Rangewide adaptive plasticity in trees provides resilience to climate change

Rangewide adaptive plasticity in trees provides resilience to climate change | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Rangewide adaptive plasticity in trees provides resilience to climate change Elizabeth Trevenen, Michael Renton, Martin Breed, Nicole Maher, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7611542/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ecosystem restoration is a critical strategy for addressing the global challenges of biodiversity loss, ecosystem degradation and climate change. Where seeds should be sourced for restoration plantings remains intensely debated. Climate-adjusted provenancing – sourcing seed from populations already experiencing the climatic conditions expected at future restoration sites – has been proposed as a proactive strategy to enhance the resilience of restoration plantings under climate change. However, the benefits of climate-adjusted provenancing over alternative strategies, such as local provenancing, remain largely untested. To address this, we established 30 large provenance trials with multiple species, locations, years, and substrates, with seeds sourced from 20 provenances across a 400 km climate gradient where mean annual rainfall doubles. Despite harsh environmental conditions, we found no clear relationship between climate at the provenance and seedling survival or growth. Occasional provenance effects were observed but were inconsistent across trials. We show that neither climate-adjusted nor local provenancing provided a predictable benefit or disadvantage. We hypothesise that the similar tolerance to a wide range of environmental stressors reflects range-wide adaptive plasticity. Such plasticity likely evolved in these ancient lineages on old landscapes in response to a long history of climatic oscillations, increasing aridity, and frequent fire. Biological sciences/Ecology/Restoration ecology Biological sciences/Ecology/Restoration ecology Biological sciences/Ecology/Climate-change ecology Biological sciences/Ecology/Climate-change ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Ecosystem restoration is a vital nature-based solution to the major global challenges of the Anthropocene: climate change, biodiversity loss and promoting human well-being 1 . Several global initiatives are driving large-scale ecosystem restoration efforts 2 , with hundreds of ‘mega-eco’ projects already underway 3 . However, climate change is making ecosystem restoration increasingly challenging 4–7 . Therefore, restored ecosystems must be resistant to gradual environmental changes like increasing temperatures, but also resilient to enable recovery from abrupt disturbances, such as drought. Hereafter, we use ‘resilience’ to refer to these ecosystem properties 8 . The restoration of diverse plant communities reflecting a pre-disturbance/reference state relies on collecting seed from wild populations. There is an urgent need to use native seed efficiently for restoration, as future demands for seed are likely unsustainable 9,10 . The geographic location from which seeds are sourced for restoration (provenance) can affect plant fitness and restoration success, yet the best strategy for sourcing seed to optimise these outcomes is widely debated 11–13 . The reliance on local provenancing is being challenged by climate change, prompting consideration of alternatives such as climate-adjusted provenancing 14 . Climate-adjusted provenancing involves sourcing seed from a range of populations that may include adaptations to the climatic conditions predicted for the future of a restoration site. Proposed as a ‘future-proofing’ strategy, it aims to enhance the resilience of restoration plantings to climate change 14–17 . This approach is a form of facilitated adaptation 18 or assisted migration 19,20 ; it addresses the possibility that local populations are maladapted to a rapidly changing climate 21 . This strategy has gained widespread popularity, with numerous predictive methods developed to identify ‘optimal’ climate-adjusted provenances for restoration plantings 22–24 . For a climate-adjusted provenancing strategy to be beneficial, seed from climate-adjusted provenances must reliably outperform those from other provenances. Yet, robust empirical tests of such provenance effects are lacking, particularly for diverse native plant communities restored in disturbed landscapes 25–27 . Consequently, it remains unknown whether climate-adjusted provenancing provides consistent and predictable benefits over alternative strategies, such as the widely used local provenancing. To address this knowledge gap, we established 30 large-scale provenance trials with multiple species, locations, years, and soil substrate conditions. We used these trials to compare the growth and survival of plants grown from seeds sourced from 20 provenances spanning a 400 km climate gradient where long-term mean annual rainfall ranges from 450 mm to 850 mm, in a region that has experienced decades of drying and warming and more frequent hot droughts 28 (Fig. 1). We monitored the plants for two years under harsh environmental conditions to explore associations between the climate at provenances and survival and growth (Fig. 2). High fitness at these early life stages is critical to successful restoration, as it is often highly stressful to young plants, particularly in dryland ecosystems that experience long summer droughts 29 . We tested the hypothesis that seeds from hotter, drier provenances would have higher survival and growth than those from local or cooler, wetter provenances, thereby supporting climate-adjusted provenancing. Results Provenance effects Relationships between plant survival, growth, and the climatic characteristics of 20 provenances were assessed across 30 unique trial combinations (2 species × 4 trial site locations × 2 planting years × 2 substrate conditions, minus one planting year × substrate combination that could not be established at one location for either species). Each trial combination contained 50 plants grown from each provenance, comprising 5 replicate plants from 5 maternal lines, planted across two replicate plots. Across all trial combinations, a relationship between a plant performance measure (i.e., survival, height or stem diameter after two years) and at least one climatic characteristic of the provenance (e.g., mean annual precipitation, mean annual temperature, or their combination, PC1) was observed in 40 out of 270 comparisons (GAMM p<0.05, Figs. 3-4, 30 combinations × 3 measures × 3 characteristics = 270 comparisons). Overwhelmingly, responses were inconsistent and unpredictable. Occasionally, there was evidence of a benefit of climate-adjusted provenancing, where seed sources from drier and hotter climates outperformed those from wetter and cooler climates (15 out of 270 comparisons). This was most evident for E. todtiana in the 2022 planting ( Fig. 3 ) . However, cooler or wetter provenances performed better in 19 out of 270 comparisons, especially for B. attenuata in the wettest location (see Banksia , Location 4 in Figs. 3-4, Extended data Fig. 1). Complex, non-linear relationships were also observed, especially for B. attenuata (Extended data Fig. 1). We conducted several analyses to examine a local provenancing effect, where plants from local or climatically similar provenances perform better in survival or growth. Evidence for a local provenance effect was rare. The local provenance performed better than the others in only 12 of 225 comparisons for E. todtiana and 17 of 225 comparisons for B. attenuata , while performing worse in 4 of 450 comparisons for E. todtiana and 2 of 450 comparisons for B. attenuata (Extended Data Fig. 2). A relationship between climate at provenance (long-term mean annual precipitation, MAP) and plant performance, together with a local effect (i.e. Fig. 2G), was observed in 4 of 135 comparisons (e.g. Fig. 3 Banksia attenuata survival at Location 1 in 2022 planting undisturbed soil). These combined effects were only observed at either the wettest or driest trial site locations (Supplementary Fig. 1-2). Ultimately, we found that no type of relationship between provenance and survival or growth occurred more frequently than could be expected by chance. General patterns Survival and growth for both species were highly variable and strongly influenced by a location x substrate interaction, and less so by planting year (Extended Data Figs. 3-4). Survival ranged from 0% to 90.7% across sites and was generally higher in undisturbed substrate (mean survival across locations and years: 68.6% for E. todtiana and 18.0% for B. attenuata ) than in disturbed substrate (38.4% and 11.8%, respectively). Plants were also larger in undisturbed substrate, with mean stem diameters of 16.0 ± 0.15 mm for E. todtiana and 6.9 ± 0.09 mm for B. attenuata , compared to 9.7 ± 0.16 mm and 4.2 ± 0.16 mm in disturbed substrate. Discussion Climate adaptation is now central to decision-making in applied ecology. For more than 15 years, an intense debate has focused on how to incorporate climate adaptation into seed provenancing, the decision of where to collect seeds from for restoration plantings. For seed provenancing guidelines to be valuable, they must be practical and supported by rigorous empirical testing that demonstrates consistent and predictable benefits. Climate-adjusted provenancing has been promoted as a pre-emptive strategy to