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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. For. Glob. Change</journal-id>
<journal-title>Frontiers in Forests and Global Change</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. For. Glob. Change</abbrev-journal-title>
<issn pub-type="epub">2624-893X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/ffgc.2022.1075787</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Forests and Global Change</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Phenotypic plasticity enables considerable acclimation to heat and drought in a cold-adapted boreal forest tree species</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Ravn</surname> <given-names>Jacob</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/2060974/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>D&#x2019;Orangeville</surname> <given-names>Lo&#x00EF;c</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1445971/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Lavigne</surname> <given-names>Michael B.</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/2092094/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Taylor</surname> <given-names>Anthony R.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/2124851/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Faculty of Forestry and Environmental Management, University of New Brunswick</institution>, <addr-line>Fredericton, NB</addr-line>, <country>Canada</country></aff>
<aff id="aff2"><sup>2</sup><institution>Natural Resources Canada, Canadian Forest Service - Atlantic Forestry Centre</institution>, <addr-line>Fredericton, NB</addr-line>, <country>Canada</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Guobao Xu, Northwest Institute of Eco-Environment and Resources (CAS), China</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Alexander G. Ivanov, Bulgarian Academy of Sciences, Bulgaria; Yassine Messaoud, Universit&#x00E9; du Qu&#x00E9;bec en Abitibi&#x2013;- T&#x00E9;miscamingue, Canada</p></fn>
<corresp id="c001">&#x002A;Correspondence: Jacob Ravn, <email>jravn@unb.ca</email></corresp>
<fn fn-type="other" id="fn004"><p>This article was submitted to Temperate and Boreal Forests, a section of the journal Frontiers in Forests and Global Change</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>5</volume>
<elocation-id>1075787</elocation-id>
<history>
<date date-type="received">
<day>20</day>
<month>10</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>28</day>
<month>11</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2022 Ravn, D&#x2019;Orangeville, Lavigne and Taylor.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Ravn, D&#x2019;Orangeville, Lavigne and Taylor</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>Increasing frequencies of severe heat waves and drought are expected to influence the composition and functioning of ecosystems globally. Our ability to predict and mitigate these impacts depends on our understanding of species- and age-specific responses to these stressors. To assess the adaptive capacity of balsam fir to climate change, a cold-adapted boreal tree species, we conducted a climate-controlled greenhouse experiment with four provenances originating from across the species biogeographic range, 12 temperature treatments ensuring a minimum of +11&#x00B0;C warming, and five drought treatment intensities. We found considerable acclimation to temperature and drought treatments across all provenances, with steady gains in biomass under temperatures well-beyond the &#x201C;worst-case&#x201D; (RCP 8.5) climate forcing scenario within the species natural range. Acclimation was supported by high phenotypic plasticity in root:shoot ratio (RSR) and photosynthesis, which were greatly increased with warming, but were not affected by drought. Our results suggest that regardless of the observed provenance variation, drought and heat are not limiting factors of the current-year balsam fir seedling growth, instead, these factors may be more impactful on later stages of regeneration or previously stressed individuals, thus highlighting the necessity of incorporating the factors of ontogeny and provenance origin in future research regarding plant and climate interactions.</p>
</abstract>
<kwd-group>
<kwd><italic>Abies balsamea</italic> (L.) Mill.</kwd>
<kwd>balsam fir</kwd>
<kwd>climate change</kwd>
<kwd>drought</kwd>
<kwd>provenance</kwd>
<kwd>phenotypic plasticity</kwd>
<kwd>intraspecific variation</kwd>
<kwd>acclimation</kwd>
</kwd-group>
<contract-sponsor id="cn001">Natural Sciences and Engineering Research Council of Canada<named-content content-type="fundref-id">10.13039/501100000038</named-content></contract-sponsor>
<contract-sponsor id="cn002">Canadian Forest Service<named-content content-type="fundref-id">10.13039/501100012394</named-content></contract-sponsor>
<contract-sponsor id="cn003">New Brunswick Innovation Foundation<named-content content-type="fundref-id">10.13039/501100000240</named-content></contract-sponsor>
<counts>
<fig-count count="3"/>
<table-count count="2"/>
<equation-count count="4"/>
<ref-count count="93"/>
<page-count count="13"/>
<word-count count="9467"/>
</counts>
</article-meta>
</front>
<body>
<sec id="S1" sec-type="intro">
<title>1 Introduction</title>
<p>Climate change is increasing the intensity and variability of temperature and precipitation regimes throughout Canada (<xref ref-type="bibr" rid="B92">Zhang et al., 2019</xref>). As a result, forest model projections predict significant decreases in the abundance of cold-adapted boreal species such as balsam fir [<italic>Abies balsamea</italic> (L.) Mill] in the majority of their southern range from increasingly unfavorable growing conditions due to rising heat and drought (<xref ref-type="bibr" rid="B9">Boulanger et al., 2017</xref>; <xref ref-type="bibr" rid="B80">Taylor et al., 2017</xref>), while in the north, these species are expected to have generally large growth increases (<xref ref-type="bibr" rid="B17">D&#x2019;Orangeville et al., 2018a</xref>; <xref ref-type="bibr" rid="B89">Wang et al., in press</xref>). The large potential ecological and economic impacts of such changes within Canada&#x2019;s forests (<xref ref-type="bibr" rid="B70">Rodenhouse et al., 2008</xref>; <xref ref-type="bibr" rid="B65">Ochuodho et al., 2012</xref>; <xref ref-type="bibr" rid="B74">Rustad et al., 2012</xref>) require a better understanding of the adaptative capacity of the region&#x2019;s most abundant tree species. Current observational studies of species responses to climate change have been asynchronous with some model predictions (<xref ref-type="bibr" rid="B93">Zhu et al., 2012</xref>; <xref ref-type="bibr" rid="B24">Fei et al., 2017</xref>), an issue that highlights the current lack of empirical data regarding age-specific and both inter- and intra-species responses to climate change across environmental gradients (<xref ref-type="bibr" rid="B39">Hijmans and Graham, 2006</xref>; <xref ref-type="bibr" rid="B28">Garz&#x00F3;n et al., 2011</xref>; <xref ref-type="bibr" rid="B5">Aubin et al., 2016</xref>; <xref ref-type="bibr" rid="B83">Urban et al., 2016</xref>).</p>
<p>Low radial growth of balsam fir is correlated with low humidity (<xref ref-type="bibr" rid="B23">Duchesne and Houle, 2011</xref>), extreme low or high temperatures, and low soil moisture (<xref ref-type="bibr" rid="B31">Goldblum and Rigg, 2005</xref>; <xref ref-type="bibr" rid="B17">D&#x2019;Orangeville et al., 2018a</xref>). However, seedlings differ from mature trees in their response to climate (<xref ref-type="bibr" rid="B25">Fisichelli et al., 2012</xref>, <xref ref-type="bibr" rid="B26">2014</xref>; <xref ref-type="bibr" rid="B33">Gray and Brady, 2016</xref>) due to their small size and position in the understory (<xref ref-type="bibr" rid="B58">McDowell et al., 2008</xref>; <xref ref-type="bibr" rid="B71">Rollinson et al., 2021</xref>), and are at a higher risk for hydraulic failure and mortality from extreme climate events due to limited root volume and photosynthetic area (<xref ref-type="bibr" rid="B58">McDowell et al., 2008</xref>). Young balsam fir are particularly reliant on consistent soil moisture levels, likely partially due to the species&#x2019; proclivity for shallow rooting (<xref ref-type="bibr" rid="B11">Burns and Honkala, 1990</xref>; <xref ref-type="bibr" rid="B81">Taylor et al., 2020</xref>). When exposed to short-term moisture depletions, balsam fir seedlings show tolerance and plasticity in xylem growth and apical growth (<xref ref-type="bibr" rid="B73">Rossi et al., 2009</xref>), while long-term droughts decrease metabolic functions that limit growth in the current and following years (<xref ref-type="bibr" rid="B16">D&#x2019;Orangeville et al., 2013</xref>; <xref ref-type="bibr" rid="B87">Vaughn et al., 2021</xref>).</p>
