5-6 Forestry paper with graphs.
Tutorials link for graphs:
Please read the first file very carefully, the rest of them are tutorials and materials.
Fixing:
Species for the second part of your report
Hi,
As described in the “Genetics Term Project Instructions 2020 Final x”, the second major part of your report involves discussing your results and including a comparison of Garry oak with another species (not all students will have the same species).
The species you will be comparing with your results is Pinus albicaulis (Whitebark pine). Attached is the paper (Bower and Aitken, 2008) from where you should extract all the information that is necessary for your comparisons (e.g. provenance locations, r, clines).
Tables 2 and 4 will give you all the information you need for great comparisons with your Garry oak data, but if you want to go deeper into local adaptation and seed transfer, this paper provides plenty of good info.
Some tips:
– Although both r (correlation coefficient) R2 (coefficient of determination) indicate a certain relationship between two variables, they are not equivalent. r is a measure of linear correlation between two variables and can vary between -1 and 1. R2 is the proportion of variation in the dependent variable that can be explained (predictable) by an independent variable (or variables) and varies between 0 and 1. r can tell you the direction of a relationship. For example, if r between growth and mean annual temperature is negative (e.g., -0.5), then you know that as temperature increases, growth decreases for that species. If you have r for one species and R2 for another and want to see whether the relationship is stronger in one species than in the other, you can square the value of r and compare it with R2 directly.
– Remember to use Climate NA map-based version (check out the tutorial on YouTube).
Thegenecology and conservation genetics of Garry oak
FRST 210 – Forest Biology II
March 24th 2020
This project is designed to give you a better idea of how differences among populations from
different provenances of trees reflect adaptation to their home climate in a common garden
experiment. It will also demonstrate some of the principles that have been presented in lectures.
There are two major parts to the report you will write. The first will include an analysis of a
provenance trial of Garry oak (Quercus garryana) growing at UBC. The second will be a discussion of
your results, including a comparison of Garry oak with another species that you will be assigned (not
all students will have the same species). A full individual report is due via Turnitin on Saturday, April
18th, by 11:59PM. Late reports will lose 10% per day.
REPORT PART I
INTRODUCTION
The Centre for Forest Conservation Genetics in the Department of Forest Sciences maintains a
range-wide provenance trial of natural populations of Garry oak (Quercus garryana Douglas ex
Hooker). This collection was established by a M.Sc. student studying conservation genetics in this
species (Huebert 2009). He collected acorns from trees in thirteen natural populations throughout
the species range, from central California to Vancouver Island (figure 1). This sampling included two
taxonomic varieties, Quercus garryana var. garryana and Q. garryana var. semota (also known as
var. breweri or var. fruticosa). Present throughout most of the species range, Q. garryana var.
garryana grows as a medium-sized tree in well-drained valley bottoms with rich soils. In the southern
portion of the range, Q. garryana var. semota grows as a short, multi-stemmed shrub in rocky soils
and at high elevations. Climatic data were estimated for each of these provenances using
ClimateWNA (Wang et al. 2012) and used to assess clinal variation in seedling growth. Seedlings
were grown in a greenhouse for one year then sown into two common garden experiments, one of
which was planted in Totem Field at UBC, and the other in Duncan, BC. You will collect and analyze
data from the Totem Field experiment.
Table 1: Geographic, taxonomic, and climatic data for thirteen provenances of Quercus garryana.
Prov. Taxonomic variety Latitude Longitude
Dist. from
Vancouver
(km)
Mean
annual
temp.
(°C)
Mean temp.
of the
warmest
month (°C)
Mean temp.
of the
coldest
month (°C)
Mean
summer
precip.
(mm)
1 semota 35.87 -118.64 1544 12.3 22.2 4.6 57
2 semota 36.8 -119.09 1425 13.5 23.6 6.3 62
3 semota 39.1 -120.85 1145 14.1 24.1 5.9 86
4 semota 40.36 -122.94 990 12.1 22.4 3.5 78
5 semota 40.85 -122.03 940 15.0 25.3 5.8 159
6 garryana 41.85 -122.84 825 11.6 22.2 2.0 81
7 garryana 42.47 -122.62 757 10.6 20.2 2.1 133
8 garryana 45.01 -123.17 472 10.8 18.5 3.7 221
9 garryana 45.28 -121.35 465 8.7 18.7 -0.7 68
11 garryana 46.83 -123.01 271 10.4 17.7 3.6 214
13 garryana 48.79 -123.7 61 9.9 17.6 3.1 172
14 garryana 48.46 -123.4 89 10.0 16.4 4.2 139
15 garryana 49.73 -125.02 138 9.1 17.0 2.1 234
Figure 1: Species range of Quercus garryana, showing sampling locations of thirteen provenances
planted in Vancouver, BC, in 2008.
MATERIALS
In the autumn of 2006, acorns were planted in individual containers with standard perennial potting
soil. In 2007, the germinated seedlings were grown in a greenhouse at UBC for their first year. The
climatic conditions in the greenhouse were very mild. Temperatures remained warm and the
seedlings were well-watered. While in the greenhouse, the seedlings were measured for date of
seedling emergence, height, circumference, and date of bud set.
One-year-old seedlings were planted into a common garden at Totem Field in 2008. The ground
below the common garden was covered with landscape cloth to prevent weeds growing in the
garden, and to insulate the soil over the winter. The common garden was established as a
randomized complete block design, with all populations represented across twelve blocks, and with
a ring of border trees around the experiment to control for extra light and soil availability at the edge
of the experiment. Trees were planted 60 cm apart to prevent root competition and to ensure that all
seedlings had adequate access to light. The experiment was watered as needed during the summer,
but was not fertilized.
The experiment has been measured several times since its establishment. In 2008, all seedlings
were measured for date of bud break, date of bud set, height, and circumference. A subset of trees
were tested for frost resistance at this time. Five-year heights and circumferences were measured in
2012. In 2013, as part of a Forest Science student’s undergraduate thesis, genetic and
morphological markers were used to determine that the five southernmost provenances (1 – 5)
belong to the shrub variety, Q. garryana var. semota. The remaining provenances are the tree variety,
Q. garryana var. garryana (Degner 2014). Ten-year height measurements were made by another
undergraduate student in 2017.
This common garden was designed to measure small seedlings, but eventually the trees grew large
enough to compete with one another for light and soil resources. To limit competitive effects, every
other tree was thinned in 2015.
METHODS
Hypothesis development
First, you should develop two hypotheses regarding genetic variation in this species. You must use
these hypotheses in your lab report. You will develop one hypothesis regarding growth pattern.
Choose one provenance climatic variable from table 1 that you think will be strongly correlated with
height in the common garden (for both 1st-year and 10th-year measurements). You might want to
read about the genecology and climate adaptation of the second species you are assigned before
developing your hypothesis. For this, hypothesize the direction you think this relationship will take
e.g., “As provenance mean annual temperature increases, seedling and tree height in the common
garden will decrease”. You will also develop a hypothesis regarding whether the growth form of the
two varieties is under environmental or genetic control i.e., will the differences in number of stems
and height observed in natural populations persist in the common garden? Remember that
hypotheses don’t need to be correct, and it is not good science to change them after you analyze
your data.
Table 2: Phenotypic data collected for a range-wide common garden of Quercus garryana
established in Vancouver, BC, in 2007.
Phenotype Year collected Units Notes
Seedling height 2007 centimeters Measured in greenhouse
Tree height 2017 centimeters May be approximate for tall trees.
Tree circumference 2019 centimeters Basal circumference of the tallest stem
Number of basal
stems 2019 Number of stems
Measured near base of tree
Measurements
The trees were measured by FRST 210 students in 2019. For each tree, students measured the
stem circumference and recorded the total number of basal stems. For circumference, trees were
measured to the nearest mm at the lowest point of the stem without any prominent swelling from
roots or branches, or at approximately 20cm above the ground if there was no section of the stem
without these features. For trees with multiple stems, the circumference of the tallest stem was
measured. The number of more-or-less vertical basal stems within the first 10 cm above the ground
was counted for each tree. The TAs compiled the circumference and stem number data from all four
lab sections, and calculated averages for each tree. These measurements were added to the 2007
and 2017 height data that we already have, and are posted on Canvas as an Excel spreadsheet for
you to use in your analyses.
Analysis
You will test your hypotheses by analyzing data in the Excel file. You will need to use MS Excel or
import the data table to another spreadsheet program to complete this lab. First, you will need to
calculate provenance means for all phenotypic traits. Then you need to create a table that includes
the provenance means for all of your phenotypic variables, as well as the climatic variable(s) used in
your analyses. You will use this table to calculate statistics and generate the scatter plots described
below. You will input these average measurements into the linear regression calculator that will be
provided with your data to determine the statistical significance of your trends and to generate
descriptive statistics for these trends (slope and R2 values). You will also input the individual
measurements for 2017 height and 2019 number of stems into the t-test calculator to determine
whether the varieties differ in either 2017 height or number of stems in 2019. When reporting your
results, only include slope and R2 values for linear regressions if they are statistically significant
(p<0.05), and only compare mean values between your varieties if the differences are significant (p
< 0.05). We’ve included a primer on understanding statistics for those who have not yet completed
FRST 231 or would like a refresher. I highly recommend that all students read this, even if you’ve
already taken FRST 231 and feel comfortable with statistics. There will be an online tutorial available
on Canvas for using Excel for this analysis.
Part I of your report
Introduction (Garry oak): 1 – 1.5 pages
Provide background knowledge for the study (e.g. genecology, common gardens, the ecology of Garry
oak), as well as scientific justification and a purpose for this study and your hypotheses.
Material and Methods (Garry oak): 0.75 – 1 page
Describe the common garden experiment and how it was established. Explain how the data was
gathered and how it was analysed. Only include information relevant to your paper.
Results (Garry oak): 0.75 – 1.5 pages, not including figures
Report the results of your data analyses and how they relate to your hypotheses. Anything that you
made a figure for or performed a statistical analysis for should be reported in your results.
Remember that results should be purely descriptive and quantitative.
(1) One table with climatic data for each provenance, as well as provenance averages for each of the
four phenotypic traits.
(2) Two bar plots showing comparisons between the two taxonomic varieties. Include error bars with
+/- standard deviation. Include p-values either in your figure or in the figure caption.
1. Mean number of stems in 2019
2. Mean height in 2017
(2) Two additional scatter plots based on your hypotheses. Include p-values either in your figure or in
the figure caption. Include a regression line (also known as a trend line) and R2 value only if your
regression is significant.
1. Provenance mean height in 2007 vs. your provenance climate variable
2. Provenance mean height in 2017 vs. your provenance climate variable
Part II of your report
Discussion (Garry oak and a second species): 3-5 pages
Your discussion should include ~one paragraph addressing five of the questions (please number the
paragraphs accordingly for our marking ease):
Answer all 4 of the following questions:
1. Do your results support your hypotheses for growth (height in 2007 and 2017? Address each
of these independently and provide biological explanations for any trends you observe.
2. Do the two Garry oak varieties differ in height or number of stems? How does this relate to
your hypothesis regarding whether variety differences are under genetic or environmental
control? How might these differences be relevant to the adaptation of these plants? Could
you selectively breed var. semota to have a tree form like var. garryana?
3. Are the phenotypic clines for growth traits in var. garryana similar to those for the other
species you were assigned? Are the same climatic variables correlated with provenance
growth in the same direction in both species?
4. How much is mean annual temperature predicted to warm by the 2080s compared to the
baseline climate normal period of 1961-1990 under either a moderate climate change
scenario (RCP 4.5) or a severe scenario (RCP 8.5) for the northernmost provenance of var.
garryana in this experiment? How does this compare with the amount of warming predicted
for the northernmost provenance of the other species you were assigned? Use the software
ClimateNA (http://www.climatewna.com/) by entering the latitude and longitude of a
provenance to estimate warming (you don’t need the elevation). A tutorial will be posted on
how to use this software, and what settings to use.
Answer 1 of the following 2 questions:
5. How would you use information from the provenance trial in a restoration plan for Garry oak
ecosystems on Vancouver Island, given predicted amounts of climate change (i.e., new
temperature and/or precipitation regime)?
6. How would you recommend the genetic diversity of Garry oak in British Columbia best be
conserved? Is Garry oak more or less of a conservation concern than the other species you
were assigned?
References: For full marks, you need to cite at least eight separate scientific publications, ideally
scientific journal articles. You should cite at least three in your introduction and at least five in your
discussion. You may use any established reference style (e.g. APA, MLA, Vancouver) as long as you
are consistent throughout. References should only be from peer-reviewed scientific journals or
government publications, and must be properly cited and referenced. In addition to the above
sources, you may cite the published chapter on genecology from the Encyclopedia of Forest Sciences
posted in the lecture notes (Aitken 2004), Colin Huebert’s unpublished MSc thesis (Huebert 2009),
and Jon Degner’s unpublished undergraduate thesis (Degner 2014). Be sure to consult the “How to
write a lab report” handout as you write your report. Do not cite any lecture notes. There will be a
short online tutorial on using the UBC Library and Web of Science or Google Scholar to find peer-
reviewed scientific publications relevant to your report.
Formatting: Your report should be double-spaced in 11-point font with 2.5cm margins on all sides.
Any deviations in formatting to circumvent page limits will receive penalties. Without figures, your
report should be a total of 4.5-7 pages. If you can’t fit your sections into the allotted page limits, it
means you need to write more concisely. Figures may be as large as you’d like, but please do not
make them smaller than 1/3 of a page.
GRADING
Introduction – 15 pts
Materials and methods – 10 pts
Results – 10 pts
Table and Graphs – 15 pts
Discussion – 40 pts
References – 10 pts
Total: 100 pts
References
Aitken, S. N. “Genecology and adaptation of forest trees.” Encyclopedia of Forest Sciences (2004): 197-204.
Degner, J. C. (2014). Using a genotyping-by-sequencing (GBS) approach to elucidate population structure in Garry
Oak (Quercus garryana) (Undergraduate thesis, University of British Columbia).
Huebert, C. A. (2009). The ecological and conservation genetics of Garry oak (Quercus garryana Dougl. ex
Hook) (Master’s thesis, University of British Columbia).
Wang, T., Hamann, A., Spittlehouse, D. L., and Murdock, T. Q. (2012). ClimateWNA—high-resolution spatial climate
data for western North America. Journal of Applied Meteorology and Climatology, 51(1): 16-29.
