Details

Autor(en) / Beteiligte

• Blum, Marcus E.
• Buderman, Frances E.
• Bennett, Joe R.
• Stewart, Kelley M.
• Cox, Mike
• Williams, Perry J.

Titel

Comparing contemporary models to traditional indices to estimate abundance of desert bighorn sheep

Ist Teil von

• The Journal of wildlife management, 2024-02, Vol.88 (2), p.n/a

Ort / Verlag

Bethesda: Blackwell Publishing Ltd

Erscheinungsjahr

2024

Quelle

Wiley Online Library - AutoHoldings Journals

Beschreibungen/Notizen

• Aerial surveys for large ungulates produce count data that often underrepresent the number of animals. Errors in count data can lead to erroneous estimates of abundance if they are not addressed. Our objective was to address imperfect detection probability by developing a framework that produces realistic and defensible estimates of bighorn sheep (Ovis canadensis) abundance. We applied our framework to a population of desert bighorn sheep (O. c. nelsoni) in the Great Basin, Nevada, USA. We captured and marked 24 desert bighorn sheep with global positioning system (GPS)‐collars and then conducted helicopter surveys naïve to the locations of collared animals. We developed a Bayesian integrated data model to leverage information from telemetry data, helicopter survey counts, and habitat characteristics to estimate abundance while accounting for availability and perception probability (i.e., detection given availability). Distance to ridgeline, terrain ruggedness, tree cover, and slope influenced perception probability of sheep given they were viewable from the helicopter. There was also annual variation in perception probability (2018: median = 0.64, credible interval [CrI] = 0.37–0.87; 2019: median = 0.81, CrI = 0.49–0.97). The abundance estimates from the integrated data model decreased from 2018 (594; 95% CrI = 537–656) to 2019 (487; 95% CrI = 436–551). In addition, accounting for availability and imperfect perception resulted in greater estimates of abundance compared to traditional directed search methods, which were 340 for 2018 and 320 for 2019. Our modeling framework can be used to generate more defensible population estimates of bighorn sheep and other large mammals that have been surveyed in a similar manner. Aerial surveys for large ungulates produce count data that often underrepresent the total number of animals because of imperfect detection, which can be caused by obstructed views and species‐specific coloration and camouflage. We developed a Bayesian integrated data model to leverage information from multiple data sources (telemetry data, helicopter survey counts, habitat characteristics) to estimate abundance while accounting for both availability and perception bias (i.e., detection given availability). We found distance to ridgeline, terrain ruggedness, tree cover, and slope influenced perception probability of sheep given they were viewable from the helicopter and that our Bayesian hierarchical model improved abundance estimates for bighorn sheep within this population.