<p>While mean summer precipitation rates are projected to increase throughout the majority of balsam fir&#x2019;s range (<xref ref-type="bibr" rid="B92">Zhang et al., 2019</xref>), higher frequencies of droughts and heatwaves will increase the prevalence of periods of low soil moisture, resulting in moisture stress and growth declines (<xref ref-type="bibr" rid="B31">Goldblum and Rigg, 2005</xref>; <xref ref-type="bibr" rid="B90">Way and Sage, 2008</xref>; <xref ref-type="bibr" rid="B73">Rossi et al., 2009</xref>; <xref ref-type="bibr" rid="B57">McDowell et al., 2011</xref>). Increases in atmospheric vapor pressure deficit (VPD) with warming and drying (<xref ref-type="bibr" rid="B91">Yuan et al., 2019</xref>) are expected to modify tree stomatal conductance and evapotranspiration rates (<xref ref-type="bibr" rid="B64">Novick et al., 2016</xref>; <xref ref-type="bibr" rid="B34">Grossiord et al., 2020</xref>). Limited soil moisture and a high transpirative demand due to warming can provoke stomatal closure in trees as a defense mechanism to mitigate water loss, therefore preventing cavitation by maintaining hydraulic pressure (<xref ref-type="bibr" rid="B21">Domec et al., 2009</xref>; <xref ref-type="bibr" rid="B14">Choat et al., 2018</xref>). In doing so, trees limit their rates of photosynthesis (<xref ref-type="bibr" rid="B34">Grossiord et al., 2020</xref>), a factor also directly inhibited by high temperature (<xref ref-type="bibr" rid="B61">McMurtrie and Wang, 1993</xref>; <xref ref-type="bibr" rid="B50">Lin et al., 2012</xref>). In turn, this reduces their capacity to produce valuable carbohydrate stores that may be used to mitigate cell death, cavitation, and hydraulic failure (<xref ref-type="bibr" rid="B35">Hacke et al., 2001</xref>; <xref ref-type="bibr" rid="B57">McDowell et al., 2011</xref>; <xref ref-type="bibr" rid="B44">Klein et al., 2018</xref>; <xref ref-type="bibr" rid="B76">Sapes et al., 2019</xref>). Long-term, persistent droughts eventually break the hydraulic tension in trees regardless of drought-resistance mechanisms, causing cavitation, which reduces the ability to transport water and solutes, leading to mortality (<xref ref-type="bibr" rid="B2">Allen et al., 2010</xref>; <xref ref-type="bibr" rid="B14">Choat et al., 2018</xref>).</p>
<p>All trees have a capacity to adapt to climate change, but the likelihood of a species to persist by both resisting and recovering from these climatic stressors is largely determined by its level and type of phenotypic plasticity and genetic diversity (<xref ref-type="bibr" rid="B5">Aubin et al., 2016</xref>). For instance, with limited moisture, some species lengthen their roots to access deeper moisture reserves, thereby increasing their root:shoot ratio (RSR), (<xref ref-type="bibr" rid="B41">Janiak et al., 2016</xref>). Trees can also adapt by increasing their photosynthesis to transpiration ratio, or water use efficiency (WUE), to reduce risk of carbon starvation and water loss (<xref ref-type="bibr" rid="B66">Osakabe et al., 2014</xref>), and refill embolized xylem to improve water conductance post-drought (<xref ref-type="bibr" rid="B44">Klein et al., 2018</xref>). Genetic adaptations resulting from local forcing events create adaptative differences between populations of species (<xref ref-type="bibr" rid="B30">Ghalambor et al., 2007</xref>), ensuring higher chances of survival under stress in certain populations (<xref ref-type="bibr" rid="B62">Moran et al., 2017</xref>). Research into the genetic variance amongst balsam fir populations, or &#x201C;provenances,&#x201D; has demonstrated high phenotypic variability within mature and sapling stage trees (<xref ref-type="bibr" rid="B27">Fryer and Ledig, 1972</xref>; <xref ref-type="bibr" rid="B54">Lowe et al., 1977</xref>; <xref ref-type="bibr" rid="B12">Carter, 1996</xref>; <xref ref-type="bibr" rid="B1">Akalusi and Bourque, 2021</xref>). In mature balsam fir, WUE was significantly different amongst provenances, with positive correlations between WUE and diameter at breast height (cm) (<xref ref-type="bibr" rid="B1">Akalusi and Bourque, 2021</xref>). A balsam fir provenance trial along an altitudinal gradient demonstrated sizeable variance of phenotypic plasticity among provenances through the adjustment of photosynthetic temperature optimums to better suit their origin climate, with a 2.4&#x00B0;C decrease in optimum temperature per 305 m increase in altitude (<xref ref-type="bibr" rid="B27">Fryer and Ledig, 1972</xref>). Incorporating these provenance-specific adaptations to climate into stand distribution models has been shown to greatly reduce the projected declines of climate change on a species&#x2019; abundance (<xref ref-type="bibr" rid="B28">Garz&#x00F3;n et al., 2011</xref>). Therefore, empirically evaluating the effects of balsam fir seedling genetic variance and phenotypic plasticity on performance and survival under a range of environmental conditions is warranted to better understand the role of climate change in the boreal and temperate forests of North America.</p>
<p>We conducted a controlled seedling provenance trial within 12 climate-controlled greenhouse phytotrons with the objective of (i) determining the response of balsam fir growth and leaf-level gas exchanges to wide gradients of heat and drought and (ii) assessing the variation of these responses between provenances. To achieve these objectives, we evaluated the rate of physiological change of each provenance when exposed to five levels of drought intensity, and a 12-level temperature gradient, ranging from an average temperature of 13.9&#x2013;30.9&#x00B0;C. To determine how seedling growth varied among treatments, we measured seedling biomass, RSRs, and CO<sub>2</sub> assimilation rates. We hypothesized that (i) rates of balsam fir seedling growth and photosynthesis will increase in response to moderate temperature increases but significant declines are expected under extreme warming conditions (i.e., a nonlinear response); (ii) balsam fir provenances from southern locations will experience less significant growth declines at higher temperatures when compared to northern provenances; and (iii) drought will limit the positive effects of moderate temperature increases, exacerbate heat-related growth declines, and increase seedling mortality.</p>
</sec>
<sec id="S2" sec-type="material|methods">
<title>2 Material and methods</title>
<sec id="S2.SS1">
<title>2.1 Experimental design</title>
<p>In the spring of 2021, we established a controlled greenhouse experiment located at the Atlantic Forestry Centre (AFC) Greenhouse operated by Natural Resources Canada&#x2014;Canadian Forest Service (CFS) (Fredericton, NB, Canada). Our experiment utilized the split-split plot design, with four balsam fir provenances, nested within five levels of drought intensity, nested within 12 temperature treatments (<xref ref-type="bibr" rid="B3">Altman and Krzywinski, 2015</xref>). Each of these 240 treatments were applied to fifteen 2-year-old seedlings, for a total of 3,600 seedlings. Instead of replicating a low number of categorical treatment levels in an ANOVA-type experimental design, we applied a larger, evenly distributed number of temperature levels to support a regression type analysis, permitting us to model non-linear response patterns (<xref ref-type="bibr" rid="B77">Schweiger et al., 2016</xref>; <xref ref-type="bibr" rid="B46">Kreyling et al., 2018</xref>). The temperature treatment phase of the experiment ran from May 1st to September 16th, with all treatment groups irrigated to field capacity once soil volumetric water content dropped below 20%. The drought trial component commenced on August 1st and finished on September 16th.</p>
</sec>
<sec id="S2.SS2">
<title>2.2 Study apparatus</title>
