Thermal performance curves (TPCs) provide a powerful framework for studying the evolution of continuous reaction norms and for testing hypotheses of thermal adaptation. Although featured heavily in comparative studies, the framework has been comparatively underutilized for quantitative genetic tests of thermal adaptation. We assayed the distribution of genetic (co)variance for TPC (locomotor activity) within and among three natural populations of Drosophila serrata and performed replicated tests of two hypotheses of thermal adaptation – that ‘hotter is better’ and that a generalist‐specialist trade‐off underpins the evolution of thermal sensitivity. We detected significant genetic variance within, and divergence among, populations. The ‘hotter is better’ hypothesis was not supported as the genetic correlations between optimal temperature (Topt) and maximum performance (zmax) were consistently negative. A pattern of variation consistent with a generalist‐specialist trade‐off was detected within populations and divergence among populations indicated that performance curves were narrower and had higher optimal temperatures in the warmer, but less variable tropical population.

Many traits studied in ecology and evolutionary biology change their expression in response to a continuously varying environmental factor. One well-studied example are thermal performance curves (TPCs); continuous reaction norms that describe the relationship between organismal performance and temperature and are useful for understanding the trade-offs involved in thermal adaptation. We characterized curves describing the thermal sensitivity of voluntary locomotor activity in a set of 66 spontaneous mutation accumulation lines in the fly Drosophila serrata. Factor-analytic modeling of the mutational variance-covariance matrix, M, revealed support for three axes of mutational variation in males and two in females. These independent axes of mutational variance corresponded well to the major axes of TPC variation required for different types of thermal adaptation; "faster-slower" representing changes in performance largely independent of temperature, and the "hotter-colder" and "generalist-specialist" axes, representing trade-offs. In contrast to its near-absence from standing variance in this species, a "faster-slower" axis, accounted for most mutational variance (75% in males and 66% in females) suggesting selection may easily fix or remove these types of mutations in outbred populations. Axes resembling the "hotter-colder" and "generalist-specialist" modes of variation contributed less mutational variance but nonetheless point to an appreciable input of new mutations that may contribute to thermal adaptation. K E Y W O R D S :Function-valued traits, G-matrix, genetic principal components, locomotor activity, M-matrix, mutational bias.Trade-offs are central to many concepts in ecology and evolutionary biology such as life-history evolution (Stearns 1992) and local adaptation (Kawecki 1995;Kawecki et al. 1997) and have been studied deeply at biochemical (Gillooly et al.

Thermal performance curves (TPCs) are continuous reaction norms that describe the relationship between organismal performance and temperature and are useful for understanding trade-offs involved in thermal adaptation. Although thermal trade-offs such as those between generalists and specialists or between hot- and cold-adapted phenotypes are known to be genetically variable and evolve during thermal adaptation, little is known of the genetic basis to TPCs - specifically, the loci involved and the directionality of their effects across different temperatures. To address this, we took a multivariate approach, mapping quantitative trait loci (QTL) for locomotor activity TPCs in the fly, Drosophila serrata, using a panel of 76 recombinant inbred lines. The distribution of additive genetic (co)variance in the mapping population was remarkably similar to the distribution of mutational (co)variance for these traits. We detected 11 TPC QTL in females and 4 in males. Multivariate QTL effects were closely aligned with the major axes genetic (co)variation between temperatures; most QTL effects corresponded to variation for either overall increases or decreases in activity with a smaller number indicating possible trade-offs between activity at high and low temperatures. QTL representing changes in curve shape such as the 'generalist-specialist' trade-off, thought key to thermal adaptation, were poorly represented in the data. We discuss these results in the light of genetic constraints on thermal adaptation.