Background & data used

As a practice, geneocological studies such as this one, utilize common gardens to monitor phenotypic measurements of plants from a dispersed set of populations in a common environment. By collecting seed from a variety of populations in the range of the species, and growing them up in a common garden, the effects of site to site variation are more controlled. Ultimately, the phenotypic traits of each plant observed at all of the common gardens are meant to be tied back to the seed source population so that we might learn about how population-level selective pressures translate to divergent traits (even when these plants are grown in a variety of climates). In theory, planting the same seed source population in multiple climate regimes will illuminate what traits are plastic, (i.e. change within one lifetime of a plant) and what traits change over generational time. For example, if you observe that no matter what common garden a seed source population is planted in, you always see wide leaves, and populations from other seed source populations always have narrow leaves, then it is possible to reach the conclusion that leaf width changes over generational /evolutionary time. This is important because if plants have been adapting to selective pressures over many generations and they are moved to a differing conditions, the traits they have developed might be maladapated to the new conditions. My dataset includes 39 population means (4-5 individuals) for each of sixteen gardens for several phenotypic traits such as leaf width, crown width and others.

One of the problems I hope to deal with is how to both quantify and visualize bluebunch wheatgrass phenotypic trait variability across soil gradients. One technique that may be useful is multiple linear regression. One drawback of my dataset however, is the fact that many soils variables are spatially auto correlated. This lack of independence leads to a violation of one of the assumptions of multiple linear regression. In order to deal with the spatially related variables in my dataset, it seemed appropriate to use a regression method that accounts for this spatial relationship.

Question

Is there a relationship between available water storage (AWS) in the top 25cm of the soil surface and the leaf width in bluebunch wheatgrass populations?

Approach and tools used

In order to make my results easier to interpret, I have restricted my analysis to a single common garden that contains 39 different bluebunch wheatgrass populations.

Tools:

1. geographically weighted regression analysis in ArcMap.
2. Spatial join tool in ArcMap
3. Join field tool in ArcMap.
4. Hot Spot analysis tool in ArcMap

Steps followed to complete the analysis

1. Surveyed gssurgo database for continuously variable soil metrics that might be relevant to plant growth (i.e. available water storage).
2. Joined AWS tabular data to gssurgo raster data for the entire study area.
3. Spatially joined soils tabular data to common garden source population x y coordinates, and plant phenotypic traits data from common garden monitoring.
4. Selected a common garden that had existing soils data for all seed source populations.
5. Reduced dataset to only include soils and trait data for the Wahluke common garden in seed zone 1.
6. Performed geographically weighted regression analysis.
   1. Analysis failed initially due to the wide geographic spread of populations.
   2. Set the minimum number of populations to be used in analysis to 8 (5 was too few, and 10 produced a smaller p-value).
7. Exported regression results to a .csv table
8. Joined local regression coefficients to seed source population shape file attribute table.
9. Symbolized local regression coefficients according to sign (negative = blue, positive = red)
10. Performed a hotspot analysis on mapped regression coefficients to look for significant clustering

Results

• Positive relationships between leaf width and available water storage occur in moderately wet areas
• Negative relationships between leaf width and available water storage occur in moderately wet to dry areas.
• Negative coefficients tend to be near other negative coefficients spatially
• Positive coefficients tend to be near other positive coefficients as well

Critique

• Limited to the analysis of one plant trait and one common garden at a time.
• The resolution and accuracy of the AWS data is questionable.
• The AWS values may not vary enough to be biologically significant.

Background:

In August 2015, the BLM released the National Seed Strategy which calls for the use of genetically appropriate seed for the restoration of damaged ecosystems. Bluebunch wheatgrass is a long-lived native perennial species that grows in most western states in the United States as well as British Columbia. This species has been shown to be genetically adapted to different climate patterns across its range. Adaptation is evident because bluebunch wheatgrass plants from distinct climates exhibit different phenotypic (physical) traits. Bluebunch wheatgrass seeds should therefore only be dispersed in areas where they are adapted to the local climate conditions.

In order to determine how far seed from this species can be moved across its range, species specific seed zones have been developed for the Great Basin ecoregion (St. Clair et al 2013). These seed zones were delineated using spatial analysis of climate patterns and observed plant traits from many populations of bluebunch wheatgrass in different climates in the Great Basin.

In order to test the efficacy of the seed zones for bluebunch wheatgrass common gardens have been established. Common gardens are a way of minimizing the effect of site history (i.e. grazing, fire, and management practices) on the growth habits of bluebunch wheatgrass and illuminate the climate-caused genetic adaptations. Wild seeds are collected from dispersed locations in the Great Basin, reared in a greenhouse to the young adult stage, and co-located into a common garden (see figure 1). Once the plants are placed within the common garden they can be monitored for phenotypic trait variability and these variations can be “mapped” back to their home climate.

