Department of Forestry and Wildlife Management- Evenstad, Inland Norway University of Applied Sciences, Elverum, Norway.
PLoS One. 2022 Jul 27;17(7):e0268710. doi: 10.1371/journal.pone.0268710. eCollection 2022.
Counting is not always a simple exercise. Specimens can be misidentified or not detected when they are present, giving rise to unidentified sources of error. Deer pellet group counts are a common method to monitor abundance, density, and population trend. Yet, detection errors and observer bias could introduce error into sometimes very large (spatially, temporally) datasets. For example, in Scandinavia, moose (Alces alces) pellet group counts are conducted by volunteer hunters and students, but it is unknown how much uncertainty observer error introduces into these datasets. Our objectives were to 1) estimate the detection probability of moose pellet groups; 2) identify the primary variables leading to detection errors including prior observer experience; and 3) compare density estimates using single and double observer counts. We selected a subset of single observer plots from a long-term monitoring project to be conducted as dependent double observer surveys, where primary and secondary observers worked simultaneously in the field. We did this to quantify detection errors for moose pellet groups, which were previously unknown in Scandinavia, and to identify covariates which introduced variation into our estimates. Our study area was in the boreal forests of southern Norway where we had a nested grid of 100-m2 plots that we surveyed each spring. Our observers were primarily inexperienced. We found that when pellet groups were detected by the primary observer, the secondary observer saw additional pellet groups 42% of the time. We found search time was the primary covariate influencing detection. We also found density estimates from double observer counts were 1.4 times higher than single observer counts, for the same plots. This density underestimation from single observer surveys could have consequences to managers, who sometimes use pellet counts to set harvest quotas. We recommend specific steps to improve future moose pellet counts.
计数并不总是一项简单的任务。当样本存在时，它们可能会被错误识别或未被检测到，从而产生无法识别的误差源。鹿粪组计数是监测丰度、密度和种群趋势的常用方法。然而，检测误差和观测者偏差可能会给有时非常大（空间上、时间上）的数据集引入误差。例如，在斯堪的纳维亚，驼鹿（Alces alces）的粪便组计数由志愿者猎人和学生进行，但尚不清楚观测者误差在这些数据集中引入了多少不确定性。我们的目标是：1）估计驼鹿粪组的检测概率；2）确定导致检测误差的主要变量，包括观测者先前的经验；3）比较单观测者和双观测者计数的密度估计值。我们从一个长期监测项目中选择了一组单观测者的样本来进行依赖的双观测者调查，其中主要和次要观测者同时在现场工作。我们这样做是为了量化驼鹿粪组的检测误差，这些误差在斯堪的纳维亚以前是未知的，并确定引入我们估计值变化的协变量。我们的研究区域位于挪威南部的北方森林，我们在那里有一个嵌套的 100 平方米的样方网格，每个春天都会对其进行调查。我们的观测者主要是没有经验的。我们发现，当主要观测者检测到粪组时，次要观测者有 42%的时间会看到额外的粪组。我们发现搜索时间是影响检测的主要协变量。我们还发现，对于相同的样方，双观测者计数的密度估计值比单观测者计数的密度估计值高 1.4 倍。由于单观测者调查的密度低估，管理者可能会受到影响，他们有时会使用粪组计数来设定狩猎配额。我们建议采取具体措施来改进未来的驼鹿粪组计数。
