Large scale wildlife monitoring studies: statistical methods for design and analysis

Abstract

SUMMARYTechniques for estimation of absolute abundance of wildlife populations have received a lot of attention in recent years. The statistical research has been focused on intensive small-scale studies. Recently, however, wildlife biologists have desired to study populations of animals at very large scales for monitoring purposes. Population indices are widely used in these extensive monitoring programs because they are inexpensive compared to estimates of absolute abundance. A crucial underlying assumption is that the population index (C) is directly proportional to the population density (D). The proportionality constant, , is simply the probability of`detection' for animals in the survey. As spatial and temporal comparisons of indices are crucial, it is necessary to also assume that the probability of detection is constant over space and time. Biologists intuitively recognize this when they design rigid protocols for the studies where the indices are collected. Unfortunately, however, in many ®eld studies the assumption is clearly invalid. We believe that the estimation of detection probability should be built into the monitoring design through a double sampling approach. A large sample of points provides an abundance index, and a smaller sub-sample of the same points is used to estimate detection probability. There is an important need for statistical research on the design and analysis of these complex studies. Some basic concepts based on actual avian, amphibian, and ®sh monitoring studies are presented in this article.

History, applications, methodological issues and perspectives for the use environmental DNA (eDNA) in marine and freshwater environments

“…Statistical analyses: Sampling methods that rely on presence/absence data to estimate the number of individuals in an area, often suffer from imperfect detection (Pollock, Nichols, Simmons, Farnworth, Bailye, & Sauer, 2002); and eDNA studies will too (Schmidt et al 2013). To date, there has been little attention given to eDNA detection probabilities with most studies assuming perfect or near perfect detection of the taxon of interest (Hyman & Collins, 2012;Thomsen et al, 2012a).…”

“…However, how these parameters might affect eDNA detection probabilities, remains unclear (Pilliod et al, 2014). Site occupancy models provide a means to account for imperfect detection of various sampling methods (Pollock et al, 2002;Andrew Royle, & Dorazio, 2008;Pilliod, Goldberg, Arkle, & Waits, 2013) including eDNA methods (Schmidt et al, 2013). Occupancy models can be used to study the effects of various abiotic and biotic factors that influence detection probabilities (both in the field and in the laboratory), and to determine the number of visits, number of samples and volume of water needed to be confident that a species is absent from a site (Schmidt et al, 2013).…”

Sources of Measurement Error, Misclassification Error, and Bias in Auditory Avian Point Count Data

“…Avian abundance estimates can vary over space and time due to actual differences in abundance, differences in detection probabilities among counts, and differences associated with measurement and misclassification errors (Nichols et al 2008;Nichols et al 2000;Farnsworth et al 2002;Pollock et al 2002;Rosenstock et al 2002;Thompson 2002).…”

“…Similarly, differences in abundance between primary and secondary forest habitats become apparent once counts are adjusted for differences in detection probability between habitats. Failure to account for such differences detection probabilities among species and habitats weakens inferences from comparative studies of avian abundance (Yoccoz et al 2001;Pollock et al 2002;Williams et al 2002).…”

Estimating wild boar (Sus scrofa) abundance and density using capture–resights in Canton of Geneva, Switzerland

“…Estimates that depend on indirect indices of presence can be used to minimize interactions with the animals. However, these indices must be directly proportional to population density to enable comparison in space and time (Nichols 1992;Pollock et al 2002), a condition rarely met in changing environmental conditions. Capture-mark-recapture (CMR) methods (Otisbe small if the trapping is not efficient, and probabilities of capture-recapture can vary among the population (Pollock et al 1990;Nichols 1992;MacKenzie et al 2005).…”