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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.

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.

Learning and resolving uncertainty should be important components of management decisions, but not every type of uncertainty is equally important to resolve. For rare, threatened, and endangered species, information may be limited and there is often great urgency to halt potential population declines. Therefore, the most important uncertainties are those that would lead to different decisions intended to improve outcomes for species of interest. The New England cottontail (Sylvilagus transitionalis) is an endemic species to the northeastern United States and is listed as a species of special concern, threatened, or endangered in all states in its current range. States within the historical New England cottontail range have made substantial investments in habitat management, captive breeding, and research, but considerable demographic, management, and monitoring uncertainties remain. Connecticut is the geographic core of the current range of the species and therefore has been the focus of many management efforts, including improving habitat, creating new habitat patches, removing eastern cottontails (Sylvilagus floridanus), and releasing captive‐bred New England cottontails. We used a value of information analysis to identify the optimal management decision given current uncertainty and to evaluate how sources of uncertainty might lead to changes in management decisions. Given the current understanding of the New England cottontail in Connecticut, we identified the optimal management decision as improving existing habitat patches without monitoring for the species and managers could expect a 3.4% increase in populations if decision‐making uncertainty could be fully resolved. Multiple sources of uncertainty influenced results, but variation in New England cottontail density and the response of the species to the removal of eastern cottontails were most likely to result in changes to management decisions. At lower New England cottontail densities, releasing captive‐bred individuals competed with improving habitat as the optimal management action. Accounting for the value of information benefits New England cottontail management by guiding research efforts toward information that is most beneficial for decision‐makers and providing insights into parameter thresholds that would lead to changes in management decisions. [ABSTRACT FROM AUTHOR]