<p>For this experiment, we built 12 climate-controlled phytotrons using pressure-treated lumber for the frames, and clear greenhouse plastic as a coating. The core design and construction process of the phytotrons is detailed in <xref ref-type="bibr" rid="B87">Vaughn et al. (2021)</xref>. The phytotrons were located inside the main AFC greenhouse and are arranged in two rows of six. The climate of each phytotron was regulated by a temperature controller (model ITC-310 T-B, Inkbird, Shenzhen, China) that cycles the attached heater and air conditioner to obtain the pre-set temperature level. The controllers were programmed to have 12-temperature levels that were timed to mimic diurnal temperature cycles. Soil moisture sensors (EC-5 Volumetric Water Content sensor, METER Group, Pullman, WA, USA) were installed within each unit to ensure a homogeneity in soil moisture levels within treatments. Two LED growing lights (HLG 100 V2, Horticulture Lighting Group, Knoxville, TN, USA) were installed within the interior of each phytotron to provide intra-chamber homogeneity of light quantity and distribution. The main AFC were painted with a darkening agent and the roof was covered with a 50% light reducing cloth to further reduce lighting variation among the phytotrons.</p>
</sec>
<sec id="S2.SS3">
<title>2.3 Seedling preparation</title>
<p>The seeds were cleaned, imbibed for two days, cold stratified at 5&#x00B0;C for 3 weeks, and were then direct sown into 45-cell trays that were filled with a 2:1 peat/vermiculite mixture. Due to the sub-optimal germination success rates advertised by the National Tree Seed Centre (NTSC), we filled each tray cell with five seeds with the intent of germinating a minimum of one seedling per cell. Therefore, we opted to subject the seedlings to the experimental treatments in their second year to ensure that each treatment group had 15, well-established, healthy seedlings rather than utilizing trays with empty or overcrowded cells and poorly situated seedlings. For the first year, seedlings grew under ambient temperatures for 50 days, fertilized with a water-based fertilizer at 100 parts per million (ppm) of 20:8:20 NPK (nitrogen, phosphorus, and potassium). To promote winter cold-hardiness, seedlings were then exposed to cooler temperatures and fertilized with a 35 ppm 8:20:30 mixture for 49 days. At the end of the first growing season, mean seedling height was 3.5 cm (SD = 0.7). Seedlings were then placed in cooling chambers until their chilling requirements of 1,000 h were met. Once dormancy requirements were met, seedlings were transplanted alongside their rooting medium to larger, 15-cell trays (approximately 440 cm<sup>3</sup> of cell rooting volume) to ensure adequate rooting area and to minimize competition for light. Seedlings were then immediately placed into the phytotrons, thus commencing the experimental portion of the project.</p>
</sec>
<sec id="S2.SS4">
<title>2.4 Experimental factors</title>
<sec id="S2.SS4.SSS1">
<title>2.4.1 Provenance</title>
<p>Considering that heterogeneous growing conditions between populations can increase genetic variance, we selected among available seedlots to maximize the temperature gradient available while controlling for variations in moisture regime and altitude in an effort to understand how the influence of climate heterogeneity affects the variance of phenotypic plasticity among provenances of balsam fir. For each provenance, seeds were collected by the NTSC<sup><xref ref-type="fn" rid="footnote1">1</xref></sup> during high-masting years to ensure optimal gene crossing and were collected from stands that represented regional environmental variation. To obtain the necessary provenance-specific climate data, the location of each provenance was intersected with North American interpolated climate grids provided by CFS (<xref ref-type="bibr" rid="B60">McKenney et al., 2011</xref>). The 30-years (1981&#x2013;2010) of extracted climate data provided maximum monthly temperature (TMAX), minimum monthly temperature (TMIN), and the monthly climate moisture index (CMI). With this data, mean summer TMAX and mean winter TMIN was calculated for the 126 balsam fir provenances available from the NTSC, and the provenances were then ordered from coldest climate to hottest climate. Due to the constraints imposed by the area of the seedling trays and phytotrons, we determined that utilizing four provenances in our experiment was an optimal trade-off between replicate count and representativeness of provenance variation. We first selected our hottest and coldest provenances, which had a summer TMAX difference of 5&#x00B0;C and a winter TMIN difference of 14&#x00B0;C. The two intermediate provenances were then selected based on their evenly spaced distance in climate rank from the hottest and coldest provenance, and their minimal variation of CMI and elevation (m) (<xref ref-type="fig" rid="F1">Figure 1</xref>). The temperature intervals between each of the four selected provenances were 4.8&#x00B0;C (<italic>winter TMIN</italic>) and 1.6&#x00B0;C (<italic>summer TMAX</italic>). Finally, we ensured that there was minimal variation in soil types between provenances; all provenances were collected from sites with well-drained, rocky, acidic soil.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Balsam fir provenance locations overlayed with the geographic extent of the species (<xref ref-type="bibr" rid="B51">Little, 1971</xref>), accompanied with their corresponding climate values.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-05-1075787-g001.tif"/>
</fig>
</sec>
<sec id="S2.SS4.SSS2">
<title>2.4.2 Temperature</title>
<p>To determine the extent of variation in provenance response to temperature increases, seedlings were exposed to twelve temperature treatment levels that covered a large temperature range. To emulate the natural variation of temperature throughout the growing season, we first averaged provenance-specific climate normals (<xref ref-type="bibr" rid="B32">Government of Canada, 2011</xref>) to create a baseline weekly temperature treatment schedule throughout the experiment growing season. We then adjusted the average temperature of this baseline schedule to cover a temperature range that was designed to (1) simulate the projected summer temperature increase under the &#x201C;worst-case&#x201D; Representative Concentration Pathway 8.5 forcing scenario (RCP; <xref ref-type="bibr" rid="B60">McKenney et al., 2011</xref>; <xref ref-type="bibr" rid="B85">van Vuuren et al., 2011</xref>) for each provenance, (2) simulate the mean summer temperature variation across the entire geographic range of balsam fir, and (3) expose the seedlings to warm enough temperatures that it may elicit a significant negative growth response in each provenance.</p>
<p>For eastern Canada, the projected mean annual temperature increases (compared to temperatures in 1986&#x2013;2005) under RCP 8.5 is approximately 5.9&#x00B0;C (<xref ref-type="bibr" rid="B92">Zhang et al., 2019</xref>). The mean summer temperature difference between the southern and northern boundaries of balsam fir is 8.5&#x00B0;C (<xref ref-type="bibr" rid="B60">McKenney et al., 2011</xref>). Therefore, the treatment levels were created to ensure that each provenance was exposed to +8.5&#x00B0;C above their current origin climate, with an additional +2.5&#x00B0;C to account for our third objective of exposing them to extreme conditions. Based on this, we determined that temperature intervals of 1.81&#x00B0;C between the 12 temperature levels would ensure each provenance experienced a temperature increase of 11&#x00B0;C above its origin baseline summer climate. Our coldest treatment level corresponded to a scenario representing 3&#x00B0;C below the historical average growing season temperature at the coldest provenance location, while the warmest treatment corresponded to an 11&#x00B0;C increase above the summer climate of the warmest provenance (<xref ref-type="table" rid="T1">Table 1</xref>). For each phytotron, the 12 daily temperature levels were set to change every 2 h, with the coldest level occurring at 2 a.m.&#x2013;4 a.m., and the hottest at 2 p.m.&#x2013;4 p.m.</p>
<table-wrap position="float" id="T1">
<label>TABLE 1</label>
<caption><p>Minimum and maximum monthly temperatures for four of the 12 temperature treatments.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left"></td>
<td valign="top" align="center" colspan="8">Treatment level<hr/></td>
</tr>
<tr>
<td valign="top" align="left"></td>
<td valign="top" align="center" colspan="2">Baseline<hr/></td>
<td valign="top" align="center" colspan="2">+5&#x00B0;C above baseline<hr/></td>
<td valign="top" align="center" colspan="2">+11.7&#x00B0;C above baseline<hr/></td>
<td valign="top" align="center" colspan="2">+18.3&#x00B0;C above baseline<hr/></td>
</tr>
<tr>
<td valign="top" align="left">Month</td>
