scholarly journals Including trait-based early warning signals helps predict population collapse

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher F. Clements ◽  
Arpat Ozgul
Keyword(s):  
Early Warning ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Population Collapse

scholarly journals Weighted trait-abundance early warning signals better predict population collapse

2018 ◽  
Author(s):  
Christopher F. Clements ◽  
Martijn van de Pol ◽  
Arpat Ozgul
Keyword(s):  
Early Warning ◽  
Experimental System ◽  
Small Scale ◽  
Phenotypic Traits ◽  
Data Types ◽  
Anthropogenic Pressures ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Population Collapse ◽  
Sampling Protocols

AbstractPredicting population collapse in the face of unprecedented anthropogenic pressures is a key challenge in conservation. Abundance-based early warning signals have been suggested as a possible solution to this problem; however, they are known to be susceptible to the spatial and temporal subsampling ubiquitous to abundance estimates of wild population. Recent work has shown that composite early warning methods that take into account changes in fitness-related phenotypic traits - such as body size - alongside traditional abundance-based signals are better able to predict collapse, as trait dynamic estimates are less susceptible to sampling protocols. However, these previously developed composite early warning methods weighted the relative contribution of abundance and trait dynamics evenly. Here we present an extension to this work where the relative importance of different data types can be weighted in line with the quality of available data. Using data from a small-scale experimental system we demonstrate that weighted indicators can improve the accuracy of composite early warning signals by >60%. Our work shows that non-uniform weighting can increase the likelihood of correctly detecting a true positive early warning signal in wild populations, with direct relevance for conservation management.

scholarly journals Effect of time-series length and resolution on abundance- and trait-based early warning signals of population declines

2019 ◽  
Author(s):  
A.A. Arkilanian ◽  
C.F. Clements ◽  
A. Ozgul ◽  
G. Baruah
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Generation Time ◽  
Time Series Data ◽  
Series Data ◽  
Series Length ◽  
Warning Signals ◽  
Relative Time ◽  
Early Warning Signals ◽  
Population Collapse

AbstractNatural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal to noise ratio of ecological systems and the need for high quality time-series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time-series length and resolution on the forecasting ability of EWS. We show that EWS’ performance decreases with decreasing length and resolution. Our simulations suggest a relative time-series length between ten and five generations and a resolution of half a generation are the minimum requirements for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths and resolutions half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

Are generic early-warning signals reliable indicators of population collapse in rotifers?

Hydrobiologia ◽  
2016 ◽  
Vol 796 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Stefan Sommer ◽  
Koen J. van Benthem ◽  
Diego Fontaneto ◽  
Arpat Ozgul
Keyword(s):  
Early Warning ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Population Collapse

scholarly journals Factors Influencing the Detectability of Early Warning Signals of Population Collapse

2015 ◽  
Vol 186 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Christopher F. Clements ◽  
John M. Drake ◽  
Jason I. Griffiths ◽  
Arpat Ozgul
Keyword(s):  
Early Warning ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Population Collapse ◽  
Factors Influencing

scholarly journals European Neolithic societies showed early warning signals of population collapse

2016 ◽  
Vol 113 (35) ◽  
pp. 9751-9756 ◽  
Author(s):  
Sean S. Downey ◽  
W. Randall Haas ◽  
Stephen J. Shennan
Keyword(s):  
Early Warning ◽  
Statistical Tests ◽  
Regime Shifts ◽  
Calibration Error ◽  
Atmospheric Effects ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Spatial And Temporal Scales ◽  
Population Collapse ◽  
European Neolithic

Ecosystems on the verge of major reorganization—regime shift—may exhibit declining resilience, which can be detected using a collection of generic statistical tests known as early warning signals (EWSs). This study explores whether EWSs anticipated human population collapse during the European Neolithic. It analyzes recent reconstructions of European Neolithic (8–4 kya) population trends that reveal regime shifts from a period of rapid growth following the introduction of agriculture to a period of instability and collapse. We find statistical support for EWSs in advance of population collapse. Seven of nine regional datasets exhibit increasing autocorrelation and variance leading up to collapse, suggesting that these societies began to recover from perturbation more slowly as resilience declined. We derive EWS statistics from a prehistoric population proxy based on summed archaeological radiocarbon date probability densities. We use simulation to validate our methods and show that sampling biases, atmospheric effects, radiocarbon calibration error, and taphonomic processes are unlikely to explain the observed EWS patterns. The implications of these results for understanding the dynamics of Neolithic ecosystems are discussed, and we present a general framework for analyzing societal regime shifts using EWS at large spatial and temporal scales. We suggest that our findings are consistent with an adaptive cycling model that highlights both the vulnerability and resilience of early European populations. We close by discussing the implications of the detection of EWS in human systems for archaeology and sustainability science.

scholarly journals Efficacy of early warning signals and spectral periodicity for predicting transitions in bipolar patients: An actigraphy study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoram K. Kunkels ◽  
Harriëtte Riese ◽  
Stefan E. Knapen ◽  
Rixt F. Riemersma - van der Lek ◽  
Sandip V. George ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Early Warning ◽  
Type I ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Research Fields ◽  
Mood Episode ◽  
Final Sample ◽  
Bipolar Patients ◽  
Direction Opposite

AbstractEarly-warning signals (EWS) have been successfully employed to predict transitions in research fields such as biology, ecology, and psychiatry. The predictive properties of EWS might aid in foreseeing transitions in mood episodes (i.e. recurrent episodes of mania and depression) in bipolar disorder (BD) patients. We analyzed actigraphy data assessed during normal daily life to investigate the feasibility of using EWS to predict mood transitions in bipolar patients. Actigraphy data of 15 patients diagnosed with BD Type I collected continuously for 180 days were used. Our final sample included eight patients that experienced a mood episode, three manic episodes and five depressed episodes. Actigraphy data derived generic EWS (variance and kurtosis) and context-driven EWS (autocorrelation at lag-720) were used to determine if these were associated to upcoming bipolar episodes. Spectral analysis was used to predict changes in the periodicity of the sleep/wake cycle. The study procedures were pre-registered. Results indicated that in seven out of eight patients at least one of the EWS did show a significant change-up till four weeks before episode onset. For the generic EWS the direction of change was always in the expected direction, whereas for the context-driven EWS the observed effect was often in the direction opposite of what was expected. The actigraphy data derived EWS and spectral analysis showed promise for the prediction of upcoming transitions in mood episodes in bipolar patients. Further studies into false positive rates are suggested to improve effectiveness for EWS to identify upcoming bipolar episode onsets.

Early warning signals using AVaRs of infinitely divisible GARCH models — evidence from stock index markets

2015 ◽  
Vol 47 (43) ◽  
pp. 4630-4652 ◽  
Author(s):  
Chia-Chien Chang ◽  
Te-Chung Hu ◽  
Chiu-Fen Kao ◽  
Ya-Chi Chang
Keyword(s):  
Early Warning ◽  
Stock Index ◽  
Garch Models ◽  
Infinitely Divisible ◽  
Warning Signals ◽  
Early Warning Signals
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