Assisted Gene Flow in the Context of Large‐Scale Forest Management In

Home , Gene flow

Assisted gene ﬂow in the context of large-scale forest management in California, USA 1, 2 2 3 DEREK J. N. YOUNG, THOMAS D. BLUSH, MICHAEL LANDRAM, JESSICA W. WRIGHT, 1 2,4 ANDREW M. LATIMER, AND HUGH D. SAFFORD

1Department of Plant Sciences, University of California, Davis, California 95616 USA 2Paciﬁc Southwest Region, USDA Forest Service, Vallejo, California 94592 USA 3Paciﬁc Southwest Research Station, USDA Forest Service, Davis, California 95618 USA 4Department of Environmental Science and Policy, University of California, Davis, California 95616 USA

Citation: Young, D. J. N., T. D. Blush, M. Landram, J. W. Wright, A. M. Latimer, and H. D. Safford. 2020. Assisted gene ﬂow in the context of large-scale forest management in California, USA. Ecosphere 11(1):e03001. 10.1002/ecs2.3001

Abstract. As climate changes, locally adapted tree populations may become maladapted to the sites in which they presently occur. When natural adaptive processes are insufficient for populations to keep pace with changing climate, human-assisted relocation of genotypes (assisted gene flow) may be a useful tool for maintaining forest resilience. While existing empirical evidence provides insight into the potential outcomes and consequences of assisted gene flow, its applicability to large-scale plantings needs to be evaluated. We conducted a test of assisted gene flow in the context of operational postfire restoration plantings in three U.S. Department of Agriculture National Forests in California. Our experimental restoration plantings included seedling provenances representing both the local planting site and lower-elevation provenances that may be adapted to hotter and drier conditions. For the duration of the experiment, the planting sites experienced anomalously hot, dry conditions, offering a window into the potential outcomes of assisted gene flow in a future climate characterized by warmer temperatures and more frequent drought. In most cases, there was no significant difference in seedling growth or survival among provenances. However, in a few cases, lower- elevation provenances performed better than local provenances, suggesting a potential benefit of assisted gene flow as a management response to climate change. Our analyses accounted for spatial variation in shrub cover and detected a consistent and substantial negative association between shrub cover and seedling growth. In addition, our study revealed that the use of operational seed collections that are not geographically precise (and therefore also not climatically precise) can complicate selection of appropriate provenances and lead to unpredictable outcomes. Numerous other risks and uncertainties—including the fact that tree populations are often adapted to local site factors other than climate and that long-term outcomes may differ from short-term observations—complicate evaluations of the potential utility of assisted gene flow.

Key words: adaptation; assisted gene ﬂow; climate change; forest; restoration; tree.

Received 24 May 2019; revised 25 October 2019; accepted 4 November 2019. Corresponding Editor: Debra P. C. Peters. Copyright: © 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. E-mail: [email protected]

INTRODUCTION When planning tree planting projects, managers select sources (e.g., collection locations or specific Seed source selection parent trees) for the tree seeds that are used. Tree planting can be an important component Based on a long history of studies that often of forest management and restoration, particu- identify strong local adaptation in tree popula- larly following severe wildfires and intensive tions (Langlet 1971, Conkle and Critchfield 1988, timber harvest (McDonald and Fiddler 2010). Ying and Liang 1994, Howe et al. 2003,

❖ www.esajournals.org 1 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Kitzmiller 2005, St Clair et al. 2005, Savolainen populations may perform better if relocated to et al. 2007, Wright 2007), many forest managers new sites. However, many of the same reciprocal have historically prioritized the use of seeds col- transplant studies that identify local adaptation lected from near the planting site while also also highlight important potential negative con- incorporating a reasonable range of genetic vari- sequences of assisted gene flow, particularly ation (Ledig and Kitzmiller 1992). This approach when populations are adapted to additional helps to ensure that most of the planted trees are local factors beyond climate (Bucharova 2017; well-adapted to the environmental conditions of also see Discussion). the planting site and that planted populations Despite the large body of literature on com- maintain adaptive capacity in future generations mon garden and provenance tests, only a few (Savolainen et al. 2007, Alberto et al. 2013). empirical studies to date have directly evaluated Matching seed source and planting site environ- the outcome of assisted gene flow—that is, ment often results in increased performance of specifically moving genotypes into sites where planted trees relative to alternative approaches the environment has changed to more closely that do not carefully account for the provenance match the relocated genotype’s historical source of planted trees (Langlet 1971, Aitken and Bem- environment (or at least some component of it) mels 2016). (Bucharova 2017). One study tested assisted gene Appropriate seed source selection methods are flow in a tree species (Populus tremuloides Michx.) less clear when environmental conditions are that the authors suggest has experienced sub- changing. Given a scenario of progressive warm- stantial adaptive lag over a long period of histori- ing and drying, for example, it may make sense cal climate warming, such that most populations to select seeds from source environments that are now occur in a climate substantially warmer than hotter and drier than the planting site, as geno- that to which they are adapted (Schreiber et al. types from those environments may be better 2013). That study observed substantial improve- adapted to certain environmental conditions ments in growth and survival in populations that (e.g., frequent drought) that may eventually were moved northward into colder sites, without characterize the planting site (Aitken and Whit- accompanying indications of cold-related mal- lock 2013). The approach of intentionally moving adaptation, representing potential positive out- genotypes of a given species to new locations comes of assisted gene flow. within the species range in order to track chang- Other studies have taken an alternative ing environmental conditions is referred to as approach, using periods of anomalously hot assisted gene flow (Ledig and Kitzmiller 1992, weather (Hancock and Hughes 2014, Bucharova Aitken and Whitlock 2013). Assisted gene flow et al. 2016) to test applications of assisted gene differs from assisted migration (also known as flow. In contrast to expectations, these studies assisted colonization or managed relocation; found that with only a few exceptions, individu- McLachlan et al. 2007) in that the latter concepts als from local populations performed better than include managed relocation of individuals or individuals from warmer provenances (i.e., can- populations of a species beyond the species’ his- didate populations for relocation), even under toric geographic range limit as opposed to mov- anomalously warm conditions. This unexpected ing them within the existing species range. result may potentially be explained by the fact However, many of the same motivations, guid- that the studies only evaluated adaptation to ing principles, and cautions apply to both temperature (as opposed to other potentially assisted gene flow and assisted colonization (Ait- important climatic factors such as moisture avail- ken and Whitlock 2013). ability) and were conducted over a single growing season with plants that began <1 yr old, Empirical evidence of assisted gene flow despite all study species being perennials. outcomes Under changing climatic conditions, local An operational forest management context adaptation to current climate implies maladapta- Despite growing recognition of the potential tion to future climate (Aitken and Bemmels importance of assisted gene flow in forest man- 2016), suggesting to some extent that agement, existing studies of assisted gene flow

