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Evolution of thermal physiology alters the projected range of threespine stickleback under climate change

Species distribution models (SDMs) are widely used to predict range shifts but could be unreliable under climate change scenarios because they do not account for evolution. The thermal physiology of a species is a key determinant of its range and thus incorporating thermal trait evolution into SDMs might be expected to alter projected ranges. We identified a genetic basis for physiological and behavioural traits that evolve in response to temperature change in natural populations of threespine stickleback (Gasterosteus aculeatus). Using these data, we created geographical range projections using a mechanistic niche area approach under two climate change scenarios. Under both scenarios, trait data were either static (“no evolution” models), allowed to evolve at observed evolutionary rates (“evolution” models) or allowed to evolve at a rate of evolution scaled by the trait variance that is explained by quantitative trait loci (QTL; “scaled evolution” models). We show that incorporating these traits and their evolution substantially altered the projected ranges for a widespread panmictic marine population, with over 7‐fold increases in area under climate change projections when traits are allowed to evolve. Evolution‐informed SDMs should improve the precision of forecasting range dynamics under climate change, and aid in their application to management and the protection of biodiversity.

provide a target for directional selection if the environment changes to include temperatures outside the range encompassed by the thermal niche. Adaptation can thus permit species to persist at temperatures that would have previously led to extirpation (Hoffman & Sgrò, 2011;Sexton et al., 2009). Under moderate climate change scenarios, mean global oceanic temperature is projected to increase in excess of 2°C by the end of the century (IPCC, 2014), with more extreme changes predicted in localized regions (Eyer et al., 2019;Walther et al., 2002) for both cooling and warming events (Hu et al., 2018). Accurately predicting species range patterns under climate change therefore requires data for temperature-associated adaptive trait evolution (Bay et al., 2017;Bush et al., 2016;Catullo et al., 2015).
The genetic basis of thermal traits has been explored in many terrestrial ectotherms (Angilletta et al., 2002;Bowler & Terblanche, 2008;Dillon et al., 2009;Rolandi et al., 2018) and there have been integrative frameworks proposed for how best to use this information to assess the vulnerability of organisms to environmental changes (Araújo et al., 2013;Bay et al., 2017;Huey et al., 2012). Given the differences in thermal biology between terrestrial and aquatic ectotherms (Sunday et al., 2011) and differential warming between terrestrial and aquatic habitats (IPCC, 2014(IPCC, , 2018, predicting how the thermal biology of aquatic organisms will respond to environmental changes requires an understanding of the genetic architecture underlying ecologically relevant thermal traits (Healy et al., 2018) in aquatic systems. The genetic basis of thermal traits has been explored in a number of socioeconomically and culturally important fish species (Everett & Seeb, 2014;Jackson et al., 1998;Jin et al., 2017;Larson et al., 2016;Muñoz et al., 2014;Perry et al., 2001) and there has been an extensive body of work on the thermal biology of the common killifish (Fundulus heteroclitus; Bryant et al., 2018;Fangue et al., 2006Fangue et al., , 2009Healy et al., 2018). These studies suggest that a significant proportion of phenotypic variance in thermal traits can be explained by genetic variation (e.g., 35 single nucleotide polymorphisms [SNPs] explained 51.9% of the variation for upper thermal tolerance in killifish; Healy et al., 2018), highlighting the potential for heritable responses to changes in thermal conditions.
