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GEOGRAPHIC CLINES IN GENETIC VARIATION
Gerald Rehfeldt
(presented by Albert R. Stage)
USDA Forest Service
Rocky Mountain Research Station
In risk mapping, the primary considerations are the presence of the host tree species, some
measure of its density, and the distribution of the pest agent. High-density or overstocked
stands are often considered to be of higher risk then stands with lower stocking levels; also
important is the climatic stress on the population. This presentation shows how the predictions from a climate model can be converted to variables that may indicate the status of the
stress of conifer species and their populations in the western USA and southwestern Canada.
Forty-eight monthlies were derived from the basic temperature and precipitation data and fit
to geographic surfaces with thin plate splines. These monthlies were then used to describe the
clines of genetic variation that exist within species for growth characteristics. The mapping of
clinal variation is useful in delineating seed zones and deriving seed transfer guidelines. The
reverse image of such maps should indicate where the species would be under stress due to
climatic conditions. For this reason, it is recommended that the monthlies and the climatic
limits could be useful in risk mapping.
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INTRODUCTION
Genetics research during the last 75 year or so has demonstrated that species of forest trees are
composed of populations, each of which is adapted (i.e., physiologically attuned) to only a
portion of the environmental gradient inhabited by the species. For most of the widespread
species, models exist that describe the clinal variation in genetic responses of populations
within species. These models are invariably driven by geographic predictors. But, now that a
climate model is available that makes point predictions on the landscape, researchers can directly relate and eventually map genetic responses to climate.
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1. Genetic variation is displayed along geographic gradients but interpretation
is invariably in terms of climate. Out of the files is a geographic cline, Douglas-fir vs. elevation. Geographic variation is acting as a surrogate for climate, which is more difficult to measure. Armed with a climate model we
then ready to assess plant-climate relationships. With climate models that
provided point predictions we cannot replace the surrogates.
In this slide, genetic variation in growth potential measured in a provenance
test of populations of Douglas-fir is related to the elevation of the stand in
which the seeds were collected.
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climate surfaces
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3006 weather stations
Hutchinson’s thin plate splines
Temperature and precipitation surfaces
Algorithms for derived variables
Splines for derived variables
Predict for DEM grids (1 km)
Map with GIS
2. There are 48 surfaces form normalized monthlies – weather variables based
on temperature and precipitation.
climate variables derived from
temperature and precipitation monthlies
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•
•
•
•
•
Degree-days > 5 °C
Degree-days < 0 °C
Frost-free period
Last spring frost
First fall frost
Growing season
degree-days > 5 °C
• Summer-winter
temperature differential
• Date degree-days > 5
°C reaches 100
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•
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•
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Mean annual temperature
Mean annual precipitation
Growing season precipitation
Mean cold month temperature
Minimum cold month
temperature
Mean warm month
temperature
Maximum warm month
temperature
Annual moisture index
Summer moisture index
3. All of these variables are of demonstrated importance in plant geography.
The model can then be used to predict the climate across the landscape.
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4. This is a map of degree-days>5C. There are nearly 6 million terrestrial pixels
in the map, and predicted values of degree days range from 0 to 6700.
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5. In this map, we’re zooming in from the previous slide on Lewiston, Idaho,
Idaho’s only seaport. This looks like a map of DEMs, but it’s not. This is a
map of degree-days that clearly shows the major drainages (Snake, Salmon,
Clearwater), the Lewiston-Clarkston valley, and the high mountains. Degree-days range from 2700 to 0 for the pixels in this map.
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6. This is the same map of Lewiston that allows some of the topography to
show through. Now, these maps are based on a 1 k grid which can be seen in
the slides. It’s important to know that the climate model itself makes point
predictions that are not necessarily tied to the DEMs.
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7. Frost-free periods vary from 0 to 365.
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8. Negetative degree-days show how severe the winters are.
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9. Then using output from the General Circulation Models and refitting the
splines, one can map climates predicted for the future. This map is for degree-days>5 and uses the greenhouse gas scenario (1% increase per year) of
the Hadley and Canadian GCMs. Upper left is contemporary climate, upper
right is that for the decade beginning in 2030, lower left for the decade beginning in 2060, and lower right for the decade beginning in 2090.
Range in contemporary values 0 to 6700. Range in 2090 will be 0 to 8344.