<td valign="top" align="center">Minimum temperature</td>
<td valign="top" align="center">Maximum temperature</td>
<td valign="top" align="center">Minimum temperature</td>
<td valign="top" align="center">Maximum temperature</td>
<td valign="top" align="center">Minimum temperature</td>
<td valign="top" align="center">Maximum temperature</td>
<td valign="top" align="center">Minimum temperature</td>
<td valign="top" align="center">Maximum temperature</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">May</td>
<td valign="top" align="center">3.9</td>
<td valign="top" align="center">14.3</td>
<td valign="top" align="center">8.9</td>
<td valign="top" align="center">19.3</td>
<td valign="top" align="center">15.6</td>
<td valign="top" align="center">26</td>
<td valign="top" align="center">22.2</td>
<td valign="top" align="center">32.6</td>
</tr>
<tr>
<td valign="top" align="left">June</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">22.1</td>
<td valign="top" align="center">12</td>
<td valign="top" align="center">27.1</td>
<td valign="top" align="center">18.7</td>
<td valign="top" align="center">33.8</td>
<td valign="top" align="center">25.3</td>
<td valign="top" align="center">40.4</td>
</tr>
<tr>
<td valign="top" align="left">July</td>
<td valign="top" align="center">13.9</td>
<td valign="top" align="center">22.1</td>
<td valign="top" align="center">18.9</td>
<td valign="top" align="center">27.1</td>
<td valign="top" align="center">25.6</td>
<td valign="top" align="center">33.8</td>
<td valign="top" align="center">32.2</td>
<td valign="top" align="center">40.4</td>
</tr>
<tr>
<td valign="top" align="left">August</td>
<td valign="top" align="center">9.5</td>
<td valign="top" align="center">21.5</td>
<td valign="top" align="center">14.5</td>
<td valign="top" align="center">26.5</td>
<td valign="top" align="center">21.2</td>
<td valign="top" align="center">33.2</td>
<td valign="top" align="center">27.8</td>
<td valign="top" align="center">39.8</td>
</tr>
<tr>
<td valign="top" align="left">September</td>
<td valign="top" align="center">8.2</td>
<td valign="top" align="center">16</td>
<td valign="top" align="center">13.2</td>
<td valign="top" align="center">21</td>
<td valign="top" align="center">19.9</td>
<td valign="top" align="center">27.7</td>
<td valign="top" align="center">26.5</td>
<td valign="top" align="center">34.3</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="S2.SS4.SSS3">
<title>2.4.3 Drought</title>
<p>To evaluate the extent of variation in provenance response to different drought intensities, seedlings were exposed to five drought treatment levels. Drought treatments had evenly spaced intervals of soil water potential (SWP) (mPa) thresholds, with the driest group only watered once SWP reached a minimum of &#x2212;2.5 mPa, a level associated with low plant water content, and near complete photosynthetic and transpirative shutdown within a range of conifer seedlings (<xref ref-type="bibr" rid="B38">Havranek and Benecke, 1978</xref>). We determined the tray SWP levels by determining the relationship between soil volumetric water content (VWC) and SWP. This was necessary because SWP measurements show the effort required by the plant roots to extract water from the soil while VWC measurements only show the water content within the soil (<xref ref-type="bibr" rid="B68">Papendick and Campbell, 1981</xref>), therefore making SWP a better indicator of drought severity for plants (see <xref ref-type="supplementary-material" rid="DS1">Supplementary material</xref>).</p>
<p>For all provenances in each phytotron, each of the five trays were designated a different SWP threshold. The four hottest treatments reached their SWP thresholds in 20 days, while the four intermediate and four cold treatments reached their thresholds in 25 and 32 days, respectively. Once the group thresholds were met, seedlings were watered to field capacity and were watered regularly until the end of the growing season.</p>
</sec>
</sec>
<sec id="S2.SS5">
<title>2.5 Response variables</title>
<sec id="S2.SS5.SSS1">
<title>2.5.1 Photosynthetic response to temperature</title>
<p>The acclimation of photosynthesis to growing condition temperatures allows a plant to mitigate temperature-related growth inhibition, therefore enhancing plant resilience to climate variability (<xref ref-type="bibr" rid="B7">Berry and Bj&#x00F6;rkman, 1980</xref>). Therefore, determining the extent of variation in photosynthetic acclimation to growing conditions within balsam fir seedlings will provide better insight of how balsam fir is able to adapt to climate change.</p>
<p>For the northernmost and southernmost provenance, we subjected two seedlings each from the coldest, intermediate, and hottest temperature treatments to temperatures ranging from 16 to 36&#x00B0;C and measured their rates of photosynthesis at each temperature level. Since adjusting the internal LiCOR temperatures is a time-intensive endeavor, this temperature range was considered an optimal trade-off between the number of seedlings measured and the extent of temperature range. Measurements were conducted with the LiCOR-6800 portable photosynthesis system, fit with a 6400-22 conifer chamber (Li-Cor, Lincoln, Nebraska, NE, USA). To ensure that internal LiCOR light levels were not limiting the measurements, we conducted two light response tests for both provenances. Based on these results, light levels within the LiCOR were set at a non-limiting level of 1,000 &#x03BC;mol m<sup>&#x2013;2</sup> s<sup>&#x2013;1</sup>. Relative humidity levels were set to match the average noontime relative humidity level of the phytotrons. Measurements were logged once humidity, CO<sub>2</sub>, and temperature became stable within the chamber. Immediately after the measurement period, seedlings were harvested and frozen to prevent desiccation and damage, allowing for accurate leaf area (LA) measurements that accounted for the curvature of the needles.</p>
<p>Because the leaf area varies between seedlings, and area measurements cannot be conducted on a live seedling, a substitute area value was used while measurements were taken. Therefore, all seedlings that were measured with the LiCOR had their LAs measured to calculate leaf-gas exchange measurements that considered seedling-specific LA. We cut and scanned the cross-sections of three needles of each seedling, (SigmaScan Pro, Systat Software Inc., Chicago, IL, USA) and then measured the projected 2D needle surface area (<italic>A</italic>), cross-section circumference (<italic>c</italic>) and width (<italic>w</italic>) (WinSEEDLE, R&#x00E9;gent Instruments, Quebec City, QC, Canada). Needles were then oven-dried at 70&#x00B0;C for a minimum of 48 h, and then weighed to determine oven-dried weight (ODW) and the specific ODW of the three measured needles.</p>
<p>The LA for each seedling was calculated by first determining the specific leaf area, which is the ratio of leaf surface area to dry mass, and then multiplying by the total ODW of the seedling needles. This process is represented with the following equation:</p>
<disp-formula id="S2.Ex1">
<mml:math display="block" id="M1"><mml:mrow><mml:mrow><mml:mi>L</mml:mi><mml:mo>&#x2062;</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mrow><mml:mfrac><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mi>c</mml:mi><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mi>w</mml:mi></mml:mfrac><mml:mo>&#x002A;</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>L</mml:mi></mml:msub></mml:mfrac><mml:mo>&#x002A;</mml:mo><mml:mi>O</mml:mi></mml:mrow><mml:mo>&#x2062;</mml:mo><mml:mi>D</mml:mi><mml:mo>&#x2062;</mml:mo><mml:mi>W</mml:mi></mml:mrow></mml:mrow></mml:math>
</disp-formula>
<p>Where <italic>c</italic> is the cross-section circumference, <italic>w</italic> is the needle width, <italic>A</italic> is the 2D needle surface area, <italic>M</italic><sub><italic>L</italic></sub> is the specific ODW of the three measured needles, and <italic>ODW</italic> is the total oven-dried weight of the needles.</p>
</sec>
<sec id="S2.SS5.SSS2">
<title>2.5.2 Seedling needle damage and biomass measurements</title>
<p>For needle damage measurements, we performed a count of seedlings that had needle damage. Seedlings were visually analyzed to determine the percent of total LA that had needle damage. We isolated the seedlings damaged only by the drought by measuring damage levels before and after the drought. Based on these measurements, each seedling was assigned a damage intensity class ranging from 0 to 3, with 0 indicating no damage, 1 being 1&#x2013;35%, 2 being 36&#x2013;70%, and 3 representing 71&#x2013;100% damage.</p>
