A new double observer‐based census framework to improve abundance estimations in mountain ungulates and other gregarious species with a reduced effort

Estimating animal abundance has a key role to play in ecology and conservation, but survey methods are always challenged by imperfect detection. Among the techniques applied to deal with this issue, Double Observer (DO) is increasing in popularity due to its cost‐effectiveness. However, the effort of using DO for surveying large territories can be significant. A DO‐based survey method that allows accurate abundance estimations with reduced effort would increase the applicability of the method. This would have positive effects on the conservation of species which are challenging to survey such as mountain ungulates. We used computer simulations based on real data and a field test to assess the reliability of the DO and of a new proposed survey procedure, the Double Observer Adjusted Survey (DOAS). DOAS is based on total block counts adjusted with some DO surveys conducted in a proportion of the total area only. Such DO surveys are then used to estimate detection probability with a mark‐recapture‐derived approach. We found that full DO is much more accurate than simple block counts for abundance estimations. DOAS is a less demanding alternative to full DO and can produce comparable abundance estimates, at the cost of a slightly lower precision. However, in the DOAS overall detectability has to be estimated within a sufficient number of sites (around a quarter of the total) to obtain a higher precision and avoid large overestimations. Practical implications. DO methods could increase the reliability of abundance estimations in mountain ungulates and other gregarious species. Full DO in particular could allow researchers to obtain unbiased estimations with high precision and its usage is therefore suggested instead of block counts in wildlife monitoring. Given the high costs of full DO, the DOAS procedure could be a viable and cost‐effective survey strategy to improve abundance estimates when resources are scarce.