enhance the resilience of restoration plantings to climate change 14 , yet direct tests in restoration contexts remain scarce. Here, we conducted a large provenance trial across multiple realistic restoration sites spanning a steep climate gradient in a region that has experienced decades of extensive drying and warming. We found no evidence that either climate-adjusted or local provenancing (the common practice) consistently improves plant survival or growth. We suggest that the strong effect of location, substrate and year, but absence of a climate-related provenance effect across multiple stressful environmental scenarios, including novel substrates, reflects adaptive phenotypic plasticity that is similar across provenances, and thus inferred to be an evolved characteristic of these species. Adaptive phenotypic plasticity allows plants to maintain fitness by adjusting their traits in response to changing environmental conditions 30–35 . It is hypothesised that adaptive plasticity will be more likely in species that have wide geographical ranges that experience consistently variable environments 31,33 , as well as in older populations that persisted in glacial refugia 36 . This is the case for Banksia and Eucalyptus that have persisted in southwestern Australia for millions of years, a tectonically stable region long free of major volcanic and glacial disturbances but marked by climatic oscillations, increasing aridity, and frequent fire. These environmental changes, particularly following the emergence of a Mediterranean-type climate around 20 million years ago 37 , would have exerted a strong selection pressure on genotypes best able to cope with extreme environmental variability, driving adaptive plasticity. In support, adaptive plasticity has been inferred as a key mechanism for tolerating environmental variability in some other eucalypts and banksias 26,38,39 . From other provenance trials, support for provenancing strategies is highly variable 27,40 . For example, Notivol et al., (2020) and Ishizuka & Goto (2012) found that for Pinus sylvestris and Abies sachalinensis, respectively, local seed-sourcing strategies generally outperformed both future climate-adjusted and admixture strategies. In contrast, Tíscar et al. (2018), using a similar reciprocal transplant design with a P. nigra , found no evidence of local adaptation to climate. Some meta-analyses suggest local adaptation is widespread. Baughman et al. (2019) reported higher survival for local genotypes in 67% of 24 experiments and greater reproductive output in 90% of 10 experiments across diverse species and life forms. In a review of 103 studies of provenance trials of mostly commercially important forest tree species from northern temperate and boreal biomes, Leites & Benito Garzón (2023) found evidence for genetic adaptation to local climate in 79% of species studied. In contrast, Leimu & Fischer (2008) found evidence of local adaptation in only 43% of 35 reciprocal transplant studies, with its occurrence being unrelated to life history traits, species longevity, mating system, or clonality. Thus, the evidence for local adaptation in plants to climate is variable, which we suggest reflects the evolutionary and landscape history of species and biomes and a neglected consideration of plasticity. Furthermore, because most provenance research has focused on northern hemisphere forestry trees, conclusions may have limited relevance for other biomes, growth forms, levels of substrate disturbance, and for the restoration of diverse plant communities under future climatic conditions. Notwithstanding other threats such as habitat clearing, disease, and increased fire, our results bring some hope in the face of sobering predictions regarding the risk of species decline with climate change 46,47 . Historically, these models have overlooked adaptive plasticity, thus may exaggerate estimates of species decline and loss (e.g. for B. attenuata 48 ). From a conservation perspective, implicit consideration of adaptive plasticity offers a more hopeful outlook 49 , particularly in implementing nature-based solutions to address global environmental changes. That is not to say that these plants are not suffering from climate stress, and they may well go locally extinct; the evidence of impacts from heatwaves and changing fire regimes is clear 50 . Rather, the positive conclusion is that the inherent plastic capacity of some plants for tolerance to climate extremes is a species-wide trait reflecting its broader evolutionary history, rather than local adaptation to climate. From a restoration perspective, our findings support a growing recognition that species with a long evolutionary history under consistently variable conditions may retain genetic variation that enhances their adaptive potential 36 , which, if the case could greatly benefit future restoration outcomes. Furthermore, our results support earlier calls to move beyond provenance, suggesting that on highly disturbed substrates, restoration success may depend more on factors such as soil quality 51 . Further understanding of the role and limits of adaptive phenotypic plasticity 44,52 , its role in altered substrate and climate change responses 53,54 , and how this might vary among biomes, is crucial for conservation and ecosystem restoration. Harnessing phenotypic plasticity that may be hidden in native environments 55 has the potential to enhance restoration outcomes in disturbed environments, but remains vastly understudied. Methods Species information Banksia attenuata (Proteaceae) and Eucalyptus todtiana (Myrtaceae) are dominant tree species of Banksia woodlands on the Swan Coastal Plain, part of the Southwest Australian Floristic Region, a global biodiversity hotspot 56 . Both species are long-lived, resprout after fire, and are priorities for ecological restoration across their distributions. See Supplementary Material Note 1 for more information on the species Experimental design Fruit containing mature seed was sourced from five arbitrarily chosen maternal plants of broadly equivalent size, age, fecundity, and health from each of 20 provenances for the species B. attenuata and E. todtiana . Provenances of E. todtiana span nearly the entire species distribution, whereas those of Banksia attenuata cover the northern extent of its broader distribution (Extended Data Fig. 5). Processed seed (Supplementary Note 2) was either sown directly ( B. attenuata ) or germinated ex situ and planted as seedlings ( E. todtiana ) at the trial sites. At each location, for each species, trial sites were established on two substrates: two replicate plots on reconstructed post-sand-mining (disturbed) soil and two replicate plots on unmined (undisturbed) soil. See Supplementary Note 3 for more details on substrate and site preparation. Each plot was divided into 5 contiguous blocks, with 100 plants in each block: 5 plants from one maternal plant from each of the 20 provenances, with a different maternal plant used in each of the 5 blocks. Each block was 10 m x 42 m and included 20 rows that each contained 5 replicate seeds or seedlings spaced 2m apart, with rows spaced 2m apart; thus, plants were in a 2m x 2m grid. The order of maternal source within a block was randomised within each plot, and the order of the 20 provenances was also randomised within each block. Edge effects were avoided by planting a random mix of provenances for the appropriate species along the edge of each plot. This setup was repeated across two planting years (2021 and 2022), resulting in a total of 64 plots (2 species × 4 locations × 2 planting years × 2 substrate conditions × 2 replicate plots), amounting to 32,000 plants in total (500 plants per plot × 64 plots). At Eneabba in 2022, no viable disturbed (post-mining) areas were available for establishing trial sites, so all four plots were set up in undisturbed (unmined) areas. This resulted in 30 unique trial combinations (2 species × 4 locations × 2 planting years × 2 substrate conditions, minus one planting year–substrate combination that could not be established at location 1 for either species (32-2=30). Provenance climate and performance We used plant survival and growth (height and stem diameter) after two years to explore relationships between provenance performance and the climatic characteristics of the seed provenance (mean annual precipitation (MAP), mean annual temperature (MAT), or their combination summarised as PC1) across the 30 unique trial combinations. Survival was measured as the cumulative survival of plants after approximately two years: 93 and 99 weeks for B. attenuata (2021 and 2022 plantings, respectively) and 90 and 94 weeks for E. todtiana (2021 and 2022 plantings, respectively). Survival was recorded as a binomial response (number survived vs. not survived) based on five plant replicates per row (treatment combination: planting year, location, substrate type, plot, and maternal block). Growth was measured as height (cm; tape measure) and stem diameter (mm; digital callipers) at 107–122 weeks. For multi-stemmed E. todtiana , the diameter of the largest stem was recorded. Weather data on long-term mean annual precipitation (1960-2024) were obtained from WorldClim 57 (https://www.worldclim.org/data/monthlywth.html, spatial resolution of 2.5 minutes ~21 km 2 ) for provenance and trial sites. We also conducted a principal component analysis (PCA) of MAT and MAP using data from WorldClim 2.1 57 (1970–2000, 30-second ~1 km² resolution). PC1, which