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Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.

Global change encompasses many co-occurring anthropogenic drivers, which can act synergistically or antagonistically on ecological systems. Predicting how different global change drivers simultaneously contribute to observed biodiversity change is a key challenge for ecology and conservation. However, we lack the mechanistic understanding of how multiple global change drivers influence the vital rates of multiple interacting species. We propose that reaction norms, the relationships between a driver and vital rates like growth, mortality, and consumption, provide insights to the underlying mechanisms of community responses to multiple drivers. Understanding how multiple drivers interact to affect demographic rates using a reaction-norm perspective can improve our ability to make predictions of interactions at higher levels of organization-that is, community and food web. Building on the framework of consumer-resource interactions and widely studied thermal performance curves, we illustrate how joint driver impacts can be scaled up from the population to the community level. A simple proof-of-concept model demonstrates how reaction norms of vital rates predict the prevalence of driver interactions at the community level. A literature search suggests that our proposed approach is not yet used in multiple driver research. We outline how realistic response surfaces (i.e., multidimensional reaction norms) can be inferred by parametric and nonparametric approaches. Response surfaces have the potential to strengthen our understanding of how multiple drivers affect communities as well as improve our ability to predict when interactive effects emerge, two of the major challenges of ecology today.

Games as a didactic tool (e. g., puzzles) are gaining recognition in environmental education to promote skill development, but also to develop a specific understanding of the natural world. However, a children’s puzzle containing representations of nature may unwillingly lead to “misconceptions” of biodiversity themes and processes, and an over-simplification of the relationship between people and nature. To solve this problem, positive connotations of biodiversity may prompt a conceptual change to a more nuanced, multifaceted conception of biodiversity.

Agriculture is likely to expand poleward with climate change, encouraging deforestation for agriculture in subarctic regions, which alters soil physical, chemical and biological properties and potentially affects microbial metabolic efficiency. Deciphering how and by which mechanisms land-use change affects microbial carbon use efficiency (CUE) will enable the development of mitigation strategies to alleviate C losses. We assessed CUE using 18O-labelled water in a paired-plot approach on soils collected from 19 farms across the subarctic region of Yukon, Canada, comprising 14 pairs of forest-to-grassland conversion and 15 pairs of forest-to-cropland conversion. Microbial CUE significantly increased following conversion to grassland and cropland. Land-use conversion resulted in a lower estimated abundance of fungi, while the archaeal abundance increased. Interestingly, structural equation modelling revealed that increases in CUE were mediated by a rise in soil pH and a decrease in soil C:N ratio rather than by shifts in microbial community composition, i.e. the ratio of fungi, bacteria and archaea. Our findings indicate a direct control of abiotic factors on microbial CUE via improved nutrient availability and facilitated conditions for microbial growth. Overall, this implies that to a certain extent CUE can be managed to achieve a more efficient build-up of stabilised soil organic C (SOC), as reflected in increased mineral-associated organic C under agricultural land use. These insights may also help constrain SOC models that generally struggle to predict the effects of deforestation, something that is likely to take place more frequently in the subarctic.

The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across system complexity, we monitored a microbial system exposed to either constant or fluctuating temperatures in a five months long laboratory experiment. We tested how forecasting of species abundances depends on number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more a species interacted, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the embedding of the species within the system and on overall system complexity could improve species forecasting and monitoring.

The temperature‐size rule (TSR) describes the inverse relationship between organism size and environmental temperature in uni‐ and multicellular species. Despite the TSR being widespread, the mechanisms for shrinking body size with warming remain elusive. Here, we experimentally test three hypotheses (differential development and growth [DDG], maintain aerobic scope and regulate oxygen supply [MASROS] and the supply–demand [SD] hypothesis) potentially explaining the TSR using the aquatic protist Colpidium striatum in three gradually changing and one constant temperature environment crossed with three different nutrient levels. We find that the constant and slowly warming environments show similar responses in terms of population dynamics, whereas populations with linear and fast warming quickly decline and show a stronger temperature‐size response. Our analyses suggest that acclimation may have played a role in observing these differences among treatments. The SD hypothesis is most parsimonious with the data, however, neither the DDG nor the MASROS hypothesis can be firmly dismissed. We conclude that the TSR is driven by multiple ecological and acclimatory responses, hence multicausal.

