EFIMED Advanced course on MODELLING MEDITERRANEAN FOREST STAND DYNAMICS FOR FOREST MANAGEMENT Mediterranean forest management and planning: the need for.

EFIMED Advanced course onMODELLING MEDITERRANEAN FOREST STAND

DYNAMICS FOR FOREST MANAGEMENT

Mediterranean forest management and planning: the need for simulation models

MARC PALAHIHead of EFIMED Office

20.8.20042

Contents

1 Some features of Mediterranean forests

2 Some concepts on forest planning

3 Simulation: a key step

20.8.20043

Features of Mediterranean forests

Long history of manipulation by man

Many types of natural vegetation and high biological diversity

Relevance of their protective, social and ecological functions

versus the productive ones (externalities)

Fragility, instability, over-exploitation (south) & fires (north)

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The natural vegetation

The climate and the altitude factor makes possible various vegetation zones:

-Thermo-Thermo-, MesoMeso-: Q. ilex, Q. suber, P. halepensis, P. brutia

-Supra-: Q. robur, Fraxinus spp., P. nigra-Montane- and Oro-: Cedrus, Fagus sylvatica, P.

sylvestris

Agro-silvo-pastoral systems: dehesa or montado

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High biological diversity

The Mediterranean area harbours 25000 plant species (50 %

endemic) whereas in central and northern Europe (an area 4

time greater) 6000 plant species can be found

In forest tree species: 100 vs. 30, respectively

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Many non-wood forest outputs

highly demanded by the society• non-wood products: cork, grazing, resin, mushrooms,

aromatic plants, honey, fruits (pinecones and acorns), truffles, game,

• services and externalities: soil protection, flood and avalanche prevention, landscape quality, nature conservation, recreation possibilities, micro-climate regulation

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Forest management planningComplex problem because of multiple

competing/complementary objectives/products

NEEDS

New models, techniques and tools to support decision

making in forestry

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Multi-objective forest planning

1 Features of Mediterranean forests

2 Some concepts on forest planning

3 Simulation: a key step

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Forest Planning?

Planning

• finds the optimal way to use forest resources

• maximises the production of goods and services

• those goods and services considered which are important to the forest owner/society (goal based)

• always utility maximisation & optimisation

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Planning, not an easy task…

Many goals to address the multiple functions

Many parties (locals, government, ecologists)

Long time horizons (as compare to agri)

Risk and uncertainty

Many alternatives

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Framework of modern planning- A quantitative approach

Decision maker Forest ecosystem

Inventory dataPreferences Models

Objectives and constraints

Information about alternatives

Comparisons

Decision

SIMULATION

OPTIMIZATION

Growth models

Fire risk models

Habitat models

Mushroom models

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Simulation: a key step in planning

Decision Support

Planning

Simulation

Data Management

Measurement

Forest Ecosystem

Decision maker Forest ecosystem

Inventory dataPreferences Models

Objectives and constraints

Information about alternatives

Optimizations

Plan

Simulation

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Stand development

GrowthMortality Ingrowth

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Forest stand development affected byRegenerationGrowth of trees MortalityHuman interventions

Models should be able to predict these processes which are

affected by factors like

• Productive capacity of an area – site quality

• Degree to which the site is occupied – density/competition

• Point in time in stand development - age

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Modeling forest development is needed to

provide tools that enable foresters to compare alternative

silvicultural treatments

predict the economic returns of a management schedule but also to

produce information about the dynamic change of less tangible

attributes of forests

generate silvicultural instructions for different species, sites and

management types

0

100

200

300

400

500

600

700

800

900

0 20 40 60 80 100 120 140

Age (years)

Vo

lum

e (

m3/h

a)

SI-17

SI-24

SI-30

Simulation of silvicultural alternatives

0

10

20

30

40

50

60

40 60 80 100 120 140

Stand age (years)

Ba

sa

l are

a (

m2 h

a-1

)

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Simulation modelscomprise of:

• a series of mathematical equations

• numerical values embedded in those equations

• the logic necessary to link these equations

• the computer code required to implement the model on a computer

Mathematically, e.g.;

Dbh-Increment = a + b dbh + c Site + d Basal area

Height = a + b dbh

Volume = a + b Height + c dbh2

….

