Lidar Prediction of Small Mammal Diversity in Wisconsin, USA

Abstract

Vegetation structure is a crucial component of habitat selection for many taxa, and airborne LiDAR (Light Detection and Ranging) technology is increasingly used to measure forest structure. Many studies have examined the relationship between LiDAR-derived structural characteristics and wildlife, but few have examined those characteristics in relation to small mammals, specifically, small mammal diversity. The aim of this study was to determine if LiDAR could predict small mammal diversity in a temperate-mixed forest community in Northern Wisconsin, USA, and which LiDAR-derived structural variables best predict small mammal diversity. We calculated grid metrics from LiDAR point cloud data for 17 plots in three differently managed sites and related the metrics to small mammal diversity calculated from five months of small mammal trapping data. We created linear models, then used model selection and multi-model inference as well as model fit metrics to determine if LiDAR-derived structural variables could predict small mammal diversity. We found that small mammal diversity could be predicted by LiDAR-derived variables including structural diversity, cover, and canopy complexity as well as site (as a proxy for management). Structural diversity and canopy complexity were positively related with small mammal diversity, while cover was negatively related to small mammal diversity. Although this study was conducted in a single habitat type during a single season, it demonstrates that LiDAR can be used to predict small mammal diversity in this location and possibly can be expanded to predict small mammal diversity across larger spatial scales.

Disentangling LiDAR Contribution in Modelling Species–Habitat Structure Relationships in Terrestrial Ecosystems Worldwide. A Systematic Review and Future Directions

“…Still, we have identified an increase of studies considering other taxonomic groups since the review of [17], including bryophytes, lichens, and fungi (see, e.g., [26,27,89]), a new broad group of organisms increasingly considered in biodiversity conservation and global change research. Studies on mammal group have also increased with successful LiDAR outcomes (see, e.g., [20,21,40,42,66,90,91]. Something similar happened with invertebrates, although to a lesser extent (see, e.g., [12,22,23]).…”

“…An alternative explanation may be that these metrics are not as standardized as canopy cover and height metrics, so that model outcomes fail to describe this trait. Interestingly, canopy vertical distribution traits were also important for invertebrates (e.g., [12,59]), even for mammals (e.g., [20,42]). All these results support the idea that environmental heterogeneity, measured through the proposed five morphological traits extracted from LiDAR, creates more niches and spatial turnover of species, favoring different habitats and thus allowing more species to coexist in accordance with the habitat heterogeneity hypothesis [3].…”

“…Canopy cover: Number of first returns above a height/total number of first returns [42]; Intensity sum of all non-ground points divided by intensity sum of all points [43]; Canopy gaps and ruggedness [44]; Fractional cover [45]; LiDAR penetration ratio (LPI), as the ratio between terrain points and total points [22].…”

Determination of Structural Characteristics of Old-Growth Forest in Ukraine Using Spaceborne LiDAR

“…More widely, these methods could be used for evaluating a woodland's importance for biodiversity: the species diversity of birds, for example, is greater in taller, more complex forests with a higher number of layers [86]. Similarly, mammal diversity has been shown to be positively related to LiDAR-derived structural complexity, and negatively related to cover [87]. Forest structure also has a strong control on the speed at which fire spreads through a forest, with structurally complex forests decreasing fire spread [88].…”

Prey-Driven Behavioral Habitat Use in a Low-Energy Ambush Predator

“…Airborne light detection and ranging (LiDAR) can describe horizontal and vertical vegetation structure across large areas, providing a valuable alternative to the use of intensive field-based methods to assess forest structure (Simonson et al, 2014). Schooler and Zald (2019) demonstrated that LiDAR-derived metrics are effective predictors of small mammal diversity in a temperate mixed-forest community. We therefore used LiDAR and other remotely sensed data to quantify forest structure and predict small mammal occupancy across the landscape.…”