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Warming water temperatures as a result of climate change pose a major threat to coldwater organisms. However, the rate of warming is not spatially uniform due to surface‐ground‐water interactions and stream and watershed characteristics. Coldwater habitats that are most resistant to warming serve as thermal refugia and identifying their locations is critical to regional aquatic conservation planning. We quantified the thermal sensitivity of 203 streams providing current and potential habitat for brook trout (Salvelinus fontinalis) across nearly 1000 linear km of their native range in the southern and central Appalachian Mountains region, USA, and characterized their spatial variability with landscape variables available in the National Hydrography Dataset. Using the Bayesian framework, we calculated the maximum slope of the logistic function relating paired weekly mean air temperature and stream temperature as an index of stream thermal sensitivity. Streams differed greatly in thermal sensitivity and those with more resistant water temperature regimes (i.e., thermal refugia) were consistently characterized by southerly latitudes and groundwater input. Landscape variables derived from a principal component analysis explained 16% of the variation in thermal sensitivity, indicating that the existing landscape variables were modestly successful in explaining spatial thermal heterogeneity. Using our model and spatial interpolation, we predicted thermal sensitivity at 8695 stream segments potentially suitable for brook trout in the study region. Thermal refugia were more common southward presumably due to higher elevations, but elsewhere they were also clustered at finer spatial scales. Our analysis informs prioritizing habitat conservation and restoration of this native salmonid and other aquatic organisms that depend on coldwater habitats in a warming world. [ABSTRACT FROM AUTHOR]

Understanding patterns of species abundance is essential for planning landscape‐level conservation. The complex hierarchies of dendritic ecosystems result in different levels of heterogeneity at distinct geographic scales. Species responses to dynamic environmental drivers may also vary spatially depending on their interactions with landscape features. Monitoring abundance by explicitly quantifying their spatial and temporal variation is important for strategic management.We analysed brook trout (Salvelinus fontinalis) count data collected from 173 sites in western North Carolina between 1989 and 2015. We developed a Bayesian hierarchical model that used single‐ and multi‐pass electro‐fishing data and characterized their respective capture probabilities. We quantified spatial variation using a multi‐scale process model representative of the nested stream habitats, and we investigated differences in population temporal trends and responses to seasonal weather patterns by space and life stage.Trout abundance was lower on the Atlantic slope of the Eastern Continental Divide than in the interior, on average, and the Atlantic slope juveniles were more adversely affected by high winter flows. However, Atlantic slope populations of both lifestages demonstrated positive temporal trends, whereas Interior juveniles demonstrated a negative trend. We found higher spatial variation than temporal variation in abundance when conditioned on the covariates, where the primary source of spatial heterogeneity was revealed at the segment level, compared to watershed or network levels.Our multi‐scale spatial model outperformed simpler models in abundance estimation and out‐of‐sample prediction. The inferred per‐pass capture probabilities indicated that single‐pass surveys were as efficient as multi‐pass surveys.Synthesis and applications. Our study suggested conservation priority should involve multiple criteria, including present‐day abundance, temporal trend and sensitivity to environmental drivers. Based on the inferred scale‐specific variations in trout abundance, we recommend that future surveys strategically combine single‐pass surveys with multi‐pass surveys to optimize abundance estimation. Our approach is widely applicable to other species and ecosystems occupying dendritic habitats. [ABSTRACT FROM AUTHOR]

Climate change affects populations over broad geographic ranges due to spatially autocorrelated abiotic conditions known as the Moran effect. However, populations do not always respond to broad‐scale environmental changes synchronously across a landscape. We combined multiple datasets for a retrospective analysis of time‐series count data (5–28 annual samples per segment) at 144 stream segments dispersed over nearly 1,000 linear kilometers of range to characterize the population structure and scale of spatial synchrony across the southern native range of a coldwater stream fish (brook trout, Salvelinus fontinalis), which is sensitive to stream temperature and flow variations. Spatial synchrony differed by life stage and geographic region: it was stronger in the juvenile life stage than in the adult life stage and in the northern sub‐region than in the southern sub‐region. Spatial synchrony of trout populations extended to 100–200 km but was much weaker than that of climate variables such as temperature, precipitation, and stream flow. Early life stage abundance changed over time due to annual variation in summer temperature and winter and spring stream flow conditions. Climate effects on abundance differed between sub‐regions and among local populations within sub‐regions, indicating multiple cross‐scale interactions where climate interacted with local habitat to generate only a modest pattern of population synchrony over space. Overall, our analysis showed higher degrees of response heterogeneity of local populations to climate variation and consequently population asynchrony than previously shown based on analysis of individual, geographically restricted datasets. This response heterogeneity indicates that certain local segments characterized by population asynchrony and resistance to climate variation could represent unique populations of this iconic native coldwater fish that warrant targeted conservation. Advancing the conservation of this species can include actions that identify such priority populations and incorporate them into landscape‐level conservation planning. Our approach is applicable to other widespread aquatic species sensitive to climate change. [ABSTRACT FROM AUTHOR]

The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio‐temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi‐scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya–Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios. [ABSTRACT FROM AUTHOR]