Dispersal is a central biological process tightly integrated into life-histories, morphology, physiology and behaviour. Such associations, or syndromes, are anticipated to impact the eco-evolutionary dynamics of spatially structured populations, and cascade into ecosystem processes. As for dispersal on its own, these syndromes are likely neither fixed nor random, but conditional on the experienced environment. We experimentally studied how dispersal propensity varies with individuals' phenotype and local environmental harshness using 15 species ranging from protists to vertebrates. We reveal a general phenotypic dispersal syndrome across studied species, with dispersers being larger, more active and having a marked locomotion-oriented morphology and a strengthening of the link between dispersal and some phenotypic traits with environmental harshness. Our proof-of-concept metacommunity model further reveals cascading effects of context-dependent syndromes on the local and regional organisation of functional diversity. Our study opens new avenues to advance our understanding of the functioning of spatially structured populations, communities and ecosystems.

Warming and nutrient enrichment are major environmental factors shaping ecological dynamics. However, cross-scale investigation of their combined effects by linking theory and experiments is lacking. We collected data from aquatic microbial ecosystems investigating the interactive effects of warming (constant and rising temperatures) and enrichment across levels of organisation and contrasted them with community models based on metabolic theory. We found high agreement between our observations and theoretical predictions: we observed in many cases the predicted antagonistic effects of high temperature and high enrichment across levels of organisation. Temporal stability of total biomass decreased with warming but did not differ across enrichment levels. Constant and rising temperature treatments with identical mean temperature did not show qualitative differences. Overall, we conclude that model and empirical results are in broad agreement due to robustness of the effects of temperature and enrichment, that the mitigating effects of temperature on effects of enrichment may be common, and that models based on metabolic theory provide qualitatively robust predictions of the combined ecological effects of enrichment and temperature.

International audience; Phenotypic plasticity is increasingly recognized as a key element of eco-evolutionary dynamics, but it remains challenging to assess because of its multidimensional nature. Indeed, organisms live in complex environments where numerous factors can impact the phenotypic expression of traits (inter-environment axis), possess multiple traits that can influence each other's expression (inter-trait axis) and differ in their genetic background (inter-genotype axis), which can not only impact the traits' values but also their plasticity. We addressed six questions related to phenotypic plasticity: (a) do different environmental gradients show similar effects on a given trait? (b) Are the effects of two environmental gradients on a trait additive? (c) Do different traits show similar plastic response to a given environmental gradient? (d) Do the (co)variances between traits vary across environmental gradients? (e) Do genotypes differ in their plastic response to a given environmental gradient? (f) Are some genotypes more plastic than others across all traits? We designed a microcosm experiment using the protist Tetrahymena thermophila aimed at encompassing all these aspects of phenotypic plasticity. We exposed 15 distinct genotypes to 25 combinations of temperature and nutrient availability and assessed the plasticity of five phenotypic traits. Our results show strong differences in the plastic response depending on the environmental gradient, not only regarding the shape of the reaction norm of the different traits tested, but also in the overall plasticity of the organisms. We did not find any covariance between traits that was consistent across all environments. Overall, our results suggest independent impacts of the environmental dimension considered on the observed plastic response. These results underline potential difficulties in generalizing findings about plasticity to all environments and all traits.

Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research.

International audience; Environmental change can alter species’ abundances within communities consistently; for example, increasing all abundances by the same percentage, or more idiosyncratically. Here, we show how comparing effects of temperature on species grown in isolation and when grown together helps our understanding of how ecological communities more generally respond to environmental change. In particular, we find that the shape of the feasibility domain (the parameter space of carrying capacities compatible with positive species’ abundances) helps to explain the composition of experimental microbial communities under changing environmental conditions. First, we introduce a measure to quantify the asymmetry of a community’s feasibility domain using the column vectors of the corresponding interaction matrix. These column vectors describe the effects each species has on all other species in the community (hereafter referred to as species’ multidimensional effects). We show that as the asymmetry of the feasibility domain increases the relationship between species’ abundance when grown together and when grown in isolation weakens. We then show that microbial communities experiencing different temperature environments exhibit patterns consistent with this theory. Specifically, communities at warmer temperatures show relatively more asymmetry; thus, the idiosyncrasy of responses is higher compared with that in communities at cooler temperatures. These results suggest that while species’ interactions are typically defined at the pairwise level, multispecies dynamics can be better understood by focusing on the effects of these interactions at the community level.