explained 92.9% of the variation, was used in subsequent analyses (Supplementary Fig. 3). Mean annual precipitation (MAP) and temperature (MAT) represent the mean over these respective periods. Data analysis Effects of provenance climate on plant performance To test relationships between growth, survival, and climatic characteristics (MAP, MAT, and PC1), we used Generalised Additive Models (GAMMs) across 30 unique trial combinations (2 species × 4 locations × 2 planting years × 2 substrate conditions, minus one planting year–substrate combination not established at location 1). All analyses were conducted in R version 4.3.2 58 . For survival, we fitted binomial GAMMs using the gamm4 function from the gamm4 package 59 , with the number of surviving versus non-surviving plants as the response. For growth, we fitted Gaussian GAMMs using the gamm4 function, with the diameter or height as the response. Because interactions among trial location, substrate condition, and planting year were significant, separate models were fitted for each unique treatment combination. Climatic predictors (MAP, MAT, or PC1) were included as continuous fixed effects, with MAP scaled. Random intercepts for plot and for maternal block nested within provenance accounted for the hierarchical structure. For E. todtiana , sow batch (glasshouse growth time before planting) was also included as a random effect (Supplementary Note 2 on seed preparation). Significance was assessed at α = 0.05. To assess support for a climate-adjusted provenancing approach, we examined whether provenances from warmer or drier regions outperformed the local provenance in the GAMM models. In cases where the GAMM was significant, if the average predicted values from the GAMM for growth or survival exceeded those of the local population, we interpreted this as evidence supporting climate-adjusted provenancing. It’s important to note that at a significance threshold of α = 0.05, approximately 5% of tests (1 in 20) will appear significant by chance alone. Thus, to infer meaningful patterns, the number of significant relationships observed must exceed those expected by chance. With few provenances with MAP drier than that at the driest site (Location 1) and few wetter than that at the wettest site (Location 4) (Fig. 1), our ability to separate climate-related provenance effects from local effects was limited at these locations. Nonetheless, we considered it important to examine whether generally drier provenances performed better than wetter ones, given the long-term rainfall decline across all locations. Local provenance effects We tested for local effects in several ways, capturing scenarios where (i) the local and a wide range of nearby provenances showed higher survival or growth (Fig. 2D), (ii) only a narrow range of nearby provenances performed better (Fig. 2E), or (iii) only the immediate local provenance outperformed others (Fig. 2F). Nearby provenances were defined as those closely matching the climatic characteristics of the local population. For some local analyses, we only considered MAP as the climatic characteristic, as it correlated reasonably well with mean annual temperature (MAT), PC1, and geographic distance among provenances (PCA). To test for wide local effects (Fig. 2D), each treatment combination was analysed with GAMMs using three climatic predictors (MAP, MAT, PC1) against three performance variables (survival, height, stem diameter). A wide local effect was considered present when the GAMM was significant ( p < 0.05), and the predicted performance of the local provenance was greater than the average of wetter and drier provenances. We also considered the opposite effects, where the predicted performance of the local provenance was worse. Models were fitted separately for each treatment combination, with plot and maternal line nested within provenance specified as random effects. The results of this analysis are summarised in Extended data Fig. 2 (green and purple lines). It captures broad quadratic-style relationships where performance peaks at the local population (green lines) or troughs there (purple lines); however, trough-shaped relationships were never observed. To test for narrow local effects (Fig. 2E), we considered the relationship between model residuals (from linear mixed-effects models and GAMMs) and the absolute difference in MAP between each provenance and the local population. A consistent pattern of residuals being more negative with greater climatic distance was considered as indicative of a positive local effect (Supplementary Fig. 4). We created nine different functions to represent the narrow local effect. Four functions were slopes showing gradual to sharp decreases in performance with increasing differences in MAP from the local population. Another four functions were step functions representing sharp drops in performance at MAP differences of 10% 20% 30% 40% of the MAP range from that of the local population. The final function was a linear relationship, representing a consistent decline in performance as MAP increasingly differs from that of the local population. The function that best fitted the data was identified using the Akaike Information Criterion (AIC). A local effect was considered significant if the p-value of the function with the best fit was below α = 0.05. See Supplementary Note 4 for the code for these functions, together with an example dataset for illustrative purposes. Lastly, to test for an immediate local provenance effect (Fig. 2F), we fitted GAMMs and linear mixed-effects models that included an extra fixed effect for immediate local provenance (coded as TRUE only for the closest provenance and FALSE otherwise). These models were again fitted separately for each treatment combination, with provenance MAP as a fixed effect and plot and maternal line nested within provenance specified as random effects. An immediate local effect was considered significant when the p-value for the local term was < 0.05. Because multiple analytical approaches were applied, we occasionally, though rarely, observed contrasting outcomes, with one analysis identifying the local provenance as best and another as worse. Such cases arose when nearby provenances performed well but the immediate local provenance itself performed poorly. Declarations Acknowledgements This work was supported by funding from the Australian Research Council [LP190100051; LP240100073] and the Australian Government under the National Environmental Science Program’s Resilient Landscapes Hub. Establishing and monitoring the field trials was only possible with the generous contributions of many volunteers. Author contributions M.R., M.F.B., E.V. and S.L.K. conceived and designed the research. N.M. and S.L.K. collected the data. E.T. and M.R. performed the analysis. E.T., M.F.B. and S.L.K. wrote the manuscript with substantial contributions from all other authors. References Seddon N, Chausson A, Berry P, Girardin CA, Smith A, Turner B. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philos Trans R Soc B. 2020;375(1794):20190120. Verdone M, Seidl A. Time, space, place, and the Bonn Challenge global forest restoration target. Restor Ecol. 2017;25(6):903–11. Levinthal R, Weller R. 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Ruthrof KX, Breshears DD, Fontaine JB, Froend RH, Matusick G, Kala J, et al. Subcontinental heat wave triggers terrestrial and marine, multi-taxa responses. Sci Rep. 2018;8(1):13094. Robinson JM, Breed MF, Maher NL, Gibson D, Ducki LC, Standish RJ, et al. Putting provenance into perspective: the relative importance of restoration site conditions over seed sourcing. Restor Ecol. 2023;31(8):e13989. Sampedro L, Alía R. A claim for a ‘next generation’of multisite range-wide forest genetic trials built on the legacy of ecological genetics to anticipate responses to climate. Glob Change Biol. 2023;29(17):4700–2. Matesanz S, Ramírez-Valiente JA. A review and meta-analysis of intraspecific differences in phenotypic plasticity: Implications to forecast plant responses to climate change. Glob Ecol Biogeogr. 2019;28(11):1682–94. Stollewerk A, Kratina P, Sentis A, Chaparro-Pedraza C, Decaestecker E, De Meester L, et al. Plasticity in climate change responses. Biol Rev. 2025; Walter GM, Clark J, Terranova D, Cozzolino S, Cristaudo A, Hiscock SJ, et al. Hidden genetic variation in plasticity provides the potential for rapid adaptation to novel environments. New Phytol. 2023;239(1):374–87. Hopper SD, Gioia P. The southwest Australian floristic region: evolution and conservation of a global hot spot of biodiversity. Annu Rev Ecol Evol Syst. 2004;35(1):623–50. Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017;37(12):4302–15. R Core Team (2023). R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2023. Available from: https://www.R-project.org/ Wood S, Scheipl F, Wood MS. Package ‘gamm4’. Am Stat. 2017;45(339):0–2. Additional Declarations There is NO Competing Interest. Supplementary Files 3.NEESUPPLEMENTARYMATERIAL2.pdf Supplementary information ExtendedDataFig1.pdf Extended Data Figure 1 ExtendedDataFig2.pdf Extended Data Figure 2 ExtendedDataFig3.pdf Extended Data Figure 3 ExtendedDataFig4.pdf Extended Data Figure 4 ExtendedDataFig5.pdf Extended Data Figure 5 ExtendeddatafiguresLegends.