Vol.3, No.5, 359-369 (2013) Open Journal of Ecology
http://dx.doi.org/10.4236/oje.2013.35041
Assessing effects of seed source and transfer
potential of white birch populations using transfer
functions
Oluwatobi A. Oke1*, Jian R. Wang2
1Department of Biology, University of New Brunswick, Fredericton, Canada; *Corresponding Author:
2Faculty of Natural Resources Management, Lakehead University, Thunder Bay, Canada
Received 4 January 2013; revised 7 August 2013; accepted 27 August 2013
Copyright © 2013 Oluwatobi A. Oke, Jian R. Wang. This is an open access article distributed under the Creative Commons Attribu-
tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
ABSTRACT
Trees have adapted to their local climates, but
with changes in the climate, they may currently
or in the near future occupy climates that are
sub-optimal for growth and survival. The goal of
current reforestation is therefore to establish a
new generation of trees with growth adapted to
the future climate(s). Here, we present prelimi-
nary data of a study assessing the effects of
seed source and trans fer po tenti al of whi te birch
populations. Seeds from twenty-five white birch
(Betula papyrifera Marsh.) populations collected
across Canada were grown in the greenhouse
and observed for emergence time, germination
and growth. The seedlings were later planted in
a common garden. After one year, the seedlings
were measured for height, root-collar diameter
(RCD) and survival rate and average volume per
seedling calculated. Transfer functions were
used to estimate the climatic distance from
which popul ations may be t ransfer red to the test
site. There was a significant effect of population
on all growth variables. Initial height was posi-
tively correlated w ith 1-year height and survival.
Germination rate negatively correlated with emer-
gence time. Principal component analysis sho w-
ed effects of seed origin on performances of the
populations in the common garden. Summer
temperature was the best predictor of the trans-
fer distance.
Keywords: Climate Change; Populations; Common
Garden; Transfer Function
1. INTRODUCTION
The global climate system is continually evolving and
significant ecological changes occur at all timescales.
This is because climate is multivariate and ecological
transitions and transformations are all related to climate
[1]. However, the rapid change in the global climate may
cause unprecedented disruption of the biological pro-
cesses [2]. One of the implications of a novel shift in the
climate system is that species may currently or in the
future be relegated to climates that are sub-optimal for
growth and survival [3]. Also, shrinkage in the coverage
of the boreal forest and extinction of some important
members of the forest is anticipated [4]. Boreal forest is
an important component of the global carbon sink system
that is sensitive to temperature [5].
There are predictions concerning the migratory pattern
of trees in response to climate change [6-8]. However,
some of the predictions were made from coarse scale
observations which may not take into consideration the
intraspecific genetic responses [9]. Some tree species
comprises of populations that are physiologically attuned
to different climates [10]. In addition, there are factors
(e.g. lakes and mountains) other than climate that may
influence the migratory pattern of a species. It is there-
fore important to assess trees’ responses at individual
species level [11].
One proposed strategy to offsetting potentially nega-
tive impacts of climate change on forest systems is to
match genotypes with the future climates [12-14]. This
idea is based on the provenance trials traditionally used
to introduce seed sources to a new climate [15]. The
provenance trials have been combined with a statistical
model (response function) to predict species response to
climate change [16,17]. The underlying hypothesis for
the model is that geographic variables are surrogate for
elusive climate that governs micro-evolution and ad-
aptation at local scale [18]. However, the resulting pre-
dictions are often complex. In addition, implementing
this approach requires a long-term provenance study and