While the climate and ocean temperatures are warming overall, thermal events characterized by both cold and heat extremes are occurring with increasing frequency (Hu et al., 2018;IPCC, 2014IPCC, , 2018Stott, 2016). Regionally downscaled environmental changes can be highly spatially heterogenous relative to global temperature trends, and are likely to be the most relevant for species-specific responses in the context of contemporary climate change (Walther et al., 2002).
For example, range expansions of marine species have been mediated by both ocean-scale warming and regional cooling (Zeidberg & Robison, 2007). In such cases, population persistence and adaptation will rely on both upper and lower critical thermal limits, forcing organisms to respond independently to extreme heat and extreme cold in separate events (Herring et al., 2015(Herring et al., , 2018(Herring et al., , 2020Hu et al., 2018;Walther et al., 2002). Importantly, adaptation in one thermal trait can shift the thresholds of correlated thermal traits (Buckley & Huey, 2016;Denny & Dowd, 2012;Hoffman & Sgrò, 2011;Huey & Kingsolver, 2011). Associations between traits influence the rate of trait adaptation and thus correlated traits must be considered when attempting to predict future species distributions under climate change (Bestion et al., 2015;Hoffman & Sgrò, 2011;Valladares et al., 2014). Recently there have been steps to incorporate theoretical trait evolution into species distribution models (SDMs) (e.g., by including the breeder's equation for a key phenotypic trait; Bush et al., 2016;Catullo et al., 2015;Kearney & Porter, 2009). However, no model has used empirical estimates of evolutionary rates to inform projected species ranges under climate change scenarios.
Threespine stickleback (Gasterosteus aculeatus, Figure 1a) is a useful vertebrate species for understanding the impact of adaptation on range dynamics under climate change. This species exhibits widespread phenotypic variation (Hendry et al., 2013), there are publicly available genomic resources (Jones, Grabherr, et al., 2012) and prior research on temperature-associated evolutionary rates for this species (Barrett et al., 2011;Morris et al., 2018). It is also feasible to artificially breed multiple hybrid generations in common garden laboratory environments. Moreover, there is a regionally downscaled climate model for the Pacific Northwest (Alexander et al., 2018;IPCC, 2014;Reynolds et al., 2007), which makes panmictic marine populations of G. aculeatus in the eastern Pacific Ocean (Kirch et al., 2021;Morris et al., 2018) an ideal system for testing how adaptive trait variation could affect projections of species ranges under climate change.
The objective of this study was to incorporate ecologically and evolutionarily relevant data on thermal traits into end-of-century projections of SDMs. To accomplish this, we assessed physiological and behavioural temperature-associated traits in two marine and two freshwater populations of threespine stickleback, as well as within-population F 1 families and marine-freshwater hybrid F 1 and F 2 families. We used the hybrid families to construct genetic linkage maps and identified multiple quantitative trait loci (QTL) associated with thermal preference, as well as upper and lower critical thermal limits. Using these genetically based traits and empirically derived evolutionary rate data for this species (Barrett et al., 2011), as well as others (Morgan et al., 2020;Sanderson et al., 2021), we constructed SDMs using a mechanistic niche area approach that integrated the trait data reported in this study and allowed those traits to evolve under two climate change scenarios, three distinct types of evolutionary models and three evolutionary rates.

