Avian Diversity Prediction Using Global Ecosystem Dynamics Investigation (GEDI) Data
Jin Xu, Conservation Biology Institute, National Zoological Park, Smithsonian Institution, xuj@si.edu (Presenter)
Qiongyu Huang, Smithsonian Institution, huangq@si.edu
Over the past few decades, global change has had a significant impact on ecosystems and avian species distribution. Biodiversity in most ecosystems is a function of resources and space, with the structural heterogeneity of habitat in the vertical dimension providing diverse ecological niches that partition space and resources and lead to high biodiversity. For birds, the vertical distribution of vegetation is a crucial characteristic that determines many aspects of their habitat suitability, such as micro-climate, food abundance, and breeding grounds. While studies have shown that canopy height is an effective predictor of bird richness, the contribution of the full vertical profile of forest foliage to mapping habitat suitability and biodiversity remains largely unknown. To address this gap, this study utilizes active remote sensing data from the Global Ecosystem Dynamics Investigation (GEDI) instrument, the first space-borne Lidar sensor launched in December 2018, to quantify the spatial heterogeneity of vegetation structural metrics for predicting bird species diversity. Specifically, we aim to model bird species richness by examining the efficacy of various canopy structure metrics derived from GEDI data in relation to North American Breeding Bird Survey (BBS) data. We also hypothesize that the model will vary across different functional guilds and aim to compare the different importance metrics for different guilds. Our results will contribute to a better understanding of the relationship between vertical vegetation structure and bird richness and could inform the development of more effective conservation strategies for avian species in the face of global change.
Associated Project(s):
Poster Location ID: 1-45
Presentation Type: Poster
Session: Poster Session 1
Session Date: Tue (May 9) 5:00-7:00 PM
CCE Program: BDEC