotion, which in general has not been studied and understood very well for aquatic macroinvertebrates to date. In this study, we aim to determine the potential effects of exposure to two neurotoxic pesticides with different modes of action at different concentrations (chlorpyrifos and imidacloprid) on the locomotion behaviour of the water louse Asellus aquaticus. We compare the effects of the different exposure regimes on the behaviour of Asellus with the effects that the presence of food and shelter exhibit to estimate the ecological relevance of behavioural changes. We found that sublethal pesticide exposure reduced dispersal distances compared to controls, whereby exposure to chlorpyrifos affected not only animal activity but also step lengths while imidacloprid only slightly affected step lengths. The presence of natural cues such as food or shelter induced only minor changes in behaviour, which hardly translated to changes in dispersal potential. These findings illustrate that behaviour can serve as a sensitive endpoint in toxicity assessments. However, under natural conditions, depending on the exposure concentration, the actual impacts might be outweighed by environmental conditions that an organism is subjected to. It is, therefore, of importance that the assessment of toxicity on behaviour is done under relevant environmental conditions.Electronic supplementary materialThe online version of this article (doi:10.1007/s10646-016-1686-y) contains supplementary material, which is available to authorized users.

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 program; (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. MERGING VALIDATION AND EVALUATION OF ECOLOGICAL MODELS