4 Discussion

We provide an efficient framework for estimating detection parameters required for SCR studies and validating empirical study designs for species where baseline detection data is not available. Our results using seven empirical datasets indicate that our genotyping protocol was highly successful, our capture and recapture rates were sufficient, and our study design was appropriate in producing precise and reliable density estimates. We followed the aerial survey protocol outlined in Hettinga et al. (2012) to inform our sampling design and obtained similar recapture rates between sampling occasions. We found that the detection parameters g0 (detection probability) and (the spatial extent of an individual’s use of the landscape) varied among our study populations and between sexes (Table 2, Table S2.1, and Table S2.2). Our results were robust to reduced sampling intensity (both in frequency and spatially), with the best study design dependent upon range size, and not dependent upon estimated population density or the spatial distribution of individuals.
For multiple species, the SCR model assumption that animals are independently and uniformly distributed over a study area is often violated, as is the case for boreal caribou (Després-Einspenner, Howe, Drapeau, & Kuhl, 2017; López-Bao et al., 2018; Stevenson et al., 2015). The fission-fusion social structure and dynamics exhibited by boreal caribou during the winter months leads to frequent exchanges between groups (Thomas & Gray, 2002). Our simulation results show that SCR models performed reliably; the grouping and movement patterns of boreal caribou during our sampling period had minimal impact on the precision or relative bias of the density estimates. We found a slight overestimation in density estimates (Appendix 2), but the precision and relative bias were not impacted. Few studies have looked at the effect that non-independence of individuals has on SCR methodologies. López-Bao et al. (2018) simulated scenarios of non-independence and spatial aggregation of individual wolves (Canis lupus ) with only a slight underestimation in population abundance estimates of aggregated individuals, while Després-Einspenner et al. (2017) were unsure to what extent the measures of uncertainty in their study of a community western chimpanzees (Pan troglodytes verus ) were underestimated.
Study designs can be inappropriate when poorly matched with the spatial behaviour of the target species (Williams et al., 2002). Detector arrays that are significantly smaller than one home range, or extreme detector spacing that leads to few or no spatial recaptures can result in biased SCR estimates (Efford, 2011; Efford & Boulanger, 2019; Sollmann et al., 2012; Tobler & Powell, 2013). Reducing the sampling intensity had a greater impact on populations with smaller range sizes regardless of density; reducing the number of transects flown led to extreme detector spacing with few or no spatial recaptures (Figs S4.4-S4.6). Increasing the temporal period of sampling can be an effective way of increasing the number of detected captures and recaptures available for analysis, which increases precision, however, increasing the temporal sampling period can also violate the assumption of population closure and lead to biased estimates (Dupont, Milleret, Gimenez, & Bischof, 2019). We found that the effects of reducing the number of sampling occasions on density estimates was influenced by the timing of the survey. If resources were only available to perform 2, rather than 3, sampling sessions, we recommend focusing on collecting samples early in the winter, rather than later in the winter, as we achieved relatively unbiased estimates (RB <20%) when retaining December, January, or February sampling occasions. Weather conditions during March surveys were not always favourable, with poor snow conditions and warm temperatures creating difficulties for finding animals and identifying fresh tracks and feeding areas.
Results from our empirical study provides a range of estimates that can be used for simulating surveys of boreal caribou in other locations. For poorly studied species, completing an initial empirical study is critical for obtaining accurate detection probability estimates. Due to the clustered, nonhomogeneous distribution of boreal caribou, extensive sampling of the entire population is recommended to ensure that clusters of caribou are not missed during sampling. Our subsampling scenarios showed how less extensive sampling in smaller ranges can miss a large portion of the population, increasing the relative bias and imprecision of the density estimates. Applying the same sampling design to all seven of our study populations proved to be suboptimal; detector spacing for the smaller populations relative to sigma led to imprecise estimates.
Our analytical framework allowed us to examine the results of empirical surveys in depth, providing confidence in the density estimates. Through different simulations we were able to explore how relative bias and precision of estimates vary when assumptions are violated. We showed that the number of individuals and recaptures of individuals can be used to predict precision, but that they cannot be used to predict relative bias. Efford & Boulanger (2019) state that subsampling of data to emulate different configurations of detectors, or different temporal sampling can be prohibitively slow, due to model fitting being computer-intensive; however, we found that even for our largest population model (24,737 km2, 386 unique individuals, and 545 recaptures), modelling with time and behaviour effects on bothg0 and \(\sigma\) ran relatively quickly (~7-10 days on a high-performance computer cluster) in a maximum likelihood framework, where the density model was fitted by maximizing the conditional likelihood.
We recommend the combination of non-invasive DNA sampling, together with SCR modeling and distribution simulations, to be an effective, accurate and precise approach to monitoring wildlife.