| Sample collection and husbandry
We collected adult Gasterosteus aculeatus from two marine popula-  (Figure 1b) at depths of less than 0.5 m.
To ensure that any genetic variation identified would be relevant for adaptation within this region, we confined our study to a single genetically panmictic cluster of populations (Morris et al., 2018).
Individuals were maintained in a flow-through system and under a photoperiod that mimicked the natural source population environment during collection periods before transport. We transported the fish to the Life and Environmental Sciences Animal Resources Centre at the University of Calgary, where we separated the fish into population-specific 113-L glass aquaria at a density of ~20 fish per aquarium. We acclimated marine individuals to freshwater salinity over 1 week and maintained fish in a common garden environment (salinity of 4-6 ppt, water temperature of 15 ± 2°C, and a photoperiod of 16:8 h light:dark). Individuals were allowed to acclimate for at least 2 weeks before experiments (1 week for stress reduction post-transfer, 1 week for common garden environment acclimation and salinity ramp). Each common garden aquarium was on a closed system with individual filters, air stones and water supply. We

| Crossing design for marine and freshwater F 1 families
We collected eggs from females and fertilized the eggs with extracted testes from euthanized males. We transferred the fertilized egg mass to a mesh-bottomed egg incubator suspended in a 37-L aquarium for hatching. Each hatching aquarium was maintained with a single air stone and a filter. Once hatched, we reared the larval fish in 37-L hatching aquaria until they reached a total length F I G U R E 1 (a) Adult threespine stickleback (Gasterosteus aculeatus) from a single genetic cluster were sampled from (b) two marine and two freshwater populations in the Canadian Pacific Northwest. These populations were assayed for (c) thermal preference (green), critical thermal minima (CTmin, blue) and maxima (CTmax, red). Thermal trait values for marine populations (M1 and M2) were incorporated into the species distribution models [Colour figure can be viewed at wileyonlinelibrary.com] (TL) of ~1 cm, after which we split the families into family-specific 113-L aquaria to maintain suitable densities. We fed the larval fish ad libitum twice daily with live Artemia spp. nauplii, and then gradually transitioned the diet to chopped, thawed bloodworms (Hikari Bio-Pure Frozen Bloodworms) ad libitum once daily as they reached ~2 cm TL. The F 1 families were maintained in a common garden environment identical to that of the F 0 populations. We produced one F 1 family for each population (M1_F 1 , M2_F 1 , FW1_F 1 , and FW2_F 1 ).

| Crossing design for hybrid mapping families
To generate genetically heterogeneous marine-freshwater F 1 families from wild F 0 parents, we collected eggs from marine females and fertilized the eggs with extracted testes from euthanized freshwater males. Egg masses were hatched and juveniles were reared as detailed above. We produced one F 1 family of M1 × FW1 hybrids (hereafter referred to as H1_F 1 ) and three F 1 families of M1 × FW2 hybrids (hereafter referred to as H2_F 1 ). The hybrid F 1 families were maintained in a common garden environment identical to that of the F 0 populations. To generate F 2 families for linkage map construction and QTL mapping, we crossed siblings from the same F 1 family using the same methodology that was used to generate the F 1 families. The amount of trait variation explained by these hybrid crosses is likely to be higher than would be observed in marinemarine or freshwater-freshwater crosses, increasing our likelihood of detecting QTL. Overall, we produced one F 2 family of H1 × H1 hybrids (referred to as H1_F 2 ) and three families of H2 × H2 hybrids (referred to as H2_F 2 _1, H2_F 2 _2 and H2_F 2 _3). All F 2 individuals were raised as described above in a common garden environment identical to that of the F 0 and F 1 individuals to ensure consistent thermal history.

