Geographic Isolation and Vulnerability Across Peru's Ecological Regions: The Influence of Regional Contexts of Extraction.

Geographically isolated places are often sites of exported environmental risks, intense resource extraction, exploitation and marginalization, and social policy neglect. These conditions create unique challenges related to vulnerability and adaptation that have direct disaster management implications. Our research investigates the relationship between geographic isolation and flood-related social vulnerability across Peru's ecological regions. Ecoregions have different relationships with colonialism and capitalism that shape vulnerability, and we hypothesize that the relationship between vulnerability and geographic isolation varies across ecoregions. Using mapping techniques and spatial regression analysis, we find that relationships between vulnerability and geographic isolation vary regionally, with differences that suggest alignment with regional contexts of extraction. We find notable differences in vulnerability related to public health infrastructure and access to services and between ecoregions with sharply contrasting histories of natural resource extraction and investment and disinvestment.

Large scale seasonal forecasting of peak season algae metrics in the Midwest and Northeast U.S.

In recent decades, many inland lakes have seen an increase in the prevalence of potentially harmful algae. In many inland lakes, the peak season for algae abundance (summer and early fall in the northern hemisphere) coincides with the peak season for recreational use. Currently, little information regarding expected algae conditions is available prior to the peak season for productivity in inland lakes. Peak season algae conditions are influenced by an array of pre-season (spring and early summer) local and global scale variables; identifying these variables for forecast development may be useful in managing potential public health threats posed by harmful algae. Using the LAGOS-NE dataset, pre-season local and global drivers of peak-season algae metrics (represented by chlorophyll-a) are identified for 178 lakes across the Northeast and Midwest U.S. from readily available gridded datasets. Forecasting models are built for each lake conditioned on relevant pre-season predictors. Forecasts are assessed for the magnitude, severity, and duration of seasonal chlorophyll concentrations. Regions of pre-season sea surface temperature, and pre-season chlorophyll-a demonstrate the most predictive power for peak season algae metrics, and resulting models show significant skill. Based on categorical forecast metrics, more than 70% of magnitude models and 90% of duration models outperform climatology.  Forecasts of high and severe algae magnitude perform best in large mesotrophic and oligotrophic lakes, however, high algae duration performance appears less dependent on lake characteristics. The advance notice of elevated algae biomass provided by these models may allow lake managers to better prepare for challenges posed by algae during the high use season for inland lakes.

Development of a sub-seasonal cyanobacteria prediction model by leveraging local and global scale predictors.

In recent decades, cultural eutrophication of coastal waters and inland lakes around the world has contributed to a rapid expansion of potentially toxic cyanobacteria, threatening aquatic and human systems. For many locations, a complex array of physical, chemical, and biological variables leads to significant inter-annual variability of cyanobacteria biomass, modulated by local and large-scale climate phenomena. Currently, however, minimal information regarding expected summertime cyanobacteria biomass conditions is available prior to the season, limiting proactive management and preparedness strategies for lake and beach safety. To address this, sub-seasonal (two-month) cyanobacteria biomass prediction models are developed, drawing on pre-season predictors including stream discharge, phosphorus loads, a floating algae index, and large-scale sea-surface temperature regions, with an application to Lake Mendota in Wisconsin. A two-phase statistical modeling approach is adopted to reflect identified asymmetric relationships between predictors (drivers of inter-annual variability) and cyanobacteria biomass levels. The model illustrates promising performance overall, with particular skill in predicting above normal cyanobacteria biomass conditions which are of primary importance to lake and beach managers.

Improving Delivery Behaviors During Exposure for Pediatric OCD: A Multiple Baseline Training Trial With Community Therapists.

This study tested whether a new training tool, the Exposure Guide (EG), improved in-session therapist behaviors (i.e., indicators of quality) that have been associated with youth outcomes in prior clinical trials of exposure therapy. Six therapists at a community mental health agency (CMHA) provided exposure therapy for 8 youth with obsessive-compulsive disorder (OCD). Using a nonconcurrent multiple baseline design with random assignment to baseline lengths of 6 to 16 weeks, therapists received gold-standard exposure therapy training with weekly consultation (baseline phase) followed by addition of EG training and feedback (intervention phase). The primary outcome was therapist behavior during in-session exposures, observed weekly using a validated coding system. Therapist behavior was evaluated in relation to a priori benchmarks derived from clinical trials. Additional outcomes included training feasibility/acceptability, therapist response to case vignettes and beliefs about exposure, and independent evaluator-rated clinical outcomes. Three therapists reached behavior benchmarks only during the EG (intervention) phase. Two therapists met benchmarks during the baseline phase; one of these subsequently moved away from benchmarks but met them again after starting the EG phase. Across all therapists, the percentage of weeks meeting benchmarks was significantly higher during the EG phase (86.4%) vs. the baseline phase (53.2%). Youth participants experienced significant improvement in OCD symptoms and global illness severity from pre- to posttreatment. Results provide initial evidence that adding the EG to gold-standard training can change in-session therapist behaviors in a CMHA setting.