The Dispersal of Giant Pandas (Ailuropoda melanoleuca) in the Qinling Mountain: the impact of climate, habitat, and anthrophonic disturbances.

Authors: Qiongyu Huang*, , Fang Wang, The Center for Systems Integration and Sustainability at Michigan State University , Audrey Lothspeich, Smithsonian Conservation Biology Institute, Haydee Hernandez, Smithsonian Conservation Biology Institute, Katherine Mertes, Smithsonian Conservation Biology Institute, Melissa Songer, Smithsonian Conservation Biology Institute
Topics: Biogeography, Animal Geographies, Asia
Keywords: giant panda, species distribution change, dispersal, anthropocentric disturbance, climate change,
Session Type: Paper
Day: 4/6/2019
Start / End Time: 5:00 PM / 6:40 PM
Room: Hoover, Marriott, Mezzanine Level
Presentation File: No File Uploaded

The population of giant pandas has been steadily recovering in the wild. The 4th national giant panda survey conducted in the early 2010s revealed that the Qinling Mountain area provides habitat for about 345 giant pandas. The population has increased by 26.4% since the 3rd national giant panda survey in the early 2000s. Giant pandas are now present in many areas that are outside of the traditionally high density areas. With the heterogeneous dispersal pattern and the involvement of multiple confounding environmental variables, two key questions arise: do climatic or aclimatic factors better predict expansion in occurrence over the past decade, and how do different environmental factors at various scale influence the probability of the latest expansion? We developed and tested the performance of contrasting species distribution models using the 3rd and 4th survey data. We also used generalized linear models and a range of environmental variables to model the presence of giant pandas outside of the core areas. Our results show that the model using bamboo suitability produced better accuracy in predicting giant panda dispersal than the model using climate variables; and that bamboo suitability, variables that serve as indices of anthropogenic disturbance, and dispersal difficulty are the key predictive variables significant at different scales in predicting dispersal outside the core areas. Our results highlight the paramount importance of restoring bamboo forest and reducing anthropogenic disturbance over giant panda habitat. The results also highlight urgent need for improved transparency in survey methodologies.

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