❖ www.esajournals.org 2 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL. have (1) been performed under highly controlled collected from one or more trees (generally 1–4) conditions that may not be representative of in each of one or more stands (usually 5 to 50) operational forest management and (2) yielded throughout the geographic region defined by contradictory and unexpected results (see previ- the seed lot’s seed zone and elevation band ous section). We sought to evaluate the potential (Table 1). Collections have historically priori- outcomes of assisted gene flow applied in large- tized more stands vs. more trees per stand in scale postfire forest restoration plantings and order to maximize diversity (Arnaldo Ferreira, gain insight into potential limitations or other Geneticist, USDA-FS Pacific Southwest Region, considerations unique to implementation of personal communication). assisted gene flow in an operational context. To USDA-FS seed lots are less geographically and this end, we studied postfire plantings of five dif- climatically specific than the provenances typi- ferent tree species conducted by the USDA Forest cally used in research studies of local adaptation Service (USDA-FS) at three sites in California. and assisted gene flow. However, they reflect the The challenges faced by the USDA-FS in Califor- current reality of large-scale forest management. nia (e.g., responsibility for managing a large and To gain realistic insight into the potential role of environmentally heterogeneous forested region assisted gene flow in large-scale management given limited resources) are not unique, so we practices (e.g., the management practices of the use the California plantings as a case study of USDA-FS), it is thus essential to evaluate (1) the assisted gene flow implementation and outcomes selection and performance of provenances as potentially relevant to any institution responsible defined by management agencies (e.g., USDA-FS for managing a large and climatically diverse seed lots) and (2) the degree to which the use of forested region. geographically imprecise seed lots constrains the When replanting following severe wildfire, potential for implementation of assisted gene many management agencies, including the flow in an operational context. USDA-FS Pacific Southwest Region (which In the present study, we evaluate the growth includes California), use seedlings grown from and survival of seedlings that originated from seed that was previously collected and then seed lots that are representative of the planting stored in a seed bank in anticipation of a future site, as well as seed lots representing lower eleva- reforestation need. In the USDA-FS Pacific tions, at three sites that were planted following Southwest Region seed bank, an individual severe wildfire. For the duration of this experi- accession is referred to as a seed lot and is iden- ment, California experienced a drought so tified by the seed zone and 500 ft wide (~150 m extreme it had no historical precedent (Robeson wide) elevation band (e.g., 4000- to 4500-ft eleva- 2015). The drought offers the potential opportu- tion) from which seeds were collected (Fig. 1; nity to gain insight into expected outcomes of Table 1; Appendix S1: Figs. S1–S6). Similar col- assisted gene flow in an overall hotter and more lection and cataloging systems are used by other variable future climate. We additionally evaluate large forest management agencies—for example, the limitations of relying on operational seed lots the California Department of Forestry and Fire with imprecise collection location data, and we Protection, or Cal Fire (Stewart McMorrow, Cal interpret our results (as well as implementation Fire Deputy Chief of Forestry Assistance, per- and expected outcomes of operational assisted sonal communication). gene flow in forests generally) in this context. The California tree seed zones are variable in size, averaging roughly 30–50 km in latitude METHODS and longitude, and are intended to define regions with relatively consistent climatic and Experimental design physiographic conditions (Buck et al. 1970). The We worked with three USDA-FS National For- reason for delineating seed lots based on seed ests (Appendix S1: Table S1) to establish experi- zone and elevation band is to maintain separate mental tree plantations in the spring of 2011 collections of seed representative of specific local during operational restoration planting in areas environmental (including climatic) conditions. A that had recently experienced high-severity wild- given USDA-FS seed lot may consist of seeds fire. At each of the three planting sites (i.e.,

❖ www.esajournals.org 3 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Fig. 1. Potential seed collection locations for the high-elevation (purple shading), mid-elevation (blue shading), and low-elevation (green shading) seed lots of incense cedar trees planted at the Klamath site. Potential collection locations are delineated based on the 500-ft source elevation band of each seed lot (Table 1) and additionally constrained by the seed zone (seed zone 301; orange outline) identiﬁed in USDA-FS seed lot records. The gray back- ground shading depicts normal mean annual precipitation for the 1981–2010 reference period (PRISM Climate Group 2019), with lighter shades reﬂecting higher precipitation. The extent of the main map is outlined in red in the inset map of California. For maps of the other species and planting sites, see Appendix S1: Figs. S1–S6.

National Forests), seedlings from multiple prove- ponderosa Lawson & C. Lawson), Jeffrey pine nances of one or more species (Table 1) were (Pinus jeffreyi Balf.), and sugar pine (Pinus lamber- planted in a randomized complete block design. tiana Douglas). We used these species because The provenances planted were unique to each site; they were present on each respective site prior to that is, each provenance was only planted at one of wildfire and they were the same species being the three planting sites (Table 1). However, we used for operational postfire reforestation in the used the same treatment group types (i.e., one local burned areas surrounding the experimental and one or two lower-elevation provenances) at study plots. each of the three planting sites. At each site, we In total, 147 USDA-FS nursery-grown seed- established three experimental blocks, each con- lings (49 seedlings per block 9 three blocks) of taining all provenances of each species tested at the each provenance of each species were planted site. Because the sites were planted by operational at each site. Seedlings were planted in a con- tree planting crews, the use of fewer, larger blocks tiguous spatial arrangement with 2–3m (as opposed to more, smaller blocks) was neces- between each tree. At this spacing, competition sary to ensure accurate planting and tracking of among these small trees should be minimal the multiple species and provenances. (Cole and Newton 1987). At the Plumas and Across all three sites, the species planted were San Bernardino sites, seedlings were planted in as follows: Douglas fir(Pseudostuga menziesii a separate 7 9 7 grid for each provenance and [Mirb.] Franco), incense cedar (Calocedrus decur- replicate. At the Klamath site, seedlings were rens [Torr.] Florin), ponderosa pine (Pinus planted in a wheel-and-spoke design (Nelder

❖ www.esajournals.org 4 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Table 1. Characteristics of seedling provenances (i.e., USDA-FS operational seed lots from which planted seedlings originated).

Trees Source Source Seed Coll. per Seed Source Elev. No. Survival precipitation temperature Species by site lot ID stands stand zone elev. (ft) symb. seedl. (%) (mm) (°C)

Klamath Douglas fir 5253 16 2 301 4500–5000 H 78 94 1170–3986 8.1–10.2 3150 43 3 301 3000–3500 M 57 82 888–3732 9.4–11.6 2327 48 4 301 2000–2500 L 72 89 875–3184 10.1–12.6 Incense cedar 5259 1 4 301 4500–5000 H 63 92 1170–3986 8.1–10.2 3569 n/a n/a 301 3000–3500 M 53 70 888–3732 9.4–11.6 4775 2 1 301 1000–1500 L 90 92 857–2505 11–13.5 Ponderosa pine 5342 11 3 301 4000–4500 H 85 94 1062–3933 8.6–10.6 3536 19 2 301 3000–3500 M 89 65 888–3732 9.4–11.6 3529 11 3 301 2000–2500 L 66 83 875–3184 10.1–12.6 Plumas Jeffrey pine 4619 20 1 523 6000–6500 H 46 98 557–2379 6.2–10.3 2895 48 2 523 5000–5500 L 49 100 533–2114 7.1–11.6 Sugar pine 7472 7 1 523 6000–6500 H 45 98 557–2379 6.2–10.3 7469 5 1 523 4500–5000 L 45 100 524–2037 7.3–13.3 San Bernardino Jeffrey pine 6625 24 1 994 7000–7500 H 130 92 342–993 7.5–11.3 7130 n/a n/a 994 5000–5500 L 138 98 201–1001 11.2–15.2 Notes: Seed lots contain seeds collected from one or more trees from one or more stands within the specified seed zone and elevation band. Seed lot ID: the USDA-FS seed lot identification code; Coll. stands: number of stands from which seeds in the seed lot were collected; Trees per stand: average number of trees per stand from which seeds in the seed lot were collected; Seed zone: California seed zone (Buck et al. 1970) from which seeds were collected; Source elev.: the 500-ft elevation band from which seeds were collected (in feet for consistency with USDA-FS delineations; see Appendix S1: Table S3 for elevations in meters); No. seedl.: the number of experimental seedlings that were followed in this study; Source precipitation and Source temperature: the range of precipitation (normal total annual precipitation over the 1981–2010 period) and temperature (normal mean annual temperature over the 1981–2010 period) within the source seed zone and elevation band of the seed lot. The high- elevation seed lot of each species is from the same 500-ft elevation band as the planting site (except for ponderosa pine at the Klamath site, which is from the 500-ft elevation band below the planting site). The text “n/a” indicates records not available.

