The dimensionality of stability depends on disturbance type.

Ecosystems respond in various ways to disturbances. Quantifying ecological stability therefore requires inspecting multiple stability properties, such as resistance, recovery, persistence and invariability. Correlations among these properties can reduce the dimensionality of stability, simplifying the study of environmental effects on ecosystems. A key question is how the kind of disturbance affects these correlations. We here investigated the effect of three disturbance types (random, species-specific, local) applied at four intensity levels, on the dimensionality of stability at the population and community level. We used previously parameterized models that represent five natural communities, varying in species richness and the number of trophic levels. We found that disturbance type but not intensity affected the dimensionality of stability and only at the population level. The dimensionality of stability also varied greatly among species and communities. Therefore, studying stability cannot be simplified to using a single metric and multi-dimensional assessments are still to be recommended.

The importance of ecological memory for trophic rewilding as an ecosystem restoration approach.

Increasing human pressure on strongly defaunated ecosystems is characteristic of the Anthropocene and calls for proactive restoration approaches that promote self-sustaining, functioning ecosystems. However, the suitability of novel restoration concepts such as trophic rewilding is still under discussion given fragmentary empirical data and limited theory development. Here, we develop a theoretical framework that integrates the concept of 'ecological memory' into trophic rewilding. The ecological memory of an ecosystem is defined as an ecosystem's accumulated abiotic and biotic material and information legacies from past dynamics. By summarising existing knowledge about the ecological effects of megafauna extinction and rewilding across a large range of spatial and temporal scales, we identify two key drivers of ecosystem responses to trophic rewilding: (i) impact potential of (re)introduced megafauna, and (ii) ecological memory characterising the focal ecosystem. The impact potential of (re)introduced megafauna species can be estimated from species properties such as lifetime per capita engineering capacity, population density, home range size and niche overlap with resident species. The importance of ecological memory characterising the focal ecosystem depends on (i) the absolute time since megafauna loss, (ii) the speed of abiotic and biotic turnover, (iii) the strength of species interactions characterising the focal ecosystem, and (iv) the compensatory capacity of surrounding source ecosystems. These properties related to the focal and surrounding ecosystems mediate material and information legacies (its ecological memory) and modulate the net ecosystem impact of (re)introduced megafauna species. We provide practical advice about how to quantify all these properties while highlighting the strong link between ecological memory and historically contingent ecosystem trajectories. With this newly established ecological memory-rewilding framework, we hope to guide future empirical studies that investigate the ecological effects of trophic rewilding and other ecosystem-restoration approaches. The proposed integrated conceptual framework should also assist managers and decision makers to anticipate the possible trajectories of ecosystem dynamics after restoration actions and to weigh plausible alternatives. This will help practitioners to develop adaptive management strategies for trophic rewilding that could facilitate sustainable management of functioning ecosystems in an increasingly human-dominated world.

Simulating plant invasion dynamics in mountain ecosystems under global change scenarios.

Across the globe, invasive alien species cause severe environmental changes, altering species composition and ecosystem functions. So far, mountain areas have mostly been spared from large-scale invasions. However, climate change, land-use abandonment, the development of tourism and the increasing ornamental trade will weaken the barriers to invasions in these systems. Understanding how alien species will react and how native communities will influence their success is thus of prime importance in a management perspective. Here, we used a spatially and temporally explicit simulation model to forecast invasion risks in a protected mountain area in the French Alps under future conditions. We combined scenarios of climate change, land-use abandonment and tourism-linked increases in propagule pressure to test if the spread of alien species in the region will increase in the future. We modelled already naturalized alien species and new ornamental plants, accounting for interactions among global change components, and also competition with the native vegetation. Our results show that propagule pressure and climate change will interact to increase overall species richness of both naturalized aliens and new ornamentals, as well as their upper elevational limits and regional range-sizes. Under climate change, woody aliens are predicted to more than double in range-size and herbaceous species to occupy up to 20% of the park area. In contrast, land-use abandonment will open new invasion opportunities for woody aliens, but decrease invasion probability for naturalized and ornamental alien herbs as a consequence of colonization by native trees. This emphasizes the importance of interactions with the native vegetation either for facilitating or potentially for curbing invasions. Overall, our work highlights an additional and previously underestimated threat for the fragile mountain flora of the Alps already facing climate changes, land-use transformations and overexploitation by tourism.

Extreme climate events counteract the effects of climate and land-use changes in Alpine treelines.

