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如何建模一个新的入侵者? 美国入侵的范围模型在100次发生后超过了全球或联合范围模型
Nicholas E Young1, Demetra A Williams2,3, Keana S Shadwell2
1Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado, USA.
概括
预测入侵物种的传播至关重要. 新的研究表明,仅经过100次观测,在物种分布模型 (SDM) 中仅使用入侵范围数据比使用全球数据更准确.
科学领域:
- 生态生态学 生态生态学
- 侵入性物种 生物学 生物学
- 生物地理学 生物地理学 生物地理学
背景情况:
- 侵入性物种对经济和生态构成重大威胁.
- 物种分布模型 (SDM) 对于绘制入侵物种的地图至关重要.
- 当前的方法往往会因为利基转移和数据问题而误解侵入性分布.
研究的目的:
- 调查入侵物种SDM的最佳数据源.
- 为了确定一个"转折点",入侵范围数据超过全球数据.
- 改善对潜在的入侵物种分布的预测.
主要方法:
- 利用了北美13种入侵植物物种的时间序列记录.
- 使用入侵范围,全球范围和联合发生数据构建的SDM进行了比较.
- 分析了不同入侵阶段的模型性能.
主要成果:
- 早期入侵阶段:使用入侵和全球数据的模型表现最好.
- 经过约100次观察:美国入侵的范围模型显著优于其他模型.
- 在入侵范围的尺度上,更高的数据质量有助于改善模型性能.
结论:
- 对于新入侵的物种,仅使用入侵范围数据来建模潜在分布,在有限的观测后变得更加准确.
- 忽视全球数据可以提高准确性,一旦有足够的入侵范围数据可用.
- 这项研究为建模新型入侵物种分布提供了强有力的方法.
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