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10. This is the same sequence of illustrations that was used in the previous slide.
It shows the effects of global warming on negative degree-days. Notice the
effects are expected to be much greater on winter temperatures than on summer temperatures.
Degree-days < 0 Contemporary ranges is 0 to 2250, 2090 range would be 0
to 1052.
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11. FFP (frost-free period) current on left, 2090 on right..
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12. To me, this one is scary. Global warming, of course, is portrayed as a temperature effect. Yet, the response of plants will be determined by the interaction of temperature with precipitation. This slide compares the contemporary annual moisture index (DD5/MAP) for the contemporary climate
(left) with that projected for 2090 (right).
AMI (annual moisture index): left is for 2000, right is 2090. dd5/map.
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13. Now, armed with the climate model, we’re ready to consider biological
effects. This slide compares our ability to predict genetic responses of populations with geographic predictors (left) and climate predictors (right).
Pinus sylvestris lattitude is a good surrogate for the climate variables.
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14. This is a similar comparison for Engelmann spruce. Engelmann spruce–
elevation is a poor surrogate for the climate effects; in fact, it leads to the
wrong interpretation.
GOALS
assess population differentiation in relation to climate
Pinus sylvestris and Larix sibirica
Picea engelmannii
compare effects of climate change
Siberia vs. western USA
15. Studies of genetic responses to climate included researchers from RMRS
and the Sukachev Institute of Forest in Krasnoyarsk, Russia. We had these
objectives. Only those dealing with USA will be considered here.
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GOALS
assess population differentiation in relation to climate
Pinus sylvestris and Larix sibirica
Picea engelmannii
compare effects of climate change
Siberia vs. western USA
16. Definitions.
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genecology of Engelmann spruce
• 295 populations sampling natural
distribution
• 18 blue spruce populations
• 20 white spruce
• common garden studies in Idaho
17. The USA example involves Engelmann spruce.
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18. This photo was taken in the provenance test conducted at low elevation at
the Priest River Experimental Forest. The populations are planted in 10tree row plots. This means that any differences that are apparent between
rows is due to genetic differences between the populations. At this mild
site, differences are obvious.
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19. This is the high elevation planting site. Growth is less at high elevation, and
differences were more difficult to detect.
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20. Yet, in studies of genetic variation of western conifers, the best variables for
assessing genetic differentiation invariably come from greenhouseshadehouse tests of shoot elongation where precise measurements can be
made while controlling extraneous environmental effects (e.g., mosquitoes
have a definite effect on the quality of the data).
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21. This slide shows different patterns of shoot elongation for spruce populations.
Engelmann results
• genetic differences are obvious
• genetic differences most pronounced for
patterns of shoot elongation
• winter temperatures drive population
differentiation
22. The tests showed these results. They can be displayed by clines in relation
to the climate where the seeds were collected.
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23. The cline is steepest for the warmest climate and flattens out in the coldest
climates. One can then describe clines like this on with regression models.
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regression models
shoot elongation
variable
start
cessation
duration
rate
amount
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predictors
R2
winter temperatures, summer
precipitation
winter temperatures, summer
maximum temperatures
winter temperature, summer
maximum temperature
winter temperatures, summer
max temperatures, freezing dates
winter temperatures, summer
maximum temperatures
0.54
0.81
0.83
0.62
0.73
24. Notice that the best predictors for spruce involve winter temperatures – the
variables that are expected to change the most with global warming. These
models, of course, are suited to predicting responses. But to map responses,
we need to know the climate at point locations on a map grid. The spline
climate model, as shown previously, can be use to estimate the climate of
each of the 6 million pixels for all of the climate variables that are important
in predicting genetic responses in spruce. Then for each pixel, one can estimate the genetic response for a population growing there as if it had been
tested in a common garden. This is what we get:
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25. This map says that the duration of shoot elongation for populations from
throughout western USA varies from 12 to 400 days. It’s nonsense. And,
the reason it doesn’t make sense is that Engelmann spruce does not grow in
all of these pixels. Before we can make sense out of this, we need an estimate
of which pixels are climatically suited for the species.
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mapping distribution of
Engelmann spruce
• 17 climate variables
• Climatic limits of 295 populations
• Canonical discriminant analysis of
9 species (1500 observations)
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26. First map now a second approximation.
