10.5061/DRYAD.62T17
Ladle, Andrew
University of Alberta
Avgar, Tal
University of Alberta
Wheatley, Matthew
University of Alberta
Boyce, Mark S.
University of Alberta
Data from: Predictive modeling of ecological patterns along linear-feature
networks
Dryad
dataset
2017
2017-09-07T00:00:00Z
2017-09-07T00:00:00Z
en
https://doi.org/10.1111/2041-210X.12660
1484358517 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Ecological patterns and processes often take place within linear-feature
networks, and this has implications when analysing the spatial
configuration of such patterns or processes across a landscape. One such
pattern is the use of landscapes by human recreationists: an important
variable in animal habitat selection and behaviour. Due to the difficulty
in obtaining data, ecologists tend to use coarse metrics such as
linear-feature density, while the extent and timing of human activity are
often ignored. Remote detector equipment and its increasing use in
ecological studies allow for large volumes of data on human activity to be
collected. However, the analysis of these data still can be challenging.
Using a combination of generalised linear mixed-effects models and
network-based ordinary kriging, we developed a method for estimating
spatial and temporal variations in motorised and non-motorised activities
across a complex linear-feature network. Trail cameras were set up between
2012 and 2014 and monitored motorised and non-motorised activities at 238
different trail sites across a 2824 km2 region of the eastern slopes and
foothills of central Alberta's Rocky Mountains. We evaluate the
predictive capacity of this approach, demonstrate its application and
discuss its merits and limitations. This method offers a straightforward
analysis that can be applied to remotely acquired data to give a useful
metric for assessing wildlife responses to human activity, and has
potential application beyond the highlighted example.
Data and codeRaw data and R coderandom_to_sample_200m_1Random points for
interpolation, part 1random_to_sample_200m_2Random points for
interpolation part 2