International audience; Ecology and evolution unfold in spatially structured communities, where dispersal links dynamics across scales. Because dispersal is multicausal, identifying general drivers remains challenging. In a coordinated distributed experiment spanning organisms from protozoa to vertebrates, we tested whether two fundamental determinants of local dynamics, top-down and bottom-up control, generally explain active dispersal. We show that both factors consistently increased emigration rates and use metacommunity modelling to highlight consequences on local and regional dynamics.

The pervasive influence of human induced global environmental change affects biodiversity across the globe, and there is great uncertainty as to how the biosphere will react on short and longer time scales. To adapt to what the future holds and to manage the impacts of global change, scientists need to predict theexpected effects with some confidence and communicate these predictions to policy makers. However, recent reviews found that we currently lack a clear understanding of how predictable ecology is, with views seeing it as mostly unpredictable to potentially predictable, at least over short time frames. However, in applied, ecology-related fields predictions are more commonly formulated and reported, as well as evaluated in hindsight, potentially allowing one to define baselines of predictive proficiency in these fields. We searched the literature for representative case studies in these fields and collectedinformation about modeling approaches, target variables of prediction, predictive proficiency achieved, as well as the availability of data to parameterize predictive models. We find that some fields such as epidemiology achieve high predictive proficiency, but even in the more predictive fields proficiency isevaluated in different ways. Both phenomenological and mechanistic approaches are used in most fields, but differences are often small, with no clear superiority of one approach over the other. Data availability is limiting in most fields, with long-term studies being rare and detailed data for parameterizing mechanistic models being in short supply. We suggest that ecologists adopt a more rigorous approach to report and assess predictive proficiency, and embrace the challenges of real world decision making to strengthen the practice of prediction in ecology.

Understanding how and why individual movement translates into dispersal between populations is a long-term goal in ecology. Movement is broadly defined as “any change in the spatial location of an individual”, whereas dispersal is more narrowly defined as a movement that may lead to gene flow. Because the former may create the condition for the latter, behavioural decisions that lead to dispersal may be detectable in underlying movement behaviour. In addition, dispersing individuals also have specific sets of morphological and behavioural traits that help them coping with the costs of movement and dispersal, and traits that mitigate costs should be under selection and evolve if they have a genetic basis. Here we experimentally study the relationships between movement behaviour, morphology and dispersal across 44 genotypes of the actively dispersing unicellular, aquatic model organism Tetrahymena thermophila. We used two-patch populations to quantify individual movement trajectories, as well as activity, morphology and dispersal rate. First, we studied variation in movement behaviour among and within genotypes (i.e. between dispersers and residents) and tested whether this variation can be explained by morphology. Then, we address how much the dispersal rate is driven by differences in the underlying movement behaviour. Genotypes expressed different movements in terms of speed and path tortuosity. We also detected marked movement differences between resident and dispersing individuals, mediated by the genotype. Movement variation was partly explained by morphological properties such as cell size and shape, with larger cells consistently showing higher movement speed and lower tortuosity. Genetic differences in activity and diffusion rates were positively related to the observed dispersal and jointly explained 45% of the variation in dispersal rate. Our study shows that a detailed understanding of the interplay between morphology, movement and dispersal may have potential to improve dispersal predictions over broader spatio-temporal scales.

Environmental change can alter species' abundances within communities consistently; for example, increasing all abundances by the same percentage, or more idiosyncratically. Here, we show how comparing effects of temperature on species grown in isolation and when grown together helps our understanding of how ecological communities more generally respond to environmental change. In particular, we find that the shape of the feasibility domain (the parameter space of carrying capacities compatible with positive species' abundances) helps to explain the composition of experimental microbial communities under changing environmental conditions. First, we introduce a measure to quantify the asymmetry of a community's feasibility domain using the column vectors of the corresponding interaction matrix. These column vectors describe the effects each species has on all other species in the community (hereafter referred to as species' multidimensional effects). We show that as the asymmetry of the feasibility domain increases the relationship between species' abundance when grown together and when grown in isolation weakens. We then show that microbial communities experiencing different temperature environments exhibit patterns consistent with this theory. Specifically, communities at warmer temperatures show relatively more asymmetry; thus, the idiosyncrasy of responses is higher compared with that in communities at cooler temperatures. These results suggest that while species' interactions are typically defined at the pairwise level, multispecies dynamics can be better understood by focusing on the effects of these interactions at the community level.

