Environmental Gradients in Old‐Growth Appalachian Forest Predict Fine‐Scale Distribution, Co‐occurrence, and Density of Woodland Salamanders

Woodland salamanders are among the most abundant vertebrate animals in temperate deciduous forests of eastern North America. Because of their abundance, woodland salamanders are responsible for the transformation of nutrients and translocation of energy between highly disparate levels of trophic organization: detrital food webs and high‐order predators. However, the spatial extent of woodland salamanders’ role in the ecosystem is likely contingent upon the distribution of their biomass throughout the forest. We sought to determine if natural environmental gradients influence the fine‐scale distribution and density of Southern Ravine Salamanders (Plethodon richmondi) and Cumberland Plateau Salamanders (P. kentucki). We addressed this objective by constructing occupancy, co‐occurrence, and abundance models from temporally replicated surveys within an old‐growth forest in the Cumberland Plateau region of Kentucky. We found that Plethodon richmondi had a more restricted fine‐scale distribution than P. kentucki (mean occupancy probability [] = 0.737) and exhibited variable density, from \u3c250 to \u3e1000 individuals per hectare, associated with increased soil moisture and reduced solar exposure due to slope face. While more ubiquitously distributed ( = 0.95), P. kentucki density varied from \u3c400 to \u3e1,000 individuals per hectare and was inversely related to increased solar exposure from canopy disturbance and landscape convexity. Our data suggest co‐occurrence patterns of P. richmondi and P. kentucki are influenced primarily by abiotic conditions within the forest, and that populations likely occur independently and without evidence of biotic interaction. Given the critical role that woodland salamanders play in the maintenance of forest health, regions that support large populations of woodland salamanders, such as those highlighted in this study—mesic forest stands on north‐to‐east facing slopes with dense canopy and abundant natural cover, may provide enhanced ecosystem services and support the stability of the total forest

Mechanisms, detection and impacts of species redistributions under climate change

International audienceShifts in species distributions are a common ecological response to climate change, and global temperature rise is often hypothesized as the primary driver. However, the directions and rates of distribution shifts are highly variable across species, systems, and studies, complicating efforts to manage and anticipate biodiversity responses to anthropogenic change. In this Review, we summarize approaches to documenting species range shifts, discuss why observed range shifts often do not match our expectations, and explore the impacts of species range shifts on nature and society. The majority (59%) of documented range shifts are directionally consistent with climate change, based on the BioShifts database of range shift observations. However, many observed species have not shifted or have shifted in directions opposite to temperature-based expectations. These lagging or expectation-contrary shifts might be explained by additional biotic or abiotic factors driving range shifts, including additional non-temperature climatic drivers, habitat characteristics, and species interactions, which are not normally considered in range shift documentations. Understanding and managing range shifts will require increasing and connecting observational biological data, generalizing range shift patterns across systems, and predicting shifts at management-relevant timescales

Bringing traits back into the equation: A roadmap to understand species redistribution

Ecological and evolutionary theories have proposed that species traits should be important in mediating species responses to contemporary climate change; yet, empirical evidence has so far provided mixed evidence for the role of behavioral, life history, or ecological characteristics in facilitating or hindering species range shifts. As such, the utility of trait-based approaches to predict species redistribution under climate change has been called into question. We develop the perspective, supported by evidence, that trait variation, if used carefully can have high potential utility, but that past analyses have in many cases failed to identify an explanatory value for traits by not fully embracing the complexity of species range shifts. First, we discuss the relevant theory linking species traits to range shift processes at the leading (expansion) and trailing (contraction) edges of species distributions and highlight the need to clarify the mechanistic basis of trait-based approaches. Second, we provide a brief overview of range shift-trait studies and identify new opportunities for trait integration that consider range-specific processes and intraspecific variability. Third, we explore the circumstances under which environmental and biotic context dependencies are likely to affect our ability to identify the contribution of species traits to range shift processes. Finally, we propose that revealing the role of traits in shaping species redistribution may likely require accounting for methodological variation arising from the range shift estimation process as well as addressing existing functional, geographical, and phylogenetic biases. We provide a series of considerations for more effectively integrating traits as well as extrinsic and methodological factors into species redistribution research. Together, these analytical approaches promise stronger mechanistic and predictive understanding that can help society mitigate and adapt to the effects of climate change on biodiversity