>Student_data_ enance
iety
_cm
_cm
_cm
7 . . 5 .2
3 13 garryana 4 9
1 4 semota 7
6 garryana 5 semota 200 8.3 5 11 garryana 8 2.3 11 garryana 9.2 2
1.3 13 garryana 4.5 9.2 1 1 semota 11 garryana 1.3 3 semota 4.5 2 semota 5.5 1 4 semota 6.8 4 6 garryana 5 2 2 semota 17 8 garryana 1 8 garryana 13 19 1.5 8 garryana 6.2 2 2 semota 8.5 2.3 8 garryana 8 14.9 1 11 garryana 3.5 4
1 15 garryana 1 3 semota 4 1.5 7 garryana 5 6.2 13 garryana 7.5 1.3 15 garryana 3 1 4 semota 7.8 6 2 semota 7.8 15 garryana 5.5 1 9 garryana 6 1 9 garryana 4.5 14.9 1.5 9 garryana 1 7 garryana 1.5 1 13 garryana 1 7 garryana 4.5 1 7 garryana 4 17.3 1 1 semota 5 3 semota 7 4.8 2.3 9 garryana 5 1 14 garryana 9.2 5
60 1 11 garryana 13.5 1 13 garryana 7 14 garryana 8 1 3 semota 1.8 7 garryana 2 1 semota 14.8 2.8 14 garryana 3.5 2.3 13 garryana 5 1.5 3 semota 3.5 24 2.6 4 13 garryana 4.2 1.5 2 semota 60 1 15 garryana 8.5 1 6 garryana 19 220 2 6 garryana 6.2 8 2.5 4 semota 5 76 4.5 4.8 13 garryana 6.5 1.5 9 garryana 8.5 17.3 2.8 14 garryana 4.5 2.3 11 garryana 6.5 1.3 9 garryana 220 1 1 semota 5.2 72 5.3 1.8 6 garryana 6 6 1 2 semota 3.5 4.2 7 garryana 6 2 13 garryana 4.2 1 4 semota 7 9 garryana 8 1 11 garryana 4 1 9 garryana 220 14.8 1.3 2 semota 4 7 1.7 14 garryana 4.2 195 1 4 semota 6.5 75 3.5 3.3 6 garryana 5 199 1.5 7 garryana 10 83 1.5 13 garryana 8 2 14 garryana 2.5 18 1 14 garryana 5 1 7 garryana 9.8 3.5 2 semota 4 8.7 2 13 garryana 5 20.1 1 7 garryana 3.5 11 2.3 13 garryana 9.5 383 24.6 2.3 14 garryana 6 400 1.5 9 garryana 3 13.7 1 6 garryana 3.5 6.6 1.3 1 semota 2.9 6.1 2 9 garryana 2 1.8 2 semota 13.5 2 semota 8.5 6.6 2 13 garryana 6 72 4.9 1.8 3 semota 4 79 2.7 5 8 garryana 1.5 14 garryana 5.2 1.8 3 semota 5 1 9 garryana 5.5 13.3 1 6 garryana 6 261 3 13 garryana 5.5 199 4 8 garryana 5 319 1 4 semota 4.5 110 6.7 3.8 15 garryana 5 326 2 1 semota 7 2 11 garryana 3.6 76 3.6 1.8 9 garryana 1 3 semota 3.2 86 5.4 6.5 13 garryana 5.5 1 1 semota 5.5 44 3.6 2 11 garryana 2.5 1 14 garryana 7.2 334 2 2 semota 8.3 70 4.6 2.3 2 semota 5 2.8 11 garryana 3 1 9 garryana 5 196 12 1 13 garryana 6.5 326 20 1 7 garryana 20.1 1 14 garryana 3.2 1 14 garryana 6.2 21 1.3 8 garryana 8 285 2.3 14 garryana 2.7 322 18.1 1.5 5 semota 2.5 52 2.9 3.3 3 semota 4.5 96 3 9 garryana 5 11.7 1.5 15 garryana 4.2 1 7 garryana 23 22 1.3 2 semota 7.5 60 3.5 2.5 3 semota 4 148 7.6 9 14 garryana 4.2 7.8 2.8 15 garryana 5.5 1 11 garryana 2.8 2.5 15 garryana 4.9 26.5 1 9 garryana 5 1.5 2 semota 6 71 4.2 3.8 6 garryana 4 1.8 5 semota 3 5.4 2 14 garryana 6 324 26.7 1 2 semota 6.5 83 4.6 1.8 8 garryana 5 13.3 1.3 11 garryana 4.2 1 1 semota 21.2 4.8 4.5 15 garryana 6 246 1 9 garryana 12 17 1 6 garryana 5.6 75 3.5 2 7 garryana 5.4 285 17 1 4 semota 3.2 7.2 2.8 13 garryana 7 277 1 14 garryana 5 269 1 5 semota 9.5 13.7 1 11 garryana 4.6 6.8 1 13 garryana 5 1 14 garryana 6 290 18.5 1 11 garryana 5.5 16 2.5 6 garryana 6.5 277 2 8 garryana 11.5 401 2.5 13 garryana 3.2 150 13.5 1 1 semota 14 200 6.5 5.5 7 garryana 8.5 290 1 5 semota 13 11.4 1.8 5 semota 3.7 6.5 2.3 1 semota 88 4.1 7.5 8 garryana 7.8 1 2 semota 8.4 6.4 1 8 garryana 8 102 16 1 4 semota 4 217 7 2 15 garryana 3 1 9 garryana 6 347 9.6 3.3 8 garryana 8.5 1 11 garryana 4.5 7.4 3 11 garryana 4.5 11.9 1 5 semota 18.5 209 7.4 6.7 6 garryana 4 19 1 1 semota 10.5 238 6.6 1 7 garryana 4.5 258 16.9 2.3 1 semota 4.4 76 4.1 4 13 garryana 6 276 19.8 1 15 garryana 4 343 19.4 2.3 6 garryana 5 60 2.9 1 9 garryana 13 1 2 semota 141 6 1 9 garryana 1.5 294 10.4 1 7 garryana 2.5 222 13.3 2.3 13 garryana 4.9 7.6 3 8 garryana 8.3 20.8 1.3 14 garryana 6.5 1 8 garryana 9.8 319 1 11 garryana 6.8 21.5 1 8 garryana 4.5 1.3 0
13 garryana 5 1.8 2 semota 5.8 2 5 semota 10 152 5.3 2 6 garryana 5 248 11.4 2 6 garryana 29 26 1 13 garryana 5 1 15 garryana 1 15 garryana 4.2 315 26.7 1.3 8 garryana 3.3 24 1 11 garryana 4 1 5 semota 2.5 13.5 2 11 garryana 4 282 17.1 1 11 garryana 7 11.5 2 8 garryana 5.5 1 11 garryana 3.7 299 1 6 garryana 7.6 323 22.6 1.3 14 garryana 4 1 2 semota 28 13.6 2 7 garryana 6.5 324 14.7 1.5 2 semota 6.5 217 6.5 1 4 semota 2.5 104 6 4.5 15 garryana 2 212 17.9 1 9 garryana 362 21.5 1.3 14 garryana 2.6 377 1 7 garryana 6 11.2 2 9 garryana 18 11.4 1 14 garryana 3.5 209 12 1 1 semota 3 102 3 2 2 semota 14 1.8 5 semota 4.4 2.8 3 semota 9 6.1 3.3 15 garryana 9.2 1 6 garryana 2 222 11.6 1 2 semota 7 7.4 3.3 2 semota 348 17.9 3.3 3 semota 8.1 336 10 4.3 9 garryana 15 1 11 garryana 3 7 1.5 2 semota 5.4 5.6 2 7 garryana 10.5 270 1 9 garryana 3.5 302 9.2 1 7 garryana 22 1 3 semota 10 245 8.7 1 6 garryana 5.2 305 2.3 8 garryana 3.4 316 1.7 8 garryana 6.5 317 19.4 1 8 garryana 6 372 2 14 garryana 6.6 283 1.7 9 garryana 6 231 1 0
4 semota 8.5 356 1 4 semota 2 112 4.9 1.3 13 garryana 4 364 1 2 semota 6.5 304 15 2 15 garryana 4.5 360 1 3 semota 12.1 5 15 garryana 3.2 305 20.2 1 8 garryana 5.7 245 16.4 1.7 3 semota 8.5 61 1.9 2.7 8 garryana 33 1 0
1 semota 9 86 3.3 3 15 garryana 4.2 294 17.5 1 15 garryana 4 305 1 5 semota 2.5 74 5 2 15 garryana 4 356 23.9 1.3 7 garryana 5.5 11.3 4 13 garryana 9 343 21.3 1 11 garryana 4 340 22.2 1 6 garryana 6 1 6 garryana 4.5 2 7 garryana 6.5 15 1 _climates
12.3 22.2 4.6 57 Prov 13.5 23.6 6.3 62 Var 14.1 24.1 86 Lat 12.1 22.4 3.5 78 Long 940 15 5.8 Dist 11.6 22.2 2 81 MAT 10.6 20.2 2.1 133 MWMT °C 472 10.8 18.5 3.7 221 MCMT °C 8.7 18.7 68 MSP er precipitation
mm 10.4 17.7 3.6 214 61 9.9 17.6 3.1 89 10 16.4 4.2 139 138 9.1 17 2.1 234 Provenance var. garryana var. semota t-value p-value 1 p-value NA NA NA 2 NA NA NA 3 8 How to Write a Lab Report
Forest Biology (FRST 200 and 210)
Read this document carefully, it outlines the content and formatting that should be used for
lab reports in FRST 200 or 210 and will be the basis for our marking system. You should note that
marking for your first lab report will be reasonably relaxed, but in the second and subsequent reports
the number of marks deducted for mistakes will increase substantially. This system encourages you
to rapidly adopt a simple, clear and systematic style of scientific writing. The easier your report is to
read the more likely you are to get good marks.
1. Report Purpose
You should aim to produce a clear, concise report that reviews current knowledge on the
subject, develops the research objectives, summarizes the methods used, and presents the results
generated. You should then discuss how the results meet the objectives and what they mean relative
to the current knowledge or literature that was used to develop your objectives, which may include
applications to which this knowledge is relevant.
2. Report Structure & Content
Use the following sections to structure your report. Pay close attention to the section details
and suggested content. All sections should be concise and informative.
Introduction
The introduction provides readers with the background information required to understand
the whole report. Introductions are funnel-like, starting broadly with an introductory sentence that
introduces the report’s context, and ending narrowly with a clear definition of specific objectives or
questions being investigated in your report (example 1). We generally leave objectives open-ended,
so you can tailor the objectives to your interests e.g., wood production, conservation, biology.
Relevant background information fills the funnel, setting up any objectives of the study, and any
hypotheses you wish to test that will be covered in your discussion section.
Example 1: Your report is comparing the wood quality of two timber species, Picea abies and Pinus
sylvestris. A good introductory sentence could be, “Wood quality, a trait in which many species vary
substantially, is an important determinant of timber value.” Your introduction then should provide
some background information that would be relevant to the reader, and narrows the focus from
wood quality in general down to more specific aspects. Potential topics might include: economic
value of high-quality wood; features that contribute to wood quality (especially whichever ones you
measured); or variability in these features. This can be further narrowed to the species being
studied: compare and contrast life histories, growth rates, relative importance to the timber industry,
etc. Now that the reader has some knowledge of wood quality and your species, you can establish
your objectives i.e., the question your study is trying to answer: “While Picea abies and Pinus
sylvestris are both important to European timber production, the quality of their mature wood has not
been adequately assessed.” Your last sentence should briefly summarize what you’ve done to
address your objective: “In this study, differences in tracheid length and extractives content were
compared between Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H. Karst.).” Assume that your reader is educated, but unfamiliar with your subject. Therefore, the
introduction should also define and contextualize any terminology that is fundamental to your study.
To do this you will need to concisely summarize relevant information from class notes and include
background research. In most cases, this will include citing relevant scientific literature. Do not
include any methods, results or conclusions in your introduction.
Your introduction section should conclude with your hypotheses. These are the scientific
statements that you wish to test in your lab report. You should provide an explanation of why you
developed your hypotheses, followed by a list of your hypotheses. These hypotheses are the basis of
your discussion. Your hypotheses must be testable, and provide a predicted direction of the
relationship (example 2). This will allow you to later assess whether your hypotheses were supported
or refuted by your data.
Example 2: For the previously mentioned research topic, a good hypothesis section might be as
follows: “As Scots pine generally has a higher growth rate than Norway spruce, it was expected that
Scots pine wood will exhibit more traits consistent with fast-growing species. As such, the authors
predicted that Scots pine wood would have longer tracheids than Norway spruce, more earlywood,
and lower wood density”.
Materials and Methods
The materials and methods is an account of how your experiment was conducted, and why it
was conducted this way. This should concisely provide enough information so that your experiment
could be repeated exactly. Materials and methods must be written in clear sentences, not in point
form. Materials should be incorporated into the methods, not included separately (example 3). Be
sure to outline all the different types of data collected, the measurements that were made, and the
units of measurement used. Also include information regarding your data analysis. How was your raw
data used to answer your hypotheses? Describe any drawings that were made and their purpose (do
not include the actual drawings here). If you used any scientific equipment, try to provide as much
information about it as possible (e.g., equipment name and model number; example 3). If you used
software to analyze your data, include what you used and which version. Any relevant equations that
were used to transform your data must be included within your methods.
Do not include unnecessary details about how the lab was performed. Remember that this is
meant to be an account of how your experiment was conducted, not your forestry lab. For example,
another scientist does not need to know that you collected data in groups of three or that the TA
compiled your class data. Do not copy the lab instruction sheet which describes what you should do
during the lab, not what you did during the experiment. You should summarise the methods in your
own words.
Example 3: Good methods might read “1 cm2 cubes of mature Picea abies and Pinus sylvestris wood
was collected from centre-cut commercial-grade dimensional lumber. To obtain tracheid samples,
these cubes were pulverized individually using an Ailence MF-1000 wood pulveriser.” Bad methods
would be “We were given small pieces of wood from the species. The TAs put them in the wood
pulveriser for us.”
Results
Use the results section to summarize your data and report observed trends. Do not discuss
how, why, or what may be the basis for these trends (this material belongs in the Discussion section).
Results should be quantitative rather than qualitative whenever possible. If you are comparing
different groups, your results should compare these groups (example 4). If your results are
presenting a correlation between measurements, include the direction and relative strength of the
relationship (e.g. slope or R2; example 5). When giving quantitative results, always include units.
Example 4: An example of a result that is both quantitative and comparative is, “Mean tracheid
length in Scots pine was estimated to be 2.57mm (standard deviation: 0.57mm). Tracheids of
Norway spruce were, on average, 35% longer, with a mean of 3.47mm (standard deviation: 0.8mm).”
A bad example of a result, that is neither sufficiently quantitative nor comparative would be,
“Tracheids in Scots pine were 2.57. Tracheids in Norway spruce were a lot longer.”
Example 5: A good result describing a correlation is “As distance of the wood sample from the pith of
the tree increased, extractives percentage decreased linearly in Norway spruce wood (slope = 0.14
µLexactives cm-1 ; R2=0.73).” A bad example that relies of qualitative descriptions would be “Distance of
the wood sample from the pith of the tree and extractives percentage were found to be related. The
relationship was very strong.”
Your results sections will also include tables or graphs that display notable trends in your
data. See ‘Writing and Formatting Rules’ below for instructions on how to present and refer to tables,
figures and appendix items. Drawings or photographs can be essential to your results for some
reports, and they may be included in the results section or the appendix at your discretion.
Discussion
Your discussion is where you address your hypotheses (from the Introduction), and
systematically explain possible reasons behind all trends reported in the results section (even if they
are not explicitly included in your hypotheses), as well as limitations of the methods used. Writing the
discussion always involves using additional sources of information (i.e., references from the scientific
literature or from texts, not course notes), statistical interpretation (if any), deductive reasoning, and
logical inference to explain the results that you reported. For each trend a simple procedure is to
make a statement of the trend you observed, then follow this statement with explanations of why you
believe this trend might have occurred (example 6).
Example 6: A good discussion sentence stating an observed trend could be, “Although there is large
variability in the data, Norway spruce tracheids were found to be generally longer than those of Scots
pine.” After making the statement, you could discuss possible explanations for why Norway spruce
produced longer tracheids in this experiment, ideally supported by outside scientific literature e.g.
