Using helicopter counts to estimate the abundance of Himalayan tahr in New Zealand's Southern Alps

Abstract

AbstractEstimating the abundance and density of mountain ungulates is difficult because of rugged and remote terrain, high elevations, and rapidly changing weather. Helicopter surveys could overcome these problems, but researchers have seldom applied helicopter‐based survey methods at large spatial scales in mountain terrain. We used helicopters to count introduced Himalayan tahr (Hemitragus jemlahicus) at 117 plots, each of 4 km2, in New Zealand's Southern Alps during 2016–2019. The sampling frame was 7,844 km2 and we located the plots at the vertices of an 8‐km grid superimposed over the sampling frame (i.e., a systematic random sampling design). We conducted 3 repeat counts at each plot during summer–autumn. We used the repeat counts to estimate tahr abundance and density, corrected for imperfect detection, using a dynamic N‐mixture model for open populations. We estimated the population of tahr in the sampling frame using design‐based, finite sampling methods and model‐based inference procedures. The mean estimated density of tahr on each plot varied from zero to 31.7 tahr/km2. The mean densities of tahr varied among management units, ranging from 0.3 to 10.7 tahr/km2, and exceeded specified intervention densities in 6 of the 7 management units. The total design‐based estimate of tahr abundance in the sampling frame was 34,500 (95% CI = 27,750–42,900), with a coefficient of variation (CV) of 0.11. The corresponding model‐based estimate of total abundance was similar (34,550, 95% CI = 30,250–38,700) but was substantially more precise (CV = 0.06) than the design‐based estimate. The precision of the estimates for the individual management units was also better than that of the design‐based estimates, with CVs of <0.20 for all but 1 management unit. Our study provides a repeatable method for sampling mountain ungulates. More generally, robust estimation of abundance and density of mountain ungulates is possible by combining aerial surveys and open population models with an objective, probabilistic sampling design.