University of Nevada, Reno
Habitat preferences, intraspecific variation, and restoration of a rare soil specialist in northern Nevada
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Natural Resources and Environmental Science
by
Jamey D. McClinton
Dr. Elizabeth A. Leger/Thesis Advisor
December, 2019
Copyright by Jamey D. McClinton 2019 All Rights Reserved
We recommend that the thesis prepared under our supervision by
Jamey D. McClinton
Entitled
Habitat preferences, intraspecific variation, and restoration of a rare soil specialist in northern Nevada
be accepted in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE
Elizabeth Leger, Ph.D., Advisor
Paul Verburg, Ph.D., Committee member
Thomas Parchman, Ph.D., Graduate School Representative
David W. Zeh, Ph.D., Dean, Graduate School
December-2019
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Abstract Edaphic specialization in plants is associated with the development of novel adaptations that frequently lead to speciation, causing unique edaphic environments to be associated with rare and endemic plant species worldwide. These species contribute significantly to global biodiversity, but are especially vulnerable to disturbance and climate change because of their inherently patchy distributions and locally adapted populations. Successful conservation of these species depends upon understanding their habitat requirements and the amounts and distributions of genetic and phenotypic diversity among populations. Little is known about the habitat requirements or levels of genetic and phenotypic diversity of edaphic specialists in the Great
Basin of the western United states. Therefore, to improve understanding of edaphic specialization in this region, and to create a foundation of knowledge for species conservation, we used phenotypic measurements in the field, greenhouse common garden studies, and next-generation genetic sequencing techniques to investigate the associations between soil variation and plant phenotypes, and between genetic and phenotypic diversity in Eriogonum crosbyae, a rare edaphic specialist on soils developed from hydrothermally altered volcanic ash in the north-western Great
Basin. We found that soil properties were poor predictors of site occupation among outcrops of known or potential habitat in our study area, and that site occupation could change over time. E. crosbyae showed phenotypically plastic responses to soil variation in the greenhouse, and there were associations between soil properties and plant form in the field. Growth was generally better in relatively milder and more fertile field soils when grown without competition, and differences in seedlings’ ability to establish in different soil types may partially explain the species’ patchy distribution in potential habitat. Our genetic analyses revealed high levels of nucleotide diversity and the presence of three highly differentiated genetic groups that often co-occurred within individual sites. The distribution of these groups across the landscape may be consistent with periods of allopatric diversification and subsequent secondary contact. Phenotypic diversity ii varied more clearly among groups than among sites dominated by a single group, and this variation was more apparent in seedlings grown in the greenhouse than in mature plants measured in the wild. Further studies exploring growth responses to variation in individual soil properties and plant performance in the presence of competition would improve understanding of the mechanisms underlying edaphic specialization in this species. Additionally, more information on the evolutionary history and taxonomy of the genetic groups and how they relate to other edaphically specialized Eriogonum in this region would improve understanding of diversity in these unique edaphic habitats in the Great Basin. Our results highlight the potential for simple- seeming systems to contain significant levels of cryptic diversity, and suggest that caution is warranted when considering potential impacts to these unique habitats.
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Acknowledgements I would like to thank my advisors, Beth Leger and Tom Parchman, for the enormous amounts of time and effort they both invested in helping me complete this project, and for all of the constructive feedback and collaboration that has helped me grow so much over the last few years. As my primary advisor, Beth’s passion for science and mentorship, infectious positivity, and constant encouragement and understanding in the face of many challenges have inspired me since we met, and I could not have done this work without her. I would also like to thank my third committee member, Paul Verburg for his valuable guidance on soil sampling and analysis and for feedback on my writing. Thank you also to my co-author, Kathy Torrence and the BLM Black
Rock Field Office for conceiving of this project, securing major funding, and for hands-on involvement with feedback, permitting and fieldwork assistance throughout the project. I am grateful to all of our undergraduate technicians and to my friend Mandy Fuller, for help with soil and tissue collection, off-road travel, and planting, monitoring, and harvesting. Thank you also to my family, especially my mom Susan Wilcher, for boundless love and support. Lastly, thank you to my husband, Logan McClinton, for the countless hours he spent helping me in the field and in the greenhouse, for helping me maintain a sense of balance as my climbing and running partner, and for his encouragement, understanding, and many hugs and laughter.
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Table of contents
Abstract ………………………………………………………………………………...…….....… i
Acknowledgements ……………………………………………………………………....…..…. iii
Table of Contents …………………………………………………………………….…..……… iv
List of Tables ………………………………………………………………………….…………. v
List of Figures …………………………………………………………………………….…...… vi
Thesis Introduction …………………………………………………………………...…….….. viii
Chapter 1. How specialized is a soil specialist? Responses of a rare Eriogonum to site-level variation in volcanic soils …………………………………………………….…………..……… 1
Chapter 2. Genetic and phenotypic diversity of a rare, soil-specialist Eriogonum in North
America. 38 ……………………………………………………………………………………... 37
Chapter 1 Appendix ………………………………………………….………………………..... 73
Chapter 2 Appendix ……………………………………………………………….……………. 95
Summary, Conclusions, and Recommendations …………………………………..……...…… 111
v
List of Tables
Chapter 1: List of Tables
Table 1. Comparisons of total biomass, root mass ratio (RMR), emergence, and survival of seedlings grown in soils from field sites with varying occupation status for E. crosbyae in the wild.
Chapter 2: List of Tables
Table 1. Differences in E. crosbyae seed and seedling characteristics among genetic groups in greenhouse common gardens.
Table 2. Differences in E. crosbyae plant form between genetic groups from field measurements at 16 sites.
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List of Figures
Chapter 1 List of Figures
Figure 1: E. crosbyae individual in flower, and an example of typical Nevada, USA habitat on hydrothermally altered volcanic outcrops.
Figure 2: Map of E. crosbyae habitat sampling locations and site occupation status in Nevada,
USA.
Figure 3: Ordination based on the first two principal components from PCA of E. crosbyae habitat soil characteristics in all sampling sites.
Figure 4: Variation in field plant form of E. crosbyae by sampling location in northwestern
Nevada, USA.
Figure 5: Significant (p<0.05) associations between native soil characteristics and morphology of
E. crosbyae individuals at field sites in northwestern Nevada, USA.
Figure 6: Comparison of total biomass, root mass ratio, and emergence of E. crosbyae seedlings grown in field soils and in a more fertile soil from Washoe Valley, NV, USA.
Figure 7: Significant (p<0.05) effects of variation in field soils from E. crosbyae habitat on seedling growth in the greenhouse at the University of Nevada, Reno, USA.
Chapter 2 List of Figures
Figure 1: Map of Eriogonum sampling sites in northern Nevada, USA colored by the genetic group assigned to the most individuals from each location.
Figure 2a: Genetic groupings of individuals sampled in the wild from populations previously identified as E. crosbyae in northern Nevada, USA. vii
Figure 2b: Phylogenetic analysis of Eriogonum individuals sampled in the wild in northern
Nevada, USA.
Figure 3: Map of Eriogonum sampling sites in northern Nevada, USA colored by the genetic group assigned to each individual.
Fig. 4: Tukey pairwise comparisons of phenotypic differences in greenhouse seed and seedling traits in Eriogonum by dominant genetic group for 16 source sites, with analyses including dominant genetic group as a categorical variable.
Fig.5: Tukey pairwise comparisons of phenotypic differences in Eriogonum plant form in the field by dominant genetic group for 16 source sites, with dominant genetic group for each field location considered as a categorical variable.
viii
Thesis Introduction Edaphic variation is a major driver of global biodiversity, and creates landscape-level patterns of different floras that have adapted to variations in soil properties (Rajakaruna 2004).
Edaphic specialists are plant species that are found primarily or exclusively on outcrops of unusual soils, such as serpentine, gypsum, limestone, and dolomite, among many others (Baskin and Baskin 1988, Kelso, Bower et al. 2003, Rajakaruna, Boyd et al. 2014, Vanderplank, Moreira-
Muñoz et al. 2014, Escudero, Palacio et al. 2015, Rajakaruna 2018). These outcrops can vary in size from dozens or hundreds of square meters to many hectares, and commonly occur as isolated edaphic “islands” (Kruckeberg 1986). These soils are frequently shallow, rocky, and may have little soil development or be so highly weathered that they are stripped of nutrients. Challenges on these soils can include low water holding capacity, high insolation, extreme pH, accumulations of toxic elements, high salinity, low nutrient availability, and soil crusts which may impede seedling establishment (Escudero, Somolinos et al. 1999, Brady, Kruckeberg et al. 2005, Palacio,
Escudero et al. 2007, Anacker 2014, Boisson, Faucon et al. 2017). Plants in these habitats commonly exhibit xeromorphic adaptations such as high root allocation, short stature, and earlier flowering phenology, as well as more specialized adaptations such as obligate mycorrhizal associations, reduced seed dispersal, cryptic growth, and the ability to partition toxins in separate cell compartments (Nagy and Proctor 1997, Brady, Kruckeberg et al. 2005, Kazakou,
Dimitrakopoulos et al. 2008, Lazarus 2010, Baythavong 2011, Anacker 2014, Escudero, Palacio et al. 2015). These adaptations are often associated with low growth rates, and can lead to competitive exclusion from more fertile environments (Anacker 2014).
In species with sufficient genetic and morphological variation, such adaptations can arise quickly, and can vary over small spatial scales, which leads edaphic specialists to make up a disproportionate amount of global floristic diversity (Kruckeberg 1986, Wright, Stanton et al.
2006, Givnish 2010, Yost, Barry et al. 2012, Gustafsson, Skrede et al. 2014). For instance, in ix
California alone, about 17% of its 1400 endemic plant species are serpentine soil endemics, even though only 1.5% of the state is underlain by the ultramafic rocks from which this soil develops
(Safford, Viers et al. 2005). Edaphic specialists provide a variety of ecosystem services, such as supporting diverse pollinator populations, acting as nurse plants for less well-adapted species, and promoting soil stabilization and development in barren areas. Some species, such as Gymnostoma leucodon can also be useful for remediating polluted areas by removing toxins from the soil
(Whiting, Reeves et al. 2004). Unfortunately, the inherently isolated, small populations of many edaphic specialists combined with high levels of local adaptation also make them especially vulnerable to habitat loss from novel disturbances, climate change, and losses of genetic variation due to drift or reductions in population size (McKinney 1997, Harrison, Damschen et al. 2009,
Givnish 2010, Damschen, Harrison et al. 2012, Escudero, Palacio et al. 2015).
In the western Great Basin in the United States, unique edaphic outcrops that have developed over hydrothermally altered volcanic ash are often associated with the presence of precious metals, and these areas can be popular for mining (Silberman 1985). Targeted impacts to these environments has generated significant interest in understanding the habitat requirements and levels and distribution of diversity in species that occur there, which are crucial for effective conservation. Our study focused on Eriogonum crosbyae Reveal, a rare edaphic specialist found in Nevada, Oregon, Idaho, and Montana (Committee 2005). E. crosbyae is a Bureau of Land
Management sensitive species in Nevada, and populations primarily occur on light-colored soil outcrops associated with hydrothermal alteration in the northwestern part of the state (Morefield
2003). Eriogonum is a highly diverse genus, and the group of yellow-flowered, capitate buckwheats to which E. crosbyae belongs is complicated taxonomically; since its discovery, the species definition for E. crosbyae has been expanded several times to encompass other taxa which were previously considered distinct (Grady and Reveal 2011). Previous studies of edaphically specialized Eriogonum species in the Great Basin have shown that even rare species x in this genus with extremely limited ranges can have high levels of genetic variation (Reveal
1981, Kaye 1990, Massey, Messinger et al. 1990, Archibald, Wolf et al. 2001, Smith and
Bateman 2002, Neel and Ellstrand 2003, Ellis, Roper et al. 2009, Brown and Mansfield 2017). In order to improve understanding of edaphic specialization in the Great Basin, and to form a foundation of knowledge for species conservation, we characterized the biotic and abiotic characteristics of E. crosbyae habitat, and used field measurements and a soil preference experiment to examine the impact of soil variation on site occupation, field plant form and greenhouse plant growth. We also collected leaf tissue and seeds from 16 E. crosbyae sampling locations, and examined the amount and distribution of genetic variation among our sample sites, and the degree to which phenotypic variation corresponded with genetic variation. We found ample genetic diversity, including the presence of three highly differentiated genetic groups that frequently co-occurred within individual sites. This complicated our analyses of phenotypic diversity, and indicates a need for further study of the taxonomy and evolution of this species before recommendations can be made as to specific management approaches based on our genetic data.
The chapters below detail this research on the habitat preferences, genetic, and phenotypic variation in the species within its range in Nevada. However, we also used our research to help choose and verify the appropriateness of a potential transplant location for seedlings from a threatened population at the Hycroft Gold Mine, and to inform approaches to propagation and out-planting at that site. I will include our methods and some preliminary results here, for the benefit of managers who may be interested in our site choice, propagation, and out- planting techniques. During site selection, we only considered areas that were within the Black
Rock Desert High Rock Canyon National Conservation Area to ensure that any population that might become established based on our efforts would not be at risk of impacts by mining in the future. We identified 8 potential transplant locations using Google Earth, because the light-color xi and barren nature of ideal E. crosbyae habitat makes it stand out in aerial photography. We then visited each site to collect soil samples and survey plant communities, for comparison with our earlier work on the habitat characteristics of known occupied sites. We used the average characteristics of field soils that produced good plant growth in the greenhouse (plants ±1 SD of mean growth) to create guidelines for ideal soil properties in E. crosbyae habitat. We chose a location near High Rock Lake that had appropriate soil properties, other plant species known to be associated with E. crosbyae habitat, and a small number of E. crosbyae individuals already established at the site periphery. This indicated appropriate climatic conditions for the species.
Seedlings from the Hycroft population were grown up for almost a year prior to transplanting, from July 2018-May 2019. Seedlings were grown in a 50/50 mix of field soil and washed decomposed granite to promote development of high root allocation, which was expected to improve field survival based on results of our earlier soil preference experiment. We tested survival of seedlings grown in both 1”x5” tubes and 4” square pots. All seedlings were hardened off outside, with individual tubes or pots buried in a sand bed from
December 2018 until they were transplanted. In the field, 1-3 seedlings were planted next to each of
>70 terra-cotta pots, which were buried 7” Buried 1-gallon terra-cotta pot that has been filled with water, with E. crosbyae deep and filled with water seedlings planted next to it. every 2-3 weeks from May-September. The holes in the bottom of the pots were plugged to prevent leakage and the rims, which protruded 1” above the soil, were painted white using an eco- xii friendly paint to reduce evaporation. Pots were covered with removable aluminum lids weighted with rocks. Terra cotta is porous, which allowed the water in the pots to slowly seep out the sides over the course of 1-2 weeks into the root zone of the seedlings that were planted next to them.
This provided near-constant moisture over the summer, and likely dramatically improved survival. As of our last monitoring date on 9/4/2019, 63% of all seedlings planted were still green and growing. This is a promising preliminary result; however, monitoring over the course of 10-
20 years, including looking for seedlings, would be required to determine the successful establishment of a self-sustaining population due to these plants’ long lifespans and extreme hardiness.
This study was able to use multiple methods to characterize genetic diversity, phenotypic diversity, soil preferences, and associated plant species for E. crosbyae populations within
Nevada, and this information was used to establish a new population of this plant. Such efforts on behalf of other rare plants would greatly enhance our ability to preserve the important diversity within these hardy and highly specialized plant species.
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1
Chapter 1
How specialized is a soil specialist? Responses of a rare Eriogonum to site-level variation in
volcanic soils
1Jamey D. McClinton
2Thomas Parchman
3Kathleen Torrence
1Paul Verburg
2Elizabeth A. Leger
1Department of Natural Resources and Environmental Science, University of Nevada, Reno,
Reno NV, USA
2Department of Biology, University of Nevada, Reno, Reno, NV, USA
3Wildlife Biologist, Black Rock Field Office, Bureau of Land Management - Nevada
1Author for correspondence. Email: [email protected]
2
ABSTRACT Understanding edaphic specialization is crucial for effectively conserving rare plants.
Focusing on Eriogonum crosbyae, a rare soil specialist from the Western United States, we examined how site-level variation among outcrops of volcanic soil affects plant growth and population distribution. We asked how variation in native soils collected from forty-two outcrops of E. crosbyae habitat affected plant growth in the greenhouse. We also measured phenotypic variation in the wild, documented biotic components of E. crosbyae habitat, and re-surveyed previously documented populations in Nevada to evaluate occupancy changes over time. We observed occupation status reversals at five locations since previous surveys in the 1980’s and
1990s. Emergence in the greenhouse was 1.6 times more likely to occur in soils from recently colonized than abandoned sites, but soil properties and overall plant growth did not differ in the greenhouse or the field based on occupation status of sampled sites. Plants were larger in soils with higher nutrient availability, and smaller in soils high in copper in both the field and the greenhouse. Plants responded to different components of soil variation at different life stages. Site occupancy was not predicted by soil chemical or physical properties within our survey areas.
