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Global change is a multifaceted, unprecedented crisis hitting the life support system of our planet. Among global changes, climate change is regarded as one of the most grave threats to biodiversity because of its direct impacts on species and ecosystems integrity and because of its indirect consequences through synergistic effects with other global change factors such as biological invasions. The challenges presented to either reduce or mitigate this biodiversity crisis derived from climate change require novel synthesis and innovation in ecological and evolutionary theory. Positive species interactions within and between trophic levels can play a key role in the resilience of ecological communities. Depending on the tolerance of nurse species to different aspects of climate change, communities can be more or less resilient to those changes. This knowledge has important implications for both natural communities and agroecosystems. Further, our fundamental understanding of the role of positive interactions can also enable both effective conservation and restoration levers in space and time. This special issue includes studies addressing the role of facilitative interactions on the response of ecological systems to climate change. Key concepts examined included stress, gradients, nurse species, spatial scale, translocation, phylogenetics alongside physiochemicals, and variation in the capacity of species to buffer changes. Alpine, tundra, drylands and temperate forests were directly tested, but salient principles were relevant to all ecosystems including a contribution on soil biota and also a call to more open data and collaborative science. Together, this corpus of work highlighted the significance of facilitative interactions in mitigating many of the effects of climate change on biodiversity. [ABSTRACT FROM AUTHOR]

Our understanding of the fundamental role that soil bacteria play in the structure and functioning of Earth's ecosystems is ever expanding, but insight into the nature of interactions within these bacterial communities remains rudimentary. Bacterial facilitation may enhance the establishment, growth, and succession of eukaryotic biota, elevating the complexity and diversity of the entire soil community and thereby modulating multiple ecosystem functions. Global climate change often alters soil bacterial community composition, which, in turn, impacts other dependent biota. However, the impact of climate change on facilitation within bacterial communities remains poorly understood even though it may have important cascading consequences for entire ecosystems. The wealth of metagenomic data currently being generated gives community ecologists the ability to investigate bacterial facilitation in the natural world and how it affects ecological systems responses to climate change. Here, we review current evidence demonstrating the importance of facilitation in promoting emergent properties such as community diversity, ecosystem functioning, and resilience to climate change in soil bacterial communities. We show that a synthesis is currently missing between the abundant data, newly developed models and a coherent ecological framework that addresses these emergent properties. We highlight that including phylogenetic information, the physicochemical environment, and species‐specific ecologies can improve our ability to infer interactions in natural soil communities. Following these recommendations, studies on bacterial facilitation will be an important piece of the puzzle to understand the consequences of global change on ecological communities and a model to advance our understanding of facilitation in complex communities more generally. [ABSTRACT FROM AUTHOR]

Understanding ecological dynamics has been a central topic in ecology since its origins. Yet, identifying dynamic regimes remains a research frontier for modern ecology. The concept of ecological dynamic regime (EDR) emerged to emphasize the dynamic property of steady states in nature and refers to the fluctuations of ecosystems around some trend or average. Identifying and characterizing EDRs is of utmost importance in the current context of global change since they form the reference against which post‐disturbance dynamics must be compared to assess ecological resilience. However, the implementation of EDRs in empirical science is still challenging given the high dimensionality and stochasticity of ecological data and the large volume of data required to distinguish stochastic dynamics from general and predictable dynamics. The era of big data and the recent advances in quantitative ecology and data science offer an opportunity to study dynamic regimes using empirical approaches from a new perspective. This paper presents a novel methodological framework to describe EDRs from a set of ecological trajectories defined by the temporal changes of state variables in a multidimensional state space. In our framework, we formally define EDRs and include analytical tools to identify, characterize, and compare EDRs based on their geometric characteristics. More specifically, we propose different ways to identify EDRs from empirical data, develop a new algorithm to identify representative trajectories summarizing the main dynamic patterns, propose a set of metrics to describe the internal distribution of ecological trajectories, and define a dissimilarity index to compare two or more dynamic regimes based on their shape and position in the state space. We used artificial data to illustrate the different elements of our framework and applied our analyses to real data, using permanent sampling plots of Canadian boreal forests as an example. Overall, our framework contributes to filling the gap between theoretical and empirical ecology by providing robust analytical tools to assess ecological resilience and study ecosystem dynamics from a multidimensional perspective and considering the variability of natural systems. [ABSTRACT FROM AUTHOR]

