In response to environmental change, organisms rely on both genetic adaptation and phenotypic plasticity to adjust key traits that are necessary for survival and reproduction. Given the accelerating rate of climate change, plasticity may be particularly important. For organisms in warming aquatic habitats, upper thermal tolerance is likely to be a key trait, and many organisms express plasticity in this trait in response to developmental or adulthood temperatures. Although plasticity at one life stage may influence plasticity at another life stage, relatively little is known about this possibility for thermal tolerance. Here, we used locally adapted populations of the copepod Tigriopus californicus to investigate these potential effects in an intertidal ectotherm. We found that low latitude populations had greater critical thermal maxima (CTmax) than high latitude populations, and variation in developmental temperature altered CTmax plasticity in adults. After development at 25°C, CTmax was plastic in adults, whereas no adulthood plasticity in this trait was observed after development at 20°C. This pattern was identical across four populations, suggesting that local thermal adaptation has not shaped this effect among these populations. Differences in the capacities to maintain ATP synthesis rates and to induce heat shock proteins at high temperatures, two likely mechanisms of local adaptation in this species, were consistent with changes in CTmax owing to phenotypic plasticity, which suggests that there is likely mechanistic overlap between the effects of plasticity and adaptation. Together, these results indicate that developmental effects may have substantial impacts on upper thermal tolerance plasticity in adult ectotherms.

Behavioral phenotyping of model organisms has played an important role in unravelling the complexities of animal behavior. Techniques for classifying behavior often rely on easily identified changes in posture and motion. However, such approaches are likely to miss complex behaviors that cannot be readily distinguished by eye (e.g., behaviors produced by high dimensional dynamics). To explore this issue, we focus on the model organism Caenorhabditis elegans, where behaviors have been extensively recorded and classified. Using a dynamical systems lens, we identify high dimensional, nonlinear causal relationships between four basic shapes that describe worm motion (eigenmodes, also called "eigenworms"). We find relationships between all pairs of eigenmodes, but the timescales of the interactions vary between pairs and across individuals. Using these varying timescales, we create "interaction profiles" to represent an individual's behavioral dynamics. As desired, these profiles are able to distinguish well-known behavioral states: i.e., the profiles for foraging individuals are distinct from those of individuals exhibiting an escape response. More importantly, we find that interaction profiles can distinguish high dimensional behaviors among divergent mutant strains that were previously classified as phenotypically similar. Specifically, we find it is able to detect phenotypic behavioral differences not previously identified in strains related to dysfunction of hermaphrodite-specific neurons.

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded 'attractor' of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach-defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error-may be of broad interest and relevance to behavioural researchers working with video-derived time series.