Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56 °C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets.

Assessing the vulnerability of species to climate change is a key step in anticipating climate impacts on species. Vulnerability assessments characterize species’ future conservation needs and can guide current planning and management actions to support species persistence in the face of climate change.  A full assessment of climate vulnerability involves characterizing three essential components: sensitivity, adaptive capacity, and exposure. Assessing sensitivity and adaptive capacity, as well as determining which aspects of exposure to assess all require detailed knowledge of species-specific traits and ecology. Such a detailed understanding is hard to come by, even for well-studied species, thus, developing vulnerability assessments for lesser-studied species can be extremely challenging.  Most vulnerability assessment methods and frameworks are developed using well-studied species and their applicability to species with poorly understood traits and ecologies is questionable.  To support climate vulnerability assessments for lesser studied species, this project aims to compile what is known about climate vulnerability from existing models, data, and frameworks into a classification model (typology).  This typology will roughly classify the sensitivity and adaptive capacity of relatively data-poor species, based on known species characteristics and ecological contexts. In an effort to identify resource management agency needs for, and challenges to, developing vulnerability assessments for data-poor species, the research team will specifically: 1) assess the ability of existing data-driven models and data-light frameworks to support vulnerability assessments for data-poor species; 2) develop a classification model based on known species vulnerabilities and basic species attributes; 3) create and test a typology to assess vulnerability for data-poor species; and 4) create a decision tree to provide guidance on how to assess vulnerability for species with varying data availability.