Losses and gains in species diversity affect ecological stability1–7 and the sustainability of ecosystem functions and services8–13. Experiments and models have revealed positive, negative and no effects of diversity on individual components of stability, such as temporal variability, resistance and resilience2,3,6,11,12,14. How these stability components covary remains poorly understood15. Similarly, the effects of diversity on overall ecosystem stability16, which is conceptually akin to ecosystem multifunctionality17,18, remain unknown. Here we studied communities of aquatic ciliates to understand how temporal variability, resistance and overall ecosystem stability responded to diversity (that is, species richness) in a large experiment involving 690 micro-ecosystems sampled 19 times over 40 days, resulting in 12,939 samplings. Species richness increased temporal stability but decreased resistance to warming. Thus, two stability components covaried negatively along the diversity gradient. Previous biodiversity manipulation studies rarely reported such negative covariation despite general predictions of the negative effects of diversity on individual stability components3. Integrating our findings with the ecosystem multifunctionality concept revealed hump- and U-shaped effects of diversity on overall ecosystem stability. That is, biodiversity can increase overall ecosystem stability when biodiversity is low, and decrease it when biodiversity is high, or the opposite with a U-shaped relationship. The effects of diversity on ecosystem multifunctionality would also be hump- or U-shaped if diversity had positive effects on some functions and negative effects on others. Linking the ecosystem multifunctionality concept and ecosystem stability can transform the perceived effects of diversity on ecological stability and may help to translate this science into policy-relevant information. Species richness was found to increase temporal stability but decrease resistance to warming in an experiment involving 690 micro-ecosystems consisting of 1 to 6 species of bacterivorous ciliates that were sampled over 40 days.

Understanding how and why individual movement translates into dispersal between populations is a long-term goal in ecology. Movement is broadly defined as ‘any change in the spatial location of an individual’, whereas dispersal is more narrowly defined as a movement that may lead to gene flow. Because the former may create the condition for the latter, behavioural decisions that lead to dispersal may be detectable in underlying movement behaviour. In addition, dispersing individuals also have specific sets of morphological and behavioural traits that help them coping with the costs of movement and dispersal, and traits that mitigate costs should be under selection and evolve if they have a genetic basis. Here, we experimentally study the relationships between movement behaviour, morphology and dispersal across 44 genotypes of the actively dispersing unicellular, aquatic model organism Tetrahymena thermophila. We used two-patch populations to quantify individual movement trajectories, as well as activity, morphology and dispersal rate. First, we studied variation in movement behaviour among and within genotypes (i.e. between dispersers and residents) and tested whether this variation can be explained by morphology. Then, we addressed how much the dispersal rate is driven by differences in the underlying movement behaviour. Genotypes revealed clear differences in terms of movement speed and linearity. We also detected marked movement differences between resident and dispersing individuals, mediated by the genotype. Movement variation was partly explained by morphological properties such as cell size and shape, with larger cells consistently showing higher movement speed and higher linearity. Genetic differences in activity and movement were positively related to the observed dispersal and jointly explained 47% of the variation in dispersal rate. Our study shows that a detailed understanding of the interplay between morphology, movement and dispersal may have potential to improve dispersal predictions over broader spatio-temporal scales.

Successfully predicting the future states of systems that are complex, stochastic, and potentially chaotic is a major challenge. Model forecasting error (FE) is the usual measure of success; however model predictions provide no insights into the potential for improvement. In short, the realized predictability of a specific model is uninformative about whether the system is inherently predictable or whether the chosen model is a poor match for the system and our observations thereof. Ideally, model proficiency would be judged with respect to the systems’ intrinsic predictability, the highest achievable predictability given the degree to which system dynamics are the result of deterministic vs. stochastic processes. Intrinsic predictability may be quantified with permutation entropy (PE), a model‐free, information‐theoretic measure of the complexity of a time series. By means of simulations, we show that a correlation exists between estimated PE and FE and show how stochasticity, process error, and chaotic dynamics affect the relationship. This relationship is verified for a data set of 461 empirical ecological time series. We show how deviations from the expected PE–FE relationship are related to covariates of data quality and the nonlinearity of ecological dynamics. These results demonstrate a theoretically grounded basis for a model‐free evaluation of a system's intrinsic predictability. Identifying the gap between the intrinsic and realized predictability of time series will enable researchers to understand whether forecasting proficiency is limited by the quality and quantity of their data or the ability of the chosen forecasting model to explain the data. Intrinsic predictability also provides a model‐free baseline of forecasting proficiency against which modeling efforts can be evaluated., Ecological Monographs, 89 (2), ISSN:0012-9615, ISSN:1557-7015, ISSN:1741-7015