Thus, the metapopulation-like behavior we observed may better explain E. crosbyae’s patchy distribution among outcrops. Our results also suggest that different life stages may be sensitive to different soil properties and that, like other soil specialists in the Great Basin, edaphic specialization in E. crosbyae is the result of high tolerance to nutrient deficiencies in soils formed on hydrothermally altered rocks.
Key Words Rare plant, edaphic specialist, hydrothermal alteration, soil preference, Great Basin 3
INTRODUCTION The mosaic of soil types and substrates that cover the Earth’s land masses is a primary factor driving landscape heterogeneity and the distribution, lineage diversification, and composition of unique plant communities, which often form through adaptation to challenging, relatively unproductive soils (Kruckeberg 1969, Denton 1979, Kruckeberg 1986, Rajakaruna
2004, Givnish 2010). This is particularly evident at locations where geological processes have created soils low in nutrients, with low water holding capacities, extreme pH, or with high levels of heavy metals or other elements that are toxic to plant growth (Rajakaruna 2018). Such sites often occur on small, scattered outcrops with sharp edaphic and floristic boundaries separating them from more hospitable soils, such as in sites with serpentine, limestone, shale, or gypsum- based soils, or soils that have developed over hydrothermally altered rock (Keener 1983, Brooks
1987, Kelso, Bower et al. 2003, Escudero, Palacio et al. 2015). Challenging edaphic conditions combined with reduced migration between distant outcrops fosters communities of locally adapted species with high rates of rarity and endemism, and these patterns have inspired much research into why certain species have come to occupy these harsh environments, the adaptations that allow them to survive, and how those adaptations affect their distributions (DeLucia,
Schlesinger et al. 1988).
Soil specialization in plants is thought to develop either as a mechanism to minimize competition, through range contractions after climate changes that restrict species distributions to edaphically harsh environments, or through random dispersal to those environments, and often involves the development of novel adaptations (Rajakaruna 2018). Consequently, edaphic specialists typically possess a variety of morphological, phenological, and physiological adaptations that allow them to tolerate or escape drought, improve efficiency of nutrient acquisition, and improve reproductive success by reducing seed dispersal, ensuring seeds remain within the edaphic habitats they are adapted to (Lipson, Bowman et al. 1996, Gemma, Koske et 4 al. 2002, Cairney and Meharg 2003, Brady, Kruckeberg et al. 2005, Kazakou, Dimitrakopoulos et al. 2008, Riba, Mayol et al. 2009, Apple 2010, Schenk 2013, Barker and Pilbeam 2015). The unique structures and compounds produced to serve these functions (such as leathery leaves, thick vestiture, high root allocation, and unique root exudates) are often associated with lower growth rates, which may be an adaptive tradeoff that improves survival and performance on harsh soils while also reducing competitiveness in more hospitable habitats (Kruckeberg 1951, Walck,
Baskin et al. 1999, Dakora and Phillips 2002, Dent and Burslem 2009, Maestri, Marmiroli et al.
2010). The tradeoffs edaphic specialists exhibit between adaptation to challenging conditions and competitive ability in fertile habitats are likely a main cause of their patchy distribution and occurrence on challenging soils (Billings 1950, DeLucia, Schlesinger et al. 1988, DeLucia,
Schlesinger et al. 1989, Walck, Baskin et al. 1999).
Understanding the factors driving species occurrence on challenging soils and the limits to their environmental tolerance is especially important from a conservation perspective and is crucial for evaluating the risk posed to edaphic specialists by environmental change. For instance, although xeromorphic adaptations of many edaphic specialists may make them more tolerant of rising global temperatures than more mesic species (Damschen, Harrison et al. 2012), specialists may be particularly vulnerable to habitat loss in response to disturbance, invasion, and dramatic climate change (McKinney 1997, Thompson 2005, Biesmeijer, Roberts et al. 2006, Colles, Liow et al. 2009). Unlike generalists, specialists may have no suitable locations available for dispersal once their unique habitats are compromised beyond their limits of tolerance (Rajakaruna, Boyd et al. 2014). In these instances, an understanding of habitat requirements and soil specialization is crucial to successful conservation through a range of actions, including evaluating risk, designating protected areas, or active species management such as restoration and relocation
(Cowling, Pressey et al. 1999, Seddon 2010, Maschinski and Haskins 2012, Pacifici, Foden et al.
2015). Specifically, knowledge of how native soil variation affects plant germination, growth, 5 and survival is crucial for improving the outcomes of restoration or relocation efforts and can improve our understanding of how edaphic specialists persist in arid environments (Khurana and
Singh 2001, Wang, Wang et al. 2016).
While much research has described the differences in plant adaptations between challenging soils and the surrounding soil matrix, there has been relatively little research (1) asking how variation in soil properties affect the intraspecific growth and distribution of edaphic specialist species, or (2) investigating what defines the limits of tolerance within challenging soil environments-- but see (Boisson, Faucon et al. 2017). Here, we ask how soil variation across potential habitat impacts the growth, distribution, and persistence of a rare edaphic specialist,
Eriogonum crosbyae Reveal, or Crosby’s buckwheat. Eriogonum is a highly diverse genus where edaphic endemism is common (Baskin and Baskin 1988, Harrison, Viers et al. 2008, Kempton
2012, Ellis, Wolf et al. 2015), and provides an excellent opportunity to study specialization in different environments. Eriogonum crosbyae likely consists of a complex of closely related taxa or lineages (Grady and Reveal 2011, Grady 2012) that inhabit scattered outcrops of nutrient-poor soils in a portion of the Great Basin, including light-colored outcrops of tuffaceous soil in Nevada developed from bedrocks that have undergone varying levels of hydrothermal alteration (Craig C.
Freeman 1993+). These soils are likely to have unique mineralogy that would significantly impact nutrient availability. Previous phylogenetic studies of E. crosbyae have suggested polyphyly among geographically proximate samples (Grady 2012), and our own preliminary genetic analyses (McClinton, Leger et. Al., Unpublished Data) indicate that our focal populations may be comprised of three differentiated genetic groups. These groups often co-occured within sites, and have been up to now considered impossible to distinguish in the field. While taxonomic work is still ongoing, for the purposes of this study we assume that coexisting and morphologically similar genotypes in our study sites would likely share comparable adaptations to the soils they inhabit. Extensive inventories of E. crosbyae in Nevada have found many 6 instances of unoccupied sites that appear to be potentially suitable based on visual cues and presence of associated species, and there is interest in understanding E. crosbyae’s specific habitat requirements and how those requirements might affect its distribution. Discerning what conditions are suitable for growth can be complicated by metapopulation-like behavior of some rare species, which exhibit populations that change occupancy status between surveys, making it difficult to determine, for example, if a site is suitable habitat, but currently unoccupied (Delong and Gibson 2012). However, field observations indicate clear gradients in soil properties across patches of volcanic soils within the region of E. crosbyae habitat (i.e. surface color, texture, etc; personal observation) that may influence site occupation. Thus, a comparison of occupied and unoccupied patches and a test of plant performance in soils collected from multiple sites should be valuable for determining the limits of edaphic tolerance and habitat requirements of this species.
In this study, we examine factors that impact E. crosbyae’s growth and distribution, complementing a management effort to find suitable habitat to relocate a population slated for mining development (hereafter referred to as the Hycroft population). We describe physical and biotic components of currently occupied E. crosbyae habitat, examine the effects of variation in light-colored volcanic soils on plant growth, and attempt to model site occupation based on edaphic variation in occupied vs. unoccupied sites. We specifically ask: 1) do sites change in occupancy status over time? 2) what are the characteristic soil properties of E. crosbyae habitat?
3) do soil properties differ between occupied and unoccupied outcrops of potential habitat? 4) how do soil properties affect plant phenotype in the field and plant growth in the greenhouse? and
5) do plants in the greenhouse grow better in soils from occupied sites than those from unoccupied sites? Field soils were analyzed for a suite of physical and chemical properties, and the effects of soil variation on plant growth were evaluated by measuring morphological variation 7 in the field and by growing seeds from the Hycroft population in soils from occupied and unoccupied soil outcrops collected from across the species’ range in Nevada.
The long-lived life history of E. crosbyae and consistency of site occupancy during previous surveys (Kaye 1990, Morefield 2003) led us to expect that site occupation would remain constant over time, and that potentially suitable habitat would vary in accordance with soil characteristics, with occupied soils less fertile than surrounding unaltered soils, but having relatively higher levels of essential nutrients, more favorable stoichiometric nutrient ratios, and lower salinity than unoccupied altered sites. We also expected soil properties to influence germination, survival, and growth of E. crosbyae in the greenhouse, with plants sown into more favorable soils generating greater biomass and lower root mass ratios (high root allocation is typical in low nutrient soils (Andrews, Sprent et al. 1999)) and having higher emergence and survival rates. Finally, we expected that growth responses we observe in the greenhouse would be consistent with models of which soil factors impact site occupation and plant growth in the field.
METHODS Study system E. crosbyae is a long-lived perennial herb with a low, matted growth form and a highly branching woody caudex, above which rise multiple scapose flower stalks ending in rounded inflorescences that retain dried tepals around mature seeds (Reveal 1985). It occupies both valleys and mountaintops of central and southwestern Idaho, western Montana, southeastern Oregon, and northwestern Nevada, occurring between 1200-3100m in elevation on substrates ranging from tuffaceous ash or metamorphic rock outcrops to sandy basaltic and granitic substrates of varying slopes and aspects. E. crosbyae, as recognized by Grady and Reveal (2011) was previously considered to be several distinct species, which may be different ecotypes, or independently evolved lineages (taxonomic work is ongoing). For a list of species currently considered to be synonymous with E. crosbyae, see Taxonomic Notes in the Appendix. Hereafter we apply the 8 taxonomic treatment of Grady and Reveal (2011), acknowledging that this definition includes plants found on a variety of substrates that may exhibit significant variation within the current E. crosbyae species designation. Here we focused only on Nevada populations of E. crosbyae, which almost exclusively inhabit relatively barren light-colored tuffaceous outcrops that have been subject to some degree of hydrothermal alteration (Fig. 1).
In Nevada, E. crosbyae is classified as a Bureau of Land Management Sensitive Species and can be found between 1200-2000m in elevation in shallow sandy to clay soils of all slopes and aspects (Morefield 2003). Annual precipitation within these sites ranges from ~6.5-
17.0cm/year, and annual average temperatures range from -0.7°C to 19.3°C, based on 30-year normals (1981-2010); Prism Climate Group (http://www.prism.oregonstate.edu). For a list of the top twenty plant species most commonly associated with E. crosbyae, see Associated Species in the Appendix.
Sampling locations We assessed occupancy and collected and compared soil samples from a subset of occupied and unoccupied outcrops across the species’ range in Nevada. The light color and barren nature of E. crosbyae’s preferred volcanic habitat allows it to stand out in comparison with the surrounding landscape, such that potentially suitable habitat can be identified from a distance or using aerial photography. As a result, many potentially suitable sites have been surveyed and characterized by their occupation status (Kaye 1990, Morefield 2003). Sampling locations for this experiment were primarily chosen from among occupied and unoccupied survey locations compiled in the Conservation Status Report for Eriogonum crosbyae (Morefield 2003), and were last surveyed during (two sites) or prior to (thirty-three sites) 1993. Additional sites were included based on survey locations documented in the Consortium of Intermountain Herbaria online repository at http://intermountainbiota.org. The four sites chosen for inclusion from this source by the methods below were surveyed between 1995-2010. The approximate center points of all 9 potential survey locations were located on topographic map layers in ArcMap and a 150x180km
(East to West x North to South) fishnet was overlaid with the resolution set to ensure that twenty- six cells contained at least one occupied site. An occupied site was randomly selected from each cell for sampling. Where possible, the nearest known unoccupied site to each occupied location along the same aspect and landform was also chosen for sampling, with a 2km maximum separation distance, chosen based on the range of wind dispersal for comparable plumed seeds
(Vittoz and Engler 2007). Not every occupied site could be paired with an unoccupied site that met these criteria, so two additional unoccupied sites were chosen using aerial imagery, also along the same aspect and landform as occupied sites in an attempt to achieve an even distribution of unoccupied sites across the species’ range. Ultimately, accessibility issues, reversals of occupation status in several sampling locations, and discovery of one new population resulted in sampling of 25 total occupied sites and 17 total unoccupied sites rather than the 26 occupied and 18 unoccupied sites that had originally been identified.
Soil characterization To characterize the soil properties of sampling locations, we collected twenty 10cm x
10cm soil cores from each site during October of 2017. In occupied locations, soil samples were taken from within 165 cm of E. crosbyae individuals, to account for potentially abrupt edaphic boundaries between suitable and unsuitable habitat. Samples were bulked and homogenized for each site. From each composited sample, one 0.5L soil sample was taken and sieved to remove fragments larger than 2mm, then sent to A&L Western Laboratories (http://www.al-labs- west.com/) for analysis. Soil properties quantified included major soil cations, selected macro and micro-nutrients, saturation percent, salinity, organic matter content, pH, and soil texture. Soil analysis methods followed A&L Western Laboratories (http://www.al-labs-west.com/) protocols; see Appendix, Table 2 for the full list of characteristics analyzed. 10
Soil variation and plant growth Field measurements Occupied sites were visited at peak bloom during the summer of 2018 for vegetation data collection, including a 15-minute meandering walk to record presence or absence of E. crosbyae and all associated plant species. This time period was sufficient to fully traverse each field site because of their small size. We also measured E. crosbyae size and growth characteristics thought to be potentially adaptive and observed to be variable in the field. These traits included: mat area
(length of the longest side of the mat multiplied by the length of the mat perpendicular to the first direction), mat depth (measured from the top of the root crown to the estimated average height of the leaves), height of the tallest flower stalk (from the base of the flower stalk to the base of the inflorescence), and number of flower stalks. Twenty plants per site were measured, beginning at the lowest elevation in the occupied area and proceeding in a systematic zig-zag pattern wide enough to cover the entire sampling area, measuring the closest plant to the sampler after walking
10 paces (~4.5m) from the previous plant.
Seed collection and greenhouse study Seeds were collected in bulk from ~100 individuals of the Hycroft test population of E. crosbyae on June 21, 2017 and cleaned by hand. Seeds were stored in a paper bag in the dark at room temperature (18°C) until planting. To determine how soil characteristics affect plant growth, we set up a soil preference experiment in the University of Nevada, Reno greenhouses using soils collected at every sampled field location, plus a native field soil from Washoe Valley,
NV (39.322771, -119.812121, 1549m elevation). This soil is characterized as a Surpass gravelly sandy loam by the NRCS Web Soil Survey, and its taxonomic class is among: coarse-loamy, mixed, superactive, mesic Aridic Haploxerolls
(https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx). The Washoe Valley field soil was included as a standard for comparison, rather than a potting soil or other commercial soil mix, because it is the substrate we use for growing a variety of Nevada native plants, including 11 other species of Eriogonum. Thus, we would expect any issues with seed quality or growing methodology to have been apparent if the seeds failed to grow in this standard soil. We employed a randomized complete block design with 12 replicate containers per soil type. Rocks larger than
2.5cm were removed and replaced with an equal volume of field soil, and a 50/50 mix of field soil and washed decomposed granite was used to improve drainage in 8.9cm x 8.9cm x 7.6cm pots. Two seeds were planted into each pot on March 1, 2018 to increase likelihood of at least one seedling establishing in all pots. Pots were watered lightly every day during germination, then 2-3 times per week as needed throughout the experiment. All pots were watered evenly when a majority appeared dry on the surface, to prevent desiccation of small seedlings and encourage germination. Germination, survival, and phenology were monitored weekly, and pots with more than one emergent seedling were thinned to one randomly-selected plant after the initial establishment period.
Temperatures in the greenhouse were set to approximate natural temperatures, within a minimum and maximum temperature limit. From March 1 to March 28th, minimum and maximum temperatures were maintained between 1.5-15.5°C; from March 28th to April 8th, between 4.5-18.5°C, and from April 9th to harvest, between 4.5- 21°C. Pots containing more than one seedling after several weeks of growth were thinned using a coin toss to determine which seedling to remove; thinned seedlings were not included in survival analyses. Above and belowground biomass was harvested, dried, and weighed during the week of July 22, 2018, after approximately 3.5 months of growth.
DATA ANALYSIS Characterizing soil properties and differences in soils and plant growth by site occupation Soil properties characteristic of E. crosbyae habitat conditions were attained by calculating the mean of individual soil properties from sites that produced average plant growth in the greenhouse (defined as sites ±1 SD from the overall means for biomass and root mass ratio; 12
Appendix, Table 2). This excluded sites where the plants performed either unusually poorly or unusually well.
