1) The study evaluated the correlation between seed dormancy levels and environmental characteristics of collection sites for plant species in the Seeds of Success program to determine if dormancy is an indicator of ecotypic variation. 2) It found that for three species - Showy goldeneye, Sandberg bluegrass, and Creosote bush - populations from locations with higher precipitation and altitudes and lower temperatures exhibited less dormancy. This suggests dormancy has evolved as an adaptation to moisture stress. 3) The correlations between dormancy and environmental variables found for some species indicate dormancy level may be a genetically-based ecotypic trait, while lack of correlations for other species suggests dormancy is a plastic response to local yearly conditions

1. Is Dormancy an Indicator of Ecotypic Variation?
1Annette Miller, 2Chrystian Sosa, 1Colin Khoury, 1Stephanie Greene
1USDA/ARS National Laboratory for Genetic Resources
Preservation, Fort Collins, CO USA
2CIAT International Center for Tropical Agriculture, Cali, Colombia
Introduction:
The seed quality lab at USDA/ARS National Laboratory for Genetic Resources
Preservation (NLGRP) tests the viability of a wide range of wild-collected seeds from the
BLM Seeds of Success program as part of preparations for long-term storage. For
species that exhibit a range of dormancy, we wondered if dormancy behavior was an
indicator of ecotypic variation. By ecotypic, we mean populations having a genetic basis
for variability in dormancy level as opposed to simply being phenotypically plastic. To
test this, we evaluated the correlation for a number of taxa between dormancy of seed
samples and the environmental characteristics of the sites where the germplasm was
collected.
Materials and methods:
Materials from the Seeds of Success program submitted to the NLGRP provide a unique
opportunity to look for indicators of ecotypic variation because the samples are of wild
species collected with standardized protocols from multiple locations.
As part of regular processing procedures, viability tests are conducted prior to storage.
In this case, viability tests were conducted using two replicates of 50 seeds planted on
germination blotters moistened with water and placed in a 20 degree incubator. No
dormancy breaking measures (no hormones or other nutrient compounds and no
prechill treatments) were used. After 2-3 weeks, normal seedlings were recorded and
the remaining seeds were tested for viability (dormancy) with a tetrazolium (TZ) test.
Total viable is the percentage of seeds that germinated readily plus the percentage of
the remaining seeds determined to be viable by the TZ test. To allow comparison
among samples with differing seed quality, the dormancy percentage was recalculated
as the proportion of total viable for these analyses:
Total viable % = Germinating readily % + Dormant %
Dormancy percentage = Dormant % / Total viable %
Using the following criteria, we chose species and sample test results:
• Exhibiting a range of dormancy.
• From germination tests conducted without dormancy-breaking measures
(dormancy measured by post-germination tetrazolium test).
• Containing geographic coordinates of collecting locations.
• Containing at least 25 samples that fit the criteria
Species and number of samples:
Number of samples per species per category
Software tools and analysis:
We used data from 27 environmental inputs including altitude and nineteen bioclimatic
variables from the WorldClim database (Hijmans et al., 2005), and seven major edaphic
drivers extracted from ISRIC- World Soil Information (Hengl et al., 2014). For the
edaphic variables, we calculated a weighted mean across 0–5, 5–15, 15–30, 30–60, and
60–100 cm soil depth values in order to derive a single data value for 0–100 cm. We
then resampled the 1 km resolution data to form 2.5 arc-minutes resolution inputs
aligned with the WorldClim dataset and we extracted the data for each occurrence
using georeferenced coordinates.
We tested pairwise association between the 27 environmental data points and
dormancy using Pearson’s product moment correlation. We highlighted those
correlations with p values ≤0.05 (Results, Figure 1). We then created boxplots for each
of the environmental variables to explore the variability among dormancy categories.
Finally, we made maps for each species and their dormancy categories. Environmental
data extraction, correlations, and figures were made using R libraries raster v2.5-8, stats
3.3.1 and ggplot2 2.2.0 respectively and the maps were made using ArcGIS Desktop
10.1.
Citations:
Hengl, Tomislav, Jorge Mendes de Jesus, Robert A. MacMillan, Niels H. Batjes, Gerard B. M. Heuvelink, Eloi Ribeiro, Alessandro Samuel-Rosa, et al.
“SoilGrids1km — Global Soil Information Based on Automated Mapping.” Edited by Ben Bond-Lamberty. PLoS ONE 9, no. 8 (August 29, 2014):
e105992. doi:10.1371/journal.pone.0105992.
