Faculty Advisor
Christie Klimas
Abstract
The endemic oak, Quercus brandegeei has been labeled as endangered by the IUCN Red List of Endangered Species due to its limited genetic diversity and lack of regeneration. The oak (Quercus) species is a keystone species in many parts of the world and has been facing various challenges to their survival (Westwood 2017) making efforts to support and protect endemic oaks all the more ecologically and socially imperative. There are challenges to identifying threats as there are many unknown characteristics of Q. brandegeei’s biology that are essential to carrying out conservation efforts. To develop a greater understanding of the species, the study characterized the size distribution and population structure of Q. brandegeei, which may be connected to the predicted small number of reproducing individuals, responsible for the lack of regeneration. Following the analysis of population structure, 73% of the trees sampled showed evidence of reproduction and 55% of trees fall within a DBH size class greater than 50 cm, indicating a population consisting of reproducing adults. There were reproduction differences between population localities and regions. With the majority of individuals reproducing, generalized linear models were then used to identify environmental factors (location, precipitation, and temperature) that served as the strongest predictors of reproduction. The strongest predictive model revealed that a combination of location by region and total wet season precipitation during the year of production were significant factors in predicting whether or not individuals reproduced. These findings may help to determine the most effective locations for reintroduction of seedlings and areas to conduct in situ conservation efforts.
Recommended Citation
Cortez, Camila
(2021)
"Modeling Reproduction Influencers of an Endangered Oak,"
DePaul Discoveries: Volume 10, Article 4.
Available at:
https://via.library.depaul.edu/depaul-disc/vol10/iss1/4
Included in
Other Ecology and Evolutionary Biology Commons, Other Forestry and Forest Sciences Commons, Statistical Models Commons