Organisms rarely experience a homogeneous environment. Rather, ecological and evolutionary dynamics unfold in spatially structured and fragmented landscapes, with dispersal as the central process linking these dynamics across spatial scales. Because dispersal is a multi-causal and highly plastic life-history trait, finding general drivers that are of importance across species is challenging but highly relevant for ecological forecasting.We here tested whether two fundamental ecological forces and main determinants of local population dynamics, top-down and bottom-up control, generally explain dispersal in spatially structured communities. In a coordinated distributed experiment spanning a wide range of actively dispersing organisms, from protozoa to vertebrates, we show that bottom-up control, that is resource limitation, consistently increased dispersal. While top-down control, that is predation risk, was an equally important dispersal driver as bottom-up control, its effect depended on prey and predator space use and whether dispersal occurred on land, in water or in the air: species that routinely use more space than their predators showed increased dispersal in response to predation, specifically in aquatic environments. After establishing these general causes of dispersal, we used a metacommunity model to show that bottom-up and top-down control of dispersal has important consequences for local population fluctuations as well as cascading effects on regional metacommunity dynamics. Context-dependent dispersal reduced local population fluctuations and desynchronized dynamics between communities, two effects that increase population and community stability.Our study provides unprecedented insights into the generality of the positive resource dependency of dispersal as well as a robust experimental test of current theory predicting that predator-induced dispersal is modulated by prey and predator space use. Our experimental and theoretical work highlights the critical importance of the multi-causal nature of dispersal as well as its cascading effects on regional community dynamics, which are specifically relevant to ecological forecasting.

Summary1. Technological advances have greatly simplified to take and analyze digital images and videos, and ecologists increasingly use these techniques for trait, behavioral and taxonomic analyses. The development of techniques to automate biological measurements from the environment opens up new possibilities to infer species numbers, observe presence/absence patterns and recognize individuals based on audio-visual information.2. Streams of quantitative data, such as temporal species abundances, are processed by machine learning (ML) algorithms into meaningful information. Machine learning approaches learn to distinguish classes (e.g., species) from observed quantitative features (phenotypes), and in-turn predict the distinguished classes in subsequent observations. However, in biological systems, the environment changes, often driving phenotypic changes in behaviour and morphology.3. Here we describe a framework for classifying species under dynamic biotic and abiotic conditions using a novel sliding window approach. We train a random forest classifier on subsets of the data, covering restricted temporal, biotic and abiotic ranges (i.e. windows). We test our approach by applying the classification framework to experimental microbial communities where results were validated against manual classification. Individuals from one to six ciliate species were monitored over hundreds of generations in dozens of different species combinations and over a temperature gradient. We describe the steps of our classification pipeline and systematically explore the effects of the abiotic and biotic environments as well as temporal effects on classification success.4. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, because phenotypic differences driven by environmental change could be captured in the learning algorithm. Importantly, automatic classification showed comparable success compared to manual identifications.5. Our framework allows for reliable classification even in dynamic environmental contexts, and may help to improve long-term monitoring of species from environmental samples. It therefore has application in disciplines with automatic enumeration and phenotyping of organisms such as eco-toxicology, ecology and evolutionary ecology, and broad-scale environmental monitoring.