<p>Immediately following the drought trial, two seedlings from each of the 240 treatment groups were harvested, and separated into needles, stems, and roots split. The 1,440 samples were dried in an oven at 65&#x00B0;C for a minimum of 48 h and were then measured for dry biomass (g). We also used these biomass measurements to calculate seedling RSR, which was calculated by dividing seedling belowground biomass by seedling aboveground biomass.</p>
</sec>
</sec>
<sec id="S2.SS6">
<title>2.6 Statistical analyses</title>
<p>For three of our four response variables, we used linear-mixed effect models to understand the influence of temperature, provenance, and drought on numerous aspects of balsam fir seedling performance. The four model response variables were characterized as: biomass, RSR, needle damage, and seedling assimilation rate of CO<sub>2</sub>. The general fixed explanatory variables for all models were provenance location, average treatment temperature, and level of drought intensity. We also included a fixed variable for LiCOR temperature within the photosynthesis model. We scaled our two temperature variables to allow for easier model interpretation. To account for potential heterogeneity between treatment blocks, we assigned a random intercept for the phytotrons within each model.</p>
<p>For each response variable, we started with a full model including all measured variables and relevant interactions between the main variables. Save for the model-specific hypothesized interactions, we removed all non-significant (<italic>p</italic> &#x003E; 0.05) predictors as well as non-significant two-way and three-way interactions within each model iteration to avoid overfitting and to simplify model interpretation. Our hypothesized interactions for the RSR and biomass models are temperature and drought, and temperature and provenance. In addition to the two prior interactions our photosynthesis model, we hypothesized that the internal LiCOR temperature would have a significant interaction with provenance. Finally, we included a single temperature and provenance interaction within our needle damage model. Our general model for biomass and RSR takes the following form:</p>
<disp-formula id="S2.Ex2">
<mml:math display="block" id="M2"><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mi>o</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>D</mml:mi></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mo>&#x00D7;</mml:mo><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mrow></mml:mrow><mml:mrow><mml:mo lspace="0pt" rspace="5.8pt">&#x00D7;</mml:mo><mml:mi>D</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>6</mml:mn></mml:msub><mml:mi>H</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mo>&#x2208;</mml:mo><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math>
</disp-formula>
<p>Where <italic>T</italic> is the average temperature treatment, fit with an orthogonal polynomial term, <italic>P</italic> is the provenance, <italic>D</italic> is the drought intensity, &#x03B2; is the slope of the fixed effects, &#x2208; is the intercept of the phytotron <italic>j</italic> random effects. We only included <italic>H</italic>, a term for seedling height, for the RSR model because tree size is an important RSR moderator, where larger trees tend to have lower RSR levels (<xref ref-type="bibr" rid="B48">Ledo et al., 2018</xref>).</p>
<p>For our photosynthesis model, the general equation took the form of:</p>
<disp-formula id="S2.Ex4">
<mml:math display="block" id="M4"><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mi>o</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>t</mml:mi></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mo>&#x00D7;</mml:mo><mml:mi>t</mml:mi></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mo>&#x00D7;</mml:mo><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mrow></mml:mrow><mml:mrow><mml:mrow><mml:mrow><mml:mo lspace="0pt" rspace="5.8pt">+</mml:mo><mml:mrow><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>6</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mo>&#x00D7;</mml:mo><mml:msup><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>&#x00D7;</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mrow><mml:mo>+</mml:mo></mml:mrow><mml:msub><mml:mo>&#x2208;</mml:mo><mml:mi>j</mml:mi></mml:msub><mml:mi/></mml:mrow></mml:math>
</disp-formula>
<p>Where <italic>T</italic> is the internal LiCOR temperature at the time of measurement, fit with an orthogonal polynomial term, <italic>t</italic> is a three-factor phytotron temperature intensity variable. Unlike the other models, we considered phytotron temperature (<italic>t</italic>), as a factor as we only sampled from three distinct temperature treatments. We included three two-way interactions, which A term for drought is not included because these measurements were conducted prior to the drought.</p>
<p>Finally, to understand how the three general explanatory variables influenced the occurrences of seedling needle damage intensity, we used a Poisson linear mixed effects model. We used this type of analysis because our count data followed a Poisson distribution. This model took the following form:</p>
<disp-formula id="S2.Ex6">
<mml:math display="block" id="M6"><mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mo>&#x2062;</mml:mo><mml:mi>o</mml:mi><mml:mo>&#x2062;</mml:mo><mml:mi>g</mml:mi><mml:mo>&#x2062;</mml:mo><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi mathvariant="normal">&#x03BB;</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mi>o</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>T</mml:mi></mml:mrow><mml:mo>&#x00D7;</mml:mo><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>&#x2062;</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mrow><mml:msub><mml:mi mathvariant="normal">&#x03B2;</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>&#x2062;</mml:mo><mml:mi>D</mml:mi></mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow><mml:msub><mml:mo>&#x2208;</mml:mo><mml:mi>j</mml:mi></mml:msub><mml:mi/></mml:mrow></mml:math>
</disp-formula>
<p>Where &#x03BB;<italic>j</italic> represents the frequency of damaged seedling observations.</p>
<p>For these analyses, we used the R package &#x201C;lme4&#x201D; (<xref ref-type="bibr" rid="B6">Bates et al., 2015</xref>; <xref ref-type="bibr" rid="B69">R Development Core Team, 2022</xref>). We conducted a Breusch&#x2013;Pagan test to assess levels of heteroscedasticity, calculated generalized variance inflation factors to test for multicollinearity, visually analyzed Q-Q plots for linearity. For <italic>post hoc</italic> comparisons, we conducted pairwise comparisons with the least-square means method using the &#x201C;emmeans&#x201D; package to determine the degree of difference in response between treatment groups (<xref ref-type="bibr" rid="B49">Lenth, 2021</xref>).</p>
</sec>
</sec>
<sec id="S3" sec-type="results">
<title>3 Results</title>
<sec id="S3.SS1">
<title>3.1 Effects of drought and heat on seedling growth and damage</title>
<p>We observed a non-linear growth response to temperature amongst all provenances, with average seedling biomass levels increasing to an optimum average temperature level threshold of approximately 23&#x00B0;C (<xref ref-type="fig" rid="F2">Figure 2A</xref>). These results support the hypothesis that seedling biomass is limited by current temperatures and responds positively to moderate warming. However, we report a positive response to temperature levels warmer than RCP 8.5 projections, suggesting a high tolerance to warming (<xref ref-type="fig" rid="F2">Figure 2A</xref>). Pairwise comparisons revealed significant differences (<italic>p</italic> &#x003C; 0.001) of total biomass (<xref ref-type="fig" rid="F2">Figure 2A</xref>) and RSR (<xref ref-type="fig" rid="F2">Figure 2B</xref>), however, the only significant interactions identified were the difference in RSR response to temperature between the north and south provenance (<xref ref-type="table" rid="T2">Table 2</xref>). Drought, applied late in the growing season to mimic July-August drought, had no impact on either aspect of growth (<xref ref-type="table" rid="T2">Table 2</xref>).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>Interaction plots (<xref ref-type="bibr" rid="B52">Long, 2019</xref>) depicting the relationship between average treatment temperature and <bold>(A)</bold> the total seedling biomass (g), <bold>(B)</bold> seedling root:shoot ratio (RSR), and <bold>(C)</bold> the number of seedlings with severe needle damage. Colored bands represent 95% confidence intervals. The black vertical line shows the mean average daily temperature (17&#x00B0;C; <italic>May&#x2014;September</italic>) within the hottest part of balsam firs range in the recent past. The red vertical line shows the RCP 8.5 projected average daily temperature (21.3&#x00B0;C; <italic>May&#x2014;September</italic>) within the same region by 2051&#x2013;2080 (<xref ref-type="bibr" rid="B67">Pacific Climate Impacts Consortium, 2014</xref>).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-05-1075787-g002.tif"/>