Anthropogenic activities are increasingly affecting ecosystems across the globe. Meanwhile, empirical and theoretical evidence suggest that natural systems can exhibit abrupt collapses in response to incremental increases in the stressors, sometimes with dramatic ecological and economic consequences. These catastrophic shifts are faster and larger than expected from the changes in the stressors and happen once a tipping point is crossed. The primary mechanisms that drive ecosystem responses to perturbations lie in their architecture of relationships, i.e. how species interact with each other and with the physical environment and the spatial structure of the environment. Nonetheless, existing theoretical work on catastrophic shifts has so far largely focused on relatively simple systems that have either few species and/or no spatial structure. This work has laid a critical foundation for understanding how abrupt responses to incremental stressors are possible, but it remains difficult to predict (let alone manage) where or when they are most likely to occur in more complex real-world settings. Here, we discuss how scaling up our investigations of catastrophic shifts from simple to more complex—species rich and spatially structured—systems could contribute to expanding our understanding of how nature works and improve our ability to anticipate the effects of global change on ecological systems. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'. [ABSTRACT FROM AUTHOR]

Human activities put ecosystems under increasing pressure, often resulting in local extinctions. However, it is unclear how local extinctions affect regional processes, such as the distribution of diversity in space, especially if extinctions show spatial patterns, such as being clustered. Therefore, it is crucial to investigate extinctions and their consequences in a spatially explicit framework. Using highly controlled microcosm experiments and theoretical models, we ask here how the number and spatial autocorrelation of extinctions interactively affect metacommunity dynamics. We found that local patch extinctions increased local diversity (α-diversity) and inter-patch diversity (β-diversity) by delaying the exclusion of inferior competitors. Importantly, recolonization dynamics depended more strongly on the spatial distribution than on the number of patch extinctions: clustered local patch extinctions resulted in slower recovery, lower α-diversity and higher β-diversity. Our results highlight that the spatial distribution of perturbations should be taken into account when studying and managing spatially structured communities. [ABSTRACT FROM AUTHOR]

A rich body of knowledge links biodiversity to ecosystem functioning (BEF), but it is primarily focused on small scales. We review the current theory and identify six expectations for scale dependence in the BEF relationship: (1) a nonlinear change in the slope of the BEF relationship with spatial scale; (2) a scale‐dependent relationship between ecosystem stability and spatial extent; (3) coexistence within and among sites will result in a positive BEF relationship at larger scales; (4) temporal autocorrelation in environmental variability affects species turnover and thus the change in BEF slope with scale; (5) connectivity in metacommunities generates nonlinear BEF and stability relationships by affecting population synchrony at local and regional scales; (6) spatial scaling in food web structure and diversity will generate scale dependence in ecosystem functioning. We suggest directions for synthesis that combine approaches in metaecosystem and metacommunity ecology and integrate cross‐scale feedbacks. Tests of this theory may combine remote sensing with a generation of networked experiments that assess effects at multiple scales. We also show how anthropogenic land cover change may alter the scaling of the BEF relationship. New research on the role of scale in BEF will guide policy linking the goals of managing biodiversity and ecosystems. [ABSTRACT FROM AUTHOR]

