Prediction of Large Whale Distributions: A Comparison o f Presence-Absence and Presence-Only Modeling Techniques
Prediction of Large Whale Distributions: A Comparison o f Presence-Absence and Presence-Only Modeling Techniques
复制标题
DOI：
10.3389/fmars.2018.00419
复制
发表时间：
2018-11-12
影响因子：
3.7
通讯作者：
Ballance, Lisa T.
中科院分区：
文献类型：
--
作者：
Fiedler, Paul C.;Redfern, Jessica, V;Ballance, Lisa T.
物种分布模型通过量化与环境变量的关系来预测物种的发生或密度，用于各种科学调查和管理应用。对于濒危物种，如大型鲸鱼，模型有助于了解影响分布变异性的生态因素，并评估航运、捕鱼和其他人类活动带来的潜在风险。系统的调查记录了物种的存在和消失，以及相关的搜索工作，但是非常昂贵。仅由目击事件组成的存在数据可以增加样本量，但在地理和生态位空间中可能存在偏差。我们使用存在-缺失观测数据和最大熵模型（Maxent）建立了广义加性模型（GAMS），该模型使用相同的存在-缺失观测数据，以及仅使用存在观测数据，对热带太平洋东部的四种大型鲸鱼：座头鲸（Megaptera novaeangliae）、蓝鲸（Balaenoptera musculus）、布氏鲸（Balaenoptera edeni）和抹香鲸（Physeter macrocephalus）建立了广义加性模型（GAMS）。环境变量包括表面温度、表面盐度、温跃层深度、分层指数和海底深度。我们比较了两种模型类型的预测分布。当从调查轨迹数据中选择Maxent缺席时，基于系统调查数据的Maxent和GAM模型预测非常相似。然而，我们表明，在只存在的Maxent预测中，空间偏差可能是由使用伪缺失而不是观察到的缺失引起的，也可能是由机会数据和分层系统调查数据的抽样偏差引起的，地层之间的覆盖不均匀。从Maxent或其他仅存在的技术中预测不常见的大型鲸鱼分布可能对科学或管理有用，但前提是在推导和解释模型预测时解决了观测中的空间偏差。
Species distribution models that predict species occurrence or density by quantifying relationships with environmental variables are used for a variety of scientific investigations and management applications. For endangered species, such as large whales, models help to understand the ecological factors influencing variability in distributions and to assess potential risk from shipping, fishing, and other human activities. Systematic surveys record species presence and absence, as well as the associated search effort, but are very expensive. Presence-only data consisting only of sightings can increase sample size, but may be biased in both geographical and niche space. We built generalized additive models (GAMS) using presence-absence sightings data and maximum entropy models (Maxent) using the same presence-absence sightings data, and also using presence-only sightings data, for four large whale species in the eastern tropical Pacific Ocean: humpback (Megaptera novaeangliae), blue (Balaenoptera musculus), Bryde's (Balaenoptera edeni), and sperm whales (Physeter macrocephalus). Environmental variables were surface temperature, surface salinity, thermocline depth, stratification index, and seafloor depth. We compared predicted distributions from each of the two model types. Maxent and GAM model predictions based on systematic survey data are very similar, when Maxent absences are selected from the survey trackline data. However, we show that spatial bias in presence-only Maxent predictions can be caused by using pseudo-absences instead of observed absences and by the sampling biases of both opportunistic data and stratified systematic survey data with uneven coverage between strata. Predictions of uncommon large whale distributions from Maxent or other presence-only techniques may be useful for science or management, but only if spatial bias in the observations is addressed in the derivation and interpretation of model predictions.