</fig>
<table-wrap position="float" id="T2">
<label>TABLE 2</label>
<caption><p>General linear mixed-effects model coefficients for the biomass, root:shoot ratio (RSR), and photosynthesis models.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">Coefficient</td>
<td valign="top" align="center">Biomass <italic>(n = 480)</italic></td>
<td valign="top" align="center">RSR <italic>(n = 480)</italic></td>
<td valign="top" align="center">Photosynthesis <italic>(n = 12)</italic></td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Intercept</td>
<td valign="top" align="center"><bold>0.36 <italic>(0.32 to 0.39)</italic></bold></td>
<td valign="top" align="center"><bold>0.77 <italic>(0.71 to 0.82)</italic></bold></td>
<td valign="top" align="center"><bold>3 <italic>(2.44 to 3.57)</italic></bold></td>
</tr>
<tr>
<td valign="top" align="left">Temperature [1]</td>
<td valign="top" align="center">0.23 <italic>(&#x2212;0.51 to 0.98)</italic></td>
<td valign="top" align="center">0.69 <italic>(&#x2212;0.56 to 1.93)</italic></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.81 <italic>(&#x2212;2.28 to 0.65)</italic></td>
</tr>
<tr>
<td valign="top" align="left">Temperature [2]</td>
<td valign="top" align="center">&#x2212;<bold>1.19 <italic>(</italic></bold>&#x2212;<bold><italic>1.93 to</italic></bold> &#x2212;<bold><italic>0.44)</italic></bold></td>
<td valign="top" align="center">&#x2212;<bold>2.1 <italic>(</italic></bold>&#x2212;<bold><italic>3.34 to</italic></bold> &#x2212;<bold><italic>0.86)</italic></bold></td>
<td valign="top" align="center">&#x2212;<bold>4.23 <italic>(</italic></bold>&#x2212;<bold><italic>5.04 to</italic></bold> &#x2212;<bold><italic>3.41)</italic></bold></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [M-S]</td>
<td valign="top" align="center">&#x2212;<bold>0.02 <italic>(</italic></bold>&#x2212;<bold><italic>0.05 to</italic></bold> &#x2212;<bold><italic>0.00)</italic></bold></td>
<td valign="top" align="center">0.03 <italic>(&#x2212;0.02 to 0.08)</italic></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [M&#x2013;N]</td>
<td valign="top" align="center"><bold>0.1 <italic>(0.07 to 0.12)</italic></bold></td>
<td valign="top" align="center"><bold>0.16 <italic>(0.11 to 0.22)</italic></bold></td>
<td valign="top" align="center">0.09 (<italic>&#x2212;0.08 to 0.27)</italic></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [N]</td>
<td valign="top" align="center">&#x2212;<bold>0.09 <italic>(</italic></bold>&#x2212;<bold><italic>0.12 to</italic></bold> &#x2212;<bold><italic>0.07)</italic></bold></td>
<td valign="top" align="center"><bold>0.18 <italic>(0.12 to 0.24)</italic></bold></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Drought</td>
<td valign="top" align="center">0 <italic>(&#x2212;0.00 to 0.01)</italic></td>
<td valign="top" align="center">0.01 <italic>(&#x2212;0.00 to 0.02)</italic></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Height</td>
<td valign="top" align="center"></td>
<td valign="top" align="center">&#x2212;<bold>0.05 <italic>(</italic></bold>&#x2212;<bold><italic>0.08 to</italic></bold> &#x2212;<bold><italic>0.03)</italic></bold></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Phytotron temperature [Hot]</td>
<td valign="top" align="center"></td>
<td valign="top" align="center"></td>
<td valign="top" align="center"><bold>1.81 <italic>(1.01 to 2.61)</italic></bold></td>
</tr>
<tr>
<td valign="top" align="left">Phytotron temperature [Int]</td>
<td valign="top" align="center"></td>
<td valign="top" align="center"></td>
<td valign="top" align="center"><bold>1.3 <italic>(0.50 to 2.10)</italic></bold></td>
</tr>
<tr>
<td valign="top" align="left">Provenance &#x00D7; Phytotron temperature [Int]</td>
<td valign="top" align="center"></td>
<td valign="top" align="center"></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.1 <italic>(&#x2212;0.35 to 0.15)</italic></td>
</tr>
<tr>
<td valign="top" align="left">Provenance &#x00D7; Phytotron temperature [Hot]</td>
<td valign="top" align="center"></td>
<td valign="top" align="center"></td>
<td valign="top" align="center">&#x2212;<bold>1.24 <italic>(</italic></bold>&#x2212;<bold><italic>1.49 to</italic></bold> &#x2212;<bold><italic>0.99)</italic></bold></td>
</tr>
<tr>
<td valign="top" align="left">Temperature [1] &#x00D7; Drought</td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.02 <italic>(&#x2212;0.15 to 0.11)</italic></td>
<td valign="top" align="center">0.21 <italic>(&#x2212;0.06 to 0.48)</italic></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Temperature [2] &#x00D7; Drought</td>
<td valign="top" align="center">0.09 <italic>(&#x2212;0.04 to 0.22)</italic></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.12 <italic>(&#x2212;0.39 to 0.15)</italic></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [M-S] &#x00D7; Temperature [1]</td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.05 <italic>(&#x2212;0.57 to 0.47)</italic></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.36 <italic>(&#x2212;1.44 to 0.73)</italic></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [M-S] &#x00D7; Temperature [2]</td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.1 <italic>(&#x2212;0.62 to 0.43)</italic></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.18 <italic>(&#x2212;1.26 to 0.91)</italic></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [M-N] &#x00D7; Temperature [1]</td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.36 <italic>(&#x2212;0.88 to 0.16)</italic></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.34 <italic>(&#x2212;1.42 to 0.75)</italic></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.26 <italic>(&#x2212;2.32 to 1.76)</italic></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [M-N] &#x00D7; Temperature [2]</td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.04 <italic>(&#x2212;0.57 to 0.48)</italic></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.41 <italic>(&#x2212;1.50 to 0.67)</italic></td>
<td valign="top" align="center">0.95 <italic>(&#x2212;0.21 to 2.10)</italic></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [N] &#x00D7; Temperature [1]</td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.04 <italic>(&#x2212;0.57 to 0.48)</italic></td>
<td valign="top" align="center"><bold>2.29 <italic>(&#x2212;3.37 to &#x2212;1.20)</italic></bold></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [N] &#x00D7; Temperature [2]</td>
<td valign="top" align="center">0.27 <italic>(&#x2212;0.26 to 0.79)</italic></td>
<td valign="top" align="center"><bold>&#x2212;</bold>0.7 <italic>(&#x2212;1.79 to 0.38)</italic></td>
<td valign="top" align="center"></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [S] &#x00D7; Phytotron temperature [Cold] &#x00D7; Temperature</td>
<td valign="top" align="center"></td>
<td valign="top" align="center"></td>
<td valign="top" align="center"><bold>&#x2212;0.49 <italic>(</italic></bold>&#x2212;<bold><italic>0.67 to</italic></bold> &#x2212;<bold><italic>0.31)</italic></bold></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [M-N] &#x00D7; Phytotron temperature [Cold] &#x00D7; Temperature</td>
<td valign="top" align="center"></td>
<td valign="top" align="center"></td>
<td valign="top" align="center">&#x2212;<bold>0.24 <italic>(</italic></bold>&#x2212;<bold><italic>0.42 to</italic></bold> &#x2212;<bold><italic>0.07)</italic></bold></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [S] &#x00D7; Phytotron temperature [Int] &#x00D7; Temperature</td>
<td valign="top" align="center"></td>
<td valign="top" align="center"></td>
<td valign="top" align="center">&#x2212;<bold>0.18 <italic>(</italic></bold>&#x2212;<bold><italic>0.36 to</italic></bold> &#x2212;<bold><italic>0.00)</italic></bold></td>
</tr>
<tr>
