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Coral reefs are under threat from disease as climate change alters environmental conditions. Rising temperatures exacerbate coral disease, but this relationship is likely complex as other factors also influence coral disease prevalence. To better understand this relationship, we meta-analytically examined 108 studies for changes in global coral disease over time alongside temperature, expressed using average summer sea surface temperature (SST) and cumulative heat stress as weekly sea surface temperature anomalies (WSSTAs). We found both rising average summer SST and WSSTA were associated with global increases in the mean and variability in coral disease prevalence. We showed global coral disease prevalence reached 9.92% compared to 3.16% in 1992, and the effect of ‘year’ became more stable (i.e., has lower variance), contrasting to the effects of the two temperature stressors. Regional patterns diverged over time and differed in response to average summer SST. Our model predicted that, under the same trajectory, 76.8% of corals would be diseased globally by 2100, even assuming moderate average summer SST and WSSTA. These results highlight the need for urgent action to mitigate coral disease. Mitigating the impact of rising ocean temperatures on coral disease alone is a complex challenge requiring global discussion and further study.

Behavioural phenotyping is often time and labour-intensive, which can come at a cost to sample size and statistical precision. This is particularly a concern given that behaviours are often highly variable within and between individuals, so naturally requires a larger sample size. Drosophila melanogaster is a common model system in many research fields, and behavioural observations are frequently required. While D. melanogaster has a rapid lifecycle that enables large numbers of flies to be reared for experiments, they are still subject to methodological bottlenecks for behavioural observations. Additionally, their small and delicate bodies make it difficult to observe certain behaviours in real-time, for example, in movement tracking or when performing repeated assays on the same individuals. Here, we present a method, pilot data, custom data processing and analysis scripts for high-throughput behavioural phenotyping in D. melanogaster, as well as general remarks for future studies. We used automatic tracking units to measure three behaviours in the same individuals: locomotor activity, exploratory behaviour in a Y-maze, and habituation to a startle response stimulus. We then examined between-individual variation and trait correlations using our pilot data. Through this, we show that these behaviours are amenable to high-throughput automated tracking, with locomotor activity generating the most straightforward and high-quality data. These methods can be used to free up time and labour to allocate to increasing sample sizes and can be used to address a range of biological questions in ecology, evolution, and beyond.