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Climate change is altering suitable habitat distributions of many species at high latitudes. Fleshy fruit-producing plants (hereafter, “berry plants”) are important in arctic food webs and as subsistence resources for human communities, but their response to a warming and increasingly variable climate at a landscape scale has not yet been examined.

We aimed to identify environmental determinants of berry plant distribution and predict how climate change might shift these distributions.

We used species distribution models to identify characteristics and predict the distribution of suitable habitat under current (2006–2013) and future climate conditions (2081–2100; representative concentration pathways 4.5, 6.0, & 8.5) for five berry plant species:Vaccinium uliginosumL.,Empetrum nigrumL.,Rubus chamaemorusL.,Vaccinium vitis-idaeaL., andViburnum edule(Michx.) Raf..

Elevation, soil characteristics, and January and July temperatures were important drivers of habitat distributions. Future suitable habitat predictions showed net declines in suitable habitat area for all species modeled under almost all future climate scenarios tested.

Our work contributes to understanding potential geographic shifts in suitable berry plant habitat with climate change at a landscape scale. Shifting and retracting distributions may alter where communities can harvest, suggesting that access to these resources may become restricted in the future. Our prediction maps may help inform climate adaptation planning as communities anticipate shifting access to harvesting locations.

 

Urban areas are often warmer than rural areas due to the phenomenon known as the “urban heat island” (UHI) effect, which can cause discomfort for those engaging in outdoor activities and can have a disproportionate impact on low-income communities, people of color, and the elderly. The intensity of the UHI effect is influenced by a variety of factors, including urban morphology, which can vary from one area to another. To investigate the relationship between outdoor thermal comfort and urban morphology in different urban blocks with varying social vulnerability status, this study developed a geographic information system (GIS)-based workflow that combined the “local climate zone” (LCZ) classification system and an urban microclimate assessment tool called ENVI-met. To demonstrate the effectiveness of this methodology, the study selected two different urban blocks in Philadelphia, Pennsylvania–with high and low social vulnerability indices (SVI)–to compare their microclimate conditions in association with urban morphological characteristics such as green coverage area, sky view factor (SVF), albedo, and street height to width (H/W) ratio. The results of the study showed that there was a strong correlation between tree and grass coverage and outdoor air and mean radiant temperature during hot seasons and extremely hot days, which in turn affected simulated predicted mean vote (PMV). The effects of greenery were more significant in the block associated with a low SVI, where nearly 50% of the site was covered by trees and grass, compared to only 0.02% of the other block associated with a high SVI. Furthermore, the investigation discovered that reduced SVF, along with increased albedo and H/W ratio, had a beneficial impact on the microclimate at the pedestrian level within the two studied urban blocks. This study provided an effective and easy-to-implement method for tackling the inequity issue of outdoor thermal comfort and urban morphology at fine geographic scales. 

Around 13,000 people from outside Alaska arrive each summer in the Bristol Bay region of Western Alaska to participate in the world’s most valuable wild salmon fishery. The small regional hub community of Dillingham is the home port of the Nushagak River salmon fishery. The National Science Foundation funded a RAPID project to assess planning needs for the fishery, community, and region. Our project developed pandemic preparedness scenarios for local residents and decision-makers through online surveys to better understand the costs and benefits of varied mitigation policies; and risk preferences from fishers, processors, local residents, and local decision-makers to better understand cooperation and decisions under risk and uncertainty. 

Seeking spatiotemporal patterns about how citizens interact with the urban space is critical for understanding how cities function. Such interactions were studied in various forms focusing on patterns of people’s presence, action, and transition in the urban environment, which are defined as human-urban interactions in this paper. Using human activity datasets that utilize mobile positioning technology for tracking the locations and movements of individuals, researchers developed stochastic models to uncover preferential return behaviors and recurrent transitional activity structures in human-urban interactions. Ad-hoc heuristics and spatial clustering methods were applied to derive meaningful activity places in those studies. However, the lack of semantic meaning in the recorded locations makes it difficult to examine the details about how people interact with different activity places. In this study, we utilized geographic context-aware Twitter data to investigate the spatiotemporal patterns of people’s interactions with their activity places in different urban settings. To test consistency of our findings, we used geo-located tweets to derive the activity places in Twitter users’ location histories over three major U.S. metropolitan areas: Greater Boston Area, Chicago, and San Diego, where the geographic context of each location was inferred from its closest land use parcel. The results showed striking spatial and temporal similarities in Twitter users’ interactions with their activity places among the three cities. By using entropy-based predictability measures, this study not only confirmed the preferential return behaviors as people tend to revisit a few highly frequented places but also revealed detailed characteristics of those activity places.