Résumé
Estimating the relative abundance (prevalence) of different population
segments is a key step in addressing fundamental research questions in
ecology, evolution, and conservation. The raw percentage of individuals in
the sample (naive prevalence) is generally used for this purpose, but it
is likely to be subject to two main sources of bias. First, the
detectability of individuals is ignored; second, classification errors may
occur due to some inherent limits of the diagnostic methods. We developed
a hidden Markov (also known as multievent) capture–recapture model to
estimate prevalence in free‐ranging populations accounting for imperfect
detectability and uncertainty in individual's classification. We
carried out a simulation study to compare naive and model‐based estimates
of prevalence and assess the performance of our model under different
sampling scenarios. We then illustrate our method with a real‐world case
study of estimating the prevalence of wolf (Canis lupus) and dog (Canis
lupus familiaris) hybrids in a wolf population in northern Italy. We
showed that the prevalence of hybrids could be estimated while accounting
for both detectability and classification uncertainty. Model‐based
prevalence consistently had better performance than naive prevalence in
the presence of differential detectability and assignment probability and
was unbiased for sampling scenarios with high detectability. We also
showed that ignoring detectability and uncertainty in the wolf case study
would lead to underestimating the prevalence of hybrids. Our results
underline the importance of a model‐based approach to obtain unbiased
estimates of prevalence of different population segments. Our model can be
adapted to any taxa, and it can be used to estimate absolute abundance and
prevalence in a variety of cases involving imperfect detection and
uncertainty in classification of individuals (e.g., sex ratio, proportion
of breeders, and prevalence of infected individuals).