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DETECTION HETEROGENEITY AND ABUNDANCE ESTIMATION IN POPULATIONS OF GOLDEN-CHEEKED WARBLERS (SETOPHAGA CHRYSOPARIA)

机译:金颊实((Setophaga chrysoparia)种群的检测异质性和丰度估计

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Abundance estimators that account for imperfect detection, such as N-mixture models, assume that detection of individuals is independent of abundance. Using spot-mapping and N-mixture models applied to point-count data, we estimated abundance of Goldencheeked Warblers (Setophaga chrysoparia) in two years at six study sites at the Balcones Canyonlands Preserve, Austin, Texas. N-mixture model estimates deviated from spot-mapping estimates at the site level by overestimating at low abundances, and at the survey-station level by underestimating at high abundance, which suggests that model assumptions may have been violated. We tested whether detection of individuals is influenced by abundance by assessing per capita song rate in relation to abundance. Per capita song rate increased with abundance, illustrating how the behavior of a territorial passerine may violate the independent-detectability assumption. We next explored violation of this assumption at the survey-station level by applying N-mixture models to simulated data exhibiting heterogeneity in detection. This exercise revealed a slight but increasingly negative bias (underestimation of abundance) in the estimator as the actual abundance increased, given positive density-dependent detection. The simulations also revealed a potential effect of sampling variation on misestimation by N-mixture model estimators. Assessing the strength, basis, and prevalence of density-dependent detection; further analyzing the effects of nonrandom heterogeneity in producing estimator bias; and accounting for nonrandom detection heterogeneity in abundance estimators are fruitful areas for further study.
机译:考虑到不完美检测的丰度估算器(例如N混合模型)假设对个体的检测与丰度无关。使用点映射和N混合模型应用于点计数数据,我们在德克萨斯州奥斯汀的Balcones Canyonlands Preserve的六个研究地点估计了两年内金毛莺(Setophaga chrysoparia)的数量。 N混合模型估计值在站点级别通过低丰度高估而偏离了点映射估计值,而在测量站级别则通过在高丰度下低估了点,从而表明模型假设可能已被违反。我们通过评估与丰富度相关的人均歌曲播放率,测试了个人检测是否受丰富度影响。人均歌曲率随着数量的增加而增加,这说明了一个区域雀形目的行为可能如何违反独立可检测性假设。接下来,我们通过将N混合模型应用于在检测中表现出异质性的模拟数据,在调查站一级探索了违反此假设的情况。此练习显示,在给定密度依赖检测为正的情况下,随着实际丰度的增加,估计量中会出现轻微但逐渐增加的负偏差(对丰度的低估)。模拟还揭示了采样变化对N混合模型估计器的误估计的潜在影响。评估密度依赖性检测的强度,基础和普遍性;进一步分析非随机异质性在产生估计偏差时的作用;在丰度估计器中考虑非随机检测异质性是有待进一步研究的硕果。

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