<p>Protected areas are a critical tool for managing and ensuring the persistence of species biodiversity and land conservation. Their spatial extents are used to measure progress towards land protections by several international targets. However, governance type, management, and enforcement of these protected areas vary sub-nationally, and can influence the efficacy of the designation. Simultaneously, climatic conditions are coupled with species resilience, and changes in climate can be associated with shifts, expansions, and contractions of viable areas for habitat maintenance. Climate change is expected to change baseline climatic conditions globally and is likely to limit the benefits of terrestrial protected areas. Improved understanding of the relationship between governance, regional climate change, and protected areas can further enhance tracking of land cover change and inform protection strategies implemented across spatial scales. To aid in informed decision making at sub-national scales, we combine information on terrestrial sites in the World Database on Protected Areas, historic and future climate projections from CMIP6, and remotely sensed data on vegetation cover (NDVI). We leverage categorical differences in protected area management, as well as climate anomalies through time to explore their relationship to land cover change, and create additional tools for risk assessment that may be used in conjunction with local governance processes</p>

Studies on the relationship between temperature and local, small scale mobility are limited, and sensitive to the region and time period of interest. We contribute to the growing mobility literature through a detailed characterization of the observed temperature‐mobility relationship in the San Francisco Bay Area at fine spatial and temporal scale across two summers (2020–2021). We used anonymized cellphone data from SafeGraph's neighborhood patterns data set and gridded temperature data from gridMET, and analyzed the influence of incremental changes in temperature on mobility rate (i.e., visits per capita) using a panel regression with fixed effects. This strategy enabled us to control for spatial and temporal variability across the studied region. Our analysis suggested that all areas exhibited lower mobility rate in response to higher summer temperatures. We then explored how several additional variables altered these results. Extremely hot days resulted in faster mobility declines with increasing temperatures. Weekdays were often more resistant to temperature changes when compared to the weekend. In addition, the rate of decrease in mobility in response to high temperature was significantly greater among the wealthiest census block groups compared with the least wealthy. Further, the least mobile locations experienced significant differences in mobility response compared to the rest of the data set. Given the fundamental differences in the mobility response to temperature across most of our additive variables, our results are relevant for future mobility studies in the region.

Indicators of biodiversity protection at the national level are used to assess progress towards global goals but provide little information at conservation-relevant scales. We provide SDG indicator 15.4.1 on mountain biodiversity protection for individual mountain ranges and further perform an area-based calculation compatible with the Kunming-Montreal Global Biodiversity Framework. We enable the identification of mountain areas in need of conservation and discuss differences between area- and official site-based indicator values.

Indicators of biodiversity protection at the national level are used to assess progress toward global goals. However, they provide little information at scales relevant for conservation and management. Hence we provide an area-based alternative to the current indicator calculated at the level of individual mountain ranges, which is directly relevant for assessing progress toward SDG 15.4.1. This allows identifying mountain areas in need of enhanced conservation efforts, within and across countries.

Heat related illnesses are one of the leading causes of weather-related mortality in the United States, and heat extremes continue to increase in frequency and duration. Public health interventions include population mobility, including travel to central cooling centers or wellness checks on vulnerable populations. Using anonymized cellphone data from Safegraph’s neighborhood patterns dataset and gridded temperature data from gridMET, we explored the mobility-temperature relationship in the San Francisco Bay Area at fine spatial and temporal scale. We leveraged spatial variability in median income and temporal variability in COVID-19 related policies across two summers (2020-2021) to analyze their influence on the mobility-temperature relationship. We completed quantile regressions for a dataset stratified by income and year. We found that mobility increased at a higher rate with higher temperatures in 2020 than 2021. However, in 2021, the relationship reversed for several wealthier income groups, where mobility decreased with higher temperatures. We then augmented the analysis and calculated a panel regression with fixed effects to characterize the mobility-temperature relationship while controlling for temporal and spatial variability. This analysis suggested that all areas exhibited lower mobility with higher summer temperatures. However, similar to the results of the quantile regression, the rate of decrease in mobility in response to high temperature was significantly greater among the wealthiest census block groups compared with the least wealthy. Given the fundamental difference in the mobility response to temperature across income groups, our results are relevant for heat mitigation efforts in highly populated regions in current and future climate conditions.