Bunching up the background betters bias in species distribution models
Bunching up the background betters bias in species distribution models
复制标题
聚集背景可以改善物种分布模型中的偏差
作者：
Julien Vollering;R. Halvorsen;Inger Auestad;K. Rydgren
用于模拟物种分布的存在记录集通常包括机会性收集的观察结果，而不是系统收集的观察结果。因此，抽样概率在地理上是不均匀的，这可能会混淆模型对物种分布的描述。建模师经常通过处理训练数据来解决采样偏差问题：或者对存在数据进行二次采样，或者在不存在的背景数据中创建类似的空间偏差。在后一类中，我们测试了一种新的方法，我们称之为‘背景加厚’。背景加厚需要按与存在位置密度成比例的方式将背景位置集中在存在位置周围。我们使用模拟数据和案例研究中的数据，比较了背景增厚和两种已建立的采样偏差校正方法-目标组背景选择和存在细化。在案例研究中，背景加厚和存在细化的表现相似，都比目标组背景选择产生更好的模型辨别力，并且比没有校正的模型产生更好的模型校准。在模拟中，当模拟存在位置的数量较少时，背景加厚比存在细化表现得更好，反之亦然。我们讨论了目标群体背景选择的缺点，为什么背景加厚和存在稀疏是保守但稳健的采样偏差校正方法，以及为什么在小样本量时背景加厚比存在稀疏更好。特别是，背景加厚对于处理数据稀缺时的采样偏差是有利的，因为它避免了丢弃存在记录。
Sets of presence records used to model species’ distributions typically consist of observations collected opportunistically rather than systematically. As a result, sampling probability is geographically uneven, which may confound the model's characterization of the species’ distribution. Modelers frequently address sampling bias by manipulating training data: either subsampling presence data or creating a similar spatial bias in non‐presence background data. We tested a new method, which we call ‘background thickening’, in the latter category. Background thickening entails concentrating background locations around presence locations in proportion to presence location density. We compared background thickening to two established sampling bias correction methods – target group background selection and presence thinning – using simulated data and data from a case study. In the case study, background thickening and presence thinning performed similarly well, both producing better model discrimination than target group background selection, and better model calibration than models without correction. In the simulation, background thickening performed better than presence thinning when the number of simulated presence locations was low, and vice versa. We discuss drawbacks to target group background selection, why background thickening and presence thinning are conservative but robust sampling bias correction methods, and why background thickening is better than presence thinning for small sample sizes. Particularly, background thickening is advantageous for treating sampling bias when data are scarce because it avoids discarding presence records.
在统计模型中，有限样本的等效性仅在于仅存在的数据。
DOI：
10.1214/13-aoas667
发表时间：
2013-12-01
期刊：
The annals of applied statistics
影响因子：
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作者：
Fithian W;Hastie T
通讯作者：
Hastie T