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The ongoing expansion of wolf (Canis lupus) populations in Europe has led to a growing demand for up‐to‐date abundance estimates. Non‐invasive genetic sampling (NGS) is now widely used to monitor wolves, as it allows individual identification and abundance estimation without physically capturing individuals. However, NGS is resource‐intensive, partly due to the elusive behaviour and wide distribution of wolves, as well as the cost of DNA analyses. Optimisation of sampling strategies is therefore a requirement for the long‐term sustainability of wolf monitoring programs. Using data from the 2020–2021 Italian Alpine wolf monitoring, we investigate how (i) reducing the number of samples genotyped, (ii) reducing the number of transects, and (iii) reducing the number of repetitions of each search transect impacted spatial capture‐recapture population size estimates. Our study revealed that a 25% reduction in the number of transects or, alternatively, a 50% reduction in the maximum number of repetitions yielded abundance estimates comparable to those obtained using the entire dataset. These modifications would result in a 2046 km reduction in total transect length and 19,628 km reduction in total distance searched. Further reducing the number of transects resulted in up to 15% lower and up to 17% less precise abundance estimates. Reducing only the number of genotyped samples led to higher (5%) and less precise (20%) abundance estimates. Randomly subsampling genotyped samples reduced the number of detections per individual, whereas subsampling search transects resulted in a less pronounced decrease in both the total number of detections and individuals detected. Our work shows how it is possible to optimise wolf monitoring by reducing search effort while maintaining the quality of abundance estimates, by adopting a modelling framework that uses a first survey dataset. We further provide general guidelines on how to optimise sampling effort when using spatial capture‐recapture in large‐scale monitoring programmes. [ABSTRACT FROM AUTHOR]

The wolf (Canis lupus) is among the most controversial of wildlife species. Abundance estimates are required to inform public debate and policy decisions, but obtaining them at biologically relevant scales is challenging. We developed a system for comprehensive population estimation across the Italian alpine region (100,000 km 2 ), involving 1513 trained operators representing 160 institutions. This extensive network allowed for coordinated genetic sample collection and landscape-level spatial capture-recapture analyses that transcended administrative boundaries to produce the first estimates of key parameters for wolf population status assessment. Wolf abundance was estimated at 952 individuals (95% credible interval 816-1120) and 135 reproductive units (i.e., packs) (95% credible interval 112-165). We also estimated that mature individuals accounted for 33-45% of the entire population. The monitoring effort was spatially estimated thereby overcoming an important limitation of citizen science data. This is an important approach for promoting wolf-human coexistence based on wolf abundance monitoring and an endorsement of large-scale harmonized conservation practices., (© 2023 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Wolves have large spatial requirements and their expansion in Europe is occurring over national boundaries, hence the need to develop monitoring programs at the population level. Wolves in the Alps are defined as a functional population and management unit. The range of this wolf Alpine population now covers seven countries: Italy, France, Austria, Switzerland, Slovenia, Liechtenstein and Germany, making the development of a joint and coordinated monitoring program particularly challenging. In the framework of the Wolf Alpine Group (WAG), researchers developed uniform criteria for the assessment and interpretation of field data collected in the frame of different national monitoring programs. This standardization allowed for data comparability across borders and the joint evaluation of distribution and consistency at the population level. We documented the increase in the number of wolf reproductive units (packs and pairs) over 21 years, from 1 in 1993-1994 up to 243 units in 2020-2021, and examined the pattern of expansion over the Alps. This long-term and large-scale approach is a successful example of transboundary monitoring of a large carnivore population that, despite administrative fragmentation, provides robust indexes of population size and distribution that are of relevance for wolf conservation and management at the transnational Alpine scale.

Wildlife dispersal directly influences population expansion patterns, and may have indirect effects on the spread of wildlife diseases. Despite its importance to conservation, little is known about dispersal for several species. Dispersal processes in expanding wolf ( Canis lupus ) populations in Europe is not well documented. Documenting the natural dispersal pattern of the expanding wolf population in the Alps might help understanding the overall population dynamics and identifying diseases that might be connected with the process. We documented 55 natural dispersal events of the expanding Italian wolf alpine population over a 20-year period through the use of non-invasive genetic sampling. We examined a 16-locus microsatellite DNA dataset of 2857 wolf samples mainly collected in the Western Alps. From this, we identified 915 individuals, recaptured 387 (42.3%) of individuals, documenting 55 dispersal events. On average, the minimum straight dispersal distance was 65.8 km (±67.7 km), from 7.7 km to 517.2 km. We discussed the potential implications for maintaining genetic diversity of the population and for wildlife diseases spreading.