“An analysis of the wood from seven spruce and pine species found that spruces tend to produce
longer tracheids than pines (Anoulli et al. 1986). This suggests that our trend may be due to broad
differences in growth strategy that have evolved between the two genera, and is unlikely to be
unique to our specific samples.”
In general, a single study cannot prove or disprove a hypothesis. Often we are using a small
sample and assuming it is representative of a broader group. Our measurements are imperfect, and
it is possible that no causal relationship exists between our variables, but rather they are both
related to a third underlying, unobserved variable, or just due to random chance. As such, we can
only use our data to support or refute a hypothesis, rather than prove or disprove it.
Your discussion should also reflect on any limitations or sources of error in your data. Make
notes as you perform your experiment or measurements about what aspects seem more or less
precise or accurate. Unless you can describe very specific errors in your dataset, the rest of your
discussion should treat your results as though they are accurate and generalizable. Saying that “this
trend exists because our data is probably wrong” is rarely a valid explanation.
A discussion should also include some broader implications or applications of your results
i.e., why should we care about these results? We will usually ask you questions in the lab handout
that attempt to draw these connections, and these should be answered within the discussion. While
it is often useful to look into the scientific literature to answer these questions, remember to draw
from your own results as well (example 7).
Example 7: If one of the questions for this hypothetical lab was “Which species’ wood would be more
appropriate for building a house?” a good response would be, “Tracheid length has been found to be
strongly correlated with microfibril angle, a critical parameter of wood strength (Kennedy 1995). As
we found Norway spruce to generally have longer tracheids than Scots pine, it is possible that
Norway spruce has a lower microfibril angle as well and therefore produces better wood for home
construction. This is supported by the results of Lichtenegger et al. (1999), who directly measured
microfibril angle in these species.” A bad response would be “Previous studies have found that
Norway spruce has stronger wood than Scots pine (Lichtenegger et al. 1999). Therefore I would use
Norway spruce to build a house”. While this technically answers the question, it does not relate to
any of the results obtained and therefore would be out of place in this discussion.
Your discussion should end with a brief conclusion. This is a concise summary of the report’s
main findings, why they occurred, and what their further implications are.
References
List in alphabetical order (according to first author’s last name) all the sources that you have
cited in your report (see the ‘Writing Rules and Formatting’ section for information on using
references). These references should be, in most cases, peer-reviewed scientific literature or
accepted text books. Do not cite course notes unless you are given explicit permission. If you cite a
source that does not appear in the references, or provide a reference for a source that is never cited,
you will not receive credit for this reference.
Appendix
The appendix contains data and figures that do not belong or fit in the rest of the report. Raw
data sheets, sampling maps, or large drawings are common appendix items. They should appear in
the appendix in the same order that they are referred to in the text. Only include items in the
appendix if they are mentioned in the report and cite them in the same style as tables or figures. To
do this, each appendix item needs to be clearly numbered. Not all reports require an appendix.
3. Writing and Formatting Rules
General Writing Etiquette
Proper paragraph formatting, clear sentence structure, good spelling and grammar are basic
expectations for all assignments. If you are not confident in your writing ability, it is a good practice to
have a friend who is proficient with English proofread your report. Reports must be typed, with size
11 or 12 font, 1.5 or 2 line spacing, and 2.5 cm margins. Do not include a cover page for your report.
Using Latin and Common Names
The first time a species name is used, include the whole Latin name and authority e.g.,
Pseudotsuga menziesii (Mirb.) Franco as well as the common name e.g., Douglas-fir. As long as there
is no potential for confusion, the genus may be abbreviated following the first use e.g., P. menziesii
or the common name may be used throughout the write-up. Latin names (in fact all non-English
words) must always be italicized or underlined. Common names do not use capitals unless they
include proper nouns such as a place or person’s name (e.g., black spruce, Sitka spruce and
Douglas-fir are all correct, whereas Black Spruce, sitka spruce and douglas-fir are all incorrect).
Citations and References
Citing and referencing appropriate literature is an essential component of all scientific
reports. We expect you to use peer-reviewed scientific papers in journals for your reports. You can
find these by using search engines such as Web of Science (available through the UBC Library) or
Google Scholar, and access them through the UBC Library. A citation is required whenever you use
information from somebody else’s text or their ideas. A ‘citation’ is the mention of literature in the
main body of your text, whereas a ‘reference’ is the full bibliographic information for a given citation
that is listed in the reference section. You should use the “(author date)” citation style (examples 6,
7). This should go at the end of the sentence using the source information, unless you are referring
to the authors within the sentence (example 7). For reference format, you can use any established
style (e.g., MLA, APA, Chicago) as long as you are consistent within your references. Don’t just copy
and paste references as you find them. They are frequently automatically generated and contain
incorrect or insufficient information.
When citing information, paraphrase the source material (example 8). Do not quote or
directly copy-paste text from references! Improper citation will, at best, lose you many marks on your
assignment. At worst, you could end up facing expulsion from UBC for plagiarism. Directly copying 7
or more consecutive words from any source material is considered plagiarism by UBC standards.
There are some rare cases where quoting information is acceptable. Generally this is when it is an
author’s specific opinion or their exact sequence of words that are relevant, or if they are providing a
technical definition (example 9).
Example 8: Here is a piece of source information, from Kennedy (1995): “There are a host of
properties that determine the quality of wood for different purposes, but the single most important
characteristic is wood specific gravity or relative density.” An appropriate citation using this
information could be:
Wood quality is affected by many properties, but relative density is the most important
characteristic (Kennedy 1995).
The following are improper uses of this information:
“There are a host of properties that determine the quality of wood for different purposes, but
the single most important characteristic is wood specific gravity or relative density” (Kennedy
1995). There are a host of properties that determine the quality of wood, but the most important is
wood relative density (Kennedy 1995).
There are a host of properties that determine the quality of wood.
Note that the last 2 examples are explicit forms of plagiarism and may lose you full credit for the
assignment or worse. Even if you are citing a source, you must paraphrase their content.
Example 9: This is an appropriate use of quotation for a reference: “Charles Darwin was among the
first to link domesticated crops and livestock to natural evolution, claiming that he ‘invariably found
that our knowledge, imperfect though it be, of variation under domestication, afforded the best and
safest clue’ to understanding natural selection (Darwin 1859).”
Measurement Units
Report your data using appropriate SI i.e., metric units and always give the units of
measurement for any data when it is first mentioned in your report as well as in every table or figure.
Think carefully about what your measurements mean in reality to determine the appropriate number
of decimal places or significant figures that should be used. Programs like excel will often provide
numbers to 10 decimal places, far more precise than the data that you input. Using these numbers
as they are given is misleading your reader into thinking your results are more precise than they truly
are. Figures and Tables
Figures such as graphs, drawings and photographs are tools for illustrating written content
that is not otherwise easy to visualize. Tables present values that summarise raw data across
several categories or variables. In your FRST 200 & 210 reports, tables and figures are essential
requirements because formal statistical analysis is usually not required, but we still expect you to
identify and discuss trends.
When creating a figure or table ask yourself what you’re trying to demonstrate to the reader and stick
to the following rules:
Do not use snazzy graphics–we will not be impressed. We are looking for figures that are
simple, clear and informative.
All graphs and tables except the first graph of the stem analysis lab (FRST 210) should be
word processed.
Size matters–don’t use small figures. As a guideline, graphs should be full-page-width. Table
size depends on the amount of data, if the table doesn’t fit on a single page it belongs in the
appendix. Figures and tables are separate entities–do not label tables as figures.
Don’t give graphs a title, even though Excel does by default. This information is provided by
the caption.
Captions for figures (graphs and drawings) are placed below the figure, but captions for
tables are placed above the table.
Label each axis of a graph with the variable it represents and the units of measurement.
Consolidate two or more trends (i.e., datasets) in the same graph if they have identical units
of measurement and values in the same order or magnitude.
Include a legend if you have more than one trend per graph and make sure that the colours
or symbols are organized such that the reader can draw fast visual conclusions about each
trend.
Drawings include a scale bar and must be clearly labelled with any parts described in your
report. Tables are meant to summarize data. If you are working with means calculated from multiple
measurements or groups, only provide the means. You can include the data used to generate
the means as a separate table in the appendix.
Figures and tables must be cited and explained in the results section. It is best to cite a
figure or table in parentheses at the end of the respective sentence (see example 10). It is never
acceptable to simply list attached figures or to say “see attached figures”. All figures and tables
should be positioned in the report close to the relevant paragraph and in the same order that they
are mentioned in the text, so the reader is not confused trying to find the correct figure.
Example 10: A good citation of a figure would be “Mean tracheid length in Scots pine was estimated
to be 2.57mm. Tracheids of Norway spruce were, on average, 35% longer, with a mean of 3.47mm
(figure 1).” A bad example would be “Figure 1 shows that mean tracheid length in Scots pine was
estimated to be 2.57mm. Figure 1 also shows that tracheids of Norway spruce were, on average,
35% longer, with a mean of 3.47mm.”
Every figure and table must have a caption that includes a number and a brief written
description (in complete sentences) of what the figure contains, but doesn’t describe any trends
(example 11). Captions should be informative enough that you could use them to understand the
figure or table in the absence of the rest of your report. If you use mean values, provide the sample
size used to generate your means (example 11). If you use error bars, say what type of error was
used.
Example 11: A good example of a figure caption might be “Figure 1: Tracheid lengths of mature wood
from Picea abies (n = 5) and Pinus sylvestris (n = 7). Error bars are given in standard deviation.” Bar Graphs, Line Graphs and Scatter plots: Your chosen graph should be determined by the type of
data on your x-axis. Bar graphs are only used when the x-axis is categorical (e.g., “pines” vs.
“spruces”), but line graphs are required whenever the data are continuous and show change from
one measurement point to the next (e.g., latitude, longitude or a time series such as day of year).
Scatterplots are used to display numerous data points for continuous x-and y-axes. Data points on
line graphs can be connected, but never use a smoothing function in your lab reports. Points in
scatterplots should never be connected with lines, but if you want to show a general trend in your
data, you can insert a line of best fit through the data points (called a trend line in Excel).
4. Useful Hints and Tips
The secret to writing great lab reports: Your first lab reports can be daunting and challenging to
write, but once you’re familiar with the format they will become much easier and obtain higher
marks. We recommend writing the materials and methods section first because it’s simple,
straightforward and familiar to you. Next, analyse your data, create your graphs and write the results
section. Then write the discussion. Finally, although it’s counterintuitive, finish by writing the
introduction. This should make the content of the introduction easier to judge and prevent it from
being too short or long.
Write concisely: Keep sentences fairly short, and don’t use long words if a short one will do. A good
scientific writer will use the minimum number of words possible to clearly state their point, so don’t
use sentences that don’t tell the reader anything meaningful. For example, “This lab was interesting
and helpful for understanding germination better” tells the reader nothing what you learned, so leave
it out. Avoid unnecessary words, e.g., the word “the” can often be deleted with no change in
meaning. After writing a first draft, edit it so that the major messages are maintained but the text is
shortened.
The meaning of ‘significant’: In science, ‘significant’ refers to the outcome of a statistical test. We’re
not doing statistical tests in most of these labs, so it is best to avoid using this term to refer to a
strong relationship in your data, unless you have actually tested whether is constitutes a statistically-
significant difference.
Avoid making overly large scientific inferences: Extrapolation of your data trends to larger scientific
concepts will be required; however, it is not acceptable to do so without adequate supporting
information. For example, applying the concepts learned from observing physiological differences
between Scots pine and Norway spruce to all firs and spruces would be inappropriate. You need to
use valid scientific references to support any conclusions you draw or speculations you make based
on your results. When you are suggesting possible explanations for results, if those are speculative,
write it in a way that reflects the uncertainty, e.g., using words such as “may result from”, or “could
be explained by”. If you want to know more about how to write good scientific papers, there is lots of
information available online. Here is one particularly user-friendly resource. http://abacus.bates.edu/~ganderso/biology/resources/writing/HTWtoc.html N N = Do not measure N N = Do not measure N N = Do not measure = Thinned/dead tree
Blue flag X = Thinned/dead tree Orange flag X = Thinned/dead tree Pink flag X = Thinned/dead tree Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7 Column 8 Column 9 Column 10 Column 11 Column 12 Column 0 X N X N X N X N X N X N X Row 0 N X N X N X N X N X N X Row 0 N X N X N X N X N X N X Row 0 N X N X N X N X N X N X X N X X X N X X X Row 1 X X N X X X X Row 1 X X X X X X Row 1 X X X X X X N X X X X X X X X Row 2 X X X X X X X Row 2 X X X X X X Row 2 X X X X X X X N X X X X X X X Row 3 X X X X X X X Row 3 X X X X X X Row 3 X X X X X X N X X X X X N X X Row 4 X X X X X X Row 4 X N X X X X X Row 4 X X X X X X X X N X X X X X X Row 5 X X X X X X Row 5 X X X N X N X X Row 5 X X X X X X N X X X X X X X X Row 6 X X X X X X Row 6 X X X X X X Row 6 N X X X X X X X X N X X X X X X Row 7 X X X X X X Row 7 X X X X X X Row 7 X X X X X X N X X X X X X X X X X Row 8 X X X X X X Row 8 X X X N X X X Row 8 X N X X X X X X N X X X X X X Row 9 X X X X X X X Row 9 X X X X X X X Row 9 X X X X X X N X X X X X X X Row 10 X X X X X X Row 10 X X X X X X Row 10 X X X X X X X X N X X X X X X Row 11 X X X X X X Row 11 X X X X X X X X Row 11 X X X X X X N X N X X X X X N X Row 12 X N X X X X N X Row 12 X X X X X X X N X Row 12 X X X X X N X X A brief primer on statistical analysis For this lab, you will be performing statistical analyses, with the help of some Excel tools. If you have already taken FRST231 or are presently taking it, this should be a refresher for you. Otherwise, you may not have ever performed statistical analysis, and will come across some confusing terms and numbers you may not know how to interpret. Hopefully this will clear up some confusion. Why do we need to use statistics? In this lab, we are seeking to explain genetic variation in tree growth by comparing measurements across taxonomic varieties, and analysing the relationship between our measurements and the climates of our trees’ provenances. When collecting data, there will always be some degree of unaccounted-for variation in our measurements. This variation may be caused by measurement error, natural variation caused by genetic differences, environmental variation, or more likely some combination of all these. As a result, real relationships are “messy”. Below are two figures showing a hypothetical relationship between provenance temperature and heights measured in a common garden. While not perfect, figure 1 clearly shows a strong relationship between these variables. But what about figure 2? It’s not as clear. Statistics allow us to ask “How likely is it that the pattern we’re seeing is the result of a real underlying relationship, rather than just random chance?” We can quantify this likelihood and assess the probability that our results reflect real relationships.
