Defining and quantifying the resilience of responses to disturbance: a conceptual and modelling approach from soil science

There are several conceptual definitions of resilience pertaining to environmental systems and, even if resilience is clearly defined in a particular context, it is challenging to quantify. We identify four characteristics of the response of a system function to disturbance that relate to “resilience”: (1) degree of return of the function to a reference level; (2) time taken to reach a new quasi-stable state; (3) rate (i.e. gradient) at which the function reaches the new state; (4) cumulative magnitude of the function (i.e. area under the curve) before a new state is reached. We develop metrics to quantify these characteristics based on an analogy with a mechanical spring and damper system. Using the example of the response of a soil function (respiration) to disturbance, we demonstrate that these metrics effectively discriminate key features of the dynamic response. Although any one of these characteristics could define resilience, each may lead to different insights and conclusions. The salient properties of a resilient response must thus be identified for different contexts. Because the temporal resolution of data affects the accurate determination of these metrics, we recommend that at least twelve measurements are made over the temporal range for which the response is expected

Slower recovery in space before collapse of connected populations

Slower recovery from perturbations near a tipping point and its indirect signatures in fluctuation patterns have been suggested to foreshadow catastrophes in a wide variety of systems. Recent studies of populations in the field and in the laboratory have used time-series data to confirm some of the theoretically predicted early warning indicators, such as an increase in recovery time or in the size and timescale of fluctuations. However, the predictive power of temporal warning signals is limited by the demand for long-term observations. Large-scale spatial data are more accessible, but the performance of warning signals in spatially extended systems needs to be examined empirically. Here we use spatially extended yeast populations, an experimental system with a fold bifurcation (tipping point), to evaluate early warning signals based on spatio-temporal fluctuations and to identify a novel spatial warning indicator. We found that two leading indicators based on fluctuations increased before collapse of connected populations; however, the magnitudes of the increases were smaller than those observed in isolated populations, possibly because local variation is reduced by dispersal. Furthermore, we propose a generic indicator based on deterministic spatial patterns, which we call ‘recovery length’. As the spatial counterpart of recovery time, recovery length is the distance necessary for connected populations to recover from spatial perturbations. In our experiments, recovery length increased substantially before population collapse, suggesting that the spatial scale of recovery can provide a superior warning signal before tipping points in spatially extended systems.United States. National Institutes of Health (NIH R00 GM085279-02)United States. National Institutes of Health (NIH DP2)Alfred P. Sloan FoundationNational Science Foundation (U.S.

Recovery and resilience of tropical forests after disturbance

The time taken for forested tropical ecosystems to re-establish post-disturbance is of widespread interest. Yet to date there has been no comparative study across tropical biomes to determine rates of forest re-growth, and how they vary through space and time. Here we present results from a meta-analysis of palaeoecological records that use fossil pollen as a proxy for vegetation change over the past 20,000 years. A total of 283 forest disturbance and recovery events, reported in 71 studies, are identified across four tropical regions. Results indicate that forests in Central America and Africa generally recover faster from past disturbances than those in South America and Asia, as do forests exposed to natural large infrequent disturbances compared with post-climatic and human impacts. Results also demonstrate that increasing frequency of disturbance events at a site through time elevates recovery rates, indicating a degree of resilience in forests exposed to recurrent past disturbance

Evidence for 'critical slowing down' in seagrass:a stress gradient experiment at the southern limit of its range

The theory of critical slowing down, i.e. the increasing recovery times of complex systems close to tipping points, has been proposed as an early warning signal for collapse. Empirical evidence for the reality of such warning signals is still rare in ecology. We studied this on Zostera noltii intertidal seagrass meadows at their southern range limit, the Banc d'Arguin, Mauritania. We analyse the environmental covariates of recovery rates using structural equation modelling (SEM), based on an experiment in which we assessed whether recovery after disturbances (i.e. seagrass & infauna removal) depends on stress intensity (increasing with elevation) and disturbance patch size (1 m(2) vs. 9 m(2)). The SEM analyses revealed that higher biofilm density and sediment accretion best explained seagrass recovery rates. Experimental disturbances were followed by slow rates of recovery, regrowth occurring mainly in the coolest months of the year. Macrofauna recolonisation lagged behind seagrass recovery. Overall, the recovery rate was six times slower in the high intertidal zone than in the low zone. The large disturbances in the low zone recovered faster than the small ones in the high zone. This provides empirical evidence for critical slowing down with increasing desiccation stress in an intertidal seagrass system