27. This slide shows the results from four different attempts to map the Engelmann distribution. They’re pretty good, but all have problems.
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28. This map is the consensus of the four on the previous slide. 11% of the 7
million pixels show suitable climate–Black Hills error, Colorado hole, Sierra Nevada and so on; remember, this is a climate unite. This map will
suffice for this presentation, but one should be aware that we’ve now developed statistical approaches that do a much better job. So, we now have a
rough species map which gives us a basis for predicting genetic responses to
climate. Still, one must remember:
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29. Climate might be right but other things are limiting.
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30. This map is more like it. Continuous variation across landscape, duration
from 21 to 9 – clines steepest for mildest climates but for the results to be
useful to forest managers we need to classify the variation into seed zones
or clime types.
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Climatypes
breadth: ± standard error of the mean for t0.2
for duration of shoot elongation:
zone
interval (days)
1
below 27
2
27-31
3
31-36
4
36-41
5
41-47
6
47-55
7
55-63
8
63-73
9
73-85
31. Climatype classifications.
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32. Geographic Zones for Duration of Shoot Elongation: all populations occupying pixels of the same color are expected to have a similar duration of
shoot elongation when grown in a common garden. Zones are smaller in
mild climates and broader in more severe. However, these zones are for
only 1 variable. For describing genetic variation in this species, we have 5
variables and all need to be taken into consideration.
.
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Engelmann spruce climatypes
variable
zones
duration of elongation
9
amount of elongation
7
cessation of elongation
8
start of elongation
5
rate of elongation
3
33. All possible combinations of these zones would give 3600!! But, we’re lucky.
For western USA, there’s only 286.
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34. Here they are. This may not be 3600, but it’s still a huge number that would
be impractical to administer by management. So, when we think about how
we got to this point, we realize that there were many sources of error along
the way.
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Sources of error
Population effects
sampling errors
experimental errors
Climate data
normalization
fitting splines
Genecology
regression models: population effects on climate
Mapping
DEMs
Climate predictions per DEM
Raster calculator
35. There were sampling errors, experimental errors, errors in climate estimates,
errors in the splines, errors in the DEMs, and errors of prediction – all, we
hope, are tiny. But, there are many sources of error such that the errors of
estimation in delineating seed zones or climatypes can’t be quantified. For
this reason, one can not assume that the boundaries between these zones
are fixed. In fact, 286 climatypes mapped for Englemann spruce are dominated by a few large climatypes.
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climatype
summary statistics
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total climatypes: 268
climatypes with pixels<10: 30
climatypes with pixels>99: 168
area of 20 largest climatypes: 66%
36. Keep in mind that 100 pixels is approximately equivalent to 1 township or
36 square miles. It’s the few large climatypes on which management should
concentrate. We can see the large ones as we zoom in:
37. There are 65 climatypes shown here for Idaho and Montana, but 20 account
for about 75% of the distribution of spruce.
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Global Warming
Amount
Siberia:
+6 to +8 °C
up to +20% (100 mm) ppt
western USA:
+4 to +5 °C
up to +17% (130 mm) ppt
Effect
Siberia: bonanza
western USA: disaster
38. So, let’s look at global warming:
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39. This slide shows a map of the distribution of spruce predicted for the climates of today (upper left), decade of 2030 (upper right), decade of 2060
(lower left), and decade of 2090 (lower right). Obviously, the climates favorable for this species move upwards off the top of the mountains and
northward off the top of the map.
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40. Here’s a gallery of some of the contemporary sites that are expected to have
a climate suitable for spruce at the end of the century. It’s hard to imagine a
orderly migration into such places.
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41. This is complicated, but this slide illustrates how populations and, therefore, species will respond to a change in climate. Each of these figures shows
genetic response functions for two populations, for lodgepole pine on the
left and Scots pine on the right. These results come from provenance tests.
