Lidar Prediction of Small Mammal Diversity in Wisconsin, USA

Sarah L Schooler,Harold S J Zald

doi:10.3390/rs11192222

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.

Highlights

A fundamental part of habitat is three-dimensional (3D) heterogeneity, which has been established as a key determinant of animal diversity, especially in forests [1,2]
First we examine if vegetation structure derived from LiDAR can predict small mammal diversity in a temperate-mixed community in the Great Lakes region, and second, we determine which LiDAR-derived vegetation structure variables best predict small mammal diversity
This study demonstrates that LiDAR has the potential to be used to predict small mammal diversity

Summary

Introduction

A fundamental part of habitat is three-dimensional (3D) heterogeneity, which has been established as a key determinant of animal diversity, especially in forests [1,2]. This has been studied extensively using field-based survey methods to measure forest structure, in conjunction with wildlife surveys [1,2]. Using field-based methods to measure the relationship between structural habitat characteristics and animal diversity at larger spatial scales is not feasible. Airborne LiDAR (Light Detection and Ranging) provides an alternative to ground based surveys and is a relatively inexpensive way to obtain detailed and spatially complete characterizations of horizontal and vertical vegetation structure across large areas [3]. LiDAR is increasingly used to study and predict habitat suitability for a variety of species across a range of taxa [4]

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