1. Climate change and extreme events, such as drought, threaten ecosystems worldwide and in particular mountain ecosystems, where species often live at their environmental tolerance limits. In the European Alps, plant communities are also influenced by land-use abandonment leading to woody encroachment of subalpine and alpine grasslands. 2. In this study, we explored how the forest-grassland ecotone of Alpine treelines will respond to gradual climate warming, drought events and land-use change in terms of forest expansion rates, taxonomic diversity and functional composition. We used a previously validated dynamic vegetation model, FATE-HD, parameterised for plant communities in the Ecrins National Park in the French Alps. 3. Our results showed that intense drought counteracted the forest expansion at higher elevations driven by land-use abandonment and climate change, especially when combined with high drought frequency (occurring every 2 or less than 2 years). 4. Furthermore, intense and frequent drought accelerated the rates of taxonomic change and resulted in overall higher taxonomic spatial heterogeneity of the ecotone than would be expected under gradual climate and land-use changes only. 5. Synthesis and applications. The results from our model show that intense and frequent drought counteracts forest expansion driven by climate and land-use changes in the forest-grassland ecotone of Alpine treelines. We argue that land-use planning must consider the effects of extreme events, such as drought, as well as climate and land-use changes, since extreme events might interfere with trends predicted under gradual climate warming and agricultural abandonment.

Cross-scale integration of knowledge for predicting species ranges: a metamodeling framework.

AIM: Current interest in forecasting changes to species ranges have resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity, and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. LOCATION: Eastern North America (as an example). METHODS: Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence-absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. RESULTS: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. MAIN CONCLUSIONS: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.

N-dimensional hypervolumes to study stability of complex ecosystems.

Although our knowledge on the stabilising role of biodiversity and on how it is affected by perturbations has greatly improved, we still lack a comprehensive view on ecosystem stability that is transversal to different habitats and perturbations. Hence, we propose a framework that takes advantage of the multiplicity of components of an ecosystem and their contribution to stability. Ecosystem components can range from species or functional groups, to different functional traits, or even the cover of different habitats in a landscape mosaic. We make use of n-dimensional hypervolumes to define ecosystem states and assess how much they shift after environmental changes have occurred. We demonstrate the value of this framework with a study case on the effects of environmental change on Alpine ecosystems. Our results highlight the importance of a multidimensional approach when studying ecosystem stability and show that our framework is flexible enough to be applied to different types of ecosystem components, which can have important implications for the study of ecosystem stability and transient dynamics.

Anticipating the spatio-temporal response of plant diversity and vegetation structure to climate and land use change in a protected area.

Vegetation is a key driver of ecosystem functioning (e.g. productivity and stability) and of the maintenance of biodiversity (e.g. creating habitats for other species groups). While vegetation sensitivity to climate change has been widely investgated, its spatio-temporally response to the dual efects of land management and climate change has been ignored at landscape scale. Here we use a dynamic vegetation model called FATE-HD, which describes the dominant vegetation dynamics and associated functional diversity, in order to anticipate vegetation response to climate and land-use changes in both short and long-term perspectives. Using three contrasted management scenarios for the Ecrins National Park (French Alps) developed in collaboration with the park managers, and one regional climate change scenario, we tracked the dynamics of vegetation structure (forest expansion) and functional diversity over 100 years of climate change and a further 400 additional years of stabilization. As expected, we observed a slow upward shift in forest cover distribution, which appears to be severely impacted by pasture management (i.e. maintenance or abandonment). The tme lag before observing changes in vegetation cover was the result of demographic and seed dispersal processes. However, plant diversity response to environmental changes was rapid. Afer land abandonment, local diversity increased and spatial turnover was reduced, whereas local diversity decreased following land use intensification. Interestingly, in the long term, as both climate and management scenarios interacted, the regional diversity declined. Our innovative spatio-temporally explicit framework demonstrates that the vegetation may have contrasting responses to changes in the short and the long term. Moreover, climate and land-abandonment interact extensively leading to a decrease in both regional diversity and turnover in the long term. Based on our simulations we therefore suggest a continuing moderate intensity pasturing to maintain high levels of plant diversity in this system.

Disentangling the drivers of metacommunity structure across spatial scales.

