3 Results
3.1.1 Capture and spatial recapture
rates
A total of 7,210 fecal samples were collected and 6,865 were
successfully genotyped (average 95.1% genotyping success), resulting in
the identification of 1,755 unique individuals from the seven
populations (Table 1). Only four allelic dropout amplification errors
occurred (error rate <0.001%). We obtained adequate number of
captures, number of unique individuals, number of recaptures, and number
of spatial recaptures for all seven populations (Table 1), with the
lowest spatial recapture rates being in the smaller populations of
Little Smoky and Slave Lake. We had similar recaptures and spatial
recaptures rates for females and males (Table S2.1, Table S2.3).
3.1.2 Empirical model
performance
Density estimation for both sexes combined and for females had good
precision (CV <30%; Table 2, Table S2.1), but modelling males
separately led to density estimates for some populations having poor
precision (Table S2.2). The average detection probability was low
(g0 < 0.06; Table 2) for all populations except the
first sampling occasion for Slave Lake (g0t1 =
0.66, g0t2 = 0.036, g0t3 =
0.44). \(\sigma\) differed among populations, ranging from 1,226 m in
Slave Lake to 3,363 m in Cold Lake (Table 2).
3.1.3 Assumptions of homogeneous
distribution
Results of simulations showed that clustering of caribou detections did
not impact the precision or relative bias of the density estimates
(Appendix 2). Median density estimates remained similar and slightly
above the starting density for all levels of clustering density
(\(\mu\)) for the three simulated populations. The simulated Cold Lake
population estimates retained the highest precision and were relatively
unbiased, despite clustering, which corresponds with the precision found
for the empirical model (Table 2). The simulated Little Smoky and Slave
Lake population density estimates had lower precision than Cold Lake
when caribou were clustered, but median density estimates were not
affected by clustering, and density estimates from both populations
remained unbiased (Appendix 2). Using a threshold value for precision of
CV <30%, Little Smoky and Slave Lake had inadequate median
levels of precision at all levels of \(\mu\). These populations had
similar (Little Smoky \(\sigma\) = 1600 m) or smaller (Slave Lake\(\sigma\) = 1200 m) \(\sigma\) values compared to the chosen detector
spacing of 1500 m (see Appendix 4). The detector spacing of 1500 m for
the empirical studies for these populations was too wide relative to\(\sigma\), with very few spatial recaptures of individuals (36 in
Little Smoky, 38 in Slave Lake over three occasions), as the detector
spacing was larger than \(\sigma\).
3.1.4 Precision and relative bias of reduced sampling
designs
In total, 36 different subsampling scenarios were run for each
population, for a total of 252 models. Precision and relative bias were
positively correlated for all sexes (both sexes r = 0.557,p < 0.0001, female r = 0.597, p< 0.0001, male r = 0.634, p < 0.0001),
with decreasing precision (increased CV) and increasing relative bias
(divergence from the estimate from the full dataset) with increased
transect spacing and reduced number of occasions (Figs 3-4). Several
scenarios failed to converge for Little Smoky and Slave Lake at 6 km and
9 km due to low numbers of individuals and no recaptures, resulting in
227 completed models. The Little Smoky and Slave Lake ranges are two of
the geographically smallest ranges (Table 1; Fig. 2), and samples in
these areas were clustered geographically (Fig. 2). The detection
function scaling parameter (\(\sigma\)) for the empirical data for
Little Smoky and Cold Lake were smaller than the detector spacing of
1500 m and reducing the number of transects increased the detector
spacing even further, leading to the detector spacing being
significantly larger than the \(\sigma\) estimates for these
populations.
Precision of the subsampling scenarios were influenced by the number of
unique individuals, number of recaptures, and number of spatial
recaptures (Fig. 5). Precision was negatively correlated with the number
of individuals, with precision decreasing with fewer captured
individuals (Table S2.5, Fig. 5); all models that failed to run had no
recaptures of individuals. The larger ranges of Cold Lake, ESAR, WSAR
and Red Earth had more unique individuals than the smaller ranges of
Little Smoky, Nipisi and Slave Lake (Fig. 5). When determining the
influence of the number of individuals on model precision, all models
with three occasions had adequate precision (<30% CV) for
both sexes in the larger populations. The number of unique individuals
had a greater influence in the smaller ranges, leading to inadequate
precision in Little Smoky, Nipisi and Slave Lake (Fig. 5), with no
significant correlation between precision and the number of unique
individuals in Slave Lake (both sexes) and Little Smoky males (Table
S2.5). Precision was negatively correlated with the number of recaptures
(Table S2.6) and spatial recaptures (Table S2.7), with lower precision
in the smaller populations compared to the larger populations. All
models with three occasions for the larger populations fell below the
30% CV threshold for all sex models (Fig. 5). Even when decreasing the
number of occasions to two, the larger ranges still performed well with
adequate precision, as these subsets still provided an adequate number
of recaptures of individuals for the models to run and precision was
significantly correlated to the number of recaptures (Table S2.6, Fig.
5). The smaller ranges did not perform as well when the data was reduced
to two occasions; several models only retained one recapture of an
individual, which resulted in a CV of nearly 100% (Fig. 5) and the
number of recaptures or spatial recaptures was not significantly
correlated with precision (Slave Lake both sexes, Little Smoky males,
Slave Lake males; Table S2.6-Table S2.7).
While there was a strong relationship between precision and the number
of individuals and recaptures, this was not the case for relative bias
(Tables S2.5-S2.7; Fig. 5). Except for Nipisi (all sexes) and Red Earth
females, the number of captures, number of unique individuals,
recaptures or spatial recaptures was not significantly correlated with
relative bias (Tables S2.5-S2.7). Removing the third session resulted in
more bias compared to removing the first and second sessions (Fig. 6).