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7611542","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":517530925,"identity":"07a966b8-f929-4dd4-a4f2-c6f274d43099","order_by":0,"name":"Elizabeth Trevenen","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0269-3168","institution":"University of Western Australia","correspondingAuthor":true,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Trevenen","suffix":""},{"id":517530926,"identity":"2843f3d3-1adc-4217-ac97-35894f681b62","order_by":1,"name":"Michael Renton","email":"","orcid":"","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Renton","suffix":""},{"id":517530927,"identity":"740c8e99-9ad4-4ab6-a376-19b47c865195","order_by":2,"name":"Martin Breed","email":"","orcid":"https://orcid.org/0000-0001-7810-9696","institution":"Flinders University","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Breed","suffix":""},{"id":517530928,"identity":"7d2404e2-7677-4f8a-b127-ac35d2f16f88","order_by":3,"name":"Nicole Maher","email":"","orcid":"","institution":"Department of Biodiversity, Conservation and Attractions","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Maher","suffix":""},{"id":517530929,"identity":"ffbc1f78-a0a1-4d76-a11c-3d2db9c543d5","order_by":4,"name":"Suzanne Prober","email":"","orcid":"","institution":"Commonwealth Scientific and Industrial Research Organisation","correspondingAuthor":false,"prefix":"","firstName":"Suzanne","middleName":"","lastName":"Prober","suffix":""},{"id":517530930,"identity":"661213d8-2195-4e58-8fd0-adff1d92201b","order_by":5,"name":"Jake Robinson","email":"","orcid":"","institution":"Flinders University","correspondingAuthor":false,"prefix":"","firstName":"Jake","middleName":"","lastName":"Robinson","suffix":""},{"id":517530931,"identity":"52ff2d4c-20bf-4741-8fd3-affe7d29bc1f","order_by":6,"name":"Rachel Standish","email":"","orcid":"https://orcid.org/0000-0001-8118-1904","institution":"Murdoch University, Perth, Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Standish","suffix":""},{"id":517530932,"identity":"b77bef62-69f2-45aa-9168-53c3829bb55c","order_by":7,"name":"Erik Veneklaas","email":"","orcid":"https://orcid.org/0000-0002-7030-4056","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"Veneklaas","suffix":""},{"id":517530933,"identity":"06ed877c-d9b8-47ea-a86c-87f18f62dda2","order_by":8,"name":"Siegfried Krauss","email":"","orcid":"","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Siegfried","middleName":"","lastName":"Krauss","suffix":""}],"badges":[],"createdAt":"2025-09-14 09:05:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7611542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7611542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91805482,"identity":"baf887c8-7465-4d69-b9ac-dd7f94e88a0d","added_by":"auto","created_at":"2025-09-22 02:20:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":686338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy location, seed provenances and trial site locations in southwest Australia. \u003c/strong\u003eA) Map showing the locations of seed provenances and trial site locations (coloured circles), with mean annual precipitation (MAP) showing the climate gradient. Colours indicate mean annual precipitation from 1960–2014 for all provenances. For trial sites, the left half of the circle shows historical MAP (1960–2014), and the right half shows more recent MAP (2014–2024). Provenance trial sites are Location 1 - Eneabba, Location 2 - Cooljarloo, Location 3a - Gingin, Location 3b - Wilbinga, and Location 4 - Gnangara. At each site, we established paired plots in undisturbed and disturbed substrates in both 2021 and 2022, except at Gingin (paired with Wilbinga for undisturbed) and Eneabba (only undisturbed in 2022). Disturbed substrates reflect typical restoration conditions after mineral sand mining, while undisturbed substrates were created by clearing native bushland or former pine plantations. B) Decadal and recent trends in mean annual precipitation (MAP) over time for each provenance, highlighting a general decline in rainfall, particularly for the wetter provenances. Vertical dashed lines indicate the years when the trial experiments were established. Note that climate stress during 2023 was severe, with all sites receiving ~30% less rainfall than the long-term mean. C) Adult plants of the study species\u003cem\u003e B. attenuata \u003c/em\u003e(flowering tree in the back left) and \u003cem\u003eE. todtiana\u003c/em\u003e (tree in the front right) in their natural habitat (photos credits SK). D) A trial site planted with \u003cem\u003eE. todtiana\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/a2af5ccf30e4852af9b035ca.png"},{"id":91805803,"identity":"c1208655-ea0b-4e94-9b10-7bb4e7e940cc","added_by":"auto","created_at":"2025-09-22 02:28:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":437227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothetical provenance responses to climate at trial sites. \u003c/strong\u003eEach point represents the hypothetical survival of a provenance, coloured by its long-term mean annual precipitation (MAP). A grey dashed line marks the MAP for the trial site. Nine hypothetical relationships between survival and provenance MAP are illustrated: A) Provenances from drier climates have higher survival, thus evidence for a benefit of climate-adjusted provenancing; B) Provenances from wetter climates have higher survival; C) A climate-matched pattern, where provenances with a long-term MAP similar to that currently experienced at the trial site have the highest survival (reflecting the trend of observed regional drying). D–F show scenarios where local or near-local provenances outperform others, indicating a local-is-best effect: D) Highest survival by local provenances and relatively high survival across a wide range of near-local provenances, thus evidence for local provenancing; E) A similar pattern to D, but over a narrower range; F) a scenario where only the immediate local provenance performs best. G) A scenario where provenances from drier climates have higher survival, and there is also a local effect. H–I show scenarios where there is no clear relationship between provenance MAP and survival: H) provenances perform equally well with low variability; I) some provenances outperform some others, but not in a way that corresponds systematically to their MAP. In these hypothetical examples, survival is used as the performance measure, although relationships with growth (i.e., plant height and stem diameter) were also explored in our study.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/dd92a25e852a6387d16b1077.png"},{"id":91805486,"identity":"8677c301-ffbf-4dcb-9b79-3ca00fd90495","added_by":"auto","created_at":"2025-09-22 02:20:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":535585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between plant survival and growth with mean annual precipitation of the 20 provenances.\u003c/strong\u003e Coloured points with standard error bars show the mean percentage survival (top row) or stem diameter (bottom row) observed for each provenance, with blue indicating source populations from wetter regions and yellow indicating those from drier regions. Solid black horizontal lines show significant non-linear relationships (GAMM \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Grey dashed vertical lines show the local mean annual precipitation at each trial location across the climate gradient (also indicated in top axis labels). Coloured dashed vertical lines indicate a significant local effect across analyses: green when the local provenance performed better than other provenances, purple when it performed worse.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/4fab2cf7f8bba0e28c07a710.png"},{"id":91805979,"identity":"a804f2cd-3cf1-45b9-aa9a-9582e250c771","added_by":"auto","created_at":"2025-09-22 02:36:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":202678,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of provenance effects. \u003c/strong\u003eDoughnut charts show the number and proportion of GAMM test results at each location for each species: yellow = drier provenances performed better, blue = wetter provenances performed better, green = local provenance performed best, dark grey = no effects, light grey = could not be assessed because high mortality limited interpretation (insufficient data). Each pie summarises 36 tests (2 years × 2 substrates × 3 climate variables × 3 performance traits) per site, except Location 1 (n = 27).