„Hoarding data for times of uncertainty”

– experiences from pan-European geneticnetworks

Csaba MátyásUniversity of West Hungary, Institute of Environmental and Earth Sciences,

NEESPI Focus Research Center for Nonboreal Eastern Europe Sopron, Hungary

Forest (genetic) research in rapidly changing times (contents)

• Do we handle the study of living systems properly?• Ailments of research: empirical (field) research

loosing ground• Does the relevance of tests and datasets expire?• Why is field experimentation indispensable?

An exemplary story of a long-term test networkfate and echo of the IUFRO Norway spruce provenance test network

Advent of an age of uncertainty

Hume: „laws of Nature”: an event may be predicted only with some level of certaintyEinstein: “As our circle of knowledge expands, so does the circumference of darkness surrounding it.” 

Troubled prestige of empirical (field) researchHeisenberg’s uncertainty principle: "The more precisely the position is determined, the less precisely the momentum is known"Analogy in forest research: Studying a population in a controlled milieu, error variation will decline; however the results are uncertainly applicable in the real environment of regeneration, competition and survival, the plants have adapted to.

Are empirical tests and their data properly appreciated and valued?

Case study: the IUFRO Norway spruce provenance experiment 1964/68

• Initiated by O. Langlet and IUFRO, managed by P. Krutzsch

• an inventory, investigation of the intraspecific genetic variation

• The goal was to select for further tree breeding• Outplanted: spring 1968 • Layout: 1100 provenances, in 11 blocks,

one-tree plots, 25 reps• Trial network: 20 field trials in 13 countries

Natural distribution of Norway spruce and originof tested 1100 provenances (star: test site Nyírjes , Hungary)

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Location of the 20 sites of the trial in Europe and Canada

Recsk, EF, 16 év (kísérlet főátlag: 11,09 m)

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The results, presented…

R2 = 0,2964

R2 = 0,0093

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Annual mean temperature of the site of origin (oC)

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Average tree height (Y axis) of Norway spruce at age 16 and within-population stdev.of height

versus annual mean temperature of the location of origin

Norway spruce provenance test, Nyírjes, Hungary

A new glance:analyzing response to

changed conditions

New goal: mimickingeffects of predicted

climate change

Multiple regression of height at age 16 of 290 provenances versus main variables of response

(spring temp. and spring prec. change)

Tested provenances: all (n = 290)3 D Contour Plot of H'16 against DTspr and DPspr

H'16 = 934.61+4.15(DTspr)+0.29(DPspr)-5.75(DTspr)2-0.16(DTspr)(DPspr)

Nyírjes

Prov. H'16

Local

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Change of spring temperature (DTspr; oC)

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Productivity and plasticity differences between groupsEffect of mean annual temperature change

on 16-year relative height

Analysis of identic populations in parallel tests

5 investigated tests: Lisjö, Abild, Lappkojberget(Sweden), Krynica (Poland ) and Nyírjes (Hungary)1. Absolute height differences: identical test

material across sites!2. Relative height: standardisation of growth

Goal: change of performance across sites→„reaction norm” = test of stability (plasticity)

16-year absolute height vs. mean ann. temperature changelow-elevation: empty symbols, dotted lineshigh-elevation: full symbols, full lines

16-year relative height vs. mean ann. temperature changelow-elevation: empty symbols, dotted lineshigh-elevation: full symbols, full lines

Comparison of responses at climatically distant sites

• Transfer functions: between-provenance variation vs. climate • Height in 3 tests: two Swedish and Hungary

– nearly equidistant, from the thermic (upper) to the xeric (lower) limits Responses of identic populations indicate:• Site potential difference: decrease of production towards

the north• Growth differentiation: with improving conditions

differentiation is increasing• Survival: strong differentiation by test conditions

Height 16 of identic Norway spruce provenances vs mean annual temp.

