Discussion
Knowledge of habitat selection is essential for evaluation of wildlife
population density in a heterogeneous landscape. We find that inclusion
of habitat heterogeneity is favored in the Spatial Capture-Recapture
(SCR) model parameters of density and detection. The SCR model without
landscape effects was 25 AIC units higher than the top model including
landscape heterogeneity. The estimated densities across the habitats
describe second-order habitat selection of the white-tailed deer
population studied. Population densities were highest in agricultural
areas and mixed forests and lowest in coniferous forests and
transitional woodlands. We can integrate this preference over all
landcover types in the study area to arrive at a landscape-specific
estimate of density. We find that including heterogeneity increases the
overall density estimate when compared with density estimated under the
best supported homogeneous (constant density) SCR model. The increase in
density is because the greatest proportion of the state space consists
of agricultural areas (76%), which is also the preferred habitat
(together with mixed forests which are, however, relatively uncommon).
In the SCR framework, we estimated habitat-specific densities, while
accounting for third- order habitat selection through the effect of
habitat on detection probability. The probability to detect an
individual is highest in transitional woodland followed by mixed forest
and is lowest in coniferous forest. Detection probability also decreases
with increasing distance to land in agricultural use. Thus, inside a
white-tailed deer’s home range, space is not used equally; the animals
prefer to be close to fields and in transitional woodland compared to
coniferous forest. In our study area, transitional woodlands are
typically clear-cuts and these, as well as fields which are
predominantly grain fields (wheat, oat, rye), likely present habitat
with good food resources for the white-tailed deer.
About half (52%) of the white-tailed deer in the study area are males.
This is an interesting finding, because the age and sex composition of
the white-tailed deer is almost entirely shaped by the strong hunting
pressure on this species. In general, harvested ungulate populations
tend to have more females than males in Nordic countries, which may
result in lower viability of the population (Langvatn and Loison 1999,
Sæther et al. 2004). Hunting-bag statistics of white-tailed deer in
Finland also imply a higher mortality of males and as a result, few
males compared to females reach old ages (Kekkonen et al. 2016).
However, DNA does not allow distinguishing fawns from adults, and fawns
are probably a large fraction of the study population. Because home
ranges of fawns are restricted to their mothers’ (Tierson et al. 1985),
and because the spatial scale of the study is small and the number of
individuals limited, our results may largely reflect fawns and their
mothers. Based on the 17 individuals recorded in both study years, we
can infer that at least 34% (14/41) of females and 12% (3/25) of males
were adults in the second study year. For these reasons, the sex ratio
of Finnish white-tailed deer would need more thorough investigation.
Our study area represents a typical Finnish rural area with low coverage
of artificial surfaces (covering only 2% of the state space). Here, we
did not find any preference or avoidance by white-tailed deer for
artificial surfaces, which includes roads and human settlements. We
expected that the distance to water, which in the study area were mainly
two bigger streams with smaller ditches, would have been an important
covariate for white-tailed deer density. Nevertheless, the models
including that covariate were not supported by AIC. Presumably, there
are other smaller stationary water sources in the area and thus larger
streams and ditches are not the crucial source of water for white-tailed
deer. It is notable that the habitat preferences later in the year
likely differ, especially during winter when food sources are different
and limited. In addition, snow cover may affect the availability of food
and movement of individuals (Andersson and Koivisto 1980).
Collecting information on space use of individuals based on non-invasive
genetic methods and analyzing this information with SCR provides a
possibility to study habitat selection of animal populations, as done in
this study. A possible advantage of this approach compared to telemetry
approaches is that population-wide inferences can be reached at a
potential lower cost and with clearly lower risk to the study organism.
In addition, multilocus genotype data provides information not only for
individual identification and density estimation, but also to study the
genetic diversity and structure of populations, or relatedness and
pedigree of individuals (Granroth-Wilding et al. 2017, Sun et al. 2017,
Hagemann et al. 2018). However, DNA data alone does not provide
information on age structure of the population, but integrating it with
camera trapping data could provide this information (Furnas et al.
2018). Furthermore, non-invasive methods combined with telemetry of a
few individuals can improve precision of SCR estimates by incorporating
additional movement information (Royle et al. 2013a, Sollmann et al.
2016, Linden et al. 2018). Although the approach may vary, our findings
add to the growing SCR-based evidence that it is important to consider
the spatial heterogeneity of an area and habitat selection of animals.
By acknowledging that landscapes are heterogeneous and incorporating
that into our analyses, we quantify habitat selection, which captures
crucial ecology in terms of how the organism uses its environment both
in terms of population density and in terms of individual space use.