Department of Biology, City College, The City University of New York, New York, NY, USA.
Ann N Y Acad Sci. 2012 Jul;1260:66-80. doi: 10.1111/j.1749-6632.2011.06440.x. Epub 2012 Feb 21.
Recent advances allow harnessing enormous stores of biological and environmental data to model species niches and geographic distributions. Natural history museums hold specimens that represent the only information available for most species. Ecological niche models (sometimes termed species distribution models) combine such information with digital environmental data (especially climatic) to offer key insights for conservation biology, management of invasive species, zoonotic human diseases, and other pressing environmental problems. Five major pitfalls seriously hinder such research, especially for cross-space or cross-time uses: (1) incorrect taxonomic identifications; (2) lacking or inadequate databasing and georeferences; (3) effects of sampling bias across geography; (4) violation of assumptions related to selection of the study region; and (5) problems regarding model evaluation to identify optimal model complexity. Large-scale initiatives regarding data availability and quality, technological development, and capacity building should allow high-quality modeling on a scale commensurate with the enormous potential of and need for these techniques.
近年来，人们可以利用大量的生物和环境数据来模拟物种的生态位和地理分布。自然历史博物馆保存的标本代表了大多数物种的唯一信息。生态位模型（有时称为物种分布模型）将此类信息与数字环境数据（特别是气候数据）相结合，为保护生物学、入侵物种管理、人畜共患病和其他紧迫的环境问题提供了关键的见解。有五个主要的陷阱会严重阻碍此类研究，特别是在跨空间或跨时间使用时：（1）不正确的分类鉴定；（2）缺乏或不充分的数据存储和地理参考；（3）地理采样偏差的影响；（4）违反与研究区域选择相关的假设；以及（5）模型评估问题，以确定最佳模型复杂度。关于数据可用性和质量、技术发展和能力建设的大规模举措，应该能够在与这些技术的巨大潜力和需求相匹配的规模上进行高质量的建模。
