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When enough is enough: Optimising monitoring effort for large‐scale wolf population size estimation in the Italian Alps

Boiani, Maria V.
; 
Dupont, Pierre
; 
Bischof, Richard
; 
Milleret, Cyril
; 
Friard, Olivier
; 
Geary, Matthew
; 
Avanzinelli, Elisa
; 
von Hardenberg, Achaz
; 
Marucco, Francesca
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Affiliation
University of Chester; Norwegian University of Life Sciences; University of Turin; Ente di Gestione Aree Protette Alpi Marittime; University of Pavia
EPub Date
Publication Date
2024-08-21
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Biological Sciences
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Abstract
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.
Citation
Boiani, M. V., Dupont, P., Bischof, R., Milleret, C., Friard, O., Geary, M., Avanzinelli, E., von Hardenberg, A., & Marucco, F. (2024). When enough is enough: Optimising monitoring effort for large‐scale wolf population size estimation in the Italian Alps. Ecology and Evolution, 14(8), article-number e70204. https://doi.org/10.1002/ece3.70204
Publisher
Wiley Open Access
Journal
Ecology and Evolution
Research Unit
URI
http://hdl.handle.net/10034/628962
DOI
10.1002/ece3.70204
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PubMed Central ID
Type
Article
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Description
© 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd.
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ISSN
EISSN
2045-7758
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Sponsors
LIFE programme. Grant Number: LIFE Wolfalps EU Project (LIFE18 NAT/IT/000972); Norges Forskningsråd. Grant Number: NFR 286886
Additional Links
https://onlinelibrary.wiley.com/doi/10.1002/ece3.70204