| Thermal tolerance and preference experiments
To assess the lower and upper limits of physiological thermal tolerance, we conducted standard critical thermal minimum (CTmin) and maximum (CTmax) experiments on adult fish (Barrett et al., 2011;Fangue et al., 2006;Hutchison, 1961). At these sublethal limits, the fish experience a loss of equilibrium (LOE) at which they lose the ability to escape conditions that would ultimately lead to their death (Beitinger et al., 2000). Before each experiment, individuals were fasted for 24 h. Our experimental tank held 1000-ml glass beakers aerated individually to prevent thermal stratification. After a 15-min acclimation to the experimental apparatus in the individual beakers, we cooled or heated the water (for CTmin or CTmax, respectively) at a rate of ~0.33°C min −1 . We assessed wild F 0 individuals (n M1 = 32, n M2 = 14, n FW1 = 15, n FW2 = 16, N = 77; Figure S2), and laboratory raised F 1 (n M1_F1 = 13, n M2_F1 = 15, n FW1_F1 = 15, n FW2_F1 = 15, N = 58; Figure S2), and F 2 individuals (n H1_F2 = 28, n H2_F2_1 = 36, n H2_F2_2 = 21, n H2_F2_3 = 17, N = 102; Figure S3). All individuals were assessed for CTmin, allowed to recover for at least 3 days, and then assessed for CTmax to keep thermal stress history consistent. The onset of erratic behaviours associated with a behavioural stress response (i.e., "agitation windows"; Turko et al., 2020) occurred below 5.0°C and above 25.0°C during CTmin and CTmax experiments, respectively. Normal behaviour was observed between 5.0 and 25.0°C, whereas outside of those temperatures, individuals gradually exhibited more extreme stress responses (e.g., increased gilling rate, erratic movement, muscle spasms, listing; as outlined by the Canadian Council of Animal Care guidelines) until reaching LOE and the inability of an individual to right itself (the experimental endpoint, measured in 0.5°C increments; Barrett et al., 2011;Fangue et al., 2006;Hutchison, 1961).
To assess the range of behavioural thermoregulation, we conducted thermal preference experiments in a temperature Shuttlebox (Loligo Systems). Experimental pools were set with a static gradient of 10°C in the cool side and 20°C in the warm side, connected with a water bridge at 15°C (acclimation temperature). Temperature was continually monitored and recorded by immersed temperature probes, connected to a computer-driven temperature controller and data acquisition system (DAQ-M; Loligo Systems). Movement in the Shuttlebox was tracked by an infrared-sensitive uEye USB 2.0 camera (IDS Imaging Development Systems) which connected to shuttlesoft (version 2; Loligo Systems) and allowed monitoring of fish movement. Following recovery from CTmax experiments (at least 3 days), individuals were acclimated in the Shuttlebox for 15 min, with entry at "cool" or "warm" pool randomized, to allow for exploration of the gradient. After acclimation to the experimental apparatus, the movement of each individual was tracked in 1-s intervals for 30 min, recording the preferred ambient water temperature (thermal preference). Individuals were assessed individually in the Shuttlebox apparatus to avoid any confounding social behaviours (e.g., as seen in shoaling behaviours; Cooper et al., 2018). At the time of data collection for thermal trait experiments, all individuals were adults.
Relationships between all thermal traits separated by population and generation were assessed using the Pearson product-moment correlation with the corrplot (version 0.84, Wei et al., 2017) and Hmisc (version 3.14-0, Harrell, 2014) packages in R (R Core Team, 2021).

| Isolation and characterization of SNPs
Genomic DNA was extracted from caudal fin tissue using a phenolchloroform-based protocol. We digested tissues overnight in digestion buffer and proteinase K at 55°C, then performed multiple phenol-chloroform and ethanol washes to isolate the DNA. We assessed the quantity of the extracted DNA using the Quant-iT PicoGreen dsDNA assay kit (ThermoFisher Scientific) and Synergy HT plate reader with the Gen5-associated software (BioTek). We prepared restriction site-associated DNA (RAD) libraries (Peterson et al., 2012) using MluCl and NlaIII restriction enzymes (New England Biolabs), ligation of individual barcodes and pooling of 48 individuals TA B L E 1 Significant quantitative trait loci (QTL) for critical thermal minimum (CTmin), critical thermal maximum (CTmax) and thermal preference (Preference) traits with the percent of trait variance explained (PVE) for each QTL, the logarithm of odds (LOD) threshold for significance and the LOD peak observed for each QTL

| Assembly of the genetic linkage maps
After barcode demultiplexing and filtering out low-quality reads in stacks (Catchen et al., 2013), we removed PCR duplicates from the raw sequences and aligned these cleaned sequences to the G. aculeatus reference genome (Jones, Grabherr, et al., 2012) using the Burrows-Wheeler transform (Li & Durbin, 2010). Individual libraries were concatenated and filtered (Puritz et al., 2014) using vcftools (version 3.0, Danecek et al., 2011) and split into chromosomespecific variant call format (VCF) files using SnpSift (Cingolani et al., 2012) to assemble the linkage maps chromosome by chromosome.
We assigned markers to a linkage group with an initial logarithm of the odds (LOD) score of 3 after filtering out markers that showed high levels of segregation distortion and missing observations (>20% missing data) in lep-map3 (Rastas, 2017). Unassigned markers were subsequently added to the existing linkage group at a LOD score of 3 and a minimum size limit of five markers per linkage group.
We ordered the markers using a minimum posterior value of 0.001 and collapsed multiple markers when the probability difference between markers was <0.01 (Rastas, 2017). The final linkage map was phased in lep-map3 (Rastas, 2017) and subset for use in R (R Core Team, 2021) to generate a list of informative SNPs to use in subsequent analyses with the qtl package (version 1.44-9, Broman et al., 2003). The final linkage maps were similar across families in length and spacing between markers, though the H2_F 2 map had a higher density of markers (Table S1).