1962) with 12 spokes, a center tree, and four masticated material was left on the site. In early trees per spoke (12 9 4 + 1 = 49). This design 2015 at the Klamath site only, newly recruited was favored by the local manager because once competing vegetation was again manually the trees are much larger it will allow for evalu- removed from within at least 1 m of most ation of competition at a range of densities. At planted trees. each site, the relative positions of each prove- Provenances were selected to represent scenar- nance cluster were randomized across blocks. ios of upslope assisted gene flow and status-quo Additional plantings were conducted at a fourth management. At each site, one provenance of site (the Angeles National Forest), but one block each species that was planted originated from of seedlings was inaccessible due to extremely the same 500-ft elevation band as the planting high density of a hazardous plant (Eriodictyon site (or in the specific case of ponderosa pine parryi [A. Gray] Greene) so the site was planted at the Klamath site, the 500-ft elevation excluded from this study. However, the plant- band below the planting site; Table 1). The high- ings remain for future monitoring. Competing elevation provenance was intended to reflect sta- vegetation was manually removed from within tus-quo management in which seeds are sourced at least 30 cm of each planted tree during plant- from as close to the planting site as possible. The ing at each site to reduce competitive/facilitative remaining planted provenance(s) of each species impacts. At the San Bernardino site, the existing originated from elevation band(s) 500 to 3500 ft vegetation (primarily shrubs) was masticated (~150–1050 m) lower (Table 1), representing sce- across the entire planting area and the narios of assisted gene flow.

❖ www.esajournals.org 5 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Field measurements unknown, we present the climatic range of We measured the experimental seedlings potential collection locations within each experi- twice: once in September 2012 after two growing mental provenance (USDA-FS seed lot) as a seasons in the field, and once in September 2015, range (Table 1) and as a point cloud (Fig. 2) rep- three years later. In 2012, we measured all sur- resenting the climate across the region defined viving planted seedlings at the Klamath and San by the seed lot’s seed zone and elevation band. Bernardino sites and a random sample of one- To do so, we first defined the geographic region third of all surviving planted seedlings (due to from which seeds in each seed lot may have been time constraints) at the Plumas site (Table 1, No. collected by using geospatial layers of seed zones seedl.). In 2015, we remeasured (or recorded (Eldorado National Forest, Geographic Informa- mortality of; Table 1) all previously measured tion Services, personal communication), and eleva- seedlings that (1) were not beneath an object such tion (USGS 2018; Fig. 1; Appendix S1: Figs. S1– as a fallen log, (2) had no apparent severe S6). Within the resulting region, we placed a grid mechanical damage (e.g., from a rolling log), (3) of points with 100 m-by-100 m spacing and com- were not apparently killed by a burrowing mam- puted temperature and precipitation values at mal, and (4) could be confidently identified to a each point using the gradient-inverse distance- specific seedling measured in 2012. Seedlings not squared (GIDS) statistical downscaling method meeting these criteria were excluded from the (Nalder and Wein 1998) as modified by Flint and dataset. We additionally tested excluding seed- Flint (2012). We obtained normal mean annual lings with signs of animal browsing, but we temperature and normal total annual precipita- found that this did not qualitatively influence tion during the 1981–2010 reference period from inferences or conclusions (results not shown). We the ~800-m resolution TopoWx dataset for tem- therefore included browsed seedlings in the anal- perature (Oyler et al. 2014) and the ~800-m reso- yses we report here in order to reflect the reality lution PRISM dataset for precipitation (PRISM of operational forest management. Climate Group 2019). We chose to use these rela- In both surveys (initial and final), we mea- tively simple climate variables because (1) they sured the height and basal diameter of each seed- are easily obtained and used in management ling. In the final survey, we also visually applications and (2) within small regions typical estimated the percent cover by shrubs of the area of our analysis (e.g., Fig. 1), spatial variation in within 1 m of each seedling. We estimated per- annual temperature and precipitation is highly cent cover in categories (0%, 0–1%, 1–5%, 5–10%, correlated with spatial variation in many other 10–25%, 25–50%, 50–75%, 75–90%, and 90–100%) biologically relevant climate variables (De Clercq and used the middle value of each category for et al. 2015). statistical analyses. This approach requires confi- We also quantified the climates of the planting dent knowledge of the location of a seedling. We sites (Fig. 2; Table 1). We identified the normal were unable to precisely locate many dead seed- climate over the 1981–2010 period by extracting lings, so we could not confidently estimate shrub values at planting site locations using the modi- cover surrounding them. We therefore did not fied GIDS downscaling method from the same include shrub cover in survival analyses. We esti- TopoWx and PRISM layers used to define the cli- mated the stem volume of each tree at each time mates of the seedling provenances. Additionally, point based on the tree’s height and basal diame- to represent the climate during the 4-yr period ter, modeling the stem as a cone (Broncano et al. that the seedlings were growing in the field, we 1998). We only measured aboveground morphol- computed mean annual temperature during the ogy, but incorporating basal diameter into the October 2011–September 2015 period from growth metric may additionally capture aspects monthly ~800-m resolution TopoWx temperature of belowground morphology better than height layers (Oyler et al. 2014) and mean annual total alone (Marx et al. 1977, Dey and Parker 1997). precipitation over the same period from monthly ~4-km resolution PRISM layers (PRISM Climate Identifying provenance and planting site climates Group 2019) and extracted values at planting site Because specific collection locations within a locations using the same modified GIDS down- given seed zone-elevation band combination are scaling method.

❖ www.esajournals.org 6 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Fig. 2. Potential climate space from which seeds in each seed lot may have been collected. Each point reﬂects a random location from within the region constrained by the seed zone and 500-foot elevation band that each seed lot represents. Each color depicts a different seed lot within each site and species combination. The gray star depicts the climate at the planting site over the 1981–2010 normal period. The orange star depicts the climate at the planting site during the four-year duration of the experiment (October 2011–September 2015). The high-elevation seed lot of each species is from the same 500-ft elevation band as the planting site, except for Ponderosa pine at the Klamath site, which was from the 500-ft elevation band below the planting site. Point clouds appear slightly different across seed lots sourced from the same seed zone and elevation band because the points are randomly drawn (see Methods).