Modelling

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SimulationTreatment schedules for stands

• Example:

1. Do nothing

2. Thinning

3. Clear-felling / selective cutting

Purpose: produce information for planning

• Stand development

• Harvested timber/firewood volume

• Costs and incomes

• Biodiversity indices

• Non-wood forest products

Results in a Decision Space

= Combinations of stands’ treatment schedules

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Simulation programSimulation program combines

• inventory data

• models

• rules for producing alternatives (”instructions”)

Models used

• models on stand dynamics

• regeneration

• growth

• mortality

• models on allometric relationships

• height = f(diameter)

• volume = f(diameter, height)

• other models (fire risk, mushroom, habitat models)

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Multi-objective Planning

Write a planning model using

• information from simulations by using models

• information on preferences -> objectives

• Planning model writer

Solve the model using

• mathematical programming

• heuristics

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Decision support systems

• Computer system which supports rather than replaces the

decision maker

• User-friendly interface

• Planning system (data base, simulation and optimisation)

augmented with e.g.

• Comparison tools

• Visualisation tool

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20.8.200422

20.8.200423

EFIMED Annual meeting 26-27 of october 2007

University of Valladolid, Palencia, Spain

Modelling the production of wild mushrooms

in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees

José Antonio Bonet , Timo Pukkala, Christine Fischer, Marc Palahí, Juan Martínez de Aragón, Carlos Colinas

20.8.200424

New forestry context

Recent socio-economic changes have accentuated the

multifunctionality of forest ecosystems

- economic development/ increasing living standards

- time for leisure and environmental awareness

- urbanization of society/depopulation of rural areas

- lack of man power and profitability (high costs, prices-globalization)

From Productive functions to Environmental and Social functions, which

need to be addressed in forest management planning (biodiversity,

recreation, scenic beauty, non-timber products, etc)

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Importance of mushroomsContrary to timber, non-timber products

have maintained their prize

Market demand has increased

Annual revenue from 478 metric tons of L. deliciosus sold in the

central Barcelona market (Mercabarna) is estimated at 1.5 - 2 million €.

Mushroom picking is a major recreational activity

valuation studies estimate that Catalans are willing to pay an

average of 6 € year-1 to be able to pick mushrooms

Pinus sylvestris forests in Catalonia can produce 60 kg ha-1 of edible

mushrooms

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Managing forests for mushroom production

The social and economic importance of mushroom picking requires

mushroom production to be an explicit management objective in forest

planning

Quantitative scientifically based forest planning requires models to

predict the yield of mushroom according to forest stand characteristics

and management practices

No such predictive models

were found in the literature

for mushrooms

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Aim of the study

To develop empirical models for predicting the production of wild

mushrooms in Scots pine (Pinus sylvestris L.) forests in the Central

Pyrenees based on mushroom production data from three

consecutive years.

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Mushroom data

In 1995, 36 plots of 10 x 10 meters were established in Pinus sylvestris

plantations of the Central Pyrenees to evaluate the productivity and

diversity of ectomycorrhizal and edible fungi in this forest community.

The plots were sampled at 1-week intervals from September through

November during the 1995, 1996 and 1997 autumn seasons

We used the following groupings in the model: all species, the edible

species, the marketed edible species, and the marketed edible

Lactarius species.

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Forest data

24 plots inventoried in 2006 to measure site and growing stock variables

(other plots had been cut or significantly transformed through management)

Plots (area varying between 0.04-0.16 ha) were established so that at least

100 trees with dbh> 7.5 cm were within the plot.

Dbh and the growth for the last ten years were measured for all trees and tree

heights, tree age and bark thicknesses were recorded for a sample of at least

20 trees per plot.

Variable Mean Standard deviation Minimum Maximum

Stand variables

T (yr) 27.9 12.4 10.4 55.3

Hdom (m) 12.3 4.4 3.3 20.0

G (m2 ha-1) 20.6 13.6 1.0 54.8

Ntrees (trees ha-1) 1171.7 392.3 717.2 2196.3

Dm (cm) 17.2 7.0 4.9 34.2

SI (m) 22.3 2.9 13.3 27.5

Elevation (m) 1238.8 220.2 846.0 1528.0

Aspect (degrees) 179.9 130.4 4.0 356.0

Slope (%) 24.1 7.2 7.0 38.0

Mushroom productions

Total (kg ha-1) 123.7 135.2 0.2 466.6

Edible (kg ha-1) 63.0 75.0 0.2 283.4

Marketed (kg ha-1) 25.6 39.1 0.2 153.4

Lactarius (kg ha-1) 7.9 21.6 0.0 104.5

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Modelling mushroom production

Mushroom production depends very much on weather conditions but

also on the forest site and growing stock characteristics

However, in forest planning, variables that can be known trough regular

inventories and simulation tools and that can be influenced through

forest management need to be used

Weather conditions cannot accurately be predicted beyond a few weeks

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Modelling approach

The predictors were chosen from stand and site variables as well as their

transformations (Age, Site index, Hdom, Basal area, N, ELE, SLO, AS, …)

