Genomically informed seed orchard design for trailing-edge tree populations: a perspective from Quercus bicolor conservation

Genomically informed seed orchard design for trailing-edge tree populations: a perspective from Quercus bicolor conservation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 30 January 2026 V2 Latest version Share on Genomically informed seed orchard design for trailing-edge tree populations: a perspective from Quercus bicolor conservation Authors : Jesse B. Parker 0009-0007-0189-2572 [email protected] , Sean Hoban , Laura M. Thompson , and Scott E Schlarbaum Authors Info & Affiliations https://doi.org/10.22541/au.176463669.93056001/v2 Published Forest Ecology and Management Version of record Peer review timeline 256 views 147 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Environmental and land use change have resulted in substantial and ongoing losses of forest genetic resources, underscoring the need to conserve diverse genetic material with potential for adaptation. Range-marginal populations, particularly those at trailing edges, are critical conservation targets, as they may harbor alleles important for resilience, yet they face heightened risk of extirpation under environmental change. However, effectively conserving these populations is challenging: their marginal position often results in small, fragmented populations that are difficult to locate and adequately sample, and may have reduced genetic diversity. Additionally, elevated rates of interspecific introgression at range edges can complicate conservation decision-making. In this perspective, we discuss considerations broadly relevant to conservation of range-marginal germplasm and illustrate how these considerations can be practically addressed, using a widespread and important tree for restoration, Quercus bicolor Willd., as a case study. We outline considerations and approaches to (1) locating source populations, using a combination of biodiversity dataset review, species distribution modeling, and field surveying; (2) genomically assessing hybridization and selecting tolerances for introgression in ex situ conservation; (3) evaluating patterns of genetic variation using population genomics techniques; and (4) using these data to optimize the selection of genotypes for the establishment of conservation seed orchards, with attention to introgression, population structure, genetic diversity, minimum sample size estimates, and inbreeding potential. We find that our approach offers a substantial improvement over an “uninformed” sampling strategy with potential long-term benefits for conservation and restoration. Restoration of plant communities through artificial regeneration is an essential component of many international and national plans (e.g., Convention on Biological Diversity, EU Nature Restoration Regulation, Montreal Process for Conservation and Restoration, the U.S. REPLANT Act) to address biodiversity loss associated with habitat loss, climate change, and disease and pest pressure (Hoban et al., 2024). A prerequisite to the implementation of such plans is stable access to provenanced seed sources for the wide diversity of species that occur in natural landscapes (Jalonen et al., 2018). For plants with variable seed production, long juvenile periods, or seeds that do not store well, including heavy-seeded forest tree species, access to seed for producing seedlings is a substantial barrier limiting restoration efforts (De Vitis et al., 2020; Fargione et al., 2021; Fernández et al., 2023). Provenanced seed orchards (i.e., wild-sourced trees of known provenance planted in managed settings to allow the large-scale production and distribution of seed) can help overcome this shortcoming and serve as living germplasm repositories to preserve the genetic diversity of populations (Engelmann & Engels, 2002; Fargione et al., 2021; Frankel et al., 1995; Parker et al., 2025a). Populations at “trailing” or “rear” range edges, such as the southern extents of North American and European species expanding northward after the last glacial period, have gained special conservation attention in recent decades due to the possibility that they harbor enhanced adaptations to climate change (Hampe & Petit, 2005; Provan & Maggs, 2012; Rehm et al., 2015). These populations may represent unique and valuable genetic resources that can be harnessed to increase the climate resilience of higher latitude populations through assisted gene flow or incorporation into breeding programs (Aitken & Bemmels, 2016; Gargano et al., 2022). Unfortunately, it is also predicted that climate extremes will reach critical thresholds at these range edges sooner, making these populations especially susceptible (Beaty et al., 2023; Cahill et al., 2014). Given that range marginal populations are often characterized by reduced population sizes and gene flow, factors that might reduce their fitness and evolutionary potential, the ability for adaptive alleles in such populations to persist in the face of environmental change will be even further compromised (Blows & Hoffmann, 2005; Eckert et al., 2008; Hengeveld & Haeck, 1982). This suggests that to make use of the potentially climate-adapted qualities of these populations, they must be conserved as soon as possible, ideally in managed ex situ locations that are less susceptible to environmental change and habitat loss. However, conserving these range-marginal populations presents distinct challenges to practitioners due to their rarity and uncertain geographic distributions, the increased potential for interspecific hybridization and introgression, and potentially impoverished genetic diversity. While environmental change continues to intensify these challenges, emerging tools offer new abilities to address them. Increasingly accessible digitized herbarium records and public biodiversity databases, improved methods for locating populations through geographic information systems and remote sensing, and the rise of affordable access to genomic data for non-model species are collectively expanding our ability to locate, characterize, and conserve vulnerable range-edge populations. In this perspective, we discuss challenges relevant to ex situ conservation at range edges and demonstrate strategies for improving the efficacy of such efforts, using the development of germplasm resources at the trailing range edge for Quercus bicolor Willd. (swamp white oak) as a case study. Trailing edge populations of Q. bicolor occurring across the state of Tennessee (USA) are fragmented with very limited distribution (Kartesz, 2023; Little & Viereck, 1971), suggesting that the extant trees are at elevated risk of extirpation as environmental change intensifies. Compounding this fragmentation is widespread introgression (recently described in Parker et al., 2025b), particularly from Quercus lyrata Walter, throughout the trailing-edge populations in Tennessee (no introgression was observed in 9 populations in the range core). To build on the findings of Parker et al. (2025b), we use the same RADseq dataset, in combination with an additional suite of geographic information systems and genomics tools, to guide the development of conservation seed orchards derived from threatened range-edge genotypes occurring in Tennessee. The seed orchards produced through this work will be vital for providing local ecotype seed for reforestation and for maintaining a stable, preserved repository of Q. bicolor germplasm for future climate-smart conservation efforts. This work was conducted as an initiative of the University of Tennessee’s Tree Improvement Program (UT-TIP) whose key mission has been the development of seed orchards for diverse species native to Tennessee to facilitate seed production for gene conservation, tree improvement, and restoration in the state. We believe that our applied perspective could be valuable to other conservationists seeking to develop much-needed germplasm resources at range edges. The four broad steps outlined are: (1) finding and documenting populations, (2) assessing hybridization and introgression, (3) using population genomics to evaluate patterns of population structure and genetic diversity, and (4) using genetic data and optimization procedures to select genotypes for conservation seed orchards, resulting in seed orchards with greater population representation, lower introgression (with hybrids kept in a separate orchard for research), higher allelic representation, lower inbreeding potential, lower homozygosity, and higher efficiency (a smaller set of core genotypes) when compared to an “uninformed” seed orchard design. In each section, we discuss relevant background factors, outline key considerations, and describe our approach to germplasm conservation at the trailing edge of Q. bicolor. Putative swamp white oak locations in Tennessee (green points) with suitable habitat shown in darker purple, along with highlighted counties where the species was historically known. The first obstacle when working at range edges is determining where occurrences persist in the wild and assessing their true extent and abundance in the focal region. Locating populations in the wild, a prerequisite for germplasm collection and propagation, is also crucial for prioritizing conservation actions based on species abundance, accurately evaluating available genetic diversity, and delineating conservation or evolutionarily significant units (Guerrant et al., 2014; Serrano et al., 2023). Unfortunately, available occurrence records and range maps often rely on historical floristic surveys that have not been systematically updated in modern times (MacKenzie et al., 2019; Wagensommer, 2023). Although such data might imply widespread occurrence across a region, species distributions may have changed in response to extensive anthropogenic alteration. As a result, species that appear common based on historical data may be far rarer in the field, particularly when their habitats overlap with heavily modified or invaded landscapes (Beck et al., 2014; Jetz et al., 2012). At trailing range edges, demographic stochasticity and reduced adaptive capacity can make populations even more susceptible to environmental change, resulting in declines or extirpations (Eckert et al., 2008; Gaston, 2009). Consequently, updated, field-based verification is essential to understand modern distributions. With increasing data availability, such efforts can be complemented by promising new techniques in species distribution modeling and remote sensing to help detect previously undocumented populations of locally rare species (Cerrejón et al., 2021; de Siqueira et al., 2009). While a variety of strategies can be employed to overcome the challenges of locating range-marginal populations, a practical