The Carrot or the Stick? Evaluation of Education and Enforcement as Management Tools for Human-Wildlife Conflicts

Evidence-based decision-making is critical for implementing conservation actions, especially for human-wildlife conflicts, which have been increasing worldwide. Conservation practitioners recognize that long-term solutions should include altering human behaviors, and public education and enforcement of wildlife-related laws are two management actions frequently implemented, but with little empirical evidence evaluating their success. We used a system where human-black bear conflicts were common, to experimentally test the efficacy of education and enforcement in altering human behavior to better secure attractants (garbage) from bears. We conducted 3 experiments in Aspen CO, USA to evaluate: 1) on-site education in communal dwellings and construction sites, 2) Bear Aware educational campaign in residential neighborhoods, and 3) elevated law enforcement at two levels in the core business area of Aspen. We measured human behaviors as the response including: violation of local wildlife ordinances, garbage availability to bears, and change in use of bear-resistance refuse containers. As implemented, we found little support for education, or enforcement in the form of daily patrolling in changing human behavior, but found more support for proactive enforcement, i.e., dispensing warning notices. More broadly we demonstrated the value of gathering evidence before and after implementing conservation actions, and the dangers of measuring responses in the absence of ecological knowledge. We recommend development of more effective educational methods, application of proactive enforcement, and continued evaluation of tools by directly measuring change in human behavior. We provide empirical evidence adding to the conservation managers' toolbox, informing policy makers, and promoting solutions to human-wildlife conflicts

Testing the Accuracy of Aerial Surveys for Large Mammals: An Experiment with African Savanna Elephants (Loxodonta africana)

Accurate counts of animals are critical for prioritizing conservation efforts. Past research, however, suggests that observers on aerial surveys may fail to detect all individuals of the target species present in the survey area. Such errors could bias population estimates low and confound trend estimation. We used two approaches to assess the accuracy of aerial surveys for African savanna elephants (Loxodonta africana) in northern Botswana. First, we used double-observer sampling, in which two observers make observations on the same herds, to estimate detectability of elephants and determine what variables affect it. Second, we compared total counts, a complete survey of the entire study area, against sample counts, in which only a portion of the study area is sampled. Total counts are often considered a complete census, so comparing total counts against sample counts can help to determine if sample counts are underestimating elephant numbers. We estimated that observers detected only 76% ± SE of 2% of elephant herds and 87 ± 1% of individual elephants present in survey strips. Detectability increased strongly with elephant herd size. Out of the four observers used in total, one observer had a lower detection probability than the other three, and detectability was higher in the rear row of seats than the front. The habitat immediately adjacent to animals also affected detectability, with detection more likely in more open habitats. Total counts were not statistically distinguishable from sample counts. Because, however, the double-observer samples revealed that observers missed 13% of elephants, we conclude that total counts may be undercounting elephants as well. These results suggest that elephant population estimates from both sample and total counts are biased low. Because factors such as observer and habitat affected detectability of elephants, comparisons of elephant populations across time or space may be confounded. We encourage survey teams to incorporate detectability analysis in all aerial surveys for mammals

Ecological Thresholds in the Savanna Landscape: Developing a Protocol for Monitoring the Change in Composition and Utilisation of Large Trees

BACKGROUND: Acquiring greater understanding of the factors causing changes in vegetation structure -- particularly with the potential to cause regime shifts -- is important in adaptively managed conservation areas. Large trees (> or =5 m in height) play an important ecosystem function, and are associated with a stable ecological state in the African savanna. There is concern that large tree densities are declining in a number of protected areas, including the Kruger National Park, South Africa. In this paper the results of a field study designed to monitor change in a savanna system are presented and discussed. METHODOLOGY/PRINCIPAL FINDINGS: Developing the first phase of a monitoring protocol to measure the change in tree species composition, density and size distribution, whilst also identifying factors driving change. A central issue is the discrete spatial distribution of large trees in the landscape, making point sampling approaches relatively ineffective. Accordingly, fourteen 10 m wide transects were aligned perpendicular to large rivers (3.0-6.6 km in length) and eight transects were located at fixed-point photographic locations (1.0-1.6 km in length). Using accumulation curves, we established that the majority of tree species were sampled within 3 km. Furthermore, the key ecological drivers (e.g. fire, herbivory, drought and disease) which influence large tree use and impact were also recorded within 3 km. CONCLUSIONS/SIGNIFICANCE: The technique presented provides an effective method for monitoring changes in large tree abundance, size distribution and use by the main ecological drivers across the savanna landscape. However, the monitoring of rare tree species would require individual marking approaches due to their low densities and specific habitat requirements. Repeat sampling intervals would vary depending on the factor of concern and proposed management mitigation. Once a monitoring protocol has been identified and evaluated, the next stage is to integrate that protocol into a decision-making system, which highlights potential leading indicators of change. Frequent monitoring would be required to establish the rate and direction of change. This approach may be useful in generating monitoring protocols for other dynamic systems