In this lab we will be performing two different statistical analyses: linear regression, and t-tests . Linear regression is a method to determine whether a relationship exists between two numeric variables, Figure 2: The effect of provenance mean annual precipitation on root-to-shoot ratios in 5-year old Pseudotsuga menziesii trees grown in a common garden in Campbell River, BC (n=5-7 per provenance). Figure 1: The effect of provenance mean annual temperature on height in 7-year old Pinus contorta trees grown in a common garden in Prince George, BC (n=10 per provenance). p-values The cornerstone of most statistical analyses is the p-value. The p-value is a statistic that reflects the probability that an observed trend could happen by random chance, and it varies from 0 to 1. If p is close to 0, there is a very low chance that the pattern we see is due to chance (i.e., it is likely to reflect a real relationship). If p is close to 1, there is no difference between our data and data generated at random, so we should not interpret there to be a relationship here. Figure 1 shows a trend with a p-value of 0.0001, meaning that if we randomly generated 10,000 datasets, we could expect one of them to show a trend this strong. Figure 2 shows a trend with a p-value of 0.1, meaning that random data could create a pattern at least as strong as this 10% of the time. We set an arbitrary threshold on p-values to assign statistical significance to a trend, meaning we are willing to accept some probability that our results are due to chance, but that number is generally low. In most scientific fields, we set a significance threshold of 0.05, meaning we are willing to accept a 5% chance that our results are due to random chance. A p-value less than 0.05 means we can consider that result “statistically significant” and therefore a reflection of a real pattern in the data. By this criterion, figure 1 shows a statistically significant relationship, while figure 2 does not. P-values are used in both linear regression and t-tests. If a linear regression has a p-value less than 0.05, we say that that the variables used in that analysis have a significant relationship. If a t-test has a p-value less than 0.05, we say that the two groups we are comparing show significant differences. Linear regression
Linear regression is a commonly-used method across many fields of biology. In this method, we are comparing data with two dimensions: an independent variable (generally meaning one that we assign or that is known ahead of time), and a dependent variable (generally one that we measure, also known as a response variable). In this lab, our independent variables will be some aspects of climate from various provenances of Garry oak. Our dependent variables will be tree heights and diameters. Linear regressions are always presented as scatter plots, where the independent variable goes on the x-axis (the horizontal axis of the scatter plot), and the dependent variable goes on the y-axis (the vertical axis of the scatter plot). In this way, we can interpret the way that our dependent variable changes in relation to our independent variable i.e., in figure 1 if the provenance mean annual temperature changes by 1°C, how much does tree height change? This is the relationship quantified by linear regression. Linear regression uses a set of equations to draw a line of fit through our data. This is shown by the grey diagonal lines in figures 1 and 2. A line of fit is a straight line that best describes the relationship in the data, and can be used to quantitatively describe that relationship using its slope. For example, the linear regression of figure 1 has a slope of 1.17m/°C, meaning for every 1°C that provenance mean annual temperature increases, we can expect height to increase by 1.17m. Another important aspect of linear regression is called the coefficient of determination, which has the mathematical symbol R2. This is a measurement of how strongly the two variables in our regression relate to one another. R2 ranges from 0 to 1, with 0 meaning there is no correlation between our data, and 1 meaning that the data is perfectly correlated i.e. all of the points in our scatter plot would fall exactly along the line of best fit. The relationship in figure 1 has an R2 of 0.62, which can be interpreted as “provenance mean annual temperature explains 62% of the variation in mean provenance heights”. Both slope and coefficient of determination are quantitative descriptions of significant relationships. Therefore, they should not be reported or interpreted for regressions with p-values greater than 0.05.
t-tests
Often, we wish to know whether two groups differ for some variable of interest. In this lab, we will be determining whether two taxonomic varieties of Garry oak differ in their average height or number of stems. A simple statistical method for this is called the t-test. A t-test determines whether the means of two groups of data are significantly different. This is determined not only by the means of those two groups, but the amount of variation in each group. This variation is referred to as error in statistics, although that term is misleading. As mentioned earlier, variation in our data is expected even if we measure things perfectly. We will quantify this variation in our groups using a parameter called standard deviation. This number is the average amount that each measurement in a group differs from the mean. For example, in figure 3, the mean height for Pinus contorta var. latifolia individuals is 9.9m, with a standard deviation of 1.1m. This means that, on average, individuals range from 8.8-11m within this group (the upper and lower end of the error bars in this figure). Figure 4: Belowground biomass of 5-year old Pseudotsuga menziesii trees grown in fertilized (n=40) and unfertilized (n=35) plots in a common garden in Campbell River, BC. Error bars represent standard deviation. Figure 3: Mean heights of 7-year old Pinus contorta var. latifolia (n=100) and P. contorta var. contorta (n=40), grown in a common garden in Prince George, BC. Error bars represent standard deviation. In figures 3 and 4, the groups both appear to differ. Pinus contorta var. latifolia individuals appear taller than var. contorta (fig. 3), and fertilized Pseudotsuga menziesii appear to grow more roots in fertilized plots (fig. 4). However, the error bars in figure 4 are larger than those in figure 3, meaning the data in figure 3 has more variation. A t-test can tell us whether these differences are statistically significant. The outputs of a t-test are a t-value and a p-value. The t-value is difficult to interpret without a much deeper dive into statistics, but a low value (near 0) represents less difference between groups than a larger value. However, the p-value is the same as discussed earlier. If p < 0.05, then the groups are significantly different, if p > 0.05, the groups are not significantly different. In figure 3, p = 0.001. These groups are significantly different. In figure 4, p = 0.09. These groups do not differ significantly. If our groups differ significantly, we can discuss the relative difference between them e.g., in figure 3, var. contorta individuals have an average height of 8.5m, 15% shorter than var. latifolia individuals. If the groups do not differ significantly, we infer that there is no difference between the mean values of our two groups and should not attempt to quantify this. American Journal of Botany 95(1): 66–76. 2008.
66
Movement of seeds from their collection site to other environ- The ranges of many trees species are predicted to shift higher in tional, aspect, and microsite adjustments because the location of Whitebark pine ( Pinus albicaulis Engelm., Pinaceae) is a 1 Manuscript received 21 September 2006; revision accepted 8 November The authors thank the USDA Forest Service regions one, fi ve, and six; 2 Author for correspondence (e-mail: andrew.bower@comcast.net)
ECOLOGICAL GENETICS AND SEED TRANSFER GUIDELINES FOR ANDREW D. BOWER 2 AND SALLY N. AITKEN
Centre for Forest Conservation Genetics, Department of Forest Sciences, University of British Columbia, 3401-2424 Main Mall, Whitebark pine ( Pinus albicaulis Engelm.) has greatly declined throughout its range as a result of introduced disease, fi re sup- Key words: genetic variation; geographic differentiation; local adaptation; Pinus albicaulis ; quantitative traits; seed transfer; 67January 2008] BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
eight replications had ambient soil temperature (ambient treatment) and the re- The fi nal data set contained 10 quantitative variables; data from all trees in Data analysis — SAS version 8 ( SAS Institute, 1999 ) was used for all statisti- y ijklmn = µ + t i + r ( t ) ij + b ( rt ) ijk + p l + pt il + pr ( t ) ijl + f ( p ) l m + e ijklmn , (Eq. 1)
where y ijklmn is the observed value for tree n in family m w ithin population l Genetic variation and population differentiation have been In this study, we analyze geographic variation and genetic MATERIALS AND METHODS
Sample materials — Open-pollinated seeds from 48 populations of white- TABLE 1. Reported values of genetic differentiation for whitebark pine ( Pinus albicaulis) and other stone pine ( Pinus subsection Cembrae ) species.
Populations Species Area F ST or G ST Reference
14 P. albicaulis BC, ID, MT, OR 0.075 A. Bower unpublished manuscript 8 P. sibirica Russia ~0.042 Goncharenko et al. 1993b P. koraiensis Coastal Russia 0.016 Potenko and Velikov 2001 Notes: AB = Alberta, BC = British Columbia, Canada; ID = Idaho, MT = Montana, USA. 68 AMERICAN JOURNAL OF BOTANY [Vol. 95
genetic variance. In this study the variance component for population ( σ 2 p ) was Climatic variables used in the analyses were mean annual tempera- means were used to test for differences between treatments for survival percent- To test differences among populations within each soil temperature, PROC y ijklm = µ + r i + b ( r ) ij + p k + rp ik + f ( p ) kl + e ijklm , (Eq. 2)
where terms for each effect are the same as in Eq. 1 without the effect of soil Genetic differentiation among populations was estimated for all quantitative TABLE 2. Pinus albicaulis populations, number of seedlings tested, geographic and climatic data.
Site no. Region Name
No. trees
Lat. o N Long. o W Elev. (m) MAT ( ° C) MTWM ( ° C) MTCM ( ° C) FFP (d) SH:MAmbient Cold
1 N Serb Creek 5 10 54.71 127.57 1385 0.7 11.4 − 10.7 52 32.7 Notes: Region: N = northern, R = Rocky Mountain, S = southern; Lat. = latitude, Long. = longitude, Elev. = elevation, See Table 3 for abbreviations and 69January 2008] BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
treatment had fewer replications, lack of cold injury testing, and the absence of To develop predictive equations for the construction of seed transfer guide- RESULTS
Soil temperature effects — Height increment and survival were Geographic patterns across the species range — In general, Growth-related traits generally had low levels of population obtained from a model using the thin plate splines of Hutchinson (2000) as il- Fig. 1. Distribution of Pinus albicaulis and locations of populations TABLE 3. Description of (A) quantitative and (B) climatic variables.
A) Quantitative trait Abbreviation Unit
3rd year height increment HTINC millimeters B) Climatic variable
Mean annual temperature MAT ° C 70 AMERICAN JOURNAL OF BOTANY [Vol. 95
nifi cant ( P = 0.006) and accounted for an additional 15% of the Regional patterns of variation — When populations were moderate to strong differentiation among populations regard- In the canonical correlation analysis of population means for TABLE 4. Correlations among population means for quantitative, climatic, and geographic variables. See Table 3 for explanation of variables.
A) Northern region
Variable HTINC a TDM a RM a SM a FL03 FL04 FCI SCI Survival Lat. Long. Elev.
MAT − 0.15 − 0.40 − 0.49 − 0.31 − 0.06 − 0.50 − 0.16 0.31 0.26 − 0.04 0.07 − 0.42 B) Rocky Mountain region
Variable HTINC a TDM a RM a SM a FL03 FL04 FCI SCI Survival Lat. Long. Elev. MAT 0.61* 0.60* 0.53* 0.63* 0.04 0.06 0.48 0.53* 0.24 0.20 − 0.33 − 0.85* C) Southern region
Variable HTINC a TDM a RM a SM a FL03 FL04 FCI SCI Survival Lat. Long. Elev. MAT 0.12 0.24 0.22 0.25 0.05 − 0.14 0.22 0.35 − 0.32 − 0.12 0.47* − 0.03 a Natural log transformed 71January 2008] BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
Scores for the fi rst pair of canonical variables from the south- Limits to seed transfer — The interval in mean temperature of needle fl ush (both years) varied signifi cantly among popula- Correlations among quantitative traits and climatic variables TABLE 5. Signifi cance level of population and family within-population effect in ANOVA, over all populations and by region, in two soil temperature Variable a Population F Family in-population Z †
Population F by region
Northern Rocky Mtn. Southern
A-HTINC b 1.84** 1.90* 1.59 1.71 1.66 C-HTINC b 1.57* 1.73* 3.52* 2.55** 0.45 a A = ambient soil temperature treatment, C = cold soil temperature treatment, see Table 3 for explanation of variables TABLE 6. Correlations between quantitative canonical variables (Quant1 and Quant2) and the quantitative variables, and between climate canonical Variable a Quant1 Quant2 Variable Clim1 Clim2 Variable Clim1 Clim2
HTINC b 0.07 0.84 MAT 0.75 0.42 HTINC 0.07 0.66 FFP 0.14 0.61
a See Table 3 for explanation of variables.
b Natural log transformed 72 AMERICAN JOURNAL OF BOTANY [Vol. 95
able seed prevented the replication of this experiment in differ- Genetic variation and population differentiation — We ob- The average level of population differentiation for quantita- P < 0.0001 for regression of mean temperature of the coldest
month on latitude). The difference for the northern region was
1.9 ° C and for the Rocky Mountain region was 1.0 ° C. Mean
temperature of the coldest month was most closely associated
with latitude in the northern region and with elevation in the
Rocky Mountain region ( Table 4 ). The interval in frost-free
period associated with the second canonical variable (which
primarily comprises growth traits) was 15 d over all regions, which
translated to a difference of 1010 m in elevation or 12.2 ° longi-
tude ( r 2 = 0.23, P = 0.001 for regression of frost-free period on
elevation) and 27.5 d in the Rocky Mountain region.
