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Modeling the geographic distribution of pawpaw (Asimina triloba [L.] Dunal) in a portion of its northern range limits, western New York State, USA

机译:纽约州西部北部范围限制的一部分北方游行(Asimina Triloba [L.] Dunal)的地理分布

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Pawpaw (Asimina triloba [L.] Dunal) is a small deciduous tree in eastern North America notable for its edible fruit. Its northern range extends into New York State (NYS) where it is listed as threatened, with only approximately 20 sites known to contain pawpaw. This research models the geographic distribution of pawpaw in western NYS (land area = 27,263 km(2)) for four reasons: to understand factors shaping its distribution, to assess the degree to which it is protected, to locate potential introduction or reintroduction sites, and to locate potential sites with undiscovered pawpaw. This study uses an ensemble of small models (ESM) approach devised for modeling the distribution of rare species, which averages predictions from simple (i.e., bivariate) models weighted by model predictive performance. The ESM in this study, constructed from boosted regression tree (BRT) models, demonstrated a high ability to discern pawpaw presences from pseudo-absences. The area under the receiver operating characteristic curve (AUC) value was 0.986 when calculated using training data, and BRT models comprising the ESM exhibited AUC values calculated using cross-validation data as high as 0.957. The ESM suggests that pawpaw is found in the warmest (mean annual temperature > 9.0 degrees C,) and driest (May-September precipitation < 44 cm) areas of western NYS, and on mildly acidic (pH = 5.5-7.0) and deep (> 110 cm) soils. Locations predicted to be suitable for pawpaw are overwhelmingly found on unprotected lands; forested areas in PAs with "high" or "highest" suitability for pawpaw only comprise 0.1-0.2% of the study area.
机译:木瓜(Asimina triloba[L.]Dunal)是北美东部的一种小型落叶树,以其可食用的果实而闻名。它的北部范围延伸到纽约州(NYS),在那里它被列为受威胁物种,已知只有大约20个地点含有木瓜。本研究对纽约州西部(陆地面积=27263 km(2))木瓜的地理分布进行了建模,原因有四:了解影响其分布的因素,评估其受保护的程度,确定潜在的引种或再引种地点,以及确定未发现木瓜的潜在地点。这项研究使用了一种小型模型集成(ESM)方法来模拟稀有物种的分布,该方法通过模型预测性能加权简单(即双变量)模型的预测平均。本研究中的ESM是由增强回归树(BRT)模型构建的,它展示了区分爪子存在和伪缺失的高能力。使用训练数据计算时,受试者工作特性曲线(AUC)值下的面积为0.986,包含ESM的BRT模型显示使用交叉验证数据计算的AUC值高达0.957。ESM表明,木瓜生长在纽约州西部最温暖(年平均温度>9.0摄氏度)和最干燥(5-9月降水量<44厘米)的地区,以及轻度酸性(pH=5.5-7.0)和深层(>110厘米)土壤上。据预测,适合木瓜生长的地点绝大多数位于未受保护的土地上;PAs中“高”或“最高”木瓜适宜性的森林区域仅占研究区域的0.1-0.2%。

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