DOI: 10.5061/dryad.4t18d
title: Data from: An efficient method to exploit LiDAR data in animal ecology

description: 1. Light detection and ranging (LiDAR) technology provides ecologists with
 high-resolution data on three-dimensional vegetation structure. Large
 LiDAR datasets challenge predictive ecologists, who commonly simplify
 point clouds into structural attributes (namely LiDAR-based metrics such
 as canopy height), which are used as predictors in ecological models,
 potentially with loss of relevant information. 2. We illustrate an
 efficient alternative approach to reduce the dimensionality of LiDAR data
 that aims at minimal data filtering with no a priori assumptions on the
 ecology of the target species. We first fit the ecological model
 exploiting the full variability of the LiDAR point cloud, then we explain
 the results using post-modelling LiDAR-data classification for ecological
 interpretation only. This is the classical logic of explorative,
 hypothesis generating and predictive statistics, rather than testing
 specific vegetation-structural hypotheses. 3. First, we reduce the
 dimensionality of the LiDAR point cloud by Principal Component Analysis
 (PCA) to fewer predictors. Secondly, we show that LiDAR-PCs are capable to
 outperforming commonly used environmental predictors in ecological
 modelling, including LiDAR-based metrics. We exemplify this by modelling
 red deer (Cervus elaphus) and roe deer (Capreolus capreolus) resource
 selection in the Bavarian Forest National Park, Germany. After fitting the
 ecological model, we provide an interpretation of the information included
 in LiDAR-PCs, which allows users to draw conclusions whenever using them
 as predictors. We make use of the PCA rotation matrix and post-modelling
 data classification, and document deer selection for understory vegetation
 at unprecedented fine scale. 4. Our approach is the first attempt in
 animal ecology to avoid the use of LiDAR-based metrics as model
 predictors, but rather generate principal components able to capture most
 of the LiDAR point cloud variability. Our study demonstrates that
 LiDAR-PCs can boost ecological models. We envision a potential use of
 LiDAR-PCs in several applications, particularly species distribution and
 habitat suitability models. We demonstrate an application of our approach
 by building suitability maps for both deer species, which can be used by
 practitioners to visualize model spatial predictions and understand the
 type of forest structures selected by deer.
description: Red deer (1-h and 15-min
 fix rates) and roe deer (1-h and 4-h fix rates) relocation data along with
 environmental predictorsAbbreviations:
 "id": deer identification code (name). "loc_id":
 relocation stratum."used": binomial response variable (1: used;
 0: available). "sun_elev": elevation of the sun with respect to
 the horizon, in degrees. "solrad_fac": categorical proxy for
 microclimate conditions (see Table S1 for additional details).
 "rugged_10": terrain ruggedness derived from the digital
 elevation model, in meters. "dist_trails": distance to the
 closest hiking trail, in meters. "ndvi_preds":
 NDVI."map_reclass": photo-interpreted habitat map of the BFNP
 from year 2012 "corine_reclass": corine land cover classes.
 "res5_PC1": 5-m resolution LiDAR, PC1. "res5_PC2": 5-m
 resolution LiDAR, PC2. "res5_PC3": 5-m resolution LiDAR, PC3.
 “res5_PC4": 5-m resolution LiDAR, PC4. "res5_PC5": 5-m
 resolution LiDAR, PC5. "res5_PC6": 5-m resolution LiDAR, PC6.
 "res5_PC7": 5-m resolution LiDAR, PC7. “res5_PC8": 5-m
 resolution LiDAR, PC8. "res5_PC9": 5-m resolution LiDAR, PC9.
 "res5_PC10": 5-m resolution LiDAR, PC10. "res5_PC11":
 5-m resolution LiDAR, PC10. "res10_PC1" : 10-m resolution LiDAR,
 PC1. "res10_PC2" : 10-m resolution LiDAR, PC2.
 "res10_PC3": 10-m resolution LiDAR, PC3. "res10_PC4":
 10-m resolution LiDAR, PC4. "res10_PC5": 10-m resolution LiDAR,
 PC5. "res10_PC6": 10-m resolution LiDAR, PC6.
 "res10_PC7": 10-m resolution LiDAR, PC7. "res10_PC8":
 10-m resolution LiDAR, PC8. "res10_PC9": 10-m resolution LiDAR,
 PC9. "st_deci": number of deciduous trees. "st_conif":
 number of conifer trees. "st_dead_all": number of standing or
 lying dead trees. "UStory": understory vegetation.
 "MStory": midstory vegetation. "OStory": overstory
 vegetation. "MeanHeight": mean height of
 vegetation.deer_satellite telemetry
 data.zip