new geographic clines in genetic variation · 2007. 2. 26. · _____geographic clines in genetic...

___________________________________________________________________________________

Workshop Proceedings: Quantitative Techniques for Deriving National Scale Data

223

GEOGRAPHIC CLINES IN GENETIC VARIATION

Gerald Rehfeldt(presented by Albert R. Stage)

USDA Forest ServiceRocky Mountain Research Station

In risk mapping, the primary considerations are the presence of the host tree species, somemeasure of its density, and the distribution of the pest agent. High-density or overstockedstands are often considered to be of higher risk then stands with lower stocking levels; alsoimportant is the climatic stress on the population. This presentation shows how the predic-tions from a climate model can be converted to variables that may indicate the status of thestress 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 fitto geographic surfaces with thin plate splines. These monthlies were then used to describe theclines of genetic variation that exist within species for growth characteristics. The mapping ofclinal variation is useful in delineating seed zones and deriving seed transfer guidelines. Thereverse image of such maps should indicate where the species would be under stress due toclimatic conditions. For this reason, it is recommended that the monthlies and the climaticlimits could be useful in risk mapping.


Rehfeldt _____________________________________________________________________________________

224

1. Genetic variation is displayed along geographic gradients but interpretationis invariably in terms of climate. Out of the files is a geographic cline, Dou-glas-fir vs. elevation. Geographic variation is acting as a surrogate for cli-mate, which is more difficult to measure. Armed with a climate model wethen ready to assess plant-climate relationships. With climate models thatprovided point predictions we cannot replace the surrogates.

In this slide, genetic variation in growth potential measured in a provenancetest of populations of Douglas-fir is related to the elevation of the stand inwhich the seeds were collected.

INTRODUCTION

Genetics research during the last 75 year or so has demonstrated that species of forest trees arecomposed of populations, each of which is adapted (i.e., physiologically attuned) to only aportion of the environmental gradient inhabited by the species. For most of the widespreadspecies, models exist that describe the clinal variation in genetic responses of populationswithin species. These models are invariably driven by geographic predictors. But, now that aclimate model is available that makes point predictions on the landscape, researchers can di-rectly relate and eventually map genetic responses to climate.

_____________________________________________________ Geographic Clines in Genetic Variation


225

climate variables derived from temperature and precipitation monthlies

• 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

• 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

2. There are 48 surfaces form normalized monthlies – weather variables basedon temperature and precipitation.

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.

climate surfaces

• 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


Rehfeldt _____________________________________________________________________________________

2264. 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.



2275. 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 amap of degree-days that clearly shows the major drainages (Snake, Salmon,Clearwater), the Lewiston-Clarkston valley, and the high mountains. De-gree-days range from 2700 to 0 for the pixels in this map.


Rehfeldt _____________________________________________________________________________________

2286. 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 inthe slides. It’s important to know that the climate model itself makes pointpredictions that are not necessarily tied to the DEMs.



2297. Frost-free periods vary from 0 to 365.

8. Negetative degree-days show how severe the winters are.


Rehfeldt _____________________________________________________________________________________

2309. Then using output from the General Circulation Models and refitting the

splines, one can map climates predicted for the future. This map is for de-gree-days>5 and uses the greenhouse gas scenario (1% increase per year) ofthe Hadley and Canadian GCMs. Upper left is contemporary climate, upperright is that for the decade beginning in 2030, lower left for the decade be-ginning 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.



23110. 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 theeffects are expected to be much greater on winter temperatures than on sum-mer temperatures.

Degree-days < 0 Contemporary ranges is 0 to 2250, 2090 range would be 0to 1052.


Rehfeldt _____________________________________________________________________________________

23211. FFP (frost-free period) current on left, 2090 on right..



23312. To me, this one is scary. Global warming, of course, is portrayed as a tem-

perature effect. Yet, the response of plants will be determined by the inter-action of temperature with precipitation. This slide compares the contem-porary 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.


Rehfeldt _____________________________________________________________________________________

23413. Now, armed with the climate model, we’re ready to consider biological

effects. This slide compares our ability to predict genetic responses of popu-lations with geographic predictors (left) and climate predictors (right).

Pinus sylvestris lattitude is a good surrogate for the climate variables.



235

GOALS

assess population differentiation in relation to climate

Pinus sylvestris and Larix sibirica

Picea engelmannii

compare effects of climate change

Siberia vs. western USA

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 thewrong interpretation.

15. Studies of genetic responses to climate included researchers from RMRSand the Sukachev Institute of Forest in Krasnoyarsk, Russia. We had theseobjectives. Only those dealing with USA will be considered here.


