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

Abstract

We estimated wild boar abundance and density using capture-resight methods in the western part of the Canton of Geneva (Switzerland) in the early summer from 2004 to 2006. Ear-tag numbers and transmitter frequencies enabled us to identify individuals during each of the counting sessions. We used resights generated by self-triggered camera traps as recaptures. Program Noremark provided Minta-Mangel and Bowden's estimators to assess the size of the marked population. The minimum numbers of wild boars belonging to the unmarked population (juveniles and/ or piglets) were added to the respective estimates to assess total population size. Over the 3 years, both estimators showed a stable population with a slight diminishing tendency. We used mean home range size determined by telemetry to assess the sampled areas and densities. Mean wild boar population densities calculated were 10.6 individuals/km 2 ±0.8 standard deviation (SD) and 10.0 ind/km 2 ± 0.6 SD with both estimators, respectively, and are among the highest reported from Western Europe. Because of the low proportion of marked animals and, to a lesser extent, of technical failures, our estimates showed poor precision, although they displayed similar population trends compared to the culling bag statistics. Reported densities were consistent with the ecological conditions of the study area.

An assessment of the applicability of dung count to estimate the wild boar population density in a forest environment

“…This value seems to be extremely high in comparison with the abundance of wild boar found in other works -i.e. in the Bialowieza National Park -[3.5-5.9 indd·km -2 ; Jedrzejewska et al (1994)] or in forests of other European countries [1.5-12 indd·km -2 ; Hebeisen et al (2008)]. The very high population density in this area is probably due to improper game management and to the concentration of animals in the winter season.…”

“…FPG count is one of the most common (Neff 1968;Hemami, Dolman 2005) and the most accurate (Barnes 2002;Campbell et al 2004) methods estimating counts of wild animals; however, in the case of wild boar it is often left out, mainly due to the lack of data on defecation rate, seasonal migrations over long distances, until recently low population density and uneven distribution of faeces in the environment (Hebeisen et al 2008). Generally speaking, FPG count can be employed to estimate numbers of wild boar similarly like abundance of ruminants, bearing in mind its limiting conditions.…”

“…The low accuracy of current estimations of wild boar density limits the effectiveness of the wild boar popula-tion control (Merli, Meriggi 2006) and for this reason, improved methods to determine wild boar numbers are crucial in the management of this species. Density of wild boar has been estimated by modifications of direct observation (Focardi et al 2002); by repeated counting of marked individuals (Waithman et al 1999); by counting of tracks on snow cover (Fonseca et al 2007) or by photo-traps combined with marked individuals (Sweitzer et al 2000;Hebeisen et al 2008;Huckschlag 2008;Morimando et al 2008).…”

Monitoring wild pig populations: a review of methods

“…Animal marking and capture-mark-recapture methods Wild pig population densities are sometimes estimated using capture-mark-recapture (CMR) methods, where animals are usually captured and marked, often with ear tags (e.g., Hebeisen et al 2008). However, CMR in wild pigs is time-, cost-, and labor-intensive and highly prone to capture heterogeneity among age and sex classes (Baber and Coblentz 1986;Sweitzer et al 2000), and marks (e.g., ear tags) may be lost, resulting in violation of analytical assumptions and estimates of questionable quality.…”

A free and open source programming library for landscape metrics calculations

“…Also, landscape metrics help to model watershed hydrological systems and to identify and analyze possible future impacts on land use pattern and hydrology (Lin et al, 2007). There are studies based on the relationship of landscape structure with the hunting community (Jimenez-Garcia et al, 2006), as well as specific studies on the wild boar (Calenge et al, 2004;Hebeisen et al, 2008;Kaden et al, 2005;Monzón & Bento, 2004;Tsachalidis and Hadjisterkotis, 2008), red-legged partridge (Nadal, 2001;Vargas et al, 2006), ducks (Guillemain et al, 2008), mouflon (Garel et al, 2005), wild rabbit (Schröpfer et al, 2000) and some predators (Rico and Torrente, 2000). Landscape metrics enable this information to be incorporated into a GIS, helping to select potential areas and take appropriate management measures (Coulson et al, 2001).…”