Proximal microclimate: Moving beyond spatiotemporal resolution improves ecological predictions

Aim: The scale of environmental data is often defined by their extent (spatial area, temporal duration) and resolution (grain size, temporal interval). Although describing climate data scale via these terms is appropriate for most meteorological applications, for ecology and biogeography, climate data of the same spatiotemporal resolution and extent may differ in their relevance to an organism. Here, we propose that climate proximity, or how well climate data represent the actual conditions that an organism is exposed to, is more important for ecological realism than the spatiotemporal resolution of the climate data. Location: Temperature comparison in nine countries across four continents; ecological case studies in Alberta (Canada), Sabah (Malaysia) and North Carolina/Tennessee (USA). Time Period: 1960–2018. Major Taxa Studied: Case studies with flies, mosquitoes and salamanders, but concepts relevant to all life on earth. Methods: We compare the accuracy of two macroclimate data sources (ERA5 and WorldClim) and a novel microclimate model (microclimf) in predicting soil temperatures. We then use ERA5, WorldClim and microclimf to drive ecological models in three case studies: temporal (fly phenology), spatial (mosquito thermal suitability) and spatiotemporal (salamander range shifts) ecological responses. Results: For predicting soil temperatures, microclimf had 24.9% and 16.4% lower absolute bias than ERA5 and WorldClim respectively. Across the case studies, we find that increasing proximity (from macroclimate to microclimate) yields a 247% improvement in performance of ecological models on average, compared to 18% and 9% improvements from increasing spatial resolution 20-fold, and temporal resolution 30-fold respectively. Main Conclusions: We propose that increasing climate proximity, even if at the sacrifice of finer climate spatiotemporal resolution, may improve ecological predictions. We emphasize biophysically informed approaches, rather than generic formulations, when quantifying ecoclimatic relationships. Redefining the scale of climate through the lens of the organism itself helps reveal mechanisms underlying how climate shapes ecological systems

Proximal microclimate : moving beyond spatiotemporal resolution improves ecological predictions

Abstract: AimThe scale of environmental data is often defined by their extent (spatial area, temporal duration) and resolution (grain size, temporal interval). Although describing climate data scale via these terms is appropriate for most meteorological applications, for ecology and biogeography, climate data of the same spatiotemporal resolution and extent may differ in their relevance to an organism. Here, we propose that climate proximity, or how well climate data represent the actual conditions that an organism is exposed to, is more important for ecological realism than the spatiotemporal resolution of the climate data.LocationTemperature comparison in nine countries across four continents; ecological case studies in Alberta (Canada), Sabah (Malaysia) and North Carolina/Tennessee (USA).Time Period1960-2018.Major Taxa StudiedCase studies with flies, mosquitoes and salamanders, but concepts relevant to all life on earth.MethodsWe compare the accuracy of two macroclimate data sources (ERA5 and WorldClim) and a novel microclimate model (microclimf) in predicting soil temperatures. We then use ERA5, WorldClim and microclimf to drive ecological models in three case studies: temporal (fly phenology), spatial (mosquito thermal suitability) and spatiotemporal (salamander range shifts) ecological responses.ResultsFor predicting soil temperatures, microclimf had 24.9% and 16.4% lower absolute bias than ERA5 and WorldClim respectively. Across the case studies, we find that increasing proximity (from macroclimate to microclimate) yields a 247% improvement in performance of ecological models on average, compared to 18% and 9% improvements from increasing spatial resolution 20-fold, and temporal resolution 30-fold respectively.Main ConclusionsWe propose that increasing climate proximity, even if at the sacrifice of finer climate spatiotemporal resolution, may improve ecological predictions. We emphasize biophysically informed approaches, rather than generic formulations, when quantifying ecoclimatic relationships. Redefining the scale of climate through the lens of the organism itself helps reveal mechanisms underlying how climate shapes ecological systems