Differential metabolic responses in bold and shy sea anemones during a simulated heatwave.

As climate change-induced heatwaves become more common, phenotypic plasticity at multiple levels is a key mitigation strategy by which organisms can optimise selective outcomes. In ectotherms, changes to both metabolism and behaviour can help alleviate thermal stress. Nonetheless, no study in any ectotherm has yet empirically investigated how changing temperatures affect among-individual differences in the associations between these traits. Using the beadlet anemone (Actinia equina), an intertidal species from a thermally heterogeneous environment, we investigated how individual metabolic rates, linked to morphotypic differences in A. equina, and boldness were related across changing temperatures. A crossed-over design and a temporal control were used to test the same individuals at a non-stressful temperature, 13°C, and under a simulated heatwave at 21°C. At each temperature, short-term repeated measurements of routine metabolic rate (RMR) and a single measurement of a repeatable boldness-related behaviour, immersion response time (IRT), were made. Individual differences, but not morphotypic differences, were highly predictive of metabolic plasticity, and the plasticity of RMR was associated with IRT. At 13°C, shy animals had the highest metabolic rates, while at 21°C, this relationship was reversed. Individuals that were bold at 13°C also exhibited the highest metabolic rates at 21°C. Additional metabolic challenges during heatwaves could be detrimental to fitness in bold individuals. Equally, lower metabolic rates at non-stressful temperatures could be necessary for optimal survival as heatwaves become more common. These results provide novel insight into the relationship between metabolic and behavioural plasticity, and its adaptive implications in a changing climate.

Quantifying the availability of seasonal surface water and identifying the drivers of change within tropical forests in Cambodia.

Surface freshwater is a vital resource that is declining globally, predominantly due to climate and land use changes. Cambodia is no exception and the loss threatens many species, such as the giant ibis a Critically Endangered waterbird. We aimed to quantify the spatial and temporal (2000-2020) change of surface water availability across northern and eastern Cambodia and to assess the impact of this on the giant ibis. We used a Random Forest Classifier to determine the changes and we tested the impact of land use and geographical covariates using spatially explicit regression models. We found an overall reduction of surface water availability of 4.16%. This was predominantly driven by the presence of Economic Land Concessions and roads which increased the probability of extreme drying and flooding events. The presence of protected areas reduced these probabilities. We found changes in precipitation patterns over the wider landscape did not correlate with changes in surface water availability, supporting the overriding influence of land use change. 98% of giant ibis nests recorded during the time period were found within 25m of surface water during the dry season, highlighting their dependency on surface water. The overall surface water decline resulted in a 25% reduction in dry season suitable habitat for the giant ibis. Although absolute changes in surface water over the whole area were relatively small, the impact on the highest quality habitat for ibis is disproportionate and therefore threatens its populations. Defining the threats to such an endangered species is crucial for effective management.

The need for One Health systems-thinking approaches to understand multiscale dissemination of antimicrobial resistance.

Although the effects of antimicrobial resistance (AMR) are most obvious at clinical treatment failure, AMR evolution, transmission, and dispersal happen largely in environmental settings, for example within farms, waterways, livestock, and wildlife. We argue that systems-thinking, One Health approaches are crucial for tackling AMR, by understanding and predicting how anthropogenic activities interact within environmental subsystems, to drive AMR emergence and transmission. Innovative computational methods integrating big data streams (eg, from clinical, agricultural, and environmental monitoring) will accelerate our understanding of AMR, supporting decision making. There are challenges to accessing, integrating, synthesising, and interpreting such complex, multidimensional, heterogeneous datasets, including the lack of specific metrics to quantify anthropogenic AMR. Moreover, data confidentiality, geopolitical and cultural variation, surveillance gaps, and science funding cause biases, uncertainty, and gaps in AMR data and metadata. Combining systems-thinking with modelling will allow exploration, scaling-up, and extrapolation of existing data. This combination will provide vital understanding of the dynamic movement and transmission of AMR within and among environmental subsystems, and its effects across the greater system. Consequently, strategies for slowing down AMR dissemination can be modelled and compared for efficacy and cost-effectiveness.