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Simulation of a black-hole binary system evolved by the SpEC code.

Simulation of a black-hole binary system evolved by the SpEC code.

Simulation of a black-hole binary system evolved by the SpEC code.

Fig 2 (heatmap) data files and R codeData and R code needed to create Fig 2 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 6 figure panels. Each file contains evolved D across the range in each of 500 generations of stable climate followed by 1000 generations of climate change.Fig 2 (heatmap).zipFig 3 (D lines) data and R codeData and R code needed to create Fig 3 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 6 models shown. Each file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change, averaged across 10 runs per cost per model.Fig 3 (D lines).zipFig 4 (delta.D) data and R codeData and R code needed to create Fig 4 in Hargreaves et al (2015) J Evol Biol. One data file for each of the 4 models (ie figure rows) shown. Each file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change for 30 runs per model.Fig 4 (delta.D).zipFig 6 (D vs density) data and R codeData and R code needed to create Fig 6 in Hargreaves et al (2015) J Evol Biol. Two data files (one for evolved D and one for density) for each of 2 model runs, one with dispersal (dispersal distance =1 as normal) and one run without dispersal (dispersal distance =0).Fig 6 (D vs density).zipAppendix S1 data and R code for each figureData and R code needed to create figures in Appendix S1 in Hargreaves et al (2015) J Evol Biol. All figures remake Fig 3 while varying one parameter. Fig S1.1 shows murate = .005; Fig S1.2 shows avshift = .01, .05, .2; Fig. S1.3 shows K=10; Fig. S1.4 shows effect of eliminating kin selection by randomizing individuals within columns before each dispersal event. For each figure there is 1 data file per model. Each data file contains evolved D across the range after 500 generations of stable climate and after 1000 generations of climate change, for 10 runs per cost.Appendix S1.zipModel code Matlab fileCode to run the model simulations.rangeshift (for dryad).mFig 5 (extinction threshold) Matlab codeMatlab code to run the simulations necessary to determine the relationship between the speed of climate change (avshift) and probability of extinction.rangeshift_thresh (for dryad).m Dispersal ability will largely determine whether species track their climatic niches during climate change, a process especially important for populations at contracting (low-latitude/low-elevation) range limits that otherwise risk extinction. We investigate whether dispersal evolution at contracting range limits is facilitated by two processes that potentially enable edge populations to experience and adjust to the effects of climate deterioration before they cause extinction: (i) climate-induced fitness declines towards range limits and (ii) local adaptation to a shifting climate gradient. We simulate a species distributed continuously along a temperature gradient using a spatially explicit, individual-based model. We compare range-wide dispersal evolution during climate stability vs. directional climate change, with uniform fitness vs. fitness that declines towards range limits (RLs), and for a single climate genotype vs. multiple genotypes locally adapted to temperature. During climate stability, dispersal decreased towards RLs when fitness was uniform, but increased when fitness declined towards RLs, due to highly dispersive genotypes maintaining sink populations at RLs, increased kin selection in smaller populations, and an emergent fitness asymmetry that favoured dispersal in low-quality habitat. However, this initial dispersal advantage at low-fitness RLs did not facilitate climate tracking, as it was outweighed by an increased probability of extinction. Locally adapted genotypes benefited from staying close to their climate optima; this selected against dispersal under stable climates but for increased dispersal throughout shifting ranges, compared to cases without local adaptation. Dispersal increased at expanding RLs in most scenarios, but only increased at the range centre and contracting RLs given local adaptation to climate.

We evaluate the extent to which males and females evolve along similar or different trajectories in response to the same environmental shift. Specifically, we use replicate experimental introductions in nature to consider how release from a key parasite (Gyrodactylus) generates similar or different defense evolution in male versus female guppies (Poecilia reticulata). After 8-12 generations of evolution, guppies were collected from the ancestral (parasite still present) and derived (parasite now absent) populations and bred for two generations in the laboratory to control for non-genetic effects. These F2 guppies were then individually infected with Gyrodactylus and infection dynamics were monitored on each fish. We found that parasite release in nature led to sex-specific evolutionary responses: males did not show much evolution of resistance, whereas females showed the evolution of increased resistance. Given that male guppies in the ancestral population had greater resistance to Gyrodactylus than did females, evolution in the derived populations led to reduction of sexual dimorphism in resistance. We argue that previous selection for high resistance in males constrained (relative to females) further evolution of the trait. We advocate more experiments considering sex-specific evolutionary responses to environmental change. Dargentetal.evolsexesJEBFile includes: individual identifier ("identifier"), names of populations used ("pop"), sex of individuals ("sex"), years of field sample ("year"), individuals standard length in mm ("startsl"), type of treatment ("type", i.e. sham or infected=2 G. turnbulli), peak parasite load ("peakpara"), parasite rate of increase r0 ("ro0to10"), outlier status of the individual ("outlier"), relative condition index ("startkn"), death during infection ("died"), and last day in which the individual was counted for parasites ("last count", i.e. day of death).

Simulation of a black-hole binary system evolved by the SpEC code.

Simulation of a black-hole binary system evolved by the SpEC code.

Simulation of a black-hole binary system evolved by the SpEC code.

Simulation of a black-hole binary system evolved by the SpEC code.

Simulation of a black-hole binary system evolved by the SpEC code.