<td valign="top" align="left">Provenance [M-N] &#x00D7; Phytotron temperature [Int] &#x00D7; Temperature</td>
<td valign="top" align="center"></td>
<td valign="top" align="center"></td>
<td valign="top" align="center">&#x2212;0.14 <italic>(&#x2212;0.32 to 0.03)</italic></td>
</tr>
<tr>
<td valign="top" align="left">Marginal <italic>R</italic><sup>2</sup> <italic>/</italic> Conditional <italic>R</italic><sup>2</sup></td>
<td valign="top" align="center">0.38 / 0.48</td>
<td valign="top" align="center">0.41 / 0.44</td>
<td valign="top" align="center">0.8 / 0.9</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p>Values in bold indicate statistically significant responses (<italic>p</italic> &#x003C; 0.05). Values in parentheses are the 95% confidence intervals. Coefficients labeled as &#x201C;M-S&#x201D;, &#x201C;M-N&#x201D;, &#x201C;N&#x201D; signify mid-southern, and mid-northern, northern locations, respectively. The &#x201C;1&#x201D; and &#x201C;2&#x201D; for the &#x201C;treatment temperature&#x201D; variable indicates the order of the polynomial.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>Severe needle damage was first observed at an average temperature of 26&#x00B0;C, with further temperature increases drastically raising incidence rates (<xref ref-type="fig" rid="F2">Figure 2C</xref> and <xref ref-type="table" rid="T2">Table 2</xref>). The effect of temperature varied between provenances; the mid-north provenance exhibited a significantly higher sensitivity to temperature than the north and mid-south provenances (<italic>p</italic> &#x003C; 0.05; <xref ref-type="fig" rid="F2">Figure 2</xref>). Similar to biomass and RSR, drought had no impact on the severity of needle damage (<italic>p</italic> = 0.1).</p>
</sec>
<sec id="S3.SS2">
<title>3.2 Acclimation of photosynthesis to growing conditions</title>
<p>The south and north provenances both exhibit a considerable ability to acclimate their photosynthesis to their growing environment (<xref ref-type="fig" rid="F3">Figure 3</xref> and <xref ref-type="table" rid="T2">Table 2</xref>), each displaying a large, significant non-linear response curve to gradual changes in measurement temperature. For the south provenance only, we identified a consistent acclimation of seedling photosynthetic optimum temperatures; optimal photosynthesis shifted at a rate of 1&#x00B0;C for every 3.6&#x00B0;C shift in average treatment temperature (<xref ref-type="fig" rid="F3">Figure 3</xref>). When we compare the effect of chronic differences in temperature, here represented by the comparison of trees grown under cold, intermediate, and hot temperature treatments, on the photosynthetic response curve, we detect multiple, significant interactions between provenances and treatment temperature indicating important effects of provenances and treatment temperature on the photosynthetic capacity of balsam fir. Specifically, when grown under hot conditions, we report a significant 29% higher photosynthetic rate when comparing the south provenance to the north provenance (<italic>p</italic> &#x003C; 0.001; <xref ref-type="fig" rid="F3">Figure 3</xref>), although the higher variation within the north-hot group may account for some of this difference.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p>Seedling temperature response curves separated by provenance location and average temperature treatment level. The colored bands represent 95% confidence intervals. Vertical lines represent photosynthetic optimum temperatures for each treatment group. Grouped vertical lines indicate a shared photosynthetic optimum temperature associated with the rightmost line.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-05-1075787-g003.tif"/>
</fig>
<p>Finally, we observed differences in heat-tolerance between treatment groups for the southern provenance only. Specifically, photosynthesis rates in the cold treatment groups dropped faster when exposed to warmer measurement temperature, when compared to the hot (<italic>p</italic> &#x003C; 0.001) and intermediate (<italic>p</italic> &#x003C; 0.05) treatment groups (<xref ref-type="fig" rid="F3">Figure 3</xref> and <xref ref-type="table" rid="T2">Table 2</xref>).</p>
</sec>
</sec>
<sec id="S4" sec-type="discussions">
<title>4 Discussion</title>
<p>Our experiment revealed considerable phenotypic plasticity in photosynthesis and RSR among provenances, which seemed to moderate balsam fir seedling stress under the warming and drought treatments. Although we cannot exclude that provenances from drier, western parts of the species range may behave differently than the provenances studied here, the results suggest that regardless of adaptations to local climate regimes, the physiology of the balsam fir seedlings studied here is capable of adapting to a warming environment resulting from climate change, as evidenced by the uniform growth declines among all provenances observed only in temperature conditions well beyond RCP 8.5 forcing scenarios within the warmest regions of the species biogeographic range.</p>
<p>Optimizing resource acquisition in limiting environments is essential for survival in seedlings due to their limited size. The observed RSR increases alongside temperature indicate an important potential ability of balsam fir seedlings to allocate resources to below-ground structures to regulate greater evapotranspiration, a strategy regularly employed by plants in moisture-limiting conditions (<xref ref-type="bibr" rid="B53">Lopez-Iglesias et al., 2014</xref>; <xref ref-type="bibr" rid="B5">Aubin et al., 2016</xref>; <xref ref-type="bibr" rid="B41">Janiak et al., 2016</xref>; <xref ref-type="bibr" rid="B62">Moran et al., 2017</xref>; <xref ref-type="bibr" rid="B48">Ledo et al., 2018</xref>). The plastic response of seedling photosynthetic levels to increasing temperature may enable seedlings a greater capacity to mitigate heat stress in needles (<xref ref-type="bibr" rid="B36">Haider et al., 2021</xref>), maintain hydraulic transport integrity through the shifting of xylem anatomy (<xref ref-type="bibr" rid="B16">D&#x2019;Orangeville et al., 2013</xref>) or through the refilling of embolized cells (<xref ref-type="bibr" rid="B44">Klein et al., 2018</xref>), thereby sustaining crucial carbohydrate distribution throughout the system (<xref ref-type="bibr" rid="B55">McDowell, 2011</xref>; <xref ref-type="bibr" rid="B45">Kono et al., 2019</xref>). Conversely, high rates of photosynthesis may exacerbate plant moisture loss through stomatal conductance, a risky but potentially beneficial strategy in hotter climates (<xref ref-type="bibr" rid="B58">McDowell et al., 2008</xref>; <xref ref-type="bibr" rid="B75">Sade et al., 2012</xref>; <xref ref-type="bibr" rid="B78">Skelton et al., 2015</xref>). In hot growing conditions, this risky behavior may have been amplified by a complete lack of water-use efficiency (WUE) acclimation to any intensity of drought (see <xref ref-type="supplementary-material" rid="DS1">Supplementary material</xref>). Although the drought treatment had no impact on seedling performance, the late application may impact growth in the following year as the majority of the current-year growth had already completed (<xref ref-type="bibr" rid="B31">Goldblum and Rigg, 2005</xref>; <xref ref-type="bibr" rid="B18">D&#x2019;Orangeville et al., 2018b</xref>; <xref ref-type="bibr" rid="B42">Kannenberg et al., 2019</xref>). Regardless, the observed positive effects from moderate temperature increases supports our first hypothesis, while the lack of effect from the drought treatment fails to support our third hypothesis.</p>
