Smith Austin M, Cropper Wendell P, Moulton Michael P
Department of Integrative Biology, University of South Florida, Tampa, Florida, United States.
School of Natural Resources and Environment, University of Florida, Gainesville, Florida, United States.
PeerJ. 2021 Apr 16;9:e11280. doi: 10.7717/peerj.11280. eCollection 2021.
Chukar partridges () are popular game birds that have been introduced throughout the world. Propagules of varying magnitudes have been used to try and establish populations into novel locations, though the relationship between propagule size and species establishment remains speculative. Previous qualitative studies argue that site-level factors are of importance when determining where to release Chukar. We utilized machine learning ensembles to evaluate bioclimatic and topographic data from native and naturalized regions to produce predictive species distribution models (SDMs) and evaluate the relationship between establishment and site-level factors for the conterminous United States. Predictions were then compared to a distribution map based on recorded occurrences to determine model prediction performance. SDM predictions scored an average of 88% accuracy and suitability favored states where Chukars were successfully introduced and are present. Our study shows that the use of quantitative models in evaluating environmental variables and that site-level factors are strong indicators of habitat suitability and species establishment.
石鸡（）是一种广受欢迎的猎鸟，已被引入世界各地。人们使用了不同规模的繁殖体，试图在新的地点建立种群，尽管繁殖体大小与物种建立之间的关系仍存在推测性。以前的定性研究认为，在确定石鸡的放归地点时，地点层面的因素很重要。我们利用机器学习集成方法，评估来自原生和归化地区的生物气候和地形数据，以生成预测物种分布模型（SDM），并评估美国本土地区建立与地点层面因素之间的关系。然后将预测结果与基于记录出现情况的分布图进行比较，以确定模型预测性能。SDM预测的平均准确率为88%，适宜性有利于石鸡成功引入并存在的州。我们的研究表明，在评估环境变量时使用定量模型，并且地点层面的因素是栖息地适宜性和物种建立的有力指标。
