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 (Sus scrofa), using removal data from aerial
gunning in 59 site/time-frame combinations (480–19,600 acres) throughout
Oklahoma and Texas, USA. 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 (more than
three) within a time frame during which demographic changes are minimized
(i.e., a closed population; ≤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.