DISCUSSION
Effects of common-garden environments — Common-gar- Fig. 2. Regression of fi rst quantitative canonical score (QS1) on stan- TABLE 7. Quantitative trait Q ST values over all populations and by region Variable a All populations Northern Rocky Mtn. Southern
A-HTINC 0.14 0.17 0.13 0.10 C-HTINC 0.13 1.00 1.00 0.00 a See Table 3 for explanation of variables. Fig. 3. Scatterplot of fi rst two quantitative canonical scores (QC1 and 73January 2008] BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
North American conifers, including subalpine fi r [ Abies lasio- Whitebark pine has a high level of cold hardiness compared Seed transfer guidelines and climate change — While white- strongly for growth traits (height growth and biomass), popula- There is a general concordance of the patterns of variation Environmental gradients associated with phenotypic traits — 74 AMERICAN JOURNAL OF BOTANY [Vol. 95
One strategy for selecting seed sources that may result in The warming predicted by global circulation models will Successful restoration plantings with preferential movement of the coldest month suggests that seed can be freely moved The problem with this approach for estimating symmetrical, New approaches to developing seed transfer recommenda- 75January 2008] BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
IPCC . 2001 . Third Assessment Report of the Intergovernmental Panel JORGENSEN , S . M . , AND J . L . HAMRICK . 1997 . Biogeography and popula- KENDALL , K . C . , AND R . E . KEANE . 2001 . Whitebark pine decline: infection, KRAKOWSKI , J . , S . N . AITKEN , AND Y . A . EL – KASSABY . 2003 . Inbreeding KRUTOVSKII , K . V . , D . V . POLITOV , AND Y . P . ALTUKHOV . 1995 . Isozyme LANNER , R . M . 1982 . Adaptations of whitebark pine for seed dispersal LEINONEN , I . , AND H . HANNINEN . 2002 . Adaptation of the timing of bud burst LITTLE , E . L . JR . 1971 . Atlas of United States trees, vol. 1, Conifers and im- LYNCH , M . , M . PFRENDER , K . SPITZE , N . LEHMAN , J . HICKS , D . ALLEN , L . Mann, A. D. 1996 . Alpha+: Experimental designs for variety trials and MCCOOL , S . F . , AND W . A . FREIMUND . 2001 . Threatened landscapes and MCKAY , J . K . , AND R . G . LATTA . 2002 . Adaptive population divergence: MCKAY , J . K . , C . E . CHRISTIAN , S . HARRISON , AND K . J . RICE . 2005 . “ How MCKINNEY , D . W . , M . F . HUTCHINSON , J . L . KESTEVEN , AND L . A . VENIER . MERILA , J . , AND P . CRNOKRAK . 2001 . Comparison of genetic differentiation MIMURA , M . , AND S . N . AITKEN . 2007 . Adaptive gradients and isolation by MORGENSTERN , K . E . 1996 . Geographic variation in forest trees. UBC Press, PATTERSON , H . D . , AND E . R . WILLIAMS . 1976 . A new class of resolvable PETERSON , D . W . , AND D . L . PETERSON . 2001 . Mountain hemlock growth PETERSON , D . L . , D . W . PETERSON , AND G . J . ETTL . 2002 . Growth responses POTENKO , V . V . , AND A . V . VELIKOV . 1998 . Genetic diversity and differen- LITERATURE CITED
ARNO , S . F . , AND R . J . HOFF . 1989 . Silvics of whitebark pine ( Pinus albi- BARTLEIN , P . J . , C . WHITLOCK , AND S . L . SHAFER . 1997 . Future climate in the BELOKON , M . M . , Y . S . BELOKON , D . V . POLITOV , AND Y . P . ALTUKHOV . BOWER , A . D . , AND S . N . AITKEN . 2006 . Geographic and seasonal variation in Bower , A. D. , and S. N. Aitken . 2007 . Mating system and inbreeding de- BRUEDERLE , L . P . , D . F . TOMBACK , K . K . KELLY , AND R . C . HARDWICK . BURGER , R . , AND M . LYNCH . 1995 . Evolution and extinction in a changing BURR , K . E . , A . ERAMIAN , AND K . EGGLESTON . 2001 . Growing whitebark pine CAMPBELL , R . K . 1979 . Genecology of Douglas-fi r in a watershed in the CAMPBELL , R . K . , AND A . I . SUGANO . 1979 . Genecology of bud-burst phe- DAVIS , M . B . , AND R . G . SHAW . 2001 . Range shifts and adaptive responses to GERNANDT , D . S . , G . GEADA L Ó PEZ , S . ORTIZ GARC Í A , AND A . LISTON . GONCHARENKO , G . G . , V . E . PADUTOV , AND A . E . SILIN . 1993a . Allozyme GONCHARENKO , G . G . , V . E . PADUTOV , AND A . E . SILIN . 1993b . Allozyme vari- HAMANN , A . , AND T . WANG . 2006 . Potential effects of climate change on HAMRICK , J . L . 2004 . Response of forest trees to global environmental HOFF , R . J . , D . E . FERGUSON , G . I . MCDONALD , AND R . E . KEANE . HOWE , G . T . , S . N . AITKEN , D . B . NEALE , K . D . JERMSTAD , N . C . WHEELER , HUFFORD , K . M . , AND S . J . MAZER . 2003 . Plant ecotypes: Genetic differentia- HUTCHINS , H . E . , AND R . M . LANNER . 1982 . The central role of Clark ’ s HUTCHINSON , M . F . 2000 . ANUSPLIN version 4.1 user ’ s guide. Australian 76 AMERICAN JOURNAL OF BOTANY [Vol. 95
POTENKO , V . V . , AND A . V . VELIKOV . 2001 . Allozyme variation and mat- PRICE , R . A . , A . LISTON , AND S . H . STRAUSS . 1998 . Phylogeny and classifi – REHFELDT , G . E . 1991 . A model of genetic variation for Pinus ponderosa REHFELDT , G . E . 1994 . Adaptation of Picea engelmannii populations to the RICHARDSON , B . A . , S . J . BRUNSFELD , AND N . B . KLOPFENSTEIN . RICHARDSON , B . A . , S . J . BRUNSFELD , AND N . B . KLOPFENSTEIN . 2002b . DNA ROGERS , D . L . , C . I . MILLAR , AND R . D . WESTFALL . 1999 . Fine-scale genetic ROMME , W . H . , AND M . G . TURNER . 1991 . Implications of global climate SAGNARD , F . , C . BARBEROT , AND B . FADY . 2002 . Structure of genetic diver- SAS Institute . 1999 . The SAS system for Windows, version 8.0. SAS SAVOLAINEN , O . , F . BOKMA , R . GARC Í A – GIL , P . KOMULAINEN , AND T . REPO . SPITZE , K . 1993 . Population structure in Daphnia obtusa : Quantitative ge- SQUILLACE , A . E . 1974 . Average genetic correlations among offspring ST . CLAIR , J . B . 2006 . Genetic variation in fall cold hardiness in coastal ST . CLAIR , J . B . , N . L . MANDEL , AND K . W . VANCE – BORLAND . STUART – SMITH , G . J . 1998 . Conservation of whitebark pine in the Canadian TANI , N . , N . TOMARU , M . ARAKI , AND K . OHBA . 1996 . Genetic diversity and TOMBACK , D . F . 1978 . Foraging strategies of Clark ’ s Nutcracker. Living TOMBACK , D . F . 1982 . Dispersal of whitebark pine seeds by Clark ’ s TOMBACK , D . F . , S . F . ARNO , AND R . E . KEANE . 2001 . The compelling case WANG , T . , A . HAMANN , D . L . SPITTLEHOUSE , AND S . N . AITKEN . WANG , T . L . , A . HAMANN , A . YANCHUK , G . A . O ’ NEILL , AND S . N . AITKEN . WESTFALL , R . D . 1992 . Developing seed transfer zones. In L. Fins, S. T. WHITLOCK , M . C . 1999 . Neutral additive genetic variance in a metapopula- WORRALL , J . 1983 . Temperature-bud burst relationships in Amabalis and YANDELL , U . G . 1992 . An allozyme analysis of whitebark pine ( Pinus albi- ZAVARIN , E . , Z . RAFII , L . G . COOL , AND K . SNAJBERK . 1991 . Geographic
2
20
1
9
Tag_ID
Prov
Var
Height_
200
7
Height_20
17
Circumference_20
19
Number_stems_2019
9
11
garryana
6
5
277
16
3
1.5
9
12
semota
10
17
4
9.2
9
13
23
20.
8
9
14
3.5
15
8.3
4.5
915
5.5
177
1
1.3
2.3
916
2.8
917
235
20.1
9
18
29
1
4.4
919
1
60
920
12.4
327
1
1.7
2.5
9
21
6.2
209
13.5
9
22
319
1
4.9
6.8
923
2
76
10.5
9
24
1
2.6
1
83
925
104
7.2
9
26
1
52
6.1
7.5
927
8.5
466
34.9
9
28
310
929
220
17.3
930
214
7.8
931
1
96
932
33
20.8
933
8.2
347
2
4.8
934
82
3.6
935
195
1.8
936
302
1
9.8
937
339
2
4.6
938
150
4.2
939
6.6
85
3.4
940
328
1
4.1
941
309
14.8
942
336
943
6.4
311
9.1
9
44
523
23.5
945
6.5
383
22.2
946
2
81
13.6
947
283
948
110
3.7
5.7
949
80
950
316
1
3.2
951
68
953
490
37.3
954
2
75
13.7
3.3
955
495
24.4
956
7.7
1
62
5.6
9
57
5.3
199
11.7
958
291
11.6
959
1
61
6.3
960
265
1
2.9
961
962
398
16.4
963
5.2
3.1
964
343
19.8
965
13.3
966
107
967
968
248
15.1
969
326
9
70
261
20.2
9
71
430
23.2
9
72
5.4
8.7
973
975
102
976
89
6.7
977
120
7.1
9
78
282
18.6
9
79
262
11.2
2.7
980
304
20.6
981
357
26.5
982
9.5
983
207
984
10.1
985
9
86
12.3
987
4.3
9
88
400
30.4
989
228
991
329
1
7.4
992
285
17.1
993
260
994
370
995
244
996
997
30.9
998
212
999
147
1000
106
1001
215
8.9
1002
276
10.4
3.8
1003
191
1004
1005
1006
21.2
337
2
7.6
1008
238
1
9.6
1009
324
14.5
1010
237
1011
15.4
1012
8.8
1013
18.5
1014
1015
1
8.1
1016
154
6.9
1017
1018
18.2
252
16.9
1019
1020
322
19.4
1021
1022
299
24.1
1023
2
2.1
1024
1025
148
8.4
1026
198
1
1.9
1027
1028
1029
11.5
380
1030
401
25.7
1031
377
1032
19.1
1033
1034
1035
5.8
1036
217
1037
246
17.6
1038
2
74
1039
1041
1042
149
1043
294
26.7
1044
183
12.2
1045
338
1046
221
11.4
1047
1048
269
11.3
1049
112
1050
1051
1052
243
1053
278
14.7
1054
141
1055
12.1
1056
290
1057
1058
1059
152
1060
22.3
1061
22.6
1062
258
1063
111
1064
416
24.3
1065
1066
231
1067
18.7
1068
21.3
1069
1070
1071
21.5
1072
189
1073
174
1074
16.5
1303
403
34.2
1304
222
1
305
1306
1307
250
16.3
1308
1309
233
17.9
1310
173
1311
256
1312
1313
318
1314
1
315
1316
1
317
1318
1319
1320
393
22.5
1321
9.9
1322
1
323
1324
156
1325
420
1326
453
29.1
1327
28.7
1328
396
1329
345
29.7
133
359
18.9
1331
270
11.1
1332
1333
1334
450
1335
445
31.4
1336
10.6
392
27.1
1337
1338
344
1339
411
21.6
1
340
362
1341
1342
263
1343
421
25.8
1344
15.3
1345
1346
376
23.9
1347
349
1
348
1349
1350
1351
1352
17.7
1353
23.6
1354
202
1355
234
1
356
1357
1358
446
22.4
1359
178
10.2
1
360
245
1361
395
28.5
1362
1363
193
1
364
17.5
1365
1366
472
26.1
1367
127
1368
146
1369
15.2
1370
1371
20.5
442
1
372
1373
18.8
1374
12.8
1375
1376
24.2
1377
20.9
1378
9.3
138
10.7
1381
1382
30.1
1383
1384
32.6
1385
14.2
295
1386
1387
1388
1389
15.5
407
139
1391
1392
19.2
1393
1394
1395
219
1396
1397
1398
292
14.1
1399
170
10.8
1400
227
Provenance
Prov Var
Lat
Long
Dist
MAT
MWMT
MCMT
MSP
Variable
Explanation
Units
1 semota
35.87
-118.64
1544
Provenance of Garry oak
arbitrary numeric value
2 semota
36.8
-119.09
1425
Taxonomic variety of provenance
none
3 semota
39.1
-120.85
1145
5.9
Provenance latitude
°N
4 semota
40.36
-122.94
990
Provenance longitude
°W
5 semota
40.85
-122.03
25.3
159
Linear distance from provenance collection site to totem field
km
6 garryana
41.85
-122.84
825
Provenance mean annual temperature
°C
7 garryana
42.47
-122.62
757
Provenance mean temperature of the warmest month
8 garryana
45.01
-123.17
Provenance mean temperature of the coldest month
9 garryana
45.28
-121.35
465
-0.7
Provenance mean su
mm
11 garryana
46.83
-123.01
271
13 garryana
48.79
-123.7
172
14 garryana
48.46
-123.4
15 garryana
49.73
-125.02
stats_calculator
Paste your data in the coloured areas below
2017 height (individual values)
t-test (2018 height)
2019 number of stems (individual values)
t-test (2019 number of stems)
Provenance climate
2007 height (provenance means)
2017 height (provenance means)
linear regression (2007 height)
var. garryana
var. semota
t-value
p-value
R-squared
slope
NA
3
4
5
linear regression (2017 height)
6 p-value R-squared slope
7 NA NA NA
8
9
11
13
14
15
Provenance Provenance climate
2019 circumference (provenance means)
linear regression (2019 circumference)
1 p-value R-squared slope
2 NA NA NA
4 5
6
7
9
11
13
14
15block_map
N
BLOCK 1
N = Do not measure
BLOCK 2
BLOCK 3
BLOCK 4
X
Blue flag
Orange flag
Pink flag
Column 0
Column 1
Column 2
Column 3
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Column 5
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Column 7
Column 8
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Column 10
Column 11
Column 12
Row 0
Row 1
0915
0926
0946
0957
0968
1309
1329
1340
1352
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1375
1384
1396
0980
0991
1002
1013
1023
1034
1047
1059
1069
Row 2
0911
0921
0932
0941
0963
1303
1315
1323
1346
1358
1369
1380
1390
0975
0985
0997
1008
1019
1029
1041
1053
1065
Row 3
0916
0927
0947
0958
0969
1310
1330
1341
1353
1364
1376
1385
1397
0981
0992
1003
1014
1024
1035
1048
1060
1070
Row 4
0912
0922
0933
0942
0964
1304
1316
1324
1335
1347
1359
1370
1391
0976
0986
0998
1009
1030
1042
1054
1066
Row 5
0917
0928
0937
0948
0959
0970
1311
1320
1331
1342
1354
1365
1377
1386
0993
1004
1015
1025
1036
1049
1061
1071
Row 6
0913
0934
0943
0953
0965
1305
1317
1325
1336
1348
1360
1371
1381
1392
0977
0987
0999
1020
1031
1043
1055
Row 7
0918
0929
0938
0949
0960
0971
1312
1321
1332
1343
1355
1366
1378
1387
1398
0982
0994
1005
1016
1026
1037
1050
1062
1072
Row 8
0923
0954
0966
1306
1318
1326
1337
1349
1361
1372
1382
1393
0988
1000
1010
1032
1044
1056
1067
Row 9
0919
0930
0939
0950
0961
0972
1313
1333
1344
1356
1367
1388
1399
0983
0995
1006
1017
1027
1038
1051
1063
1073
Row 10
0914
0924
0935
0944
0955
0967
1307
1319
1327
1338
1350
1362
1373
1383
1394
0978
0989
1001
1011
1021
1045
1057
1068
Row 11
0920
0931
0940
0951
0962
0973
1314
1322
1334
1345
1357
1368
1389
1400
0984
0996
1018
1028
1039
1052
1064
1074
Row 12
0925
0936
0945
0956
1308
1328
1339
1351
1374
1395
0979
1012
1022
1033
1046
1058
e.g., height and provenance mean annual temperature, as shown in figures 1 and 2. A t-test is used to determine whether a relationship exists between one numeric variable and one categorical variable, e.g., height and taxonomic variety, as shown in figures 3 and 4.
ments within a species range for reforestation or restoration may
increase the risk of maladaptation ( Campbell, 1979 ). Reduced
growth or mortality resulting from maladaptation could reduce
the success of restoration projects, and gene fl ow from maladapted
planted trees into adjacent native populations could negatively
affect adaptation to local conditions ( McKay et al., 2005 ). How-
ever, the planting of individuals adapted to new environmental
conditions, e.g., a warmer climate, could be a method to facilitate
migration and provide a source of genotypes well adapted to local
populations. Seed transfer should be guided by natural levels of
genetic variation and local adaptation in quantitative traits spe-
cifi c to the species in question ( Morgenstern, 1996 ; Hufford and
Mazer, 2003 ; McKay et al., 2005 ). Understanding genetic struc-
ture is also necessary for managing breeding programs, evaluat-
ing conservation of genetic resources, and predicting the possible
effects of climate change ( St. Clair et al., 2005 ).
latitude and elevation as a result of climate change ( Davis and
Shaw, 2001 ; Hamann and Wang, 2006 ). However, at a local scale,
projected vegetation responses include a combination of eleva-
suitable conditions for each taxon shifts within a region ( Bartlein
et al., 1997 ). The potential impacts of predicted warming under-
score the importance of understanding genetic structure and adap-
tation of populations to their local environment. For species
threatened by pests and diseases in addition to climate change,
minimizing maladaptation may mean the difference between es-
tablishing or maintaining viable populations and local extirpation.
high elevation, fi ve-needle pine, and the only North American
member of the stone pines ( Pinus subsection Cembrae ) ( Arno
and Hoff, 1989 ; Price et al., 1998 ; but see Gernandt et al., 2005 ).