Global environmental change has negative impacts on ecological systems, impacting the stable provision of functions, goods, and services. Whereas effects of individual environmental changes (e.g. temperature change or change in resource availability) are reasonably well understood, we lack information about if and how multiple changes interact. We examined interactions among four types of environmental disturbance (temperature, nutrient ratio, carbon enrichment, and light) in a fully factorial design using a microbial aquatic ecosystem and observed responses of dissolved oxygen saturation at three temporal scales (resistance, resilience, and return time). We tested whether multiple disturbances combine in a dominant, additive, or interactive fashion, and compared the predictability of dissolved oxygen across scales. Carbon enrichment and shading reduced oxygen concentration in the short term (i.e. resistance); although no other effects or interactions were statistically significant, resistance decreased as the number of disturbances increased. In the medium term, only enrichment accelerated recovery, but none of the other effects (including interactions) were significant. In the long term, enrichment and shading lengthened return times, and we found significant two-way synergistic interactions between disturbances. The best performing model (dominant, additive, or interactive) depended on the temporal scale of response. In the short term (i.e. for resistance), the dominance model predicted resistance of dissolved oxygen best, due to a large effect of carbon enrichment, whereas none of the models could predict the medium term (i.e. resilience). The long-term response was best predicted by models including interactions among disturbances. Our results indicate the importance of accounting for the temporal scale of responses when researching the effects of environmental disturbances on ecosystems.

Summary Individual behaviour, that is, the reaction of an organism to internal state, conspecifics and individuals of other species as well as the environment, is a crucial building block of their ecology. Modern tracking techniques produce high-frequency observations of spatial positions of animals and accompanying speed and tortuosity measurements. However, inferring behavioural modes from movement trajectories remains a challenge. Changes in behavioural modes occur at different temporal and spatial scales and may take two forms: abrupt, representing distinct change points; or continuous, representing smooth transitions between movement modes. The multi-scale nature of these behavioural changes necessitates development of methods that can pinpoint behavioural states across spatial and temporal scales. We propose a novel segmentation method based on the discrete wavelet transform (DWT), where the movement signal is decomposed into low-frequency approximation and high-frequency detail sub-bands to screen for behavioural changes at multiple scales. Approximation sub-bands characterizes broad changes by taking the continuous variations between behavioural modes into account, whereas detail sub-bands are employed to detect abrupt, finer scale change points. We tested the ability of our method to identify behavioural modes in simulated trajectories by comparing it to three state-of-the-art methods from the literature. We further validated the method using an annotated dataset of turkey vultures (Cathartes aura) relating extracted segments to the expert knowledge of migratory vs. non-migratory patterns. Our results show that the proposed DWT segmentation is more versatile than other segmentation methods, as it can be applied to different movement parameters, performs better or equally well on the simulated data, and correctly identifies behavioural modes identified by the experts. It is hence a valuable addition to the toolbox of land managers and conservation practitioners to understand the behavioural patterns expressed by animals in natural and human-dominated landscapes.

A high level of plant and insect diversity, and more specifically high butterfly diversity characterizes the Mediterranean Basin. However, alarming negative trends have been reported for butterfly populations in that region emphasizing the urgent need to better understand the drivers of their population declines. Habitat specialists of grasslands are strongly affected, mainly by land use change and climate change. Thorough assessments of habitat requirements and dispersal abilities are crucial to establish appropriate conservation measures to counter these threats. Here, we investigate the ecological requirements and dispersal ability of Euphydryas desfontainii, one of Portugal's rarest butterflies, to develop targeted conservation strategies. The assessment of habitat requirements showed differences between occupied and unoccupied patches in terms of host plant abundance and area. Mark-release-recapture data were used to model demographic parameters: survival rates decreased linearly over the flight period and recruitment followed a parabolic curve with separate peaks for males and females. The movement data were fitted to an inverse power function and used to predict the probability of long-distance dispersal. The obtained probabilities were compared to related checkerspot butterflies and interpreted regarding the structural connectivity of the investigated habitat network. We suggest focusing on the preservation of remaining habitat patches, whilst monitoring and safeguarding that their vegetation structure does provide sufficiently diversified microclimates in order to best conserve E. desfontainii populations. © 2014 Springer International Publishing Switzerland.