Statistical analyses respectively. We always considered the high-ele- To test for differentiation in seedling growth vation (local) provenance as the baseline prove- and survival among the provenances (seed lots) nance represented by the intercept of the model of each species, we developed statistical models (as opposed to a dummy variable), as this was that use initial (2012) observations to establish a the provenance representing status-quo man- baseline that accounts for variation due to nurs- agement. We allowed the model’s intercept and ery and planting practices (Jacobs et al. 2005), coefficient(s) for provenance dummy variable(s) and the change between measurements (2012– to vary randomly across experimental blocks to 2015) to identify variation potentially attributa- account for block effects and for the existence ble to provenance differences. Specifically, for of multiple experimental units (trees) within seedling growth, we fit hierarchical Gaussian each block-by-provenance combination (Quinn (normal) linear models to explain final (2015) and Keough 2002). The survival models thus stem volume using initial (2012) stem volume, effectively incorporate a random intercept for shrub cover, and provenance. This modeling each data row (i.e., block-by-provenance combi- approach allowed us to evaluate the effect of nation of survival/mortality counts), which seedling provenance on final seedling size, additionally serves to account for any overdis- independent of the influence of initial seedling persion in the data (Elston et al. 2001, Agresti size and shrub cover. We treated provenance as 2002). Prior to fitting models, we log-trans- a categorical variable, with 1 or 2 dummy vari- formed initial and final stem volume values in ables for species with 2 or 3 provenances, order to satisfy assumptions of normality, and

❖ www.esajournals.org 7 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL. we standardized continuous predictor variables responses across all 1000 sets of coefficients (Gel- by subtracting the mean and dividing by the man and Hill 2007, McElreath 2016). We addi- standard deviation (separately for each site-by- tionally computed pairwise contrasts (the species combination). difference in predicted response value and asso- We fit a separate model for each site-by-species ciated 95% confidence interval) between all combination. The model specification given tree i provenance pairs for each site-by-species combi- within experimental block j for a site-by-species nation using the same sets of sampled model combination with two provenances is as follows: coefficients. We performed all statistical analyses 2 in R version 3.5.0 (R Core Team 2018) using the final volumei Nl ; r Þ i package lme4 (Bates et al. 2015) for fitting gener- l ¼ b b i 0j½i 1j½i provenance lowi alized linear mixed-effects models.

b initial volumei 2 RESULTS b 3 shrub coveri b l ; r2 Þ Provenance and planting site climates 0j N b0 b0 The geographic region from which seeds of a b l ; r2 Þ 1j N b1 b1 given provenance were reported to have been collected (based on the recorded seed zone and We repeated the same modeling procedure for 500-ft elevation band of the corresponding seed survival between initial (2012) and final (2015) lot) was generally very broad (e.g., Fig. 1; surveys, except that we used a binomial response Appendix S1: Figs. S1–S6). Mean climate gener- distribution (with a logit link) and did not ally varied widely across space within each of include shrub cover or initial stem volume as these regions (Fig. 2). For example, the high-ele- predictors. We excluded shrub cover because it vation seed lots of Douglas fir and incense cedar was often impossible to precisely locate the planted at the Klamath site (defined by seed zone planting locations (and thus quantify the sur- 301 and the 4500- to 5000-ft elevation band) rounding shrub cover) of dead seedlings. It was encompassed a region with normal annual pre- not possible to fit survival models for ponderosa cipitation ranging from under 1200 mm to nearly pine at the Klamath site or for Jeffrey and sugar 4000 mm and normal mean annual temperature pines at the Plumas site, either because survival ranging from 8.1° to 10.2°C (Fig. 2). Most other was consistently very high (Plumas species; provenances evaluated in the experiment also Table 1), or because one experimental block con- originated from seed lots representing regions tained too few seedlings to achieve robust model that encompass a >twofold range in mean annual fits (Klamath ponderosa pine). precipitation and a 2°–4°C range in mean tem- To visualize differences in seed lot growth and perature (Fig. 2). survival among provenances, we used fitted Source precipitation and temperature values models to predict final stem volume and survival also differed among provenances of a given spe- for a hypothetical average experimental block, cies, to varying extents (Fig. 2). The among- average shrub cover, and average initial stem provenance differentiation is driven by geo- volume. To do so, in making model predictions, graphic variation in climate, with cooler, wetter we held shrub cover and initial stem volume conditions generally more common at higher explanatory variables constant at their means for elevations and in more coastally influenced each respective site-by-species combination, and regions. However, the ranges of precipitation we held all effects that vary by block at the over- values overlapped substantially among prove- all mean. We randomly sampled 1000 sets of nances, despite the fact that the elevation limits model fixed-effect coefficients using the fitted of all provenances were separated by at least multivariate normal distribution of coefficients. 500 ft (Table 1). For example, for Douglas fir For each set of coefficients, we predicted the planted at the Klamath site, the potential source response (stem volume or survival probability) precipitation range for the high-elevation seed for each provenance, and we then computed the lot was 1170–3986 mm, for the mid-elevation median and 95% confidence interval of the seed lot was 888–3732 mm, and for the low-

❖ www.esajournals.org 8 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL. elevation seed lot was 875–3184 mm (Table 1). low-elevation provenance also had a signifi- Potential source temperature ranges were gener- cantly higher model-predicted survival rate ally more clearly differentiated among prove- (mean 98%) than the high-elevation provenance nances, though overlap did occur to some extent (mean 92%; Fig. 4; Appendix S1: Table S2). (Fig. 2). The overlap was generally greater for With few exceptions, in models that included provenances from elevation bands that were clo- shrub cover as a predictor of stem volume, shrub ser in elevation (e.g., Plumas species and Kla- cover was significantly and substantially nega- math ponderosa pine; Table 1). tively associated with final stem volume The normal climate of the planting sites (i.e., (Table 2). The only exceptions were the individ- the mean annual temperature and total annual ual species-by-site models for two species at the precipitation over the 1981–2010 reference per- Klamath site, where shrubs had recently been iod) generally fell within the climate space of manually removed from within approximately potential seed collection sites of the high-eleva- 1 m surrounding each tree. tion provenance for each species and site combination (Fig. 2). At each planting site, the climate DISCUSSION during the 4-yr duration of the experiment was substantially hotter and drier than the long-term Differentiation in performance among some average climate (Fig. 2). As a result, in most cases provenances the climate conditions at the planting site during In two separate cases (Klamath incense cedar the experiment fell within the climate space of and San Bernardino Jeffrey pine), seedlings from potential seed collection sites of a lower-elevation a lower-elevation provenance exhibited greater provenance planted at the site. growth and survival than the high-elevation provenance that was selected to reflect status- Provenance performance quo local seed sourcing (Figs. 3–4; Appendix S1: In several cases, the low-elevation provenances Table S2). Within a given seed zone, lower eleva- outperformed the high-elevation provenances in tions are most often climatically hotter and drier terms of growth and/or survival, but in most than higher elevations (though there are many cases the differences were not individually signif- exceptions; Fig. 2), so the stronger growth of the icant for a given species at a given site (Figs. 3–4; low-elevation provenances (also seen in all other Appendix S1: Table S2). Performance among species and sites, but not significantly) may provenances differed significantly only for reflect adaptations to the anomalously hot, dry incense cedar planted at the Klamath site and for conditions that prevailed at the (higher- Jeffrey pine planted at the San Bernardino site. elevation) planting site for the duration of the For incense cedar, the low-elevation provenance experiment. This observation aligns with other had significantly larger model-predicted final observations of local adaptation along climatic stem volume (mean 30 cm3) than both the mid- gradients in pine species in California (e.g., Kitz- and high-elevation provenances (mean 14 and miller 2005), including Jeffrey pine (Martnez- 16 cm3 stem volumes, respectively), accounting Berdeja et al. 2019); however, we note that there for differences in starting size and shrub cover is a lack of existing data for incense cedar. Our (Fig. 3; Appendix S1: Table S2). Additionally, observation also aligns with the conceptual both the low- and high-elevation provenances of notion that in a future with hotter temperatures incense cedar had significantly higher model- and more frequent drought, assisted gene flow fitted survival rates (mean 92% and 97%, respec- has the potential to yield increased tree growth tively) than the mid-elevation provenance (73%; and survival relative to status-quo management Fig. 4; Appendix S1: Table S2). (Aitken and Whitlock 2013). As with the incense cedar at the Klamath site, Faster growth and greater short-term survival the low-elevation provenance of Jeffrey pine at relative to other provenances may not, however, the San Bernardino site had a significantly larger necessarily reflect better adaptation to the condi- model-predicted final stem volume (mean tions at a site. Plants from warmer provenances 200 cm3) than the high-elevation provenance are often observed to grow faster than plants (126 cm3; Fig. 3; Appendix S1: Table S2). The from cooler provenances, even when planted

