Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys
Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys
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DOI：
10.1111/j.1365-2664.2007.01441.x
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发表时间：
2008-04-01
影响因子：
5.7
通讯作者：
Royle, J. A.
中科院分区：
文献类型：
--
作者：
Kery, M.;Royle, J. A.
1. 物种丰富度是最广泛使用的生物多样性指标，但由于某些物种通常被忽视，因此无法直接观察到。因此，为了获得无偏的物种丰富度估计，必须考虑到不完美的可探测性。当在多个样点评估物种丰富度时，可以采用两种方法来估计物种丰富度：分别对每个样点进行估计或将所有样本集中起来进行估计。第一种方法产生不精确的估计，而第二种方法则失去了特定地点的信息。相比之下，层次贝叶斯（HB）多物种站点占用模型受益于跨站点的信息组合，而不会丢失站点特定信息，并且还可以产生每个物种的占用估计。该模型的核心是对未完全观察到的存在-缺失矩阵的估计，这是生物地理学和监测研究的核心。我们使用瑞士繁殖鸟类调查数据来说明该模型，并将其估计值与广泛使用的jackknife物种丰富度估计值和原始物种计数进行了比较。两名独立观察员分别在26个1公里(2)的样方上进行了三次调查，发现了27-56种（共103种）物种。在HB模型下，三次调查后发现的物种平均估计比例为0.87。与原始计数相比，刀切估计的精确度较低（观察者之间的可重复性较差），但HB估计的可重复性与原始计数相同。因此，HB模型中的信息组合导致物种丰富度估计可能至少与先前校正可检测性的方法一样无偏，但相对于未校正的、有偏的物种计数，没有精度上的成本。全区物种丰富度估计为113.1 (CI 106 ~ 123)；物种可检测性范围为0.08 ~ 0.99，表明物种可检测性非常异质性；物种占用率为0.06 ~ 0.96。即使在六次调查之后，观察到的占用率的绝对偏差估计高达0.0.4。合成与应用。用于物种丰富度估计的HB模型结合了不同地点的信息，比以前的方法更精确，估计偏差更小。它还提供了几种衡量社区规模和组成的估计。可包括占用率和可检测性的协变量。我们认为，它在监测方案以及生物地理学和社区生态学方面具有相当大的潜力。
1. Species richness is the most widely used biodiversity metric, but cannot be observed directly as, typically, some species are overlooked. Imperfect detectability must therefore be accounted for to obtain unbiased species-richness estimates. When richness is assessed at multiple sites, two approaches can be used to estimate species richness: either estimating for each site separately, or pooling all samples. The first approach produces imprecise estimates, while the second loses site-specific information.2. In contrast, a hierarchical Bayes (HB) multispecies site-occupancy model benefits from the combination of information across sites without losing site-specific information and also yields occupancy estimates for each species. The heart of the model is an estimate of the incompletely observed presence-absence matrix, a centrepiece of biogeography and monitoring studies. We illustrate the model using Swiss breeding bird survey data, and compare its estimates with the widely used jackknife species-richness estimator and raw species counts.3. Two independent observers each conducted three surveys in 26 1-km(2) quadrats, and detected 27-56 (total 103) species. The average estimated proportion of species detected after three surveys was 0.87 under the HB model. Jackknife estimates were less precise (less repeatable between observers) than raw counts, but HB estimates were as repeatable as raw counts. The combination of information in the HB model thus resulted in species-richness estimates presumably at least as unbiased as previous approaches that correct for detectability, but without costs in precision relative to uncorrected, biased species counts.4. Total species richness in the entire region sampled was estimated at 113.1 (CI 106-123); species detectability ranged from 0.08 to 0.99, illustrating very heterogeneous species detectability; and species occupancy was 0.06-0.96. Even after six surveys, absolute bias in observed occupancy was estimated at up to 0.40.5. Synthesis and applications. The HB model for species-richness estimation combines information across sites and enjoys more precise, and presumably less biased, estimates than previous approaches. It also yields estimates of several measures of community size and composition. Covariates for occupancy and detectability can be included. We believe it has considerable potential for monitoring programmes as well as in biogeography and community ecology.