Five fundamental ways in which complex food webs may spiral out of control

Theory suggests that increasingly long, negative feedback loops of many interacting species may destabilize food webs as complexity increases. Less attention has, however, been paid to the specific ways in which these ‘delayed negative feedbacks’ may affect the response of complex ecosystems to global environmental change. Here, we describe five fundamental ways in which these feedbacks might pave the way for abrupt, large‐scale transitions and species losses. By combining topological and bioenergetic models, we then proceed by showing that the likelihood of such transitions increases with the number of interacting species and/or when the combined effects of stabilizing network patterns approach the minimum required for stable coexistence. Our findings thus shift the question from the classical question of what makes complex, unaltered ecosystems stable to whether the effects of, known and unknown, stabilizing food‐web patterns are sufficient to prevent abrupt, large‐scale transitions under global environmental change

Linking human impacts to community processes in terrestrial and freshwater ecosystems

Human impacts such as habitat loss, climate change and biological invasions are radically altering biodiversity, with greater effects projected into the future. Evidence suggests human impacts may differ substantially between terrestrial and freshwater ecosystems, but the reasons for these differences are poorly understood. We propose an integrative approach to explain these differences by linking impacts to four fundamental processes that structure communities: dispersal, speciation, species-level selection and ecological drift. Our goal is to provide process-based insights into why human impacts, and responses to impacts, may differ across ecosystem types using a mechanistic, eco-evolutionary comparative framework. To enable these insights, we review and synthesise (i) how the four processes influence diversity and dynamics in terrestrial versus freshwater communities, specifically whether the relative importance of each process differs among ecosystems, and (ii) the pathways by which human impacts can produce divergent responses across ecosystems, due to differences in the strength of processes among ecosystems we identify. Finally, we highlight research gaps and next steps, and discuss how this approach can provide new insights for conservation. By focusing on the processes that shape diversity in communities, we aim to mechanistically link human impacts to ongoing and future changes in ecosystems

Linking human impacts to community processes in terrestrial and freshwater ecosystems.

Human impacts such as habitat loss, climate change and biological invasions are radically altering biodiversity, with greater effects projected into the future. Evidence suggests human impacts may differ substantially between terrestrial and freshwater ecosystems, but the reasons for these differences are poorly understood. We propose an integrative approach to explain these differences by linking impacts to four fundamental processes that structure communities: dispersal, speciation, species-level selection and ecological drift. Our goal is to provide process-based insights into why human impacts, and responses to impacts, may differ across ecosystem types using a mechanistic, eco-evolutionary comparative framework. To enable these insights, we review and synthesise (i) how the four processes influence diversity and dynamics in terrestrial versus freshwater communities, specifically whether the relative importance of each process differs among ecosystems, and (ii) the pathways by which human impacts can produce divergent responses across ecosystems, due to differences in the strength of processes among ecosystems we identify. Finally, we highlight research gaps and next steps, and discuss how this approach can provide new insights for conservation. By focusing on the processes that shape diversity in communities, we aim to mechanistically link human impacts to ongoing and future changes in ecosystems

NextGen speaks

Peer Reviewe

Finding the direction of lowest resilience in multivariate complex systems

The dynamics of complex systems, such as ecosystems, financial markets and the human brain, emerge from the interactions of numerous components. We often lack the knowledge to build reliable models for the behaviour of such network systems. This makes it difficult to predict potential instabilities. We show that one could use the natural fluctuations in multivariate time series to reveal network regions with particularly slow dynamics. The multidimensional slowness points to the direction of minimal resilience, in the sense that simultaneous perturbations on this set of nodes will take longest to recover. We compare an autocorrelation-based method with a variance-based method for different time-series lengths, data resolution and different noise regimes. We show that the autocorrelation-based method is less robust for short time series or time series with a low resolution but more robust for varying noise levels. This novel approach may help to identify unstable regions of multivariate systems or to distinguish safe from unsafe perturbations

Foreseeing the future of mutualistic communities beyond collapse

Changing conditions may lead to sudden shifts in the state of ecosystems when critical thresholds are passed. Some well-studied drivers of such transitions lead to predictable outcomes such as a turbid lake or a degraded landscape. Many ecosystems are, however, complex systems of many interacting species. While detecting upcoming transitions in such systems is challenging, predicting what comes after a critical transition is terra incognita altogether. The problem is that complex ecosystems may shift to many different, alternative states. Whether an impending transition has minor, positive or catastrophic effects is thus unclear. Some systems may, however, behave more predictably than others. The dynamics of mutualistic communities can be expected to be relatively simple, because delayed negative feedbacks leading to oscillatory or other complex dynamics are weak. Here, we address the question of whether this relative simplicity allows us to foresee a community’s future state. As a case study, we use a model of a bipartite mutualistic network and show that a network’s post-transition state is indicated by the way in which a system recovers from minor disturbances. Similar results obtained with a unipartite model of facilitation suggest that our results are of relevance to a wide range of mutualistic systems

Leaf phenology as an indicator of ecological integrity

Abstract Climate change leads to an increased frequency of severe weather events as well as stressful growing conditions. Together these changes may impact the resilience of ecosystems. To keep track of such effects, conservation managers monitor the “ecological integrity” or coherence of ecosystem processes, such as the cycling of carbon and water. Networked phenocams can produce near‐continuous observations of leaf function in the context of climate change, capturing declines due to disturbance or stress. Here we explore the application of phenocams to detect responses to disturbance and stress using 14 examples from the PhenoCam Network. We selected these previously published and new examples to include a variety of disturbances in the form of hurricanes, a windstorm, frost, insect defoliation, and stress due to drought. Frost and herbivory disturbances led to both reductions and extensions in the duration of the rising section of the greenness curve, while hurricanes generally led to reductions in the duration of the plateau section and entire leaf‐on period. We found that changes of at least ±20% in the duration of the rising section in the seasonal greenness curve, ±20% in the duration of the plateau section following the seasonal greenness peak, and ±10% in the duration of the entire leaf‐on period were a reliable signal of leaf functional declines due to disturbance or stress. If such declines become increasingly frequent and severe as a consequence of climate change, this could impact ecological integrity through interruptions to ecosystem processes. Comparing the duration of these periods in a given year to the average for other years with these thresholds resulted in average true detection rates of 86% and false‐positive detection rates of 11% when sampling from probability density functions of 344 broadleaf and needleleaf PhenoCam site‐years. Here we show that phenocams are powerful ecological integrity monitoring tools, which can be efficiently applied to quantify dynamic responses to disturbance or stress