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Defining practical and robust study designs for interventions targeted at terrestrial mammalian predators
1Department of Conservation Biology, Georg-August-Universität Göttingen, Göttingen, Germany.
Most human-predator conflict studies use less reliable designs, with a rise in their use over time. More robust study designs, like randomized controlled trials, remain underutilized for evaluating conservation interventions.
Area of Science:
- Conservation Biology
- Wildlife Management
- Ecology
Background:
- Human-mammalian predator conflicts are increasing globally, posing challenges for conservation and livelihoods.
- Effective conflict mitigation relies on evidence from robust study designs, but current practices are unclear.
- There's a need to understand study design trends and improve best practices for predator-targeted interventions.
Purpose of the Study:
- To examine the evolution of study designs used in human-predator conflict interventions.
- To assess the relationship between study designs and factors like intervention type, predator species, and country.
- To identify limitations and applicability of different study designs for future research.
Main Methods:
- Analysis of 434 cases from 244 studies published between 1955 and 2020.
- Application of multinomial regression modeling to track changes in study design usage.
- Categorization of interventions into types: aversion, husbandry, mixed, invasive, lethal, and noninvasive management.
Main Results:
- Study design choice was primarily related to intervention type, not other factors.
- Less reliable designs (before-after and control-impact) were most common (47.7% and 38.2%) and increased over time.
- Robust designs like randomized controlled trials (5.3%) and crossover designs (1.4%) remained infrequent.
Conclusions:
- The prevalence of less rigorous study designs in human-predator conflict research is a concern.
- Before-after-control-impact designs offer broad applicability for predator interventions.
- Randomized controlled trials and crossover designs, while robust, have limitations in practical application for certain interventions.
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