Improving School Environments for Preventing Sexual Violence Among LGBTQ+ Youth.

BACKGROUND: Sexual violence (SV) is a serious public health concern, and lesbian, gay, bisexual, transgender, queer and questioning (LGBTQ+) youth report higher rates than their heterosexual and cisgender peers. This qualitative study aimed to understand LGBTQ+ students' perspectives on how middle and high school environments can better prevent and address SV. METHODS: In partnership with a school-based LGBTQ+ support group in Washington State, we recruited 31 LGTBQ+ students ages 13-18 for virtual interviews (n = 24) and for providing text-based answers to interview questions (n = 7). We used inductive thematic analysis to analyze data and identify themes. RESULTS: To prevent and respond to SV, students highlighted schools having: (1) access to gender-neutral spaces; (2) LGBTQ+ competency training for staff; (3) enforcement of school policies (eg, SV, anti-bullying) and accountability; (4) LGBTQ+-competent mental health support; and (5) comprehensive sexual health education that addresses LGBTQ+ relationships and SV. IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY: Students expressed the need for changes in school physical and social environments to address SV among LGBTQ+ youth. CONCLUSIONS: Incorporating youth perspectives, particularly LGBTQ+ youth at high risk of SV, can help schools implement strategies that are supported by youth and thus potentially more sustainable and effective.

Widespread habitat loss and redistribution of marine top predators in a changing ocean.

The Northwest Atlantic Ocean and Gulf of Mexico are among the fastest warming ocean regions, a trend that is expected to continue through this century with far-reaching implications for marine ecosystems. We examine the distribution of 12 highly migratory top predator species using predictive models and project expected habitat changes using downscaled climate models. Our models predict widespread losses of suitable habitat for most species, concurrent with substantial northward displacement of core habitats >500 km. These changes include up to >70% loss of suitable habitat area for some commercially and ecologically important species. We also identify predicted hot spots of multi-species habitat loss focused offshore of the U.S. Southeast and Mid-Atlantic coasts. For several species, the predicted changes are already underway, which are likely to have substantial impacts on the efficacy of static regulatory frameworks used to manage highly migratory species. The ongoing and projected effects of climate change highlight the urgent need to adaptively and proactively manage dynamic marine ecosystems.

Building use-inspired species distribution models: Using multiple data types to examine and improve model performance.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.