AIM: Metacommunity theories attribute different relative degrees of importance to dispersal, environmental filtering, biotic interactions and stochastic processes in community assembly, but the role of spatial scale remains uncertain. Here we used two complementary statistical tools to test: (1) whether or not the patterns of community structure and environmental influences are consistent across resolutions; and (2) whether and how the joint use of two fundamentally different statistical approaches provides a complementary interpretation of results. LOCATION: Grassland plants in the French Alps. METHODS: We used two approaches across five spatial resolutions (ranging from 1 km × 1 km to 30 km × 30 km): variance partitioning, and analysis of metacommunity structure on the site-by-species incidence matrices. Both methods allow the testing of expected patterns resulting from environmental filtering, but variance partitioning allows the role of dispersal and environmental gradients to be studied, while analysis of the site-by-species metacommunity structure informs an understanding of how environmental filtering occurs and whether or not patterns differ from chance expectation. We also used spatial regressions on species richness to identify relevant environmental factors at each scale and to link results from the two approaches. RESULTS: Major environmental drivers of richness included growing degree-days, temperature, moisture and spatial or temporal heterogeneity. Variance partitioning pointed to an increase in the role of dispersal at coarser resolutions, while metacommunity structure analysis pointed to environmental filtering having an important role at all resolutions through a Clementsian assembly process (i.e. groups of species having similar range boundaries and co-occurring in similar environments). MAIN CONCLUSIONS: The combination of methods used here allows a better understanding of the forces structuring ecological communities than either one of them used separately. A key aspect in this complementarity is that variance partitioning can detect effects of dispersal whereas metacommunity structure analysis cannot. Moreover, the latter can distinguish between different forms of environmental filtering (e.g. individualistic versus group species responses to environmental gradients).

Working toward integrated models of alpine plant distribution.

Species distribution models (SDMs) have been frequently employed to forecast the response of alpine plants to global changes. Efforts to model alpine plant distribution have thus far been primarily based on a correlative approach, in which ecological processes are implicitly addressed through a statistical relationship between observed species occurrences and environmental predictors. Recent evidence, however, highlights the shortcomings of correlative SDMs, especially in alpine landscapes where plant species tend to be decoupled from atmospheric conditions in micro-topographic habitats and are particularly exposed to geomorphic disturbances. While alpine plants respond to the same limiting factors as plants found at lower elevations, alpine environments impose a particular set of scale-dependent and hierarchical drivers that shape the realized niche of species and that require explicit consideration in a modelling context. Several recent studies in the European Alps have successfully integrated both correlative and process-based elements into distribution models of alpine plants, but for the time being a single integrative modelling framework that includes all key drivers remains elusive. As a first step in working toward a comprehensive integrated model applicable to alpine plant communities, we propose a conceptual framework that structures the primary mechanisms affecting alpine plant distributions. We group processes into four categories, including multi-scalar abiotic drivers, gradient dependent species interactions, dispersal and spatial-temporal plant responses to disturbance. Finally, we propose a methodological framework aimed at developing an integrated model to better predict alpine plant distribution.

Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances.

Although abiotic factors, together with dispersal and biotic interactions, are often suggested to explain the distribution of species and their abundances, species distribution models usually focus on abiotic factors only. We propose an integrative framework linking ecological theory, empirical data and statistical models to understand the distribution of species and their abundances together with the underlying community assembly dynamics. We illustrate our approach with 21 plant species in the French Alps. We show that a spatially nested modelling framework significantly improves the model's performance and that the spatial variations of species presence-absence and abundances are predominantly explained by different factors. We also show that incorporating abiotic, dispersal and biotic factors into the same model bring new insights to our understanding of community assembly. This approach, at the crossroads between community ecology and biogeography, is a promising avenue for a better understanding of species co-existence and biodiversity distribution.

Consequences of climate change on the tree of life in Europe.

Many species are projected to become vulnerable to twenty-first-century climate changes, with consequent effects on the tree of life. If losses were not randomly distributed across the tree of life, climate change could lead to a disproportionate loss of evolutionary history. Here we estimate the consequences of climate change on the phylogenetic diversities of plant, bird and mammal assemblages across Europe. Using a consensus across ensembles of forecasts for 2020, 2050 and 2080 and high-resolution phylogenetic trees, we show that species vulnerability to climate change clusters weakly across phylogenies. Such phylogenetic signal in species vulnerabilities does not lead to higher loss of evolutionary history than expected with a model of random extinctions. This is because vulnerable species have neither fewer nor closer relatives than the remaining clades. Reductions in phylogenetic diversity will be greater in southern Europe, and gains are expected in regions of high latitude or altitude. However, losses will not be offset by gains and the tree of life faces a trend towards homogenization across the continent.