1. Experimental laboratory systems (ELS) are widely applied research tools to test theoretical predictions in ecology and evolution. Combining ELS with automated image analysis could significantly boost information acquisition due to the ease at which abundance and morphological data is collected. Despite the advantages of image analysis, the technology has not been fully adopted yet, presumably due to the difficulties of technical implementation. 2. The tools needed to integrate image analysis in ELS are nowadays readily available: digital camera equipment is purchased at limited costs and free software solutions which allow sophisticated image processing and analysis exist. Here, we give a concise description how to integrate these pieces into a largely automated image analysis workflow. We provide researchers with necessary background information on the principles of image analysis,explaining how to standardize image acquisition and how to validate the results to reduce bias. 3. Three cross-platform and open-source software solutions for image analysis are compared: ImageJ, the EB-Image package in R, and Python with the SciPy/scikit image libraries. The relative strengths and limitations of each solution are compared and discussed. In addition, a set of test images and three scripts are provided in the Online Supplementary Material to illustrate the use of image analysis and help biologists to implement image analysis in their own systems. 4. To demonstrate the reliability and versatility of a validated image analysis workflow, we introduce our own Tetrahymena thermophila ELS. Then, examples from evolutionary ecology are provided showing the advantages of image analysis to study different ecological questions, aiming at both the population and individual level. 5. Experimental laboratory systems that integrate the advantages of image analysis extend their application and versatility compared with regular ELS. Such improvements are necessary to understand complex processes such as eco-evolutionary feedbacks, community dynamics and individual behaviour in ELS.

Corresponding to theory, the persistence of metapopulations in fragmented landscapes depends on the area of suitable habitat patches and their degree of isolation, mediating the individual exchange between habitats. More recently, habitat quality has been highlighted as being equally important. We therefore assess the role of habitat area, isolation and quality for the occupancy of larval stages of the regionally threatened butterfly Euphydryas desfontainii occurring in grassland habitats comprising the host plant Dipsascus comosus. We put a special focus on habitat quality which was determined on two spatial scales: the landscape (among patches) and the within-patch level. On the landscape level, occupancy of caterpillars was determined by a presence-absence analysis at 28 host plant patches. On the within-patch level, oviposition site selection was studied by comparing 159 host plants with egg clutches to a random sample of 253 unoccupied host plants within six habitat patches. The occupancy of caterpillars and presence of egg clutches on host plants was then related to several predictors such as patch size and isolation on the landscape level and host plant characteristics and immediate surroundings on the within patch level. On the landscape level, only host plant abundance was related to the presence of caterpillars, while size and isolation did not differ between occupied and unoccupied patches. However, the weak discrimination of larval stages among patches changed on the within-patch level: here, several microclimatic predictors such as sunshine hours and topography, host plant morphology and phenology as well as further potential host plants in the immediate surroundings of the plant chosen for oviposition strongly determined the presence of egg clutches. We strongly suggest promoting the presence of the host plant in topographically and structurally rich habitat patches to offer potential for microclimatic compensation for a species considered threatened by climate change.

Forecasts of how ecological systems respond to environmental change are increasingly important. Sufficiently inaccurate forecasts will be of little use, however. For example, weather forecasts are for about one week into the future; after that they are too unreliable to be useful (i.e., the forecast horizon is about one week). There is a general absence of knowledge about how far into the future (or other dimensions, e.g., space, temperature, phylogenetic distance) useful ecological forecasts can be made, in part due to lack of appreciation of the value of ecological forecast horizons. The ecological forecast horizon is the distance into the future (or other dimension) for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g., parameter uncertainty, environmental and demographic stochasticity, evolution), level of ecological organisation (e.g., population or community), organismal properties (e.g., body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. We propose that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability, and for motivating and guiding agenda setting for ecological forecasting research and development.

Microbes are critical components of ecosystems and provide vital services (e.g., photosynthesis, decomposition, nutrient recycling). From the diverse roles microbes play in natural ecosystems, high levels of functional diversity result. Quantifying this diversity is challenging, because it is weakly associated with morphological differentiation. In addition, the small size of microbes hinders morphological and behavioral measurements at the individual level, as well as interactions between individuals. Advances in microbial community genetics and genomics, flow cytometry and digital analysis of still images are promising approaches. They miss out, however, on a very important aspect of populations and communities: the behavior of individuals. Video analysis complements these methods by providing in addition to abundance and trait measurements, detailed behavioral information, capturing dynamic processes such as movement, and hence has the potential to describe the interactions between individuals. We introduce BEMOVI, a package using the R and ImageJ software, to extract abundance, morphology, and movement data for tens to thousands of individuals in a video. Through a set of functions BEMOVI identifies individuals present in a video, reconstructs their movement trajectories through space and time, and merges this information into a single database. BEMOVI is a modular set of functions, which can be customized to allow for peculiarities of the videos to be analyzed, in terms of organisms features (e.g., morphology or movement) and how they can be distinguished from the background. We illustrate the validity and accuracy of the method with an example on experimental multispecies communities of aquatic protists. We show high correspondence between manual and automatic counts and illustrate how simultaneous time series of abundance, morphology, and behavior are obtained from BEMOVI. We further demonstrate how the trait data can be used with machine learning to automatically classify individuals into species and that information on movement behavior improves the predictive ability., Ecology and Evolution, 5 (13), ISSN:2045-7758