O. A. Oke, J. R. Wang / Open Journal of Ecology 3 (2013) 3 59-369
360
populations are necessarily planted in multiple sites [17].
With the bourgeoning availability of climate data, cli-
mate-transfer function was developed to estimate the
climatic distance to which populations may be trans-
ferred. It is based on the view that organismal distribu-
tions are primarily controlled by climate with other fac-
tors being secondary. Its advantage is that populations
may not necessarily be planted in more than one site to
generate a reliable result [19]. This approach has been
popularly used to assess species level response to climate
change [2,20-22].
In this paper, we present preliminary data of a study
where we used transfer functions to assess the transfer-
ability of 25 populations of white birch to a test site in
Northern Ontario. White birch (Betula papyrifera Marsh.)
is a widely distributed species in North America and the
most prevalent of all the birches [23]. It is an ecologi-
cally important hardwood species in the Canadian boreal
forest. There is a rising commercial interest in its pro-
ducts and its inclusion in hardwood-conifer stand mana-
gement. Although there is an increased silvicultural
knowledge base for the species [23-30], information
about local populations, transfer potentials and climatic
guidelines for its transfer in order to take advantage of its
genetic diversity is still limited. Previous studies have
reported variations in climatic response between seed
sources from its southern and northern limits. The north-
ern seed sources appear to germinate at low temperatures
compared with the southern sources [31-32]. Such varia-
tion has been reported for some other temperate species
[33] but caution is sounded against any generalization
without first testing the seed sources in a uniform envi-
ronment [34,35]. So far, no such testing exists for white
birch. Although one seed source testing of white birch
was done, the sources used in the study and the test sites
were only restricted to the interior of British Columbia
[36]. The objective of this study was to investigate how
variations in seed source determine the success of white
birch in the field and to understand the underlying cli-
matic factors that may influence the transfer potential of
the species. In this study we were able to directly relate
the influence of the seed sources to the outcomes of the
transfer functions.
2. MATERIALS AND METHODS
2.1. Greenhouse Operations
White birch seeds from different (twenty-five) forest
regions covering seven provinces (Ontario, British Co-
lumbia, New Brunswick, Newfoundland, Nova Scotia,
Quebec and Prince Edward Island) were grown under
ambient condition at the Lakehead University’s green-
house. The selected populations ranged from latitude 45˚
16'N - 54˚43'N and elevation 70 - 800 m. The seeds were
sown in styroblocks (5 seeds per cavity) on 18th of April
2008. Each styroblock consisted of 45 cavities. The pot-
ting medium was a pre-mixed peat moss. The seedlings
were fertilized with a regular fertilizer (N-P-K 20:20:20).
During the germination phase, the seedlings were ob-
served for emergence time, germination percentage and
height in the greenhouse. The seedling emergence time
was recorded as the number of days after sowing when
the seedlings were visible. Germination was expressed as
a percentage, based on the numbers of cavities with
seedlings and the total number of cavities in the styrob-
lock. Height (initial) was measured with a ruler on 28th
of July 2008 (10 weeks after sowing of seeds). The seed-
lings were grown for 12 weeks in the greenhouse.
2.2. Field Operations
The common garden site is located in northwest On-
tario (Thunder Bay) on a forested land that was recently
disturbed by a wild fire. It is located on 48'22'N, 89'19'W
with elevation of 183.3 m. The site has mean January
temperature of −15˚C, mean July temperature of 18˚C
and an average annual precipitation of 704 mm. The field
preparation and fencing were carried out between 25th
and 31st of July 2008. Field preparation involved re-
moval of weeds, dead woods and stumps on the site. The
fencing was necessary to prevent damages to the seed-
lings by deer. The seedlings were planted in a completely
randomized design between 5th and 7th of August 2008.
Thirty three seedlings were planted for each population
using a spacing of 1.5 m × 1.5 m. The site was kept weed
free. On July 25th 2009 (after 1 year), the seedlings were
measured for height and root-collar diameter and were
also scored for survival. The volume per seedling was
also calculated using the formula:


24hd
where h = tree height, d = root-collar diameter and

=
22/7.
2.3. Climate Data and Analysis
All the climate data were normalized climate data
(1971- 2000) collected from weather stations closest to
the population’s origins or use of multiple weather sta-
tions where the former was not applicable. Fifty climate
variables; minimum, maximum and mean monthly tem-
peratures, mean monthly precipitation, mean annual tem-
perature, and mean annual precipitation were used as
independent variables. Six derived climate variables;
mean temperature of the coldest month (MTCM), mean
temperature of the warmest month (MTWM), annual
moisture index (AMI), summer-winter temperature dif-
ference (SWTD), degree days (DD) < 0˚C, degree days
>5˚C and 3 geographic variables (latitude, longitude and

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361
minus population climate) and e is the residual. elevation) were also used in the analyses. The data were
collected from Environment Canada. The growth vari-
ables were tested for normality. Where there was a depar-
ture from normality, the data were transformed using a
polynomial or logarithm transformation. One way ana-
lysis of variance (ANOVA) was used to determine if
there were significant effects of populations on the grow-
th variables.
Principal component analysis (PCA) was used to sum-
marize the growth variables. The component that ex-
plained most of the variations was used to build a trans-
fer regression. The principal component regression mo-
del was:
2
01121
1PCbb XbXe

