Intraspecific variation in climate response of Norway spruce in the eastern Alpine range: Selecting appropriate provenances for future climate

Abstract Enhancing adaptation of forest ecosystems to prospective climate change is a major challenge in current forest management. Beyond potential negative effects of climate change such as decreasing productivity due to an increasing number of drought periods and damages from intensified disturbance regimes, there is also a potential for increasing productivity due to prolonged vegetation periods and higher photosynthetic rates. Quantitative genetic variation is crucial for adaptability of species towards environmental changes. The use of suitable reproductive material for forest regeneration will be a key factor essential for both, mitigating negative effects and making the most of potential positive effects. Therefore, insights into intraspecific variation within and among tree populations in climate response are of paramount importance. In our study we investigated intraspecific variation in climate response among Norway spruce (Picea abies) populations in the eastern Alpine range. Results from a comprehensive Austrian provenance test, comprising tree heights at age 15 from 379 populations planted at 29 test sites across Austria, were used to calibrate climate response functions for groups of Norway spruce populations. Potential future changes in productivity for climate change conditions as represented by a regionalized A1B scenario were estimated using height at age 15 as a productivity proxy. Climate response functions were calculated for single populations and aggregated clusters of populations from climatically similar origins. Our results hardly revealed any declines in employed proxies for productivity of Norway spruce throughout its current distribution range in Austria. For most parts of Austria an increase of tree heights up to 45 percent can be expected until 2080. However, the impact of a warming climate is different for individual population groups. Generally, variation in climate response increases with higher temperatures and less precipitation. Thus, an optimized choice of seed material according to prospective future climate conditions has the potential for an additional increase of productivity up to 11 percent. In general, populations from currently warm and drought prone areas seem to be well adapted to respective climate conditions and may be appropriate candidates for extended utilization in future. Furthermore, populations showing the best productivity indices originate from regions, which are phylogenetically distinct from the core distribution area of Norway spruce, suggesting that population history might explain part of the variation in climate response among populations.

[1]  O. Junttila,et al.  Climatic control of bud burst in young seedlings of nine provenances of Norway spruce. , 2008, Tree physiology.

[2]  P. Krutzsch IUFRO's role in coniferous tree improvement: Norway spruce (Picea abies (L.) Karst.). , 1992 .

[3]  S. Aitken,et al.  Ecological genetics and seed transfer guidelines for Pinus albicaulis (Pinaceae). , 2008, American journal of botany.

[4]  N. Crookston,et al.  Quantifying the abundance of co-occurring conifers along Inland Northwest (USA) climate gradients. , 2008, Ecology.

[5]  K. K. Carter Provenance tests as indicators of growth response to climate change in 10 north temperate tree species , 1996 .

[6]  E. Beale,et al.  Confidence Regions in Non‐Linear Estimation , 1960 .

[7]  H. Spiecker,et al.  Radial growth variation of Norway spruce (Picea abies (L.) Karst.) across latitudinal and altitudinal gradients in central and northern Europe , 2002 .

[8]  F. Gugerli,et al.  Genetic consequences of glacial survival and postglacial colonization in Norway spruce: combined analysis of mitochondrial DNA and fossil pollen , 2008, Molecular ecology.

[9]  C. Andalo,et al.  The impact of climate change on growth of local white spruce populations in Québec, Canada , 2005 .

[10]  Andy Jarvis,et al.  Downscaling Global Circulation Model Outputs: The Delta Method Decision and Policy Analysis Working Paper No. 1 , 2010 .

[11]  D. Spittlehouse,et al.  GENETIC RESPONSES TO CLIMATE IN PINUS CONTORTA: NICHE BREADTH, CLIMATE CHANGE, AND REFORESTATION , 1999 .

[12]  R. Schmidtling Use of provenance tests to predict response to climate change: loblolly pine and Norway spruce. , 1994, Tree physiology.

[13]  Response of Picea abies populations from elevational transects in the Polish Sudety and Carpathian mountains to simulated drought stress , 2002 .

[14]  W. Connolley,et al.  An Antarctic assessment of IPCC AR4 coupled models , 2007 .

[15]  R. Schubert,et al.  Genetic Response of Forest Systems to Changing Environmental Conditions , 2001, Forestry Sciences.

[16]  Tongli Wang,et al.  Accounting for population variation improves estimates of the impact of climate change on species’ growth and distribution , 2008 .

[17]  J. Hamrick,et al.  Effects of life history traits on genetic diversity in plant species , 1996 .

[18]  Hugh G. Gauch,et al.  Fitting the Gaussian Curve to Ecological Data , 1974 .

[19]  F. Maghuly,et al.  Differentiation among Austrian populations of Norway spruce [Picea abies (L.) Karst.] assayed by mitochondrial DNA markers , 2007, Tree Genetics & Genomes.