workflow might proceed as follows . Candidate occurrences are first compiled from herbaria and biodiversity databases, after which records are manually inspected to confirm that specimens represent natural and correctly identified occurrences of the target taxon. Verified records can then be used to build species distribution models and generate predictions of suitable habitat across the focal region. These predictions, together with known locations, can then guide targeted field surveys that both refine distributional knowledge and help identify extant populations suitable for germplasm collection. Before initiating conservation efforts for swamp white oak in Tennessee, key questions raised include: Do occurrence records align with the true modern-day distribution of swamp white oak in the focal area? How many biodiversity database records are misidentifications? What factors might account for discrepancies between publicly available occurrence records and field-confirmed occurrences? Where do swamp white oak populations occur that are suitable for germplasm collection? Addressing these questions requires both intensive review of public datasets and comprehensive field surveying. In our approach, we first compiled occurrences from the Southeast Regional Network of Expertise and Collection (SERNEC, 2024; accessed from January 2022–April 2024), the Global Biodiversity Information Facility (GBIF.org, 2024), and iNaturalist (iNaturalist, 2024). Records were then manually inspected to verify species identity and to exclude cultivated trees. We then attempted to locate these filtered occurrences through extensive field surveying. Because relatively few populations could be confirmed using herbaria and public datasets alone, we also employed species distribution modeling to identify suitable areas and attempt to locate previously undocumented occurrences. Models were built using the MaxEnt algorithm (Phillips et al., 2006) with a suite of environmental variables and range-wide occurrence records obtained from GBIF and SERNEC (see Supplement section 1.1, Figure S1, and Table S1). The efficacy of species distribution modeling is highly dependent on the quality of occurrence data used. After manual vetting of range-wide occurrence data, only 48% of GBIF occurrences were retained; cultivated specimens accounted for 14%, while obvious misidentifications accounted for 3%. Another 22.5% of specimens lacked sufficient information to attempt verification (e.g., blurry photographs, lack of identifiable features), and the final 12.5% were likely Q. bicolor but could not be confirmed to be naturally occurring. SERNEC records were more reliable than GBIF records as they typically included more detailed locality information and allowed easier confirmation of species identity, although numerous specimens were still determined to be misidentified. Additional details on modeling can be found in Parker (2025) and in the Supplementary Materials (see Figure S2 and Table S2). To prioritize locations for field surveys in Tennessee, highly suitable areas identified by the model were inspected in ArcGIS Pro (ESRI, 2023) for land-use characteristics (using the National Land Cover Database, Dewitz, 2023) and proximity to known swamp white oak populations. The effectiveness of model-guided surveying was mixed. Targeted searches in high-suitability areas (Figure 1) led to the discovery of three new Q. bicolor populations in Campbell, Stewart, and Rutherford counties. However, surveys of optimal swamp white oak habitat across 20 other Tennessee counties were unsuccessful. Confirmed occurrences were generally found in small pockets of floodplain forest (<0.5ha) within a matrix of developed and agricultural land. Across the region, most currently suitable land (52%) was classified as “agriculture”, followed by “forest” (29%), and “developed land” (16%). Numerous historically documented locations could not be relocated despite thorough field surveys, suggesting population declines or local extirpation. Ultimately, after a multiyear, resource-intensive search effort, field surveys confirmed the presence of swamp white oak in only 11 Tennessee counties and only 9 of the 23 counties with historical occurrence records. Several broad conclusions relevant to conservation efforts can be made from our exploration of swamp white oak distribution. First, the effort required to locate natural populations can be a substantial barrier to adequate sampling of range-edge populations. As species often become rarer at their range margins, populations become harder to find not only because there are fewer of them, but also because there may be an associated decline in institutional knowledge of the species. For instance, in our search for Tennessee swamp white oak genotypes, many Tennessee-based professionals who were consulted for information on possible occurrences (including botanists and foresters) were either unaware that the species occurred in the state, confused it with other oaks, or were unfamiliar with it altogether. While extensive hybridization (as described in Parker et al., 2025b) may contribute to taxonomic confusion, a broader lack of awareness was apparent, particularly when contrasted with the high level of knowledge and concern for the species encountered in the core parts of its range. If representing a larger trend across taxa, this experience suggests an additional factor that could complicate range-edge conservation efforts. Our experimentation with species distribution modeling demonstrates its potential utility for locating wild genotypes and characterizing habitat suitability. However, models are only as robust as the data and computational resources that support them. Incomplete or biased occurrence records can yield misleading predictions, while coarse or biologically irrelevant environmental layers may obscure important habitat heterogeneity. Local adaptation at range edges, a likely phenomenon, can also distort the modeling process (Hällfors et al., 2016). Subdivision of species into smaller, locally adapted units prior to modeling might provide more regionally tailored predictions (as done here, described in the Supplementary Materials) but will also constrain the available sample sizes for modeling, thereby reducing the explanatory and predictive power of models. Such subdivision may be infeasible for understudied or uncommon species with few occurrence records, or in poorly documented range-edge environments. Under these limitations, species distribution models may fail to capture a species’ true distribution and thus offer limited utility for on-the-ground conservation work. In our case study, combined evidence suggests that swamp white oak habitats in Tennessee were once more extensive but have been reduced by land conversion, altered hydrology, and urban expansion. Remaining populations occur mostly on unprotected land and are further threatened by habitat fragmentation and climate change. This exemplifies that modern-day distributions of species are often not reflected in historical occurrence records, especially at range edges where populations may be particularly prone to fragmentation and habitat loss. In fact, it appears that our understanding of plant distributions is unable to keep pace with the profound impacts of anthropogenic change, a significant obstacle to the efficient and informed development of conservation and management priorities. Continued investment in updating floras and occurrence datasets at local and regional scales is an important antidote to this ever-deepening “Wallacean shortfall” (Lomolino, 2004; Serrano et al., 2023). Through our combined methods of occurrence record review, species distribution modeling, and extensive field surveying, we have established a more complete picture of the trailing-edge distribution of swamp white oak in the study area, allowing us to document historic population decline, identify taxonomic errors in occurrence datasets, and locate previously undocumented populations. Ultimately, this knowledge will translate into increased accuracy when assessing conservation concern and improved population representation in germplasm repositories. While the methods outlined here can be resource and time-intensive, they offer a substantial improvement over opportunistic sampling protocols and distribution assessments that lack rigorous review and field-based verification. Such methods are becoming increasingly necessary to facilitate and inform conservation efforts in the face of environmental change. † One additional swamp white oak specimen was discovered in Hawkins County, TN but was not sampled due to access issues (not pictured) Impact of hybrid thresholds on (A) number of "unadmixed" genotypes and (B) Allele counts for samples. Note the drastic drop below the dotted line, which was chosen via the ‘broken stick’ method. Hybridization presents another possible complication for conservation of trailing-edge genotypes. Range edges often serve as zones of secondary contact, where species that are largely allopatric come into close proximity, increasing opportunities for hybridization (Arnold, 2006; Muniz et al., 2020; Parker et al., 2025b; Taylor & Larson, 2019). Range margins might experience increased hybrid formation due to demographic imbalances and “pollen swamping” (Beatty et al., 2010; Ellstrand, 1992; Ellstrand & Elam, 1993), or hybrid persistence due to marginal ecological conditions and altered adaptive landscapes (Ortego et al., 2014; Ribicoff et al., 2025; Villa-Machío et al., 2024). At range margins where genotypes may already be scarce, widespread hybridization presents an acute challenge to practitioners, requiring a careful reevaluation of priorities and desired outcomes. Hybridization may play an important role in evolutionary and ecological processes (Arnold, 2006; Rieseberg et al., 2003). For example, hybrid populations in foundational tree genera such as Populus L. (Evans et al., 2012; Wimp et al., 2004) and Eucalyptus L’Hér (Whitham et al., 1999) have been shown to host unique arthropod assemblages, with effects cascading to the ecosystem level. Such hybrid populations representing ecologically novel units may be worthy of conservation attention in their own right, particularly if managers are primarily interested in ecological outcomes. Increases in fitness-related traits through hybridization can also strengthen resilience to abiotic stressors, as demonstrated in Populus (Hord et al., 2025), Helianthus L. (Whitney et al., 2010), and Iris Tourn. ex L. (Martin et al., 2006), or to biotic stressors, as demonstrated in Castanea dentata (Marshall) Borkh. (Newhouse & Powell, 2021) and Juglans cinerea L. (Brennan et al., 2020). Hybridization in these cases might represent a significant evolutionary advantage, allowing