Soil variation across all sites was summarized and visualized using Principal Components
Analysis (PCA). In addition, 2-sided t-tests were used to test for differences in individual soil variables between occupied and unoccupied sites. We present t-tests both with and without
Bonferroni corrections for multiple comparisons. For these analyses, all sites with extant populations of E. crosbyae at the time of sampling were considered occupied, while all sites without populations were considered unoccupied, regardless of whether occupancy status had changed since previous surveys. One-way analysis of variance was used to test whether emergence, total biomass, and root mass ratio (RMR; root weight/total biomass) of seedlings grown in the greenhouse varied based on field occupation status of the soil they were grown in; for these tests, we also included categories for recently colonized or abandoned sites. Tukey’s
HSD post-hoc tests were performed on significant models for multiple comparisons.
Plant responses to soil variation An exploratory approach to variable selection and model generation was used to discern which soil characteristics had the greatest effects on site occupancy, plant characteristics in the field, and total biomass, root mass ratio, emergence, and survival in the greenhouse. Soils from the more fertile Washoe Valley site were excluded during variable selection and model creation to improve comparability of greenhouse and field data. To reduce autocorrelation in measurements of plant characteristics in the field, one orthogonal variable with the strongest loadings on each of the first three axes of a Principal Components Analysis were chosen as responses of interest. These included mat area, mat depth, and height of the tallest flower stalk, all with Pearson correlation coefficients ≤ 0.40. Field growth traits of interest were further thinned upon consideration of preliminary genetic analyses of populations from across E. crosbyae’s range, which revealed three differentiated genetic groups (Unpublished Data). Several 13 populations were composed of individuals from more than one genetic group. However, we found that when the dominant genetic group from each site was included as a categorical variable during model selection it was not included in top models of mat area or mat depth. Therefore, we chose to focus on results from these traits, which showed little dependence on this metric of genetic variation. In the greenhouse, emergence and survival were recorded as the percentage of seeds per pot that emerged, and the percentage of un-thinned seedlings that survived from emergence until the date of harvest, respectively.
Thirty-three possible soil variables were initially considered during variable selection.
We then eliminated variables with Pearson correlation coefficients ≥0.70. To do this, out of each pair of highly correlated soil variables, the variable with highest deviance with the response of interest in univariate generalized linear models was removed, with this selection process proceeding separately for each response (site occupation, field flower stalk height, mat area, and mat depth, and greenhouse total biomass, RMR, emergence, and survival). Additional thinning of soil variables was performed using the R package ‘randomForestSRC’, to facilitate final model selection (Breiman 2001, Ishwaran, Kogalur et al. 2008, Ishwaran and Kogalur 2019). Random
Forest is a nonparametric machine learning technique that uses iterative decision trees to estimate the predictive capability of variables, and is an ideal method of variable selection when there are more potential explanatory factors than can be accommodated in a single model. The top 16 variables with the highest RandomForest VIMP (variable importance) were chosen for inclusion.
In all RandomForest runs for greenhouse response variables, block was shown to be the least predictive of all variables, and so was omitted from further analyses.
Univariate and final models were constructed with gaussian distributions for measures of plant characteristics in the field, total biomass and RMR, and with binomial distributions with a logit link function for site occupation and percentages of emergence and survival. For models of 14 total biomass and RMR in the greenhouse, plant age (days since emergence) was included as a potential covariate, and block was also included. Sites with three or fewer surviving seedlings were removed from analyses of greenhouse growth due to high variation in responses. A Tukey
Ladder of Powers transformation was performed on total biomass and RMR to help meet the assumption of normally distributed residuals, and transformed values were scaled to a mean of zero and standard deviation of one. All potential explanatory variables (except block) were similarly scaled.
Final model selection and multi-model averaging were performed using a genetic algorithm in the R package “glmulti” using AICc for model comparison. Coefficient estimates were calculated based on the subset of models which yielded 95% of the total evidence weight.
Comparisons of R2, Adjusted R2, and Allen’s (1971) predicted R2 (calculated using leave-one-out cross-validation) for the top models were made to test for model over-fitting, and model assumptions were checked using Normal Q-Q and Residuals vs. Fitted plots of top models.
RESULTS Field data Site occupancy and soil properties Five locations reversed occupancy status since they were last surveyed between 1982 and
1993 (Fig. 2, Appendix Table 1). This included three previously occupied locations (M32, M8, and T12116, to be referred to as “abandoned”) and two previously unoccupied locations (U48 and
U91, to be referred to as “colonized”). On average, soils in E. crosbyae habitat were lower in extractable Bray-P, Olsen-P, NO3-N, Zn, Mn, Fe, and percent sand than our standard Washoe
Valley soil, and higher than the Washoe Valley soil in extractable K, Mg, Ca, Na, cation exchange capacity (CEC), SO4-S, KCL- extractable Al, silt, and clay (Appendix, Table 2). A principal components analysis (PCA) of soil properties in E. crosbyae habitat sites explained
57.70% of the total variation in the first three PCA axes (Appendix, Table 4). PC1 (27%) was 15 dominated by traits describing soil pH and salinity. PC2 (16%) was dominated mainly by P, K,
Na, Ca:Na, and SO4-S. PC3 (15%) was dominated by Ca, KCl-Extr. Al, and the micronutrients
Mn and B. Although soil properties varied by location, a scatterplot of PC1 and PC2 scores shows little distinction in soil characteristics among sites that differ in occupancy status (Fig. 3). This was true for principal component three as well (data not shown). The lack of soil differentiation by site occupation status was further illustrated by two-sided t-tests comparing soil properties in occupied and unoccupied soils. Without correction for multiple comparisons, occupied sites had a slightly lower pH (P= 0.056), a higher Zn concentration (P= 0.033), a higher concentration of Fe
(P= 0.043); however, these differences were no longer significant when using a Bonferroni- corrected alpha for multiple comparisons (α/n; 0.05/34= 0.001); see Appendix, Table 2 for all comparisons. Finally, logistic models of site occupation based on differences in soil characteristics were tested and found to be unpredictive (results not shown).
Soils associations with field plant form One-way ANOVA for mat area and mat depth showed significant differences in plant form by site (mat area: DF= 19, F = 8.093, p<0.05; mat depth: DF= 19, F = 12.65, p<0.05, Fig.
4), and these traits also had differing associations with soil properties at field sites (Fig. 5). Mat depth was positively associated with Mn, sand, and organic matter, and mat area was positively associated with higher N:P ratios and with increasing KCL-extractable Al concentrations
(p<0.05). Mat depth was negatively associated with Na and Mg percent cation saturation, and mat area was negatively associated with higher proportions of Na:Mg and Ca:Na on the cation exchange complex, as well as with increasing Cu concentrations (p<0.05). All relationships were linear, and partial plots from the best model of each response to soil variation did not indicate optimal minimum or maximum nutrient concentrations within the sampled range (i.e., there were no quadratic effects). 16
Soil preference experiment Emergence, survival, total biomass, and root mass ratio of Hycroft population seedlings grown in the greenhouse varied in forty-two different soils from sites identified as potential E. crosbyae habitat (Fig. 6; Appendix, Greenhouse Measurements, Figs. 2,3,4). Above and belowground biomass of seedlings grown in field soils were highly correlated (Adj. R2=0.72, p<0.001), and the average root to total biomass ratio of these seedlings was 0.71, high compared with the average root mass ratio of desert plants reported in Poorter, Niklas et al. (2012) of 0.56.
The average total biomass of E. crosbyae seedlings grown in the greenhouse was higher and average root mass ratio was lower in the more fertile Washoe Valley standard soil than in all
E. crosbyae habitat soils when considered as a group in two-sided t-tests (p<0.05; Fig. 6).
Emergence and survival rates did not significantly differ between the Washoe Valley soil and E. crosbyae habitat soils (Fig. 6; Appendix, Table 8). However, percent emergence in the greenhouse did vary based on occupation status of the source soil (DF= 3, Chisq= 14.27, P=
0.0026; Table 1). Emergence was 1.612 times more likely to occur in soils from recently colonized sites than in soils from abandoned sites, and 1.144 times as likely in soils from unoccupied sites than in those from abandoned sites (p<0.05; Table 1). Total biomass, root mass ratio, and survival of seedlings in the greenhouse were not significantly different between soils in sites that were occupied, unoccupied, colonized, or abandoned by E. crosbyae in the field (Table
1, Fig. 6).
Excluding the Washoe soil and testing the effects of soil characteristics on plant growth in the greenhouse, we found that emergence rate was positively correlated with Mg and negatively correlated with Mn, Cu, and percent organic matter (p<0.05) (Fig. 7). Total biomass was positively correlated with Bray P and Mg, and negatively correlated with increasing N to P ratios, soluble salts, KCl-extractable Al, and Cu (p<0.05). Among E. crosbyae habitat field soils, 17 root mass ratio was negatively correlated with organic matter and with increasing concentrations of NO3-N, and B (p<0.05). No soil factors were predictive of survival.
DISCUSSION The process of colonization and adaptation to challenging soils is a major force in plant diversification, and these soils throughout the world are characterized by a unique and diverse flora that faces unique ecological challenges (Rajakaruna 2018). Although edaphic specialists generally benefit from reduced competition, they may be at increased risk of extirpation due to disturbance and changing climatic conditions relative to edaphic generalists because of their limited ranges, low dispersal capacity, small populations, and tendency to inhabit soils valuable for resource extraction (Brooks 1963, Harrison, Damschen et al. 2009, Escudero, Palacio et al.
2015, Bernardo, Albrecht et al. 2016). Investigating plant responses to variation in edaphic characteristics can help determine the habitat tolerances of soil specialists and the mechanisms driving their geographic extent, which are important for understanding the ecology and evolution of specialization and for determining appropriate conservation measures (Boisson, Faucon et al.
2017). Here, we used analyses of soil properties, observations of population persistence over time, and measurements of potentially adaptive traits in the field and in the greenhouse to study the habitat specificity and growth requirements of E. crosbyae, an edaphic specialist, within its range in Northern Nevada.
Within our study area, E. crosbyae habitat soils were characterized by severe deficiencies of available nitrogen and phosphorus, relatively high silt and clay contents, and high levels of K,
Mg, Na, SO4-S, and KCl-Extr. Al. These soils have largely developed from hydrothermally altered volcanic ash deposits, and appear to have unique chemistry and mineralogy that impacts nutrient availability. For instance, high estimates of cation exchange capacity, SO4-S, and Al may have resulted from the dissolution of gypsum and other salts during soil testing, or be associated with the presence of minerals forms of potash, which are commonly associated with hydrothermal 18 activity (we aim to better understand this by characterizing the mineralogy of these soils for future work). (Millot 2013). E. crosbyae showed great resilience to nutrient deficiencies and a high degree of phenotypic plasticity in response to soil variation, similar to observations in other soil specialists (Baythavong 2011, Sánchez, Alonso-Valiente et al. 2017). Contrary to our expectations, we observed reversals in site occupation in several locations, suggesting that E. crosbyae may exhibit metapopulation dynamics within its Nevada range. Such dynamics have also been observed in other edaphic specialists despite the possible evolution of reduced seed dispersal (Astegiano, Guimarães Jr et al. 2015, García-Fernández, Iriondo et al. 2018, Santamaría,
Sánchez et al. 2018). We also found that there were no significant differences in soil properties or greenhouse plant growth based on site occupation, and that soil physical and chemical characteristics were poor predictors of site occupancy among our sampling locations.
However, soil properties did vary by sampling location, and we observed significant associations between soil variation and plant form in the field and plant performance in the greenhouse. As with other soil specialists, we found that E. crosbyae responded to this soil variation in different ways at different life stages (Boisson, Faucon et al. 2017, Sánchez, Alonso-
Valiente et al. 2017). We also found that the responses in plant form in the field and growth in the greenhouse indicated sensitivity to similar components of soil variation. For instance, organic matter and Mn were both associated with increased mat depth in the field, but with reduced emergence in the greenhouse. Similarly, higher N:P ratios were associated with higher mat area in the field, but lower total biomass in the greenhouse, and mat area in the field and emergence and total biomass in the greenhouse were negatively correlated with Cu. However, despite the significant associations we observed between plant traits and soil characteristics, we note that the variations in plant form we observed in the field are likely the result of both environmental differences among sites as well as genetic differences among populations. The effect of these soil 19 properties on plant performance at different life history stages could be verified with additional manipulative experiments.
The contrast between the positive effects of organic matter and Mn on mat depth in the field and their negative effects on emergence in the greenhouse may reflect a difference in the effects of soil properties at different life history stages, but could also be related to the artificial nature of the greenhouse environment. In the greenhouse, attempting to water a variety of soil types evenly while maintaining adequate conditions for germination and seedling growth in all pots could have resulted in higher than optimal water content in poorer-draining soils, despite general similarity in texture between sites. Both organic matter and Mn have been shown to promote disease by either contributing to excessively moist conditions (Libohova, Seybold et al.
2018, You, Rensing et al. 2018) or by increasing virulence of pathogens under moist conditions
(Falcon, Fox et al. 1984, Hasannejad, Zad et al. 2006, Orr and Nelson 2018), and could have caused seed rot or failure of seedlings to emerge from the soil after germination. However, both soil elements are likely to be beneficial to plant growth in E. crosbyae’s native, dryer environment (Barker and Pilbeam 2015). Manganese in particular is likely to be a limiting nutrient for E. crosbyae in the field because, at ~4ppm, the concentrations present in E. crosbyae habitat are less than half the level found in the fertile Washoe Valley soil, and even minor Mn deficiencies can inhibit photosynthesis (Barker and Pilbeam 2015), which might account for the positive associations observed between this soil characteristic and plant growth in the field.
We also observed variations in root vs. shoot growth in the greenhouse in response to differences in nutrient availability, and significantly higher biomass of E. crosbyae seedlings grown in in the fertile test soil compared with those in native soils. These variations demonstrate
E. crosbyae’s ability to respond via phenotypic plasticity to major shifts in soil fertility-- a trait that is somewhat unusual for highly specialized edaphic endemics (Kazakou, Dimitrakopoulos et 20 al. 2008, Madawala Weerasinghe, Chandrasekara et al. 2010). However, despite this morphological flexibility, certain soils, such as from M31, seemed especially challenging for the seedlings during propagation, as evidenced by extremely low total biomass and high root mass ratios attained in the greenhouse. M31 had the lowest pH and highest KCL- Extr. Al concentration of all our sites (4.7 and 102ppm, respectively) and the growth response in this soil may suggest limits to tolerance of nutrient toxicity in E. crosbyae. Managers may wish to consider how the soil properties of potential relocation sites are likely to affect plant growth based on models of plant responses to edaphic variations in native soils, because vitality may have indirect effects on fecundity and population persistence (Chen, Li et al. 2017). In the greenhouse, E. crosbyae also performed better under relatively more fertile conditions; however, the barren nature of the volcanic outcrops E. crosbyae inhabits suggests that low competitive ability may play a role in their habitat preferences. Identifying sites with soil properties within the criteria we designated for “average” E. crosbyae soil characteristics may prevent inadvertently choosing sites too edaphically extreme or too high in competition from other species for survival.
Community composition and soil water availability of potential habitat are also likely to be important limiting factors for habitat suitability (DeLucia, Schlesinger et al. 1988), and should be studied further in the field.
Further study of E. crosbyae’s demography, including understanding how soil and climate variation impacts seed production, seed viability, and the establishment and survival of seedlings in the field would be helpful in determining which mechanisms are the most important drivers of population persistence within our sampling areas and across the species’ range. For example, it is possible that including populations from the entire geographic range might elucidate patterns of soil preferences that were not apparent within our focal sampling area in northern Nevada. Additionally, further studies on the mechanistic bases behind the morphological variation we observed and how that variation contributes to E. crosbyae’s adaptation to its unique 21 soil types might offer further insight into this species’ tolerances and vulnerability to changing conditions.
CONCLUSIONS Contrary to our expectations, variations in soil characteristics among outcrops of populated or potentially suitable habitat did not predict site occupation, indicating that factors other than soil chemistry and texture could be affecting occupation among our sample sites.
However, variations in the levels of key nutrients within these impoverished substrates had significant effects on plant phenotype in the field, and on establishment, biomass and root allocation in the greenhouse, indicating that there may be enhanced sensitivity to certain limiting nutrients that could affect the establishment and persistence of populations. Consistent with previous work, we found generally stronger associations with variation in native soils in the field than in the greenhouse, which suggests that non-edaphic traits are also likely drivers of plant growth and survival. Focusing on growth responses to edaphic variation within native soils was an effective first step in characterizing habitat suitability and the likely mechanisms driving edaphic specialization, and these methods, combined with plant community surveys, helped us identify an unoccupied, potentially suitable site where relocation of a threatened population is currently being tested. These methods may be applicable to edaphic specialists in other environments. Understanding the degree of specialization and habitat requirements for edaphic endemics is an important step in understanding species’ levels of imperilment by a variety of threats, and in our ability to conserve these unique species that make up such a valuable portion of global biodiversity.