Understanding the stability of ecological communities is a matter of increasing importance in the context of global environmental change. Yet it has proved to be a challenging task. Different metrics are used to assess the stability of ecological systems, and the choice of one metric over another may result in conflicting conclusions. Although each of the multitude of metrics is useful for answering a specific question about stability, the relationship among metrics is poorly understood. Such lack of understanding prevents scientists from developing a unified concept of stability. Instead, by investigating these relationships we can unveil how many dimensions of stability there are (i.e., in how many independent components stability metrics can be grouped), which should help build a more comprehensive concept of stability. Here we simultaneously measured 27 stability metrics frequently used in ecological studies. Our approach is based on dynamical simulations of multispecies trophic communities under different perturbation scenarios. Mapping the relationships between the metrics revealed that they can be lumped into 3 main groups of relatively independent stability components: early response to pulse, sensitivities to press, and distance to threshold. Selecting metrics from each of these groups allows a more accurate and comprehensive quantification of the overall stability of ecological communities. These results contribute to improving our understanding and assessment of stability in ecological communities. [ABSTRACT FROM AUTHOR]

Biological diversity is essential for the maintenance of the ecosystem functions that support life on the planet. Inherent to this diversity is the seemingly endless way in which the biological entities of a natural system interact and affect each other at local and regional scales, conforming complex ecological networks permeable to external forcing. Existing approaches to capture and model such complexity typically make unrealistic or excessively restrictive assumptions. Here we use concepts from open dynamical systems and metacommunity theory to develop a framework in which the system dynamics is a function of both interspecific interactions in the focal system (e.g., a local community of coexisting species) and unobserved biotic and abiotic interactions with the local and regional environment (e.g., the metacommunity). Species in the focal system interact through direct exchanges of biomass (i.e., trophic interactions), as well as through altering the acquisition and/or transformation of biomass by other species (nontrophic interactions). Interactions are affected by environmental fluctuations and by migration and emigration processes, which can take place at different time scales and can be modeled by stochastic differential equations driven by a mixture of continuous and discontinuous processes. In this manner, the proposed framework provides a wider and more flexible representation of the complexity of ecological systems, in comparison with the closed-system paradigm that isolates the system from the environment. Because the core model explicitly recognizes the existence of local and regional processes, it is also a natural starting point to examine spatially structured networks. [ABSTRACT FROM AUTHOR]

Facilitation among plants mediated by grazers occurs when an unpalatable plant extends its protection against grazing to another plant. This type of indirect facilitation impacts species coexistence and ecosystem functioning in a large array of ecosystems worldwide. It has nonetheless generally been understudied so far in comparison with the role played by direct facilitation among plants. We aimed at providing original data on indirect facilitation at the community scale to determine the extent to which indirect facilitation mediated by grazers can shape plant communities. Such experimental data are expected to contribute to refining the conceptual framework on plant-plant-herbivore interactions in stressful environments. We set up a 2-year grazing exclusion experiment in tropical alpine peatlands in Bolivia. Those ecosystems depend entirely on a few, structuring cushion-forming plants (hereafter referred to as 'nurse' species), in which associated plant communities develop. Fences have been set over two nurse species with different strategies to cope with grazing (direct vs. indirect defenses), which are expected to lead to different intensities of indirect facilitation for the associated communities. We collected functional traits which are known to vary according to grazing pressure ( LDMC, leaf thickness, and maximum height), on both the nurse and their associated plant communities in grazed (and therefore indirect facilitation as well) and ungrazed conditions. We found that the effect of indirectly facilitated on the associated plant communities depended on the functional trait considered. Indirect facilitation decreased the effects of grazing on species relative abundance, mean LDMC, and the convergence of the maximum height distribution of the associated communities, but did not affect mean height or cover. The identity of the nurse species and grazing jointly affected the structure of the associated plant community through indirect facilitation. Our results together with the existing literature suggest that the 'grazer-nurse-beneficiary' interaction module can be more complex than expected when evaluated in the field. [ABSTRACT FROM AUTHOR]