populations to adapt and persist despite disease and pest pressure and environmental change. Given these possible realities, the incorporation of introgressed individuals into conservation plans may be intended in well-considered cases (see Galbusera et al., 2025; Hamilton & Miller, 2016). Conversely, the inadvertent inclusion of hybrids into conservation repositories can be highly undesirable (Rossetto et al., 2021; Winkler & Massatti, 2020). Introgression in seed orchards intended to provide plant material for landscape-level reforestation can result in the proliferation of unwanted traits related to growth or form (Xu et al., 2008). The presence of hybrids can also alter the optimal planting habitats of progeny by changing alleles related to site specificity; ecologically divergent taxa could produce offspring with novel site preferences when allowed to interbreed (Arnold, 2006; Rieseberg et al., 1999). This divergence can result in a failure to properly match genotypes to their optimal site and undermine planting success. Furthermore, hybridization events can result in loss of fitness due to the disruption of coadapted gene complexes (Orr, 1996). In situations where such outbreeding depression is evident, where the conservation of traits specific to one of the parental taxa is highly desired, or where maintaining species purity is a top priority (as in many conservation-oriented projects), introgression within orchards can be minimized through careful selection of genotypes. Regardless of whether a practitioner is interested in the exclusion or the intentional preservation of hybrid genotypes, an adequate understanding of introgression levels present in a focal area is helpful prior to initiating ex situ conservation actions. After putative populations are located on the landscape, a thorough review of documented crosses with the focal species, followed by morphological or genomic analysis, can help elucidate their taxonomic status. Sampling the focal range edge in tandem with populations where hybridization is less likely (such as at range cores, in some instances) can provide a reference for detecting introgression and its frequency. Genomic hybrid analysis can then be performed using genetic assignment and admixture modeling tools, such as programs like NewHybrids (Anderson, 2008) or STRUCTURE (Pritchard et al., 2000). While it may not be feasible to perform genomic analyses for every species for which conservation actions are intended, there is a heightened need to do so when working with taxa known for hybridizing potential, and where detection of hybrids based solely on morphology can be unreliable. For plantings intended for the large-scale and long-term production of plant material (i.e., many decades), the cost of genotyping individuals might pay off hundreds or thousands of times in reforestation or restoration benefits. In our case study , the discovery of widespread introgression from Q. lyrata in Tennessee swamp white oak populations (documented in Parker et al., 2025b), while intriguing from an evolutionary standpoint, severely complicates germplasm conservation planning. What might otherwise be a straightforward process of selecting genotypes based on distribution and genetic diversity instead raises difficult questions: How should hybrid populations be valued? Are hybrid swarms natural components of the landscape or products of anthropogenic disturbance? Does hybridization enhance local adaptation? What are the risks of tolerating versus excluding admixed individuals in conservation plantings? Even the technical task of setting thresholds for acceptable admixture becomes a consequential decision that can strongly influence the genetic diversity available for orchards and the genetic and phenotypic composition of future plantings (Allendorf et al., 2001). In this case, to assess admixture levels and guide genotype selection, all mature trees within the focal area were sampled and sequenced with RADseq (methodology detailed in Parker et al., 2025b). We used admixture estimates from sNMF (Frichot & François, 2015; Frichot et al., 2014) and STRUCTURE analyses to classify samples as either “admixed” ( Q sNMF < 0.84, Q STRUCTURE 0.84, Q STRUCTURE > 0.9), using the standard threshold ( Q STRUCTURE = 0.9) applied in other studies (Field et al., 2011; Porto-Hannes et al., 2021; van Wyk et al., 2017). While necessary to guide genotype selection, applying a binary threshold to continuous admixture data is inherently arbitrary. In situations like that seen at the trailing range edge of Q. bicolor , with widespread introgression and few pure genotypes, a more context-dependent approach to hybrid thresholding may be preferred. In this case, we also employed an ad hoc “broken-stick” method to identify the point on the hybrid index–genotype count curve (Figure 2) where an increasingly stringent threshold would lead to an accelerated decline in the number of genotypes available for orchards, and where decreasing thresholds would result in greater tolerance for introgression with only marginal gain of genotypes. This method might prove helpful more broadly when practitioners need to balance the minimization of extraspecific genomic elements with the retention of sufficient genotypes to maximize within-orchard diversity. Tailoring admixture tolerances based on conservation and management goals, the particularities of the focal species, and available resources, is likely to be more successful than a blind application of standardized thresholds across taxa. Incidentally, in this case, the “broken-stick” threshold coincided with the standard 0.9 Q STRUCTURE level (see Figure 2). Of the 70 Tennessee trees analyzed, 24 failed to meet this threshold (34%), with 46 “unadmixed” trees from eight locations remaining (65%) (“unadmixed” is used because the majority of these trees had Q values above the threshold but still well below 1.00, such as 0.95). In Parker et al. (2025b), genetic diversity analysis demonstrated that admixture thresholds have strong effects on allele counts and heterozygosity: less stringent admixture thresholds increase both metrics due to the inclusion of Q. lyrata- specific alleles in introgressed individuals (as shown in Figure 2B and Figure S3). Such increases might translate to enhanced adaptive potential, permitting range-edge populations to persist despite marginal conditions; alternatively, they could introduce undesirable traits or erode species-specific alleles in captive populations. These outcomes may even occur simultaneously, depending on the practitioner’s definition of “undesirable traits” and their specific management goals. The discovery of introgression within a species of interest often necessitates a reevaluation of conservation priorities and strategies (Wayne & Shaffer, 2016). Navigating the costs and benefits of hybrid conservation can be guided by decision-making models such as the RAD (Resist–Accept–Direct) framework (Lynch et al., 2021; Thompson et al., 2020), which provides a structured approach for the evaluation of management decisions in the face of uncertainty. Depending on conservation objectives, this framework can help managers determine whether goals are best served by resisting hybridization to preserve parental lineages, accepting hybridization as part of the existing ecological reality, or actively directing it to harness potential adaptive benefits. A precursor to using such a framework effectively is the intentional testing and evaluation of hybrids relative to management goals. While there are numerous examples from forestry of utilizing hybridization to enhance growth traits or disease resistance (for example, Steiner et al., 2017; Stettler & Canada, 1996), the deliberate production, testing, and deployment of hybrid genotypes for conservation-oriented goals has not been widely undertaken. Concerns over outbreeding depression, loss of locally adapted gene complexes, and genetic swamping may make managers hesitant to introduce hybrids into wild populations, let alone invest increasingly scarce time and money into their development (Allendorf et al., 2001; Rhymer & Simberloff, 1996). Effectively evaluating hybrid genotypes could take decades for many tree species, a prohibitively lengthy amount of time for most research programs. Additionally, infrastructure and policy related to conservation and restoration are generally organized around species rather than hybrids, a reality that can complicate or prevent the allocation of resources to projects involving hybrids (Allendorf et al., 2001; Piett et al., 2015). Given widespread genetic erosion due to land use change, climate change, and increasing disease and pest pressure, pursuing the testing and generation of hybrids where possible is an important endeavor. At the southern range margin of Q. bicolor , where introgression is pervasive, strict exclusion of admixed individuals from conservation plans is shortsighted. To provide a controlled setting to assess the suitability of admixed genotypes for reforestation, we have initiated development of a research orchard to contain the Q. bicolor lyrata hybrid genotypes. The orchard will be established using grafted admixed genotypes and will be planted following the same protocols outlined for the unadmixed orchards (described in section 5.3), though located in an area widely separate from the unadmixed orchards or other white oak (subsection Quercus ) seed orchards. This hybrid orchard will facilitate practical research into the value of hybrids for tree improvement as well as basic research into the nature of hybridization in Quercus. For example, the variation of introgressed alleles can be more deeply analyzed to determine specific allele combinations that might increase desirable traits like drought or heat tolerance. With a large amount of such information, genotypes can be precisely matched to planting locations while considering local environmental conditions and potential climate change. Such methods represent the technological cutting edge of forestry, tree improvement, and genomics; however, their advancement is contingent on the availability of robust sources of provenanced plant material. Seed orchards of relatively pure individuals will be established simultaneously to provide genetically diverse seed for reforestation at the southern range edge. Collectively, these seed orchards will allow for the preservation of diverse genotypes, including natural hybrids, without introducing opportunities for unpredictable crossbreeding among admixture levels. In the following sections, seed orchard development for the “unadmixed” trailing-edge genotypes will be discussed with attention to genetic considerations. Rarefaction curve for allelic representation of random samples with prediction curves (solid lines) based on the Michaelis-Menten Model. Genomically informed selection exceeds random selection from range core and Tennessee. Meanwhile, selection from all populations (yellow [JB1] [JP2] ) exceeds any other strategy. Notably, when compared with range cores, trailing-edge populations often display lower within-population genetic diversity, increased inbreeding, and greater among-population diversity due to fragmentation and reduced effective population size (Eckert et al., 2008; Hampe & Petit, 2005). If unaccounted for, these conditions can affect progeny fitness and erode the long-term value of ex situ collections for gene conservation (Brown & Marshall, 1995; Charlesworth & Charlesworth, 1987). As genetic diversity underpins a species' capacity to adapt to changing conditions (Allendorf et al., 2022), maximizing it in captive populations is a central goal for ex situ conservation. However, as genetic diversity estimates can vary widely among species (Frankham et al., 2010; Hamrick & Godt, 1996; Nevo et al., 1984), standard targets for such metrics are not readily applicable across taxa. Instead, species-specific baselines can be established by comparing diversity across spatial scales within a species. For example, differences in heterozygosity or allele counts can reveal whether marginal populations are genetically depauperate relative to robust core populations, or whether ex situ collections adequately capture species-wide variation (Gapare et al., 2008; Hoban & Schlarbaum, 2014). In addition to within-population diversity, analysis of population structure or among-population differentiation can provide insight into gene flow, historic demography, and local adaptation, as well as inform the delineation of conservation units (Casacci et al., 2014; Hoban & Schlarbaum, 2014; Moritz, 1994). In ex situ contexts, strong structure may result in mating incompatibilities or outbreeding depression when provenances are mixed, whereas weak structure might imply that provenance mixing is less risky (Bucharova et al., 2019; Lesica & Allendorf, 1999; Tong et al., 2020). These risks are often context and taxon-specific and are best avoided by assessing population structure genetically and minimizing unnecessary mixture between detectably diverged lineages. In summary, patterns of genetic variation across a species’ range (e.g., due to demography, migration, local adaptation) can leave lasting imprints on the health and adaptive capacity of natural populations. Careful evaluation of genetic variation, particularly through range core–range margin sampling designs, can facilitate the development of ex situ conservation strategies tailored to match the genetic characteristics of natural populations and ultimately improve conservation outcomes. Key questions related to genetic variation at the trailing edge of Q. bicolor include: Are trailing-edge populations genetically depauperate compared to those at the range core? Will such discrepancies impact allele counts (and ultimately allelic representation in orchards and minimum sample size estimates)? Do trailing-edge populations display enhanced population structure compared to the core, and should orchards be adjusted to accommodate this? Parker et al. (2025b) found that genetic diversity measures such as heterozygosity, allelic richness, and nucleotide diversity did not differ significantly between the range core and Tennessee populations sampled, indicating that demographic contraction (due to post-glacial migration and more recent anthropogenic impacts) has not yet caused genome-wide genetic erosion. However, some differences were detected between the range positions. For one, all of these measures had greater variance at the trailing edge (see section 3.3 in Parker et al., 2025b), perhaps due to the effects of demographic stochasticity or hybridization (for example, note the platykurtic distribution of H O in Tennessee populations compared to the range core, Figure 4). To evaluate if such differences would result in altered rates of allele capture at the trailing edge compared to the core, we used an allele rarefaction approach (Kalinowski, 2004) (methodology described in section 4 of the Supplementary Materials). Rarefaction showed that allele accumulation by random sampling was slower in Tennessee than in the range core. Consequently, achieving 90% allele representation in a random sample required an average of 95 Tennessee individuals but only 80 from the range core, while sampling from both simultaneously required just 50 (see where the prediction curves intersect the 90% allele dashed line, Figure 3), consistent with previous studies (for example, Hoban et al., 2018). This demonstrates that demographic constraint limits the efficacy of geographically restricted sampling (as suggested by Hoban & Schlarbaum, 2014). We also found that, as expected, filtering admixed individuals considerably reduced Tennessee’s total allele count from 7,092 to 6,104. These findings suggest that while genetic diversity measures were comparable, the relative dearth of unadmixed genotypes in Tennessee will limit the overall allelic representation possible in seed orchards without broader geographic sampling. To assess differences in among-population diversity and population differentiation between the range core and trailing edge, pairwise F ST , isolation by distance, and analysis of molecular variance (AMOVA) tests were conducted by Parker et al. (2025b). Despite detecting greater differentiation in Tennessee than at the range core, overall measures were low. Pairwise F ST ranged from 0.044 to 0.108 in Tennessee (compared to 0.039 to 0.061 in the range core), isolation by distance was absent, and AMOVA attributed only 7.37% of variation to among-population differences (compared to 4.08% in the range core). This pattern is consistent with other studies in Quercus that reported limited population structure, even in fragmented and intensively managed stands (Berg & Hamrick, 1995; Hung et al., 2025; Streiff et al., 1998), and suggests that Tennessee populations can be treated as broadly panmictic for orchard design. Slightly elevated differentiation at the trailing edge, possibly due to local adaptation, nonetheless highlights the conservation importance of these populations. Histogram of individual-level observed heterozygosity for Q. bicolor in the core and the trailing edge (Tennessee) of the range. Variances of H O differed (Levene’s test, p = 0.011), while central tendency did not (Wilcoxon test, p = 0.54). Although overall genetic diversity is comparable between core and trailing-edge populations, demographic constraint and reduced numbers of unadmixed genotypes limit the efficacy of random sampling at the range edge. This supports the conclusion that sampling guidelines derived from studies of genetic diversity distributions in range cores may not apply equally at range edges (similar to Gapare et al., 2008), particularly if populations are genetically depauperate, have differing patterns of spatial genetic structure, or if few genotypes are available. Together these findings define the genetic constraints and sampling targets that must be accommodated during genotype selection and seed orchard development. Under these conditions, the principal challenge for orchard development is selecting individuals for inclusion that balance maximizing allelic diversity, limiting relatedness, and retaining biologically meaningful structure within the limits of a spatially constrained seed orchard. Meeting this challenge requires a transparent framework for genotype selection that translates genetic patterns into practical conservation decisions. Networks based on the genomic relationship matrix with a kinship threshold of 0.092. (A) Opportunistic sample ( n = 34) and (B) Genomically informed orchard selection ( n = 33). Note the absence of high kinship groupings in B, showing that the inbreeding potential is reduced through intentional genotype selection. The genetic goals of an ex situ seed orchard include conserving alleles, representing and maintaining meaningful population structure (adaptive differences across diverged populations), and preventing breeding among closely related individuals. Given the high costs of land acquisition and long-term orchard maintenance, it is crucial to size orchards efficiently while still achieving genetic and seed production goals. Comprehensive delineation of populations across the study area (as described in section 2), followed by genomic characterization of those populations (as described in sections 3 and 4), enables more effective optimization of genotype selection and more rigorous evaluation of orchard design relative to genetic objectives. Allele counts can be used to appraise the allelic representation of germplasm repositories, when compared to the focal area or whole range. Previously established guidelines concerning the ideal representation of alleles in germplasm conservation repositories have recommended sampling plans that capture thresholds of 90 or 95% of the total alleles found in the species, termed a minimum sample size estimate (Brown & Marshall, 1995; Hoban, 2019). Because allele diversity declines after establishment due to both intentional and unintentional selection, maximizing initial diversity is essential (Basey et al., 2015; Kitzmiller, 1990; Mitton & Jeffers, 1989). Maximum initial genetic diversity of repositories can be achieved through optimized sampling practices (Brown & Marshall, 1995; Guerrant et al., 2014), although it is important for collection plans to account for the expected losses noted above (Hoban, 2019). By using the range-core sampling as a benchmark, other standard metrics can be used to quantify and compare genetic diversity in range-edge populations and their corresponding ex situ collections. For example, the distribution of individual observed heterozygosity (H O ) can be compared between range-core and focal-area samples to identify outliers during genotype selection. Because low heterozygosity, often reflective of inbreeding, is frequently associated with elevated mutation load and reduced fitness, excluding such outliers helps minimize the risk of transmitting deleterious genetic effects to orchard progeny. Furthermore, when populations are geographically and demographically constrained, intensive sampling within them risks oversampling close relatives, resulting in increased inbreeding within orchards despite capturing higher numbers of alleles (as shown with range edge populations of European ash by Hoban et al., 2018). Genomic estimates of relatedness can thus permit deeper sampling of constrained populations while excluding redundant genotypes, thereby optimizing the balance between maximizing allele capture and minimizing