Wolf survival and population trend using non‐invasive capture–recapture techniques in the Western Alps

Abstract

Summary 1.Reliable estimates of population parameters are often necessary for conservation management but these are hard to obtain for elusive, rare and wide-ranging species such as wolves Canis lupus. This species has naturally recolonized parts of its former habitat in Western Europe; however, an accurate and cost-effective method to assess population trend and survival has not been implemented yet. 2.We used open-model capture-recapture (CR) sampling with non-invasive individual identifications derived from faecal genotyping to estimate survival and trend in abundance for wolves in the Western Alps between 1999 and 2006. Our sampling strategy reduced individual heterogeneity in recaptures, thus minimizing bias and increasing the precision of the estimates. 3. Young wolves had lower apparent annual survival rates (0AE24 ± 0AE06) than adult wolves (0AE82 ± 0AE04); survival rates were lower in the summer than in the winter for both young and adults. The wolf population in the study area increased from 21 ± 9AE6 wolves in 1999 to 47 ± 11AE2 wolves in late winter 2005; the population growth rate (k = 1AE04 ± 0AE27) was lower than that recorded for other recolonizing wolf populations. 4. We found a positive trend in wolf abundance, regardless of the method used. However, the abundance estimate based on snow-tracking was on average 36AE2% (SD = 13AE6%) lower than that from CR modelling, because young dispersing wolves are likely to have lower sign detection rates in snow-track surveys, a problem adequately addressed by CR sampling. 5. Synthesis and applications. We successfully implemented a new method to assess large carnivore population trend and survival at large spatial scales. These are the first such estimates for wolves in Italy and in the Alps and have important management implications. Our approach can be widely applied to broader spatial and temporal scales for other elusive and wide-ranging species in Europe and elsewhere.

Monitoring wolves (Canis lupus) by non-invasive genetics and camera trapping: a small-scale pilot study

“…Free-ranging dogs and hybrids often occur within the wolf range Verardi et al 2006), but field monitoring methods are seldom able to identify reliably the species, be they wolf, dog or hybrid. However, new methods such as non-invasive genetic sampling (NGS) and camera trapping have been recently applied to large predator monitoring programmes, and promise to solve these problems (Karanth and Nichols 1998;Kohn and Wayne 1997;Marucco et al 2009;Sarmento et al 2009;Taberlet et al 1997;Waits and Paetkau 2005).…”

Designing cost-effective capture-recapture surveys for improving the monitoring of survival in bird populations

“…Survival estimations often rely on the use of GPS/VHF tracking that is not well suited for long-term monitoring. Camera-trapping and DNA-based identification are increasingly used to improve 480 CR surveys in such species (Marucco et al 2009, Cubaynes et al 2010, O'Connell et al 2010 and we believe that a cost-efficiency approach may be helpful for carefully designing optimal surveys in such monitoring. For example, one could simulate different sampling designs varying by trap number, inter-trap distance and the area covered for carnivores having small or large home-ranges to assess the effect of these components on the detection of 485 survival variation.…”

“…Our simulations included standard CR techniques and alternative methods may be achievable to decrease the cost of the more effective but less efficient design components. For instance, collecting biological materials to implement identification of individuals through DNA analyses might provide valuable data for ON long-lived species (Marucco et al 2009, Bulut et 440 al. 2016, Woodruff et al 2016.…”

Genetic and genomic monitoring with minimally invasive sampling methods

“…There is now a wealth of published evidence that MIS is more cost-effective than traditional methods that require other technological approaches (e.g., camera trapping, tracks and signs and even trapping animals) and that collection and analysis of larger sample sizes are often possible (De Barba et al, 2010;Marucco et al, 2009;Solberg et al, 2006;Stenglein et al, 2010), prompting many wildlife managers to shift to MIS approaches. Extensive methodological and analytical development has been invested in establishing protocols to maximize success rates and minimize error rates when using these low-quality DNA sources for genetic monitoring (Beja-Pereira et al, 2009;Broquet & Petit, 2004;Miquel et al, 2006;Morin et al, 2010;Smith & Wang, 2014;Taberlet, Griffin, et al, 1996;Taberlet & Luikart, 1999;Wang, 2016).…”