Die Waldvegetation des Onsernonetals und ihreEntwicklungstendenzen

Gabriele Carraro Pippo Gianoni Annette Fietz

04.01.2021

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q IntersiziplinäreArbeitsgruppe mit10 versch. Beiträge(Vegetationskunde, Klimatologie, Palynonlogie, Geschichte, …)

q Bristol-Stiftungq Nationalpark-

Projektq Dionea SA

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Overview of the research

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q Area of investigation14’890 ha with 75% Wald

Investigation area

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q Border region of Switzerland and Italy, 300 – 2550 m a.s.l

Investigation area

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q Big, quitenaturalforestcomplexes.

q Mainly lessdisturbedduring the past 70 years

q National Park Projekt

A forest landscape

Val Onsernone Foto:PNL

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The remaning forest 70 years ago

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The remaning forest 117 years ago

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q Today 75% of the study area are covered by dense compact forest

Change of landscape - forest

Forest development in the Onsernone valley Source: PNL, 2016

Forest Open areasSparse woodland

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Change of landscape - agriculture

q Decrease of agricultural areas

1885

today

Foto archive: R&R Carazzetti

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Ravine forest formation of low altitude (300-700m)

Luzuloniveae-Tilietum

25f

Schneesimsen-Linden-mischwaldin Schlucht

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IL NOSTRO PAESAGGIO SI TRASFORMA…Mountain Maple-Alpine Laburnum forest

Laburno-Aceretum

21*

Goldregen-Ahornwald

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IL NOSTRO PAESAGGIO SI TRASFORMA…Typical forest of the mountain belt (600-1200m)

Luzuloniveae-Fagetumtypicum und drayoperidetosum

3SLMontane Schneesimsen-Buchen-wälder (Ein. 3+4)

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Climax forest of fir and beech (600-1500m)

Source: Dionea SA

Luzulo-Abieti-Fagetumcalamagrostietosumund Laburno-Abieti-Fagetum

19SL

Buchen-Tannenwälder(Ein. 19a+19L)

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Source: Dionea SA

Subalpine forest formation (1650-1900m)

Rhododendro-Abietetum

47*SL

Alpine rose- FirLarch

AlpenrosenLärchen-Tannenwälder (47* + 47*F + 47*L, …)

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Objectives

q Elaboration of an area-wide, up-to-date forest vegetation map (large-scale mapping)

q Presentation of the forest and landscape development as useful information for the management of forest ecosystems and biodiversity

q Enhancement of the Johannes Bär vegetation map (1915) as well a climate change scenario (2075 RegCM3 T +3,5°C; P -10%mm)

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Johannes Bär (1877-1957) in Onsernone 1905-1911

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q Surveys by diverse of the past: Klötzli, Keller, Stampfli + Dionea (100km)

q 57 forest types, 31 zonal, 26 azonalregrouped in 32 macro-units

q Sampling design of the terrestrialsurveys >> 3200 sampling points (7-10% of the area)

Terrestrial survey based on typology system

Map of sampling areas

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Vegetation map 1:50’000 of Johannes Bär 1915

q Outstanding, detailed work

q Differentiation of vegetation units based on distribution of main forest-trees and their density

q Great scientific benefit for today’s understanding of evolutionary dynamics Ufficio Federale di Topografia, Dionea SA

Updating Bär’s vegetation map21

q Harmonisation and transformation of Bär’s vegetation units to current classification

q Resulting in 31 current vegetation macro-units

original

Updated version

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Environmental variables

Daily maximum temperature in July °C 25x25mMean temperature July °C 25x25mAbsolut minimum temperature of the year °C 25x25mDuration of global radiation within the vegetationperiod h 5x5Global radiation July W/m2 25x25mRelation actual to potential evapotranspiration 1 25x25mRelative humidity % 25x25mFrequency of Föhn wind % 25x25mKontinentalität in April °C 25x25mAltitude a.s.l m 5x5Distance to water surfaces m 5x5Screes m2 5x5Geology 5 classes 5x5Profile shape (convex, concave) 3 classes 5x5Slope gradient degree 5x5Exposition 4 classes 5x5Development of forest 7 classes 5x5

