When can the cause of a population decline be determined?
Inferring the factors responsible for declines in abundance is a prerequisite to preventing the extinction of wild populations. Many of the policies and programmes intended to prevent extinctions operate on the assumption that the factors driving the decline of a population can be determined. Exogenous factors that cause declines in abundance can be statistically confounded with endogenous factors such as density dependence. To demonstrate the potential for confounding, we used an experiment where replicated populations were driven to extinction by gradually manipulating habitat quality. In many of the replicated populations, habitat quality and density dependence were confounded, which obscured causal inference. Our results show that confounding is likely to occur when the exogenous factors that are driving the decline change gradually over time. Our study has direct implications for wild populations, because many factors that could drive a population to extinction change gradually through time.
Department of Statistics and Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, 80523, USA. trevor.hefley@colostate.edu.
Trevor J
TJ
Hefley
Department of Statistics and Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, 80523, USA.|U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Unit, Fort Collins, CO, 80523, USA.
Mevin B
MB
Hooten
Odum School of Ecology, University of Georgia, Athens, GA, 30602.
John M
JM
Drake
U.S. Geological Survey, National Wildlife Health Center, Madison, WI, 80523, USA.
Robin E
RE
Russell
U.S. Geological Survey, National Wildlife Health Center, Madison, WI, 80523, USA.
Daniel P
DP
Walsh
D000818:Animals; D003198:Computer Simulation; D003621:Daphnia / Q000502:physiology*; D017753:Ecosystem; D016015:Logistic Models; D008954:Models, Biological; D011157:Population Dynamics; D013997:Time Factors
Allee effect; autocorrelation; causal inference; density dependence; extinction; multicollinearity; population dynamics; temporal confounding; time series