Protected areas are the cornerstone of global conservation, yet financial support for basic monitoring infrastructure is lacking in 60% of them. Citizen science holds potential to address these shortcomings in wildlife monitoring, particularly for resource-limited conservation initiatives in developing countries - if we can account for the reliability of data produced by volunteer citizen scientists (VCS)., This study tests the reliability of VCS data vs. data produced by trained ecologists, presenting a hierarchical framework for integrating diverse datasets to assess extra variability from VCS data., Our results show that while VCS data are likely to be overdispersed for our system, the overdispersion varies widely by species. We contend that citizen science methods, within the context of East African drylands, may be more appropriate for species with large body sizes, which are relatively rare, or those that form small herds. VCS perceptions of the charisma of a species may also influence their enthusiasm for recording it., Tailored programme design (such as incentives for VCS) may mitigate the biases in citizen science data and improve overall participation. However, the cost of designing and implementing high-quality citizen science programmes may be prohibitive for the small protected areas that would most benefit from these approaches., Synthesis and applications. As citizen science methods continue to gain momentum, it is critical that managers remain cautious in their implementation of these programmes while working to ensure methods match data purpose. Context-specific tests of citizen science data quality can improve programme implementation, and separate data models should be used when volunteer citizen scientists' variability differs from trained ecologists' data. Partnerships across protected areas and between protected areas and other conservation institutions could help to cover the costs of citizen science programme design and implementation. [ABSTRACT FROM AUTHOR]

Evaluation of population dynamics for rare and declining species is often limited to data that are sparse and/or of poor quality. Frequently, the best data available for rare bird species are based on large-scale, population count data. These data are commonly based on sampling methods that lack consistent sampling effort, do not account for detectability, and are complicated by observer bias. For some species, short-term studies of demographic rates have been conducted as well, but the data from such studies are typically analyzed separately. To utilize the strengths and minimize the weaknesses of these two data types, we developed a novel Bayesian integrated model that links population count data and population demographic data through population growth rate ( λ) for Gunnison sage-grouse ( Centrocercus minimus). The long-term population index data available for Gunnison sage-grouse are annual (years 1953-2012) male lek counts. An intensive demographic study was also conducted from years 2005 to 2010. We were able to reduce the variability in expected population growth rates across time, while correcting for potential small sample size bias in the demographic data. We found the population of Gunnison sage-grouse to be variable and slightly declining over the past 16 years. [ABSTRACT FROM AUTHOR]

World-wide declines in amphibian populations are often attributed to loss of habitat and exploitation; additionally, climate change may play an important role in future declines. Despite protection of habitat, amphibians relying on temporary habitats, such as vernal pools, may need active management to maintain their populations under forecasts of warming temperatures and more variable precipitation. However, few studies have examined the factors influencing where these species choose to breed (breeding occurrence) and the conditional likelihood of successful metamorphosis, while accounting for imperfect detection., We developed an occupancy model and estimated parameters within a Bayesian framework to investigate the factors influencing probabilities of wood frog Lithobates sylvatica breeding and successful metamorphosis at Patuxent Research Refuge, Maryland, USA. Our objectives were to obtain estimates of breeding occurrence and metamorph occupancy and evaluate the success of current management actions., The probabilities of wood frog breeding and successful metamorphosis varied by year and were positively related to the pond's typical hydroperiod length and annual precipitation. Contrary to our predictions, previous occupancy states had little effect on breeding and metamorph occupancy probabilities, which is likely to be due to high correlation of occupancy with hydroperiod length. Additionally, we did not observe a relationship between breeding occupancy probabilities and the spatial arrangement of pools. Although sample sizes were small, management actions resulted in an increase in both breeding and metamorph occupancy probabilities., Synthesis and applications. We demonstrate that management actions targeting short-hydroperiod pools favourably influence both components of breeding success. However, continued monitoring is needed to determine whether managed pools remain suitable for wood frogs. With predicted changes in climate and a positive relationship between breeding occupancy and winter precipitation, a proactive focus on active management of vernal pools may provide a means to maintain wood frog populations in the future. [ABSTRACT FROM AUTHOR]

Core areas are important descriptors of animal space-use patterns, but current estimation methods rely on arbitrary rules and potentially lead to imprecise or erroneous area estimates. We proposed a Bayesian statistical model that incorporates an individual-based method for estimating core area boundaries. The model accounts for boundary uncertainty and multiple scales of clustering by partitioning a home range into ⩾2 completely spatially random point patterns defined by a kernel density isopleth. We used data from coyotes (Canis latrans), bobcats (Lynx rufus), and red-shouldered hawks (Buteo lineatus) to estimate core areas for individual animals. We also estimated core areas from simulated point patterns with known boundaries, varying numbers of points, and relative densities of points inside core areas, and compared estimates to those obtained using the 50% isopleth. Optimal isopleths for the empirical data ranged between 18.7% and 71.5%. We found no species-specific range of core area isopleths. Across all simulated scenarios, our method outperformed the 50% isopleth-based estimate, which consistently overestimated core areas. Minta overlap values were 20-40% higher across all scenarios for our method compared to the 50% isopleth. Minta overlap values were .75% in 90% of scenarios using our method. Objectively estimating core areas using our individual-based method may lead to improved inference about which behavioral and ecological processes underlie observed space-use patterns because of greater estimate precision. [ABSTRACT FROM AUTHOR]