Alex Arkilanian

University of Zurich
The Effect of Relative Sampling Timeseries Length and Resolution on Early Warning Signals of Population Collapse

Alex Arkilanian, Gaurav Baruah, Christopher F. Clements, Arpat Ozgul

Natural populations are increasingly threatened with collapse at the hands of expanding human populations and their associated impacts. Predicting population collapse in response to anthropogenic forcing with the help of generic early warning signals (EWS) may provide an optimistic 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 timeseries data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the achieved length and resolution of sampling timeseries are highly variable from one system to another. The effect of these timeseries’ length and resolution relative to the rate of their respective dynamic system on the efficacy of EWS and the newly proposed trait-based EWS is still unknown. We take a simulation- and experimental-based approach to assess the impacts of relative timeseries length and resolution on the forecasting ability of EWS. We show with model simulations and experimental data that EWS performance decreased with decreasing length and resolution. However, when trait information was included alongside abundance based EWS, we found strong and reliable signals of population collapse even when the length of the timeseries was very short and of low resolution. We suggest that whenever possible trait data should be monitored along with abundance such that more reliable forecasting can be derived on the stability of the underlying system.