A unifying model to estimate thermal tolerance limits in ectotherms across static, dynamic and fluctuating exposures to thermal stress

Abstract

Temperature tolerance is critical for defining the fundamental niche of ectotherms and researchers classically use either static (exposure to a constant temperature) or dynamic (ramping temperature) assays to assess tolerance. The use of different methods complicates comparison between studies and here we present a mathematical model (and R-scripts) to reconcile thermal tolerance measures obtained from static and dynamic assays. Our model uses input data from several static or dynamic experiments and is based on the well-supported assumption that thermal injury accumulation rate increases exponentially with temperature (known as a thermal death time curve). The model also assumes thermal stress at different temperatures to be additive and using experiments with Drosophila melanogaster, we validate these central assumptions by demonstrating that heat injury attained at different heat stress intensities and durations is additive. In a separate experiment we demonstrate that our model can accurately describe injury accumulation during fluctuating temperature stress and further we validate the model by successfully converting literature data of ectotherm heat tolerance (both static and dynamic assays) to a single, comparable metric (the temperature tolerated for 1 h). The model presented here has many promising applications for the analysis of ectotherm thermal tolerance and we also discuss potential pitfalls that should be considered and avoided using this model.

Interactions between developmental and adult acclimation have distinct consequences for heat tolerance and heat stress recovery

“…The inherent methodological tradeoffs between static and ramping assays have long been argued to induce diverse molecular stress responses and therefore perhaps constitute distinct forms of genetic variation, with different dynamics, in exposed animals (Cooper et al, 2008;Mitchell and Hoffmann, 2010;Sgrò et al, 2010;Santos et al, 2011;Terblanche et al, 2011;Rezende et al, 2014). Thus, stress duration, intensity and potential ramping rates are critical parameters to interpret heattolerance metrics in a biologically relevant context (Kovacevic et al, 2019;Kingsolver and Umbanhowar, 2018;Ma et al, 2021;Jørgensen et al, 2021). From this perspective, our results also highlight that warmer developmental acclimation gained secondary importance in increasing flies' heat tolerance as the stress persisted (Table 1).…”

Into the wild—a field study on the evolutionary and ecological importance of thermal plasticity in ectotherms across temperate and tropical regions

“…[20,21]). Efforts to explain this perplexity have suggested that species' thermoregulatory behaviour [22][23][24][25], low genetic variability for heat tolerance [15,17,26,27], physiological adjustments [28][29][30][31] and methodology [32][33][34][35][36] obscure the effectiveness of using heat tolerance as a predictor of range limits [35,37,38].…”

Half a century of thermal tolerance studies in springtails (Collembola): A review of metrics, spatial and temporal trends

“…These physiological points used as thermal limits can, in turn, be used to measure the responses of ectothermic animals to changes in climate ( Sinclair et al., 2016 ). Examination of multi-species thermal tolerance datasets worldwide can be very useful to better understand patterns and processes in the field of macrophysiology ( Addo-Bediako et al., 2000 ; Bennett et al., 2018 ; Bennett et al., 2021 ), provided that the methodological variations between different studies are addressed ( Rezende et al., 2014 ; Jørgensen et al., 2021 ). Indeed, the need to disentangle environmental signal from experimental noise when documenting large-scale interspecific patterns in physiological variation and understanding its causes, has long been an issue in macrophysiology ( Chown et al., 2003 ).…”

“…The CTL approach, on the other hand, is considered a ‘dynamic’ method since it consists of a progressive increase or decrease in temperature marked by a rate ( Lutterschmidt and Hutchison, 1997 ; Cowles and Bogert, 1944 ). Even so, these metrics may be related since variation in temperature and exposure time cause cumulative effects on organisms ( Jørgensen et al., 2021 ; Rezende et al., 2020 ). Lower limit measurements have an additional complication, as animal responses may be different when facing freezing ( Sinclair et al., 2003 ).…”