ACKNOWLEDGEMENTS This research was funded by a Bureau of Land Management Conservation Area
Improvement Grant initially written by Kathy Cadigan of the Black Rock Field Office, with ideas developed in collaboration with co-authors at the University of Nevada, Reno, and funded under 22
Assistance Agreement # L16AC00318. Funding was also generously provided by the University of Nevada, Reno Graduate Student Association Research Grant Program, the Eriogonum Society
James Reveal Eriogonum Project Grant, and the Nevada Native Plant Society Margaret Williams
Research Grant. Thank you to Marenna Disbro for assistance with soil collection, Sage Ellis and
Trevor Carter for assistance with experiment setup, Logan McClinton and Miranda Fuller for assistance with planting and field data collection, Jerry Tiehm for assistance with plant identification, and Logan McClinton, Meagan O’Farrell, Katelyn Josifko, and Amber Durfee for assistance with biomass harvesting and processing. Thank you also to the BLM Black Rock Field
Office for facilitating data and soil collection on public land, and for excellent field assistance, and to Tom Dilts for assistance with ArcMap during site selection.
AUTHOR CONTRIBUTIONS Kathleen Cadigan conceived of the project and secured major funding, Elizabeth Leger and Thomas Parchman designed the research questions with assistance from Kathleen Cadigan and contributed critically to methodology and drafts of the manuscript; Paul Verburg assisted with methods and supplies for soil collection and gave valuable advice on soil analysis and interpretation, as well as comments to improve accuracy and flow in the manuscript. Jamey
Wilcher assisted with methodological design, secured funding from the UNR Graduate Student
Association, Eriogonum Society, and Nevada Native Plant Society, implemented the greenhouse experiment and field data collection, analyzed the data, and led the writing of the manuscript. 23
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37
Chapter 2
Genetic and phenotypic diversity of a rare, soil-specialist Eriogonum in North America
1Jamey D. McClinton
2Thomas Parchman
3Kathleen Torrence
4Elizabeth A. Leger
1Department of Natural Resources and Environmental Science, University of Nevada, Reno,
Reno Nv, USA; 2,4Department of Biology, University of Nevada, Reno, Reno, NV, USA
3Wildlife Biologist, Black Rock Field Office, Bureau of Land Management - Nevada
1Author for correspondence. Email: [email protected]
38
ABSTRACT Edaphic specialists frequently occur in small, isolated and locally adapted populations, and are often of conservation concern. We assessed phenotypic variation and genetic diversity in populations of plants previously identified as Eriogonum crosbyae, a rare soil specialist that occurs primarily in the western Great Basin. We analyzed phenotypic variation of potentially adaptive traits in sixteen field sites within north-western Nevada, and in seedlings from those same sites grown in a common garden greenhouse experiment. We also surveyed genetic variation within and across these sites using a high-throughput genotyping by sequencing approach. Surprisingly, genetic data revealed that Eriogonum sampled across our sites consisted of three fairly divergent lineages (mean FST = 0.25), with individuals from multiple groups occasionally found at the same site. Phenotypes measured in both the field and the greenhouse differed consistently between these groups, especially for seedling traits, as well as between populations within groups for some traits. Nucleotide diversity estimates for the most common and widespread of these groups suggest relatively high genetic diversity (pi = 0.027) coupled with limited spatial genetic structure across the sampled region. Our results indicate the existence of significant cryptic diversity within E. crosbyae, and that special care toward preserving this diversity throughout the species’ range is warranted when making conservation and land-use decisions.
39
INTRODUCTION Edaphically restricted plant species make up a large, diverse, and ecologically valuable component of the global flora (Anacker 2011, Rajakaruna and Harrison 2011, Rajakaruna, Boyd et al. 2014). They tend to inhabit small, isolated patches of unique soils within geographically restricted areas, and can be important for supporting diverse populations of pollinators, for promoting soil stability and development, and for acting as nurse plants for less well-adapted species (Escudero, Somolinos et al. 1999, Wolf and Thorp 2011, Zhang, Aradottir et al. 2016,
Foronda, Pueyo et al. 2019). Successful conservation of edaphic specialists depends upon understanding the unique patterns of genetic and phenotypic diversity that often result from demographic and ecological processes unique to species with small, isolated populations
(Matesanz, Rubio Teso et al. 2017). This natural isolation between populations can mean that edaphic specialists tend to show less sensitivity to habitat fragmentation than generalists, with even small amounts of gene flow through pollen transfer or sporadic long-distance dispersal events maintaining diversity (Ellstrand and Elam 1993, García-Fernández, Iriondo et al. 2018).
Similarly, in highly differentiated populations that are not well connected, differences in functional diversity can contribute to species longevity by allowing plants to persist in refugia under different climatic conditions (Nicotra, Beever et al. 2015). However, if small populations become too isolated, recruitment failures, genetic drift and inbreeding depression can result in losses of diversity (Franks 2010, Gómez-Fernández, Alcocer et al. 2016). Low genetic diversity can threaten population persistence in plants by reducing seed production, viability, and vigor of seedlings, which can lead to recruitment failure (Ellstrand and Elam 1993, Frankham 2015, De
Vriendt, Lemay et al. 2016, Hens, Pakanen et al. 2017). Maintaining diversity in rare species is also crucial for ensuring their survival because genetic and phenotypic diversity dictate species’ ability to adapt to environmental variation and impact the level of resistance/resilience species have to changing climatic conditions, invasion, habitat fragmentation, and other stressors (Leimu, 40
Mutikainen et al. 2006, Honnay and Jacquemyn 2007, Goergen, Leger et al. 2011, Nicotra,
Beever et al. 2015).
However, preserving genetic and phenotypic diversity is requires understanding its levels and distribution, and the extent to which phenotypes observed in the field result from underlying genetic variation and/or phenotypic plasticity to varying environmental conditions (Noel, Machon et al. 2007). Rare species may fall anywhere along a spectrum of genetic and phenotypic variation—some, such as Lasthenia maritima, have low genetic variation at both the population and species level (Crawford, Ornduff et al. 1985), while others, like Achillea millefolium ssp. megacephala, have been shown to harbor more genetic diversity within and among populations than even their widespread congeners (relative to; A. m. ssp. lanulosa) (Purdy and Bayer 1996).
Species with high levels of genetic variation and/or phenotypic plasticity are generally resilient to stressors but may require management oriented toward preserving diversity throughout their range or in large target locations to protect that resilience (Crawford, Ruiz et al. 2001, Noel,
Machon et al. 2007). Conversely, in species with lower variation or variation that is distributed unevenly among populations, successful conservation may require focusing on individual populations known to harbor the most variation (Whitlock, Hipperson et al. 2016). Knowledge of where rare species fall along this spectrum can help with identification of critical conservation areas and management techniques that will best promote long-term survival. Similarly, understanding the link between phenotypic diversity and genetic diversity can help inform decisions about how to best preserve the variation that forms the foundations of species’ resilience.
Here, we describe genetic and phenotypic diversity within populations of Eriogonum crosbyae Reveal. E. crosbyae is one of eighty-one species of Eriogonum in Nevada, and occurs on scattered outcrops of nutrient-poor soils in Northwestern Nevada, Southern Oregon, central and southwestern Idaho, and western Montana. It is a long-lived, mat-forming perennial herb that 41 produces dense clusters of bright yellow flowers from May to August and is important for its aesthetic and ecological value. Although E. crosbyae’s reproductive biology is not fully understood, it has been observed to be pollinated by insects from eleven different families (Kaye
1990), and related species are self-compatible but primarily outcrossing (Archibald, Wolf et al.
2001, Neel, Ross-Ibarra et al. 2001). Populations in Nevada range in size from a few dozen to over 45,000 individuals, growing at densities between 22 to 32,000 plants per hectare (Morefield
2003). Initial studies on the biology of the species, taxonomic notes, and species status reports have categorized E. crosbyae as an edaphic specialist due to its apparent restriction to unproductive, island-like volcanic ash outcrops over the majority of its range in northern Nevada and Southern Oregon (Reveal 1981, Kaye 1990, Morefield 2003). However, the species description in the Flora of North America (Craig C. Freeman 1993+) and a study on the genetics and evolution of edaphic endemism in Eriogonum by Grady (2012) implies that it may have more generalist growth capabilities and a wider distribution than other narrow edaphic endemics in this genus due to its ability to colonize other relatively unproductive sites, including basaltic, metamorphic, and granitic outcrops, washes, and ridges.
E. crosbyae is part of the E. chrysops group, a complex group of capitate buckwheats whose taxonomy is still evolving (Reveal 1981). The current treatment of E. crosbyae in Flora of
North America encompasses seven taxa that had been previously considered distinct species (See
Taxonomic Notes in the Appendix), following a grouping by Grady and Reveal (2011). Grady
(2012) suggested E. crosbyae is a polyphyletic group based on Sanger sequencing of a small number of both chloroplast and nuclear regions from five accessions. E. crosbyae’s taxonomic evolution and Grady’s phylogenetic results suggest high diversity within the species, and the potential for additional undocumented cryptic variation. Despite E. crosbyae’s unresolved taxonomic status, this rare, edaphically restricted taxon has already lost several major populations to mining since its discovery in 1978, and it is listed as a Bureau of Land Management Sensitive 42 species in Nevada. There is interest in protecting the remaining diversity and resilience within the species as it is currently known; however, there are also major gaps in scientific understanding of how much diversity there is and where it is located on the landscape-- questions this study attempts to address.
We analyzed patterns of genetic and phenotypic diversity in E. crosbyae, focusing on populations inhabiting light-colored soil outcrops from across the species’ range in Nevada in an attempt to minimize the likelihood of taxonomic complications. We considered both adult phenotypes in the wild and potentially adaptive seed and seedling traits for plants grown in a common garden environment. We studied both adult and seedling phenotypes because adult phenotypes demonstrate growth outcomes in response to environmental variation in the wild, while seedling traits are closely tied to successful reproduction and colonization of new habitats and can be important indicators of local adaptation (Baughman, Agneray et al. 2019). Seedling traits are also important to consider in the contexts of active species management and restoration and in-situ conservation, because variation in these traits can indicate the degree of habitat specificity species exhibit and, by extension, their vulnerability to environmental changes (Leger,
Atwater et al. 2019). Although much of the past genetic work on edaphically restricted
Eriogonum in Nevada has been based on small sets of traditional DNA markers (Kaye 1990,
Archibald, Wolf et al. 2001), these studies show that even rare edaphic specialist species in this genus with extremely limited ranges have relatively high levels of total genetic variation compared to widespread species in other genera (Gitzendanner and Soltis 2000). We sought to determine how diversity is distributed in E. crosbyae, and whether variation in potentially adaptive traits in the field or greenhouse corresponds with genetic diversity. This information will create a baseline for species conservation, and help add to the body of knowledge regarding genetic and phenotypic diversity in edaphic specialists. 43
METHODS Sampling locations In Nevada, E. crosbyae grows within an approximately 8,700km2 area in the northwestern areas of the state (Fig. 1). Plants grow in sites between 1200-2000m of elevation and receive
~6.5-17.0cm of precipitation per year, with annual average low temperatures of -0.7°C and highs of 19.3°C based on 30-year normals (1981-2010); Prism Climate Group
(http://www.prism.oregonstate.edu). For this study, sampling locations were randomly selected from among previously known occupied sites documented in Morefield (2003), with additional sites included that were documented on the Consortium of Intermountain Herbaria website at http://intermountainbiota.org. Potential sites were located on digitized topographic maps in
ArcMap, and a 150 x 180km (East to West x North to South) grid was overlaid on the map such that twenty-six cells contained at least one occupied site, spaced evenly across the species’ range in Nevada. Sites were assigned random numbers, and the site with the lowest random number from each square was selected for sampling. Sites are remote and some roads are impassible after localized rain events, and thus not all selected sites were available for access for all parts of this study.
We additionally sampled one population each of E. prociduum, E. ochrocephalum, E. alexanderae, and E. anemophilum, for comparison in phylogenetic analyses. These species have been shown to be closely related to E. crosbyae in earlier phylogenetic and taxonomic work
(Kaye 1990, Grady and Reveal 2011, Grady 2012). Sites containing each of these species were identified using the Consortium for Intermountain Herbaria website, and the closest accessible population of each of these species to the sampling area we covered for E. crosbyae was selected.
Genetic material collection and library preparation Leaf tissue samples for genetic analysis were collected from twenty-two accessible E. crosbyae sites in October 2017 (Fig. 1; Appendix Table 1). Leaf samples were collected from at 44 least twenty plants spaced at least 15 paces (~7.5m) apart in all directions, and tissue was stored and dried in silica gel prior to DNA extraction. Tissue was pulverized using a TissueLyser
(Qiagen, Valencia, CA), and DNA was extracted using Qiagen DNeasy plant kits. DNA extracts were evaluated for quantity and quality using a Qiexpert microfluidic electrophoresis device
(Qiagen, Valencia, CA).
Reduced-representation libraries for Illumina sequencing were generated using a ddRADseq approach (Parchman et al. 2012; Peterson et al. 2012). Genomic DNA was digested with the restriction enzymes EcoRI and MseI, and T4 DNA ligase was used to attach uniquely barcoded adaptors to digested fragments representing each individual. EcoRI cut-site adaptors consisted of the base Illumina adaptors embedded with eight to ten base pair (bp) DNA barcode sequences, while the MseI cut site adaptors were based on the alternate base Illumina adaptor.
Digested, ligated, and barcoded fragments from each individual were then pooled and PCR amplified using standard Illumina primers and a proofreading polymerase (Iproof; Biorad,
Hercules, CA). Libraries were size selected for a region between 350 and 450bp using a
PippinPrep quantitative electrophoresis unit (Sage Science, Beverly, MA). We generated single end, 100bp reads on three lanes of an Illumina HiSeq 2500 platform at the University of Texas
Genomic Sequencing and Analysis Facility (Austin, TX).
Phenotypic Diversity Field Measurements and Seed Collection We then conducted non-invasive plant form measurements on 20 individuals per site at
16 of the same locations during summer 2018 and collected seeds from the same 20 plants in a bulk collection, supplemented with collection from additional plants if fecundity was low
(Appendix Table 1). Plants were measured in a systematic zig-zag pattern beginning at the lowest elevation in the sampling area and aiming to traverse the entire width of the site, stopping at the closest plant to the sampler after every ten paces (~4.5m). Measured traits were observed to be 45 variable in the field and are potentially adaptive. They included: mat area (length of the longest side of the mat multiplied by the length of the mat perpendicular to the first direction), mat depth
(measured from the top of the root crown to the estimated average height of the leaves), height of the tallest flower stalk (from the base of the flower stalk to the base of the inflorescence), and number of flower stalks.
Greenhouse Measurements Seeds from the same 16 sites were sown into 8.9x8.9cx7.6 cm pots containing a 50/50 mix of field soil from one easily-accessible location and 3/8” decomposed granite. Rocks larger than 2.5cm removed from the field soil and replaced with an equal amount of additional soil. Pots were planted on January 10th, 2019, grown for roughly seven months in a University of Nevada,
Reno greenhouse (depending on emergence date) and harvested the week of July 19th, 2019. We aimed to plant 30 pots per seed collection site, with three seeds in each pot where quantities allowed (90 total seeds per site). We planted three seeds per pot to compensate for potentially low seed viability, based on an average viability of 31% in “good” seeds found for the closely related species E. prociduum (Kaye 1990). For sites where less than 90 seeds were collected, the number of seeds per pot was reduced to one or two to maintain the maximum number of pots possible, up to 30. Pots were arrayed randomly and were monitored daily for emergence and survival for two months, then every other day for the remainder of the experiment. Pots were watered daily during germination, then 2-3 times per week as needed for the remainder of the experiment. Pots containing more than one seedling after several weeks of growth were thinned using a coin toss to determine which seedling to remove. Temperatures in the greenhouse were set to approximate natural temperatures, within a minimum and maximum temperature limit. From January 10th to
March 28th, minimum and maximum temperatures were maintained between 1.5-15.5°C; from
March 28th to April 8th, between 4.5-18.5°C, and from April 9th to harvest, between 4.5- 21°C.
There was obvious variation in leaf-length among plants, and on June 3rd, 2019, the length of the 46 longest leaf of each seedling (cm) was measured from the base of the petiole to the tip of the leaf.
Upon harvest, roots and shoots were separated, dried, and weighed, and total biomass and root mass ratio were calculated. Prior to planting, 50 seeds per site (or all seeds in sites with low fecundity) were weighed in batches of five, and average seed weight was calculated for a total of
81 samples.
Genetic Data Analysis We removed sequences potentially representing common contaminants or Illumina oligos using series of bash and perl scripts (https://github.com/ncgr/tapioca), and demultiplexed reads by individual barcodes using a perl script. Because no reference genome is available for any closely related taxa, we used CD-HIT (Fu, Niu et al. 2012) to de novo cluster the unique sequences in our data set into contig consensus sequences which represented the genomic regions sampled with our ddRADseq approach (partial reference hereafter). We mapped all reads to the partial reference using bowtie2 (Langmead and Salzberg 2012), and used samtools and bcftools v1.3 (Li, Handsaker et al. 2009) to identify and call variants across the alignments. We obtained genotype likelihoods for SNPs at sites with a minimum base quality of 20, maximum coverage depth of 100, minimum map quality of 20, minimum site quality of 20, and minimum genotype quality of 10. We filtered variants using vcftools v0.1.14 (Danecek, Auton et al. 2011) and retained SNPs with minor allele frequency > 0.05, summed depth across samples > 100 or <
10000, and alternate allele call quality > 999. We took several additional steps to ameliorate genotyping bias stemming from the potential misassembly of paralogous genomic regions.
