Dr. Jelle Lever

As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research.

Climate change exerts a profound impact on lakes, eliciting responses that range from gradual to abrupt transitions. When reaching critical tipping points, the established lake dynamics stand to undergo substantial modifications, setting off a chain reaction that reverberates through the entire ecosystem. This lake shift ripples into related ecosystem services and even influences the well-being of human communities. Despite the importance of lake shifts, we lack a systematic overview of their occurrence, mainly due to the lack of systematic data at the global scale. We reviewed the literature focusing on climate-related lake shifts and assessed how satellite Earth Observation (EO) has contributed to the research topic, and what we can unlock from this novel data. Our results show that EO data are used in only 9% of studies on lake shifts, although this fraction has increased since 2012. EO data is most commonly used to assess shifts in surface extent, ice coverage, or phytoplankton phenology. These variables are directly observable and the spatio-temporal resolution of EO satellites is of great advantage. But lake shifts can also be identified indirectly from EO data, as in the example of the vertical mixing of lake water, which can be described on the basis of surface patterns. In all possible applications, we expect increasing use of EO satellites in the future, including the development of early warning systems that promise to provide timely alerts regarding impending lake shifts, thus serving as a vanguard against abrupt alterations that could ripple through interconnected ecosystem services.

Complex systems ranging from societies to ecological communities and power grids may be viewed as networks of connected elements. Such systems can go through critical transitions driven by an avalanche of contagious change. Here we ask, where in a complex network such a systemic shift is most likely to start. Intuitively, a central node seems the most likely source of such change. Indeed, topological studies suggest that central nodes can be the Achilles heel for attacks. We argue that the opposite is true for the class of networks in which all nodes tend to follow the state of their neighbors, a category we call two-way pull networks. In this case, a well-connected central node is an unlikely starting point of a systemic shift due to the buffering effect of connected neighbors. As a result, change is most likely to cascade through the network if it spreads first among relatively poorly connected nodes in the periphery. The probability of such initial spread is highest when the perturbation starts from intermediately connected nodes at the periphery, or more specifically, nodes with intermediate degree and relatively low closeness centrality. Our finding is consistent with empirical observations on social innovation, and may be relevant to topics as different as the sources of originality of art, collapse of financial and ecological networks and the onset of psychiatric disorders.

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.

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.

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.