❖ www.esajournals.org 9 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Fig. 3. Median model-fitted stem volume (and 95% confidence interval) of the seedlings of each seed lot at the end of the study, following three years of growth in the field. Fitted final stem volume accounts for (and holds constant across seed lots within each site-by-species combination) initial seedling stem volume and shrub competition (see Methods). The high-elevation seed lot of each species is from the same 500-ft elevation band as the planting site (except for Ponderosa pine at the Klamath site, which was from the 500-ft elevation band below the planting site), and the other seed lots were collected from 500 to 3500 ft (~150–1050 m) lower in elevation.

Fig. 4. Median model-fitted survival probability (and 95% confidence interval) of the seedlings over three years of growth in the field. Survival of Ponderosa pine at the Klamath site and Jeffrey and sugar pine at the Plu- mas site is not shown because survival was either consistently very high (Plumas species; Table 1) or because one experimental block contained too few seedlings for robust model fits (Klamath ponderosa pine). The high-elevation seed lot of each species is from the same 500-ft elevation band as the planting site, and the other seed lots were collected from 500 to 3500 ft (~150–1050 m) lower in elevation.

❖ www.esajournals.org 10 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Table 2. Median model coefﬁcient estimates (with 95% conﬁdence intervals in parentheses) for the stem volume model (a) and survival model (b) for each planting site and species combination.

Model coefﬁcient (mean and 95% conﬁdence interval) Model Species by site Intercept Shrub cover Source elev. (L) Source elev. (M) Year 1 volume AUC

(a) Stem volume (cm3) models Klamath Douglas fir 2.95 0.1 0.25 0.03 0.71 (2.46, 3.47) (0.24, 0.03) (0.01, 0.52) (0.68, 0.61) (0.59, 0.84) Incense cedar 2.31 0.01 0.66 0.17 0.67 (1.81, 2.86) (0.14, 0.1) (0.33, 0.95) (0.73, 0.37) (0.54, 0.8) Ponderosa pine 3.49 0.27 0.03 0.16 0.79 (3.26, 3.74) (0.39, 0.13) (0.19, 0.27) (0.11, 0.44) (0.68, 0.9) Plumas Jeffrey pine 3.3 0.33 0.28 0.91 (2.55, 4) (0.49, 0.19) (0.33, 0.88) (0.74, 1.07) Sugar pine 2.92 0.23 0.02 0.72 (2.51, 3.32) (0.36, 0.1) (0.51, 0.59) (0.55, 0.89) San Bernardino Jeffrey pine 4.01 0.65 0.44 0.89 (3.77, 4.25) (0.75, 0.55) (0.09, 0.8) (0.77, 1.02) (b) Survival (%) models Klamath Douglas fir 2.65 0.28 1.09 0.73 (1.47, 3.84) (1.66, 1.09) (2.58, 0.45) Incense cedar 3.06 0.54 2.14 0.80 (0.57, 5.7) (3.23, 1.8) (4.22, 0.24) San Bernardino Jeffrey pine 2.39 1.44 0.69 (1.75, 2.99) (0.09, 2.7) Notes: Models were fitted using standardized predictor variables (see Methods) to facilitate comparison among coefficients and species. A separate model was fitted for each site-by-species combination. The source elevation coefficients correspond to dummy variables for seed lots (low elevation and mid-elevation), with high elevation as the base level incorporated into the model intercept. For pairwise contrasts among seed lots, see Appendix S1: Table S2. Coefficient estimates with 95% confidence intervals that exclude zero are bolded. Coefficients for survival models for Ponderosa pine at the Klamath site and Jeffrey and sugar pine at the Plumas site are not shown because survival was either consistently very high (Plumas species; Table 1) or because one experimental block contained too few seedlings for robust model fits (Klamath ponderosa pine). AUC: area under the receiver-operating characteristic curve. into cooler environments (Mangold and Libby idea that upslope assisted gene flow leads to 1978, Morgenstern 2011). This observation may stronger performance. This observation may be a be attributed to a tradeoff between growth rate consequence of the limited geographic specificity and cold hardiness (Loehle 1998, Koehler et al. of seed lot collection locations: The potential cli- 2012), as trees that invest in cold hardiness (e.g., matic range from which the seeds may have been increased solute accumulation and delayed bud collected includes conditions very distinct from break) have fewer resources available for growth. the planting site’s climate (see Limited geographic Thus, although warmer- and wetter-provenance specificity of seed collection locations, below). trees may grow faster than local trees, they may It is also possible that the provenance differen- suffer more extensive damage in rare cold or dry tiation we observed was driven by nongenetic events that are not captured in short-term studies factors such as differential treatment of the seed- like ours. lings in the nursery (Jacobs et al. 2005) and Notably, the mid-elevation seed lot of Klamath maternal provisioning (Roach and Wulff 1987), incense cedar showed significantly lower sur- but our model, which accounts for initial size vival than either the low-elevation or high-eleva- and survival after one year of growth in the field, tion (local) incense cedar seed lots, thus should help to rule out (but potentially not com- providing an important counterexample to the pletely exclude) such effects.