In a rapidly changing world, ecology has the potential to move from empirical and conceptual stages to application and management issues. It is now possible to make large-scale predictions up to continental or global scales, ranging from the future distribution of biological diversity to changes in ecosystem functioning and services. With these recent developments, ecology has a historical opportunity to become a major actor in the development of a sustainable human society. With this opportunity, however, also comes an important responsibility in developing appropriate predictive models, correctly interpreting their outcomes and communicating their limitations. There is also a danger that predictions grow faster than our understanding of ecological systems, resulting in a gap between the scientists generating the predictions and stakeholders using them (conservation biologists, environmental managers, journalists, policymakers)., Here, we use the context provided by the current surge of ecological predictions on the future of biodiversity to clarify what prediction means, and to pinpoint the challenges that should be addressed in order to improve predictive ecological models and the way they are understood and used., Synthesis and applications. Ecologists face several challenges to ensure the healthy development of an operational predictive ecological science: (i) clarity on the distinction between explanatory and anticipatory predictions; (ii) developing new theories at the interface between explanatory and anticipatory predictions; (iii) open data to test and validate predictions; (iv) making predictions operational; and (v) developing a genuine ethics of prediction. [ABSTRACT FROM AUTHOR]

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development. [ABSTRACT FROM AUTHOR]

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data. [ABSTRACT FROM AUTHOR]

The increasing incidence of extreme events combined with long-term pressures due to climate change and anthropogenic activities are seriously threatening ecosystems worldwide. Promoting ecological resilience (i.e., the ability of ecosystems to absorb changes and maintain their processes and functioning) is a pivotal target for biological conservation. Many empirical approaches to assess resilience have relied on the underlying assumption that, in the absence of disturbances, ecosystems would be in a 'static' baseline state. In reality, ecological systems are highly dynamic and undergo phases of development and reorganization resulting from natural successional changes and their response to multiple interacting variables. As such, ecosystem states can be better described by 'dynamic regimes' rather than stationary states. In this context, there is an urgent need to expand the common 'equilibrium-based' approaches used in empirical ecology to evaluate resilience so that they can be applied to non-static ecosystems. Here, we present our perspective on the relevance of approaches based on dynamic regimes to assess ecological resilience empirically. More specifically, we briefly review the concept of resilience and discuss the main challenges for empirical applications. Then, we review an approach based on ecological dynamic regimes and temporal trajectories that can be used to assess the ecological resilience of non-static ecosystems from empirical data. Approaches focused on dynamic regimes help evaluate the management effort necessary to restore a disturbed ecosystem. Besides, they facilitate the identification of the factors that must receive special attention for enhancing ecosystem resilience. • Many empirical studies assume 'static' reference states to quantify resilience. • Equilibrium paradigms contradict resilience theory and challenge biological conservation. • Transient dynamics, the stochasticity and dimensionality of data, and unstable basins of attraction should be considered. • Ecological dynamic regimes from empirical data bring new opportunities to assess resilience. • Non-equilibrium perspectives provide accurate information for conservation strategies. [ABSTRACT FROM AUTHOR]

Effective conservation requires prioritizing areas that are vulnerable to large, irreversible changes. Unfortunately, rigorously documenting these changes with experiments and long-term monitoring is not only costly, but may provide evidence that is too late to facilitate proactive decisions. We use a simple model to illustrate that commonly available short-term spatial, "snapshot", data from a given ecosystem along an environmental gradient can be used to identify environmental conditions under which different ecosystem states (e.g. different species compositions) co-occur in space. These environmental conditions are those under which future perturbations have the potential for discontinuous large, sometimes irreversible, effects; and can be mapped in space to predict potential spatial hotspots of ecosystem fragility. We apply these insights to ecologically important high-elevation subalpine meadows of the Sierra Nevada (California). Our analysis reveals specific areas within meadows that may be more vulnerable than others because their plant communities have the potential to shift to a different state. These shifts can be mechanistically explained by interactions between the vegetation and the local water regimes and/or the upper soil conditions. Our study provides a simple workflow using commonly available data to help prioritize conservation areas based on their potential sensitivity to upcoming perturbations. Such an approach could be very valuable to make most efficient use of conservation and management resources in the context of ongoing global changes. [ABSTRACT FROM AUTHOR]