<p>The emergence of genetic variation can result from the selective pressures of heterogeneity in growing conditions within a species geographic range, and from geographic isolation (<xref ref-type="bibr" rid="B88">Via and Lande, 1985</xref>; <xref ref-type="bibr" rid="B47">Lande, 2009</xref>). Advantageous phenotypic plasticity resulting from this genetic variation can potentially improve the persistence of a population in uncertain conditions (<xref ref-type="bibr" rid="B22">Donohue et al., 2000</xref>; <xref ref-type="bibr" rid="B13">Chevin and Lande, 2010</xref>; <xref ref-type="bibr" rid="B63">Nicotra et al., 2010</xref>), potentially promoting further population divergence (<xref ref-type="bibr" rid="B88">Via and Lande, 1985</xref>; <xref ref-type="bibr" rid="B43">Kelly, 2019</xref>). Our results indicate a low to moderate level of variation in phenotypic plasticity between provenances, here represented as RSR and photosynthesis. As hypothesized, the southern provenances exhibited greater performance in hot conditions when compared to the northern provenances; however, these divergences had limited impacts on growth and needle damage rates overall. This low variation between provenances may relate to the theorized ecological cost of adaptive phenotypic plasticity, where adaptations to a specific environment could limit a species plastic response to further change or cause unintended consequences within plant responses to other stimuli (<xref ref-type="bibr" rid="B19">DeWitt et al., 1998</xref>; <xref ref-type="bibr" rid="B84">Van Kleunen and Fischer, 2005</xref>; <xref ref-type="bibr" rid="B29">Ghalambor et al., 2015</xref>). Alternatively, considering that the magnitude and type of plasticity can change alongside tree development (<xref ref-type="bibr" rid="B10">Bouvet et al., 2005</xref>), advantageous phenotypic plasticity may have a more noticeable influence on provenance growth variation within older trees that may be more prone to moisture stress due to a greater exposure to climatic extremes (<xref ref-type="bibr" rid="B82">Tyree and Ewers, 1991</xref>; <xref ref-type="bibr" rid="B20">Domec et al., 2008</xref>; <xref ref-type="bibr" rid="B56">McDowell and Allen, 2015</xref>; <xref ref-type="bibr" rid="B59">McGregor et al., 2021</xref>; <xref ref-type="bibr" rid="B71">Rollinson et al., 2021</xref>).</p>
<p>Model projections of balsam fir&#x2019;s future range distribution (<xref ref-type="bibr" rid="B40">Iverson et al., 2008</xref>; <xref ref-type="bibr" rid="B37">Hassan and Bourque, 2009</xref>), abundance and productivity (<xref ref-type="bibr" rid="B9">Boulanger et al., 2017</xref>; <xref ref-type="bibr" rid="B80">Taylor et al., 2017</xref>) under varying climate change scenarios fail to consider age-specific provenance growth responses to climate, which contributes to uncertainty in model projections. Considering responses to climate found in previous studies (<xref ref-type="bibr" rid="B12">Carter, 1996</xref>; <xref ref-type="bibr" rid="B1">Akalusi and Bourque, 2021</xref>) and our own findings, it is important to further highlight the two factors in modeling approaches.</p>
<p>Although our experiment was conducted in a controlled greenhouse setting, stressful factors found only in natural growing conditions such as competition (<xref ref-type="bibr" rid="B72">Rollinson et al., 2016</xref>), pests, and pathogens (<xref ref-type="bibr" rid="B2">Allen et al., 2010</xref>; <xref ref-type="bibr" rid="B57">McDowell et al., 2011</xref>) may limit the observed positive growth response to temperature. Nonetheless, our research indicates that the southward extent of balsam fir is not directly limited by temperature, as seedling growth increases well beyond the current average temperature of the species&#x2019; southern range boundary. Recent research has also reported similar temperature-related growth benefits in young balsam fir in natural conditions (<xref ref-type="bibr" rid="B15">Collier et al., 2022</xref>), and no effect of winter warming on germination success (<xref ref-type="bibr" rid="B86">Vaughn and Taylor, 2022</xref>). Interestingly, the southward range of balsam fir saplings was observed expanding with climate warming, while mature fir migrated poleward (<xref ref-type="bibr" rid="B8">Boisvert-Marsh et al., 2014</xref>), suggesting that balsam fir may be more sensitive to climate stress in later stages of life. Though plagued with a risky hydraulic framework (<xref ref-type="bibr" rid="B79">Sperry and Tyree, 1990</xref>), shorter balsam fir have less distance to transport water therefore avoiding the risk of embolism and moisture stress that larger, mature trees face due to greater xylem tension, a higher exposure to drought conditions, and a greater moisture requirement (<xref ref-type="bibr" rid="B79">Sperry and Tyree, 1990</xref>; <xref ref-type="bibr" rid="B82">Tyree and Ewers, 1991</xref>; <xref ref-type="bibr" rid="B20">Domec et al., 2008</xref>; <xref ref-type="bibr" rid="B56">McDowell and Allen, 2015</xref>; <xref ref-type="bibr" rid="B4">Aubin et al., 2018</xref>; <xref ref-type="bibr" rid="B59">McGregor et al., 2021</xref>; <xref ref-type="bibr" rid="B71">Rollinson et al., 2021</xref>). The moisture dependence of balsam fir has been noted in previous studies (<xref ref-type="bibr" rid="B16">D&#x2019;Orangeville et al., 2013</xref>, <xref ref-type="bibr" rid="B17">2018a</xref>; <xref ref-type="bibr" rid="B15">Collier et al., 2022</xref>), and may be explained by the growth efficiency trade-off between tracheid embolism resistance and hydraulic conductivity; in non-moisture limiting conditions, a low conductivity may reduce competitiveness (<xref ref-type="bibr" rid="B79">Sperry and Tyree, 1990</xref>; <xref ref-type="bibr" rid="B82">Tyree and Ewers, 1991</xref>; <xref ref-type="bibr" rid="B20">Domec et al., 2008</xref>). Therefore, the generally hydric conditions throughout the range of balsam fir promote a high-risk hydraulic framework, fast growth, and a subsequent moisture dependency, thereby potentially reducing climate resilience and competitiveness in conditions with high hydrological variability.</p>
</sec>
<sec id="S5" sec-type="conclusions">
<title>5 Conclusion</title>
<p>All balsam fir provenances exhibit a consistent, striking ability to acclimate their physiological traits via phenotypic plasticity, enabling high growth in considerable heat. This suggests that temperature and drought may not be the limiting factors that moderate the establishment and growth of balsam fir seedlings at range boundaries, instead, climatic stress may be exacerbated with age and cumulative abiotic and biotic stressors, thereby influencing the reproductive success and competitive vigor of older individuals. Although the implications of our research are limited by the controlled nature of our experimental design and can only be generalized to the eastern, wetter part of the species range where the species is most dominant, these findings support the integration of age- and provenance-specific growth responses into future modeling attempts aiming to evaluate species distributions and abundance under climate change scenarios. To do so, more empirical data relating to these interactions needs to be collected in both controlled and natural environments. Furthermore, while our research aimed to evaluate how range wide temperature heterogeneity influenced the variance of genetics and phenotypic plasticity between provenances, future research should incorporate greater provenance diversity to increase our holistic understanding of balsam fir climate resilience. Future research should also be conducted to decipher how competition, legacy effects, provenance origin, and biotic stressors influence the success of balsam fir under differing climatic conditions, and at different life stages.</p>
</sec>
<sec id="S6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="S7" sec-type="author-contributions">
<title>Author contributions</title>
<p>JR performed the experiments, analyzed the data, and prepared the manuscript. All authors conceived the study design and contributed to the final version of the manuscript.</p>
</sec>
</body>
<back>
<sec id="S8" sec-type="funding-information">
<title>Funding</title>
<p>Project funding was provided through an NSERC Discovery Grant (RGPIN-2019-04353), the New Brunswick Innovation Foundation (RIF 2019-029), and the Canadian Forest Service.</p>
</sec>
<ack>
<p>We greatly appreciate the support and resources provided by the Atlantic Forestry Centre throughout the project. We thank the National Tree Seed Centre for their generous contribution of the tree seed used in this project. Thanks to Rob Vaughn, John Letourneau, Gretta Goodine, Peter Tucker, and Rachel Aske for their guidance and help throughout the project.</p>
</ack>
<sec id="S9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="S10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="S11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/ffgc.2022.1075787/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/ffgc.2022.1075787/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.docx" id="DS1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<fn-group>
<fn id="footnote1"><label>1</label><p><ext-link ext-link-type="uri" xlink:href="https://cfs.nrcan.gc.ca/publications?id=36773">https://cfs.nrcan.gc.ca/publications?id=36773</ext-link></p></fn>
</fn-group>
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