Obtaining reliable information on species distributions is often the first step in conservation. Distribution information can be used to focus survey efforts to estimate population size and examine drivers of occupancy or other population parameters. We compared detection rates and survey costs for 2 techniques (remote cameras and scent detection teams) used to evaluate distribution of a rare carnivore, Humboldt subspecies of Pacific marten (Martes caurina humboldtensis). We used remote cameras at randomly placed sites in coastal Oregon, USA, to compare camera set type (baited, unbaited), bait height (<0.5 m, 1.37 m), and bait types (chicken and lure, cat food) for a 21‐day survey period during winter (27 Jan–12 Apr) and summer (04 Jul–14 Oct) 2015. When a marten was detected, we completed an additional survey within 25 m. Scent detection teams (human handler and dog) performed area‐constrained searches for scat deposited. We used a Bayesian occupancy model to compare detection probabilities for baited camera treatments. We detected martens at 26 sites, and offset stations detected martens at 15 of 24 sites (62.5%). Survey‐level detection probability for martens (the probability of detecting ≥1 marten during a survey) was high for the camera survey during summer and winter (estimates ranged from 60% to 99% and 33% to 82%, respectively), and similar for all treatments. We surveyed 50 sites with both remote cameras and scent detection teams 25 April–25 May 2015. Of 33 sites with ≥1 marten detection, 36.4% of units had a marten detected only by scent detection teams, 30.3% only by remote camera, and 33.3% by both techniques. When the objective was marten detection, survey costs were less for scent detection teams. For identifying individual martens and their sex, cameras paired with hair snares were more cost‐efficient. Scent detection teams provided a complementary method to baited remote cameras for assessing distribution. Using multiple techniques provided an opportunity to quantify limitations of survey methods. © 2018 The Wildlife Society.

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Black‐throated Sparrows (Amphispiza bilineata) are common breeding birds throughout the desert regions of North America and can be considered nest‐site generalists. Information about how spatial (e.g., vegetation) and temporal factors influence nest survival of these sparrows is lacking throughout their range. Our objective was to examine the spatial and temporal factors associated with nest survival of Black‐throated Sparrows at the nest and nest‐patch scales in the predator‐rich environment of the northern Chihuahuan Desert of New Mexico. We used a logistic‐exposure model fit within a Bayesian framework to model the daily survival probability of Black‐throated Sparrow nests. Predation was the leading cause of nest failure, accounting for 86% of failed nests. We found evidence of negative associations between nest survival and both vegetative cover above nests and shrub density within 5 m of nests. We found no support for other habitat covariates, but did find strong evidence that daily survival rate was higher earlier in the breeding season and during the egg‐laying stage. A decline in nest survival later in the breeding period may be due to increased predator activity due to warmer ambient temperatures, whereas lower survival during the incubation and nestling stages could be a result of increased activity at nests. A generalist approach to nest‐site selection may be an adaptive response to the presence of a diverse assemblage of nest predators that results in the reduced influence of spatial factors on nest survival for Black‐throated Sparrows.

e Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species-environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species, Bromus inermis. The census of B. inermis provides a good example of a species distribution that is both sparsely (1.9 % prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey B. inermis. However, inferences about the relationships between B. inermis presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.