❖ www.esajournals.org 11 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Limited differentiation in performance among intersection of the recorded seed zone and 500-ft other provenances elevation band) can cover wide geographic areas Growth and survival were not significantly and a large range of climates (Fig. 1; different among provenances for the remaining Appendix S1: Figs. S1–S6). Without any further site-by-species combinations that we tested, information, the seeds of a given seed lot may although most combinations trended toward have been collected from one extreme of the greater growth in low-elevation seed lots. This potential geographic/climatic space, the opposite limited differentiation was unexpected given that extreme, or anywhere in between—each leading local adaptation along environmental gradients, to different expectations of relative provenance even at relatively fine scales, is often observed performance. Further, seed lots often consist of for many of the species in our study, including seed collections from multiple locations (stands) Douglas fir (Campbell 1979, St Clair et al. 2005), within a seed zone and elevation band (Table 1). ponderosa pine (Kitzmiller 2005), and sugar pine In all of the cases in which our study identified (Eckert et al. 2015). seed lot performance differentiation (growth and The lack of significant differentiation in our survival of Klamath incense cedar and San Ber- study could be due to several factors. First, we nardino Jeffrey pine), the potential source climate may simply not have had enough statistical range of the best-performing provenance does power to detect these differences. The planting not even include the planting site climate. Fur- sites reflect actual postfire conditions in which ther, in the case of Klamath incense cedar sur- tree planting is conducted, and these conditions vival, the potential source climate range of the are variable. Our models detected and accounted worst-performing (mid-elevation) provenance for a generally strong negative influence of shrub does contain the planting site climate. Given the cover on seedling growth (Table 2), consistent large range of potential source climate of each with results of manipulative studies in this sys- seed lot, it may be the case that seeds from the tem that identify strong competitive effects of poorly performing seed lots were actually col- shrubs both aboveground (i.e., for light) and lected from a region of their potential climate belowground (i.e., for water and/or nutrients; space that was more dissimilar to the planting e.g., Conard and Radosevich 1982). However, site than the collection locations of the other seed there are numerous other sources of potentially lots, potentially explaining the stronger perfor- important environmental variation, including mance of the other seed lots. shrub height and species identity, soil character- Our analyses further demonstrate that given istics, and microenvironments created by logs limited information about collection locations, and other objects (Gray and Spies 1997, Gray common assumptions (e.g., that lower-elevation et al. 2005). This variability reflects the reality of provenance climates are warmer and drier) are managed landscapes, and in this regard our not necessarily valid. All else equal, lower eleva- results reflect the outcomes that may be expected tions are, indeed, generally hotter and drier than in a management context. Progressively increas- higher elevations. However, when comparing ing the sample size would eventually almost low- and high-elevation points at different loca- always achieve significance (Quinn and Keough tions within a seed zone, this pattern does not 2002), but our sample sizes are already sufficient always hold. In fact, in comparing two 500-ft ele- for detecting differences that are large enough in vation bands separated by even 1000 ft or more magnitude to be relevant in the context of post- of elevation, some sites in the higher-elevation fire management (Table 2). band can actually be hotter and drier than some sites in the lower-elevation band (Fig. 2). This Limited geographic specificity of seed collection issue is exacerbated when comparing elevation locations bands from disparate seed zones. Another potential explanation for the lack of Dependence on seed lots collected and/or cata- substantial provenance differentiation is the loged with limited geographic specificity is a chal- uncertainty about the exact collection location lenge common to many large land management and therefore the source climate of each prove- institutions beyond the USDA-FS PacificSouth- nance. USDA-FS seed lot designations (i.e., the west Region (e.g., Cal Fire; Stewart McMorrow,

❖ www.esajournals.org 12 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Cal Fire Deputy Chief of Forestry Assistance, per- growth by far (Jeffrey pine at the San Bernardino sonal communication). Given current staffing and site) also showed significant provenance differ- financial resources, it is infeasible to store, track, entiation. Adaptive differentiation among prove- and manage seed collections from individual nances when trees are younger or smaller may source trees separately (Sara Wilson, former Seed be realized in phenotypic attributes that we did Bank Manager, USDA-FS Pacific Southwest not measure, including phenology, freezing toler- Region, personal communication). This reality high- ance, xylem density, and rooting depth (Aitken lights the fact that many principles of seed lot and Adams 1997, Oleksyn et al. 1999, St Clair selection and assisted gene flow that are proposed et al. 2005). While the seedling stage is often con- and evaluated in the academic literature may not sidered the most sensitive to environmental be directly applicable to large-scale management stress (Grubb 1977)—and thus potentially the scenarios. It also highlights the importance of most likely to exhibit performance differentiation updating management practices to address the —it is possible that any signals of environmental challenges of climate change. maladaptation were dampened by planting vig- Options for revising management practices orous nursery-grown seedlings (Ledig and Kitz- include requiring greater geographic specificity miller 1992). To the extent that this is the case, it when collecting and cataloging seeds (e.g., highlights the fact that planting tree seedlings recording geographic coordinates of each parent can exclude opportunities for natural selection, tree or collection site), maintaining individual and it emphasizes the importance of carefully seed collections separately rather than pooling selecting the genotypes used for seedling plant- them (even when they originate from the same ing projects (Millar and Libby 1989). seed zone and elevation band), and developing An additional challenge in implementing quantitative seed transfer guidelines that incor- assisted gene flow is that we often hope for the porate empirical data on the climatic (and other trees we plant today to persist on a site for dec- environmental) tolerances of species and popula- ades to centuries, but climate change is expected tions as well as expectations of climate change to present a continuously moving target for at and associated uncertainty. Management agen- least decades to come (Aitken and Bemmels cies including the USDA-FS; Cal Fire; and the 2016). Thus, assisted gene flow may require British Columbia, Canada, Ministry of Forests choosing between (1) planting trees that may be are already prioritizing many such revisions well-adapted in the future but are not at present (Arnaldo Ferreira, Geneticist, and Sara Wilson, or (2) vice versa. Given the potential for limited former Seed Bank Manager, USDA-FS Pacific cold hardiness in heat- and drought-adapted Southwest Region, personal communications; Ste- populations (Loehle 1998, Koehler et al. 2012), it wart McMorrow, Cal Fire Deputy Chief of For- may be advantageous to wait to move hot- and estry Assistance, personal communication; dry-provenance genotypes into historically Snetsinger 2004, O’Neill et al. 2017). Tools to cooler, wetter sites in response to (not in anticipa- facilitate climate-based seed transfer of precisely tion of) climate change, when rare cold events located seed collections have recently been devel- may be less common. This approach would only oped (e.g., the Seedlot Selection Tool; seedlotse- be effective, however, to the extent that trees can lectiontool.org). persist through some amount of climate change in their present sites. Nuances, challenges, and risks of implementing Existing provenance studies—particularly assisted gene flow those that involve planting trees of a given It is possible that even small provenance differ- provenance into multiple environments—can entiation will affect performance in the long term also help to identify the extent to which prove- despite being less apparent in early years (Schu- nances can be safely transferred in anticipation ler 1994, Rice and Knapp 2008), when growth of future warming (Hufford and Mazer 2003). rates are relatively low and microenvironments For example, a transplant study in Populus fre- can have a large influence (Gray and Spies 1997). montii S. Watson determined that the spatial seed This interpretation is supported by our observa- transfers necessary so that trees planted today tion that the species trial exhibiting the most are well-adapted in 100 yr would result in

