Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests
EntityUAM. Departamento de Ecología
PublisherPublic Library of Science
10.1371/journal.pone.0182451PLos One 12.8 (2017): e018245
Funded byCJ was partially funded by the Universidad Autónoma de Madrid (Spain) for performing the fieldwork, thanks to an aid program for postgraduates students. This paper is for postgraduates students. This paper is a contribution to the REMEDINAL 3 (S2013/MAE-2719) network of the CAM 2719) network of the CAM
ProjectComunidad de Madrid. S2013-MAE-2719/REMEDINAL 3
Editor's Versionhttps://doi. org/10.1371/journal.pone.0182451
SubjectsLiDAR technology; Pinus sylvestris; Exploring habitat preferences; Forest vertical structure; Medio Ambiente
Rights© 2017 Jaime-González et al.
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
LiDAR technology has firmly contributed to strengthen the knowledge of habitat structurewildlife 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 fieldbased 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
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