Warming reduces the effects of enrichment on stability and functioning across levels of organisation in an aquatic microbial ecosystem

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

Habitat requirements and dispersal ability of the Spanish Fritillary (Euphydryas desfontainii) in southern Portugal: evidence-based conservation suggestions for an endangered taxon

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

Predicting effects of multiple interacting global change drivers across trophic levels

International audienceGlobal 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

Refocusing multiple stressor research around the targets and scales of ecological impacts

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

How puzzles are shaping our understanding of biodiversity: A call for more research into biodiversity representation in educational games

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

The intrinsic predictability of ecological time series and its potential to guide forecasting

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

Biodiversity increases and decreases ecosystem stability

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