Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests

• Published in: PloS one Vol. 12; no. 8; p. e0182451
• Main Authors: Jaime-González, Carlos, Acebes, Pablo, Mateos, Ana, Mezquida, Eduardo T
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.08.2017; Public Library of Science (PLoS)
• Subjects: Animal Distribution - physiology; Animal models; Animals; Apodemus sylvaticus; Biodiversity; Biology and Life Sciences; Canopies; Complexity; Coniferous forests; Ecology and Environmental Sciences; Ecosystem; Elevation; Engineering and Technology; Flight; Food availability; Forests; Habitat conservation; Habitat preferences; Habitats; Lidar; Long-tailed field mouse; Mice; Optical radar; Partitioning; Perturbation methods; Physiological aspects; Pine; Pine trees; Pines; Pinus - chemistry; Pinus sylvestris; Predation; Principal components analysis; Remote Sensing Technology - methods; Rodentia; Shrubs; Small mammals; Studies; Technology; Variance; Variance analysis; Vertebrates; Wildlife; Wildlife habitats; Wood; Wood - chemistry; Austria; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0182451

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.