Department of Wildlife Ecology and Conservation, University of Florida, P.O. Box 110430, Gainesville, FL 32611-0430, USA.
Conserv Biol. 2012 Feb;26(1):68-77. doi: 10.1111/j.1523-1739.2011.01754.x. Epub 2011 Oct 19.
Predictive models of species distributions are typically developed with data collected along roads. Roadside sampling may provide a biased (nonrandom) sample; however, it is currently unknown whether roadside sampling limits the accuracy of predictions generated by species distribution models. We tested whether roadside sampling affects the accuracy of predictions generated by species distribution models by using a prospective sampling strategy designed specifically to address this issue. We built models from roadside data and validated model predictions at paired locations on unpaved roads and 200 m away from roads (off road), spatially and temporally independent from the data used for model building. We predicted species distributions of 15 bird species on the basis of point-count data from a landbird monitoring program in Montana and Idaho (U.S.A.). We used hierarchical occupancy models to account for imperfect detection. We expected predictions of species distributions derived from roadside-sampling data would be less accurate when validated with data from off-road sampling than when it was validated with data from roadside sampling and that model accuracy would be differentially affected by whether species were generalists, associated with edges, or associated with interior forest. Model performance measures (kappa, area under the curve of a receiver operating characteristic plot, and true skill statistic) did not differ between model predictions of roadside and off-road distributions of species. Furthermore, performance measures did not differ among edge, generalist, and interior species, despite a difference in vegetation structure along roadsides and off road and that 2 of the 15 species were more likely to occur along roadsides. If the range of environmental gradients is surveyed in roadside-sampling efforts, our results suggest that surveys along unpaved roads can be a valuable, unbiased source of information for species distribution models.
物种分布的预测模型通常是利用沿道路收集的数据开发的。路边采样可能提供有偏差（非随机）的样本；然而，目前尚不清楚路边采样是否会限制物种分布模型生成的预测的准确性。我们通过使用专门设计的前瞻性采样策略来测试路边采样是否会影响物种分布模型生成的预测的准确性，该策略旨在解决这个问题。我们从路边数据中建立模型，并在未铺砌道路上的配对位置以及距离道路 200 米处（远离道路）验证模型预测，这些位置在空间和时间上与用于建模的数据独立。我们根据蒙大拿州和爱达荷州（美国）的陆鸟监测计划的点计数数据预测了 15 种鸟类的物种分布。我们使用分层占用模型来解释不完全检测。我们预计，从路边采样数据得出的物种分布预测在使用远离道路采样数据验证时的准确性将低于使用路边采样数据验证时的准确性，并且模型的准确性将因物种是否为广域种、与边缘相关或与内部森林相关而受到不同影响。模型性能指标（kappa、接收者操作特征图曲线下的面积和真技巧统计）在路边和远离道路的物种分布模型预测之间没有差异。此外，尽管路边和远离道路的植被结构存在差异，并且 15 种物种中有 2 种更有可能沿路边出现，但边缘、广域种和内部物种之间的性能指标没有差异。如果在路边采样工作中调查了环境梯度的范围，我们的结果表明，未铺砌道路沿线的调查可以成为物种分布模型的有价值的、无偏倚的信息来源。
