Gomez Juan Pablo, Robinson Scott K, Blackburn Jason K, Ponciano José Miguel
Department of Biology, University of Florida, Gainesville, Florida.
Florida Museum of Natural History, Gainesville, Florida.
Methods Ecol Evol. 2018 Feb;9(2):340-353. doi: 10.1111/2041-210X.12856. Epub 2017 Jul 24.
In this study we propose an extension of the N-mixture family of models that targets an improvement of the statistical properties of rare species abundance estimators when sample sizes are low, yet typical for tropical studies. The proposed method harnesses information from other species in an ecological community to correct each species' estimator. We provide guidance to determine the sample size required to estimate accurately the abundance of rare tropical species when attempting to estimate the abundance of single species.We evaluate the proposed methods using an assumption of 50 m radius plots and perform simulations comprising a broad range of sample sizes, true abundances and detectability values and a complex data generating process. The extension of the N-mixture model is achieved by assuming that the detection probabilities are drawn at random from a beta distribution in a multi-species fashion. This hierarchical model avoids having to specify a single detection probability parameter per species in the targeted community. Parameter estimation is done via Maximum Likelihood.We compared our multi-species approach with previously proposed multi-species N-mixture models, which we show are biased when the true densities of species in the community are less than seven individuals per 100 hectares. The beta N-mixture model proposed here outperforms the traditional Multi-species N-mixture model by allowing the estimation of organisms at lower densities and controlling the bias in the estimation.We illustrate how our methodology can be used to suggest sample sizes required to estimate the abundance of organisms, when these are either rare, common or abundant. When the interest is full communities, we show how the multi-species approaches, and in particular our beta model and estimation methodology, can be used as a practical solution to estimate organism densities from rapid inventory datasets. The statistical inferences done with our model via Maximum Likelihood can also be used to group species in a community according to their detectabilities.
在本研究中，我们提出了N - 混合模型家族的一种扩展，其目标是在样本量较低但在热带研究中较为典型的情况下，改善珍稀物种丰度估计器的统计特性。所提出的方法利用生态群落中其他物种的信息来校正每个物种的估计器。当试图估计单一物种的丰度时，我们提供了指导，以确定准确估计珍稀热带物种丰度所需的样本量。我们使用半径为50米的样地假设来评估所提出的方法，并进行模拟，模拟包括广泛的样本量、真实丰度和可检测性值以及复杂的数据生成过程。N - 混合模型的扩展是通过假设检测概率以多物种方式从贝塔分布中随机抽取来实现的。这种层次模型避免了为目标群落中的每个物种指定单个检测概率参数。参数估计通过最大似然法进行。我们将我们的多物种方法与先前提出的多物种N - 混合模型进行了比较，结果表明，当群落中物种的真实密度低于每100公顷7个个体时，先前的模型存在偏差。这里提出的贝塔N - 混合模型通过允许在较低密度下估计生物体并控制估计偏差，优于传统的多物种N - 混合模型。我们说明了我们的方法如何用于建议估计生物体丰度所需的样本量，无论这些生物体是珍稀、常见还是丰富的。当关注的是整个群落时，我们展示了多物种方法，特别是我们的贝塔模型和估计方法，如何可以用作从快速清查数据集中估计生物体密度的实际解决方案。通过我们的模型通过最大似然法进行的统计推断，也可用于根据群落中物种的可检测性对它们进行分组。