❖ www.esajournals.org 13 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL. substantially reduced performance today (Grady potentially be appropriate under different et al. 2015). Ironically, the trees that can tolerate assumptions—are combined. Such approaches, the largest transfers today in anticipation of however, should account for the possibility of future climate change (i.e., those that are the least increased mortality (due to trees that prove to climatically sensitive) may be the most able to be maladapted; Ledig and Kitzmiller 1992) in tolerate climate change in the absence of assisted determining planting densities and future fol- gene flow (Wang et al. 2006). low-up management. Additional research into Additional risks surround implementation of the potential outcomes and consequences of assisted gene flow. Introgression between local assisted gene flow—including studies of the cli- and introduced populations could result in out- matic tolerances of tree populations and the breeding depression (Weeks et al. 2011, Aitken strength of adaptation to non-climatic biophysi- and Whitlock 2013); alternatively, reproductive cal factors that trees may experience differently phenological mismatches between local and if relocated—could meaningfully inform seed introduced genotypes could prevent desirable selection decisions. introduced alleles from establishing in the local population (Wadgymar and Weis 2017). Popula- ACKNOWLEDGMENTS tions of tree species, including some of the species we studied, often exhibit local adaptation to We thank the local USDA Forest Service staff who factors that do not vary with climate (e.g., soil coordinated installation of the experiments and facili- tated monitoring: R. Tompkins, L. Smith, J. Hooper, C. properties, photoperiod, pathogens, and mutual- fi ists; Wright 2007, der Putten 2012, Kranabetter Stith, and T. Drake. We also thank the committed eld technicians who assisted with monitoring: J. Borba, A. et al. 2012, Way and Montgomery 2015, Grady Le, B. Baatar, and A. Merritt. We gratefully acknowl- et al. 2015), likely impacting the success of popu- edge funding from the USDA Forest Service Region 5 fi lations relocated speci cally to track climate Ecology Program. (Schiffers et al. 2013, Bucharova 2017). The existence of these other adaptations may provide an LITERATURE CITED alternative explanation for our unexpected observations. Existing common garden studies Agresti, A. 2002. Categorical data analysis. Second edi- designed to evaluate the extent of local adaptation. Wiley & Sons, New York, New York, USA. tion to individual environmental attributes (e.g., Aitken, S. N., and W. T. Adams. 1997. Spring cold har- climate or soil type) provide an important foun- diness under strong genetic control in Oregon pop- dation for predicting outcomes of assisted gene ulations of Pseudotsuga menziesii var. menziesii. – flow. However, the most directly relevant infor- Canadian Journal of Forest Research 27:1773 1780. Aitken, S. N., and J. B. Bemmels. 2016. Time to get mation comes from studies that explicitly com- moving: assisted gene flow of forest trees. Evolu- pare performance of relocated vs. local tionary Applications 9:271–290. genotypes under altered climate. Such studies Aitken, S. N., and M. C. Whitlock. 2013. Assisted gene are relatively rare (Schreiber et al. 2013, Hancock flow to facilitate local adaptation to climate change. and Hughes 2014, Bucharova et al. 2016). Annual Review of Ecology, Evolution, and System- While results of existing and future research atics 44:367–388. can help guide assisted gene flow decisions, Alberto, F. J., et al. 2013. Potential for evolutionary there will always exist some uncertainty regard- responses to climate change: evidence from tree ing the best tree provenance(s) for any given populations. Global Change Biology 19:1645–1661. € application. Uncertainty exists due to imperfect Bates, D., M. Machler, B. Bolker, and S. Walker. 2015. information regarding many important factors, Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67:1–48. including future climate, likelihood of introgres- Broadhurst, L. M., A. Lowe, D. J. Coates, S. A. Cun- sion and outbreeding depression, and extent of ningham, M. McDonald, P. A. Vesk, and C. Yates. local adaptation to non-climatic factors. Given 2008. Seed supply for broadscale restoration: maxi- fl this reality, assisted gene ow programs could mizing evolutionary potential. Evolutionary Appli- be designed around a composite provenancing cations 1:587–597. approach (Broadhurst et al. 2008) in which mul- Broncano, M. J., M. Riba, and J. Retana. 1998. Seed ger- tiple provenances—each of which might mination and seedling performance of two

❖ www.esajournals.org 14 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

Mediterranean tree species, holm oak (shape Quer- Elston, D. A., R. Moss, T. Boulinier, C. Arrowsmith, cus ilex L.) and Aleppo pine (shape Pinus halepensis and X. Lambin. 2001. Analysis of aggregation, a Mill.): a multifactor experimental approach. Plant worked example: numbers of ticks on red grouse Ecology 138:17–26. chicks. Parasitology 122:563–569. Bucharova, A. 2017. Assisted migration within species Flint, L. E., and A. L. Flint. 2012. Downscaling future range ignores biotic interactions and lacks evi- climate scenarios to fine scales for hydrologic and dence. Restoration Ecology 25:14–18. ecological modeling and analysis. Ecological Pro- Bucharova, A., W. Durka, J.-M. Hermann, N. Holzel,€ S. cesses 1:2. Michalski, J. Kollmann, and O. Bossdorf. 2016. Gelman, A., and J. Hill. 2007. Data analysis using Plants adapted to warmer climate do not outper- regression and multilevel/hierarchical models. form regional plants during a natural heat wave. Cambridge University Press, New York, New York, Ecology and Evolution 6:4160–4165. USA. Buck, J. M., R. S. Adams, J. Cone, M. T. Conkle, W. J. Grady, K. C., T. E. Kolb, D. H. Ikeda, and T. G. Whi- Libby, C. J. Eden, and M. J. Knight. 1970. California tham. 2015. A bridge too far: cold and pathogen tree seed zones. California Region, Forest Service, constraints to assisted migration of riparian forests. U.S. Department of Agriculture, San Francisco, Restoration Ecology 23:811–820. California, USA. Gray, A. N., and T. A. Spies. 1997. Microsite controls Campbell, R. K. 1979. Genecology of Douglas-firina on tree seedling establishment in conifer forest watershed in the Oregon Cascades. Ecology canopy gaps. Ecology 78:2458–2473. 60:1036–1050. Gray, A. N., H. S. J. Zald, R. A. Kern, and M. North. Cole, E. C., and M. Newton. 1987. Fifth-year responses 2005. Stand conditions associated with tree regen- of Douglas-fir to crowding and nonconiferous eration in Sierran mixed-conifer forests. Forest competition. Canadian Journal of Forest Research Science 51:198–210. 17:181–186. G r u b b , P. J . 1 9 7 7 . The maintenance of species- Conard, S. G., and S. R. Radosevich. 1982. Growth richness in plant communities: the importance of responses of white fir to decreased shading and the regeneration niche. Biological Reviews 52:107– root competition by Montane chaparral shrubs. 145. Forest Science 28:309–320. Hancock, N., and L. Hughes. 2014. Turning up the heat Conkle, M. T., and W. B. Critchfield. 1988. Genetic vari- on the provenance debate: testing the ‘local is best’ ation and hybridization of ponderosa pine. Pages paradigm under heatwave conditions. Austral 27–43 in D. M. Baumgartner and J. E. Lotan, edi- Ecology 39:600–611. tors. Ponderosa pine: the species and its manage- Howe, G. T., S. N. Aitken, D. B. Neale, K. D. Jermstad, ment. Washington State University Cooperative N. C. Wheeler, and T. H. Chen. 2003. From geno- Extension Service, Pullman, Washington, USA. type to phenotype: unraveling the complexities of De Clercq, E. M., S. Leta, A. Estrada-Pena,~ M. Madder, cold adaptation in forest trees. Canadian Journal of S. Adehan, and S. O. Vanwambeke. 2015. Species Botany 81:1247–1266. distribution modelling for Rhipicephalus microplus Hufford, K. M., and S. J. Mazer. 2003. Plant ecotypes: (Acari: Ixodidae) in Benin, West Africa: comparing genetic differentiation in the age of ecological datasets and modelling algorithms. Preventive restoration. Trends in Ecology & Evolution 18:147– Veterinary Medicine 118:8–21. 155. der Putten, W. H. V. 2012. Climate change, above- Jacobs, D. F., K. F. Salifu, and J. R. Seifert. 2005. Rela- ground-belowground interactions, and species’ tive contribution of initial root and shoot morphol- range shifts. Annual Review of Ecology, Evolution, ogy in predicting field performance of hardwood and Systematics 43:365–383. seedlings. New Forests 30:235–251. Dey, D. C., and W. C. Parker. 1997. Morphological indi- Kitzmiller, J. H. 2005. Provenance trials of ponderosa cators of stock quality and field performance of red pine in northern California. Forest Science 51:595– oak (Quercus rubra L.) seedlings underplanted in a 607. central Ontario shelterwood. Volume 14. Pages Koehler, K., A. Center, and J. Cavender-Bares. 2012. 145–156. New Forests, Dordrecht, The Nether- Evidence for a freezing tolerance–growth rate lands. trade-off in the live oaks (Quercus series Virentes) Eckert, A. J., P. E. Maloney, D. R. Vogler, C. E. Jensen, across the tropical–temperate divide. New Phytolo- A. D. Mix, and D. B. Neale. 2015. Local adaptation gist 193:730–744. at fine spatial scales: an example from sugar pine Kranabetter, J. M., M. U. Stoehr, and G. A. O'Neill. (Pinus lambertiana, Pinaceae). Tree Genetics & Gen- 2012. Divergence in ectomycorrhizal communities omes 11:42. with foreign Douglas-fir populations and

