The magnitude and direction of sexual size dimorphism (SSD) varies greatly across the animal kingdom, reflecting differential selection pressures on the reproductive and/or ecological roles of males and females. If the selection pressures and constraints imposed on body size change along environmental gradients, then SSD will vary geographically in a predictable way. Here, we uncover a biogeographical reversal in SSD of lizards from Central and North America: in warm, low latitude environments, males are larger than females, but at colder, high latitudes, females are larger than males. Comparisons to expectations under a Brownian motion model of SSD evolution indicate that this pattern reflects differences in the evolutionary rates and/or trajectories of sex-specific body sizes. The SSD gradient we found is strongly related to mean annual temperature, but is independent of species richness and body size differences among species within grid cells, suggesting that the biogeography of SSD reflects gradients in sexual and/or fecundity selection, rather than intersexual niche divergence to minimize intraspecific competition. We demonstrate that the SSD gradient is driven by stronger variation in male size than in female size and is independent of clutch mass. This suggests that gradients in sexual selection and male-male competition, rather than fecundity selection to maximize reproductive output by females in seasonal environments, are predominantly responsible for the gradient.

Aim The distribution of Yersinia pestis, the pathogen that causes plague in humans, is reliant upon transmission between host species; however, the degree to which host species distributions dictate the distribution of Y. pestis, compared with limitations imposed by the environmental niche of Y. pestis per se, is debated. We test whether the present‐day environmental niche of Y. pestis differs between its native range and an invaded range and whether biotic factors (host distributions) can explain observed discrepancies. Location North America and Central Asia. Major taxa studied Yersinia pestis. Methods We use environmental niche models to determine whether the current climatic niche of Y. pestis differs between its native range in Asia and its invaded range in North America. We then test whether the inclusion of information on the distribution of host species improves the ability of models to capture the North American niche. We use geographical null models to guard against spurious correlations arising from spatially autocorrelated occurrence points. Results The current climatic niche of Y. pestis differs between its native and invaded regions. The Asian niche overpredicted the distribution of Y. pestis across North America. Including biotic factors along with the native climatic niche increased niche overlap between the native and invaded models, and models containing only biotic factors performed better than the native climatic niche alone. Geographical null models confirmed that the increased niche overlap through inclusion of biotic factors did not, with a couple of exceptions, arise solely from spatially autocorrelated occurrences. Main conclusions The current climatic niche in Central Asia differs from the current climatic niche in North America. Inclusion of biotic factors improved the fit of models to the Y. pestis distribution data in its invaded region better than climate variables alone. This highlights the importance of host species when investigating zoonotic disease introductions and suggests that climatic variables alone are insufficient to predict disease distribution in novel environments.

Climate change and invasive species are among the biggest threats to global biodiversity and ecosystem function. Although the individual impacts of climate change and invasive species are commonly assessed, we know far less about how a changing climate may impact invading species. Increases in water temperature due to climate change are likely to alter the thermal regime of UK rivers, and this in turn may influence the performance of invasive species such as signal crayfish (Pacifastacus leniusculus), which are known to have deleterious impacts on native ecosystems. We evaluate the relationship between water temperature and two key performance traits in signal crayfish—feeding and burrowing rate—using thermal experiments on wild‐caught individuals in a laboratory environment. Although water temperature was found to have no significant influence on burrowing rate, it did have a strong effect on feeding rate. Using the thermal performance curve for feeding rate, we evaluate how the thermal suitability of three UK rivers for signal crayfish may change as a result of future warming. We find that warming rivers may increase the amount of time that signal crayfish can achieve high feeding rate levels. These results suggest that elevated river water temperatures as a result of climate change may promote higher signal crayfish performance in the future, further exacerbating the ecological impact of this invasive species.

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Background COVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingdom’s response to the pandemic, and informed public health policies and clinical guidelines. However, these data are held by different organizations, and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find relevant data to access and interrogate the data they need to inform the pandemic response at pace. Objective We aimed to transform UK COVID-19 diagnostic data sets to be findable, accessible, interoperable, and reusable (FAIR). Methods A federated infrastructure model (COVID - Curated and Open Analysis and Research Platform [CO-CONNECT]) was rapidly built to enable the automated and reproducible mapping of health data partners’ pseudonymized data to the Observational Medical Outcomes Partnership Common Data Model without the need for any data to leave the data controllers’ secure environments, and to support federated cohort discovery queries and meta-analysis. Results A total of 56 data sets from 19 organizations are being connected to the federated network. The data include research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal health care records and demographics. The infrastructure is live, supporting aggregate-level querying of data across the United Kingdom. Conclusions CO-CONNECT was developed by a multidisciplinary team. It enables rapid COVID-19 data discovery and instantaneous meta-analysis across data sources, and it is researching streamlined data extraction for use in a Trusted Research Environment for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions while maintaining patient confidentiality and local governance procedures.

BACKGROUND n.a OBJECTIVE COVID-19 data have been generated across the UK as a by-product of clinical care and public health provision, and numerous bespoke and repurposed research endeavours. Analysis of these data has underpinned the UK’s response to the pandemic and informed public health policies and clinical guidelines. However, these data are held by different organisations and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find, navigate permissions to access and interrogate the data they need to inform the pandemic response at pace. METHODS A federated infrastructure model was rapidly built to enable the automated and reproducible mapping of health Data Partners’ pseudonymised data to the OMOP common data model without the need for any data to leave the data controllers’ secure environments and to support federated cohort discovery queries and meta-analysis. RESULTS 56 datasets from 19 organisations are being connected to the federated network. The data includes research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal healthcare records and demographics. The infrastructure is live, supporting aggregate level querying of data across the UK. CONCLUSIONS CO-CONNECT is a multidisciplinary infrastructure project enabling rapid COVID-19 data discovery, instantaneous meta-analysis across data sources, and is researching streamlined data extraction for egress into a Trusted Research Environment (TRE) for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions whilst maintaining patient confidentiality and local governance procedures.

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