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/2ed71827746f3b2d9fe732b1.png"},{"id":95228700,"identity":"3d7a98b0-17bd-405a-9fc9-e0aec9eaee7a","added_by":"auto","created_at":"2025-11-05 16:34:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2606139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/a3dc3264-3138-4744-aa65-0582ef78fce5.pdf"},{"id":91805492,"identity":"04464ba8-d60c-474d-85cd-142a711dbf78","added_by":"auto","created_at":"2025-09-22 02:20:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5221870,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"3.NEESUPPLEMENTARYMATERIAL2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/5009f62511b8285a3a5ce694.pdf"},{"id":91805487,"identity":"11d12bd4-518e-43cc-9655-e1259cb93176","added_by":"auto","created_at":"2025-09-22 02:20:36","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":910029,"visible":true,"origin":"","legend":"Extended Data Figure 1","description":"","filename":"ExtendedDataFig1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/b5bd9593b91e73baaa3df16b.pdf"},{"id":91805484,"identity":"8ac196d7-f433-4c98-9770-cd3acae10965","added_by":"auto","created_at":"2025-09-22 02:20:36","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":157525,"visible":true,"origin":"","legend":"Extended Data Figure 2","description":"","filename":"ExtendedDataFig2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/8c044ba45bf36dfacdaae899.pdf"},{"id":91805805,"identity":"9154c1f9-f443-4f90-a113-b76adffbd52a","added_by":"auto","created_at":"2025-09-22 02:28:36","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":618412,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Figure 3\u003c/p\u003e","description":"","filename":"ExtendedDataFig3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/f0e21de77b1818ac267c53cb.pdf"},{"id":91805491,"identity":"363e3c1e-d2e2-4aad-b68f-d23eac31b357","added_by":"auto","created_at":"2025-09-22 02:20:36","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":409596,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Figure 4\u003c/p\u003e","description":"","filename":"ExtendedDataFig4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/345343224aa2c4501e1213ed.pdf"},{"id":91805490,"identity":"5af76696-8dc3-41a6-8993-98ea6e700584","added_by":"auto","created_at":"2025-09-22 02:20:36","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":823983,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Figure 5\u003c/p\u003e","description":"","filename":"ExtendedDataFig5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/a37332c5d694d6564740fb4c.pdf"},{"id":91805489,"identity":"8730eef5-be32-4e91-b2f6-2d9e83af60d3","added_by":"auto","created_at":"2025-09-22 02:20:36","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16756,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendeddatafiguresLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-7611542/v1/476b0cf439348a0c087ea3a5.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Rangewide adaptive plasticity in trees provides resilience to climate change","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEcosystem restoration is a vital nature-based solution to the major global challenges of the Anthropocene: climate change, biodiversity loss and promoting human well-being\u003csup\u003e1\u003c/sup\u003e. Several global initiatives are driving large-scale ecosystem restoration efforts\u003csup\u003e2\u003c/sup\u003e, with hundreds of \u0026lsquo;mega-eco\u0026rsquo; projects already underway\u003csup\u003e3\u003c/sup\u003e. However, climate change is making ecosystem restoration increasingly challenging\u003csup\u003e4\u0026ndash;7\u003c/sup\u003e. Therefore, restored ecosystems must be resistant to gradual environmental changes like increasing temperatures, but also resilient to enable recovery from abrupt disturbances, such as drought. Hereafter, we use \u0026lsquo;resilience\u0026rsquo; to refer to these ecosystem properties\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe restoration of diverse plant communities reflecting a pre-disturbance/reference state relies on collecting seed from wild populations. There is an urgent need to use native seed efficiently for restoration, as future demands for seed are likely unsustainable\u003csup\u003e9,10\u003c/sup\u003e. The geographic location from which seeds are sourced for restoration (provenance) can affect plant fitness and restoration success, yet the best strategy for sourcing seed to optimise these outcomes is widely debated\u003csup\u003e11\u0026ndash;13\u003c/sup\u003e. The reliance on local provenancing is being challenged by climate change, prompting consideration of alternatives such as climate-adjusted provenancing\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eClimate-adjusted provenancing involves sourcing seed from a range of populations that may include adaptations to the climatic conditions predicted for the future of a restoration site. Proposed as a \u0026lsquo;future-proofing\u0026rsquo; strategy, it aims to enhance the resilience of restoration plantings to climate change\u003csup\u003e14\u0026ndash;17\u003c/sup\u003e. This approach is a form of facilitated adaptation\u003csup\u003e18\u003c/sup\u003e or assisted migration\u003csup\u003e19,20\u003c/sup\u003e; it addresses the possibility that local populations are maladapted to a rapidly changing climate\u003csup\u003e21\u003c/sup\u003e. This strategy has gained widespread popularity, with numerous predictive methods developed to identify \u0026lsquo;optimal\u0026rsquo; climate-adjusted provenances for restoration plantings\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e. For a climate-adjusted provenancing strategy to be beneficial, seed from climate-adjusted provenances must reliably outperform those from other provenances. Yet, robust empirical tests of such provenance effects are lacking, particularly for diverse native plant communities restored in disturbed landscapes\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e.\u0026nbsp;Consequently, it remains unknown whether climate-adjusted provenancing provides consistent and predictable benefits over alternative strategies, such as the widely used local provenancing.\u003c/p\u003e\n\u003cp\u003eTo address this knowledge gap, we established 30 large-scale provenance trials with multiple species, locations, years, and soil substrate conditions. We used these trials to compare the growth and survival of plants grown from seeds sourced from 20 provenances spanning a 400 km climate gradient where long-term mean annual rainfall ranges from 450 mm to 850 mm, in a region that has experienced decades of drying and warming and more frequent hot droughts\u003csup\u003e28\u003c/sup\u003e (Fig. 1). We monitored the plants for two years under harsh environmental conditions to explore associations between the climate at provenances and survival and growth (Fig. 2). High fitness at these early life stages is critical to successful restoration, as it is often highly stressful to young plants, particularly in dryland ecosystems that experience long summer droughts\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe tested the hypothesis that seeds from hotter, drier provenances would have higher survival and growth than those from local or cooler, wetter provenances, thereby supporting climate-adjusted provenancing.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eProvenance effects\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRelationships between plant survival, growth, and the climatic characteristics of 20 provenances were assessed across 30 unique trial combinations (2 species \u0026times; 4 trial site locations \u0026times; 2 planting years \u0026times; 2 substrate conditions, minus one planting year \u0026times; substrate combination that could not be established at one location for either species). Each trial combination contained 50 plants grown from each provenance, comprising 5 replicate plants from 5 maternal lines, planted across two replicate plots.\u003c/p\u003e\n\u003cp\u003eAcross all trial combinations, a relationship between a plant performance measure (i.e., survival, height or stem diameter after two years) and at least one climatic characteristic of the provenance (e.g., mean annual precipitation, mean annual temperature, or their combination, PC1) was observed in 40 out of 270 comparisons (GAMM p\u0026lt;0.05, Figs. 3-4, 30 combinations \u0026times; 3 measures \u0026times; 3 characteristics = 270 comparisons).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverwhelmingly, responses were inconsistent and unpredictable. Occasionally, there was evidence of a benefit of climate-adjusted provenancing, where seed sources from drier and hotter climates outperformed those from wetter and cooler climates (15 out of 270 comparisons). This was most evident for \u003cem\u003eE. todtiana\u0026nbsp;\u003c/em\u003e\u003cem\u003ein the 2022 planting (\u003c/em\u003eFig. 3\u003cem\u003e)\u003c/em\u003e. However, cooler or wetter provenances performed better in 19 out of 270 comparisons, especially for \u003cem\u003eB. attenuata\u003c/em\u003e in the wettest location (see \u003cem\u003eBanksia\u003c/em\u003e, Location 4 in Figs. 3-4, Extended data Fig. 1). Complex, non-linear relationships were also observed, especially for \u003cem\u003eB. attenuata\u003c/em\u003e (Extended data Fig. 1).\u003c/p\u003e\n\u003cp\u003eWe conducted several analyses to examine a local provenancing effect, where plants from local or climatically similar provenances perform better in survival or growth. Evidence for a local provenance effect was rare. The local provenance performed better than the others in only 12 of 225 comparisons for \u003cem\u003eE. todtiana\u003c/em\u003e and 17 of 225 comparisons for \u003cem\u003eB. attenuata\u003c/em\u003e, while performing worse in 4 of 450 comparisons for \u003cem\u003eE. todtiana\u003c/em\u003e and 2 of 450 comparisons for \u003cem\u003eB. attenuata\u003c/em\u003e (Extended Data Fig. 2).