Rehfeldt _____________________________________________________________________________________

236

GOALS

assess population differentiation in relation to climate

Pinus sylvestris and Larix sibirica

Picea engelmannii

compare effects of climate change

Siberia vs. western USA

genecology of Engelmann spruce

• 295 populations sampling natural distribution

• 18 blue spruce populations

• 20 white spruce

• common garden studies in Idaho

16. Definitions.

17. The USA example involves Engelmann spruce.



23718. This photo was taken in the provenance test conducted at low elevation at

the Priest River Experimental Forest. The populations are planted in 10-tree row plots. This means that any differences that are apparent betweenrows is due to genetic differences between the populations. At this mildsite, differences are obvious.


Rehfeldt _____________________________________________________________________________________

23819. This is the high elevation planting site. Growth is less at high elevation, and

differences were more difficult to detect.



23920. Yet, in studies of genetic variation of western conifers, the best variables for

assessing genetic differentiation invariably come from greenhouse-shadehouse tests of shoot elongation where precise measurements can bemade while controlling extraneous environmental effects (e.g., mosquitoeshave a definite effect on the quality of the data).


Rehfeldt _____________________________________________________________________________________

240

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 relationto the climate where the seeds were collected.

21. This slide shows different patterns of shoot elongation for spruce popula-tions.



24123. 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.


Rehfeldt _____________________________________________________________________________________

242

regression models

0.73winter temperatures, summer maximum temperatures

amount

0.62winter temperatures, summer max temperatures, freezing dates

rate

0.83winter temperature, summer maximum temperature

duration

0.81winter temperatures, summer maximum temperatures

cessation

0.54winter temperatures, summer precipitation

start

R2predictorsshoot elongation variable

24. Notice that the best predictors for spruce involve winter temperatures – thevariables that are expected to change the most with global warming. Thesemodels, 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 splineclimate model, as shown previously, can be use to estimate the climate ofeach of the 6 million pixels for all of the climate variables that are importantin predicting genetic responses in spruce. Then for each pixel, one can esti-mate the genetic response for a population growing there as if it had beentested in a common garden. This is what we get:



24325. 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 inall of these pixels. Before we can make sense out of this, we need an estimateof which pixels are climatically suited for the species.


Rehfeldt _____________________________________________________________________________________

244

27. This slide shows the results from four different attempts to map the Engel-mann distribution. They’re pretty good, but all have problems.

mapping distribution of Engelmann spruce

• 17 climate variables

• Climatic limits of 295 populations

• Canonical discriminant analysis of 9 species (1500 observations)

26. First map now a second approximation.



24528. 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, Si-erra Nevada and so on; remember, this is a climate unite. This map willsuffice for this presentation, but one should be aware that we’ve now devel-oped statistical approaches that do a much better job. So, we now have arough species map which gives us a basis for predicting genetic responses toclimate. Still, one must remember:


Rehfeldt _____________________________________________________________________________________

246

30. This map is more like it. Continuous variation across landscape, durationfrom 21 to 9 – clines steepest for mildest climates but for the results to beuseful to forest managers we need to classify the variation into seed zonesor clime types.

29. Climate might be right but other things are limiting.



247

Climatypesbreadth: ± standard error of the mean for t0.2for duration of shoot elongation:

interval (days)zone

73-859

63-738

55-637

47-556

41-475

36-414

31-363

27-31 2

below 27 1

31. Climatype classifications.


Rehfeldt _____________________________________________________________________________________

24832. Geographic Zones for Duration of Shoot Elongation: all populations occu-

pying pixels of the same color are expected to have a similar duration ofshoot elongation when grown in a common garden. Zones are smaller inmild climates and broader in more severe. However, these zones are foronly 1 variable. For describing genetic variation in this species, we have 5variables and all need to be taken into consideration.

.



249

Engelmann spruce climatypes

3rate of elongation

5start of elongation

8cessation of elongation

7amount of elongation

9duration of elongation

zonesvariable

33. All possible combinations of these zones would give 3600!! But, we’re lucky.For western USA, there’s only 286.


Rehfeldt _____________________________________________________________________________________

250 34. Here they are. This may not be 3600, but it’s still a huge number that wouldbe impractical to administer by management. So, when we think about howwe got to this point, we realize that there were many sources of error alongthe way.



251

Sources of errorPopulation effects

sampling errorsexperimental errors

Climate datanormalizationfitting splines

Genecologyregression models: population effects on climate

MappingDEMsClimate predictions per DEMRaster 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, wehope, are tiny. But, there are many sources of error such that the errors ofestimation in delineating seed zones or climatypes can’t be quantified. Forthis reason, one can not assume that the boundaries between these zonesare fixed. In fact, 286 climatypes mapped for Englemann spruce are domi-nated by a few large climatypes.


