Individual heterogeneity as a pitfall in population estimates based on non‐invasive genetic sampling: a review and recommendations

Abstract

In recent years, much progress has been made in non-invasive genetic methods for various purposes including population estimation. Previous research focused on optimising laboratory protocols and assessing genotyping errors. However, an important source of bias in population estimates still remains in the field sampling methods. The probability of animals being sampled can vary according to sex, age, social status or home-range location. In this article, we present relevant literature reviewed to provide an overview of the occurrence of individual heterogeneity (IH) in the field, and how it can be minimised, e.g. by adaptation of sampling design. We surveyed 38 articles describing noninvasive population estimation for 12 mammal and two bird species. The majority of these studies discussed IH as a potential problem. The detectability of IH via goodness-of-fit testing depended on the average capture probability reported in the studies. Field tests for assessing variation in sampling probabilities or validating estimations were carried out in only 11 of the 38 studies. The results of these tests point out that IH is a widespread problem in noninvasive population estimation, which deserves closer attention not only in the development of laboratory protocols but also concerning the sampled species' characteristics and the field methods. IH can be reduced in the field by carefully adapting the sampling design to the characteristics of the studied population. If this is not reasonable, it may be better to switch to a different sampling strategy.

Evidence for improved connectivity between Cantabrian brown bear subpopulations

“…Palomero et al (2007b) important source of IH in our data set, similar to that reported in many bear studies (Mace et al 1994, Boulanger et al 2002, Bellemain et al 2005. We minimized these problematic issues by combining diff erent sampling strategies (systematic and opportunistic) and sample types (feces and hair) (Pollock et al 1982, Williams et al 2002, Ebert et al 2010. Hair sampling using traps requires an active approach by the animal that could aff ect the sampling probability.…”

“…IH occurs as a result of biological characteristics of the study species, individual attributes such as sex, age, seasonal changes of habitat use due to individual or habitat features, territoriality or even sampling procedures, e.g. seasons or sessions (Ebert et al 2010). As we have already shown, sex is an been caught on a large number of occasions (13 times; Supplementary material Appendix 1 Table A4), thus showing a high capture probability.…”

“…Th e fact that the combination of diff erent sampling strategies is a good approach to reduce IH in the fi eld has already been reported in the literature (Boulanger et al 2008). When multiple approaches are used simultaneously, the impact of IH caused by any single method can be minimized (Pollock et al 1982, Williams et al 2002, Boulanger et al 2008, Ebert et al 2010. Furthermore, we adapted the estimates of population size from CMR-estimators to IH.…”

“…feces, along transects appears to be a good approach as it is less aff ected by individual behavior. However, passive sampling can be aff ected by other sources of IH such as diff erences in deposition rate and pattern (Ebert et al 2010). Th e fact that the combination of diff erent sampling strategies is a good approach to reduce IH in the fi eld has already been reported in the literature (Boulanger et al 2008).…”

Bias in estimation of adult survival and asymptotic population growth rate caused by undetected capture heterogeneity

“…In addition, when using DNA samples, it is possible to allow for misidentification using modelling techniques (Lukacs & Burnham 2005;Wright et al 2009). A related issue is that CH can sometimes be avoided by the use of an appropriate sampling design; Ebert et al (2010) provided an example in which intense sampling effort on a small study area with few sampling occasions led to CH being less likely to be detected. It is worth noting that in the context of abundance estimation, use of a mixture model to allow for individual CH (Pledger, Pollock & Norris 2003) can sometimes lead to identifiability issues (Holzmann, Munk & Zucchini 2006).…”