relatedness. Here, with these considerations in mind, we outline some of our approaches to genotype selection and evaluate the efficacy of orchards established at the trailing range edge of Q. bicolor . By integrating information on introgression, diversity, population structure, and kinship, genotype selection is optimized to achieve conservation goals within practical orchard constraints. As plant material collection and cloning were initiated prior to genomic analysis, an orchard of successful grafts was already established at the University of Tennessee’s Plateau AgResearch and Education Center near Crossville, Tennessee prior to genomic analysis. Relatedness and allele counts were contrasted between this subset of opportunistically grafted genotypes (referred to hereafter as the “opportunistic sample”) and the genomically informed genotype selection to evaluate how an informed sampling strategy might differ from an uninformed sampling strategy. To begin genotype selection, an admixture threshold was first applied to the available Tennessee genotypes (QSTRUCTURE < 0.9) based on the hybrid index–genotype count curve (Figure 2), as described in Section 3.2. Then, using the H O distribution for the range core as a guide, one outlier with low H O was removed (far left green bar, Figure 4). Relatedness among the remaining genotypes was then estimated using an identity by state (IBS) approach implemented in the gl.grm function in dartR (Mijangos et al., 2022). To visualize kinship estimates for different subsets of individuals, networks based on the genomic relationship matrix for the Tennessee trees were generated using the 0.092 kinship estimation threshold (e.g., Figure 5), corresponding to the lower bound of the 95% confidence interval for the half-sibling kinship estimation (mean kinship = 0.125, C.I. = 0.092 – 0.158) based on Speed and Balding (2015). This threshold will ensure that individuals selected are at least less related than half-siblings. Kinship estimates detected several closely related pairs in Tennessee populations (see Supplemental Materials, Figure S4). Individuals with numerous close relationships were strategically removed. Following these initial filtering steps, the CoreHunter program implemented in R was used to guide genotype selections (De Beukelaer et al., 2018). CoreHunter optimizes the selection of germplasm subsets by maximizing genetic distance based on the modified Rogers’ distance between samples to increase the average entry-to-nearest-entry distance (Wright, 1984). Sample sizes ranging from 20 to 40 individuals were tested, and measures of relatedness were calculated for each sample. The CoreHunter subset with the largest sample size that did not contain any pairwise kinship estimates greater than 0.092 was considered the optimal subsample for seed orchard establishment. Using this method, the resulting genomically informed orchard selection included 33 individuals from all eight populations remaining after admixture thresholding. Details of the 33 samples selected in this subset are provided in the Supplementary Materials (Table S3). This selection will allow 95.3% of the Tennessee alleles detected and 76.3% of the total alleles detected (after hybrid filtering) to be represented in the orchard, a significant achievement for a regionally focused conservation effort. Despite being a smaller sample size than the opportunistic sample (33 in this subset compared to the 34 in the opportunistic sample), this optimized orchard selection offers a significant improvement in allelic representation over the opportunistic sample, which only contains 72.6% of the total alleles and 90.6% of the Tennessee alleles. Furthermore, the selection includes no familial relationships greater than the first cousin level, in contrast to several more closely related pairs contained in the opportunistic sample (Figure 5). When compared to the opportunistic sample, this selection represents more of the species’ total allelic diversity and drastically reduces relatedness within the orchard while requiring fewer genotypes. In addition to increasing genetic diversity of progeny and potentially reducing postzygotic inbreeding depression, maximizing genetic distance between ortets can result in increased seed set for outbreeding species like oaks (Dow & Ashley, 1998; Stauffer & Adams, 1993; Streiff et al., 1999; Wojacki et al., 2019). By informing orchard designs with genomic data, these seed orchards will more fully preserve the adaptive and reproductive potential of southern swamp white oak populations while providing a robust source of seed for restoration. † Blue-yellow lines suggest half-sibling relationships, yellow-red suggest full-sibling or parent-offspring relationships. Node colors correspond to populations. The final outcome of this work is the establishment of conservation seed orchards based on trailing-edge swamp white oak genotypes. To develop seed orchards, clones of selected genotypes were generated via whip-and-tongue grafting, a method that has proven successful in other oak species (S.E. Schlarbaum, personal communication). Of the 33 samples in the optimized orchard selection, 23 have already been successfully grafted, with several genotypes containing enough replicates to complete both a primary and a duplicate orchard. The primary orchard will be located at the University of Tennessee’s Highland Rim AgResearch and Education Center near Springfield, Tennessee. A secondary duplicate orchard will be established in a disjunct location (location yet to be determined) to ensure redundancy in case of natural disaster, disease outbreak, or other catastrophe. A third orchard will be established using admixed genotypes (as described in Section 4.3). Further details on grafting and orchard design are available in the Supplemental Materials. Importantly, close partnerships between UT–TIP and state and federal agencies, such as the Tennessee Department of Agriculture’s Division of Forestry, the Tennessee Wildlife Resources Agency, the Tennessee Valley Authority, and the U.S. Forest Service, ensure that appropriate outlets are in place for the propagation, distribution, and planting of orchard-produced seed. The success of ex situ conservation actions ultimately hinges on such partnerships. If adequate resources do not exist to manage orchards and enable the harvesting of seed and production of seedlings when orchards begin bearing (which may be decades after orchard planting), then the ultimate value of orchards will be severely undermined. Effective advancement of conservation goals thus depends on collaboration among diverse agencies. Table 1. Summary of key steps in the development of informed range edge seed orchards Step What happens Data/ Software/ Tools/ Resources/ Skills Outcomes and application to Q. bicolor case study I. Compile Occurrence Knowledge ↓ Aggregate occurrence records ● Verify that records are properly identified and naturally occurring GBIF (free, online, often has specimen images), herbarium records (often digitized, available online) ● Skills in species identification Historic herbarium records in TN spanned 23 counties ● Putative locations compiled from across the Q. bicolor range and filtered, resulting in 740 occurrences (Figure S1) I. Predict Occurrence ↓ Create species distribution model ● Incorporate land use information to understand preferred habitat and lost habitat Biologically relevant rasterized environmental data for the study area ● GIS-capable environment (e.g., ArcGIS/QGIS, R) and species distribution modeling tools (e.g., MaxEnt) ● Considerable computing power may be required Species distribution models built using MaxEnt (Figure 1) ● Models indicate that suitable habitat intersects heavily with altered landscapes ● Highly suitable areas targeted for field surveys and to guide restoration I. Update Occurrence Knowledge ↓ Survey areas of predicted habitat from the model to find new occurrences ● Compile confirmed locations into comprehensive list of individuals available for conservation Commitment of time and resources (transportation costs, labor, etc.) ● Access to land (may require coordinating with landowners/ managers) ● Ability to identify species in the field Numerous records were determined to be misidentified, no longer extant, or of cultivated origin ● Putative Q. bicolor confirmed in 13 locations (Figure 1) II. Detect Hybrids ↓ Obtain tissue samples from range edge and range core populations, and from co-occurring congeners ● Extract and sequence DNA ● Use hybrid detection software ● Establish threshold for designating individuals as hybrids Costs associated with tissue collection (travel, field supplies, etc.) and DNA extraction and sequencing ● Ability to process sequence reads (may require bioinformatics training) ● Assignment or admixture modeling tools (e.g., NewHybrids, STRUCTURE, PCA) ● Consideration of trade-offs when using threshold based hybrid identification Tissue collected from 76 trees in TN (all mature trees found through field surveying) and 66 trees in the range core ● Widespread introgression from Q. lyrata detected in TN ● Thresholds established ( Q sNMF < 0.84, Q STRUCTURE < 0.9) for designating genotypes as “admixed” (Figure 2) ● 46 unadmixed individuals identified III. Assess Genetic Diversity ↓ Determine if genetic diversity is reduced in range edge ● Resample DNA dataset to identify minimum sample size estimate (MSSE) Population genetics software or R packages for measuring heterozygosity, F IS , allelic richness, nucleotide diversity, etc. ● R code for resampling simulations Genetic diversity was found to be comparable between range core and TN ● However, fewer available genotypes limits allele representation (larger MSSE when only sampling from TN) (Figure 3) III. Assess Genetic Structure ↓ Determine if strong population boundaries exist using neutral and ‘adaptive’ genes Population structure inference tools (e.g., STRUCTURE) and genetic differentiation metrics (e.g., F ST , AMOVA; can be calculated using various R packages) ● Consideration of impact of differentiation on orchard (population representation, outbreeding depression, assortative mating) More population structure detected in TN than the range core ● Yet, overall weak population structure supports combining provenances into a single trailing edge orchard IV. Create an Optimal Collection ↓ Create a collection that minimizes relatedness and maximizes genetic distance among individuals, while meeting genetic diversity goals Core subset selection tools (e.g., CoreHunter program) ● Estimates of relatedness ● Data regarding genetic diversity and structure from the previous steps Optimal subset of 33 minimally related, maximally genetically distant individuals selected for unadmixed orchard establishment (with 95.3% of