phys

iolo

gic

base

d ph

ysio

grap

hic

base

d

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Objectives:

q Provide an instrument for large scale mapping in regions with difficult accessibility

q Current or potential area-wide distribution of the forest vegetation

q Prediction of development under current or withclimate change condition

Modelling

Forest types Environmental variables

Sampling design3200 points

GIS Database

Random Forest (RF) Model

R Softwarentrees

…

…

1st

2nd

…

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Prediction results

Abb.41 grafische Darstellung der Confusion matrix Ffür Tannenbuchenwald (19SL) und Birken-Hasel Buschwald

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Model performance and evaluation

q Zonal formations reach better results than azonal formations

Correct prediction CLU MAX

Overall model 53,3 % 58,8%

Best forecasted forest type ~85% ~ 90%

1st and 2nd most predicted type together 70 % 88%

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Random Forest Model results

q Given the large number of possible forest units (32), the model provides interesting results: a total of 53.6 percent were correctly forecasted = forest type predicted with the highest probability + another 19.6 percent from the second forecast.

q Validity of the model depends on quality of terrain data (observed forest type vegetation) and of the environmental variables (problematic data for geology, morphology, azonal drivers). Prediction of new types is not possible (e.g Mediterranean units like macchia).

q Further improvements by the vegetation raw model map remain indispensable, based on counter-slope observations, aerial plots and on-site controls.

Actual forest vegetation27

Updated version

Actual forest vegetation map 1:25’00028

Updated version

Potential vegetation under actual climate 1960-199029

Updated version

Potential vegetation with moderate climate warming30

Updated version

Scenario Reg CM3 for 2075:

+3.5°C- 10%mm/Y

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Winner and losers by Bär and today 2015

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Historic development of vegetation

q Reforestation across all altitude belts at the expense of open areas, remarkable increase of dense forest

q Decrease of pioneer formations with birch tree

q Stability of azonal formations in areasalready not used for agriculture in the past

Abb.51

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Historic development of climax formations

q Regeneration towards the respective climax of zonal formations (more stability)

q Delayed development towards the climax in lower altitudes due to former anthropogenic influences

q Decrease of spruce tree (Picea)

q Regeneration of Lime, Maple, Elm and Alder (absent or very rare species in the past: local extinction)

1915

2015

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Winner and losers potentials by climate warming

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Synthesis of the dynamic

q Forest formations succeded in overcoming the strong degradation of the past, with a today‘s coverage of 70%, mostly dense forests

q Fir-beech tree formation can be considered as stable elements spreading towards higher altitudes even with climate warming scenario

q New formations of beech-holly and Laurisilva (3L / 93) are the mainly potential vegetation in lower altitudes (Klötzli, 1988)

q Increase of oak formation with mediterranean character (Q.pubescens), could reach 1500m on shallow, sun-exposed slope with potential for new mediterranean species

q Loss of larch forest and open alpine areas, in a middle term are to considered at risk

Non lnear phenomena: paraclimax: Fagetum <> Abieti-Fagetum

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Updated version

Laurisilvae37

Updated version

q Carraro G., Klötzli F., Gianoni P. Walther (1999)

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Perspectives for the vegetation mapping based on forest typology approach

q The reference to a modern forest typology system is of great advantage for the dynamic studies and the development of models.

q A modern forest typology system allow to develop a guidancefor specific evolutionary processes in favour of biodiversity or other ecosystem performance: resettlement of (new) species (tree-app), control of game, evaluating para-climax situations,

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Dynamic and forest typology approach

q Forest typologies are powerful tool understanding and forecasting the potential climax and for a better performance in the forest management (resilience, biodiversity, storage of biomass and CO2, protection functions, …)

q Hystorical materials of the past century as vegetation inventories, maps, plots, surveys, … represent an important heritage for the research of the forest ecosystem dynamic.

q Grazie !