Canada-US Fulbright Visiting Research Chair, Jackson School, University of Washington, Seattle, WA 98195, USA.
Conserv Biol. 2011 Jun;25(3):526-35. doi: 10.1111/j.1523-1739.2011.01656.x. Epub 2011 Mar 8.
Often abundance of rare species cannot be estimated with conventional design-based methods, so we illustrate with a population of blue whales (Balaenoptera musculus) a spatial model-based method to estimate abundance. We analyzed data from line-transect surveys of blue whales off the coast of Chile, where the population was hunted to low levels. Field protocols allowed deviation from planned track lines to collect identification photographs and tissue samples for genetic analyses, which resulted in an ad hoc sampling design with increased effort in areas of higher densities. Thus, we used spatial modeling methods to estimate abundance. Spatial models are increasingly being used to analyze data from surveys of marine, aquatic, and terrestrial species, but estimation of uncertainty from such models is often problematic. We developed a new, broadly applicable variance estimator that showed there were likely 303 whales (95% CI 176-625) in the study area. The survey did not span the whales' entire range, so this is a minimum estimate. We estimated current minimum abundance relative to pre-exploitation abundance (i.e., status) with a population dynamics model that incorporated our minimum abundance estimate, likely population growth rates from a meta-analysis of rates of increase in large baleen whales, and two alternative assumptions about historic catches. From this model, we estimated that the population was at a minimum of 16.5% (95% CI 7.3-34.4%) of pre-exploitation levels in 1998 under one catch assumption and 12.4% (CI 5.4-26.3%) of pre-exploitation levels under the other. Thus, although Chilean blue whales are probably still at a small fraction of pre-exploitation abundance, even these minimum abundance estimates demonstrate that their status is better than that of Antarctic blue whales, which are still <1% of pre-exploitation population size. We anticipate our methods will be broadly applicable in aquatic and terrestrial surveys for rarely encountered species, especially when the surveys are intended to maximize encounter rates and estimate abundance.
通常情况下，传统的基于设计的方法无法估计稀有物种的丰度，因此，我们以智利沿海的蓝鲸（Balaenoptera musculus）种群为例，介绍了一种基于空间模型的方法来估计丰度。我们分析了在该海域进行的蓝鲸线路截距调查数据，该种群曾被大量捕杀至低水平。野外协议允许偏离计划的轨迹线，以收集鉴定照片和组织样本进行遗传分析，这导致在密度较高的区域增加了临时采样设计的努力。因此，我们使用空间建模方法来估计丰度。空间模型越来越多地被用于分析海洋、水生和陆地物种的调查数据，但这种模型的不确定性估计往往存在问题。我们开发了一种新的、广泛适用的方差估计器，该估计器表明，在研究区域内可能有 303 头蓝鲸（95%置信区间为 176-625）。该调查并未涵盖鲸鱼的整个范围，因此这只是一个最小估计值。我们用一个种群动态模型来估计当前的最小丰度与开发前的丰度（即状态）相对比，该模型结合了我们的最小丰度估计值、从大型须鲸增长率的荟萃分析中得出的可能种群增长率，以及关于历史捕捞量的两种替代假设。根据该模型，我们估计在一种捕捞假设下，1998 年种群数量至少为开发前水平的 16.5%（95%置信区间为 7.3-34.4%），在另一种假设下为开发前水平的 12.4%（置信区间为 5.4-26.3%）。因此，尽管智利蓝鲸的数量可能仍然只占开发前水平的一小部分，但即使是这些最小丰度估计值也表明，它们的状况要好于南极蓝鲸，后者的数量仍不到开发前种群规模的 1%。我们预计，我们的方法将广泛适用于水生和陆地稀有物种的调查，特别是当调查旨在最大限度地提高发现率并估计丰度时。
