Tests of density dependence using indices of relative abundance in a deer population

Abstract

A major question in animal ecology is explaining the causes of population fluctuations. Consensus about the most reliable method to detect density dependence (DD) or environmental effects from time‐series data, however, has not yet been achieved. Times series analyses have been used with indices of relative abundance in numerous studies, although these indices are rarely validated. Here, we used three different time series of relative abundance (number of deer seen per hunter per day, hunting success and proportion of males in the harvest) to explore direct and delayed DD in a white‐tailed deer Odocoileus virginianus population on Anticosti Island, Québec, Canada. Three mathematical approaches were tested: linear models, autoregressive (AR) models, and total DD in life history. Tests of DD using different indices of abundance on the same population should lead to similar results if all indices exhibit similar behaviour. Indices of relative abundance correlated with each other, although sometimes weakly, such that we obtained similar DD estimates with each index using detrended non‐stationary series. In most time series, linear regression of Nt−1 and Nt and AR models did not detect DD, while we obtained strong evidence for DD from the life‐history approach. This meant that contrasting conclusions about the role of density dependence within this population were reached depending on which method was used. We conclude that the method that incorporates most biological realism, the life‐history approach, provided a different result than classical interpretation of autoregressive coefficients. Only the life‐history interpretation supported our a priori belief that density dependence operating through competition for food regulates the Anticosti deer population. Phenomenological analysis aiming to investigate changes in abundance should be carefully conducted as the use of inappropriate indices or methods could lead to inappropriate conclusions or management strategies. Preferably, the method used should match the time scale of the population sampling regime and species life history.

Is hunting an effective tool to control overabundant deer? A test using an experimental approach

“…Because aerial surveys could not be performed each year, we used 2 other indices to monitor variations in deer density in control and experimental sites. We used the yearly average number of deer seen per hunter per day, which was correlated with density estimates from aerial surveys, both spatially (Pettorelli et al 2007) and temporally (Simard et al 2012). This index also correlated with population estimates in other ungulate populations (Solberg et al 1999, Mysterud et al 2007).…”

Advances in population ecology and species interactions in mammals

“…This requires more comprehensive data than does simpler linear modeling, and consequently has seen limited use. Simard et al (2012) applied linear and autoregressive modeling as well as Lande-style analyses to evaluate DD in white-tailed deer in Quebec. Their understanding of the life history of this population led the authors to expect both direct and delayed DD, but neither linear nor autoregressive models supported this.…”

Density‐dependent fitness, not dispersal movements, drives temporal variation in spatial genetic structure in dark‐eyed juncos (Junco hyemalis)

“…We censused juvenile juncos (<1‐year‐old predispersal non‐nesting juncos) and adult juncos (sexually mature, >1‐year‐old, postdispersal potentially breeding juncos) at our site for 11 years, developing two density indices for each year (Simard, Cote, Gingras, & Coulson, ): predispersal density (juvenile population at the end of the previous breeding season) and postdispersal density (adult population at the start of the current breeding season). Using microsatellite DNA amplified from blood collected from nesting adults, we measured fine‐scale spatial genetic structure relative to pre‐ and postdispersal density.…”