Finally, loci with a mean coverage >20X per individual and with observed FIS > 0.5 or < -0.5 were excluded in order to remove sites that could be associated with the mis-assembly of paralogous regions.
We executed principal components analysis (PCA) on a genotype likelihood covariance matrix including all individuals, using the prcomp function in R. We additionally used a 47 hierarchical Bayesian ancestry-based approach, similar to structure (Pritchard, Stephens et al.
2000), implemented in the program entropy (Gompert, Lucas et al. 2014). This approach determines the number of ancestral populations (k) best represented by the data and assigns individuals to them based on ancestry proportions. Both of these analyses indicated the sampled individuals actually represent three genetically distinct groups that are not spatially segregated
(see results). As summary metrics of genetic differentiation among these groups, we calculated
Hudson’s FST (Hudson, Slatkin et al. 1992) and Nei’s D (Nei 1972) based on allele frequencies.
We calculated nucleotide diversity (pi) within these groups using methods implemented in
ANGSD (Korneliussen, Albrechtsen et al. 2014).
We also used phylogenetic analyses to characterize patterns of evolutionary divergence among. E. crosbyae samples and four additional Erigonum species (E. alexanderae, E. anemophilum, E. ochrocephalum, and E. prociduum) sampled near the same geographic range as the E. crosybae populations. We generated multiple alignments and identified polymorphisms across 107 individuals for phylogenetic analyses using ipyRAD v0.9.15 (Eaton 2014). Reads were clustered with a 90% similarity threshold, mapped to the partial reference with bwa, and merged with MUSCLE (Edgar 2004). Most parameter values were set to default; full parameter value information is available with the supplement (DRYAD doi:XXXXXXX). After discarding loci with more than 20 SNPs or 10 indels, the final nexus alignment contained 548,432 SNPs across 107 samples. Multiple alignments generated above were used as input for maximum likelihood phylogenetic inference using RAxML version 8.2.12 and run using the GTR+CAT model of nucleotide substitution (Stamatakis 2014). We quantified support using 1000 nonparametric bootstrap replicates.
Greenhouse and Field Data Analysis Analyses relating genetic and phenotypic data were ultimately conducted on sixteen populations that were accessible in the field, grown in the greenhouse, and for which sufficient 48 genetic data was generated (Appendix Table 1). Because our results identified three differentiated genetic groups, we used generalized linear models to analyze differences in seed and seedling traits of E. crosbyae across these groups (see results). Each site sampled in the wild (results below) contained individuals from either one, two, or three major genetic groups, with some individuals occasionally falling between groups; we had not anticipated this result when we collected and bulked seeds from plants growing together in a single site. Thus, each sampling site was assigned a dominant genetic group (the genetic group represented by the largest number of plants at each site), coded as a categorical variable, and additional continuous variables were created to represent the percent of individuals within each site that fell into each genetic group. A principle components analysis was used to select trait measurements from the field that were correlated less than 0.40, by eliminating all variables but those with the strongest loadings on each of the first three principal components axes.
The existence of multiple genetic groups changed the methods we used to ask how populations varied in phenotypic traits. First, we asked how genetic groups differed in phenotypic traits. Two versions of these analyses were performed, using either the categorical dominant genetic group variable or the continuous variable, with all phenotypic analyses performed in R.
Average seed weight, days to emergence, length of longest leaf, root mass ratio, total biomass, height of tallest flower stalk, mat area, and mat depth were analyzed separately as dependent variables for both sets of analyses. For the first set, the categorical dominant genetic group variable and plant age (where appropriate for total biomass, root mass ratio, and length of longest leaf) were used as predictive variables. Tukey pairwise comparisons between genetic groups were performed using the “multcomp” package in R (Hothorn, Bretz et al. 2008). For the second set, the continuous dominant genetic group variables and plant age (where appropriate) were used as predictive variables. Models were created using untransformed values and gaussian error distributions, except for total biomass, emergence timing, mat area, and mat depth, which were 49 modeled with a gamma distribution and inverse link function to reduce heteroscedasticity of residuals.
Second, to answer our original question about phenotypic differences among populations, we analyzed phenotypic differences between sites that were dominated by genetic group three, which was the most common. This was done for both field plant form measurements and greenhouse growth responses. We used generalized linear models with untransformed values and gaussian error distributions to model differences among sites for average seed weight, emergence timing, and length of longest leaf in the greenhouse, and height of the tallest flower stalk in the field.
Differences in total biomass by site in the greenhouse and in mat area and mat depth in the field were modeled using a generalized linear model with a gamma distribution and inverse link function. Lastly, differences in root mass ratio between sites were modeled using beta regression with an identity link, following the methods proposed by Ferrari and Cribari-Neto (2004).
RESULTS Population Genetic Analyses After filtering potential contaminants and removing individuals with poor sequencing performance, we retained a total of ~550 million Illumina reads. After identifying and calling variants using samtools and bcftools and bioinformatic filtering based on sequencing depth and quality, we retained 18,754 loci across 229 individuals for population genetic analyses. Model based (entropy) and model free (PCA) analyses both indicated that the E. crosbyae samples represented three genetically differentiated groups which overlapped in geographic space. PCA revealed three tight clusters of individuals (Fig. 2a), and these groups were characterized by moderate to strong levels of differentiation (Hudson’s FSTavg = 0.27; Nei’s Davg = 0.18).
Similarly, entropy assigned ancestry proportions nearly fully to each of three ancestral population clusters. Each site sampled contained individuals belonging to one or more genetic group (Fig. 3), and four individuals, one each from populations M10 and M31 and two from M20, were not 50 clearly assigned to a genetic group (Figs. 2a, 3), and instead appeared intermediate between groups two and three. Nucleotide diversity estimates indicated relatively high levels of standing variation in each of the groups (1 =0.021, 2 =0.029, 3 =0.028).
When each population was assigned a single dominant genetic group (Fig. 1), geographic patterns of genetic distribution were apparent. Populations dominated by genetic group three were the most common and formed the central cluster of sites geographically (thirteen populations)
(Fig. X). Populations dominated by group one were the least common (four populations) and were tightly centered near Hog Ranch Mountain. The five populations dominated by group two had the greatest geographic distances between populations; this group also encompassed the single populations we sampled of E. prociduum, E. ochrocephalum, E. alexanderae, and E. anemophilum. Only 9 populations had all sampled individuals from the same genetic group. All but three populations sampled in the field for genetic analyses were comprised of >75% individuals from a single genetic group; the remainder were between 60-69% (Fig. 3).
Within genetic group three, spatial genetic structure across the landscape was low, indicated by the scattered phylogenetic locations of individuals from different populations dominated by group three within our final tree (Fig. 2b). Spatial genetic structuring was more apparent within group two, and to a lesser extent, group one, where individuals from each population dominated by that group clustered closely next to each other on the phylogenetic tree.
Phenotypic diversity Greenhouse Results Out of 1,374 seeds that were planted in the greenhouse, only 396 plants emerged, frequently with more than one seedling per pot, giving a germination rate of 28.8%. A total of
272 seedlings survived to harvest after thinning and the natural deaths of 12 seedlings. Analysis of variance in seed and seedling traits detected differences among the three dominant genetic groups, with the degree of differentiation varying by trait (Appendix Table 2). Groups two and 51 three were more similar to each other than they were to group one for average seed weight, days to emergence, and root mass ratio (Fig. 4). However, group one had leaf lengths most similar to those observed in group two (Fig. 4). Group one had the highest average seed weight, shortest time to emergence, and the lowest total biomass and root mass ratios (p<0.05) (Appendix Table
2). Group three had the highest total biomass root mass ratios, and longest leaf lengths (p<0.05)
(Appendix Table 2). When greenhouse traits were analyzed using continuous variables for the percent of individuals belonging to each genetic group in generalized linear models, results for differences in traits between groups were similar. Seed weight increased the most as the proportion of individuals per population belonging to group one rose (p<0.001), while length of longest leaf (p<0.001) and root mass ratio (p<0.008) increased the most as proportions of individuals belonging to group three rose (Table 1). Within group three, we found no significant differences in total biomass of seedlings grown in the greenhouse among sites (p<0.1). However, there was moderate to high variation among sites in average seed weight, emergence times, length of longest leaf, and root mass ratio (p<0.05) (Appendix Figs. 1-5).
Field Results Fewer differences among genetic groups were detected in field plant traits. However, analysis of variance did show that sites dominated by group three had taller flower stalks than sites dominated by groups one or two (z= 4.50, 3.91, respectively; p<0.00), and sites dominated by group two had the largest mats, with the greatest mat area and mat depth (mat area: z= 5.50, -
9.82 respectively; mat depth: z= 5.68, -10.78 respectively; p<1e-04 for all). Analyses of field plant form using continuous variables for the percent of individuals belonging to each genetic group somewhat agreed with these results. Flower stalk height increased the most as the proportion of individuals in group three rose (p<0.01). However, the estimates for change in mat area as the proportions of individuals in groups one and three increased were very similar (p≤
0.059), while the estimate of the effect for proportion of individuals in group two was non- 52 significant (Table 2). Within group three, we found no significant differences in mat area between sites (p<0.1), but we did find moderate to high variation among sites in mat depth and flower stalk height (p<0.05) (Appendix Figs. 6-8).
DISCUSSION Edaphic specialization to island-like patches of unique soils within the landscape matrix is an important source of diversity in floras around the world, and especially in western North
America (Kruckeberg 1986, Givnish 2010). Although soil specialists can be relatively resilient to habitat fragmentation because of their inherently scattered distributions, the potential for soil specialists to exist within small populations and restricted areas of occupancy make them vulnerable to disturbance, dramatic changes in climate, and stochastic losses of genetic variation
(Ellstrand and Elam 1993, Harrison, Damschen et al. 2009, García-Fernández, Iriondo et al.
2018). Effective conservation of these species requires preservation of enough genetic and phenotypic diversity to allow them to maintain resilience and population viability. Here, we quantified the amount and distribution of genetic and phenotypic diversity in populations of plants previously identified as E. crosbyae, a rare edaphic specialist in the western Great Basin.
This creates a baseline of knowledge for future studies to facilitate conservation efforts involving the species in Nevada and improve our understanding of diversity in edaphically specialized
Eriogonums.
Surprisingly, despite our narrow geographic focus, we found that individuals from our sample sites belonged to one of three strongly differentiated genetic groups that frequently co- occurred. Phenotypic measurements taken in the wild showed statistically significant differences between these groups for some traits; however, the overlapping ranges of variation in many traits would make groups difficult to distinguish in the wild. In contrast, differences among groups were more pronounced in seedlings that were grown in a greenhouse common garden, indicating that consideration of seedling morphology may aid in future investigations of diversity in this 53 species. Intraspecific diversity was arranged such that the most similar groups (groups 1 and 2) dominated central populations, while sites dominated by group 3 were geographically separated from the central site cluster by larger distances and topographic barriers, possibly indicating a history of colonization and dispersal after previous isolation. Our results suggest that these soil specialists have complex histories, and that it is possible to use genetic and phenotypic variation to identify unique sites and hotspots of Eriogonum genetic diversity in this region.
The distinct genetic clusters we observed in E. crosbyae and their associated morphological differences in the field and greenhouse reveal that this study system harbors greater biodiversity than originally assumed. Although Eriogonum is a highly diverse genus and previous phylogenetic studies have suggested a lack of support for E. crosbyae as an evolutionarily cohesive unit (Kaye 1990, Grady 2012), we assumed that within our small study area, individual sites would contain single populations. However, we found that many of the sites we had sampled contained individuals belonging to more than one genetic group, with high levels of differentiation between the groups. Interestingly, we observed four individuals from three different populations that appeared to have intermediate genotypes between groups two and three, which are the most highly differentiated. This could potentially indicate hybridization; however, fitness and fertility of these hybrids is unknown, and further studies would be needed to determine reproductive compatibility among groups. This pattern of multiple genetic groups coexisting within single sites is unusual for plants, but has been observed previously in Lasthenia californica DC. ex Lindl. and L. gracilis (DC.) Green, two cryptic species that co-occur on the same serpentine hillside in southern California, and in species of Draba in the Arctic circle, among few others (Grundt, Kjolner et al. 2006, Yost, Barry et al. 2012, Gale, Li et al. 2015). In addition to individual study sites containing surprisingly high levels of biodiversity, our estimates of nucleotide diversity are fairly high within each group, similar to levels observed in eight broadly endemic plant species reviewed in Leffler, Bullaughey et al. (2012). These results 54 indicate that diversity, and likely connectivity, has been maintained despite the apparent isolation between our study sites and the small population sizes they contain.
The biogeographic history of the Great Basin may offer some insight to the unusual patterns of diversity we found. During the last glacial maximum in North America, the western
Great Basin became a landscape of glaciated mountaintops surrounded by vast lakes, including, in our area of interest, Lake Lahontan (Reveal 1980, Wells 1983). Plant tissue and pollen preserved in ancient packrat middens in western Nevada suggest that several species of pines experienced range changes potentially leading to allopatric divergence during this period, followed by dispersal into new habitat types as the climate warmed (Wells 1983). This process could similarly have caused the high Eriogonum diversity we observed within our study sites, if populations of one ancestral lineage were isolated by glaciation or large bodies of water, diverged, then dispersed back into their present challenging edaphic habitats when lake levels dropped. The spatial distribution of sites dominated by each genetic group also could be consistent with periods of allopatric differentiation and subsequent secondary contact. According to our phylogenetic analyses and our estimates of genetic distance, groups one and three are more closely related to each other than either is to group two, indicating that these two groups also diverged the most recently. Groups one and three are the dominant plants in the central cluster of our sample sites, while sites dominated by the older lineage, group two, were generally found around the outskirts of our sample range, on the far side of potential geographic barriers including
Hog Ranch Mountain and the Black Rock Desert. Interestingly, phenotypic differentiation in seedlings in the greenhouse was often highest between sites dominated by genetic groups one and three, despite their recent divergence, which may indicate strong selective forces acting on these groups during early life stages. 55
The extent of variation we found within individual sample sites may not have been fully recognized in previous studies because plant taxonomy and field botany often rely heavily on the features of mature plants observed in the wild to distinguish between species, subspecies, and ecotypes. Such features include flower morphology, leaf size and shape, plant growth habit and stature, pubescence, etc. and by these measures, sites containing E. crosbyae had been assumed to be clearly identifiable, with few exceptions to morphological distinctiveness in mature plants likely resulting from hybridization in populations that occur near the ranges of other related species such as E. prociduum Reveal (Kaye 1990, Rabeler 1993). Our field results mirror these previous observations and justification for taxonomic classification, with the ranges in values we measured for flower stalk height, mat area, and mat depth in different groups all overlapping strongly among divergent genetic groups, despite statistically significant differences for some traits. These similarities could be caused by relatively short amounts of evolutionary time since divergence (only about 12,000-110,000 years if it occurred during the last glacial maximum) and/or strong selective pressures, such as those observed in some species of Lupinus in the Andes
(Gustafsson, Skrede et al. 2014, Contreras-Ortiz, Atchison et al. 2018, Struck, Feder et al. 2018).
However, our common garden experiment revealed differences in seed and seedling traits between groups that were not obvious in the field. For instance, it was readily apparent that some seedlings in the greenhouse produced a small number of leaves that were much longer than the majority of those in their developing mats, and these leaves began to show signs of senescence early in the summer that would likely have resulted in their eventual loss in the field. However, when we measured the length of the longest leaf in our seedling experiment, this trait clearly distinguished seedlings from populations dominated by group three from those dominated by groups one and two. Similarly, seed mass readily distinguished sites dominated by genetic group one from groups two or three. These findings underscore the importance of pairing genetic or morphological analyses of variation in the field with studies of plant growth in a controlled 56 environment, because underlying biodiversity may sometimes be more apparent at early life stages (M. Griffith and E. Sultan 2006). We recommend further study of the floral traits in these populations, to determine whether there is yet undescribed variation among these genetic groups.
Our results indicate that much remains to be learned about the distribution of variation in
Eriogonum soil specialists in this region, its evolutionary origins, and the ecological significance of this cryptic diversity. Although our genetic results are preliminary, and precautionary bioinformatic filtering and reference mapping are ongoing in order to verify them, the genetic and morphological patterns we found may have important implications for species conservation. Sites dominated by different genetic groups may benefit from different management strategies; however, substantial further study is needed before specific recommendations can be made.
