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Expanding gray wolf (Canis lupus) populations in Europe and North America contribute to increased risks of livestock predation, which can threaten human livelihoods and lead government agencies to target wolves for lethal removal. Public wolf hunting is a highly contentious strategy for mitigating these risks, yet few empirical studies examine its effectiveness in doing so. Using difference-in-differences and structural equation modeling of data from the northwestern US between 2005 and 2021, we analyzed impacts of wolf hunting on livestock predation by wolves and government removal of wolves in the same year and with a 1-year time lag while controlling for social and environmental variables. We found that public wolf hunting had a small negative effect on livestock predation but had no effect on government lethal removal of wolves in the same or subsequent years. Our findings challenge the assumption that wolf hunting is an effective management strategy for reducing livestock predation and lethal removal. 

Instrument fidelity in message testing research hinges upon how precisely messages operationalize treatment conditions. However, numerous message testing studies have unmitigated threats to validity and reliability because no established procedures exist to guide construction of message treatments. Their construction typically occurs in a black box, resulting in suspect inferential conclusions about treatment effects. Because a mixed methods approach is needed to enhance instrument fidelity in message testing research, this article contributes to the field of mixed methods research by presenting an integrated multistage procedure for constructing precise message treatments using an exploratory sequential mixed methods design. This work harnesses the power of integration through crossover analysis to improve instrument fidelity in message testing research through the use of natural language processing (NLP).