Fig. 1: Simulations of the consequences of global and local adaptation on the population responses to local and global climatic anomalies.

a, d show the performance of species at five sites with climatic means spanning across the range of the climatic variable. We expected locally adapted species to present multiple different performance curves representing distinct populations at sites distributed along the species’ distributional range, as shown in panel a. This expectation implies that population change will be more sensitive to local weather anomalies (simulation in b) than to weather anomalies calculated from all sites across the species’ distribution (simulation in c). In the case of global adaptation, performance is represented by a single curve through its entire range (d). Therefore, observed population change will be more sensitive to global weather anomalies calculated from all sites across the species’ distribution (f) than to the local site anomalies (e). Performance curves were based on a Briere type I function⁷⁴, which is a simple function that matches empirical data on thermal performance⁷⁵. We included a fixed area under the curve as consistent with expectations of specialist/generalist trade-offs⁷⁶. Beyond this hypothetical example, in practice, the mean and variance of curves may vary across species; for example, some species may have broader climatic tolerances than others, i.e., the curves in panels b or f would be broader and shallower, meaning there is a greater range of temperatures in which population growth can remain positive. Broader tolerances may be driven in part by phenotypic plasticity, i.e., gene-by-environment interactions (for example, oviposition microsite preferences varying between locations depending on the local macroclimate). This phenotypic plasticity may be exhibited across the entire range, or it might only occur in certain areas, i.e., there might conceivably be a local adaptation of the phenotypic plasticity. Alternatively, local adaptation can occur in fixed traits, such as lighter-colored insects in warmer areas⁷⁷. Both of these evolutionary adaptations produce patterns akin to that in panel a, whereby optimum of a thermal performance differs amongst the populations of species so that they perform best in their “home” conditions. To generate weather across the range, we standardized an observed 19-year time series of global yearly temperatures (min = 0, max = 1, mean = 0.5) and then shifted the values of each year to predict mean expectation at local sites across the range, a local value for each site and year was then sampled with Gaussian noise. The performance was subsequently used as input into a discrete logistic growth model (N_t+1 = RN_t(1 – N_t/K)) as proportional to R the intrinsic growth rate. Each population was seeded with a small number of individuals and was allowed to recover by immigration should the population size go to zero. A time series of population change for each of the sites was collected from the simulation (ΔN after initialization and immigration was excluded). Models for population change were then fitted using local and global anomalies and are shown in (b, c, e, f). Colors in (a, d) indicate location in the distributional (e.g., blue to red, cold to hot extremes, respectively). Colors in the rest of the panels indicate spatial scale (blue—local climate anomalies; orange—global climate anomalies), circles indicate populations (i.e., from distinct sites), lines show predicted trend with 95% confident intervals.