Data from: Inferring invasive species abundance using removal data from management actions

Evaluation of the progress of management programs for invasive species is crucial for demonstrating impacts to stakeholders and strategic planning of resource allocation. Estimates of abundance before and after management activities can serve as a useful metric of population management programs. However, many methods of estimating population size are too labor intensive and costly to implement, posing restrictive levels of burden on operational programs. Removal models are a reliable method for estimating abundance before and after management using data from the removal activities exclusively, thus requiring no work in addition to management. We developed a Bayesian hierarchical model to estimate abundance from removal data accounting for varying levels of effort, and used simulations to assess the conditions under which reliable population estimates are obtained. We applied this model to estimate site-specific abundance of an invasive species, feral swine (<I>Sus scrofa</I>), using removal data from aerial gunning in 59 site/time-frame combinations (480-19,600 acres) throughout Oklahoma and Texas, U.S. Simulations showed that abundance estimates were generally accurate when effective removal rates (removal rate accounting for total effort) were above 0.40. However, when abundances were small (<50) the effective removal rate needed to accurately estimates abundances was considerably higher (0.70). Based on our post-validation method 78% of our site/time frame estimates were accurate. To use this modeling framework it is important to have multiple removals (3+) within a time frame during which demographic changes are minimized (i.e., a closed population; {less than or equal to} 3 months for feral swine). Our results show that the probability of accurately estimating abundance from this model improves with increased sampling effort (8+ flight hours across the 3-month window is best) and increased removal rate. Based on the inverse relationship between inaccurate abundances and inaccurate removal rates, we suggest auxiliary information that could be collected and included in the model as covariates (e.g., habitat effects, differences between pilots) to improve accuracy of removal rates and hence abundance estimates.

入侵物种管理项目的进展评估，对于向利益相关方展示项目成效以及开展资源分配的战略规划至关重要。管理活动前后的种群丰度估算，可作为种群管理项目成效的有效评估指标。然而，诸多种群规模估算方法的实施成本高昂且耗时费力，给实际运营的项目带来了沉重的约束性负担。移除模型是一种可靠的方法，仅通过移除活动产生的数据即可完成管理前后的丰度估算，无需在管理工作之外额外开展工作。我们构建了贝叶斯分层模型（Bayesian hierarchical model），基于不同努力程度下的移除数据估算丰度，并通过模拟实验评估了可获得可靠种群估算结果的条件。我们将该模型应用于估算某入侵物种——野猪（Sus scrofa）的位点特异性丰度，使用了美国俄克拉荷马州与得克萨斯州范围内59个位点/时间窗口组合（面积480至19600英亩）的航空狩猎移除数据。模拟实验结果显示，当有效移除率（计入总努力程度的移除率）高于0.40时，丰度估算结果整体较为准确。然而，当种群丰度较低（<50）时，要实现准确的丰度估算，所需的有效移除率需显著更高（0.70）。基于我们的事后验证方法，59个位点/时间窗口的估算结果中有78%是准确的。若要使用该建模框架，需在人口动态变化最小化的时间窗口内开展多次移除操作（≥3次），即保持种群封闭；对于野猪而言，该窗口时长应≤3个月。我们的研究结果表明，通过该模型实现丰度准确估算的概率，会随着采样努力程度的提升（3个月窗口内飞行时长≥8小时为最优）以及移除率的提高而升高。基于不准确的丰度估算结果与不准确的移除率之间的负相关关系，我们建议可收集辅助信息并将其作为协变量纳入模型（例如栖息地效应、飞行员间的差异），以提升移除率的估算准确性，进而优化丰度估算结果。