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.

评估外来入侵物种管理项目的进展，对于向利益相关方论证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实用衡量指标。然而，诸多种群规模估算方法的实施需耗费大量人力与资金，给实际运营的管理项目带来了沉重的负担。移除模型是仅利用移除活动相关数据即可估算管理前后种群丰度的可靠方法，无需在管理工作之外额外开展其他作业。我们开发了贝叶斯层级模型（Bayesian hierarchical model），可基于移除数据估算种群丰度，并纳入对不同水平移除作业强度的考量，同时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外来入侵物种——野猪（Sus scrofa）的位点特异性丰度，所用数据来自美国俄克拉荷马州与得克萨斯州境内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。模拟实验结果表明，当有效移除率（即考虑总作业投入后的移除率）高于0.40时，种群丰度估算结果整体较为准确。然而，当种群丰度较低（<50）时，要准确估算丰度所需的有效移除率需大幅提升至0.70。基于我们的事后验证方法，78%的位点-时间窗口估算结果准确可靠。若要使用该建模框架，需在人口统计学变化最小化的时间窗口内开展3次及以上的移除作业（针对野猪而言，该窗口时长应≤3个月，即封闭种群）。我们的研究结果显示，通过该模型准确估算丰度的概率，会随着采样作业投入的增加（在3个月窗口内完成8小时以上的飞行时长为最优）以及移除率的提升而提高。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、飞行员间的差异等），以提升移除率估算的准确性，进而优化丰度估算结果。