DOI: 10.5061/dryad.zkh1893ks
title: A new Double Observer based census framework to improve abundance estimations in mountain ungulates and other gregarious species with a reduced effort

description: 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.
description: Main data were obtained through computer simulations, with the
 script provided "DOAS script.R" With the
 provided dataset we aimed to test the robustness of the full DO and our
 proposed DOAS (see related paper for more info), in different field
 conditions. The DOAS method is based on Double Observer surveys being
 performed in only a portion of the total target area to estimate the
 detection probability, that is then used to adjust the counts obtained
 with total block counts conducted in the whole area. To
 test DO and DOAS methods we carried out simulated surveys on populations
 randomly built under real-case parameters, using as a case study the
 population of Alpine ibex (<em>Capra ibex</em>) in Gran
 Paradiso National Park (GPNP, Italy). This simulation results in the
 provided "MAIN data). Using these simulated surveys, we tested
 whether DO conducted in the entire area (full DO) and DOAS are able to
 provide reliable abundance estimates, compared to block counts, and the
 relative costs of the two DO methods. Moreover, we analyzed factors that
 can influence the proportion of individuals estimated with full DO and
 DOAS, including abundance, group size and detection probability
description: # A new Double Observer based census framework to improve abundance
 estimations in mountain ungulates and other gregarious species with a
 reduced effort
 [https://doi.org/10.5061/dryad.zkh1893ks](https://doi.org/10.5061/dryad.zkh1893ks) ## Description of the data and file structure We provide the R script to perform computer simulations (DOAS script.R) on the reliability of the 3 census methods testes: block counts, full DO and our proposed DOAS procedure. The "DOAS_maindata.csv" is a representative subset of the final dataset obtained running the provided script. Each row is a single simulation, the columns are (see paper for details): * det.p = simulated detection probability * pop.size= simulated population size  * mean.group = average simulated group size * size.effect= multiplying factor for the detectability of a single group compared to a large group * miscount= miscount effect (average group abundance counted compared to the real one) * surv.sites= number of sites in which DO is performed for the DOAS * obs.effect= observer effect (det.p of the second observer compared to the first) * detp.var= coefficient of variation of detection probability across sites * totalDO.groupdetect= detectability estimated with the total DO * totalDO= ratio of individuals estimated with total DO * census= ratio of individuals estimated with block counts * groupadjust.censusSR= ratio of individuals estimated with DOAS * group.detectionSR= detectability estimated with DOAS ## Code/software DOASscript.R is the R script to obtain all the data in DOAS_maindata.csv