A Gis-Based Species Distribution Model for the Endangered Smith's Blue Butterfly, Euphilotes enoptes smithi (Lycaenidae)

R. A. Arnold, Robert B. Jensen
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Abstract

ABSTRACT. A species distribution model (SDM) was developed for the endangered Smith's Blue butterfly, Euphilotes enoptes smithi (Lycaenidae), to predict target areas where it might be found in the rugged Santa Lucia Mountains that extend for 230 km along the Big Sur coast in Monterey County, CA. Field surveys identified 1,506 locations of the butterfly's larval foodplant and primary adult nectar plant, Eriogonum parvifolium (Polygonaceae), in this mountain range. Vegetation, soil, and geology types were identified for each of these known locations. Because all life stages of the endangered butterfly are closely associated with this foodplant, it served as a surrogate to identify new locations that might support the butterfly. Binary logistic regression analysis was used to evaluate the relationships between foodplant occurrence and each of these environmental attributes. Logistic regression identified specific soil and geology types that were useful predictors of potential foodplant occurrence, but it was unable to identify comparable specific vegetation types. GIS analyses were performed to identify target areas that share the same combinations of these environmental attributes in locations known to support the foodplant. The top target areas were all characterized by soil and geology types that the logistic regression analyses corroborated were the best predictors of potential foodplant occurrence. To validate our SDM, field surveys for the foodplant and butterfly were performed at 300 predicted target locations. The foodplant and butterfly were observed at 88.4% and 80.7% of target areas or immediately adjacent to them, respectively. Results of this SDM not only provide a better understanding of this endangered butterfly's potential and actual geographic range but also the environmental attributes that constitute its suitable habitat. Furthermore, these findings will assist resource agencies in their conservation efforts to benefit this endangered butterfly.
基于gis的濒危史密斯蓝蝶Euphilotes enoptes Smith物种分布模型
摘要为濒临灭绝的史密斯蓝蝴蝶Euphilotes enoptes smithi (Lycaenidae)开发了一个物种分布模型(SDM),以预测在加利福尼亚州蒙特雷县大索尔海岸绵延230公里的崎岖的圣卢西亚山脉中可能发现它的目标区域。实地调查确定了该山脉中蝴蝶幼虫食物植物和主要成年花蜜植物Eriogonum parvifolium (Polygonaceae)的1506个地点。每个已知地点的植被、土壤和地质类型都被确定。因为濒临灭绝的蝴蝶的所有生命阶段都与这种食物植物密切相关,它可以作为替代品来确定可能支持蝴蝶的新地点。使用二元逻辑回归分析来评估食源性植物的发生与这些环境属性之间的关系。逻辑回归确定了特定的土壤和地质类型,这些类型是潜在食用植物发生的有用预测因子,但无法确定可比的特定植被类型。进行了GIS分析,以确定在已知支持食品工厂的地点具有相同这些环境属性组合的目标区域。结果表明,土壤和地质类型是预测潜在粮食作物发生的最佳预测因子。为了验证我们的SDM,在300个预测的目标地点对食用植物和蝴蝶进行了实地调查。在88.4%的目标区及邻近目标区有食用植物,80.7%的目标区有蝴蝶。该SDM的结果不仅可以更好地了解这种濒危蝴蝶的潜力和实际地理范围,还可以更好地了解构成其适宜栖息地的环境属性。此外,这些发现将有助于资源机构的保护工作,使这种濒危蝴蝶受益。
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