| Quantitative trait loci mapping
We analysed the four F 2 families separately with the same methodology to assess the presence of QTL associated with the thermal traits and avoid the confounding effects of unique meiotic events. We calculated conditional genotype probabilities using a hidden Markov model, allowing for possible genotyping errors at a level of 0.0001 using a Kosambi mapping function with a fixed step width, prior to running genome scans with a single QTL model (Arends et al., 2014;Broman & Sen, 2009 Figure 2).

| Environmental variables used to construct SDMs
We compiled environmental data widely used in the construction of SDMs to estimate suitable habitat in both present-day and endof-century forecasts (Wiens et al., 2009), including bathymetry, sea ice extent and concentration, salinity, and sea surface temperature (SST). We used 2014 data as our baseline year to match the forecasting baseline of the Fifth Assessment Report (IPCC, 2014).
We assumed a suitable habitat range for this species in the Pacific Northwest to consist of coastal areas (water depth <200 m) where sea ice is never present (i.e., no sea ice at the maximum extent). G.
aculeatus has a broad salinity tolerance (Bayly, 2003;Divino et al., 2016) and salinity is not limiting in any part of the range (Zweng et al., 2013), so salinity was not included in the final present-day or forecasted models. We obtained bathymetry data from the

General Bathymetric Chart of the Oceans (GEBCO) of the British
Oceanographic Data Centre (Weatherall et al., 2015), and maximum sea ice extent data from the Multisensory Analyzed Sea Ice Extent -Northern Hemisphere (MASIE-NH) product (Fetterer et al., 2010).
We obtained maximum and minimum daily statistical mean SST from

| Trait inclusion in SDMs
We incorporated experimental data on the wild marine popula- to end-of-century (i.e., not changed) in the "no evolution" projections. In contrast, in the "evolution" projections, we allowed traits to change based on empirically derived evolutionary rate estimates. We allowed CTmin to evolve at a rate of 0.63 haldanes estimated from a selection experiment previously conducted on populations belonging to the same admixed genetic cluster as the sampled populations (Barrett et al., 2011;Morris et al., 2018). At present, there are no empirical estimates of CTmax evolution for threespine stickleback.
Thus, as a proxy we used a known estimate of CTmax evolution in fish, which was observed in zebrafish (0.19 haldanes; Morgan et al., 2020). Given that the evolutionary rates for CTmin and CTmax from Barrett et al. (2011) and Morgan et al. (2020) are quite high relative to other recorded evolutionary rates for phenotypic traits (particularly physiological traits; Sanderson et al., 2021), we also explored the influence of evolution on projected ranges when evolution occurred at more "typical" rates. To do so, we allowed both CTmin and CTmax to evolve at a mean rate from a large meta-analysis of phenotypic trait evolution (0.14 haldanes; Sanderson et al., 2021).
Since the rates of evolution used here are estimates based on whole-organism tolerance (Barrett et al., 2011;Morgan et al., 2020), and trait evolution is constrained by the underlying genetic architecture of the trait (Barghi et al., 2020;Fisher, 1930;Orr, 1998;Ungerer & Riesebero, 2003), we next explored how selection acting on discrete, identifiable loci could impact evolutionary rates, and the subsequent effects on projected habitat range. To do so, we constructed models that scaled the evolutionary rate of CTmin or CTmax based on the observed genetic architecture from our QTL mapping (i.e., we scaled by the percent trait variation that is explained by each locus; "scaled evolution" projections). However, an important restriction in the evolution of CTmin for all models incorporating trait evolution ("evolution" and "scaled evolution" projections) was a hard boundary at 0°C, with the assumption that population persistence in a sub-