Although the relationship between bluebunch wheatgrass phenotypes is fairly well understood, there is a knowledge gap surrounding bluebunch phenotypes and soils. I aim to use data gathered from existing common garden studies and soil maps to determine if relationships between soils and bluebunch phenotypes exist.

Study Design:

Two common gardens are situated within each of 8 seed zones resulting in 16 gardens total. At each common garden, 4-5 populations from each zone are represented. Twice per growing season, phenotypic trait data such as leaf length and width, crown width, and number of reproductive stalks are gathered for each individual plant at all common gardens. The resulting dataset contains population level means for each trait at each garden.

Generalized Common Garden Schematic

(figure 1)

Research Question:

Do phenotypic traits of bluebunch wheatgrass vary with soil order?

Tools and Approaches:

Soils Dataset:

• Soils data were gathered from the Geospatial Data Gateway and were downloaded separately for each state in the study region to reduce the file size.
• Soils raster layers for each state were loaded into ArcMap.
• Tabular data for soil order was joined to raster data using the “Join Field” tool in ArcMap.
• Soil order layers were symbolized using graduated colors by category.

Bluebunch Trait Dataset:

• Latitude and longitude decimal degrees (X Y coordinates) were added to each population in the common garden plant trait dataset.
• Blank cells were replaced with NA values.
• X Y locations for each row in the dataset were “jittered” to remove duplicate coordinates (see figure 2 & 3).
  • Using the rand() function in excel, two new columns with random positive decimal values were added.
  • The difference between the two random value columns was subtracted from the X and Y coordinates for each row in the dataset.
  • The resulting “jittered” X and Y coordinates were stored in new columns.
• The bluebunch trait dataset was loaded in to ArcMap and visualized using the jittered X and Y coordinates.
• Hot spot analysis was performed separately for each of four plant traits; leaf width, leaf length, crown width, and number reproductive stalks and visualized with soil order.

(figure 2) Un-jittered population X Y locations

(figure 3) Jittered population X Y locations

(figure 3) Jittered population locations

Results:

In the hot spot analysis of leaf width one hotspot and three cold spots were revealed. This indicates that a significant number of plants that were sourced from those areas had either wide or narrow leaves.

In the hot spot analysis of leaf length, one hotspot and two cold spots were revealed. This indicates that a significant number of plants that were sourced from those areas had either wide or narrow leaves. In this analysis, the hot spot was in a different location than previously indicating that wider leaves were not necessarily longer or vice versa. Contrastingly, the two cold spots in this analysis were in a similar location as with leaf width. This indicates that short narrow leaves tend to also co-occur.

In the analysis of crown width, the hotspot occurred in the same location as with the leaf width analysis. This leads me to believe that plants from this vicinity tend to be larger in general than plants from other areas.

In the analysis of reproductive stalks only one large hot spot was found. This indicates that the larger plants in this region also tend to produce significantly more flowering stalks per plant than in other areas.

Critique:

■  The jittering effect could be reduced to make it easier to distinguish the populations. It may also be possible that this type of analysis is not appropriate for this data set because the phenotypic trait data is for one population being grown at multiple common gardens and this creates a situation where the data values have the same X Y coordinate. Even though, the jittering effect was done at random, there is still a chance that the results we are seeing are a product of the data transformation itself.

■  90-95% CI might be too stringent for reasonable genetic inference. In many ecological contexts p-values of less than 95% are a regular occurrence. In this case, leaf width / length, crown width, and the number of reproductive stalks of each plant probably co-vary. It does not appear that this type of analysis is meant to deal with co-variance.

■  Soil traits are de-emphasized. Although soil order was used as the background it was difficult to see a pattern between soils and plant trait hot spots. This may be partially due to the large scale of the analysis or may also be a product of the jittering.

■  Only one phenotypic trait can be visualized at once. Since only one trait can be mapped in the hot spot analysis at once, there is no way to know if the observed similarity in hot spot locations is truly significant.

■  Hot spot analysis does not work with raster data. In the future, I would like to find a way to look at the clustering patterns within soils and it appears that this analysis does not recognize raster data.

■  A less regional, and more zoomed in analysis might improve the interpretation. Since the soils are highly variable in this study area it may be more productive to narrow the scope of inference to include smaller areas. Doing this may make the soil – plant trait interpretation easier.

References:

St. Clair Bradley John, Francis F. Kilkenny, Richard C. Johnson, Nancy L. Shaw, and George Weaver. 2013. “Genetic Variation in Adaptive Traits and Seed Transfer Zones for Pseudoroegneria Spicata (bluebunch Wheatgrass) in the Northwestern United States.” Evolutionary Applications 6 (6): 933–48. doi:10.1111/eva.12077.