❖ www.esajournals.org 15 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

implications for assisted migration. Ecological experiment. Scandinavian Journal of Forest Applications 22:550–560. Research 14:7–17. Langlet, O. 1971. Two hundred years genecology. O'Neill, G., T. Wang, N. Ukrainetz, L. Charleson, L. Taxon 20:653–721. McAuley, A. Yanchuk, and S. Zedel. 2017. A pro- Ledig, F. T., and J. H. Kitzmiller. 1992. Genetic strate- posed climate-based seed transfer system for Bri- gies for reforestation in the face of global climate tish Columbia. Technical report 099. Province of change. Forest Ecology and Management 50:153– British Columbia Forests, Lands, and Natural 169. Resource Operations, Victoria, British Columbia, Loehle, C. 1998. Height growth rate tradeoffs deter- Canada. mine northern and southern range limits for trees. Oyler, J. W., A. Ballantyne, K. Jencso, M. Sweet, and S. Journal of Biogeography 25:735–742. W. Running. 2014. Creating a topoclimatic daily air Mangold, R. D., and W. J. Libby. 1978. A model for temperature dataset for the conterminous United reforestation with optimal and suboptimal tree States using homogenized station data and remo- populations. Silvae Genetica 27:66–68. tely sensed land skin temperature. International Martnez-Berdeja, A., J. A. Hamilton, A. Bontemps, J. Journal of Climatology 35:2258–2279. Schmitt, and J. W. Wright. 2019. Evidence for popu- PRISM Climate Group. 2019. http://prism.oregonsta lation differentiation among Jeffrey and Ponderosa te.edu. Oregon State University. pines in survival, growth and phenology. Forest Quinn, G. P., and M. J. Keough. 2002. Experimental Ecology and Management 434:40–48. design and data analysis for biologists. Cambridge Marx, D. H., W. C. Bryan, and C. E. Cordell. 1977. Sur- University Press, New York, New York, USA. vival and growth of pine seedlings with Pisolithus R Core Team. 2018. R version 3.5.0. R: a language and Ectomycorrhizae after two years on reforestation environment for statistical computing. R Founda- sites in North Carolina and Florida. Forest Science tion for Statistical Computing, Vienna, Austria. 23:363–373. Rice, K. J., and E. E. Knapp. 2008. Effects of competi- McDonald, P. M., and G. O. Fiddler. 2010. Twenty-five tion and life history stage on the expression of local years of managing vegetation in conifer plantations adaptation in two native bunchgrasses. Restoration in northern and central California: results, applica- Ecology 16:12–23. tion, principles, and challenges. Gen. Tech. Rep. Roach, D. A., and R. D. Wulff. 1987. Maternal effects in PSW-GTR-231. U.S. Department of Agriculture, plants. Annual review of ecology and systematics Forest Service, Pacific Southwest Research Station, 18:209–235. Albany, California, USA. Robeson, S. M. 2015. Revisiting the recent California McElreath, R. 2016. Statistical rethinking: a Bayesian drought as an extreme value. Geophysical course with examples in R and Stan. CRC Press, Research Letters 42:6771–6779. Boca Raton, Florida, USA. Savolainen, O., T. Pyhaj€ arvi,€ and T. Knurr.€ 2007. Gene McLachlan, J. S., J. J. Hellmann, and M. W. Schwartz. flow and local adaptation in trees. Annual Review 2007. A framework for debate of assisted migration of Ecology, Evolution, and Systematics 38:595–619. in an era of climate change. Conservation Biology Schiffers, K., E. C. Bourne, S. Lavergne, W. Thuiller, 21:297–302. and J. M. J. Travis. 2013. Limited evolutionary res- Millar, C. I., and W. J. Libby. 1989. Disneyland or cue of locally adapted populations facing climate native ecosystem: genetics and the restorationist. change. Philosophical Transactions of the Royal Ecological Restoration 7:18–24. Society B 368:20120083. Morgenstern, M. 2011. Geographic variation in forest Schreiber, S. G., C. Ding, A. Hamann, U. G. Hacke, B. trees: genetic basis and application of knowledge R. Thomas, and J. S. Brouard. 2013. Frost hardiness in silviculture. UBC Press, Vancouver, British vs. growth performance in trembling aspen: an Columbia, Canada. experimental test of assisted migration. Journal of Nalder, I. A., and R. W. Wein. 1998. Spatial interpola- Applied Ecology 50:939–949. tion of climatic Normals: test of a new method in Schuler, T. M. 1994. Survival, growth, and juvenile- the Canadian boreal forest. Agricultural and Forest mature correlations in a West Virginia sugar maple Meteorology 92:211–225. provenance test 25 years after establishment. Nelder, J. A. 1962. New kinds of systematic designs for Research Paper NE-689. United States Department spacing experiments. Biometrics 18:283–307. of Agriculture, Forest Service, Northeastern Forest Oleksyn, J., P. B. Reich, W. Chalupka, and M. G. Experiment Station, Randor, Pennsylvania, USA. Tjoelker. 1999. Differential above- and below- Snetsinger, J. 2004. Chief forester's standards for seed ground biomass accumulation of European Pinus use. http://www2.gov.bc.ca/assets/gov/farming-na sylvestris populations in a 12-year-old provenance tural-resources-and-industry/forestry/tree-seed/le

❖ www.esajournals.org 16 January 2020 ❖ Volume 11(1) ❖ Article e03001 YOUNG ET AL.

gislation-standards/chief_forester_standards.pdf. migration in a warming world. Plant, Cell & Envi- British Columbia Ministry of Forests. ronment 38:1725–1736. St Clair, J. B., N. L. Mandel, and K. W. Vance-Borland. Weeks, A. R., C. M. Sgro, A. G. Young, R. Frankham, 2005. Genecology of Douglas fir in western Oregon N. J. Mitchell, K. A. Miller, M. Byrne, D. J. Coates, and Washington. Annals of Botany 96:1199–1214. M. D. Eldridge, and P. Sunnucks. 2011. Assessing USGS. 2018. 3DEP products and services: the National the benefits and risks of translocations in changing Map, 3D Elevation Program. https://nationalma environments: a genetic perspective. Evolutionary p.gov/3DEP/ Applications 4:709–725. Wadgymar, S. M., and A. E. Weis. 2017. Phenological Wright, J. W. 2007. Local adaptation to serpentine soils mismatch and the effectiveness of assisted gene in Pinus ponderosa. Plant and Soil 293:209–217. flow. Conservation Biology 31:547–558. Ying, C. C., and Q. Liang. 1994. Geographic pattern of Wang, T., A. Hamann, A. Yanchuk, G. A. O'Neill, and adaptive variation of lodgepole pine (Pinus contorta S. N. Aitken. 2006. Use of response functions in Dougl.) within the species’ coastal range: field per- selecting lodgepole pine populations for future cli- formance at age 20 years. Forest Ecology and Man- mates. Global Change Biology 12:2404–2416. agement 67:281–298. Way, D. A., and R. A. Montgomery. 2015. Photoperiod constraints on tree phenology, performance and

SUPPORTING INFORMATION

Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2. 3001/full

❖ www.esajournals.org 17 January 2020 ❖ Volume 11(1) ❖ Article e03001