Introduction
Wildlife management requires knowledge of population parameters such as
density, sex ratio, and productivity. Population densities are often
dependent on the quality of occupied habitat as animals rarely use all
available habitats equally (Fretwell 1969, Maier et al. 2005, Bjørneraas
et al. 2012). Thus, habitat selection of animals is an important part of
management and conservation of many species (Allen and Singh 2016). When
landscapes are heterogeneous and animals preferentially use certain
habitats, the study of habitat selection is necessary in order to
understand how habitat is linked to population abundance (Royle et al.
2018). In particular, by assuming a homogeneous landscape or ignoring
the effect of landscape heterogeneity on populations, estimates of
density can be biased (Royle et al. 2013a).
The preference of animals for certain habitats, i.e. the habitat (or
resource) selection can be viewed as a process with three orders
(Johnson 1980). The first order is the spatial distribution of the
species, the topic often studied in landscape ecology. Second and the
third order habitat selection is more of interest in population ecology.
Second order habitat selection describes how individuals are distributed
within the species’ range in relation to environmental features. Third
order selection describes within home range selection of habitats by
individuals.
Commonly, habitat selection has been studied invasively using telemetry
e.g. by attaching GPS or VHF collars on animals (Morris et al. 2016,
Bose et al. 2018). Live capturing a large number of individuals,
especially of large species, can be not only harmful for animals but
also expensive and impractical. Therefore, even though information on
space usage by telemetry can be detailed, it often represents only a few
individuals that may not be representative of the population. Telemetry
data thus represents individual-level rather than population-level
habitat selection. The use of non-invasive genetic methods provides the
possibility to study animal space use without physically marking and
recapturing them, for instance by collecting feces (Granroth-Wilding et
al. 2017, Hagemann et al. 2018) or hair (Sun et al. 2017, O’Meara et al.
2018) left in specific trap or collection devices in the environment
(Waits and Paetkau 2005). Spatial information of the individuals is
recorded from the capture locations and individual identification is
obtained by extracting DNA from the sample and genotyping the samples.
Even though sampling in the field and analyzing non-invasive DNA in the
laboratory can be laborious and expensive, the resulting genotype data
can provide valuable population-level information of space use.
Spatially explicit records of individuals can be analyzed using spatial
capture-recapture (SCR), a spatial extension of long-established
capture-recapture methods (Efford 2004, Royle et al. 2014). Apart from
inferring density, SCR can also be simultaneously used to examine
spatial processes of the populations e.g. habitat selection (Royle et
al. 2013a) and landscape connectivity (Royle et al. 2013b, Sutherland et
al. 2015, Fuller et al. 2016). SCR connects population-level information
to the spatial structure of the landscape by accounting for spatial
location of the sampling sites and for spatial distribution of the
individual encounters. Because SCR includes space explicitly, it allows
inclusion of habitat covariates into the models of both density and
detection probability. The relationship between the habitat and the
density distribution of populations, i.e. second order habitat
selection, is modeled by SCR as a density of activity centers as a
function of habitat covariates. To study the habitat use of individuals
within their home ranges, i.e. third order habitat selection, the
habitat structure around the sampling locations or traps can be
incorporated to SCR analyses to model how those covariates affect
encounter probabilities (Royle et al. 2018). SCR can estimate the effect
of certain habitat types on density and encounter probability, even if
individuals are not directly encountered in that habitat, by predicting
the locations of individual home range centers in the vicinity of the
sample units. One main application of SCR has been research on large
carnivores (Proffitt et al. 2015, Sun et al. 2017, Stetz et al. 2019,
Welfelt et al. 2019), but non-invasive DNA sampling with SCR has also
been used to study the relationship of mule deer (Odocoileus
hemionus) population density and habitat structure (Brazeal et al.
2017).
White-tailed deer (Odocoileus virginianus) inhabit a large
variety of terrestrial habitats and feed on various vegetation types
(Halls 1984). In Finland, it has become one of the most important game
species after its remarkable and continuing growth in abundance since
1934, when the species was introduced from North America. It is listed
as a potentially or locally harmful species in Finland’s National
Strategy on Invasive Alien Species (2012). One of the biggest impacts of
white-tailed deer are deer-vehicle collisions, but in the areas of the
densest population the species can also cause damage to agriculture and
forestry for instance by eating vegetable crops and tree seedlings. For
the management of this species, it is important not only to estimate
abundance, but also to understand what habitat types the white-tailed
deer prefers in Finland and how that affects the species densities. To
this end, we conducted a SCR based study using fecal DNA in southwestern
Finland. Our aim was to understand how white-tailed deer use available
habitat types in a short time period (about two to three weeks) just
prior to the start of the hunting season. The study was repeated in the
same study area in two consecutive years. Additionally, we evaluated the
importance of habitat covariates when inferring this species’ density by
comparing the “standard” SCR model where density is assumed to be
homogeneously distributed over space to inhomogeneous SCR models
considering a heterogeneous landscape.