[20]  Nicholas K. Ukrainetz,et al.  Comparison of fixed and focal point seed transfer systems for reforestation and assisted migration: a case study for interior spruce in British Columbia , 2011 .

[21]  Juho Matala,et al.  Modelling the response of tree growth to temperature and CO2 elevation as related to the fertility and current temperature sum of a site , 2006 .

[22]  E. Morgenstern Geographic Variation in Forest Trees: Genetic Basis and Application of Knowledge in Silviculture , 2004 .

[23]  Ø. Johnsen,et al.  Provenances and families show different patterns of relationship between bud set and frost hardiness in Picea abies , 2000 .

[24]  A. Hamann,et al.  Use of response functions in selecting lodgepole pine populations for future climates , 2006 .

[25]  Wolfgang Cramer,et al.  A simulation model for the transient effects of climate change on forest landscapes , 1993 .

[26]  H. Kromp-Kolb,et al.  The sensitivity of Austrian forests to scenarios of climatic change: a large-scale risk assessment based on a modified gap model and forest inventory data , 2002 .

[27]  Marc Hanewinkel,et al.  Modelling and economic evaluation of forest biome shifts under climate change in Southwest Germany. , 2010 .

[28]  Manfred J. Lexer,et al.  Assessing trade-offs between carbon sequestration and timber production within a framework of multi-purpose forestry in Austria , 2007 .

[29]  S. Schueler,et al.  Sub-montane Norway spruce as alternative seed source for a changing climate? A genetic and growth analysis at the fringe of its natural range in Austria. , 2010 .

[30]  M. Bindi,et al.  Impacts of Present and Future Climate Variability on Agriculture and Forestry in the Temperate Regions: Europe , 2005 .

[31]  Andreas Hamann,et al.  Developing seed zones and transfer guidelines with multivariate regression trees , 2011, Tree Genetics & Genomes.

[32]  S. Yeaman,et al.  Regional heterogeneity and gene flow maintain variance in a quantitative trait within populations of lodgepole pine , 2006, Proceedings of the Royal Society B: Biological Sciences.

[33]  G. O'neill,et al.  Using a spatiotemporal climate model to assess population-level Douglas-fir growth sensitivity to climate change across large climatic gradients in British Columbia, Canada , 2011 .

[34]  G. Nigh,et al.  Linking population genetics and tree height growth models to predict impacts of climate change on forest production , 2011 .

[35]  J. Favre,et al.  Geographical variation in random amplified polymorphic DNA and quantitative traits in Norway spruce , 2002 .

[36]  Alexei G. Sankovski,et al.  Special report on emissions scenarios : a special report of Working group III of the Intergovernmental Panel on Climate Change , 2000 .

[37]  G. Howe,et al.  Genetic maladaptation of coastal Douglas‐fir seedlings to future climates , 2007 .

[38]  T. Knürr,et al.  Gene Flow and Local Adaptation in Trees , 2007 .

[39]  H. Peltola,et al.  Diameter growth of Scots pine (Pinus sylvestris) trees grown at elevated temperature and carbon dioxide concentration under boreal conditions. , 2002, Tree physiology.

[40]  O. Langlet Two hundred years genecology. , 1971 .

[41]  T. Hlásny,et al.  Adaptation to common optimum in different populations of Norway spruce (Picea abies Karst.) , 2011, European Journal of Forest Research.

[42]  C. Mátyás Modeling climate change effects with provenance test data. , 1994, Tree physiology.

[43]  R. Petit,et al.  Forests of the Past: A Window to Future Changes , 2008, Science.

[44]  C. Graham,et al.  Within‐taxon niche structure: niche conservatism, divergence and predicted effects of climate change , 2010 .

[45]  G. Rehfeldt,et al.  Assessing Population Responses to Climate in Pinus sylvestris and Larix spp. of Eurasia with Climate-Transfer Models , 2003 .

[46]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[47]  A. Barbati,et al.  Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems , 2010 .

[48]  N. Breda,et al.  Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences , 2006 .

[49]  J. Hamrick,et al.  Factors influencing levels of genetic diversity in woody plant species , 1992, New Forests.

[50]  M. Schwartz,et al.  A Framework for Debate of Assisted Migration in an Era of Climate Change , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[51]  R. Shaw,et al.  Range shifts and adaptive responses to Quaternary climate change. , 2001, Science.

[52]  G. Rehfeldt,et al.  Intraspecific responses to climate in Pinus sylvestris , 2002 .

[53]  CONFIDENCE INTERVALS FOR THE OPTIMUM IN THE GAUSSIAN RESPONSE FUNCTION , 2001 .

[54]  A. O. Nicholls,et al.  Determining species response functions to an environmental gradient by means of a β‐function , 1994 .