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Understanding connectivity between populations is key to identifying hotspots of diversity, dispersal sinks and sources, and effective management units for natural resources. Multi-species connectivity seeks to overcome species-specific idiosyncrasies to identify shared patterns that are most critical to spatial management. The linear Hawaiian archipelago provides an excellent platform to assess multi-species connectivity patterns, with shared boundaries to gene flow identified across a majority of the 41 coral reef species surveyed to date. Here, we evaluate genome-scale data by comparing consistency and resolution to previous connectivity studies using far fewer loci. We used pool-seq to genotype 22,503–232,730 single nucleotide polymorphisms per species (625,215 SNPs total) from the same individuals published in previous studies of two fishes, two corals, and two lobsters. Additionally, one coral species (Pocillopora meandrina) without previous archipelago-wide population genetic data was included. With greater statistical power, most genetic differences between pairwise comparisons of islands were significant (250 of 308), consistent with the most recent larval dispersal models for the Hawaiian Archipelago. These data reveal significant differentiation at a finer scale than previously reported using single-marker studies, yet did not overturn any of the conclusions or management implications drawn from previous studies. We confirm that population genomic datasets are consistent with previously reported patterns of multispecies connectivity but add a finer layer of population resolution that is pertinent to management. 

Aridification in the U.S. Southwest has led to tension about conservation and land management strategy. Strain on multi-generational agricultural livelihoods and nearly 150-year-old Colorado River water adjudication necessitates solutions from transdisciplinary partnerships. In this study, farmers and ranchers in a small San Juan River headwater community of southwestern Colorado engaged in a participatory, convergent research study prioritizing local objectives and policy. Acknowledging the historic and sometimes perceived role of academic institutions as representing urban interests, our goal was to highlight how research can support rural governance. This process involved creating community partnerships, analyzing data, and supporting results distribution to the surveyed population through social media. The survey was designed to support a local waterway management plan. Survey results showed lack of water availability and climate changes were selected by producers as most negatively affecting their operations, and many were extremely interested in agroforestry methods and drought-resistant crop species. Statistical analysis identified that satisfaction with community resources was positively correlated with scale of production, satisfaction with irrigation equipment, and familiarity with water rights. We hope to contribute our framework of a convergent, place-based research design for wider applications in other regions to uncover solutions to resource challenges. 

Current research on ferroelectric polymers centers predominantly on poly(vinylidene fluoride) (PVDF)–based fluoropolymers because of their superior performance. However, they are considered “forever chemicals” with environmental concerns. We describe a family of rationally designed fluorine-free ferroelectric polymers, featuring a polyoxypropylene main chain and disulfonyl alkyl side chains with a C3 spacer: −SO₂CH₂CHRCH₂SO₂− (R = −H or −CH₃). Both experimental and simulation results demonstrate that strong dipole-dipole interactions between neighboring disulfonyl groups induce ferroelectric ordering in the condensed state, which can be tailored by changing the R group: ferroelectric for R = −H or relaxor ferroelectric for R = −CH₃. At low electric fields, the relaxor polymer exhibits electroactuation and electrocaloric performance comparable with those of state-of-the-art PVDF-based tetrapolymers. 

Abstract Habitat loss due to changes in land cover is one of the main causes of biodiversity decline worldwide. Habitat loss occurs disproportionately in areas of high biodiversity because these same areas are particularly suitable for development. We assessed the effect of development risk on the biodiversity of breeding birds in the United States. We compared the effect of two predictors of habitat loss on the richness, abundance, and rarity of woodland, open-habitat, and urban birds at the local and regional levels. We used the House Price Index—as a measure of development risk—and primary productivity as predictors in simulations of habitat loss. For local scale analysis, we used generalized regression models. For regional-scale habitat loss simulations, we statistically compared the results obtained from each predictor. Locally, development risk and primary productivity interacted in their effect on the richness, abundance, and rarity index of all birds. At the regional level, developme 

BackgroundAs artificial intelligence (AI) tools are integrated more widely in psychiatric medicine, it is important to consider the impact these tools will have on clinical practice. ObjectiveThis study aimed to characterize physician perspectives on the potential impact AI tools will have in psychiatric medicine. MethodsWe interviewed 42 physicians (21 psychiatrists and 21 family medicine practitioners). These interviews used detailed clinical case scenarios involving the use of AI technologies in the evaluation, diagnosis, and treatment of psychiatric conditions. Interviews were transcribed and subsequently analyzed using qualitative analysis methods. ResultsPhysicians highlighted multiple potential benefits of AI tools, including potential support for optimizing pharmaceutical efficacy, reducing administrative burden, aiding shared decision-making, and increasing access to health services, and were optimistic about the long-term impact of these technologies. This optimism was tempered by concerns about potential near-term risks to both patients and themselves including misguiding clinical judgment, increasing clinical burden, introducing patient harms, and creating legal liability. ConclusionsOur results highlight the importance of considering specialist perspectives when deploying AI tools in psychiatric medicine.