Hijmans, Robert J., Susan E. Cameron, Juan L. Parra, Peter G. Jones, and Andy Jarvis. “Very High Resolution Interpolated Climate Surfaces for
Global Land Areas.” International Journal of Climatology 25, no. 15 (December 2005): 1965–78. doi:10.1002/joc.1276.
very low 1-9%
(readily germinable)
low 10-49% medium 50-89% high 90-100%
Dormancy categories (tests with H20 only and no prechill)
Precipitation
TemperatureAltitude
Soil
Heliomeris multiflora Nutt.
Showy goldeneye
Asteraceae
Perennial subshrub. Great basin North to
Montana, South to Texas.
Low dormancy samples correlate to:
Higher precipitation:
• annually
o in driest month
o in driest quarter
• in wettest month
• in wettest quarter
• in coldest quarter
• in warmest quarter
Lower temperature:
o minimum in the coldest month
o maximum in the warmest month
o mean in the warmest quarter
o mean of the coldest quarter
o mean annual temperature
• mean diurnal range
Soil
• higher percentage of organic carbon
• Lower pH in soil water
• Lower bulk density of soil
Other
o higher altitudes
Poa secunda J. Presl.
Sandberg bluegrass
Poaceae
Perennial, cool season bunchgrass. Seeds
mature June-July at lower elevations and in
August for higher elevation populations.
Alaska, Canada and Western US south to
Arizona.
Low dormancy samples correlate to:
Higher precipitation
• annually
o in warmest quarter
• In driest quarter
• in driest month
• Lower precipitation seasonality
Lower temperature
• minimum in the coldest month
• mean in the coldest quarter
• mean diurnal range
• mean annual temperature
• maximum of the warmest month
• mean in the driest quarter
Soil
• Lower pH in soil water
• Lower cation exchange capacity
Other
• higher altitudes
Discussion:
Many temperate species require a moist chilling (stratification) period that mimics winter. Because of this, we might
guess that species at higher elevations would also evolve to have this requirement. However, in these three species,
populations at higher altitudes exhibited less dormancy. In these cases, the higher altitudes also correlate with places
that receive higher precipitation overall, perhaps thus reducing the need for a dormancy period. The lower altitude
populations may encounter more moisture stress, which would provide selection pressure for more dormancy in dry
seasons. Increased physiologic dormancy in these populations may prevent germination when conditions are too hot
and dry. This points to a dormancy mechanism that has more to do with water potential than with cold exposure. Soil
chemistry may also play a role.
Species with low or no correlation to these environmental attributes may have more variability within populations
rather than among populations, or the expression of dormancy may be a response to variable yearly conditions at a
particular locale.
One constraint of this study is that the data points only reflect one sample and one harvest year per location. Future
studies will be improved by including multiple samples from multiple years at each location.
Is dormancy level an ecotypic (genetically based) attribute? In species with significant correlations to several
environmental attributes, it may be. In species with few, it is probably not.
Larrea tridentata (DC.) Coville
Creosote bush
Zygophyllaceae
Perennial shrub. California, Nevada, Utah,
Arizona, New Mexico, and Texas.
Low dormancy samples correlate to:
Higher precipitation
• in driest quarter
o In driest month
• In warmest quarter
• Lower precipitation seasonality
Lower temperature
• minimum in the coldest month
• mean in the coldest quarter
• mean in the driest quarter
• mean annual temperature
Soil
• higher cation exchange capacity
o higher percent silt
o lower percent sand
Other
• higher altitudes
• higher latitudes
http://extension.usu.edu/rangeplants/images/uploads/Forb%20Photos/WILLshowygoldeneyeSMALL.jpg Sheri Hagwood. USDI Bureau of Land Management (BLM).
http://src.sfasu.edu/~jvk/TransPecosPlants/TransPecosPlants/Zygophyllaceae/lrLarrea_tridentata11.jpg
5
4
5
0
30
21
13
21
4
13
7
12
44
7
6
9
6
14
3
24
12
4
8
5
15
0
1
1
Poa secunda
Eriogonum umbellatum
Larrea tridentata
Heliomeris multiflora
Heterotheca villosa
Atriplex canescens
Ambrosia dumosa
Series1 Series2 Series3 Series4
Ambrosia dumosa 33
Atriplex canescens 90
Heterotheca villosa 45
Heliomeris multiflora 36
Larrea tridentata 29
Eriogonum umbellatum 25
Poa secunda 36
Results:
o Attributes with higher confidence: see Pearson correlation values in chart.
The table above highlights the significant correlations between
seed dormancy and various environmental attributes for each
species. Orange represents a positive correlation, and purple
represents a negative correlation. Darker hues represent a
stronger correlation.
In the boxplots below, note the separations and upward or
downward trends.