1. Microbes are critical components of ecosystems and vital to the services they provide. The essential role of microbes is due to high levels of functional diversity, which are, however, not always mirrored in morphological differentiation hampering their taxonomic identification. In addition, the small size of microbes hinders the measurement of morphological and behavioural traits at the individual level, as well as interactions between individuals. 2. Recent advances in microbial community genetics and genomics, flow cytometry and digital image analysis are promising approaches, however they miss out on a very important aspect of populations and communities: the behaviour of individuals. Video analysis complements these methods by providing in addition to abundance and trait measurements, detailed behavioural information, capturing dynamic processes such as movement, and hence has the potential to describe the interactions between individuals. 3. We introduce bemovi, a package using R–the statistical computing environment–and the free image analysis software ImageJ. Bemovi is an automated digital video processing and analysis work flow to extract abundance and morphological and movement data for numerous individuals on a video, hence characterizing a population or community by multiple traits. Through a set of functions, bemovi identifies individuals present in a video and reconstruct their movement trajectories through space and time, merges measurements from all treated videos into a single database to which information on experimental conditions is added, readily available for further analysis in R. 4. We illustrate the validity, precision and accuracy of the method for experimental multi-species communities of protists in aquatic microcosms. We show the high correspondence between manual and automatic counts of individuals and illustrate how simultaneous time series of abundance, morphology and behaviour are constructed. We demonstrate how the data from videos can be used in combination with supervised machine learning algorithms to automatically classify individuals according to the species they belong to, and that information on movement behaviour can substantially improve the predictive ability and helps to distinguish morphologically similar species. In principle, bemovi should be able to extract from videos information about other types of organism, including microbes, so long as the individuals move relatively fast compared to their background.

Dispersal and phenotypic plasticity are two main ways for species to deal with rapid changes of their environments. Understanding how genotypes (G), environments (E), and their interaction (genotype and environment; G × E) each affects dispersal propensity is therefore instrumental for predicting the ecological and evolutionary responses of species under global change. Here we used an actively dispersing ciliate to quantify the contributions of G, E, and G × E on dispersal propensity, exposing 44 different genotypes to three different environmental contexts (densities in isogenotype populations). Moreover, we assessed the condition dependence of dispersal, that is, whether dispersal is related to morphological, physiological, or behavioral traits. We found that genotypes showed marked differences in dispersal propensity and that dispersal is plastically adjusted to density, with the overall trend for genotypes to exhibit negative density-dependent dispersal. A small, but significant G × E interaction indicates genetic variability in plasticity and therefore some potential for dispersal plasticity to evolve. We also show evidence consistent with condition-dependent dispersal suggesting that genotypes also vary in how individual condition is linked to dispersal under different environmental contexts thereby generating complex dispersal behavior due to only three variables (genes, environment, and individual condition).

Dispersal and phenotypic plasticity are two main ways for species to deal with rapid changes of their environments. Understanding how genotypes (G), environments (E), and their interaction (genotype and environment; G × E) each affects dispersal propensity is therefore instrumental for predicting the ecological and evolutionary responses of species under global change. Here we used an actively dispersing ciliate to quantify the contributions of G, E, and G × E on dispersal propensity, exposing 44 different genotypes to three different environmental contexts (densities in isogenotype populations). Moreover, we assessed the condition dependence of dispersal, that is, whether dispersal is related to morphological, physiological, or behavioral traits. We found that genotypes showed marked differences in dispersal propensity and that dispersal is plastically adjusted to density, with the overall trend for genotypes to exhibit negative density-dependent dispersal. A small, but significant G × E interaction indicates genetic variability in plasticity and therefore some potential for dispersal plasticity to evolve. We also show evidence consistent with condition-dependent dispersal suggesting that genotypes also vary in how individual condition is linked to dispersal under different environmental contexts thereby generating complex dispersal behavior due to only three variables (genes, environment, and individual condition).