Although of little commercial value, it has tremendous ecologi-
cal value and is considered a keystone species ( Tomback et al.,
2001 ). The large, wingless seeds of whitebark pine are an im-
portant food source for the Clark ’ s nutcracker ( Nucifragia co-
lumbiana Wilson), which is its primary dispersal agent and
mutualist ( Tomback, 1978 ; Hutchins and Lanner, 1982 ; Lanner,
1982 ; Tomback, 1982 ). However, whitebark pine is in decline
throughout most of its range from a synergism of natural
and human-driven causes. Outbreaks of mountain pine beetle
( Dendroctonus ponderosae Hopkins) and decades of fi re sup-
pression have led to mortality and successional replacement by
shade-tolerant species. However, the greatest agent driving the
current decline is the introduced disease white pine blister rust
(caused by the fungus Cronartium ribicola J. C. Fisch. ex
Rabh.). Scientists agree that whitebark pine ecosystems require
immediate restoration to reduce the effects of fi re exclusion and
blister rust ( McCool and Freimund, 2001 ). Silvicultural tech-
niques can be used to encourage natural regeneration, but in
stands with a compromised seed source or those that need to be
regenerated quickly, planting seedlings (if available) is the sug-
gested restoration practice ( Hoff et al., 2001 ), using blister-rust-
resistant seedlings when they are available. There is a widespread
need for restoration and often a limited supply of seed for white-
bark pine, thus geographic guidelines on seed transfer are
needed for restoration and conservation of this species.
2007.
the British Columbia Ministry of Forests; E. C. Manning and Tweedsmuir
Provincial Parks of British Columbia; and B. Brett of Snowline Ecological
Consulting, Whistler, B.C. for seed. Many people provided assistance to
this project, including D. Kolotelo, J. Tuytel, C. Chourmouzis, D. Watson,
K. Keir, M. Harrison, D. Szohner, P. Smets, J. Krakowski, S, Trehearne,
and all of the members of the Aitken laboratory at UBC. Climate data were
provided by Drs. T. Wang and G. Rehfeldt. Funding for this study came
from the British Columbia Forestry Investment Account through the Forest
Genetics Council of B.C. to the Centre for Forest Conservation Genetics
at UBC. Thank you to Drs. A. Yanchuk, M. Whitlock, J. Whitton, Y. El-
Kassaby, S. Graham, D. Tomback, B. St. Clair, and an anonymous reviewer
for their helpful comments on earlier drafts of this manuscript.
PINUS ALBICAULIS (PINACEAE) 1
Vancouver, British Columbia V6T 1Z4 Canada
pression, and other factors, and climate change is predicted to accelerate this decline. Restoration is needed; however, no informa-
tion regarding the degree of local adaptation is available to guide these efforts. A seedling common-garden experiment was
employed to assess genetic diversity and geographic differentiation ( Q ST ) of whitebark pine for traits involved in growth and ad-
aptation to cold and to determine climatic variables revealing local adaptation. Seedlings from 48 populations were grown for two
years and measured for height increment, biomass, root to shoot ratio, date of needle fl ush, fall and spring cold injury, and survival.
Signifi cant variation was observed among populations for most traits. The Q ST was low (0.07 – 0.14) for growth traits and moderate
(0.36 – 0.47) for cold adaptation related traits, but varied by region. Cold adaptation traits were strongly correlated with mean
temperature of the coldest month of population origins, while growth traits were generally correlated with growing season length.
We recommend that seed transfer for restoration favor seed movement from milder to colder climates to a maximum of 1.9 ° C in
mean annual temperature in the northern portion of the species range, and 1.0 ° C in the U. S. Rocky Mountains to avoid maladapta-
tion to current conditions yet facilitate adaptation to future climates.
whitebark pine; white pine blister rust.
maining four replications (cold treatment) had cooled water pumped through
hoses buried approximately 25 cm below the surface, which kept soil tempera-
ture consistently ~8 ° C cooler during the warmest part of the day. Populations
that were represented in fewer than half of the replications (i.e., < 4 in the ambi-
ent or < 2 in the cold treatment) because of mortality were excluded from the
analysis. The fi nal data set included 40 populations in the ambient treatment
and 37 in the cold treatment, with 33 populations common to both treatments
( Table 2 ). The AlphaPlus program ( Mann, 1996 ) was used to design the plant-
ing layout and assign seedlings randomly within replications. Seedlings were
planted at 9.5 × 10 cm spacing, with one row of buffer trees surrounding each
raised nursery bed for which data were not collected. They were kept well wa-
tered and were fertilized and weeded as needed to provide conditions optimal
for growth for most temperate conifers. Timing of initiation of growth in the
spring was observed for 2003 and 2004, and at the end of the 2004 growing
season, survival, height growth, aboveground and belowground oven-dry bio-
mass of seedlings were measured on all replications. Artifi cial freeze testing
was performed on 5-mm needle segments from all seedlings in the ambient
treatment in three replications in the fall of 2003 and four replications in the
spring of 2004. The electrolyte leakage method was used to quantify cold in-
jury. Details of cold hardiness testing are given in Bower and Aitken (2006) .
both temperature treatments were available for third-year height increment,
root biomass, shoot biomass, total biomass, root to shoot ratio, date of needle
fl ush in 2003 and 2004 ( Table 3 ). Measurements of fall and spring cold injury
were available from the ambient treatment only. In addition, the percentage
survival in each soil temperature treatment was tested for treatment effects.
cal analyses. Preliminary analysis showed an increase in variability of residuals
with an increase in predicted values, so a natural-log transformation was ap-
plied to height increment, root, shoot, and total biomass, and root to shoot ratio
for all analyses, which helped to equalize variance. For testing for differences
between soil temperature treatments and genotype-by-environment interactions
in the quantitative traits, PROC MIXED was used with the following model for
populations included in both treatments:
in incomplete block k in rep j in soil temperature i , µ is the overall mean, t i is
the effect of temperature i, r ( t ) ij is the effect of rep j nested within temperature
i , b ( rt ) ijk is the effect of incomplete block k nested within rep j within tem-
perature i, p l is the effect of population l , pt il is the interaction of temperature
i and population l , pr ( t ) ijl is the interaction of population l and rep j within
temperature i , f ( p ) lm is the effect of family m nested within population l , and
e ijlkmn Temperature, population, and population-by-temperature interaction
were considered fi xed, while all other effects were considered random. The
same model was used to analyze each geographic region separately. Population
assessed in whitebark pine using molecular markers and mono-
terpenes ( Yandell, 1992 ; Jorgensen and Hamrick, 1997 ; Brued-
erle et al., 1998 ; Stuart-Smith, 1998 ; Rogers et al., 1999 ;
Krakowski et al., 2003 ), and results have indicated average to
above average expected heterozygosity compared to other pines
(average H e of 0.16 for whitebark pine vs. 0.13 – 0.16 for pines
in general [Ledig, 1998]). Population differentiation in white-
bark pine was reported to be low to moderate in all studies ( F ST
or G ST < 0.09) ( Table 1 ), with signifi cant evidence of inbreed-
ing ( F is signifi cantly greater than zero). Populations in the
northern (western British Columbia), eastern (Rocky Moun-
tains), and southern regions of the species range (California and
Oregon) are differentiated for monoterpenes ( Zavarin et al.,
1991 ), isozymes ( Yandell, 1992 ) and organelle DNA ( Richard-
son et al., 2002b ). However, levels of genetic variation and
population differentiation in phenotypic traits potentially in-
volved in local adaptation in whitebark pine have not previ-
ously been determined.
differentiation in phenotypic seedling traits in a common-gar-
den experiment in whitebark pine and evaluate degree of local
adaptation to climate for the purpose of developing seed trans-
fer recommendations and predicting the ability of whitebark
pine to adapt to climate change.
bark pine from across most of the species range ( Table 2, Fig. 1 ) were germi-
nated in 2002 following seed stratifi cation using the protocol described by Burr
et al. (2001) . Germinants were sown into individual 10 in 3 (164 cm 3 ) Ray Leach
Cone-tainer super cells (Stuewe and Sons, Corvallis, Oregon, USA) for their
fi rst growing season. In November 2002, 10-mo-old seedlings were trans-
planted into a raised nursery bed common garden in Vancouver, British Colum-
bia (49 ° 13 ’ N, 123 ° 6 ’ W) and grown for two growing seasons. Seedlings were
planted in an incomplete block alpha design ( Patterson and Williams, 1976 )
with 12 replications, and 10 four-tree by four-tree incomplete blocks within
replications. Each replication contained 160 test trees, with populations repre-
sented by 1 – 18 families (mean 7.9, SE 0.37), with each family usually repre-
sented once per replication. Because temperatures in Vancouver are higher than
those in the native environment, two temperature treatments were imposed:
30 P. albicaulis USA rangewide and northern AB 0.034 Jorgensen and Hamrick 1997
14 P. albicaulis USA Great Basin 0.088 Yandell 1992
29 P. albicaulis Canadian Rockies 0.062 Stuart-Smith 1998
17 P. albicaulis British Columbia 0.061 Krakowski et al. 2003
18 P. albicaulis Rangewide 0.046 a Richardson et al. 2002b
11 P. sibirica Russia 0.025 Krutovskii et al. 1995
19 P. koraiensis Russia 0.015 Potenko and Velikov 1998
3 P. koraiensis Russian far east 0.040 Krutovskii et al. 1995
5 P. pumila Russia 0.043 Goncharenko et al. 1993a
3 P. pumila Kamchatka penn., Russia 0.021 Krutovskii et al. 1995
18 P. pumila Japan 0.170 Tani et al. 1996
5 P. cembra Alps and eastern Carpathians, Ukraine 0.040 Belokon et al. 2005
a Φ ST from cpDNA microsatellite data
used as the among-population variance, and three times the variance compo-
nent for family within-population (3 σ 2 f ( p ) ) was used as the within-population
variance. The within-population genetic variation was approximated as three
times the family variance instead of four as is used for true half-sibs, because
open-pollinated progeny of whitebark pine are more closely related than half-
sibs due to moderate inbreeding and correlated paternity ( Squillace, 1974 ; Kra-
kowski et al., 2003 ; Bower and Aitken, 2007 ). Values of Q ST were compared to
all published estimates for whitebark pine for genetic markers ( F ST or G ST ).
ture, mean temperature of the coldest month, mean temperature of the
warmest month, mean annual precipitation, mean summer precipitation, annual
heat : moisture index, summer heat : moisture index, and frost-free period. Cli-
matic variables for populations north of 48 ° N were obtained from PRISM cli-
matic data corrected for local elevation using the Climate BC model described
by Wang et al. (2006a) . For populations south of 48 ° N, climatic data were
age using the above model with only the temperature and population effects,
and their interaction.
MIXED was used with the REML variance component estimator and the fol-
lowing model:
temperature. All terms were considered random except for population, which
was fi xed. To obtain estimates of variance components, the analysis was re-
peated with all effects considered random.
traits by calculation of Q ST ( Spitze, 1993 ): Q ST = σ 2 b / ( σ 2 b + 2 σ 2 w ), where σ 2 b is
the among-population variance and σ 2 w is the within-population additive
2 N Hunters Basin 13 29 54.53 127.18 1446 0.3 11.1 − 11 48 34.8
3 N Morice Lake 2 — 54.04 127.48 1231 0.6 11.3 − 11.1 31 44.9
4 N Kimsquit river 1 — 53.19 127.18 900 3 12.5 − 7.2 83 32.9
5 N Heckman Pass 6 — 52.52 125.82 1526 0 9.9 − 10.6 58 46.8
6 N Perkins Peak 3 1 51.83 125.05 1916 − 1.8 7.9 − 11.5 35 31.9
7 N Jesamond 42 19 51.27 121.87 1846 0.9 11.4 − 9.1 45 45
8 N Lime Mtn. 19 13 51.10 121.67 1900 0.5 11.1 − 9.4 39 52.9
9 N Darcy 46 9 50.53 122.58 1800 0.5 11.7 − 9.8 46.1 45.3
10 N Blackcomb 57 40 50.10 122.90 1908 0.6 10 − 7.5 43.7 22.4
11 N Thynne Mtn. 20 1 49.71 120.92 1785 1.9 12.7 − 7.7 48.8 27.4
12 N Manning Park 93 40 49.10 120.67 2000 0.3 10.8 − 8.6 43.8 48.1
13 N Baldy 14 — 49.17 119.25 2154 1.2 12.1 − 8.6 39.6 37.5
14 R Copper Butte 11 9 48.70 118.46 2185 − 0.5 10.4 − 10.2 48.2 30.7
15 R Colville 6 4 48.66 118.46 2154 − 0.1 10.7 − 10.1 49 32.5
16 R Snow Peak — 7 48.58 118.48 2185 0.5 11.2 − 9.7 48.7 35.6
17 R Salmo Mtn. 13 11 48.97 117.10 2092 0 10.7 − 9.3 61.2 21.2
18 R Hooknose Mtn. — 2 48.94 117.43 2215 0.5 11.5 − 8.9 54 28.8
19 R Farnham Ridge 31 26 48.84 116.51 1846 1.5 12.4 − 8.3 70.6 35.4
20 R North Baldy — 7 48.55 117.16 1877 2.6 13.9 − 7 89 41.6
21 R Lunch Peak 37 14 48.38 116.19 1846 2.1 12.4 − 6.6 84 23.9
22 R Our Lake 6 13 47.84 112.81 2277 0.2 11.4 − 9.6 31.3 35.1
23 R Sheep Shed 22 19 47.52 112.80 2154 1 12.3 − 8.9 33.4 43.5
24 R Granite Butte 19 8 46.87 112.47 2338 0.5 12 − 9.1 32.8 47.1
25 R Blacklead Mtn. 23 41 46.64 114.86 2062 1.2 12.3 − 8.1 32.1 26.2
26 R Gospel Peak 22 16 45.63 115.95 2154 1 11.9 − 8.3 26.4 33.9
27 R Heavens Gate 10 3 45.38 116.51 2154 1.2 12.3 − 8.3 40.6 49.2
28 R Mudd Ridge 23 15 45.90 113.45 2400 0.2 11.8 − 9.5 17.5 29
29 R Quartz Hill 27 44 45.71 112.93 2646 − 0.8 10.9 − 10.2 15.8 37
30 R Little Bear 22 26 45.40 111.28 2154 2.1 14.4 − 8.9 40.5 43.4
31 R Picket Pin 4 1 45.44 110.05 2892 − 1.8 9.9 − 11 20.6 24.9
32 R Hellroaring II 20 24 45.04 109.45 2892 − 1.4 10.3 − 10.4 21.7 36.5
33 R Sawtel Peak 26 18 44.54 111.41 2400 − 0.1 12.9 − 11.9 25.9 45.8
34 R Vinegar Hill 14 26 44.72 118.57 2338 0.5 11.4 − 8.6 39.8 54.1
35 S Mt. Hood 20 22 45.39 121.66 1969 1.7 10.8 − 4.9 48.4 15
36 S Newberry Crater 30 13 43.72 121.23 2100 2.9 12.9 − 4.7 45.5 58.1
37 S Paulina Peak 18 11 43.69 121.25 2250 2 11.9 − 5.3 42.3 59.6
38 S Batchelor Butte 16 1 43.26 122.68 2200 1.9 10.7 − 3.9 44.1 40
39 S Tipsoo Peak — 13 43.22 122.04 2462 0.7 10 − 5.4 34.4 35.3
40 S Moon Mtn. 6 4 43.20 122.65 2201 1.9 10.8 − 3.9 43.9 45.2
41 S Pelican Butte 40 17 42.51 122.15 2462 1.1 10.4 − 5 35.2 50.6
42 S Ball Mtn. 22 15 41.80 122.16 2363 2.2 11.4 − 4.4 39.1 107.1
43 S Goosenest Summit 6 6 41.72 122.23 2506 1.5 10.6 − 4.6 35.3 99.3
44 S Drakes Peak 26 16 42.30 120.15 2462 2.5 13.1 − 5.3 48.1 80.3
45 S Crane Mountain 19 22 42.07 120.24 2538 2.2 12.7 − 5.6 46.3 72.4
46 S Mt. Rose 11 — 39.30 119.90 2754 2.4 12.6 − 4.8 61 88.4
47 S Stevens Peak 7 — 38.70 119.98 2923 1.6 11 − 5.2 46.6 61.6
48 S Ebbetts Pass 2 — 38.50 119.80 2769 2.5 12 − 4.4 46.9 72
explanation of variables.
