Doctoral Dissertations

Date of Award


Degree Type


Degree Name

Doctor of Philosophy



Major Professor

Liem Tran

Committee Members

Monica Papes, Sally Horn, Qiusheng Wu


Species Distribution Models (SDMs) are important tools for biological conservation and wildlife management as they detail the distributions of biota across landscapes. In this dissertation I explored two emerging Big Data sources that can be used to enhance SDMs, lidar and Voluntary Geographic Information (VGI). Lidar data can be used in ecological models as explanatory variables that provide information about 3D attributes of space (i.e., structural ecology), and observation data from VGI projects (like eBird) can help inform models about species presence across spatial and temporal scales. In my first research study, I employ a multiscale analysis to address the challenges associated with developing SDMs with high-resolution data from lidar. I present an approach, SBBS, in which the output of SDMs developed with variables that had aspatial resolution of 30-m were used to improve SDMs developed with variables that had a 10-m resolution. This approach produced better models than both a model developed with the default Maxent background sampling area, and a model developed using the conventional approach of resampling environmental data to a common resolution. In my second study I focused on model thresholds to explore the differences between an SDM developed with data from citizen scientists through eBird and one developed with data from wildlife professionals. Results corroborated past research that found SDMs developed with citizen science favor anthropogenic landscapes, but also found factors related to elevation and habitat fragmentation contributed to the mismatch between these models. In my third study, I used inferences from an SDM developed with a scientific occurrence dataset and the statistical concept of influence to evaluate, categorize, and filter eBird points. Through my methods, I was able to isolate species presence locations from eBird that best matched the environmental characteristics of observation locations from the scientific dataset and analyze attributes of points that differed from that profile. This research contributes to knowledge at the nexus of Biogeography and GIScience, as spatial data methods are used to better understand species distributions, while knowledge about ecological relationships across space serves as a basis to better understand these two emerging spatial data sources.


Chapter 2 was previously published in Ecological Modelling in January 2020.

Files over 3MB may be slow to open. For best results, right-click and select "save as..."