\u003c/p\u003e\n\u003cp\u003eA relationship between climate at provenance (long-term mean annual precipitation, MAP) and plant performance, together with a local effect (i.e. Fig. 2G), was observed in 4 of 135 comparisons (e.g. Fig. 3 \u003cem\u003eBanksia attenuata\u003c/em\u003e survival at Location 1 in 2022 planting undisturbed soil). These combined effects were only observed at either the wettest or driest trial site locations (Supplementary Fig. 1-2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUltimately, we found that no type of relationship between provenance and survival or growth occurred more frequently than could be expected by chance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGeneral patterns\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSurvival and growth for both species were highly variable and strongly influenced by a location x substrate interaction, and less so by planting year (Extended Data Figs. 3-4). Survival ranged from 0% to 90.7% across sites and was generally higher in undisturbed substrate (mean survival across locations and years: 68.6% for \u003cem\u003eE. todtiana\u003c/em\u003e and 18.0% for \u003cem\u003eB. attenuata\u003c/em\u003e) than in disturbed substrate (38.4% and 11.8%, respectively). Plants were also larger in undisturbed substrate, with mean stem diameters of 16.0 \u0026plusmn; 0.15 mm for \u003cem\u003eE. todtiana\u003c/em\u003e and 6.9 \u0026plusmn; 0.09 mm for \u003cem\u003eB. attenuata\u003c/em\u003e, compared to 9.7 \u0026plusmn; 0.16 mm and 4.2 \u0026plusmn; 0.16 mm in disturbed substrate.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eClimate adaptation is now central to decision-making in applied ecology. For more than 15 years, an intense debate has focused on how to incorporate climate adaptation into seed provenancing, the decision of where to collect seeds from for restoration plantings. For seed provenancing guidelines to be valuable, they must be practical and supported by rigorous empirical testing that demonstrates consistent and predictable benefits. Climate-adjusted provenancing has been promoted as a pre-emptive strategy to enhance the resilience of restoration plantings to climate change\u003csup\u003e14\u003c/sup\u003e, yet direct tests in restoration contexts remain scarce. Here, we conducted a large provenance trial across multiple realistic restoration sites spanning a steep climate gradient in a region that has experienced decades of extensive drying and warming. We found no evidence that either climate-adjusted or local provenancing (the common practice) consistently improves plant survival or growth.\u003c/p\u003e\n\u003cp\u003eWe suggest that the strong effect of location, substrate and year, but absence of a climate-related provenance effect across multiple stressful environmental scenarios, including novel substrates, reflects adaptive phenotypic plasticity that is similar across provenances, and thus inferred to be an evolved characteristic of these species. Adaptive phenotypic plasticity allows plants to maintain fitness by adjusting their traits in response to changing environmental conditions\u003csup\u003e30\u0026ndash;35\u003c/sup\u003e. \u0026nbsp;It is hypothesised that adaptive plasticity will be more likely in species that have wide geographical ranges that experience consistently variable environments\u003csup\u003e31,33\u003c/sup\u003e, as well as in older populations that persisted in glacial refugia\u003csup\u003e36\u003c/sup\u003e. This is the case for \u003cem\u003eBanksia\u003c/em\u003e and \u003cem\u003eEucalyptus\u003c/em\u003e that have persisted in southwestern Australia for millions of years, a tectonically stable region long free of major volcanic and glacial disturbances but marked by climatic oscillations, increasing aridity, and frequent fire. These environmental changes, particularly following the emergence of a Mediterranean-type climate around 20 million years ago\u003csup\u003e37\u003c/sup\u003e, would have exerted a strong selection pressure on genotypes best able to cope with extreme environmental variability, driving adaptive plasticity. In support, adaptive plasticity has been inferred as a key mechanism for tolerating environmental variability in some other eucalypts and banksias\u003csup\u003e26,38,39\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFrom other provenance trials, support for provenancing strategies is highly variable\u003csup\u003e27,40\u003c/sup\u003e. For example, Notivol et al., (2020) and Ishizuka \u0026amp; Goto (2012) found that for \u003cem\u003ePinus sylvestris\u003c/em\u003e and \u003cem\u003eAbies sachalinensis,\u003c/em\u003e respectively, local seed-sourcing strategies generally outperformed both future climate-adjusted and admixture strategies. In contrast, T\u0026iacute;scar et al. (2018), using a similar reciprocal transplant design with a \u003cem\u003eP. nigra\u003c/em\u003e, found no evidence of local adaptation to climate. Some meta-analyses suggest local adaptation is widespread. Baughman et al. (2019) reported higher survival for local genotypes in 67% of 24 experiments and greater reproductive output in 90% of 10 experiments across diverse species and life forms. In a review of 103 studies of provenance trials of mostly commercially important forest tree species from northern temperate and boreal biomes, Leites \u0026amp; Benito Garz\u0026oacute;n (2023) found evidence for genetic adaptation to local climate in 79% of species studied. In contrast, Leimu \u0026amp; Fischer (2008) found evidence of local adaptation in only 43% of 35 reciprocal transplant studies, with its occurrence being unrelated to life history traits, species longevity, mating system, or clonality. Thus, the evidence for local adaptation in plants to climate is variable, which we suggest reflects the evolutionary and landscape history of species and biomes and a neglected consideration of plasticity. Furthermore, because most provenance research has focused on northern hemisphere forestry trees, conclusions may have limited relevance for other biomes, growth forms, levels of substrate disturbance, and for the restoration of diverse plant communities under future climatic conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotwithstanding other threats such as habitat clearing, disease, and increased fire, our results bring some hope in the face of sobering predictions regarding the risk of species decline with climate change\u003csup\u003e46,47\u003c/sup\u003e. Historically, these models have overlooked adaptive plasticity, thus may exaggerate estimates of species decline and loss (e.g. for \u003cem\u003eB. attenuata\u003c/em\u003e \u003csup\u003e48\u003c/sup\u003e). From a conservation perspective, implicit consideration of adaptive plasticity offers a more hopeful outlook\u003csup\u003e49\u003c/sup\u003e, particularly in implementing nature-based solutions to address global environmental changes. That is not to say that these plants are not suffering from climate stress, and they may well go locally extinct; the evidence of impacts from heatwaves and changing fire regimes is clear\u003csup\u003e50\u003c/sup\u003e. Rather, the positive conclusion is that the inherent plastic capacity of some plants for tolerance to climate extremes is a species-wide trait reflecting its broader evolutionary history, rather than local adaptation to climate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom a restoration perspective, our findings support a growing recognition that species with a long evolutionary history under consistently variable conditions may retain genetic variation that enhances their adaptive potential\u003csup\u003e36\u003c/sup\u003e, which, if the case could greatly benefit future restoration outcomes. Furthermore, our results support earlier calls to move beyond provenance, suggesting that on highly disturbed substrates, restoration success may depend more on factors such as soil quality\u003csup\u003e51\u003c/sup\u003e. Further understanding of the role and limits of adaptive phenotypic plasticity\u003csup\u003e44,52\u003c/sup\u003e, its role in altered substrate and climate change responses\u003csup\u003e53,54\u003c/sup\u003e, and how this might vary among biomes, is crucial for conservation and ecosystem restoration. Harnessing phenotypic plasticity that may be hidden in native environments\u003csup\u003e55\u003c/sup\u003e has the potential to enhance restoration outcomes in disturbed environments, but remains vastly understudied.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSpecies information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBanksia attenuata\u003c/em\u003e (Proteaceae) and \u003cem\u003eEucalyptus todtiana\u003c/em\u003e (Myrtaceae) are dominant tree species of Banksia woodlands on the Swan Coastal Plain, part of the Southwest Australian Floristic Region, a global biodiversity hotspot\u003csup\u003e56\u003c/sup\u003e. Both species are long-lived, resprout after fire, and are priorities for ecological restoration across their distributions. See Supplementary Material Note 1 for more information on the species\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFruit containing mature seed was sourced from five arbitrarily chosen maternal plants of broadly equivalent size, age, fecundity, and health from each of 20 provenances for the species \u003cem\u003eB. attenuata\u003c/em\u003e and \u003cem\u003eE. todtiana\u003c/em\u003e. Provenances of \u003cem\u003eE. todtiana\u003c/em\u003e span nearly the entire species distribution, whereas those of \u003cem\u003eBanksia attenuata\u003c/em\u003e cover the northern extent of its broader distribution (Extended Data Fig. 5). Processed seed (Supplementary Note 2) was either sown directly (\u003cem\u003eB. attenuata\u003c/em\u003e) or germinated ex situ and planted as seedlings (\u003cem\u003eE. todtiana\u003c/em\u003e) at the trial sites. At each location, for each species, trial sites were established on two substrates: two replicate plots on reconstructed post-sand-mining (disturbed) soil and two replicate plots on unmined (undisturbed) soil. See Supplementary Note 3 for more details on substrate and site preparation.