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 Double Observer (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. Double Observer 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.