NyírjesR2 = 0,09

Mean: 97 %

AbildR2 = 0,19

Mean: 65 %

LappkojbergetR2 = 0,10

Mean: 41 %

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Mean annual temperature at origin (Tyear; C)

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Survival at age 16 (%) of 291 identic provenances at 3 test sites, versus mean temperature at origin. Arrows: temp. at test

The provenance trial as a reference basis

Reference basis for molecular genetic analysis Postglacial recolonisation confirmed:• identification of haplotypes: G. Mátyás, C. Sperisen 1995-

1998 (Sperisen et al.1999) • variation assessed in the mitochondrial nad1 gene (Tollefsrud

et al. 2008).Reference basis for reconstitution of genetic resources• Extinct provenance 0293 Kolonowskie interest for

reforestation and breeding (Giertych 1978). • Scions collected in the Polish trial used for reconstitution seed

orchard

PCA clusters establishedfrom mtDNA marker data,sampled in Hungary (Tollefsrud et al. 2008)

“Trees live longer than research concepts” (D. Lindgren)

Provenance tests in rapidly changing timesamong the largest contributions of forest science

to evolutionary biology? (Mátyás 1996)The experiments and data provide unique opportunitiesPermit to glance into an uncertain future:

study the genetic background of adaptation

Did the trial network gratify the expectations?Fate of a long-term trial network

over two human generations

Echo of trial over four decadesField experiments Authors

01 Canada Coles (1976), Fowler (1979)

02 Ireland Nieuwenhuis –Rostami (1993)

03 England Lines (1973,1979)

04, 05 Norway

Dietrichson (1976), Magnesen(1976), Dietrichson (1979),Skrøppa –Dietrichson (1986), Fottland – Skrøppa (1989), Skrøppa (1993)

06, 07, 08 SwedenKrutzsch (1975, 1976, 1979), A. Persson – B. Persson (1992, 1997)

09 Finland

10 France Christophe (1976), V. D. Sype(1997)

Field experiments Authors

11, 12 Belgium de Jamblinne (1976), Nanson (1976)

13, 14, 15 Germany

König (1976, 1981, 1989), Rau (1993), Kannenberg – Gross (1999), Liesebach et al. (2001) [Weisgerber (1976 a,b, 1977, 1984), Liesebach (2010)]

16 Scotland Lines (1973, 1976, 1979)

17 Czechia Vins (1976, 1979)

18 Austria Günzl (1979a, 1979b)

19 PolandBałut (1979), Sabor (1979,1997), Bałut – Sabor (2001, 2002), Masternak et al. (2009)

20 Hungary

Szőnyi – Ujvári (1970, 1975), Ujvári-Jármay & Ujvári (1979, 1980, 1992/93, 2006), Ujvári-Jármay et al. (2000/2001), Liesebach et al. (2001), Mátyás et al. (2009, 2010)

Diagnosis: general ailments of contemporary research policy

exemplifiedShortsightedness of human endeavour and power of political- economic actualitiesPrestige loss of empirical investigation• absence of holistic, wide-ranging overview• Dominance of indoor modelling and lab work• Weak links to practice (IF as substitute?)• Short-term projects and policy decisions• indifference to reinvent, reinterpret

Preparing for an uncertain future: why do we need the field trials?

Chaotic regulation in living systems: a system changing in time

• determined by at least 3 independent variables• responses are nonlinear• sensitive to starting conditions (response

depends on initial situation)• responses appear within a certain n-dimensional region (attractor

space)

Past observations cannot be projected into far future: close following of field trials indispensable!

Importance of extreme events

Instant genetic selection: snowbrake of Plat. de Jura (F) Beech provenance test, Straza, Slovenia, 2013 photo Bozic

Torup, Skane, S. Sweden

Farchau, Schleswig-H.

„Empty plot”: direct evidence of (genetic) tolerance limit

Beech prov. test, Hungary

Low infection tolerance of provenance from arid environment:heavy needlecast infection of Central Asian Scots pine

Beshkarachaisk, Kazakhstan, in common garden test, Hungary

Résumé: necessity of long-term field datareason: unpredictable system behaviourchaotic regulation requires constant followupchanging determining factors: past empiric

experiences insufficienthuman linear thinking vs non-linear responses reliable projections require follow-up +

documentation of system behaviour indispensable for policy & management adjustment: maintain long-term trials,systematically monitor permanent plots,protect time series and baseline data sets,AND: adjust research tasks to new challenges!

Thank you for the privilege of having shared my thoughts with you!

Source of the spruce examples:Ujvári-Jármay, É. – L. Nagy – Cs. Mátyás (2016) : The IUFRO 1964/68 inventory provenance trial of Norway spruce in Nyírjes, Hungary – results and conclusions of five decades. Acta Silvatica & Lignaria Hungarica , Vol. 12, special edition, 178 p. DOI: 10.1515/aslh-2016-0001