They show that populations have a climatic optimium within which growth
(and survival is optimum). This is the point at the peak of the respective
curves. However, populations differ in growth potential, as shown by the
different heights of the curves. They also differ in cold hardiness, and this is
illustrated by the differences in thex-axis coordinate of the optimum. And,
there is a negative relationship between growth potential of populations
and cold hardiness. Together, these characteristics mean that most populations are competitively excluded from their climatic optima. In fact, only
one population, the one with the highest sit index growing in the mildest
climates, actually occupies its optima. Other populations are relegated to
suboptimal conditions and the degree of suboptimality increases as the climate becomes more and more severe. It’s the degree of suboptimality that
will determine initial responses to global warming. For populations occupying their optima, any warming will be deleterious to growth and survival. But, for populations occupying sites that are colder than their optima,
a warming climate will be advantageous. Consequently, for western USA,
global warming has disastrous consequences in both the short and long terms.
But in Siberia, global warming should be a stimulant to growth and productivity.
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the time factor
• Interspecific effects
– Immigration is slow; extirpation can be fast
– Result is a temporarily impoverished flora
• Intraspecific effects
– Accommodating global warming requires more change
per generation than genetic systems can provide
– Result is a lag in response to change
• Adjusting to global warming may require natural
systems up to 1000 years
• Scariest part of global warming is the speed not
the amount of change
42. When we think about global warming, one tends to concentrate on the
amount of warming. But, in historical perspective, the amount of change
isn’t very much: temperatures fluctuated by about 7C during the Pleistocene.
Plants can adjust to this amount of change. The scary part about global
warming from the viewpoint of plants is the speed.
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finale
• when converted to variables with physiological
importance, 4-5°C increase has huge impact
-- alter species distributions
-- wholesale redistribution of genotypes within
species
• to mitigate the impact, mankind can participate in
the evolutionary processes
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43. Concluding points about global warming. How does mankind participate?
By assisting migration of genotypes to the novel location of their optimal
climates. By planting more trees.
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44. Maps like this one can provide assistance to the manager. The blue shows
the distribution of a climatypes in the contemporary climate. The orange
shows the 2030 projected distribution of the climate inhabited by the
climatypes, the yellow the 2060 distribution, and the pink the 2090 distribution. For mankind to be participating in the evolutionary process, seeds
today could be collected in the blue zone and planted in the orange zone in
anticipation of the change in climate.
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opinion page
• yes, it’s happening
• no, the GCM’s don’t quite have it right
• yes, there is something we can do to
mitigate the effects
• but, it’s the cause not the effect that
needs attention
• buy now Siberian or Yukonian estates,
sit back, and watch the show
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45. Because the cause of the problem is not being addressed, what we can accomplish as individuals is almost nothing compared to the scope of the problem. My suggestion is to buy now while permafrost is still cheap and watch
natural history unfold.
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REFERENCES
Rehfeldt, G.E., C.C. Ying, D.L. Spittlehouse, and D.L. Hamilton. 1999. Genetic responses to
climate in Pinus contorta: niche breadth, climate change, and reforestation. Ecological Monographs 69: 375-407.
Rehfeldt, G.E., C.C. Ying, and W.R. Wykoff. 2001. Physiologic Plasticity, Evolution, and
Impacts of a Changing climate on Pinus contorta. Climatic Change 50: 55-376.
Rehfeldt, G.E., Ying CC, Spittlehouse DL, Hamilton DL (1999) Genetic responses to climate in
Pinus contorta: niche breadth, climate change, and reforestation. Ecological Monographs 69:
375-407.
Rehfeldt, G.E., N.M. Tchebakova, Y.I. Parfenova, W.R. Wykoff, N.A. Kouzmina, and L.I.
Milyutin. 2002. Intraspecific responses to climate in Pinus sylvestris. Global Change Biology 8:
1-18.
Rehfeldt, G.E. 2004, Inter- and intra-specific variation in Picea engelmannii and its congeneric
cohorts: biosystematics, genecology and climate-change. Gen. Tech. Rep. RMRS-GTR-134.
Ft Collins, CO: U.S. Department of Agriculture, Forest Service, Rock Mountain Research
Station.
Rehfeldt, G.E., N.M. Tchebakova, and E. Parfenova. 2004. Genetic responses to climate and
climate change in conifers of the temperate and boreal forests. Recent Advances in Genetics
and Breeding 1: 113-130.
Rehfeldt, G.E. 2005. A spline climate model for western United States. Gen. Tech. Rep. RMRSGTR. Ft Collins, CO: U.S. Department of Agriculture, Forest Service, Rock Mountain
Research Station. In Press.
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