| Genetic basis of thermal traits
Threespine stickleback exhibited a wide thermal tolerance range bounded by a mean CTmin of 1.89°C (±1.04°C SD) and a mean CTmax of 30.1°C (±1.78°C SD) (Figure 1c; Figure S2), with these physiological traits being highly correlated (r = .79, Figure S1). These marine populations also tolerated a wide range of temperatures within which there was no observable stress response (5.0-25.0°C).
Thermal preference values were slightly above the acclimation temperature, but with more variance around the mean than is observed in the physiological traits (16.7 ± 3.19°C, Figure 1c; Figure S2). To determine if these measured thermal traits have a genetic basis and could therefore evolve in response to natural selection, we raised hybrid marine-freshwater F 1 (N = 2) and F 2 (N = 4; Figure S3) families under common garden conditions and used these fish for genomewide linkage map construction (Table S1)

| Geographical range of projected suitable habitat
Based on sea ice extent and bathymetry alone, our present-day suitable habitat model suggests a marine range for this population from the southern Bering Sea to northern Washington State, and along the southeast Alaskan Panhandle (combined shaded area in Figure 3a, ~654,122 km 2 ). End-of-century IPCC projections resulted in a substantial increase in the overall suitable habitat area for stickleback, with an 873,059-km 2 increase in total area (combined shaded area in Figure 3b-e, ~1,527,181 km 2 ) in association with a reduction in sea ice concentration at the northern end of the range.

| "No evolution" SDM
We used our observed values of CTmin, CTmax and thermal pref-

| "Evolution" SDMs
Incorporating the evolution of CTmin and CTmax into the SDMs to "no evolution" models ( Figure S4). Reducing the rate of evolution for these traits to the mean rate of trait evolution reported in Sanderson et al. (2021) still results in all suitable habitat falling within the NB area under both RCP 4.5 and RCP 8.5. Similarly, scaling CTmin to evolve at a rate equivalent to the response expected if selection acted solely on the detected CTmin QTL on either linkage group III (PVE = 28.9%) or XXI (PVE = 53.7%) resulted in identical range distributions as the full "evolution" models. Given that CTmax evolution had no impact on these range projections, we did not construct "scaled evolution" models for this trait.