TN alleles represented) ● All populations containing unadmixed genotypes represented IV. Establish Seed Orchards Clone selected genotypes and plant into managed orchards for germplasm conservation and long-term and large-scale seed production Resources and staff to travel to field sites and collect germplasm ● Facilities and staff for generating clones, housing and caring for plant material, etc. ● Land for planting with long-term ownership, management, and maintenance stability Germplasm collected and clones generated through grafting (with 49 individuals cloned to date) ● Duplicate Q. bicolor orchards established from select unadmixed genotypes ● A third, hybrid orchard established using admixed genotypes (in progress) † Roman numerals correspond to the major headings of the main text (minus the introduction). By integrating geospatial modeling, population genomic analysis, and ecological and evolutionary considerations with field surveying, grafting, and orchard design, we have presented a holistic strategy for conserving range marginal populations of tree species. The application of this framework to our case study with Q. bicolor has resulted in improved knowledge of the species’ occurrence, habitat preferences, introgression levels, hybridization potential, genetic diversity, and genetic structure at the trailing range edge. By guiding field surveying and genotype selection, this information has ultimately translated into significant improvements in seed orchard design when compared to relatively uninformed sampling strategies. The strategy outlined here (and summarized in Table 1) can be adapted to other taxa to ensure efficient and robust ex situ conservation implementation. In this study, we demonstrate that conservation of trailing edge genotypes can present unique challenges to practitioners and that standard protocols guiding the establishment of ex situ resources may need to be tailored for range margins. Of special note is the discovery of cryptic hybrids in trailing edge populations of Q. bicolor. We encourage greater awareness amongst practitioners of the possibility of undetected introgression in trailing-edge populations and their associated ex situ resources and recommend more intentional evaluation of hybrid genotypes relative to management goals and the evolutionary potential of range-edge populations. Conservation of forest genetic resources at trailing range edges is a critical endeavor given the rampant loss of forested land and the pressing effects of climate change. While many studies have called for the need to conserve trailing-edge genotypes (Hampe & Petit, 2005; Rehm et al., 2015) and the need to facilitate seed transfer from “warm-edge” range positions (Aitken & Bemmels, 2016; Girardin et al., 2021), comprehensive accounts of the methodologies and decision-making processes involved in establishing range-edge germplasm resources are lacking. By combining practical descriptions of applied actions with theoretical considerations, we hope that this work will fill this void and aid and inspire others to undertake germplasm conservation projects across trailing range edges for a wide diversity of species, thereby bolstering the adaptive potential and long-term viability of these species in the face of environmental change. Jesse B. Parker: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Resources, Data Curation, Writing – review & editing, Writing – original draft, Visualization, Supervision, Project Administration, Funding Acquisition. Sean Hoban: Writing – review & editing, Validation, Methodology. Laura M. Thompson: Writing – review & editing, Validation, Supervision, Methodology, Funding Acquisition. Scott E. Schlarbaum: Writing – review & editing, Validation, Supervision, Methodology, Project Administration, Funding Acquisition. The authors declare no conflicts of interest. We thank Dr. John Bradford for providing helpful comments on the manuscript. Financial support for this work came from the McIntire-Stennis Cooperative Forestry Research funding (Project E11-2215) and from the Dr. F. Martin Bronson Endowment to UT-TIP. This work was conducted as an initiative of the University of Tennessee Tree Improvement Program (UT-TIP). Jason Hogan and TJ Graveline were responsible for grafting and graft care. Further support was provided by Ami Sharp, Matthew Barnicki, and Erin Victorson. Field permits and site access were generously granted by the Tennessee Wildlife Resources Agency, Pilcher Park Nature Center, Indiana Dunes National Park, the Indiana Department of Natural Resources, ACRES Land Trust, the Ohio Department of Natural Resources, and several private landowners. Mention of trade names or commercial products is solely for descriptive purposes and does not imply endorsement by the U.S. Government. Raw RADseq reads will be available in the NCBI sequence read archive (BioProject: PRJNA1260989) upon manuscript publication. Code and additional data used for this study are available on GitHub ( https://github.com/jesseparkertrees/Quercus_bicolor _conservation). Aitken, S. N., & Bemmels, J. B. (2016). Time to get moving: assisted gene flow of forest trees. Evol Appl , 9 (1), 271-290. https://doi.org/10.1111/eva.12293 Allendorf, F. W., Funk, W. C., Aitken, S. N., Byrne, M., Luikart, G., & Antunes, A. (2022). Conservation and the Genomics of Populations . OUP Oxford. https://books.google.com/books?id=OmlbEAAAQBAJ Allendorf, F. W., Leary, R. F., Spruell, P., & Wenburg, J. K. (2001). The problems with hybrids: setting conservation guidelines. Trends in Ecology & Evolution , 16 (11), 613-622. https://doi.org/https://doi.org/10.1016/S0169-5347(01)02290-X Anderson, E. C. (2008). Bayesian inference of species hybrids using multilocus dominant genetic markers. Philos Trans R Soc Lond B Biol Sci , 363 (1505), 2841-2850. https://doi.org/10.1098/rstb.2008.0043 Arnold, M. L. (2006). Evolution Through Genetic Exchange . OUP Oxford. https://books.google.com/books?id=R5QUDAAAQBAJ Basey, A. C., Fant, J. B., & Kramer, A. T. (2015). Producing native plant materials for restoration: 10 rules to collect and maintain genetic diversity. Native Plants Journal , 16 (1), 37-53. https://doi.org/10.3368/npj.16.1.37 Beatty, G. E., Philipp, M., & Provan, J. (2010). Unidirectional hybridization at a species’ range boundary: implications for habitat tracking. Diversity and Distributions , 16 (1), 1-9. https://doi.org/https://doi.org/10.1111/j.1472-4642.2009.00616.x Beaty, F., Gehman, A.-L. M., Brownlee, G., & Harley, C. D. G. (2023). Not just range limits: Warming rate and thermal sensitivity shape climate change vulnerability in a species range center. Ecology , 104 (12), e4183. https://doi.org/https://doi.org/10.1002/ecy.4183 Beck, J., Böller, M., Erhardt, A., & Schwanghart, W. (2014). Spatial bias in the GBIF database and its effect on modeling species' geographic distributions. Ecological Informatics , 19 , 10-15. https://doi.org/https://doi.org/10.1016/j.ecoinf.2013.11.002 Berg, E. E., & Hamrick, J. L. (1995). FINE-SCALE GENETIC STRUCTURE OF A TURKEY OAK FOREST. Evolution , 49 (1), 110-120. https://doi.org/https://doi.org/10.1111/j.1558-5646.1995.tb05963.x Blows, M. W., & Hoffmann, A. A. (2005). A reassessment of genetic limits to evolutionary change. Ecology , 86 (6), 1371-1384. https://doi.org/https://doi.org/10.1890/04-1209 Brennan, A. N., McKenna, J. R., Hoban, S. M., & Jacobs, D. F. (2020). Hybrid Breeding for Restoration of Threatened Forest Trees: Evidence for Incorporating Disease Tolerance in Juglans cinerea [Original Research]. Frontiers in Plant Science , Volume 11 - 2020 . https://doi.org/10.3389/fpls.2020.580693 Brown, A., & Marshall, D. (1995). A basic sampling strategy: theory and practice. Collecting plant genetic diversity: technical guidelines. CAB International, Wallingford , 75-91. Bucharova, A., Bossdorf, O., Hölzel, N., Kollmann, J., Prasse, R., & Durka, W. (2019). Mix and match: regional admixture provenancing strikes a balance among different seed-sourcing strategies for ecological restoration. Conservation Genetics , 20 (1), 7-17. https://doi.org/10.1007/s10592-018-1067-6 Cahill, A. E., Aiello-Lammens, M. E., Caitlin Fisher-Reid, M., Hua, X., Karanewsky, C. J., Ryu, H. Y., Sbeglia, G. C., Spagnolo, F., Waldron, J. B., & Wiens, J. J. (2014). Causes of warm-edge range limits: systematic review, proximate factors and implications for climate change. Journal of Biogeography , 41 (3), 429-442. https://doi.org/https://doi.org/10.1111/jbi.12231 Casacci, L. P., Barbero, F., & Balletto, E. (2014). The “Evolutionarily Significant Unit” concept and its applicability in biological conservation. Italian Journal of Zoology , 81 (2), 182-193. https://doi.org/10.1080/11250003.2013.870240 Cerrejón, C., Valeria, O., Marchand, P., Caners, R. T., & Fenton, N. J. (2021). No place to hide: Rare plant detection through remote sensing. Diversity and Distributions , 27 (6), 948-961. https://doi.org/https://doi.org/10.1111/ddi.13244 Charlesworth, D., & Charlesworth, B. (1987). INBREEDING DEPRESSION AND ITS EVOLUTIONARY CONSEQUENCES. Annual Review of Ecology, Evolution, and Systematics , 18 (Volume 18, 1987), 237-268. https://doi.org/https://doi.org/10.1146/annurev.es.18.110187.001321 De Beukelaer, H., Davenport, G. F., & Fack, V. (2018). Core Hunter 3: flexible core subset selection. BMC Bioinformatics , 19 (1), 203. https://doi.org/10.1186/s12859-018-2209-z de Siqueira, M. F., Durigan, G., de Marco Júnior, P., & Peterson, A. T. (2009). Something from nothing: Using landscape similarity and ecological niche modeling to find rare plant species. Journal for Nature Conservation , 17 (1), 25-32. https://doi.org/https://doi.org/10.1016/j.jnc.2008.11.001 De Vitis, M., Hay, F. R., Dickie, J. B., Trivedi, C., Choi, J., & Fiegener, R. (2020). Seed storage: maintaining seed viability and vigor for restoration use. Restoration Ecology , 28 (S3), S249-S255. https://doi.org/https://doi.org/10.1111/rec.13174 Dewitz, J. (2023). National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey data release. https://doi.org/10.5066/P9JZ7AO3 . Dow, B., & Ashley, M. (1998). High levels of gene flow in bur oak revealed by paternity analysis using microsatellites. Journal of Heredity , 89 (1), 62-70. https://doi.org/10.1093/jhered/89.1.62 Eckert, C. G., Samis, K. E., & Lougheed, S. C. (2008). Genetic variation across species’ geographical ranges: the central–marginal hypothesis and beyond. Molecular Ecology , 17 (5), 1170-1188. https://doi.org/https://doi.org/10.1111/j.1365-294X.2007.03659.x Ellstrand, N. C. (1992). Gene Flow by Pollen: Implications for Plant Conservation Genetics. Oikos , 63 (1), 77-86. https://doi.org/10.2307/3545517 Ellstrand, N. C., & Elam, D. R. (1993). Population Genetic Consequences of Small Population Size: Implications for Plant Conservation. Annual Review of Ecology and Systematics , 24 , 217-242. http://www.jstor.org/stable/2097178 Engelmann, F., & Engels, J. M. M. (2002). Technologies and strategies for ex situ conservation. ESRI. (2023). ArcGIS Pro . In (Version 3.2) Environmental Systems Research Institute. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview Evans, L. M., Allan, G. J., & Whitham, T. G. (2012). Populus hybrid hosts drive divergence in the herbivorous mite, Aceria parapopuli: implications for conservation of plant hybrid zones as essential habitat. Conservation Genetics , 13 (6), 1601-1609. https://doi.org/10.1007/s10592-012-0409-z Fargione, J., Haase, D. L., Burney, O. T., Kildisheva, O. A., Edge, G., Cook-Patton, S. C., Chapman, T., Rempel, A., Hurteau, M. D., Davis, K. T., Dobrowski, S., Enebak, S., De La Torre, R., Bhuta, A. A. R., Cubbage, F., Kittler, B., Zhang, D., & Guldin, R. W. (2021). Challenges to the Reforestation Pipeline in the United States [Original Research]. Frontiers in Forests and Global Change , 4 . https://doi.org/10.3389/ffgc.2021.629198 Fernández, A., León-Lobos, P., Contreras, S., Ovalle, J. F., Sershen, van der Walt, K., & Ballesteros, D. (2023). The potential impacts of climate change on ex situ conservation options for recalcitrant-seeded species [Review]. Frontiers in Forests and Global Change , Volume 6 - 2023 . https://doi.org/10.3389/ffgc.2023.1110431 Field, D. L., Ayre, D. J., Whelan, R. J., & Young, A. G. (2011). Patterns of hybridization and asymmetrical gene flow in hybrid zones of the rare Eucalyptus aggregata and common E. rubida. Heredity , 106 (5), 841-853. https://doi.org/10.1038/hdy.2010.127 Frankel, O. H., Brown, A. H. D., & Burdon, J. J. (1995). The Conservation of Plant Biodiversity . Cambridge University Press. https://books.google.com/books?id=KMNqyaNpTSAC Frankham, R., Ballou, J. D., & Briscoe, D. A. (2010). Introduction to Conservation Genetics . Cambridge University Press. https://books.google.com/books?id=vLZKnsCk89wC Frichot, E., & François, O. (2015). LEA: An R package for landscape and ecological association studies. Methods in Ecology and Evolution , 6 (8), 925-929. https://doi.org/https://doi.org/10.1111/2041-210X.12382 Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G., & François, O. (2014). Fast and efficient estimation of individual ancestry coefficients. Genetics , 196 (4), 973-983. https://doi.org/10.1534/genetics.113.160572 Galbusera, P., Bertola, L. D., Ball, A. D., Bishop von Wettberg, E., Bruford, M. W., Helsen, P., Hoban, S., Fienieg, E., Quinzin, M. C., Russo, I.-R. M., Segelbacher, G., Ting, N., Waits, L. P., Stronen, A. V., & Kopatz, A. (2025). Hybrids Along a Natural-Anthropogenic Gradient: Improving Policy and Management Across All Levels of Biodiversity. Conservation Letters , 18 (6), e13158. https://doi.org/https://doi.org/10.1111/conl.13158 Gapare, W. J., Yanchuk, A. D., & Aitken, S. N. (2008). Optimal sampling strategies for capture of genetic diversity differ between core and peripheral populations of Picea sitchensis (Bong.) Carr. Conservation Genetics , 9 (2), 411-418. https://doi.org/10.1007/s10592-007-9353-8 Gargano, D., Bernardo, L., Rovito, S., Passalacqua, N. G., & Abeli, T. (2022). Do marginal plant populations enhance the fitness of larger core units under ongoing climate change? Empirical insights from a rare carnation. AoB PLANTS , 14 (3). https://doi.org/10.1093/aobpla/plac022 Gaston, K. J. (2009). Geographic range limits: achieving synthesis. Proceedings of the Royal Society B: Biological Sciences , 276 (1661), 1395-1406. https://doi.org/doi:10.1098/rspb.2008.1480 GBIF.org. (2024). GBIF Occurrence Download (The Global Biodiversity Information Facility. https://doi.org/10.15468/DL.BDUJ8C Girardin, M. P., Isabel, N., Guo, X. J., Lamothe, M., Duchesne, I., & Lenz, P. (2021). Annual aboveground carbon uptake enhancements from assisted gene flow in boreal black spruce forests are not long-lasting. Nature Communications , 12 (1), 1169. https://doi.org/10.1038/s41467-021-21222-3 Guerrant, E. O., Havens, K., & Vitt, P. (2014). Sampling for Effective Ex Situ Plant Conservation. International Journal of Plant Sciences , 175 (1), 11-20. https://doi.org/10.1086/674131 Hällfors, M. H., Liao, J., Dzurisin, J., Grundel, R., Hyvärinen, M., Towle, K., Wu, G. C., & Hellmann, J. J. (2016). Addressing potential local adaptation in species distribution models: implications for conservation under climate change. Ecological Applications , 26 (4), 1154-1169. https://doi.org/https://doi.org/10.1890/15-0926 Hamilton, J. A., & Miller, J. M. (2016). Adaptive introgression as a resource for management and genetic conservation in a changing climate. Conservation Biology , 30 (1), 33-41. https://doi.org/https://doi.org/10.1111/cobi.12574 Hampe, A., & Petit, R. J. (2005). Conserving biodiversity under climate change: the rear edge matters. Ecology Letters , 8 (5), 461-467. https://doi.org/https://doi.org/10.1111/j.1461-0248.2005.00739.x Hamrick, J. L., & Godt, M. J. W. (1996). Effects of life history traits on genetic diversity in plant species. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences , 351 (1345), 1291-1298. https://doi.org/doi:10.1098/rstb.1996.0112 Hengeveld, R., & Haeck, J. (1982). The Distribution of Abundance. I. Measurements. Journal of Biogeography , 9 (4), 303-316. https://doi.org/10.2307/2844717 Hoban, S. (2019). New guidance for ex situ gene conservation: Sampling realistic population systems and accounting for collection attrition. Biological Conservation , 235 , 199-208. https://doi.org/https://doi.org/10.1016/j.biocon.2019.04.013 Hoban, S., da Silva, J. M., Hughes, A., Hunter, M. E., Kalamujić Stroil, B., Laikre, L., Mastretta-Yanes, A., Millette, K., Paz-Vinas, I., Bustos, L. R., Shaw, R. E., Vernesi, C., & Genetics, t. C. f. C. (2024). Too simple, too complex, or just right? Advantages, challenges, and guidance for indicators of genetic diversity. BioScience , 74 (4), 269-280. https://doi.org/10.1093/biosci/biae006 Hoban, S., Kallow, S., & Trivedi, C. (2018). Implementing a new approach to effective conservation of genetic diversity, with ash ( Fraxinus excelsior ) in the UK as a case study. Biological Conservation , 225 , 10-21. https://doi.org/https://doi.org/10.1016/j.biocon.2018.06.017 Hoban, S., & Schlarbaum, S. (2014). Optimal sampling of seeds from plant populations for ex-situ conservation of genetic biodiversity, considering realistic population structure. Biological Conservation , 177 , 90-99. https://doi.org/https://doi.org/10.1016/j.biocon.2014.06.014 Hord, A. M., Fischer, D. G., Schweitzer, J. A., LeRoy, C. J., Whitham, T. G., & Bailey, J. K. (2025). Hybrid introgression as a mechanism of rapid evolution and resilience to climate change in a riparian tree species. Communications Biology , 8 (1), 1173. https://doi.org/10.1038/s42003-025-08410-3 Hung, T. H., Formaggia, E., Morley, L., Kirby, K., Salguero-Gómez, R., Sheldon, B. C., & MacKay, J. J. (2025). Genetic diversity and population structure of pedunculate oaks (Quercus robur) in Wytham Woods. PLANTS, PEOPLE, PLANET , 7 (6), 1789-1802. https://doi.org/https://doi.org/10.1002/ppp3.70042 iNaturalist. (2024). iNaturalist https://www.inaturalist.org/ Jalonen, R., Valette, M., Boshier, D., Duminil, J., & Thomas, E. (2018). Forest and landscape restoration severely constrained by a lack of attention to the quantity and quality of tree seed: Insights from a global survey. Conservation Letters , 11 (4), e12424. https://doi.org/https://doi.org/10.1111/conl.12424 Jetz, W., McPherson, J. M., & Guralnick, R. P. (2012). Integrating biodiversity distribution knowledge: toward a global map of life. Trends in Ecology & Evolution , 27 (3), 151-159. https://doi.org/https://doi.org/10.1016/j.tree.2011.09.007 Kalinowski, S. T. (2004). Counting Alleles with Rarefaction: Private Alleles and Hierarchical Sampling Designs. Conservation Genetics , 5 (4), 539-543. https://doi.org/10.1023/B:COGE.0000041021.91777.1a Kartesz, J. T. (2023). Biota of North America Program (BONAP): North American Plant Atlas (Biota of North America Program (BONAP). http://bonap.net/napa Kitzmiller, J. H. (1990). Managing genetic diversity in a tree improvement program. Forest Ecology and Management , 35 (1), 131-149. https://doi.org/https://doi.org/10.1016/0378-1127(90)90237-6 Lesica, P., & Allendorf, F. W. (1999). Ecological Genetics and the Restoration of Plant Communities: Mix or Match? Restoration Ecology , 7 (1), 42-50. https://doi.org/https://doi.org/10.1046/j.1526-100X.1999.07105.x Little, E. L., & Viereck, L. A. (1971). Atlas of United States Trees: (no.1146). Conifers and important hardwoods, by E.L. Little, Jr . U.S. Government Printing Office. https://books.google.com/books?id=mlovAAAAYAAJ Lomolino, M. V. (2004). Conservation biogeography. Frontiers of Biogeography: new directions in the geography of nature , 293 , 293-296. Lynch, A. J., Thompson, L. M., Morton, J. M., Beever, E. A., Clifford, M., Limpinsel, D., Magill, R. T., Magness, D. R., Melvin, T. A., Newman, R. A., Porath, M. T., Rahel, F. J., Reynolds, J. H., Schuurman, G. W., Sethi, S. A., & Wilkening, J. L. (2021). RAD Adaptive Management for Transforming Ecosystems. BioScience , 72 (1), 45-56. https://doi.org/10.1093/biosci/biab091 MacKenzie, C. M., Mittelhauser, G., Miller-Rushing, A. J., & Primack, R. B. (2019). Floristic Change in New England and New York: Regional Patterns of Plant Species Loss and Decline. Rhodora , 121 (985), 1-36, 36. https://doi.org/10.3119/18-04 Martin, N. H., Bouck, A. C., & Arnold, M. L. (2006). Detecting Adaptive Trait Introgression Between Iris fulva and I. brevicaulis in Highly Selective Field Conditions. Genetics , 172 (4), 2481-2489. https://doi.org/10.1534/genetics.105.053538 Mijangos, J. L., Gruber, B., Berry, O., Pacioni, C., & Georges, A. (2022). dartR v2: An accessible genetic analysis platform for conservation, ecology and agriculture. Methods in Ecology and Evolution , 13 (10), 2150-2158. https://doi.org/https://doi.org/10.1111/2041-210X.13918 Mitton, J., & Jeffers, R. (1989). The genetic consequences of mass selection for growth rate in Engelmann spruce. Silvae Genetica , 38 (1), 6–12. Moritz, C. (1994). Defining ‘evolutionarily significant units’ for conservation. Trends in Ecology & Evolution , 9 (10), 373-375. Muniz, A. C., Lemos-Filho, J. P., Souza, H. A., Marinho, R. C., Buzatti, R. S., Heuertz, M., & Lovato, M. B. (2020). The protected tree Dimorphandra wilsonii (Fabaceae) is a population of inter-specific hybrids: recommendations for conservation in the Brazilian Cerrado/Atlantic Forest ecotone. Annals of Botany , 126 (1), 191-203. https://doi.org/10.1093/aob/mcaa066 Nevo, E., Beiles, A., & Ben-Shlomo, R. (1984). The Evolutionary Significance of Genetic Diversity: Ecological, Demographic and Life History Correlates. In G. S. Mani, Evolutionary Dynamics of Genetic Diversity Berlin, Heidelberg. Newhouse, A. E., & Powell, W. A. (2021). Intentional introgression of a blight tolerance transgene to rescue the remnant population of American chestnut. Conservation Science and Practice , 3 (4), e348. https://doi.org/https://doi.org/10.1111/csp2.348 Orr, H. A. (1996). Dobzhansky, Bateson, and the genetics of speciation. Genetics , 144 (4), 1331-1335. https://doi.org/10.1093/genetics/144.4.1331 Ortego, J., Gugger, P. F., Riordan, E. C., & Sork, V. L. (2014). Influence of climatic niche suitability and geographical overlap on hybridization patterns among southern Californian oaks. Journal of Biogeography , 41 (10), 1895-1908. https://doi.org/https://doi.org/10.1111/jbi.12334 Parker, J. (2025). Conservation at the Trailing Edge: Hybridization, Population Genomics, and Environmental Change in Quercus bicolor [Master's Thesis, University of Tennessee]. https://trace.tennessee.edu/utk_gradthes/15506 Parker, J. B., Hansbrough, M., Lance, R., & Schlarbaum, S. E. (2025a). Saving the Near Extinct Harbison Hawthorn (Crataegus harbisonii): An Ex Situ Approach for Woody Plant Species Conservation. Forests , 16 (9), 1394. https://www.mdpi.com/1999-4907/16/9/1394 Parker, J. B., Hoban, S., Thompson, L. M., & Schlarbaum, S. E. (2025b). Evaluating the Central–Marginal Hypothesis: Introgression and Genetic Variation at the Trailing Edge of Quercus bicolor. Molecular Ecology , e70185. https://doi.org/https://doi.org/10.1111/mec.70185 Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling , 190 (3), 231-259. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2005.03.026 Piett, S., Hager, H. A., & Gerrard, C. (2015). Characteristics for evaluating the conservation value of species hybrids. Biodiversity and Conservation , 24 (8), 1931-1955. https://doi.org/10.1007/s10531-015-0919-3 Porto-Hannes, I., Burlakova, L. E., Zanatta, D. T., & Lasker, H. R. (2021). Boundaries and hybridization in a secondary contact zone between freshwater mussel species (Family:Unionidae). Heredity (Edinb) , 126 (6), 955-973. https://doi.org/10.1038/s41437-021-00424-x Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of Population Structure Using Multilocus Genotype Data. Genetics , 155 (2), 945-959. https://doi.org/10.1093/genetics/155.2.945 Provan, J., & Maggs, C. A. (2012). Unique genetic variation at a species' rear edge is under threat from global climate change. Proceedings of the Royal Society B: Biological Sciences , 279 (1726), 39-47. https://doi.org/doi:10.1098/rspb.2011.0536 Rehm, E. M., Olivas, P., Stroud, J., & Feeley, K. J. (2015). Losing your edge: climate change and the conservation value of range-edge populations. Ecology and Evolution , 5 (19), 4315-4326. https://doi.org/https://doi.org/10.1002/ece3.1645 Rhymer, J. M., & Simberloff, D. (1996). Extinction by hybridization and introgression. Annual Review of Ecology, Evolution, and Systematics , 27 (Volume 27, 1996), 83-109. https://doi.org/https://doi.org/10.1146/annurev.ecolsys.27.1.83 Ribicoff, G., Garner, M., Pham, K., Althaus, K. N., Cavender-Bares, J., Crowl, A. A., Gray, S., Gugger, P., Hahn, M., Liao, S., Manos, P. S., Mohn, R. A., Pearse, I. S., Steichmann, N. R., Tuffin, A. L., Whittemore, A. T., & Hipp, A. L. (2025). Introgression, Phylogeography, and Genomic Species Cohesion in the Eastern North American White Oak Syngameon. Molecular Ecology , n/a (n/a), e17822. https://doi.org/https://doi.org/10.1111/mec.17822 Rieseberg, L. H., Archer, M. A., & Wayne, R. K. (1999). Transgressive segregation, adaptation and speciation. Heredity (Edinb) , 83 ( Pt 4) , 363-372. https://doi.org/10.1038/sj.hdy.6886170 Rieseberg, L. H., Raymond, O., Rosenthal, D. M., Lai, Z., Livingstone, K., Nakazato, T., Durphy, J. L., Schwarzbach, A. E., Donovan, L. A., & Lexer, C. (2003). Major Ecological Transitions in Wild Sunflowers Facilitated by Hybridization. Science , 301 (5637), 1211-1216. https://doi.org/doi:10.1126/science.1086949 Rossetto, M., Yap, J.-Y. S., Lemmon, J., Bain, D., Bragg, J., Hogbin, P., Gallagher, R., Rutherford, S., Summerell, B., & Wilson, T. C. (2021). A conservation genomics workflow to guide practical management actions. Global Ecology and Conservation , 26 , e01492. https://doi.org/https://doi.org/10.1016/j.gecco.2021.e01492 SERNEC. (2024). SERNEC Specimen Records (Southeast Regional Network of Expertise and Collections. https://sernecportal.org/portal/collections/harvestparams.php Serrano, F. C., Vieira-Alencar, J. P. d. S., Díaz-Ricaurte, J. C., Valdujo, P. H., Martins, M., & Nogueira, C. d. C. (2023). The Wallacean Shortfall and the role of historical distribution records in the conservation assessment of an elusive Neotropical snake in a threatened landscape. Journal for Nature Conservation , 72 , 126350. https://doi.org/https://doi.org/10.1016/j.jnc.2023.126350 Speed, D., & Balding, D. J. (2015). Relatedness in the post-genomic era: is it still useful? Nat Rev Genet , 16 (1), 33-44. https://doi.org/10.1038/nrg3821 Stauffer, A., & Adams, W. (1993). Allozyme variation and mating system of three Douglas-fir stands in Switzerland. Silvae Genetica , 42 , 254-254. Steiner, K. C., Westbrook, J. W., Hebard, F. V., Georgi, L. L., Powell, W. A., & Fitzsimmons, S. F. (2017). Rescue of American chestnut with extraspecific genes following its destruction by a naturalized pathogen. New Forests , 48 (2), 317-336. https://doi.org/10.1007/s11056-016-9561-5 Stettler, R. F., & Canada, N. R. C. (1996). Biology of Populus and Its Implications for Management and Conservation . NRC Research Press. https://books.google.com/books?id=HvuTJC32C3YC Streiff, Ducousso, Lexer, Steinkellner, Gloessl, & Kremer. (1999). Pollen dispersal inferred from paternity analysis in a mixed oak stand of Quercus robur L. and Q. petraea (Matt.) Liebl. Molecular Ecology , 8 (5), 831-841. https://doi.org/https://doi.org/10.1046/j.1365-294X.1999.00637.x Streiff, R., Labbe, T., Bacilieri, R., Steinkellner, H., Glössl, J., & Kremer, A. (1998). Within‐population genetic structure in Quercus robur L. and Quercus petraea (Matt.) Liebl. assessed with isozymes and microsatellites. Molecular Ecology , 7 (3), 317-328. Taylor, S. A., & Larson, E. L. (2019). Insights from genomes into the evolutionary importance and prevalence of hybridization in nature. Nature Ecology & Evolution , 3 (2), 170-177. https://doi.org/10.1038/s41559-018-0777-y Thompson, L. M., Lynch, A. J., Beever, E. A., Engman, A. C., Falke, J. A., Jackson, S. T., Krabbenhoft, T. J., Lawrence, D. J., Limpinsel, D., Magill, R. T., Melvin, T. A., Morton, J. M., Newman, R. A., Peterson, J. O., Porath, M. T., Rahel, F. J., Sethi, S. A., & Wilkening, J. L. (2020). Responding to Ecosystem Transformation: Resist, Accept, or Direct? Fisheries , 46 (1), 8-21. https://doi.org/10.1002/fsh.10506 Tong, Y., Durka, W., Zhou, W., Zhou, L., Yu, D., & Dai, L. (2020). Ex situ conservation of Pinus koraiensis can preserve genetic diversity but homogenizes population structure. Forest Ecology and Management , 465 , 117820. https://doi.org/https://doi.org/10.1016/j.foreco.2019.117820 van Wyk, A. M., Dalton, D. L., Hoban, S., Bruford, M. W., Russo, I.-R. M., Birss, C., Grobler, P., van Vuuren, B. J., & Kotzé, A. (2017). Quantitative evaluation of hybridization and the impact on biodiversity conservation. Ecology and Evolution , 7 (1), 320-330. https://doi.org/https://doi.org/10.1002/ece3.2595 Villa-Machío, I., Heuertz, M., Álvarez, I., & Nieto Feliner, G. (2024). Demography-driven and adaptive introgression in a hybrid zone of the Armeria syngameon. Molecular Ecology , 33 (20), e17167. https://doi.org/https://doi.org/10.1111/mec.17167 Wagensommer, R. P. (2023). Floristic Studies in the Light of Biodiversity Knowledge and Conservation. Plants (Basel) , 12 (16). https://doi.org/10.3390/plants12162973 Wayne, R. K., & Shaffer, H. B. (2016). Hybridization and endangered species protection in the molecular era. Molecular Ecology , 25 (11), 2680-2689. https://doi.org/https://doi.org/10.1111/mec.13642 Whitham, T. G., Martinsen, G. D., Keim, P., Floate, K. D., Dungey, H. S., & Potts, B. M. (1999). Plant hybrid zones affect biodiversity: tools for a genetic-based understanding of community structure. Ecology , 80 (2), 416-428. https://doi.org/https://doi.org/10.1890/0012-9658(1999)080[0416:PHZABT]2.0.CO;2 Whitney, K. D., Randell, R. A., & Rieseberg, L. H. (2010). Adaptive introgression of abiotic tolerance traits in the sunflower Helianthus annuus. New Phytologist , 187 (1), 230-239. https://doi.org/https://doi.org/10.1111/j.1469-8137.2010.03234.x Wimp, G. M., Young, W. P., Woolbright, S. A., Martinsen, G. D., Keim, P., & Whitham, T. G. (2004). Conserving plant genetic diversity for dependent animal communities. Ecology Letters , 7 (9), 776-780. https://doi.org/https://doi.org/10.1111/j.1461-0248.2004.00635.x Winkler, D. E., & Massatti, R. (2020). Unexpected hybridization reveals the utility of genetics in native plant restoration. Restoration Ecology , 28 (5), 1047-1052. https://doi.org/https://doi.org/10.1111/rec.13189 Wojacki, J., Eusemann, P., Ahnert, D., Pakull, B., & Liesebach, H. (2019). Genetic diversity in seeds produced in artificial Douglas-fir (Pseudotsuga menziesii) stands of different size. Forest Ecology and Management , 438 , 18-24. https://doi.org/https://doi.org/10.1016/j.foreco.2019.02.012 Wright, S. (1984). Evolution and the Genetics of Populations, Volume 4: Variability Within and Among Natural Populations . University of Chicago Press. https://books.google.com/books?id=q0RDJf3K_aUC Xu, S., Tauer, C. G., & Nelson, C. D. (2008). Natural hybridization within seed sources of shortleaf pine (Pinus echinata Mill.) and loblolly pine (Pinus taeda L.). Tree Genetics & Genomes , 4 (4), 849-858. https://doi.org/10.1007/s11295-008-0157-x Information & Authors Information Version history V1 Version 1 02 December 2025 V2 Version 2 30 January 2026 Peer review timeline Published Forest Ecology and Management Version of Record 1 Sep 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords central-marginal hypothesis ex situ genetics germplasm range margin Authors Affiliations Jesse B. Parker 0009-0007-0189-2572 [email protected] View all articles by this author Sean Hoban View all articles by this author Laura M. Thompson View all articles by this author Scott E Schlarbaum View all articles by this author Metrics & Citations Metrics Article Usage 256 views 147 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jesse B. Parker, Sean Hoban, Laura M. Thompson, et al. Genomically informed seed orchard design for trailing-edge tree populations: a perspective from Quercus bicolor conservation. Authorea . 30 January 2026. DOI: https://doi.org/10.22541/au.176463669.93056001/v2 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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