a few key populations at the northern and southern ends of the range compared
to the ambient treatment, thus only data from the ambient treatment were used
in the canonical correlation analysis. Climatic data and least-squares population
means for each seedling phenotypic trait demonstrating signifi cant ( P ≤ 0.05)
population differentiation were included in this analysis. Canonical redundancy
analysis was used to determine the proportion of variation in phenotypic traits
accounted for by canonical correlations with the climatic or geographic data
sets. To assess potential differences in relationships between seedling pheno-
typic traits and climatic variables between the two soil temperature treatments,
canonical correlation analysis was repeated for the two treatments separately
using only the populations common to both.
lines, values of signifi cant canonical variables for the seedling phenotypic traits
were regressed on the standardized climatic variable with the highest loading
for that canonical variable. The slope of this regression estimates the rate of
change in the phenotypic canonical variable relative to the selected climatic
variable. Rates of differentiation along climatic gradients were interpreted rela-
tive to the least signifi cant difference among populations at the 20% level (least
signifi cant difference: LSD 0.2). This conservatively reduces type II error (ac-
cepting no differences among populations when differences actually exist) and
minimizes maladaptation risk accordingly ( Rehfeldt, 1991 ). Values of LSD for
the phenotypic canonical variables were obtained from a Duncan ’ s multiple
range test in PROC GLM using the model for testing variation among popula-
tions described. The fl oating seed transfer model developed by Rehfeldt (1991 ,
1994 ) was used to determine seed transfer guidelines for restoration programs
of whitebark pine. The maximum recommended environmental transfer dis-
tance between seed collection population and planting site was calculated as the
difference in the standardized climate variable associated with the LSD ( P =
0.20) value of the phenotypic canonical variable multiplied by the standard
deviation of the climate variable. Univariate regressions of climate variables on
latitude, longitude, and elevation were used to determine the geographic dis-
tances associated with the rates of differentiation in climate variables to make
simple seed transfer recommendations.
signifi cantly greater, on average, in the cold treatment than in
the ambient treatment (least squares mean = 6.7 and 8.9 mm,
P = 0.04 for height increment and 66.9 and 82.3%, P < 0.001
for survival, in the ambient and cold treatment respectively).
Means for biomass traits were also greater in the cold treatment,
and the temperature treatment difference greater for root mass
than shoot mass, although the difference between treatments for
these traits was not signifi cant. The date of needle fl ush did not
differ signifi cantly between treatments. Population-by-treat-
ment interaction was not signifi cant for any of the traits. The
foliage of seedlings in the cold temperature treatments gener-
ally appeared darker green and healthier than those in the ambi-
ent treatment. No treatment-specifi c geographic trends were
evident, and separate canonical correlation analyses of individ-
ual treatments yielded the same results.
seedlings from populations originating from colder climates
had less overall growth, earlier needle fl ush in spring, and less
cold injury in fall than seedlings originating from milder cli-
mates when grown in the common garden. Populations differed
signifi cantly in the ambient soil temperature treatment for all
variables except root : shoot ratio and spring cold injury ( Table
5 ). Despite a lack of signifi cant differences among populations
in the ANOVA, root : shoot ratio differed signifi cantly among
populations in a Duncan ’ s multiple range test.
differentiation (0 ≤ Q ST ≤ 0.14), while the cold-adaptation re-
lated traits (date of needle fl ush and fall cold injury) showed
lustrated for North America by McKinney et al. (2001) . Clines in quantitative
traits can be obscured when there are correlations among traits or if geographi-
cal structure is complex. In these cases, canonical correlation analysis is more
effi cient than regressing each trait on environmental variables separately ( West-
fall, 1992 ). Several of the seedling phenotypic traits and climatic or geographic
variables were strongly intercorrelated ( Table 4 ), so canonical correlation anal-
ysis was used to examine the relationships among these variables. The cold
tested in common-garden experiment. Dashed lines separate the southern,
Rocky Mountain and northern regions.
Total dry biomass TDM grams
Root dry biomass RM grams
Shoot dry biomass SM grams
Root : shoot ratio RSR unitless
2003 Date of needle fl ush FL03 days from Jan. 1
2004 Date of needle fl ush FL04 days from Jan. 1
Fall cold injury FCI index of injury (%)
Spring cold injury SCI index of injury (%)
Mean temperature, warmest month MTWM ° C
Mean temperature, coldest month MTCM ° C
Mean annual precipitation MAP millimeters
Mean summer precipitation MSP millimeters
Annual heat : moisture index AH:M [(MAT + 10)/(MAP/1000)]
Summer heat : moisture index SH:M [(MWMT/(MSP/1000)]
Frost-free period FFP days
variation. The second pair of variables demonstrates the posi-
tive relationship between the length of the frost-free period and
growth, both height and biomass ( Table 6 ). The regression of
the second phenotypic canonical score on frost-free period was
also signifi cant ( P = 0.001) but weak ( r 2 = 0.24). Canonical re-
dundancy analysis showed that the fi rst two climatic canonical
variables account for 24 and 17% (41% total) of the variation in
population phenotypic trait means, indicating substantial ge-
netic structure along climatic gradients.
analyzed separately by region, some broad-scale geographic
differences in patterns of population differentiation emerged
( Table 7 ). In the ambient soil temperature treatment, in the
northern region, signifi cant differences were detected among
populations for all three biomass traits. In the Rocky Mountain
region, only date of needle fl ush in 2004 varied signifi cantly
among populations, while in the southern region, only date of
less of treatment (0.36 ≤ Q ST ≤ 0.65). A comparison of Q ST
values with previously published values of F ST for whitebark
pine ( Table 1 ) shows that the phenotypic traits with the weakest
differentiation are similar to the highest estimates of differen-
tiation in presumably neutral molecular markers from rangewide
studies ( Jorgensen and Hamrick, 1997 ; Richardson et al.,
2002b ), and the quantitative traits with the strongest differentia-
tion have substantially higher Q ST estimates.
seedling phenotypic traits and climatic variables for population
origins, the fi rst canonical correlation was signifi cantly differ-
ent from zero ( P < 0.0001) and explained 72% of the variance
in the data. The fi rst pair of canonical variables summarizes
relationships between cold-related phenotypic traits and mean
temperature of the coldest month ( Table 6 ). Mean temperature
of the coldest month explained a substantial proportion of the
variance in the fi rst phenotypic canonical score ( r 2 = 0.79, P <
0.0001, Fig. 2 ). The second canonical correlation was also sig-
MTWM 0.03 − 0.16 − 0.26 − 0.07 0.14 − 0.38 − 0.21 0.30 0.38 − 0.07 0.19 − 0.27
MTCM − 0.37 − 0.34 − 0.48 − 0.21 − 0.25 − 0.04 0.20 0.45 0.13 − 0.59* 0.52 0.12
MAP − 0.18 − 0.37 − 0.41 − 0.32 0.02 − 0.05 0.35 0.37 − 0.03 0.03 − 0.19 − 0.30
MSP − 0.15 − 0.29 − 0.33 − 0.24 0.20 0.03 0.20 0.50 0.02 − 0.09 0.02 − 0.14
AH:M 0.33 0.45 0.47 0.43 − 0.09 − 0.22 − 0.55 − 0.24 0.17 − 0.10 0.31 0.26
SH:M 0.25 0.33 0.31 0.32 − 0.17 − 0.20 − 0.28 − 0.41 0.25 − 0.04 0.16 0.10
FFP − 0.23 − 0.48 − 0.52 − 0.42 − 0.57* − 0.62* − 0.01 − 0.42 0.05 0.25 − 0.34 − 0.63
Lat. 0.41 − 0.01 0.14 − 0.11 0.10 − 0.63* − 0.42 − 0.24 − 0.08
Long. − 0.24 0.19 0.04 0.29 − 0.07 0.55* 0.19 0.29 0.14 − 0.95*
Elev. − 0.12 0.39 0.30 0.44 0.01 0.76* 0.21 0.30 − 0.04 − 0.81* 0.86*
MTWM 0.51* 0.58* 0.54* 0.59* 0.08 − 0.14 0.13 0.40 0.13 − 0.18 0.11 − 0.53*
MTCM 0.44 0.51* 0.46* 0.51* − 0.12 0.23 0.69 0.55* 0.17 0.29 − 0.45 − 0.71*
MAP 0.21 0.08 0.03 0.11 − 0.21 0.01 0.48 0.24 0.18 0.54* − 0.43 − 0.53*
MSP − 0.15 − 0.15 − 0.15 − 0.14 − 0.43 − 0.19 − 0.12 − 0.11 0.03 0.35 0.22 0.00
AH:M − 0.14 0.00 − 0.02 0.03 0.06 0.05 − 0.45 − 0.12 − 0.09 − 0.49* 0.40 0.39
SH:M − 0.11 0.08 0.02 0.12 0.34 0.36 0.00 0.17 − 0.17 − 0.52* 0.08 0.09
FFP 0.41 0.15 0.06 0.22 − 0.13 0.00 0.47 0.30 0.33 0.70* − 0.60* − 0.77*
Lat. 0.39 0.01 − 0.06 0.07 − 0.07 − 0.09 0.07 − 0.11 0.47*
Long. − 0.32 − 0.07 0.02 − 0.12 − 0.14 − 0.27 − 0.66* − 0.27 − 0.22 − 0.51*
Elev. − 0.68* − 0.44 − 0.34 − 0.49* − 0.08 − 0.03 − 0.50* − 0.39 − 0.38 − 0.61* 0.66*
MTWM − 0.16 0.00 0.00 0.00 0.18 − 0.02 0.13 0.51 − 0.43 − 0.16 0.70* 0.15
MTCM 0.43 0.43 0.42 0.43 − 0.26 − 0.13 0.17 − 0.35 0.24 0.05 − 0.60* − 0.28
MAP 0.40 0.20 0.22 0.19 − 0.47 − 0.20 0.03 − 0.36 0.51 0.37 − 0.68* − 0.43
MSP 0.46 0.24 0.27 0.22 − 0.50 − 0.21 − 0.05 − 0.29 0.55* 0.55* − 0.67 − 0.57*
AH:M − 0.17 − 0.08 − 0.06 − 0.11 0.63* 0.55* − 0.07 0.11 − 0.43 0.02 0.03 − 0.01
SH:M − 0.47 − 0.21 − 0.23 − 0.21 0.59* 0.51 0.00 − 0.10 − 0.61* − 0.55* 0.26 0.54
FFP − 0.23 − 0.15 − 0.19 − 0.13 − 0.08 − 0.21 0.38 0.34 − 0.25 − 0.31 0.65* 0.24
Lat. 0.62* 0.38 0.46 0.31 − 0.25 − 0.06 − 0.23 0.19 0.56*
Long. − 0.49 − 0.31 − 0.37 − 0.27 0.11 − 0.16 0.10 0.26 − 0.50 − 0.64*
Elev. − 0.76* − 0.52 − 0.58* − 0.47 0.31 0.16 0.10 − 0.15 − 0.60* − 0.96* 0.67*
* Signifi cant at α = 0.05
ern (Oregon and California) populations were clearly separated
from the Rocky Mountain and Canadian populations ( Fig. 3 ).
The southern populations had a large infl uence on the relation-
ship between the fi rst phenotypic canonical score and mean
temperature of the coldest month ( Fig. 2 ). Regressions con-
ducted within each region separately revealed a signifi cant rela-
tionship between the fi rst phenotypic canonical score and mean
temperature of the coldest month in both the northern region
( r 2 = 0.41, P = 0.05) and the Rocky Mountain region ( r 2 = 0.32,
P = 0.01), but not in the southern region ( r 2 = 0.08, P = 0.38).
The relationship between the second phenotypic canonical
score (largely representing growth traits) and frost-free period
was only signifi cant in the Rocky Mountain region ( P =
0.003).
the coldest month associated with a signifi cant difference in the
fi rst quantitative canonical variable (which largely refl ects date
of needle fl ush and fall cold injury) over all regions was esti-
mated as 1.1 ° C, which translates to a geographic distance
of approximately 2.8 ° latitude or 300 kilometers ( r 2 = 0.49,
tions. In the cold treatment, height increment differed signifi –
cantly among populations in the northern region; all traits
except root : shoot ratio differed in the Rocky Mountain region;
and date of needle fl ush differed among populations in the
southern region. Estimates of population differentiation ( Q ST )
were lower, on average, within regions than those for all popu-
lations pooled across region ( Table 7 ). In the ambient treatment,
the northern region had higher estimates of Q ST, on average,
than other regions, while in the cold treatment, estimates of
population differentiation were highest for the Rocky Mountain
region.
also varied by region ( Table 4 ). In the northern region, only the
date of needle fl ush had clinal variation that was positively as-
sociated with frost-free period. In the Rocky Mountain region,
growth traits (height growth and biomass) and spring cold in-
jury were positively associated with temperature variables. In
the southern region, survival was correlated positively with
summer precipitation and negatively with summer aridity in-
dex, and date of needle fl ush was positively correlated with
aridity.
treatments.
A-TDM b 1.69** 2.63** 2.23* 1.20 1.73
A-RM b 1.65* 2.12* 2.07* 1.17 1.57
A-SM b 1.73** 2.72** 2.47* 1.21 1.78
A-RSR b 1.09 1.02 1.34 0.82 1.05
A-FL03 6.51** 2.52** 0.76 0.98 3.25*
A-FL04 5.49** 1.93* 1.70 1.83* 2.12*
A-FCI 2.59** 1.02 1.85 1.17 2.04
A-SCI 1.11 1.25 0.40 0.91 1.76
C-TDM b 1.24 2.06* 1.28 2.56** 0.78
C-RM b 1.22 1.86* 1.21 2.31** 0.73
C-SM b 1.23 2.14* 1.15 2.30** 0.85
C-RSR b 1.09 1.50* 0.30 0.99 1.82
C-FL03 3.29** 1.23 0.47 2.30** 2.01
C-FL04 6.36** 0.48 1.99 3.89** 3.31**
b Natural log transformed
* Signifi cant at α = 0.05
** Signifi cant at α = 0.01
† Wald test of covariance parameter = estimate/approximate standard error ( SAS Institute, 1999 )
variables (Clim1 and Clim2) and both climate and quantitative variables.