| DISCUSS ION
In this study, we have produced the first "evolution-informed" SDMs that incorporate empirical data for temperature-associated traits and their evolution to understand how end-of-century projections change with the inclusion of these data. We assessed physiological (critical thermal minimum, [CTmin] and maximum [CTmax]), and behavioural (thermal preference) thermal traits for threespine stickleback from wild marine and freshwater populations, as well as hybrid F 1 and F 2 families. We provide novel characterization of QTL underlying these traits in this species, which are likely to be important genetic targets of selection under climate change. Finally, we incorporated these trait data and three empirical estimates of evolutionary rates into mechanistic niche area SDMs under two climate change scenarios. When trait data are included in projections but held constant (i.e., "no evolution" models), there is an ~6-fold (RCP 4.5) and 7-fold (RCP 8.5) increase in the geographical area of the NB area. The geographical ranges projected for the end-of-century increased by over 7-fold when CTmin was allowed to evolve at observed trait-specific rates in the "evolution" models. Additionally, when a slower rate of CTmin was used or the rate was scaled by the percent of the trait variance that is explained by individual loci (reflecting a scenario where selection acted solely on those loci), this substantial increase in NB area was still attained by the end of the century under either RCP scenario. These changes to the projected species ranges under climate change underline the importance of incorporating behavioural as well as physiological data into SDMs (Sunday et al., 2012), as well as the key role that thermal trait evolution could play in range shifts (Buckley et al., 2010;Evans et al., 2015;Lyon et al., 2019).
Our models project a northward range expansion under climate change. This is unsurprising because climate change opens newly available thermal niche space in waters north of the current-day geographical range (Alexander et al., 2018) (as is seen in the "no evolution" model northward expansion, Figure 3b,c). Northward range expansion with climate change due to increasing habitat availability has also been documented in birds (Melles et al., 2011;Rushing et al., 2020;Tingley et al., 2009;Tombre et al., 2019), plants (D'Andrea et al., 2009), other fishes (Fossheim et al., 2015;Spies et al., 2020;Yapıcı et al., 2016) and pest species (e.g., ticks and mountain pine beetle; Clow et al., 2017;Kurz et al., 2008;Ogden et al., 2006;Sagurova et al., 2019;Sambaraju et al., 2019), as well as in large-scale analyses of diverse taxa assessing the "fingerprints" of climate change impacts (Parmesan & Yohe, 2003;Platts et al., 2019). What is perhaps less expected is that our models reveal that evolution of cold tolerance, but not heat tolerance, had a substantial impact on predicted ranges under climate change, despite end-of-century climate change scenarios projecting an overall warmer, not cooler, world. This result occurs because even with climate change, waters within the marine range of this stickleback population are not projected to reach temperatures that would surpass their current CTmax limit. However, there is reason to think that the relevance of cold tolerance in climate change scenarios is not specific to this system and could be generally important to consider. Climate change is leading to an increase in the frequency of extreme temperature events (IPCC, 2014;Stott, 2016), including both extreme heat and extreme cold (Herring et al., 2015(Herring et al., , 2018(Herring et al., , 2020, which could drive selection on both CTmin and CTmax (Buckley & Huey, 2016;Denny & Dowd, 2012;Hoffman & Sgrò, 2011;Kingsolver et al., 2011). Moreover, although northern waters are warming on average, there has been considerable spatial heterogeneity in temperature change (Walther et al., 2002).
Importantly, northern waters are not predicted to be uniformly warmer under climate change (Alexander et al., 2018;IPCC, 2014) relative to the southern waters in this range, especially when seasonal variation is considered (Alexander et al., 2018). Seasonal variation and more extreme thermal events under climate change scenarios may force organisms to respond independently to both extreme heat and extreme cold in separate events (Herring et al., 2015(Herring et al., , 2018(Herring et al., , 2020Walther et al., 2002), and coupled with the correlation between CTmin and CTmax (as observed here, Figure S1), population persistence will rely on adaptation in both upper and lower critical limits. Further investigations to test the empirical rate of evolution of thermal behaviour, physiology and the molecular underpinnings of these key traits would be well served by assessing additional subpopulations along the latitudinal gradient inhabited by stickleback to gain a more detailed understanding of these temperature-associated traits over a wider environmental range.
