Evaluating the efficacy of management actions to control invasive species is crucial for maintaining funding and to provide feedback for the continual improvement of management efforts. However, it is often difficult to assess the efficacy of control methods due to limited resources for monitoring. Managers may view effort on monitoring as effort taken away from performing management actions. We developed a method to estimate invasive species abundance, evaluate management effectiveness, and evaluate population growth over time from a combination of removal activities (e.g., trapping, ground shooting) using only data collected during removal efforts (method of removal, date, location, number of animals removed, and effort). This dynamic approach allows for abundance estimation at discrete time points and the estimation of population growth between removal periods. To test this approach, we simulated over 1 million conditions, including varying the length of the study, the size of the area examined, the number of removal events, the capture rates, and the area impacted by removal efforts. Our estimates were unbiased (within 10% of truth) 81% of the time and were correlated with truth 91% of the time. This method performs well overall and, in particular, at monitoring trends in abundances over time. We applied this method to removal data from Mingo National Wildlife Refuge in Missouri from December 2015 to September 2019, where the management objective is elimination. Populations of feral swine on Mingo NWR have fluctuated over time but showed marked declines in the last 3–6 months of the time series corresponding to increased removal pressure. Our approach allows for the estimation of population growth across time (from both births and immigration) and therefore, provides a target removal rate (above that of the population growth) to ensure the population will decline. In Mingo NWR, the target monthly removal rate is 18% to cause a population decline. Our method provides advancement over traditional removal modeling approaches because it can be applied to evaluate management programs that use a broad range of removal techniques concurrently and whose management effort and spatial coverage vary across time.

Evaluation of the efficacy of management actions to control invasive species is crucial for maintaining funding and to provide feedback for the continual improvement of management efficiency. However, it is often difficult to assess the efficacy of control methods due to limited resources for monitoring. To evaluate management actions there is often a trade-off in effort aimed at performing management actions and effort aimed at collecting monitoring data to evaluate management actions. We developed a method to estimate invasive species abundance, evaluate management effectiveness, and evaluate population growth overtime from a combination of removal activities (e.g., aerial gunning, trapping, ground shooting) using only data collected during removal efforts (the method of removal, the date, location, number of animals removed, and the effort). This dynamic approach allows for estimating abundance at discrete time points and the estimation of population growth between removal periods. We applied this method to removal data from Mingo National Wildlife Refuge in Missouri from December 2015 to September 2019, where the management objective is elimination. Populations of feral swine on Mingo NWR have fluctuated over time but have shown more marked declines in the last 3-6 months. More dramatic declines were observed in the center of the refuge. To counteract population growth (from both births and immigration) the percent of the population of feral swine removed monthly must be greater than the growth rate. On average, we found that removing 10% of the population monthly had only a 50% chance of causing a population decline, whereas removing 15% of the population monthly had a 95% chance of causing a population decline. Our method provides advancement over traditional removal modeling approaches because it can be applied to evaluate management programs that use a broad range of removal techniques concurrently and whose management effort and spatial coverage vary across time.

Documented coyote attacks on humans are rare events distributed throughout North America. Geospatial monitoring and categorization of coyote behavior type provides essential information necessary for the focused management of coyotes that pose a risk to human safety. Indices of behavior have been used to measure trends in observed behavior with respect to management effort over time. A method is presented for evaluating coyote behavior density for use in developing a human dimension-based decision model with management implementation thresholds. The proposed model allows for the geo-specific adaptive management of coyotes while considering potential environmental, ecological, and social impacts in the course of protecting human safety. KEY WORDS: adaptive management, behavior density, behavior index, behavior threshold, Canis latrans, coyote, geospecific, Integrated Wildlife Damage Management, Texas, urban coyote Proc. 27 th Vertebr. Pest Conf. (R. M. Timm and R. A. Baldwin, Eds.) Published at Univ. of Calif., Davis. 2016. Pp. 78-84.