Future work in this area should strive for more extensive geographic sampling, and should involve careful measurement of the locations and phenotypic traits of individuals sampled in the wild or grown in the greenhouse that could be directly compared to genetic results. This sampling strategy would allow researchers to better understand the taxonomic boundaries and degree of phenotypic plasticity and underlying genetic differentiation among these taxa. In light of this, although the individuals we sampled from E. anemophilum, E ochrocephalum, E. alexanderae, and E. prociduum all clustered within the second genetic grouping we identified, our study does not provide enough evidence of similarities between them to imply a need for any taxonomic changes to those species. Rather, our results highlight the potential for even simple-seeming systems to contain significant hidden variation, and act as an invitation to better understand how that variation is maintained and what, if any, cascading impacts this genotypic variation might have on other community components via differences in plant chemistry, soil interactions, or phenology that are not readily apparent to the casual observer. 57
ACKNOWLEDGEMENTS This research was funded by a Bureau of Land Management Conservation Area
Improvement Grant written by Kathleen Torrence of the Black Rock Field Office under
Assistance Agreement # L16AC00318. Funding was also generously provided by the University of Nevada, Reno Graduate Student Association Research Grant Program, the Eriogonum Society
James Reveal Eriogonum Project Grant, and the Nevada Native Plant Society Margaret Williams
Research Grant. Thank you to Logan McClinton for assistance with field data collection and planting, and to Lana Sheta for assistance with genetic sample preparation and processing. Thank you also to the BLM Black Rock Field Office for facilitating data and tissue collection on public land, and to Tom Dilts for assistance with ArcMap during site selection.
AUTHOR CONTRIBUTIONS Kathleen Torrence conceived of the project and secured major funding, Elizabeth Leger and Thomas Parchman designed the research questions with assistance from Kathleen Torrence and contributed critically to methodology and drafts of the manuscript; Jamey McClinton assisted with methodological design, secured funding from the UNR Graduate Student Association,
Eriogonum Society, and Nevada Native Plant Society, implemented the greenhouse experiment and field data collection, analyzed the data with assistance with genetic data from Tom Parchman and Trevor Faske, and led the writing of the manuscript.
58
TABLES
59
60
FIGURES Figure 1: Map of Eriogonum sampling sites in northern Nevada, USA colored by the genetic group assigned to the most individuals from each location. Purity of dominant groupings ranges from 60-100%.
61
Figure 2a: Genetic groupings of individuals sampled in the wild from populations previously identified as E. crosbyae.
Figure 2b: Phylogenetic analysis of E. crosbyae individuals sampled in the wild.
62
Figure 3: Map of Eriogonum sampling sites in northern Nevada, USA colored by the genetic group assigned to each individual sampled from each location. Panels from left to right increase in level of detail for the central populations.
63
Fig. 4: Tukey pairwise comparisons of phenotypic differences in greenhouse seed and seedling traits by dominant genetic group for 16 source sites, with analyses including dominant genetic group as a categorical variable and plant age (where appropriate). Letters indicate significant (P<0.05) differences among groups.
64
Fig.5: Tukey pairwise comparisons of phenotypic differences in plant form in the field by dominant genetic group for 16 source sites, with only dominant genetic group for each field location considered as a categorical variable. Letters indicate significant (P<0.05) differences among groups.
65
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Chapter 1 Appendix Taxonomic Notes E. capistratum, E. capistratum var. capistratum, E. capistratum var. muhlickii, E. capistratum var. welshii, E. meledonum, E. ochrocephalum var. alexanderae, and E. verrucosum are all considered synonymous with E. crosbyae, following the grouping by Grady and Reveal
(2011), which adds E. prociduum var. mystrium, updating the species’ 2005 treatment in Flora of
North America.
DNA sequence data and differences in morphology suggest that E. alexanderae may warrant its acceptance as a separate species, rather than as a variety of E. ochrecephalum (Grady and Reveal
2011).
Associated Species According to our surveys in Nevada, the top twenty species most commonly associated with E. crosbyae are (in order): Elymus elymoides (Raf.) Swezey, Ericameria nauseosa (Pallas ex
Pursh) G.L. Nesom & Baird, Penstemon speciosus Douglas ex Lindl., Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young, Tetradymia glabrata Torr. & A. Gray, Bromus tectorum L.,
Artemisia arbuscula Nutt., Chenactis douglasii (Hook.) Hook. & Arn., Grayia spinosa (Hook.)
Moq., Stipa hymenoides Roem. & Schult., Poa secunda J. Presl, Ipomopsis congesta (Hook.)
V.E. Grant, Pleiacanthus spinosus (Nutt.) Rydb., Astragalus tetrapterus A. Gray, Chrysothamnus viscidiflorus (Hook.) Nutt., Phlox hoodia Richardson, Sphaeralcea grossulariifolia (Hook. &
Arn.) Rydb., Stenotus acaulis (Nutt.) Nutt., Arenaria sp. L., and Mentzelia albicaulis (Hook.)
Torr. & A. Gray.
74
Site Characteristics Appendix Table 1: Site names, coordinates, and occupation status of Eriogonum crosbyae sampling locations in Nevada. Key to occupation status: Occupied = E. crosbyae present in the field, Unoccupied = No E. crosbyae present, Colonized = E. crosbyae present in previously empty site, Abandoned = Site once supported a population, but population not found during our 2017 survey. Previous site occupation status as designated in the Morefield (2003) survey.
Site Name Occupation Status Longitude Latitude
Hycroft Occupied -118.703 40.85563
M321 Abandoned -119.469 41.22749
M81 Abandoned -119.475 41.21985
M10 Occupied -119.474 41.24459
M11 Occupied -119.426 41.24772
M12 Occupied -119.499 41.18389
M17 Occupied -119.556 41.24583
M18 Occupied -119.36 41.34631
M20 Occupied -119.544 41.45799
M29 Occupied -119.491 41.11524
M31 Occupied -119.41 41.10117
M35 Occupied -119.358 41.52695
M38 Occupied -119.488 41.24423
M5 Occupied -119.441 41.16252
M51 Occupied -119.423 41.16699
M59 Occupied -119.436 41.4807
M6 Occupied -119.479 41.16055
M60B2 Occupied -119.374 41.50898
M60W2 Occupied -119.374 41.50898
M61 Occupied -119.439 41.18266 75
PU3 Occupied -118.994 41.81449
T12134 Occupied -119.151 41.71832
T13833 Occupied -119.394 40.44244
T16225 Occupied -119.331 41.50723
T8075 Occupied -119.488 41.12297
WO2 Occupied -118.997 41.81529
T121163 Abandoned -119.001 41.81756
U22 Unoccupied -119.442 41.49174
U484 Colonized -119.34 41.51409
U54 Unoccupied -119.554 41.25299
U59 Unoccupied -119.528 41.22663
U62 Unoccupied -119.491 41.10951
U68 Unoccupied -119.525 41.11477
U70 Unoccupied -119.534 41.13742
U72 Unoccupied -119.483 41.23591
U74 Unoccupied -119.454 41.25149
U77 Unoccupied -119.49 41.24378
U85 Unoccupied -119.484 41.21164
U87 Unoccupied -119.431 41.24676
U89 Unoccupied -119.449 41.15785
U911 Colonized -119.438 41.19092
U93 Unoccupied -119.494 41.18797
WU1 Unoccupied -119.534 41.45287
1Observed 10/12/17; 2Sites adjacent to each other- both occupied, soil color brown and white, respectively; 3Observed 10/27/2017; 4Observed 10/26/17 76
Soil and Plant Growth Characteristics Appendix Table 2: Descriptive statistics (mean +/- SD) for 38 soil characteristics in occupied and unoccupied E. crosbyae habitat soils and the more fertile Washoe Valley, NV soil. P-values for occupied and unoccupied field soils were obtained using 2-sided t-tests of differences in soil characteristics by site occupation (note: significance level not corrected for multiple comparisons). Sampling accomplished through methods described above; 20 sub-samples per each site were composited, homogenized, and tested by A&L Western Laboratories. Average E. crosbyae All soils tested habitat1 P-value Occupied Unoccupied (Occupied Washoe Soil Field Sites Field Sites vs. Mean(±SD) Range N = 1 N = 25 N = 17 Unoccupied; Mean (±SD) Mean (±SD) α=0.05) % Organic 3.04 2.95 2.96 3.3 0.70 1.7 - 4.3 Matter2 (±0.83) (±0.76) (±0.72) Weak Bray P 18.72 16.47 17.48 196 0.62 2.0 - 47.0 (ppm) 2 (±10.16) (±8.06) (±29.78) 853.48 816.24 840.90 227.0 - K (ppm) 2 163 0.18 (±1061.75) (±570.41) (±868.73) 4285.0 445.72 505.24 473.30 146.0 - Mg (ppm) 2 103 0.84 (±206.84) (±281.88) (±245.31) 1359.0 2232.20 2826.47 2527.00 1010.0 - Ca (ppm) 2 1186 0.069 (±763.27) (±1181.14) (±984.66) 5725.0 229.28 285.77 236.50 Na (ppm) 2 30 0.25 39.0 - 901.0 (±217.24) (±313.41) (±180.42) 6.45 6.67 6.62 pH 5.8 0.056 4.7 - 7.5 (±0.66) (±1.00) (±0.64) CEC 23.62 23.90 23.91 9 0.19 12.9 - 104.0 (meq/200g) (±18.51) (±8.81) (±15.64) K % Cation 9.59 8.84 9.41 4.6 0.56 0.6 - 33.6 Saturation (±8.20) (±4.77) (±6.85) Mg % Cation 19.40 18.00 18.82 9.4 0.43 1.4 - 40.9 Saturation (±8.85) (±6.96) (±7.83) Ca % Cation 56.51 60.27 59.41 65.5 0.38 23.7 - 85.6 Saturation (±12.92) (±14.13) (±11.92) H % Cation 10.02 7.74 7.71 19 0.068 0.0 - 50.5 Saturation (±13.47) (±18.05) (±11.57) Na % Cation 4.49 5.17 4.66 1.5 0.87 0.4 - 12.0 Saturation (±2.60) (±4.25) (±2.42) NO3 N 5.20 5.65 5.26 69 0.14 3.0 - 18.0 (ppm) (±3.32) (±2.40) (±10.49) 77
Average E. crosbyae All soils tested habitat1 P-value Occupied Unoccupied (Occupied Washoe Soil Field Sites Field Sites vs. Mean(±SD) Range N = 1 N = 25 N = 17 Unoccupied; Mean (±SD) Mean (±SD) α=0.05) 373.84 452.65 374.40 SO4 S (ppm) 17 0.18 2.0 - 7306.0 (±1455.44) (±703.64) (±1189.39) 0.40 0.19 0.32 Zn (ppm) 2 9.9 0.033 0.1 - 2.7 (±0.51) (±0.08) (±1.57) 4.24 4.06 3.59 Mn (ppm) 2 10 0.24 1.0 - 12.0 (±3.13) (±6.30) (±2.89) 7.76 5.53 6.37 Fe (ppm) 2 46 0.043 2.0 - 35.0 (±6.91) (±5.69) (±8.52) 0.86 0.67 0.77 Cu (ppm) 2 0.9 0.13 0.2 - 1.7 (±0.41) (±0.31) (±0.39) 0.42 0.47 0.45 B (ppm) 2 0.4 0.49 0.2 - 1.0 (±0.18) (±0.22) (±0.19) Soluble Salts 0.84 1.45 0.98 1.2 0.11 0.2 - 3.1 (mmhos/cm) (±0.82) (±1.45) (±0.87) KCl extractable 5.84 67.53 4.42 1 0.94 1.0 - 102.0 aluminum (±20.21) (±273.79) (±16.06) (ppm) 1.96 2.22 1.98 SAR 0.7 0.61 0.5 - 5.5 (±1.18) (±1.79) (±1.06) 1.62 1.95 1.65 ESP 0.1 0.65 0.1 - 6.4 (±1.59) (±2.34) (±1.42) 3.31 5.01 3.52 Na (meq/L) 2.1 0.18 0.7 - 13.4 (±3.32) (±6.52) (±2.88) 5.41 8.87 6.73 Ca (meq/L) 15.2 0.18 0.6 - 24.0 (±7.41) (±9.50) (±8.13) 1.31 3.56 1.87 Mg (meq/L) 3.9 0.12 0.2 - 10.4 (±1.17) (±4.68) (±2.08) 0.84 1.45 0.98 EC (dS/m) 1.2 0.11 0.2 - 3.1 (±0.82) (±1.45) (±0.87) 1.28 1.35 1.28 Cl (meq/L) 1.2 0.35 0.3 - 6.1 (±1.63) (±1.32) (±1.39) Extractable 0.18 0.16 0.18 0.4 0.90 0.1 - 0.8 B (ppm) (±0.15) (±0.08) (±0.13) 52.04 54.24 53.93 % Sand 79 0.54 31.0 - 81.0 (±10.42) (±9.83) (±10.70) 78
Average E. crosbyae All soils tested habitat1 P-value Occupied Unoccupied (Occupied Washoe Soil Field Sites Field Sites vs. Mean(±SD) Range N = 1 N = 25 N = 17 Unoccupied; Mean (±SD) Mean (±SD) α=0.05) 25.68 25.29 24.84 % Silt 12 0.77 10.0 - 42.0 (±6.73) (±5.74) (±6.51) 22.28 20.47 21.23 % Clay 9 0.21 9.0 - 39.0 (±6.34) (±8.35) (±6.95) 1“Average” habitat conditions attained by averaging sites with mean plant growth ±1 SD from the overall means for biomass and root mass ratio in the greenhouse. Soils included are: M11, M12, M17, M18, M20, M29, M31, M32, M35, M38, M5, M5, M6, M60B, M60W, M61, M8, T12134, T13833, T16225, T8075, U22, U54, U59, U62, U68, U72, U74, U77, U85, U91, U93, WOCC2, U93, M59, M10, PU3, and U87. 2Extractable, not total values.
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Appendix Table 3: Summary statistics of E. crosbyae characteristics at field sites in northern Nevada and in the greenhouse at the University of Nevada, Reno.
Response Nobs* Mean SD
Mat Depth 369 2.21 (cm) 1.26 (cm) Field Measurements 109.30 Mat Area 369 119.00 (cm2) (cm2)
Percent emergence 466 0.48 0.39
Total biomass 284 228.77 mg 156.15 Greenhouse Measurements Root mass ratio 284 0.71 0.09
Percent survival 332 0.76 0.37
* Nobs varies. For field measurements, each variable was measured for all plants. Percent emergence in the greenhouse was calculated using every pot planted. Percent survival was calculated only out of those pots where seedlings emerged (332), and total biomass and root mass ratio were calculated using pots with seedlings that survived until harvest.
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Appendix Table 4: PCA loadings and cumulative proportions explained for first 10 PC axes of soil variation between sites. Variation explained: 93%. PC 1 (27%) is dominated by traits describing soil pH and salinity. PC2 (16%) is dominated mainly by Bray P, K, Na. PC 3 (15%) is dominated by calcium, KCl-extractable aluminum, and the micronutrients manganese and boron.
