POISSON POINT PROCESS MODELS SOLVE THE "PSEUDO-ABSENCE PROBLEM" FOR PRESENCE-ONLY DATA IN ECOLOGY
POISSON POINT PROCESS MODELS SOLVE THE "PSEUDO-ABSENCE PROBLEM" FOR PRESENCE-ONLY DATA IN ECOLOGY
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DOI：
10.1214/10-aoas331
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发表时间：
2010-09-01
影响因子：
1.8
通讯作者：
Shepherd, Leah C.
中科院分区：
文献类型：
--
作者：
Warton, David I.;Shepherd, Leah C.
仅存在数据，即物种被记录为存在的点位置，通常用于将物种分布建模为一组解释变量的函数，无论是绘制物种发生情况、了解其与环境的关联，还是预测其对环境变化的响应。目前，生态学家最常通过向数据中添加随机选择的“伪不存在”来分析仅存在的数据，以便可以使用逻辑回归进行分析，这种方法在模型规范、解释和实施方面存在弱点。为了解决这些问题，我们提出了存在强度的泊松点过程建模。我们还得出了所提出的方法和逻辑回归之间的联系，具体来说，我们表明，随着伪缺席数量的增加（以规则或均匀随机排列），逻辑回归斜率参数及其标准误差收敛于相应的泊松点过程模型的参数及其标准误差。我们讨论这些结果的实际意义。特别是，点过程建模为选择伪缺席的数量和位置提供了一个框架，这两者目前都是通过生态学中的临时且有时无效的方法来选择的，我们通过示例来说明这一点。
Presence-only data, point locations where a species has been recorded as being present, are often used in modeling the distribution of a species as a function of a set of explanatory variables-whether to map species occurrence, to understand its association with the environment, or to predict its response to environmental change. Currently, ecologists most commonly analyze presence-only data by adding randomly chosen "pseudo-absences" to the data such that it can be analyzed using logistic regression, an approach which has weaknesses in model specification, in interpretation, and in implementation. To address these issues, we propose Poisson point process modeling of the intensity of presences. We also derive a link between the proposed approach and logistic regression-specifically, we show that as the number of pseudo-absences increases (in a regular or uniform random arrangement), logistic regression slope parameters and their standard errors converge to those of the corresponding Poisson point process model. We discuss the practical implications of these results. In particular, point process modeling offers a framework for choice of the number and location of pseudo-absences, both of which are currently chosen by ad hoc and sometimes ineffective methods in ecology, a point which we illustrate by example.