Mitigating Risk For Anxiety Among Preschool-Age Children Living In Poverty: Evaluating The Impact Of Adult-Provided Social Support On Autonomic Stress Reactivity

Poverty increases children's exposure to stress, elevating their risk for developing patterns of heightened sympathetic and parasympathetic stress reactivity. Repeated patterns of high sympathetic activation and parasympathetic withdrawal place children at risk for anxiety disorders. This study evaluated whether providing social support to preschool-age children during mildly stressful situations helps reduce reactivity, and whether this effect partly depends on children's previously assessed baseline reactivity patterns. The Biological Sensitivity to Context (BSC) theory proposes that highly reactive children may be more sensitive than less reactive children to all environmental influences, including social support. In contrast, conventional physiological reactivity (CPR) theory contends that highly reactive children are more vulnerable to the impact of stress but are less receptive to the potential benefits present within their social environments. In this study, baseline autonomic reactivity patterns were measured. Children were then randomly assigned to a high-support or neutral control condition, and the effect of social support on autonomic response patterns was assessed. Results revealed an interaction between baseline reactivity profiles and experimental condition. Children with patterns of high-reactivity reaped more benefits from the social support in the experimental condition than did their less reactive peers. Highly reactive children experienced relatively less reactivity reduction in the neutral condition while experiencing relatively greater reactivity reduction in the support condition. Despite their demonstrated stability over time, reactivity patterns are also quite susceptible to change at this age; therefore understanding how social support ameliorates reactivity will further efforts to avert stable patterns of high-reactivity among children with high levels of stress, ultimately reducing risk for anxiety disorders.

Nesting In The Clouds: Evaluating And Predicting Sea Turtle Nesting Beach Parameters From Lidar Data

Humans' desire for knowledge regarding animal species and their interactions with the natural world have spurred centuries of studies. The relatively new development of remote sensing systems using satellite or aircraft-borne sensors has opened up a wide field of research, which unfortunately largely remains dependent on coarse-scale image spatial resolution, particularly for habitat modeling. For habitat-specialized species, such data may not be sufficient to successfully capture the nuances of their preferred areas. Of particular concern are those species for which topographic feature attributes are a main limiting factor for habitat use. Coarse spatial resolution data can smooth over details that may be essential for habitat characterization. Three studies focusing on sea turtle nesting beaches were completed to serve as an example of how topography can be a main deciding factor for certain species. Light Detection and Ranging (LiDAR) data were used to illustrate that fine spatial scale data can provide information not readily captured by either field work or coarser spatial scale sources. The variables extracted from the LiDAR data could successfully model nesting density for loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) sea turtle species using morphological beach characteristics, highlight beach changes over time and their correlations with nesting success, and provide comparisons for nesting density models across large geographic areas. Comparisons between the LiDAR dataset and other digital elevation models (DEMs) confirmed that fine spatial scale data sources provide more similar habitat information than those with coarser spatial scales. Although these studies focused solely on sea turtles, the underlying principles are applicable for many other wildlife species whose range and behavior may be influenced by topographic features.