Biology subjects

Scheffer, M.

Publications and source records attributed to Scheffer, M..

6 recordsLinked to original sources

bioRxiv2024

Improved potential analysis for inadequate ecological data

Potential analysis is used in many ecological studies to infer whether or not an ecosystem can have alternative stable states, to estimate the tipping points and, to assess the resilience of ecosystems. The main reason behind its frequent use is that such a frequency-based analysis is a minimalistic modelling strategy and therefore, is well-suited for limited ecological data with measurement errors. It has been used extensively in tree cover studies to discern alternative states of savannah and forest, the regime upon which savannah and forest coexist and, their corresponding resilience. Classical potential analysis may produce biased results due to inherent theoretical and practical limitations. This letter introduces a more sophisticated approach to address these shortcomings and enhance predictive capabilities regarding ecological dynamics, especially when working with insufficient data whose incorrect analysis can significantly impact management strategies.

ecology↗

bioRxiv2024

Stochastic regimes can hide the attractors in data, reconstruction algorithms can reveal them

Tipping points and alternative attractors have become an important focus of research and public discussions about the future of climate, ecosystems and societies. However, empirical evidence for the existence of alternative attractors remains scarce. For example, bimodal frequency distributions of state variables may suggest bistability, but can also be due to bimodality in external conditions. Here, we bring a new dimension to the classical arguments on alternative stable states and their resilience showing that the stochastic regime can distort the relationship between the probability distribution of states and the underlying attractors. Simple additive Gaussian white noise produces a one-to-one correspondence between the modes of frequency distributions and alternative stable states. However, for more realistic types of noise, the number and position of modes of the frequency distribution do not necessarily match the equilibria of the underlying deterministic system. We show that data must represent the stochastic regime as thoroughly as possible. When data are adequate then existing methods can be used to determine the nature of the underlying deterministic system and noise simultaneously. This may help resolve the question of whether there are tipping points, but also how realized states of a system are shaped by stochastic forcing vs internal stability properties. Open Research StatementData and MATLAB codes for results reported here are available in the Github repository https://github.com/mshoja/Reconst (Babak M. S. Arani 2023) The original data source is cited in the text.

ecology↗

bioRxiv2023

Monitoring Resilience in Bursts

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in a variety of fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers increasingly slowly from external perturbations when approaching a tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems it may be more feasible to collect several high-resolution time series in short periods of time, i.e. in bursts. Resilience indicators can then be calculated to detect a change of resilience in a system between such bursts. Here, we compare the performance of both methods using simulated data, and showcase possible use of bursts in a case-study using mood data to anticipate depression in a patient. Using the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to down-sample the continuous time series and still signal of an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in systems resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring is costly or unfeasible. Significance statementGauging the risk of tipping points is of great relevance in complex systems ranging from health to climate, and ecosystems. For this purpose, dynamical indicators of resilience are being derived from long continuous time series to monitor the system and obtain early warning signals. However, gathering such data is often prohibitively expensive or practically unfeasible. Here we show that collecting data in brief, intense bursts may often solve the problem, making it possible to estimate change in resilience between the bursts withrelatively high precision. This may be particularly useful for monitoring resilience of humans or animals, where brief time series of blood pressure, balance, mood or other relevant markers may be collected relatively easily to help estimating systemic resilience.

ecology↗

bioRxiv2022

Quantifying the Human Cost of Global Warming

The costs of climate change are often estimated in monetary terms1,2 but this raises ethical issues3. Here we express them in terms of numbers of people left outside the human climate niche4 - defined as the historically highly-conserved distribution of relative human population density with respect to mean annual temperature (MAT). We show that climate change has already put [~]8% of people (>500 million) outside this niche. By end-of-century (2080-2100), current policies leading to around 2.7 {degrees}C global warming5-9 could leave one third (21-42% or 2-4 billion) of a future 9.5 billion population outside the niche. Limiting global warming to 1.5 {degrees}C could halve this exposure, reducing it by [~]1.5 billion people. For the two countries with the most people affected - India and Nigeria - reducing global warming from 2.7 to 1.5 {degrees}C results in a >6-fold decrease in the population exposed to unprecedented temperatures, MAT [≥]29 {degrees}C. The lifetime emissions of [~]3.5 global average citizens today (or [~]1.2 average US citizens) expose 1 future person to MAT [≥]29 {degrees}C by end-of-century. That person comes from a place where emissions today are around half of the global average. These results highlight the need for more decisive policy action to limit the human costs and inequities of climate change.

ecology↗

bioRxiv2021

Deep learning for early warning signals of regime shifts

Many natural systems exhibit regime shifts where slowly changing environmental conditions suddenly shift the system to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems all simplify down to a small number of possible normal forms that determine how the new regime will look. Indicators such as increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) by detecting how dynamics slow down near the tipping point. But they do not indicate what type of new regime will emerge. Here we develop a deep learning algorithm that can detect EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behaviour of dynamics near tipping points that are common to many dynamical systems. The algorithm detects EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that will characterize the oncoming regime shift. Such approaches can help humans better manage regime shifts. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally-occurring tipping points.

ecology↗

bioRxiv2018

Ecosystem tipping points in an evolving world

There is growing concern over tipping points arising in ecosystems due to the crossing of environmental thresholds. Tipping points lead to strong and possibly irreversible shifts between alternative ecosystem states incurring high societal costs. Traits are central to the feedbacks that maintain alternative ecosystem states, as they govern the responses of populations to environmental change that could stabilize or destabilize ecosystem states. However, we know little about how evolutionary changes in trait distributions over time affect the occurrence of tipping points, and even less about how big scale ecological shifts reciprocally interact with trait dynamics. We argue that interactions between ecological and evolutionary processes should be taken into account for understanding the balance of feedbacks governing tipping points in nature.

ecology↗