TDM b − 0.05 0.58 MTWM 0.22 0.07 TDM − 0.05 0.46
RM b − 0.13 0.55 MTCM 0.95 0.27 RM − 0.12 0.43
SM b − 0.01 0.59 MAP 0.21 0.62 SM − 0.01 0.46
FL03 0.91 − 0.27 MSP − 0.25 0.24 FL03 0.85 − 0.21
FL04 0.90 − 0.31 AH_M 0.20 − 0.31 FL04 0.85 − 0.24
FCI 0.66 0.28 SH_M 0.74 − 0.35 FCI 0.62 0.22
ent environments, so the two temperature treatments were
intended to assess potential levels of genotype-by-environment
interaction. The common-garden environment (at Vancouver,
British Columbia; elevation ~100 m, MAT = 10 ° C, MTWM =
17.3 ° C, MTCM = 3.2 ° C) is considerably warmer than white-
bark pine ’ s native habitat, where frosts can occur during any
month of the year ( Arno and Hoff, 1989) ( Table 2 ). Although
milder air temperatures and warmer soil would enhance growth
for most tree species, for whitebark pine, the ambient soil tem-
perature appeared more stressful than the cold soil treatment,
even with the soil kept moist. The darker color and superior
health of the seedlings in the cold treatment indicated that
higher soil temperature was a stress that appeared to be cumula-
tive over the two growing seasons. However, it appears that all
populations suffered equally in the warm environment because
there was no evidence of genotype-by-environment interaction
between soil temperature treatments for any of the traits as-
sessed. Although this experiment was outside of the natural
range of the species, phenotypic differences in a common gar-
den among populations demonstrate genetic differences and
provide a better basis for estimating limits to seed transfer and
likelihood of maladaptation than do estimates from molecular
marker studies.
served signifi cant differences among population means in most
quantitative traits ( Table 5 ), similar to many other widespread
North American conifers ( Morgenstern, 1996 ). In the subalpine
environments where whitebark pine grows, traits affecting tol-
erance of abiotic stresses most likely play a larger role in deter-
mining fi tness than do growth traits related to competitive
ability.
tive traits studied ( Q ST ) was within the range of previous esti-
mates for other conifers ( Merila and Crnokrak, 2001 ; McKay
and Latta, 2002 ; Howe et al., 2003 ; Savolainen et al., 2004 ; St.
Clair et al., 2005; St. Clair, 2006 ; Mimura and Aitken, 2007 ).
Population differentiation ( Q ST ) was strongest across all popu-
lations for traits related to phenology and cold injury. However,
patterns of population differentiation varied among regions,
with populations in the northern region differentiated most
den experiments are often replicated in different environments
within a species ’ range to detect genotype-by-environment in-
teractions as well as population differentiation. This experiment
was outside of the natural range of whitebark pine; therefore
results may have differed from what would have been observed
in the natural habitat of this species. A limited supply of avail-
dardized mean temperature of the coldest month (MTCM) for 41 popula-
tions of Pinus albicaulis in three geographic regions. Y-axis scale is
standard deviation, and bracket indicates value of LSD 0.20.
for two temperature treatments.
A-TDM 0.07 0.24 0.00 0.07
A-RM 0.08 0.37 0.00 0.05
A-SM 0.07 0.30 0.00 0.08
A-RSR 0.00 0.04 0.00 0.00
A-FL03 0.47 0.03 0.00 0.24
A-FL04 0.47 0.08 0.19 0.15
A-FCI 0.36 1.00 0.05 0.25
A-SCI 0.12 0.00 0.05 0.11
C-TDM 0.09 0.05 0.89 0.03
C-RM 0.08 0.04 0.48 0.02
C-SM 0.08 0.04 1.00 0.03
C-RSR 0.01 0.00 0.00 1.00
C-FL03 0.43 0.00 0.41 0.12
C-FL04 0.65 1.00 0.47 0.18
QC2) based on eight quantitative traits for 41 populations of Pinus albi-
caulis . Axis scales are standardized values. Symbols refer to geographic
regions shown in Table 2 .
carpa (Hook) Nutt.] ( Peterson et al., 2002 ), mountain hemlock
[ Tsuga mertensiana (Bong.) Carr.] ( Peterson and Peterson,
2001 ), and Douglas fi r ( St. Clair et al., 2005 ).
to other conifers, but signifi cant differences exist among geo-
graphic regions ( Bower and Aitken, 2006 ). In the common-
garden environment, populations from colder (higher latitude)
populations (with lower mean temperature of the coldest month)
fl ushed earlier in the spring and suffered greater spring cold
injury. Earlier fl ushing of these cold-adapted populations may
be due to lower chilling requirements in winter, lower heat sum
requirements in spring, or both ( Howe et al., 2003 ). In their
natural environments, these populations are likely to experience
shorter growing seasons than populations further south, so de-
spite a higher risk of spring cold injury, earlier fl ushing in the
spring may be a mechanism to allow trees at these higher lati-
tudes to extend their growing season relative to trees from lower
latitudes ( Sagnard et al., 2002 ). In species that can tolerate frost
damage well or that have high recovery potential the length
of the growing season may be a more important driving force
in adaptation than the avoidance of damage ( Leinonen and
Hanninen, 2002 ). Worrall (1983) reported differences in both
threshold temperature and heat-sum needed for fl ushing in am-
abilis fi r [ Abies amabilis (Dougl.) Forbes] and subalpine fi r and
also found that populations from higher elevations fl ushed ear-
lier than warmer, lower elevation populations in a common gar-
den. A faster response to warming spring temperatures of higher
elevation or more northerly sources has also been reported for
a number of other conifer species (see references in Campbell
and Sugano, 1979 ; Morgenstern, 1996 ). For coastal Douglas fi r
( P. menziesii var. menziesii ), however, where growth may be
more limited by drought than cold in some environments, pat-
terns were opposite ( Campbell and Sugano, 1979 ).
bark pine is distributed over a large latitudinal range, the clinal
variation observed indicates that trees from a particular popula-
tion are expected to be optimally adapted for only a portion of
the environmental conditions experienced across the species
range. We have used the fl oating seed transfer model developed
by Rehfeldt (1991 , 1994 ) to determine seed transfer guidelines
for restoration in current climates of whitebark pine. Of the
traits we assessed, the date of needle fl ush gives the strongest
signature for local adaptation because it has the highest Q ST es-
timates (0.43 – 0.63), thus it was considered fi rst for developing
regional estimates of maximum potential seed transfer distances
for restoration without substantial risk of maladaptation. In the
northern region, it should be possible to move seed from cone
collection sites to planting sites that differ by up to 1.9 ° C in
mean temperature of the coldest month and maintain growth
phenology suitable for the current local climate with acceptable
risk of fall cold injury. In the Rocky Mountain region, the cli-
matic transfer maximum is reduced to 1.0 ° C in current climates.
Restoration ecologists, park managers, and foresters can more
easily use seed transfer guidelines based on geographic dis-
tances than climatic differences and these differences in mean
temperature of the coldest month translate to approximately
4.6 ° latitude, or 505 km, for the northern region, and 320 m in
elevation in the Rocky Mountain region, based on signifi cant
correlations between climatic and geographic variables. In the
southern region, the lack of correspondence between the fi rst
seedling phenotypic canonical variable and mean temperature
tions in the southern region differentiated by cold adaptation
traits (date of needle fl ush and cold injury), while populations in
the Rocky Mountain region were differentiated by both height
growth and date of needle fl ush ( Table 7 ). The Q ST values for
these traits in these regions were greater than estimates of dif-
ferentiation in neutral molecular markers ( F ST and G ST ) for
whitebark pine ( Table 1 ). Levels of population differentiation
( F ST ) previously reported in whitebark pine range from 0.034
to 0.088 and average 0.058, which is slightly higher than most
values reported for other stone pine species ( Pinus subsection
Cembrae ) ( Table 1 ). This value indicates that the vast majority
of selectively neutral genetic variation in whitebark pine is
among individuals within populations, as for most conifers.
Our results show that differentiation is stronger for about half
of the quantitative traits we studied than for the upper estimate
of differentiation for neutral genetic markers in whitebark pine,
indicating a moderate degree of local adaptation. Greater dif-
ferentiation based quantitative traits than on neutral markers
suggests a prominent role of natural selection driving local ad-
aptation of populations for many of these polygenic phenotypic
traits ( Lynch et al., 1999 ; Whitlock, 1999 ).
we detected for seedling phenotypic traits with those reported
by both Zavarin et al. (1991) using monoterpenes and Richard-
son et al. (2002b) using mtDNA. Both of these studies found
differentiation among the Rocky Mountain, northern, and
southern regions of the species range, which may indicate some
historical effects such as isolation, genetic drift, and migration
on quantitative genetic structure in addition to the effects of
selection for local adaptation to climate. Richardson et al.
(2002b) also suggested that genetic evidence supports two
Pleistocene refugia for whitebark pine, one in the Yellowstone
region and one in the southern Oregon Cascades, with subse-
quent northward postglacial colonization patterns that have re-
sulted in a secondary contact zone in the southern Washington
Cascades. An assessment of quantitative traits across the con-
tact zone suggested by Richardson et al. (2002a , b ) might help
support the molecular results, but we are unable to test for this
pattern due to the lack of representation of Washington Cascade
populations in our study.
Populations differed signifi cantly for nearly all traits, and at a
broad geographic scale, those from higher latitude environ-
ments with lower winter temperatures fl ushed earlier in the
spring, suffered less cold injury in the fall, and allocated more
biomass to shoots in the common-garden environment than
those from milder environments. However, clinal variation pat-
terns that corresponded to climatic gradients varied by region
( Table 4 ), indicating local adaptation is driven by selection
pressures from different environmental factors in different re-
gions. In the northern region, local adaptation to available
growing season length appears to be important because the date
of needle fl ush has a clinal variation associated with frost-free
period. In the Rocky Mountain region, annual and seasonal
mean temperatures appear to be driving local adaptation, with
height growth increasing with temperature at population ori-
gins. In the southern region, where survival and date of needle
fl ush were both associated with rainfall patterns, water avail-
ability appears to be the factor associated with population dif-
ferentiation. Regional differences in relationships between
phenotypes and source climates have been reported for other
successful establishment of seedlings under current climates, at
least some of which can tolerate warming over the next several
decades, is to transfer seed unidirectionally to the maximum
extent allowable from mild to cold climates on the basis of the
estimates from the fl oating seed transfer model. Most popula-
tions of temperate conifers have a reasonably broad tempera-
ture tolerance, although populations vary in breadth of reaction
norm (e.g., Wang et al., 2006b ). To balance the risks of malad-
aptation in both current and future environments, the challenge
is to plant seedlings into environments near their lower-temper-
ature limits to ensure adequate survival and growth, yet have at
least some of those trees as adults remain within their tempera-
ture tolerances on the warmer side of the reaction norm.
likely cause phenotypes to shift northward and upward to track
conditions to which they are locally adapted ( Davis and Shaw,
2001 ), but climatic differences among regions should maintain
clinal variation. The guidelines we propose are established
based on a 20% risk of assuming no difference among popula-
tions when a difference actually exists. This threshold may be
too conservative, given the risks of climate change, and it may
become optimal to transfer seed greater distances than esti-
mated and accept a greater risk of type II error (assuming popu-
lations are the same when they are different). However,
exceeding these distances increases the chance of maladapta-
tion under current conditions and should only be done after
weighing this risk against the need for restoration. Mixing seeds
from different populations within the acceptable transfer range
may also be a reasonable strategy for mitigating uncertain fu-
ture climates because it may increase the probably that at least
some trees survive. Outbreeding depression from the mixing of
populations is unlikely to be a problem for whitebark pine;
there are no published examples of this phenomenon in conifers
that we are aware of, and it is unlikely to evolve in species with
high levels of gene fl ow (e.g., via wind-dispersed pollen).
of seeds from south to north or to higher elevations, both within
and exceeding the current species range, could facilitate popu-
lation migration, particularly if planted genotypes have some
resistance to white pine blister rust. Without such human inter-
vention, whitebark pine may continue to decline, and while
selection may result in adaptation by favoring survival and
reproduction of those local genotypes adapted to new condi-
tions, this may be insuffi cient demographically to maintain vi-
able populations. A comparison of predictions of the current
range of whitebark pine in British Columbia and the current
predicted range based on bioclimatic modeling indicates a
considerably poorer fi t of predicted to actual distribution than
is typical of most of the 49 forest tree species in the province
( Hamann and Wang, 2006 ). This lack of fi t between actual and
current predicted range of whitebark pine may refl ect a migra-
tional lag from the dependence on seed dispersal by the Clark ’ s
nutcracker and may be indicative of a slow potential rate of migra-
tion in response to climate change. This unoccupied potential
habitat is also predicted to be one of the few areas that have
climates that could support whitebark pine both currently and in
seven or eight decades. Facilitated migration via restoration
plantings that move blister-rust-resistant genotypes along envi-
ronmental gradients and into areas of new potential habitat may
be the only way that populations of this species can maintain
viability. Experimental fi eld plantings will be needed to test this
concept operationally.
within this region. However, in the absence of further data and
until populations from the Washington Cascades can be studied
and compared with other southern populations, we suggest that
transfer between mountain ranges (e.g., Sierra Nevadas and
Cascades) should be avoided. Height growth and biomass in
whitebark pine populations are signifi cantly correlated with the
length of the growing season (frost-free period); however, these
clines are gentle, and estimates of maximum seed transfer dis-
tances based on these traits are also too large to be of practical
use in a conservation or restoration program.
static seed transfer maxima based on climate is that it assumes
local genotypes are optimally adapted to current climates and
that these climates will remain constant. The evidence that we
are in a period of record rates of warming is mounting ( IPCC,
2001 ). While populations of most temperate and boreal tree
species have high levels of genetic variation that could enable
adaptation to new environments ( Hamrick, 2004 ), long genera-
tion lengths will greatly constrain their ability to adapt to rapid
climate change ( Burger and Lynch, 1995 ), and seed dispersal is
unlikely to be suffi ciently rapid to allow populations to migrate
and track climates spatially ( Davis and Shaw, 2001 ). One re-
sponse to modest climate change may be for trees to migrate
within local areas among microsites or aspects. However,
whitebark pine inhabits a relatively small range of aspects,
slopes, and microsites, so this is not a likely mechanism for
maintaining high levels of adaptive diversity. Whitebark pine
has reasonably high levels of variation; however, it requires
several to many decades to reach sexual maturity, its habitats
are discontinuous, largely consisting of high-elevation “ islands ”
separated by lower-elevation valleys, and its migration is de-
pendent on the Clark ’ s nutcracker ( Arno and Hoff, 1989 ). All
reduce the likelihood that this species can adapt or migrate suf-
fi ciently rapidly to avoid the collapse of many populations. Cli-
mate models predict a dramatic decrease in the range of habitat
suitable for whitebark pine with increases in temperature and
CO 2 ( Romme and Turner, 1991 ; Bartlein et al., 1997 ; Hamann
and Wang, 2006 ). While increasing temperature may result in
new habitat available north of its current range, it is also likely
to lead to an upward shift of the timberline and the range of
whitebark pine, resulting in a smaller potential area for it to oc-
cupy. Many populations currently have negative growth rates
due to white pine blister rust infection; fi re exclusion and re-
sulting competition from shade-tolerant, fi re-intolerant, faster-
growing conifer species; and mountain pine beetle (Kendall
and Keane, 2001). Interactions among this introduced disease,
the current mountain pine beetle epidemic, and climate change
could drive populations toward extirpation and the species to-
ward extinction ( Hamrick, 2004 ).
tions should balance anticipated future climates with the need
to restrict seed movement to environmental distances that can
lead to successful seedling establishment under current, albeit
transient, conditions. If seed is transferred an excessive distance
from warmer to colder climates in anticipation of future warm-
ing, cold injury, or mortality may result during establishment;
yet if predictions of future climate are ignored, local seed
sources that are fi t in current local environments may result in
restoration plantings of trees that are not adequately competi-
tive with other species or will never achieve reproductive matu-
rity as a result of slow growth rates under the conditions of the
decades to come.
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