Thermal drone surveys to detect arboreal fauna: Improving population estimates and threatened species monitoring

Abstract

Sound methods to determine species occurrence and abundance are crucial for successful wildlife management and conservation. When species communities cannot be readily detected using camera traps or acoustic monitoring, ground survey methods such as spotlighting on foot are commonly used. While able to provide precise detection and density estimates, these methods can be laborious and time consuming and are restricted to surveying small areas. Advances in drone technology now allow for the detection of heat signatures of endothermal wildlife using thermal cameras from the sky, which we contrast to traditional ground surveys. We found that drone and ground surveys achieve similar detection probabilities for nocturnal arboreal mammals of southeastern Australia. Drones achieved high detection rates for targeted arboreal wildlife occurrence and consistently recorded more species and individuals than ground-based surveys via spotlighting. Ground surveys often missed specialist species like the endangered southern greater glider (Petauroides volans) when populations had low densities. Drone-derived density estimates for surveyed areas of 100-200 ha were significantly lower than those extrapolated from 10-ha ground survey results. Thermal drone surveys present a promising tool for measuring and monitoring nocturnal arboreal wildlife populations due to their ability to cover larger areas with comparable detection rates to ground surveys. Drone surveys provide comprehensive information on species assemblage, density, and distribution across management compartment-scale survey areas, offering valuable insights into species occurrence and population status. Drones were particularly effective in areas with dense vegetation or that were otherwise inaccessible for ground-based surveys, enhancing the ability to estimate populations, quantify recovery following large-scale disturbances, and to discover previously undocumented populations. Drone-based wildlife survey methods have the potential to reduce uncertainty in compartment-scale population estimates for improved wildlife monitoring and conservation.

Keywords: arboreal fauna; distance sampling; drone; population estimates; spotlighting; thermal imagery; threatened species; wildlife survey.

FIGURE 1

Number of individual observations of common ringtail possums (A) and southern greater gliders (B) and respective density estimates (C, common ringtail possums; D, southern greater gliders) from ground and drone observations within the footprint of a single‐ (the first, 4 ha) or three‐transect (10 ha) ground survey and across the entire compartment (drone only). Note that one compartment with a total number of 146 common ringtail possum observations by drone was removed from the figure as an outlier. Outliers are illustrated as red points, boxplots show the median, upper, and lower quantiles as well as minimum and maximum values. The error bar overlay illustrates the mean (dot) and 95% CIs (bars). Animal silhouettes were adapted from Lindenmayer (1996).

FIGURE 2

Species likelihood of detection curves based on estimated animal density across the compartment from drone surveys for common ringtail possums (A) and southern greater gliders (B). Data points were slightly jittered to avoid overlapping. Animal silhouettes were adapted from Lindenmayer (1996).

FIGURE 3

Three examples of the interplay of transect placement, spatial distribution of individuals, and population density estimates for southern greater gliders from ground and drone surveys. Compartment (A) recorded no observations from spotlighting transects (gray lines) within the 10‐ha ground survey area (blue boundary), but four animals were recorded outside the ground survey footprint by thermal drone survey. The area surveyed was 131 ha (dark gray), leading to a population density estimate of 0.05 animals/ha. The nearest neighbor index (NNI) was 2.1, indicating a highly dispersed, small population. In compartment (B), the same number of southern greater gliders (21) was detected by ground surveys within the 10‐ha survey area and in the 30‐ha compartment area by drone. Population density from ground surveys was estimated at 1.9 animals/hectare, while drone survey results led to an estimate of ~0.9 animals/ha. The NNI for this compartment was 0.75, indicating a clustered, dense population. Compartment C represents conditions where both ground survey and drone survey density estimates aligned. Five observations within the ground survey area resulted in estimates of 0.4 animals/ha, while the estimate for the entire compartment from drone observations was 0.3 animals/ha from a total of 25 observations over 118 ha. The NNI was 1.2, indicating a moderately dispersed population. Clustering of individuals in the southern part of the survey area can be observed.

FIGURE 4

Relationship of number of total observations (A and B, individuals of all species observed), common ringtail possums (CRP, C and D), and southern greater gliders (SGG, E and F) by drone with survey length and survey area. Similar relationships were observed from ground survey results for survey length (see Appendix S1: Figure S6). Animal silhouettes were adapted from Lindenmayer (1996).