College of Behavioral & Social Sciences

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    TREETOP: A Shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists
    (Wiley, 2022-06-06) Silva, Carlos Alberta; Hudak, Andrew T.; Vierling, Lee A.; Valbuena, Ruben; Cardil, Adrian; Mohan, Midhun; Alves de Almeida, Danilo Roberti; Broadbent, Eben N.; Almeyda Zambrano, Angelica M.; Wilkinson, Ben; Sharma, Ajay; Drake, Jason B.; Medley, Paul B.; Vogel, Jason G.; Atticciati Prata, Gabriel; Atkins, Jeff W.; Hamamura, Caio; Johnson, Daniel G.; Klauberg, Carine
    Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR. The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open-source web-based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting-edge analyses of properties related to forest ecology and management. We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post-hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest. treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open-source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function.
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    Tightness-Looseness in the United States: Ecological Predictors and State Level Outcomes
    (2014) Harrington, Jesse Ryan; Gelfand, Michele J; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This research demonstrates wide variation in tightness-looseness (strength of punishment and degree of permissiveness) at the state level in the United States, and its association with various ecological and historical factors, psychological characteristics, and state-level outcomes. Consistent with theory and past research, ecological and man-made threats--more natural disasters, greater disease prevalence, fewer natural resources, and greater external threat--predict increased tightness at the state level. Tightness is also associated with higher trait conscientiousness and lower trait openness. Compared with loose states, tight states have more social stability, indicated by lowered drug and alcohol use, lower rates of homelessness, and lower social disorganization. However, tight states also have relatively higher incarceration rates, greater discrimination and inequality, lower creativity, and lower happiness. In all, tightness-looseness provides a parsimonious explanation of the wide variation seen across the 50 states of the United States of America.