In interpreting the projections we present here, it is important to note that using a different methodology to assess thermal limits could result in different thresholds relating to tolerance and behaviour (Moyano et al., 2017). In addition, in the absence of an empirical estimate of the rate of evolutionary change for CTmax in threespine stickleback, we are using a rate observed for this trait in zebrafish as a proxy, which will necessarily be inaccurate for stickleback. It is unclear whether the true rate of CTmax evolution in stickleback might be slower or more rapid than the rate for zebrafish. For instance, it is possible that in threespine stickleback CTmax could evolve at similar rates as observed for CTmin, particularly given similarities in genetic architecture and the correlation between these traits in this study. Although incorporating evolution of CTmax had no impact on our projected ranges because temperatures under RCP 4.5 and RCP 8.5 never exceeded current CTmax values, we suspect this trait will be of major importance in many other systems where populations reside in environments closer to their current upper thermal limits (e.g., Deutsch et al., 2008;Morgan et al., 2019;Nati et al., 2021). Ultimately, given the dramatic temperature changes that are projected for end-ofcentury marine systems (IPCC, 2014(IPCC, , 2018, there is a critical need for empirical measures of evolutionary rates in thermal traits for predicting future species ranges.
While this set of mechanistic niche models builds upon current SDM work by incorporating functional traits critical to population persistence as well as varied evolutionary scenarios based on empirical measurements, there are limitations to these projections. We present niche models of geographical species ranges, but future work in this area would benefit from including a robust metric of species distribution and population genetic structure within these mechanistic geographical ranges. Deriving distributional maps from statistical algorithms that rely on species occurrence and bioclimatic variables would move beyond binary outcomes of habitat suitability and provide a map of suitable habitat that is scored as an index and could underlie the static and evolving trait areas. Species distributions that build upon these evolution-informed species ranges could be an important tool for projecting population persistence under climate change.
The efficiency of translating the selection acting on a trait into evolutionary responses across generations will depend on the genetic architecture of the trait and the extent of environmental change (Dittmar et al., 2016;Orr, 2005;Rogers et al., 2012). Amongtrait genetic correlations can also either speed or slow adaptive evolution depending on whether correlations are reinforcing or antagonistic to the direction of selection (Etterson & Shaw, 2001).
In this case, we observe a positive correlation between CTmin and CTmax that could facilitate selection for increased temperature tolerance at both upper and lower limits. However, the empirical rates of trait evolution that we incorporated into our SDMs were estimated using change in phenotypic variation across generations (Barrett et al., 2011;Morgan et al., 2020), rather than evolution at underlying loci. As such, it is likely that some proportion of the observed phenotypic change was due to plastic responses. In our "scaled evolution" models, we took a conservative approach by restricting phenotypic evolution to occur only through heritable change via the loci shown here to be associated with the trait. The large-effect loci that we identified are consistent with expectations from theory that suggest prolonged bouts of adaptation with gene flow (as expected in this system; Rogers et al., 2012;Schluter & Conte, 2009;Schluter et al., 2010Schluter et al., , 2021 will favour architectures characterized by fewer, larger effect, more tightly linked alleles (Griswold & Whitlock, 2003;Via et al., 2012;Yeaman & Otto, 2011;Yeaman & Whitlock, 2011).
However, the effects of the QTL identified from these populations are probably overestimated, while other loci may have gone undetected (sensu the Beavis Effect; Beavis, 1994). These loci may have been detected in some F 2 families and not in others because each mapping family has its own specific meiotic events that enable the identification of certain loci. Additionally, there are many key functional traits that are relevant to population persistence under a changing climate, and it is possible that the traits focused on here, or unexplored traits related to thermal performance, could display antagonistic pleiotropy.
Collectively, the inclusion of thermal traits and their evolution alters the projected ranges of marine threespine stickleback, with a substantial increase in the projected area that the species may occupy under climate change forecasts. Many traits are evolving in response to climate change (Eliason et al., 2011;Gómez-Ruiz & Lacher, 2019;Horton et al., 2020;Hovel et al., 2016) and SDMs that do not take trait data (and trait evolution) into account could provide less accurate predictions about future species distributions under climate change (Bush et al., 2016;Guisan et al., 2013;Huey et al., 2012;Kearney & Porter, 2004)-an issue of particular concern for species at risk and pest species undergoing range expansion (Bebber, 2015;Cullingham et al., 2011;McLeod et al., 2012). Our results provide a framework for addressing this problem, which will have critical implications for the application of these models in policy, sustainable resource management and the protection of biodiversity in a changing climate.

ACK N OWLED G M ENTS
The species collections took place on Huu-ay-aht and Sechelt First Nations traditional territories and we are grateful to them for the opportunity to conduct research in protected and sacred areas.
We thank the Bamfield Marine Sciences Centre for the resources required to conduct this research, Sam Owens for the photo in

O PE N R E S E A RCH BA D G E S
This article has earned an Open Data Badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5061/dryad.70rxw dbz6 and https://github.com/sjswu itchi k/gasAcu_qtl_sdm

DATA AVA I L A B I L I T Y S TAT E M E N T
The data associated with this work are available as follows: ddRAD