Improving broad scale forage mapping and habitat selection analyses with airborne laser scanning: the case of moose
Lone, Karen; van Beest, Floris; Mysterud, Atle; Gobakken, Terje; Milner, Jocelyn Margarey; Ruud, Hans-Petter; Loe, Leif Egil
Original version
10.1890/ES14-00156.1Abstract
Determining the spatial distribution of large herbivores is a key challenge in ecology and
management. However, our ability to accurately predict this is often hampered by inadequate data on
available forage and structural cover. Airborne laser scanning (ALS) can give direct and detailed
measurements of vegetation structure.We assessed the effectiveness of ALS data to predict (1) the distribution
of browse forage resources and (2) moose (Alces alces) habitat selection in southern Norway. Using ground
reference data from 153 sampled forest stands, we predicted available browse biomass with predictor
variables from ALS and/or forest inventory. Browse models based on both ALS and forest inventory variables
performed better than either alone. Dominant tree species and development class of the forest stand remained
important predictor variables and were not replaced by the ALS variables. The increased explanatory power
from including ALS came from detection of canopy cover (negatively correlated with forage biomass) and
understory density (positively correlated with forage biomass). Improved forage estimates resulted in
improved predictive ability of moose resource selection functions (RSFs) at the landscape scale, but not at the
home range scale. However, when also including ALS cover variables (understory cover density and canopy
cover density) directly into the RSFs, we obtained the highest predictive ability, at both the landscape and
home range scales. Generally, moose selected for high browse biomass, low amount of understory vegetation
and for low or intermediate canopy cover depending on the time of day, season and scale of analyses. The
auxiliary information on vegetation structure from ALS improved the prediction of browse moderately, but
greatly improved the analysis of habitat selection, as it captured important functional gradients in the habitat
apart fromforage.We conclude that ALS is an effective and valuable tool for wildlife managers and ecologists
to estimate the distribution of large herbivores.
Description
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