\u003c/p\u003e\n\u003cp\u003eEach plot was divided into 5 contiguous blocks, with 100 plants in each block: 5 plants from one maternal plant from each of the 20 provenances, with a different maternal plant used in each of the 5 blocks. Each block was 10 m x 42 m and included 20 rows that each contained 5 replicate seeds or seedlings spaced 2m apart, with rows spaced 2m apart; thus, plants were in a 2m x 2m grid. The order of maternal source within a block was randomised within each plot, and the order of the 20 provenances was also randomised within each block. Edge effects were avoided by planting a random mix of provenances for the appropriate species along the edge of each plot. This setup was repeated across two planting years (2021 and 2022), resulting in a total of 64 plots (2 species \u0026times; 4 locations \u0026times; 2 planting years \u0026times; 2 substrate conditions \u0026times; 2 replicate plots), amounting to 32,000 plants in total (500 plants per plot \u0026times; 64 plots). At Eneabba in 2022, no viable disturbed (post-mining) areas were available for establishing trial sites, so all four plots were set up in undisturbed (unmined) areas. This resulted in 30 unique trial combinations (2 species \u0026times; 4 locations \u0026times; 2 planting years \u0026times; 2 substrate conditions, minus one planting year\u0026ndash;substrate combination that could not be established at location 1 for either species (32-2=30).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProvenance climate and performance\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used plant survival and growth (height and stem diameter) after two years to explore relationships between provenance performance and the climatic characteristics of the seed provenance (mean annual precipitation (MAP), mean annual temperature (MAT), or their combination summarised as PC1) across the 30 unique trial combinations. Survival was measured as the cumulative survival of plants after approximately two years: 93 and 99 weeks for \u003cem\u003eB. attenuata\u0026nbsp;\u003c/em\u003e(2021 and 2022 plantings, respectively) and 90 and 94 weeks for \u003cem\u003eE. todtiana\u0026nbsp;\u003c/em\u003e(2021 and 2022 plantings, respectively). Survival was recorded as a binomial response (number survived vs. not survived) based on five plant replicates per row (treatment combination: planting year, location, substrate type, plot, and maternal block). \u0026nbsp;Growth was measured as height (cm; tape measure) and stem diameter (mm; digital callipers) at 107\u0026ndash;122 weeks. For multi-stemmed \u003cem\u003eE. todtiana\u003c/em\u003e, the diameter of the largest stem was recorded.\u003c/p\u003e\n\u003cp\u003eWeather data on long-term mean annual precipitation (1960-2024) were obtained from WorldClim\u003csup\u003e57\u003c/sup\u003e (https://www.worldclim.org/data/monthlywth.html, spatial resolution of 2.5 minutes ~21 km\u003csup\u003e2\u003c/sup\u003e) for provenance and trial sites. We also conducted a principal component analysis (PCA) of MAT and MAP using data from WorldClim 2.1\u003csup\u003e57\u003c/sup\u003e (1970\u0026ndash;2000, 30-second ~1 km\u0026sup2; resolution). PC1, which explained 92.9% of the variation, was used in subsequent analyses (Supplementary Fig. 3). \u0026nbsp;Mean annual precipitation (MAP) and temperature (MAT) represent the mean over these respective periods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of provenance climate on plant performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test relationships between growth, survival, and climatic characteristics (MAP, MAT, and PC1), we used Generalised Additive Models (GAMMs) across 30 unique trial combinations (2 species \u0026times; 4 locations \u0026times; 2 planting years \u0026times; 2 substrate conditions, minus one planting year\u0026ndash;substrate combination not established at location 1). All analyses were conducted in R version 4.3.2\u003csup\u003e58\u003c/sup\u003e. For survival, we fitted binomial GAMMs using the gamm4 function from the gamm4 package\u003csup\u003e59\u003c/sup\u003e, with the number of surviving versus non-surviving plants as the response. For growth, we fitted Gaussian GAMMs using the gamm4 function, with the diameter or height as the response. Because interactions among trial location, substrate condition, and planting year were significant, separate models were fitted for each unique treatment combination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClimatic predictors (MAP, MAT, or PC1) were included as continuous fixed effects, with MAP scaled. Random intercepts for plot and for maternal block nested within provenance accounted for the hierarchical structure. For \u003cem\u003eE. todtiana\u003c/em\u003e, sow batch (glasshouse growth time before planting) was also included as a random effect (Supplementary Note 2 on seed preparation).\u003c/p\u003e\n\u003cp\u003eSignificance was assessed at \u0026alpha; = 0.05. To assess support for a climate-adjusted provenancing approach, we examined whether provenances from warmer or drier regions outperformed the local provenance in the GAMM models. In cases where the GAMM was significant, if the average predicted values from the GAMM for growth or survival exceeded those of the local population, we interpreted this as evidence supporting climate-adjusted provenancing. It\u0026rsquo;s important to note that at a significance threshold of \u0026alpha; = 0.05, approximately 5% of tests (1 in 20) will appear significant by chance alone. Thus, to infer meaningful patterns, the number of significant relationships observed must exceed those expected by chance. With few provenances with MAP drier than that at the driest site (Location 1) and few wetter than that at the wettest site (Location 4) (Fig. 1), our ability to separate climate-related provenance effects from local effects was limited at these locations. Nonetheless, we considered it important to examine whether generally drier provenances performed better than wetter ones, given the long-term rainfall decline across all locations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLocal provenance effects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe tested for local effects in several ways, capturing scenarios where (i) the local and a wide range of nearby provenances showed higher survival or growth (Fig. 2D), (ii) only a narrow range of nearby provenances performed better (Fig. 2E), or (iii) only the immediate local provenance outperformed others (Fig. 2F). Nearby provenances were defined as those closely matching the climatic characteristics of the local population. For some local analyses, we only considered MAP as the climatic characteristic, as it correlated reasonably well with mean annual temperature (MAT), PC1, and geographic distance among provenances (PCA).\u003c/p\u003e\n\u003cp\u003eTo test for wide local effects (Fig. 2D), each treatment combination was analysed with GAMMs using three climatic predictors (MAP, MAT, PC1) against three performance variables (survival, height, stem diameter). A wide local effect was considered present when the GAMM was significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and the predicted performance of the local provenance was greater than the average of wetter and drier provenances. We also considered the opposite effects, where the predicted performance of the local provenance was worse. \u0026nbsp; Models were fitted separately for each treatment combination, with plot and maternal line nested within provenance specified as random effects. The results of this analysis are summarised in Extended data Fig. 2 (green and purple lines). It captures broad quadratic-style relationships where performance peaks at the local population (green lines) or troughs there (purple lines); however, trough-shaped relationships were never observed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo test for narrow local effects (Fig. 2E), we considered the relationship between model residuals (from linear mixed-effects models and GAMMs) and the absolute difference in MAP between each provenance and the local population. A consistent pattern of residuals being more negative with greater climatic distance was considered as indicative of a positive local effect (Supplementary Fig. 4). We created nine different functions to represent the narrow local effect. Four functions were slopes showing gradual to sharp decreases in performance with increasing differences in MAP from the local population. Another four functions were step functions representing sharp drops in performance at MAP differences of 10% 20% 30% 40% of the MAP range from that of the local population. The final function was a linear relationship, representing a consistent decline in performance as MAP increasingly differs from that of the local population. The function that best fitted the data was identified using the Akaike Information Criterion (AIC). A local effect was considered significant if the p-value of the function with the best fit was below \u0026alpha; = 0.05. See Supplementary Note 4 for the code for these functions, together with an example dataset for illustrative purposes.