  (3)
The climate variables were screened with simple linear
regression model. Regressions that were significant at α
= 0.05 were retained for use in further analysis. The lin-
ear model was:
where PC1 is the first principal component which ex-
plained most of the variation in the data, b0 is the inter-
cept, b1 and b2 are the regression coefficients, X1 is cli-
matic variable (common garden climate minus popula-
tion climate) and e is the residual. All the analyses were
performed using SAS 9.1 (SAS institute Cary, NC) and
sigma plot 11 (Systat Software, San Jose, CA).
011i
YbbXe  (1)
where Yi is the predicted height, root collar diameter
(RCD), survival or average volume, b0 is the intercept, b
is the regression coefficient, X is climatic or geographic
variables of the populations and e is the residual. The
successfully screened climatic or geographic variables
from the simple linear models were used in the develop-
ment of transfer functions. A transfer function is a re-
gression used to describe performance of multiple seed
sources at a single test site. The model is given as:
3. RESULTS
3.1. Greenhouse
There were significant differences in emergence time,
germination percentage, and initial height among the popu-
lations (p < 0.0001). Seedlings emergence time ranged
from 7 to 15 days (Figure 1) while germination percent-
age ranged from 29% to 100% (Figure 2). Initial height
ranged from 22.5 cm in Porcupine population to 38.6 cm
in Millvale population (Figure 3). Some of the popula-
tions germinated within 7 days especially the populations
2
01121i
YbbXbX e  (2)
where Yi is the predicted height or productivity, b0 is the
intercept, b1 and b2 are the regression coefficients, X1 is
climatic or geographic variable (common garden climate
Figure 1. Emergence time (east-west) in days of the 25 white birch populations after sowing.
Figure 2. Percentage germination of the 25 populations of white birch.

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362
from the west coast. Most of the populations germinated
within 10 days. With the exception of the population from
St. Georges which germinated within 10 days, populations
from Newfoundland generally took much longer to ger-
minate. Most of the populations that germinated early had
higher germination percentages and higher initial height
compared with those that germinated late. The latitude,
longitude and elevation of the populations had no bear-
ing on either of germination or seedlings emergence time.
3.2. Common Garden
There were significant differences in 1-year height
and root-collar diameter (p < 0.0001) among the 25
populations. The 1-year height ranged from 30.1 cm in St.
Georges population to 57.9 cm in Skimikin (Figure 4).
The root-collar diameter (RCD) ranged from 4.35 mm in
St. Georges population to 6.67 mm in Wayerton (Figure
5). Survival among the populations ranged from 21% in
NL-TW1 population to 88% in Skimikin (Figure 6). The
average survival was 65%. The average volume per tree
was also significant (p < 0.0001). Volume ranged from
approximately 4.4 cm3 in population from Timmins
(Moist) to 20.99 cm3 in Skimikin (Figure 7).
There were correlations between the greenhouse and
Figure 3. Initial heights of the 25 white birch populations.
Figure 4. Height (in cm) after 1 year in the common garden.
Figure 5. Root-collar diameter (in cm) after 1 year in the common garden.