Due to the hierarchical structure of the data, mushroom measurements of

the same year were correlated observations as were the measurements on

the same plot, the generalised least squares (GLS) technique was applied

to fit mixed linear models

ijjinij xxxfy ),...,,( 21

Models for the total production, edible species, marketed species, and individual species or species groups were fitted

20.8.200432

Results

The regression analyses showed that stand basal area, elevation, aspect and slope were the most significant predictors:Total productionln(yij) = 0.981 +2.483ln(G) -0.128G +0.934cos(Asp) -0.0135Slo1.5 + ui + uj + eijEdible mushroomsln(yij) = -4.329 +1.966ln(G) -0.118G +0.636cos(Asp) +0.00331Alt + ui + uj + eij Marketed mushroomsln(yij) = -6.236 +1.246ln(G) -0.0599G +0.00459Alt + ui + uj + eijMarketed Lactariusln(yij) = -0.192 +1.016ln(G) -0.106G +1.489cos(Asp) -0.0151Slo1.5 + ui + uj + eij

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ResultsElevation 1240m, Slope 24%, East

0

20

40

60

80

100

120

0 10 20 30 40

Basal area, m2ha-1

Fre

sh w

eigh

t, k

g ha

-1

Total

Edible

Marketed

Lactarius

Altitude 1240m, Slope 24%

0

50

100

150

200

250

300

350

400

0 10 20 30 40

Basal area, m2ha-1

Tot

al f

resh

wei

ght,

kg

ha-1

North

EastWest

South

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Results

050

100150200250300350400450500

0 20 40 60

Basal area, m2ha-1

To

tal y

ield

, kg

ha

-1

0

100

200

300

400

500

600

700

0 10 20 30 40

Slope,%

To

tal y

ield

, kg

ha

-1

0

100

200

300

400

500

-1 -0.5 0 0.5 1

CosAspect ("Northness")

To

tal y

ield

, kg

ha

-1

20.8.200435

ResultsProductions were greatest when stand basal area was approximately 20

m2 ha-1.

Increasing elevation and northern aspect and decreasing slope

increased total mushroom production, edible and marketed

Marketed Lactarius spp., the most important group collected in the region,

showed similar relationships.

The annual variation in mushroom production correlated with autumn

rainfall.

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Discussion It seems that highest mushroom production coincides with the peak in forest

volume growth Previous studies shows that mushroom production correlates with growth

and photosynthetic rate of host trees

• Flux of current photosynthates is critical for soil respiration and

ectomycorrhizal sporocarp production

Since stand basal area is correlated with site conditions (soil quality, water

availability, humidity, etc.), and other variables like age, volume, etc.

estimating the effects of growing stock variables requires more plot

measurements or a population in which stand variables are less correlated

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Discussion Elevation, aspect and slope in the Prepyrenees range reflect water

availability and soil quality which clearly affects mushroom production Stands near canopy closure with vigorous growth rates located at high

elevations, in northern aspects and with low slopes seems to be optimal sites

for mushroom production in Scots pine forests of the Spanish pre-Pyrenees. Such models can be used to optimized forest stand management for

mushroom and timber productionDespite of the limitations of our data in number of measurements and plots

the results of the study are encouraging because they demonstrate that

mushroom production are related to stand characteristics that can be

influenced by silvicultural interventions

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Next step is to collect a larger data set including more variability in

growing stock characteristics as well as other tree species.

Collecting large quantities of empirical data over several years is required

because there are multiple factors responsible for high temporal variation

in mushroom productions.

The effect of silvicultural treatments needs to be studied

Climate change should be considered as it will affect both forest growth

(and composition) and weather conditions = mushroom production

Final remarks

20.8.200439

BONET, J.A. PUKKALA, T.; FISCHER, C.R.; PALAHÍ, M.; MARTÍNEZ DE ARAGON, J. i COLINAS, C. 2007. “Empirical models for predicting the yield of wild mushrooms in Scots pine forests in the Central Pyrenees”. Annals of Forest Sciences (in press).

MARTÍNEZ DE ARAGÓN, J.; BONET, J.A.; FISCHER, C.R. i COLINAS, C. 2007. “Productivity and richness of ectomycorrhizal and edible forest fungi in three pine forests of the pre-Pyrenees, Spain: Development of predictive models as a basis for forest management of the mycologic resource”.  Forest, Ecology & Management (doi:10.1016/j.foreco.2007.06.040).

 BONET, J.A.; FISCHER, C.R. y COLINAS, C., 2004. “The relationship between orientation and forest age on the production of sporocarps of ectomycorrhizal fungi in Pinus sylvestris forests of the Central Pyrenees”. Forest, Ecology and Management, 203: 157-175.

REFERENCES

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