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 Proportion of 0.27 0.16 0.15 0.11 0.07 0.05 0.04 0.03 0.03 0.02 Variance Cumulativ e 0.27 0.43 0.57 0.68 0.75 0.80 0.84 0.88 0.90 0.93 Proportion % Organic 0.10 -0.12 0.11 -0.04 0.15 -0.11 0.18 -0.20 0.38 0.10 Matter Bray P -0.11 -0.22 -0.08 -0.30 0.07 0.07 0.11 0.05 0.26 -0.11 (ppm) K (ppm) -0.18 0.20 0.09 -0.11 -0.13 -0.34 0.07 -0.25 0.05 -0.18 Mg (ppm) 0.13 -0.01 -0.05 0.05 0.34 -0.42 -0.29 0.03 0.08 0.13 Ca (ppm) -0.03 -0.05 0.27 0.13 0.26 -0.30 -0.08 0.14 0.18 -0.03 Na (ppm) -0.24 0.22 0.12 -0.09 0.04 -0.09 -0.05 0.00 0.02 -0.24 CEC (meq -0.16 -0.27 0.17 -0.05 0.01 -0.21 -0.16 -0.14 -0.01 -0.16 /100g) K % -0.09 0.25 0.09 -0.11 -0.18 -0.21 0.20 -0.37 -0.11 -0.09 Cation Sat. Mg % 0.23 0.06 -0.14 -0.06 0.22 -0.08 -0.14 0.07 -0.03 0.23 Cation Sat. Ca % 0.17 -0.02 0.30 0.09 0.13 0.17 0.12 0.18 0.04 0.17 Cation Sat. Na % -0.19 0.29 0.11 -0.10 0.06 0.10 -0.02 0.00 -0.08 -0.19 Cation Sat. NO3-N -0.08 0.03 -0.10 0.19 0.24 0.33 0.08 -0.44 0.27 -0.08 (ppm) SO4-S -0.16 -0.29 0.19 -0.03 -0.05 0.01 -0.02 -0.04 -0.16 -0.16 (ppm) Zn (ppm) -0.06 -0.05 -0.17 -0.14 -0.19 0.07 -0.49 -0.05 0.26 -0.06 Mn (ppm) -0.15 -0.08 -0.34 0.05 0.00 0.10 0.08 0.06 -0.13 -0.15 Fe (ppm) -0.19 -0.10 -0.24 -0.11 -0.16 -0.03 -0.07 -0.06 0.28 -0.19 Cu (ppm) -0.05 0.00 -0.16 -0.29 0.24 0.02 0.10 0.16 0.40 -0.05 B (ppm) -0.08 -0.10 0.27 -0.08 0.27 0.21 -0.03 0.04 -0.03 -0.08 Soluble Salts -0.28 -0.03 0.03 0.22 0.01 -0.05 0.11 0.15 0.07 -0.28 (mmhos /cm) 81
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 Proportion of 0.27 0.16 0.15 0.11 0.07 0.05 0.04 0.03 0.03 0.02 Variance Cumulativ e 0.27 0.43 0.57 0.68 0.75 0.80 0.84 0.88 0.90 0.93 Proportion KCl-Extr. -0.13 -0.12 -0.27 0.22 -0.06 0.04 0.18 0.03 -0.11 -0.13 Al (ppm) SAR -0.21 0.23 0.03 -0.13 0.13 0.16 -0.16 0.14 -0.12 -0.21 ESP -0.21 0.22 0.03 -0.14 0.12 0.17 -0.17 0.13 -0.12 -0.21 Na -0.27 0.17 0.09 0.00 0.06 0.01 -0.04 0.15 0.02 -0.27 (meq/L) Ca -0.23 -0.11 0.14 0.19 -0.04 -0.12 0.11 0.17 0.14 -0.23 (meq/L) Mg -0.20 -0.08 -0.12 0.31 0.07 -0.09 0.17 0.15 0.00 -0.20 (meq/L) pH 0.19 0.19 0.26 -0.01 0.13 0.09 0.08 0.03 0.01 0.19 EC (dS/m) -0.28 -0.03 0.03 0.22 0.01 -0.05 0.11 0.15 0.07 -0.28 Cl (meq/L) -0.17 0.02 -0.06 0.23 0.25 0.23 -0.14 -0.17 0.00 -0.17 Extractabl -0.15 -0.23 0.17 -0.15 -0.02 0.25 -0.19 -0.08 0.02 -0.15 e B (ppm) % Sand 0.11 0.12 0.16 0.22 -0.34 0.13 -0.14 0.12 0.29 0.11 % Silt -0.03 -0.04 -0.04 -0.30 0.24 0.04 0.43 -0.14 -0.09 -0.03 % Clay -0.13 -0.13 -0.19 -0.05 0.27 -0.22 -0.18 -0.05 -0.32 -0.13 Ca:Mg Ratio % -0.12 -0.28 0.24 -0.09 -0.11 0.06 -0.03 -0.11 -0.09 -0.12 Cation Sat. Ca:Na Ratio % 0.02 -0.34 0.18 -0.06 -0.08 0.01 -0.05 -0.05 -0.15 0.02 Cation Sat. Na:Mg Ratio % -0.25 0.18 0.10 -0.12 -0.12 -0.10 0.00 -0.04 0.12 -0.25 Cation Sat. N:P Ratio -0.01 0.08 0.06 0.35 0.14 0.04 -0.18 -0.46 0.01 -0.01 (ppm)
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Soils and Plant Growth Modeling Field Measurements Appendix Table 5: Top models of E. crosbyae growth responses to soil variation in the field for all populations measured. Variable effect estimates reported in Fig. 5 were averaged between top models. Models displayed if AICc is within 2 units of best model (N=373).
McFadden Δ Predicted Response Predictors1 AICc pseudo-R2 AICc R22 (Adj.)
Al (ppm) + Cu (ppm) + % Sand + Na:Mg Ratio * + Mg (meq/L) + 941.23 0.00 0.274 0.251 Ca:Na Ratio * + N:P Ratio (ppm) + Mg (ppm)
Al (ppm) + Cu (ppm) + % Organic Matter + % Sand + Na:Mg Ratio * + 941.85 0.62 0.276 0.249 Mg (meq/L) + Ca:Na Ratio * + N:P Ratio (ppm) + Mg (ppm)
Al (ppm) + Cu (ppm) + % Sand + Na:Mg Ratio * + Mg (meq/L) + 942.22 0.99 0.273 0.249 Ca:Na Ratio * + N:P Ratio (ppm) + Mg (ppm) + Ca*
Al (ppm) + Cu (ppm) + % Sand + Na:Mg Ratio * + Mg (meq/L) + 942.42 1.19 0.275 0.250 Ca:Na Ratio * + N:P Ratio (ppm) + Mat Area Mg (ppm) + Mn (ppm)
Al (ppm) + Cu (ppm) + % Sand + Na:Mg Ratio * + Mg (meq/L) + 942.50 1.27 0.273 0.247 Ca:Na Ratio * + N:P Ratio (ppm) + Ca (ppm) + Mg (ppm)
Al (ppm) + % Silt + Cu (ppm) + % Sand + Na:Mg Ratio * + Ca:Na Ratio 942.55 1.32 0.272 0.247 * + N:P Ratio (ppm) + Ca (ppm) + Mg (ppm)
Al (ppm) + Cu (ppm) + % Organic Matter + % Sand + Na:Mg Ratio * + 942.86 1.63 0.274 0.248 Mg (meq/L) + Ca:Na Ratio * + N:P Ratio (ppm) + Mg (ppm) + Ca*
Al (ppm) + Cu (ppm) + % Sand + 942.88 1.65 0.274 0.249 Na:Mg Ratio * + Mg (meq/L) + 83
McFadden Δ Predicted Response Predictors1 AICc pseudo-R2 AICc R22 (Adj.)
Ca:Na Ratio * + N:P Ratio (ppm) + Mg (ppm) + Ca* + Mn (ppm)
Al (ppm) + Cu (ppm) + % Organic Matter + % Sand + Na:Mg Ratio * + 942.98 1.75 0.271 0.247 Mg (meq/L) + Ca:Na Ratio * + N:P Ratio (ppm)
% Clay + Cu (ppm) + Mg* + % Silt + Mn (ppm) + % Organic Matter + 889.33 0.00 0.372 0.346 Ca:Mg Ratio * + % Sand + NO3-N Mat (ppm) Depth % Clay + Cu (ppm) + Mg* + % Silt + Mn (ppm) + % Organic Matter + 891.01 1.68 0.367 0.346 Ca:Mg Ratio * + % Sand 1Units: * = (% Cation Saturation). 2Predicted R2 method according to that suggested in Allen (1971). Values within 0.2 units of Adjusted R2 are considered good evidence that models are not over-fitted (Kundu, Sengupta et al. 2015).
84
Appendix Figure 2: Association of mat area of E. crosbyae individuals measured at field sites in northern Nevada with soil variables, using all measured populations. Estimates are averaged from models that add up to 95% of multi-model evidence weight and are in units of standard deviations from the mean for both soil variables and plant responses. 85
Appendix Figure 3: Association of mat depth of E. crosbyae individuals measured at field sites in northern Nevada with soil variables, using all measured. Estimates are averaged from models that add up to 95% of multi-model evidence weight and are in units of standard deviations from the mean for both soil variables and plant responses.
86
Greenhouse Measurements Appendix Table 8: Top models of Biomass, Root Mass Ratio, and Emergence with McFadden pseudo-R2 (Adj.) and Predicted R2 for the best models, displayed if AICc is within 2 units of top model. Variable effect estimates reported in Fig. 7 were averaged between top models.
Pred. Response Predictors1 AICc ΔAICc Adj. R2 R22
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + K* + % Silt + Mg (ppm) + CEC 529.16 0.00 0.644 0.623 (meq/100g) + % Org. Matter + Cl (meq/L) + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + % Silt + Mg (ppm) + CEC (meq/100g) + 529.70 0.54 0.642 0.623 % Org. Matter + Na:Mg Ratio* + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + % Silt + Mg (ppm) + CEC (meq/100g) + 530.22 1.06 0.641 0.622 % Org. Matter + Cl (meq/L) + Al (ppm) + Cu (ppm) Total Biomass Plant Age + Sol. Salts (mmhos/cm) + (mg) Bray P (ppm) + N:P Ratio (ppm) + % Silt + Mg (ppm) + Mn (ppm) + CEC 530.38 1.22 0.644 0.622 (meq/100g) + % Org. Matter + Na:Mg Ratio* + Cl (meq/L) + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + % Silt + Mg (ppm) + CEC (meq/100g) + 530.44 1.28 0.641 0.621 Na:Mg Ratio* + Cl (meq/L) + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + % 530.46 1.30 0.638 0.621 Silt + Mg (ppm) + CEC (meq/100g) + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + 530.55 1.38 0.639 0.621 Bray P (ppm) + N:P Ratio (ppm) + % 87
Silt + Mg (ppm) + CEC (meq/100g) + Na:Mg Ratio* + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + % 530.56 1.40 0.639 0.621 Silt + Mg (ppm) + CEC (meq/100g) + Cl (meq/L) + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + K* + % Silt + Mg (ppm) + CEC 530.60 1.44 0.640 0.621 (meq/100g) + % Org. Matter + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + % Silt + Mg (ppm) + Mn (ppm) + pH + % 530.72 1.56 0.642 0.622 Org. Matter + Na:Mg Ratio* + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + K* + % Silt + Mg (ppm) + CEC 530.95 1.79 0.640 0.620 (meq/100g) + Cl (meq/L) + Al (ppm) + Cu (ppm)
Plant Age + Sol. Salts (mmhos/cm) + Bray P (ppm) + N:P Ratio (ppm) + K* + Mg (ppm) + Mn (ppm) + % Org. 531.09 1.93 0.640 0.622 Matter + Cl (meq/L) + Al (ppm) + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Org. Matter + Na (meq/L) + 741.58 0.00 0.238 0.204 Extr. B (ppm) + Mn (ppm) + Mg (meq/L) + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Org. Matter + Na (meq/L) + 741.65 0.07 0.232 0.208 Root Mass Extr. B (ppm) + Cu (ppm) Ratio Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Silt + % Org. Matter + K* + 742.10 0.52 0.237 0.207 Extr. B (ppm) + Mn (ppm) + N:P Ratio (ppm)
Plant Age (Days) + NO3-N (ppm) + B 742.17 0.59 0.236 0.207 (ppm) + % Org. Matter + Na (meq/L) + 88
Extr. B (ppm) + Mn (ppm) + N:P Ratio (ppm) + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Org. Matter + Extr. B (ppm) 742.19 0.60 0.233 0.208 + Mn (ppm) + Na* + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Org. Matter + Na (meq/L) + 742.49 0.91 0.232 0.208 Extr. B (ppm) + Al (ppm) + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Silt + % Org. Matter + Extr. 743.31 1.73 0.233 0.203 B (ppm) + Mn (ppm) + Mg (ppm) + N:P Ratio (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Silt + % Org. Matter + Extr. 743.45 1.87 0.233 0.203 B (ppm) + Mn (ppm) + Na* + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Silt + % Org. Matter + Na 743.50 1.92 0.230 0.203 (meq/L) + Extr. B (ppm) + Mn (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Org. Matter + Na (meq/L) + 743.50 1.92 0.236 0.206 Extr. B (ppm) + Al (ppm) + Mn (ppm) + Mg (ppm) + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Org. Matter + Na (meq/L) + 743.50 1.92 0.236 0.200 Extr. B (ppm) + Mn (ppm) + Mg (meq/L) + Mg (ppm) + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Org. Matter + Na (meq/L) + 743.54 1.96 0.236 0.204 Extr. B (ppm) + Mn (ppm) + Mg (ppm) + N:P Ratio (ppm) + Cu (ppm)
Plant Age (Days) + NO3-N (ppm) + B (ppm) + % Silt + % Org. Matter + K* + 743.56 1.98 0.236 0.203 Extr. B (ppm) + Mn (ppm) + N:P Ratio (ppm) + Cu (ppm)
% Org. Matter + Mg (ppm) + Ca (ppm) % + Ca* + Mn (ppm) + Fe (ppm) + Cu 988.42 0.00 0.097 0.092 Emergence (ppm) + B (ppm) + ESP + Extr. B (ppm) + % Sand + N:P Ratio (ppm) 89
% Org. Matter + Mg (ppm) + Mn (ppm) + Cu (ppm) + B (ppm) + Al 988.69 0.27 0.092 0.100 (ppm) + Na (meq/L) + Extr. B (ppm) + N:P Ratio (ppm)
% Org. Matter + Mg (ppm) + Ca (ppm) + K* + Mn (ppm) + Fe (ppm) + Cu 988.78 0.36 0.093 0.087 (ppm) + B (ppm) + Extr. B (ppm) + N:P Ratio (ppm)
% Org. Matter + Mg (ppm) + K* + Mn (ppm) + Cu (ppm) + B (ppm) + Al 988.98 0.56 0.091 0.094 (ppm) + Na (meq/L) + Extr. B (ppm)
% Org. Matter + Mg (ppm) + Ca (ppm) + K* + Mn (ppm) + Fe (ppm) + Cu 989.25 0.83 0.092 0.094 (ppm) + Na (meq/L) + Extr. B (ppm) + N:P Ratio (ppm)
% Org. Matter + Mg (ppm) + Ca (ppm) + Mn (ppm) + Fe (ppm) + Cu (ppm) + 989.36 0.94 0.090 0.089 ESP + Extr. B (ppm) + N:P Ratio (ppm)
% Org. Matter + Mg (ppm) + Ca (ppm) + Ca* + Mn (ppm) + Fe (ppm) + Cu 989.55 1.12 0.091 0.107 (ppm) + % Clay + Ca Na Ratio* + N:P Ratio (ppm)
% Org. Matter + Mg (ppm) + Ca (ppm) + K* + Mn (ppm) + Fe (ppm) + Cu 989.93 1.51 0.097 0.101 (ppm) + Al (ppm) + Extr. B (ppm) + % Clay + N:P Ratio (ppm)
% Org. Matter + Mg (ppm) + Ca (ppm) + Ca* + Mn (ppm) + Cu (ppm) + B 990.01 1.58 0.092 0.102 (ppm) + Na (meq/L) + pH + Cl (meq/L) + Extr. B (ppm)
% Org. Matter + Mg (ppm) + Ca (ppm) + Ca* + Mn (ppm) + Fe (ppm) + Cu 990.13 1.71 0.090 0.091 (ppm) + % Sand + Ca Na Ratio* + N:P Ratio (ppm)
% Org. Matter + Mg (ppm) + Mg* + Ca* + Mn (ppm) + Fe (ppm) + Cu 990.32 1.90 0.090 0.092 (ppm) + B (ppm) + ESP + Extr. B (ppm) 90
% Org. Matter + Mg (ppm) + Ca (ppm) + Ca* + Mn (ppm) + Cu (ppm) + B 990.37 1.95 0.092 0.093 (ppm) + Al (ppm) + Extr. B (ppm) + % Sand + N:P Ratio (ppm)
1Units: * = (% Cation Saturation). 2Method for total biomass and root mass ratio according to that suggested in Allen (1971). Values for emergence according to 10-fold cross-validation, repeated 3 times for each model. Values within 0.2 units of Adjusted R2 are considered acceptable evidence that models are not over- fitted (Kundu, Sengupta et al. 2015).
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Appendix Table 9: Differences in growth between field soils from E. crosbyae in Nevada and the fertile test soil. The fertile test soil refers to a fertile test soil from Washoe Valley, NV (39.322771, -119.812121) used to grow native plants, including other Eriogonum species, in the University of Nevada, Reno greenhouse. Plant growth was significantly lower in the native soils, while percent emergence and survival were not different between the native soils and test soil. R2 /
Predictors Estimates Conf. Interval p Nobs adjusted R2
Soil Type: Aboveground Field Soil -169.31 – 0.222 / -138.97 <0.001 284 Biomass (mg) vs. Washoe -108.64 0.220 Soil
Soil Type: Belowground Field Soil 0.017 / -98.71 -186.52 – -10.89 0.028 284 Biomass (mg) vs. Washoe 0.013 Soil
Soil Type: Total Biomass Field Soil -351.69 – 0.056 / -237.68 <0.001 284 (mg) vs. Washoe -123.67 0.053 Soil
Soil Type: Percent Field Soil 0.911 0.41 – 2.05 0.823 466 0 Emergence vs. Washoe Soil
Soil Type: Percent Field Soil 1.141 0.31 – 4.19 0.841 333 0 Survival vs. Washoe Soil
1Odds ratio from logistic generalized linear model with logit link function.
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Appendix Figure 5: Effect of soil variation on emergence of E. crosbyae seedlings grown in the greenhouse at the University of Nevada, Reno, USA. Estimates are averaged from models that add up to 95% of multi-model evidence weight, and are in units of standard deviations from the mean for both soil variables and plant responses.
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Appendix Figure 6: Effect of soil variation on total biomass of E. crosbyae seedlings grown in the greenhouse at the University of Nevada, Reno, USA. Estimates are averaged from models that add up to 95% of multi-model evidence weight, and are in units of standard deviations from the mean for both soil variables and plant responses.
