Abstract
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 from forage. We conclude that ALS is an effective and valuable tool for wildlife managers and ecologists to estimate the distribution of large herbivores.
Highlights
Among ungulates, density-dependent food limitation is a main limiting factor in population dynamics (Bonenfant et al 2009)
All models tended to over-predict at low biomass and under-predict at higher biomass, so the estimated quantity is better interpreted as a relative rather than an absolute measure of forage biomass (Appendix: Fig. A2)
Our study further showed that the ALSbased structural information on cover increased the predictive performance of moose habitat selection models
Summary
Density-dependent food limitation is a main limiting factor in population dynamics (Bonenfant et al 2009). Most studies rely on environmental proxies of forage availability and cover, such as NDVI (Mueller et al 2008), land cover classes (Uzal et al 2013), or forest stand characteristics like productivity (Godvik et al 2009), dominant tree species (Dussault et al 2005a) and age class (Mabille et al 2012). Often, such proxies are used without quantifying levels of food and cover, though exceptions occur (van Beest et al 2010b, Avgar et al 2013, Blix et al 2014). It is well known that the physical structure of the habitat is important for habitat selection as cover is used for concealment and thermal shelter (Mysterud and Østbye 1999, DePerno et al 2003)
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