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Figure 6. Percentage survival after 1 year in the common garden.
Figure 7. Volume per populations after 1 year in the common garden.
the field observations. The initial height positively cor-
related with 1-year height and survival (r = 0.74 and 0.51
respectively). Populations with higher initial heights
maintained best height growth after one year. Also, sur-
vival was higher in the populations with higher initial
height. Germination was negatively correlated with
emergence time (r = −0.71) meaning that the longer it
takes a population to germinate, the lower the germina-
tion percentage. Negative correlation (r = −0.47) between
emergence time and 1-year RCD indicated that most
populations that took longer time to germinate had lower
RCD.
3.3. Transfer Functions
Screening of the 56 climate and 3 geographic variables
using simple linear regressions were generally significant
for regression of mean summer temperatures (mostly
June and July) against height, RCD and volume (Table
1). No significant regression was observed for survival.
Mean summer temperature was also a significant predic-
tor of volume. In addition to mean summer temperatures,
regressions of growth variables against some derived
climate variables (DD > 5˚C, MTWM and AMI) were
also significant.
All significant regressions and previously established
key variables (MTWM, DD > 5˚C, MTWM, SWTD, DD <
0˚C, AMI, MAT and MAP) (Rehfeldt 1995) were used to
build the transfer functions. Transfer functions with p >
0.1 were discarded. Seven populations were excluded
from further analyses because their locations were too
close to one or more populations which would have re-
quired the use of a single weather station for more than
one population.
In the transfer functions, all the variables that were
successfully screened produced statistically significant
regressions with the exception of May minimum tem-
perature which had a p-value greater than 0.1. The trans-
fer functions showed that the mean summer temperature
(June, July and August) is a stronger predictor of white
birch performance compared with the derived climate
variables (Figures 8-10); r2 ranged from 0.27 - 0.39 for
height and 0.35 - 0.58 for RCD and 0.30 - 0.57 for vo-
lume (Ta b le 2 ). AMI was the only predictor of survival
(r2 = 0.34). Also, performance was generally better in
populations from warmer climates. Since transfer dis-
tance was calculated as common garden climate minus
population climate, positive values denote transfers from
climate that are cooler than the climate of the test site
while negative values represent transfers from climates
warmer than that of the test site. Zero denotes the climate
of the test site and the best match.
Two principal components (PC) were retained by the
PCA. Both components explained a cumulative variation
of 73% with PC1 explaining 51% of the variation and
PC2 explaining 22%. Initial height, 1-year height, RCD

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364
Figure 8. 1-year height transfer distances (common garden climate minus popula-
tion climate) of the white birch populations in the units of (a) June mean tempera-
ture, (b) Mean temperature of the coldest month, (c) June minimum temperature
and (d) Annual moisture index.
Figure 9. Root-collar diameter transfer distances (common garden climate minus
population climate) of the white birch populations in the units of (a) July maximum
temperature, (b) June mean temperature, (c) June maximum temperature and (d) De-
gree days > 5˚C.

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(a) (b)
Figure 10. Volume transfer distances (common garden climate minus population climate) of the white birch populations in the
units of (a) June minimum temperature and (b) Degree days > 5˚C.
Table 1. Simple linear regression of height, RCD and volume against each of 54 climate and 3 geographic variables.
Variables R2 Sig. Predictors
1yr-Height 0.38 0.0059 JunMinT
0.24 0.0355 JunMeanT
RCD 0.35 0.0097 MayMeanT
0.57 0.0003 JunMeanT
0.44 0.0026 JulMeanT
0.30 0.0166 AugMeanT
0.44 0.0026 MTWM
0.49 0.0011 JunMaxT
0.42 0.0035 JulMaxT
0.36 0.0088 AugMaxT
0.40 0.0048 JunMinT
0.25 0.0339 MayMinT
Volume 0.41 0.0041 JunMinT
0.57 0.0164 JunMeanT
0.48 0.0014 JunMaxT
0.32 0.0148 AugMeanT
0.32 0.0140 AugMaxT
0.49 0.0013 JulMeanT
0.55 0.0058 DD > 5˚C
0.48 0.0040 MTWM
Significant at p < 0.05.

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Table 2. Statistical significance and r2 of the quadratic transfer functions of 1-year height, RCD, volume and survival against climate
predictors (summer mean temperatures).
Variables R2 Sig. Predictors
1yr-Height 0.39 0.0255 JunMinT
0.28 0.0826 JunMeanT
0.31 0.0628 MTWM
0.27 0.0914 AMI
RCD 0.35 0.0413 MayMeanT
0.58 0.0014 JunMeanT
0.46 0.0095 JulMeanT
0.33 0.0503 AugMeanT
0.46 0.0095 MTWM
0.50 0.0057 JunMaxT
0.43 0.0141 JulMaxT
0.39 0.0245 AugMaxT
0.40 0.0211 JunMinT
0.42 0.0161 DD > 5˚C
Volume 0.42 0.0174 JunMinT
0.57 0.0017 JunMeanT
0.52 0.0040 JulMeanT
0.51 0.0050 JunMaxT
0.33 0.0477 AugMaxT
0.35 0.0418 AugMeanT
0.45 0.0117 DD > 5˚C
0.52 0.0042 MTWM
0.30 0.0703 AMI
Survival 0.34 0.0437 AMI
Significant at p < 0.1.
positively loaded on PC1. Emergence also loaded (nega-
tively) on PC1. Germination loaded negatively on PC2.
Regression of PC1 against volume resulted in r2 of 0.88.
Simple linear regression of PC1 against each of the 56
climate variables produced only two significant regres-
sions (June minimum temperature and June mean tem-
perature). R2 equals 0.32 and 0.29 respectively. Because
germination was the only variable loading on PC2, cli-
mate variables were not regressed against PC2.
4. DISCUSSION
What is striking in this study is the direct relationships
between the greenhouse growth performances and the
first year field data, especially the correlation between
the initial height and survival. Indeed, seedling growth
investment guarantees survival and it may also be a re-
quisite for resource acquisition and resource balance of
seedlings [37]. Moreover, collectively, emergence time,
height and RCD are considered factors of growth and
fitness [38,39]. This result suggests that relative per-
formances of white birch populations in the field, to
some extent, could be estimated from germination and
pre-planting performances.
We expected that survival among the populations
would be influenced by cold winter temperature at the
common garden site because most of the populations in
the experiment were from climates that are warmer than
the common garden climate: That was not the case. Sur-

