Camera trapping is a standard tool in ecological research and wildlife conservation. Study designs, particularly for small-bodied or cryptic wildlife species often attempt to boost low detection probabilities by using non-random camera placement or baited cameras, which may bias data, or incorrectly estimate detection and occupancy. We investigated the ability of non-baited, multi-camera arrays to increase detection probabilities of wildlife. Study design components were evaluated for their influence on wildlife detectability by iteratively parsing an empirical dataset (1) by different sizes of camera arrays deployed (1–10 cameras), and (2) by total season length (1–365 days). Four species from our dataset that represented a range of body sizes and differing degrees of presumed detectability based on life history traits were investigated: white-tailed deer (Odocoileus virginianus), bobcat (Lynx rufus), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana). For all species, increasing from a single camera to a multi-camera array significantly improved detection probability across the range of season lengths and number of study sites evaluated. The use of a two camera array increased survey detection an average of 80% (range 40–128%) from the detection probability of a single camera across the four species. Species that were detected infrequently benefited most from a multiple-camera array, where the addition of up to eight cameras produced significant increases in detectability. However, for species detected at high frequencies, single cameras produced a season-long (i.e, the length of time over which cameras are deployed and actively monitored) detectability greater than 0.75. These results highlight the need for researchers to be critical about camera trap study designs based on their intended target species, as detectability for each focal species responded differently to array size and season length. We suggest that researchers a priori identify target species for which inference will be made, and then design camera trapping studies around the most difficult to detect of those species.

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Adaptive management is a well-established approach to managing natural resources, but there is little evidence demonstrating effectiveness of adaptive management over traditional management techniques. Peer-reviewed literature attempts to draw conclusions about adaptive management effectiveness using social perceptions, but those studies are largely restricted to employees of US federal organizations. To gain a more comprehensive insight into perceived adaptive management effectiveness, this study aimed to broaden the suite of disciplines, professional affiliations, and geographic backgrounds represented by both practitioners and scholars. A questionnaire contained a series of questions concerning factors that lead to or inhibit effective management, followed by another set of questions focused on adaptive management. Using a continuum representing strategies of both adaptive management and traditional management, respondents selected those strategies that they perceived as being effective. Overall, characteristics (i.e., strategies, stakeholders, and barriers) identified by respondents as contributing to effective management closely aligned with adaptive management. Responses were correlated to the type of adaptive management experience rather than an individual's discipline, occupational, or regional affiliation. In particular, perceptions of characteristics contributing to adaptive management effectiveness varied between respondents who identified as adaptive management scholars (i.e., no implementation experience) and adaptive management practitioners. Together, these results supported two concepts that make adaptive management effective: practitioners emphasized adaptive management's value as a long-term approach and scholars noted the importance of stakeholder involvement. Even so, more communication between practitioners and scholars regarding adaptive management effectiveness could promote interdisciplinary learning and problem solving for improved resources management.