Rehfeldt _____________________________________________________________________________________

252

36. Keep in mind that 100 pixels is approximately equivalent to 1 township or36 square miles. It’s the few large climatypes on which management shouldconcentrate. We can see the large ones as we zoom in:

climatypesummary statistics

• total climatypes: 268

• climatypes with pixels99: 168

• area of 20 largest climatypes: 66%

37. There are 65 climatypes shown here for Idaho and Montana, but 20 accountfor about 75% of the distribution of spruce.



25338. So, let’s look at global warming:

Global Warming

AmountSiberia:

+6 to +8 °Cup to +20% (100 mm) ppt

western USA:+4 to +5 °Cup to +17% (130 mm) ppt

EffectSiberia: bonanzawestern USA: disaster


Rehfeldt _____________________________________________________________________________________

25439. This slide shows a map of the distribution of spruce predicted for the cli-

mates of today (upper left), decade of 2030 (upper right), decade of 2060(lower left), and decade of 2090 (lower right). Obviously, the climates fa-vorable for this species move upwards off the top of the mountains andnorthward off the top of the map.



25540. 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 aorderly migration into such places.


Rehfeldt _____________________________________________________________________________________

25641. This is complicated, but this slide illustrates how populations and, there-

fore, species will respond to a change in climate. Each of these figures showsgenetic response functions for two populations, for lodgepole pine on theleft 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 respectivecurves. However, populations differ in growth potential, as shown by thedifferent heights of the curves. They also differ in cold hardiness, and this isillustrated by the differences in thex-axis coordinate of the optimum. And,there is a negative relationship between growth potential of populationsand cold hardiness. Together, these characteristics mean that most popula-tions are competitively excluded from their climatic optima. In fact, onlyone population, the one with the highest sit index growing in the mildestclimates, actually occupies its optima. Other populations are relegated tosuboptimal conditions and the degree of suboptimality increases as the cli-mate becomes more and more severe. It’s the degree of suboptimality thatwill determine initial responses to global warming. For populations occu-pying their optima, any warming will be deleterious to growth and sur-vival. 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 pro-ductivity.



25742. When we think about global warming, one tends to concentrate on the

amount of warming. But, in historical perspective, the amount of changeisn’t very much: temperatures fluctuated by about 7C during the Pleistocene.Plants can adjust to this amount of change. The scary part about globalwarming from the viewpoint of plants is the speed.

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


Rehfeldt _____________________________________________________________________________________

25843. Concluding points about global warming. How does mankind participate?

By assisting migration of genotypes to the novel location of their optimalclimates. By planting more trees.

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



25944. Maps like this one can provide assistance to the manager. The blue shows

the distribution of a climatypes in the contemporary climate. The orangeshows the 2030 projected distribution of the climate inhabited by theclimatypes, the yellow the 2060 distribution, and the pink the 2090 distri-bution. For mankind to be participating in the evolutionary process, seedstoday could be collected in the blue zone and planted in the orange zone inanticipation of the change in climate.


Rehfeldt _____________________________________________________________________________________

26045. Because the cause of the problem is not being addressed, what we can ac-

complish as individuals is almost nothing compared to the scope of the prob-lem. My suggestion is to buy now while permafrost is still cheap and watchnatural history unfold.

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



261

REFERENCES

Rehfeldt, G.E., C.C. Ying, D.L. Spittlehouse, and D.L. Hamilton. 1999. Genetic responses toclimate in Pinus contorta: niche breadth, climate change, and reforestation. Ecological Mono-graphs 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 inPinus 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 congenericcohorts: biosystematics, genecology and climate-change. Gen. Tech. Rep. RMRS-GTR-134.Ft Collins, CO: U.S. Department of Agriculture, Forest Service, Rock Mountain ResearchStation.

Rehfeldt, G.E., N.M. Tchebakova, and E. Parfenova. 2004. Genetic responses to climate andclimate change in conifers of the temperate and boreal forests. Recent Advances in Geneticsand Breeding 1: 113-130.

Rehfeldt, G.E. 2005. A spline climate model for western United States. Gen. Tech. Rep. RMRS-GTR. Ft Collins, CO: U.S. Department of Agriculture, Forest Service, Rock MountainResearch Station. In Press.

new geographic clines in genetic variation · 2007. 2. 26. · _____geographic clines in genetic...

Documents