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

Assessing ecological resilience to human induced environmental change in shallow lakes

Sudden unpredictable changes in ecosystems are an increasing source of concern because of their inherent unpredictability and the difficulties involved in restoration. Our understanding of the changes that occur across different trophic levels and the form of this change is lacking. This is especially true of large shallow lakes, where characteristics such as fetch and depth are close to theoretical boundary values for hysteretic behaviour. The development of reliable indicators capable of predicting these changes has been the focus of much research in recent years. The success of these early warning indicators (EWIs) has so far been mixed. There remain many unknowns about how they perform under a wide variety of conditions and parameters. Future climate change is predicted to have a wide range of impacts through the interaction of combined pressures, making the understanding of EWIs and the in-lake processes that occur during regime shifts imperative. Loch Leven, Scotland, UK, is a large shallow lake with a history of eutrophication, research and management and as such is an ideal study site to better understand resilience and regime shifts under a range of interacting stressors. The objectives of this research are to: (1) analyse long term data to identify the occurrence of common tipping points within the chemical (water column nutrient concentrations) and biological (macrophytes, phytoplankton, zooplankton) components of the loch, then test these tipping points using five statistical early warning indicators (EWIs) across multiple rolling window sizes; and (2) quantify the changes in lake ecology using a before/after analysis and testing for non-linearity, combined with modelling using the aquatic ecosystem process model PCLake to determine the level of resilience following a regime shift during recovery from eutrophication; (3) using PCLake, examine the sensitivity of Loch Leven to regime shifts in the face of predicted environmental change (e.g. climate change, nutrient pollution). Statistical analysis identified tipping points across all trophic levels included, from physical and chemical variables through to apex predators. The success of EWIs in predicting the tipping points was highly dependent on the number of EWIs used, with window size having a smaller impact. The 45% window size had the highest overall accuracy across all EWIs but only detected 16.5% more tipping points than the window size with the lowest overall accuracy. Differences between individual EWI performance and usage of them as a group was substantial with a 29.7% increase between the two. In both individual and group use of EWIs, false positives (early warning without a tipping point) were more common than true positives (tipping point preceded by EWI), creating significant doubts about their reliability as management tools. Significant change was seen across multiple variables and trophic levels in the before/after analysis following sudden recovery from eutrophication, with most variables also showing evidence of non-linear change. Modelling of responses to nutrient loading for chlorophyll, zooplankton and macrophytes, under states from before and after the shift, indicate hysteresis and thus the presence of feedback mechanisms. The modelling of responses to nutrient loading and predicted climate change in temperature and precipitation demonstrated that increases in temperature and decreases in summer precipitation individually had large impacts on chlorophyll and zooplankton at medium to high phosphorus (P) loads. However, modelling of the combined effects of these changes resulted in the highest lake chlorophyll concentrations of all tested scenarios. At low P loads higher temperatures and increased winter precipitation had the greatest impact on system resilience with a lower Critical Nutrient Load (CNL). The difference between chlorophyll and zooplankton as opposed to macrophytes was in the presence of a lower CNL for the increased winter precipitation-only scenarios which was not seen in the macrophytes. This highlights the potential role of high winter inputs potentially loaded with particulate matter in reducing resilience at lower P loads. This research has highlighted the vulnerability and low resilience of Loch Leven to environmental change. The presence of multiple tipping points and high levels of EWI activity show a high level of flexibility in the system. Coupled with the occurrence of widespread trophic change during a sudden recovery and a small level of hysteresis and high levels of sensitivity to climate change, the low levels of resilience become clear. The impact of lake-specific characteristics such as moderate depth, large fetch and a heterogeneous bed morphology is particularly evident in the limitations on macrophyte cover and the reliance on zooplankton to determine the hysteresis offset (amount of phosphorus (P) loading between the two CNL). The presence of these characteristics can be used to identify other lakes vulnerable to change. Improving the predictive capabilities of resilience indicators such as EWIs, and better understanding of the ecological changes that occur during non-linear change in response to recovery and climate change, can help target relevant ecosystem components for preventative management. These actions may become necessary under even the most conservative estimates of environmental change