\u003c/p\u003e\n\u003cp\u003eLastly, to test for an immediate local provenance effect (Fig. 2F), we fitted GAMMs and linear mixed-effects models that included an extra fixed effect for immediate local provenance (coded as TRUE only for the closest provenance and FALSE otherwise). These models were again fitted separately for each treatment combination, with provenance MAP as a fixed effect and plot and maternal line nested within provenance specified as random effects. An immediate local effect was considered significant when the p-value for the local term was \u0026lt; 0.05. Because multiple analytical approaches were applied, we occasionally, though rarely, observed contrasting outcomes, with one analysis identifying the local provenance as best and another as worse. Such cases arose when nearby provenances performed well but the immediate local provenance itself performed poorly.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from the Australian Research Council [LP190100051; LP240100073] and the Australian Government under the National Environmental Science Program\u0026rsquo;s Resilient Landscapes Hub. Establishing and monitoring the field trials was only possible with the generous contributions of many volunteers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.R., M.F.B., E.V. and S.L.K. conceived and designed the research. N.M. and S.L.K. collected the data. E.T. and M.R. performed the analysis. E.T., M.F.B. and S.L.K. wrote the manuscript with substantial contributions from all other authors.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSeddon N, Chausson A, Berry P, Girardin CA, Smith A, Turner B. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philos Trans R Soc B. 2020;375(1794):20190120. \u003c/li\u003e\n\u003cli\u003eVerdone M, Seidl A. Time, space, place, and the Bonn Challenge global forest restoration target. Restor Ecol. 2017;25(6):903\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eLevinthal R, Weller R. Mega-eco projects: a global assessment of large-scale ecological restoration initiatives. Socio-Ecol Pract Res. 2023;5(3):341\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eBustamante MM, Silva JS, Scariot A, Sampaio AB, Mascia DL, Garcia E, et al. Ecological restoration as a strategy for mitigating and adapting to climate change: lessons and challenges from Brazil. Mitig Adapt Strateg Glob Change. 2019;24:1249\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eHarris JA, Hobbs RJ, Higgs E, Aronson J. Ecological restoration and global climate change. Restor Ecol. 2006;14(2):170\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eSimonson WD, Miller E, Jones A, Garc\u0026iacute;a-Rangel S, Thornton H, McOwen C. Enhancing climate change resilience of ecological restoration\u0026mdash;A framework for action. Perspect Ecol Conserv. 2021;19(3):300\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eSvejcar LN, Davies KW, Ritchie AL. Ecological restoration in the age of apocalypse. Glob Change Biol. 2023;29(17):4706\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eTrevenen E, Standish R, Price C, Hobbs R. Restoration and resilience. In: Routledge Handbook of Ecological and Environmental Restoration. Routledge; 2017. p. 509\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eBroadhurst L, Driver M, Guja L, North T, Vanzella B, Fifield G, et al. Seeding the future\u0026ndash;the issues of supply and demand in restoration in Australia. Ecol Manag Restor. 2015;16(1):29\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eJalonen R, Valette M, Boshier D, Duminil J, Thomas E. Forest and landscape restoration severely constrained by a lack of attention to the quantity and quality of tree seed: Insights from a global survey. 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Evolutionary Applications, 7 (1), 1\u0026ndash;14. 2014; \u003c/li\u003e\n\u003cli\u003eNicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U, et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 2010;15(12):684\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eRichter S, Kipfer T, Wohlgemuth T, Calder\u0026oacute;n Guerrero C, Ghazoul J, Moser B. Phenotypic plasticity facilitates resistance to climate change in a highly variable environment. Oecologia. 2012;169:269\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eSnell-Rood EC, Ehlman SM. Ecology and evolution of plasticity. Phenotypic Plast Evol. 2021;139\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eShay JE, Pennington LK, Mandussi Montiel-Molina JA, Toews DJ, Hendrickson BT, Sexton JP. Rules of plant species ranges: Applications for conservation strategies. Front Ecol Evol. 2021;9:700962. \u003c/li\u003e\n\u003cli\u003eLamont BB, He T. 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Modeling intraspecific adaptation of Abies sachalinensis to local altitude and responses to global warming, based on a 36-year reciprocal transplant experiment. Evol Appl. 2012;5(3):229\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eT\u0026iacute;scar PA, Lucas-Borja ME, Candel-P\u0026eacute;rez D. Lack of local adaptation to the establishment conditions limits assisted migration to adapt drought-prone Pinus nigra populations to climate change. For Ecol Manag. 2018;409:719\u0026ndash;28. \u003c/li\u003e\n\u003cli\u003eBaughman OW, Agneray AC, Forister ML, Kilkenny FF, Espeland EK, Fiegener R, et al. Strong patterns of intraspecific variation and local adaptation in Great Basin plants revealed through a review of 75 years of experiments. Ecol Evol. 2019;9(11):6259\u0026ndash;75. \u003c/li\u003e\n\u003cli\u003eLeites L, Benito Garz\u0026oacute;n M. Forest tree species adaptation to climate across biomes: Building on the legacy of ecological genetics to anticipate responses to climate change. Glob Change Biol. 2023;29(17):4711\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eLeimu R, Fischer M. A meta-analysis of local adaptation in plants. PloS One. 2008;3(12):e4010. \u003c/li\u003e\n\u003cli\u003eFitzpatrick MC, Gove AD, Sanders NJ, Dunn RR. Climate change, plant migration, and range collapse in a global biodiversity hotspot: the Banksia (Proteaceae) of Western Australia. Glob Change Biol. 2008;14(6):1337\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eHamer JJ, Veneklaas EJ, Poot P, Mokany K, Renton M. Shallow environmental gradients put inland species at risk: Insights and implications from predicting future distributions of E ucalyptus species in S outh W estern A ustralia. Austral Ecol. 2015;40(8):923\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eHe T, D\u0026rsquo;Agui H, Lim SL, Enright NJ, Luo Y. 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The southwest Australian floristic region: evolution and conservation of a global hot spot of biodiversity. Annu Rev Ecol Evol Syst. 2004;35(1):623\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eFick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017;37(12):4302\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eR Core Team (2023). R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2023. Available from: https://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eWood S, Scheipl F, Wood MS. Package \u0026lsquo;gamm4\u0026rsquo;. Am Stat. 2017;45(339):0\u0026ndash;2. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7611542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7611542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Ecosystem restoration is a critical strategy for addressing the global challenges of biodiversity loss, ecosystem degradation and climate change. Where seeds should be sourced for restoration plantings remains intensely debated. Climate-adjusted provenancing – sourcing seed from populations already experiencing the climatic conditions expected at future restoration sites – has been proposed as a proactive strategy to enhance the resilience of restoration plantings under climate change. However, the benefits of climate-adjusted provenancing over alternative strategies, such as local provenancing, remain largely untested. To address this, we established 30 large provenance trials with multiple species, locations, years, and substrates, with seeds sourced from 20 provenances across a 400 km climate gradient where mean annual rainfall doubles. Despite harsh environmental conditions, we found no clear relationship between climate at the provenance and seedling survival or growth. Occasional provenance effects were observed but were inconsistent across trials. We show that neither climate-adjusted nor local provenancing provided a predictable benefit or disadvantage. We hypothesise that the similar tolerance to a wide range of environmental stressors reflects range-wide adaptive plasticity. Such plasticity likely evolved in these ancient lineages on old landscapes in response to a long history of climatic oscillations, increasing aridity, and frequent fire.","manuscriptTitle":"Rangewide adaptive plasticity in trees provides resilience to climate change","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 02:20:31","doi":"10.21203/rs.3.rs-7611542/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c069b4a3-e669-4562-8ec0-3da1b79a671c","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55000278,"name":"Biological sciences/Ecology/Restoration ecology"},{"id":55000279,"name":"Biological sciences/Ecology/Restoration ecology"},{"id":55000280,"name":"Biological sciences/Ecology/Climate-change ecology"},{"id":55000281,"name":"Biological sciences/Ecology/Climate-change ecology"}],"tags":[],"updatedAt":"2025-11-05T14:00:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 02:20:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7611542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7611542","identity":"rs-7611542","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}