O. A. Oke, J. R. Wang / Open Journal of Ecology 3 (2013) 3 59-369 367
prisingly, most of the populations with good performan-
ces in common garden were from locations with warm-
er climates. However, a study has shown that it is possi-
ble for species to perform differently at different climatic
extremes. For instance, in a 6 year provenance study of
white ash, the provenances with tallest height in the
coldest climate were the shortest in a relatively warmer
environment and vice versa [40]. The authors pointed out
that such result underpins the genetic basis for trade-off
between growth and cold tolerance. However, white bir-
ch appears to be a generalist with regards to frost tole-
rance [36]. Mortality was only observed later in the
spring and could not be attributed to frost damage. More
importantly, mortality was lowest among the populations
from Western Canada where the climate is much warmer
than in Northern Ontario. Nonetheless, we exercise cau-
tion here because short-term growth investment may lead
to future mortality when populations from warm climates
are transferred to cold climates [41,42].
Growth potential of some species is directly linked to
summer temperature [3]. Also in this study, summer tem-
perature proved to be a strong predictor of climatic dis-
tance to which white birch populations may be trans-
ferred. This was consistent with the parallel factorial
experiment that we conducted in the greenhouse. We
used two temperatures (30oC day/20˚C night and 22˚C
day/14˚C night) and two water regimes (regular watering
and drought stressed). Seedlings in the high temperature
environment outperformed those in the low temperature
environment (unpublished data). In theory, it implies that
white birch populations will benefit from transfers to
warmer climates. However, successful transfer is most
probable if the climate of the test site matches that of the
populations [18]. This is because there is a potential risk
of maladaptation when transferring species along climate
or geographic range [43,44]. Also, populations might
express adaptation to their original environments even
when planted in common gardens [17]. For these reasons,
a conservative climatic distance will be more appropriate.
Out of the populations tested, the closest matches for the
common garden site were populations from Timmins
(T-Dry) in Northern Ontario and St George’s in New-
foundland. Although St George’s is a climatic match for
the common garden site, the population’s performance
was not very impressive.
Apart from being a match, in the absence of a popula-
tion from Thunder Bay (due to lack of viable seeds) in
this experiment, populations from Timmins invariably
serve as surrogate for the test site. Timmins is located in
Northern Ontario with similar cold northern climate as
the test site. However, this is not a conclusive outcome
because the performances of the remaining two popula-
tions from Timmins are less than average even though
they were from locations which are a few kilometers
apart. This poses a question of how much influence do
seed sources have on the post-planting performance of
white birch. Also, it should be pointed out that white
birch has different ploidy levels with polyploids being
generally more tolerant than the diploids [45]. It is diffi-
cult to know if there is a confounding effect of ploidy
level in addition to population effects. Growth and sur-
vival is controlled by many factors other than climates
and it is important to understand genotype performance
from both genetic, developmental and growth viewpoints
to adequately capture the dimension of variations among
the populations. However, the prediction of the transfer
functions is not trivial. The models summarized the im-
portant climate variables relevant to the species transfer
and aided the matching of populations with the test site.
This will provide a useful guide in the decision making
process. At this stage of this experiment, we use the term
“match” with caution and we are itching to know what
the populations’ performances might be in a few years
from now.
5. ACKNOWLEDGEMENTS
This study was funded by Canadian National Science and Engineer-
ing Research Council (NSERC) discovery grant to JW.
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