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Appendix Figure 7: Effect of soil variation on root mass ratio of E. crosbyae seedlings grown in the greenhouse at the University of Nevada, Reno, USA. Estimates are averaged from models that add up to 95% of multi-model evidence weight, and are in units of standard deviations from the mean for both soil variables and plant responses.
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Chapter 2 Appendix Taxonomic Notes E. capistratum, E. capistratum var. capistratum, E. capistratum var. muhlickii, E. capistratum var. welshii, E. meledonum, E. ochrocephalum var. alexanderae, and E. verrucosum are all considered synonymous with E. crosbyae, following the grouping by Grady and Reveal (2011), which adds E. prociduum var. mystrium, updating the species’ 2005 treatment in Flora of North America.
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Tables Appendix Table 1: Site names and coordinates of sampling sites in Nevada.
Tissue Seeds/ Incl. in for pheno. comb. % % % Planted genetics msrmts. Gen./ Indiv’s Indivs. Indivs. Site Long. Lat. Spp. Jan. collecte in wild pheno. in Gen. in Gen. in Gen. 2019? d Oct. July analyse Grp. 1 Grp. 2 Grp. 3 2017?1 2018? s?
M10 -119.474 41.24459 E. crosbyae Y Y Y Y 0 0 0.9
M11 -119.426 41.24772 E. crosbyae Y Y Y Y 0 0.13 0.88
M12 -119.499 41.18389 E. crosbyae Y Y Y Y 0 0 1
M17 -119.556 41.24583 E. crosbyae Y Y Y Y 0 0 1
M18 -119.36 41.34631 E. crosbyae Y Y Y Y 0 0.17 0.83
M20 -119.544 41.45799 E. crosbyae Y Y Y Y 0 0 0.78
M29 -119.491 41.11524 E. crosbyae Y Y Y Y 0 0 0.83
M31 -119.41 41.10117 E. crosbyae Y Y Y Y 0.05 0.77 0.14
M35 -119.358 41.52695 E. crosbyae Y Y Y Y 0 0 1
M38 -119.488 41.24423 E. crosbyae Y Y Y Y 0 0.33 0.67
M5 -119.441 41.16252 E. crosbyae Y Y Y Y 0.75 0.17 0.08
M51 -119.423 41.16699 E. crosbyae Y Y Y Y 0 0.31 0.69
M61 -119.439 41.18266 E. crosbyae Y Y Y Y 0.6 0 0.4
T13833 -119.394 40.44244 E. crosbyae Y Y Y Y 0 1 0
T16225 -119.331 41.50723 E. crosbyae Y Y Y Y 0 0.14 0.86
T8075 -119.488 41.12297 E. crosbyae Y Y Y Y 0 0 1
- T16149 118.7027 40.855633 E. crosbyae Y Y2 N N 0 1 0 5
LGJ -119.481 41.21845 E. crosbyae Y N N N 0 1 0
M6 -119.479 41.16055 E. crosbyae Y N N N 1 0 0
M32 -119.469 41.22749 E. crosbyae Y N N N 0 0 1 97
Tissue Seeds/ Incl. in for pheno. comb. % % % Planted genetics msrmts. Gen./ Indiv’s Indivs. Indivs. Site Long. Lat. Spp. Jan. collecte in wild pheno. in Gen. in Gen. in Gen. 2019? d Oct. July analyse Grp. 1 Grp. 2 Grp. 3 2017?1 2018? s?
U91 -119.438 41.19092 E. crosbyae Y N N N 1 0 0
PU3 -118.994 41.81449 E. crosbyae Y N N N 0 1 0
E. ERAN -118.747 40.27419 anemophilu N3 N N N 0 1 0 m
E. EROC -119.800 39.58083 ochrocephal N3 N N N 0 1 0 um
E. ERAL -119.719 39.75223 N3 N N N 0 1 0 alexanderae
E. ERPR -120.432 41.58583 N3 N N N 0 0.71 0.29 prociduum
1All sites in this column containing E. crosbyae were included in analyses of genetic groups. 2Phenotypic measurements not collected for this population. 3Collected 4/28/2019-4/29/2019.
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Appendix Table 2: Tukey pairwise comparisons of phenotypic differences in greenhouse seed and seedling traits by dominant genetic group for 16 source sites, with only dominant genetic group considered as a categorical variable and plant age (where appropriate for total biomass, root mass ratio, and length of longest leaf). Trait Comparison Estimate Std. Error z-value Pr(>|z|) 2 - 1 -0.22 0.08 -2.77 0.02 Seed Weight (mg) 3 - 1 -0.23 0.06 -3.76 0.00 3 - 2 -0.01 0.06 -0.13 0.99
2 - 1 -0.01 0.00 -2.63 0.02 Days to 3 - 1 -0.01 0.00 -2.20 0.07 Emergence 3 - 2 0.00 0.00 1.19 0.44 2 - 1 0.16 0.27 0.59 0.82 Length of Longest 3 - 1 1.43 0.21 6.68 0.00 Leaf (cm) 3 - 2 1.27 0.20 6.50 0.00 2 - 1 0.00 0.00 -1.35 0.35 Total Biomass 3 - 1 0.00 0.00 -3.66 0.00 (mg) 3 - 2 0.00 0.00 -2.38 0.04
2 - 1 0.04 0.02 1.84 0.15 Root Mass Ratio 3 - 1 0.05 0.02 3.01 0.01 3 - 2 0.01 0.02 0.76 0.72 Adjusted p values reported -- single-step method
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Appendix Table 3: Tukey pairwise comparisons of phenotypic differences in field plant form by genetic group, with only dominant genetic group considered as a categorical variable. Std. Trait Comparison Estimate z-value Pr(>|z|) Error
2 - 1 1.05 0.75 1.41 0.33
Flower stalk height (cm) 3 - 1 2.65 0.66 3.99 <1e-04
3 - 2 1.59 0.49 3.24 0.00
2 - 1 119.34 21.46 5.56 <1e-04
Mat Area (cm2) 3 - 1 -25.48 19.10 -1.33 0.37
3 - 2 -144.82 14.17 -10.22 0
2 - 1 1.19 0.21 5.77 <1e-04
Mat Depth (cm) 3 - 1 -0.38 0.18 -2.06 0.10
3 - 2 -1.57 0.14 -11.51 0
Adjusted p values reported -- single-step method
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Appendix Table 4: Least-squares mean estimates for variation in greenhouse growth responses by site, for sites dominated by genetic group three. Estimates are based upon each response’s respective generalized linear model. Total biomass and root mass ratio included both source site and plant age as predictors, while the models for average seed weight and days until emergence only included source site. Estimates obtained using the “emmeans()” function in R version 3.5.3. Least-squares Response Source Site SE df1 LCL UCL mean estimates M10 0.829 0.038 Inf 0.755 0.904 M11 0.886 0.038 Inf 0.812 0.961 M12 0.752 0.038 Inf 0.678 0.827 M17 0.838 0.038 Inf 0.763 0.912 Avg. 1- M18 0.768 0.038 Inf 0.693 0.842 Seed M20 0.599 0.038 Inf 0.525 0.674 Weight M29 0.656 0.038 Inf 0.582 0.730 2 (N= 61) M35 0.613 0.038 Inf 0.538 0.687 M38 0.735 0.038 Inf 0.661 0.809 M51 1.049 0.035 Inf 0.981 1.117 T16225 0.551 0.038 Inf 0.476 0.625 T8075 1.019 0.038 Inf 0.945 1.094 M10 55.188 4.623 Inf 46.127 64.248 M11 25.240 3.698 Inf 17.992 32.488 M12 34.056 4.358 Inf 25.513 42.598 M17 37.042 3.774 Inf 29.644 44.440 Days to M18 23.091 3.942 Inf 15.364 30.818 Emergenc M20 50.333 7.549 Inf 35.538 65.129 e M29 42.800 5.847 Inf 31.339 54.261 (N= 211) M35 38.450 4.135 Inf 30.346 46.554 M38 35.682 3.942 Inf 27.955 43.409 M51 28.818 3.942 Inf 21.091 36.545 T16225 45.833 7.549 Inf 31.038 60.629 T8075 37.600 4.135 Inf 29.496 45.704 M10 1.781 0.240 Inf 1.309 2.252 M11 3.160 0.191 Inf 2.785 3.535 M12 2.737 0.217 Inf 2.311 3.163 M17 3.274 0.188 Inf 2.906 3.643 Length of M18 3.739 0.201 Inf 3.344 4.133 Longest M20 1.523 0.380 Inf 0.779 2.268 Leaf M29 3.365 0.292 Inf 2.792 3.938 (N= 210) M35 1.883 0.206 Inf 1.479 2.288 M38 3.068 0.196 Inf 2.683 3.453 M51 1.810 0.198 Inf 1.423 2.198 T16225 1.837 0.378 Inf 1.097 2.578 T8075 3.468 0.206 Inf 3.063 3.872 M10 0.004 0.000 Inf 0.003 0.005 101
Least-squares Response Source Site SE df1 LCL UCL mean estimates M11 0.003 0.000 Inf 0.002 0.003 M12 0.003 0.000 Inf 0.002 0.004 M17 0.003 0.000 Inf 0.002 0.003 M18 0.003 0.000 Inf 0.003 0.004 Total M20 0.004 0.001 Inf 0.002 0.005 Biomass M29 0.002 0.000 Inf 0.002 0.003 (N= 211) M35 0.003 0.000 Inf 0.003 0.004 M38 0.003 0.000 Inf 0.002 0.003 M51 0.004 0.000 Inf 0.003 0.004 T16225 0.003 0.001 Inf 0.002 0.005 T8075 0.003 0.000 Inf 0.002 0.003 M10 0.656 0.020 Inf 0.617 0.695 M11 0.730 0.014 Inf 0.702 0.758 M12 0.752 0.016 Inf 0.720 0.784 M17 0.739 0.014 Inf 0.711 0.767 M18 0.705 0.016 Inf 0.674 0.736 Root Mass M20 0.661 0.031 Inf 0.599 0.722 Ratio M29 0.736 0.022 Inf 0.692 0.779 (N= 211) M35 0.743 0.016 Inf 0.712 0.774 M38 0.726 0.015 Inf 0.696 0.755 M51 0.671 0.016 Inf 0.640 0.703 T16225 0.721 0.029 Inf 0.663 0.778 T8075 0.736 0.016 Inf 0.705 0.767 1Estimates listed are asymptotic results (i.e. z-tests using the standard normal distribution rather than the t-distribution); therefore, degrees of freedom are infinite. 2Total number of average 1-seed weights analyzed between all sites. 1-seed weights were calculated based on 5-seed batches drawn 5-6 times per site, without replacement.
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Appendix Table 5: Least-squares mean estimates for variations in field plant form between sites, for sites dominated by genetic group three. Estimates are based upon each response’s respective generalized linear model. Estimates obtained using the “emmeans()” function in R version 3.5.3. Least- squares mean Response Site estimates SE df1 LCL UCL M10 5.345 0.894 Inf 3.592 7.098 M11 9.789 0.943 Inf 7.941 11.637 M12 10.444 0.943 Inf 8.596 12.292 Height of M17 13.179 0.918 Inf 11.380 14.978 Tallest M18 10.189 0.918 Inf 8.391 11.988 Flower M29 8.528 0.943 Inf 6.680 10.376 Stalk M35 8.722 0.943 Inf 6.874 10.570 (n= 206) M38 7.720 0.894 Inf 5.967 9.473 M51 7.312 0.970 Inf 5.410 9.213 T16225 5.220 0.894 Inf 3.467 6.973 T8075 9.979 0.918 Inf 8.180 11.778 M10 0.008 0.001 Inf 0.005 0.010 M11 0.009 0.002 Inf 0.006 0.013 M12 0.013 0.003 Inf 0.008 0.018 M17 0.010 0.002 Inf 0.006 0.013 M18 0.009 0.002 Inf 0.005 0.012 Mat Area M29 0.009 0.002 Inf 0.006 0.013 (n= 206) M35 0.013 0.003 Inf 0.008 0.018 M38 0.013 0.002 Inf 0.008 0.017 M51 0.005 0.001 Inf 0.003 0.008 T16225 0.008 0.002 Inf 0.005 0.011 T8075 0.007 0.001 Inf 0.005 0.010 M10 0.597 0.054 Inf 0.492 0.702 M11 0.474 0.045 Inf 0.386 0.562 M12 0.352 0.033 Inf 0.287 0.418 M17 0.448 0.041 Inf 0.367 0.529 Mat M18 0.400 0.037 Inf 0.328 0.472 Depth M29 0.453 0.043 Inf 0.369 0.538 (n= 206) M35 0.534 0.051 Inf 0.435 0.633 M38 0.556 0.050 Inf 0.458 0.653 M51 0.384 0.037 Inf 0.310 0.457 T16225 0.635 0.057 Inf 0.523 0.747 T8075 0.459 0.042 Inf 0.376 0.542 1Estimates listed are asymptotic results (i.e. z-tests using the standard normal distribution rather than the t-distribution); therefore, degrees of freedom are infinite.
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Figures
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Appendix Fig. 2: Variation in average seed weight between collection sites for E. crosbyae seedlings grown in the University of Nevada, Reno greenhouse, for collection sites that were dominated by genetic group 3. Letter groupings indicate significant differences at the p<0.05 level.
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Appendix Fig. 3: Variation in emergence timing between collection sites for E. crosbyae seedlings grown in the University of Nevada, Reno greenhouse, for collection sites that were dominated by genetic group 3. Letter groupings indicate significant differences at the p<0.05 level.
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Appendix Fig. 4: Variation in longest leaf length between collection sites for E. crosbyae seedlings grown in the University of Nevada, Reno greenhouse, whose collection sites were dominated by genetic group 3. Letter groupings indicate significant differences at the p<0.05 level.
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Appendix Fig. 5: Variation in estimates1 of root to total biomass ratio between collection sites for E. crosbyae seedlings grown in the University of Nevada, Reno greenhouse, for collection sites that were dominated by genetic group 3, estimated using beta regression with canonical link. Letter groupings indicate significant differences at the p<0.05 level.
1Points represent least-squares mean estimates of average seed weight for each site, and error bars are the upper and lower 95% confidence intervals.
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Appendix Fig. 6: Variation in height of the tallest flower stalk between sites for E. crosbyae individuals measured in the field in northern Nevada, for sites that were dominated by genetic group 3. Letter groupings indicate significant differences at the p<0.05 level.
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Appendix Fig. 7: Variation in mat area between sites for E. crosbyae individuals measured in the field in northern Nevada, for sites that were dominated by genetic group 3. Letter groupings indicate significant differences at the p<0.05 level.
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Appendix Fig. 8: Variation in mat depth between sites for E. crosbyae individuals measured in the field in northern Nevada, for sites that were dominated by genetic group 3. Letter groupings indicate significant differences at the p<0.05 level.
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Summary, conclusions and recommendations We have characterized the biotic and abiotic characteristics of Eriogonum crosbyae habitat and examined impacts of soil variation on site occupation and plant form/plant growth in this species. We found that the soils characteristic of E. crosbyae habitat are unique, with high clay contents and possible presence of minerals associated with hydrothermal alteration, potentially including gypsum and potash, which we are investigating further. These soils are high in calcium, aluminum, and sulfur, and low in phosphorus, manganese, nitrate-nitrogen, and iron; but have close to neutral pH and little salinity. E. crosbyae exhibits high tolerance of nutrient deficiencies and significant phenotypic plasticity in both the field and in the greenhouse in response to edaphic variation. However, the soil properties we measured were poor predictors of site occupation within our study areas, and sites were observed to change in occupation status over time. Differences in ability to establish in different soils may partially underlie the patchy distribution of the species among our field sites; however, further mechanistic studies of plant growth responses to edaphic variation would improve understanding of factors driving species distributions.
We also quantified genetic and phenotypic diversity in E. crosbyae using a combination of next-generation genetic sequencing techniques and measurements of phenotypic variation in the field and in a greenhouse common garden. We found high nucleotide diversity in E. crosbyae, and the presence of three highly differentiated genetic groups that often co-occurred in individual sites. The distribution of these genetic groups may be consistent with periods of allopatric speciation followed by subsequent secondary contact. Seed and seedling phenotypes varied strongly among genetic groups, but phenotypic variation among groups was more pronounced in the greenhouse than in the field for our measured traits. These genetic groups may have varying levels of genetic diversity and degrees of connectivity among populations, but further research, including broader sampling, will be needed to better understand the taxonomic relationships and 112 evolutionary histories of these groups, as well as the relationship between phenotypic and genetic diversity in these edaphic specialists.
Our research was an effective first step toward understanding the habitat requirements and levels of diversity present in Eriogonum crosbyae, and we were able to use our results to identify and verify the appropriateness of a transplant location for seedlings from a threatened population. Results from this transplant effort are preliminary, but encouraging. These methods could be successfully applied to other taxa and systems, including to other rare Eriogonum species in the Great Basin. Our results highlight the potential for even simple-seeming systems to contain significant cryptic variation, and suggest that caution is warranted when considering impacts to these unique habitats and complex species.