Human-wildlife conflict has emerged as the central vocabulary for cases requiring balance between resource demands of humans and wildlife. This phrase is problematic because, given traditional definitions of conflict, it positions wildlife as conscious human antagonists. We used content analysis of wildlife conservation publications and professional meeting presentations to explore the use of the phrase, human-wildlife conflict, and compared competing models explaining its usage. Of the 422 publications and presentations using human-wildlife conflict, only 1 reflected a traditional definition of conflict, >95% referred to reports of animal damage to entities human care about, and <4% referred to human-human conflict. Usage of human-wildlife conflict was related to species type (herbivores with human food, carnivores with human safety, meso-mammals with property), development level of the nation where the study occurred (less developed nations with human food and more developed nations with human safety and property damage), and whether the study occurred on private lands or protected areas (protected areas with human-human conflict and other areas with property damage). We argue that the phrase, human-wildlife conflict, is detrimental to coexistence between humans and wildlife, and suggest comic reframing to facilitate a more productive interpretation of human-wildlife relationships.

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Participatory modeling engages the implicit and explicit knowledge of stakeholders to create formalized and shared representations of reality and has evolved into a field of study as well as a practice. Participatory modeling researchers and practitioners who focus specifically on environmental resources met at the National Socio-Environmental Synthesis Center (SESYNC) in Annapolis, Maryland, over the course of 2 years to discuss the state of the field and future directions for participatory modeling. What follows is a description of 12 overarching groups of questions that could guide future inquiry.Participatory approaches to resource management must involve those who are affected by the decisions that stem from environmental management decisions (Reed et al., 2009). Environmental resource management often requires a combination of descriptive and normative knowledges as well as local capacity for action (and inaction). Because resource users have such knowledge and capacity, local engagement is crucial in PM. However, there are imbricated layers of power between researchers and locals-often with researchers holding the balance of power due to their increased access to social goods such as money, formal education,

A hidden Markov model ͑HMM͒ system is presented for automatically classifying African elephant vocalizations. The development of the system is motivated by successful models from human speech analysis and recognition. Classification features include frequency-shifted Mel-frequency cepstral coefficients ͑MFCCs͒ and log energy, spectrally motivated features which are commonly used in human speech processing. Experiments, including vocalization type classification and speaker identification, are performed on vocalizations collected from captive elephants in a naturalistic environment. The system classified vocalizations with accuracies of 94.3% and 82.5% for type classification and speaker identification classification experiments, respectively. Classification accuracy, statistical significance tests on the model parameters, and qualitative analysis support the effectiveness and robustness of this approach for vocalization analysis in nonhuman species.

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Governance networks in large‐scale landscape conservation are constantly changing as their constituent programs are created, are reconfigured, or cease to exist. Here, a four‐stage life‐cycle model is used to outline the challenges that network members face in designing healthy and useful governance processes, and a short description of the evolution of the Chicago Wilderness alliance helps to illustrate the dynamic nature of network governance. We deliberately use the concept of a “healthy and useful life” in place of more traditional measures of success because it highlights the constant nurturing that network processes require. The concept also draws attention to the fact that governance networks are a functional enterprise – even though they eventually come to the end of a life cycle, they most likely served useful functions while in operation.

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