www.elsevier.com/locate/foreco

Forest Ecology and Management 213 (2005) 209–228

Sustainability of forest management practices: Evaluation through

a simulation model of nutrient cycling

Juan A. Blanco a, Miguel A. Zavala b, J. Bosco Imbert a, Federico J. Castillo a,*

a Departamento de Ciencias del Medio Natural, Edificio los Olivos, Universidad Publica de Navarra, E-31006 Pamplona, Navarra, Spainb Departamento de Ecologıa, Edificio de Ciencias, Universidad de Alcala, E-28871 Alcala de Henares, Madrid, Spain

Received 11 May 2004; received in revised form 25 January 2005; accepted 30 March 2005

Abstract

Forest harvesting may interfere with long-term ecosystem structure and function and different harvesting methods will differ

in their effects on soil fertility (e.g. whole-tree harvesting versus stem removal). In the case of thinning, effects of thinning

intensity, rotation length and site quality must be assessed in order to formulate sustainable management practices. Assessment

of the relative impact of these practices is difficult, however, given the long temporal scales involved. In this study, we implement

a process-based model of nutrient cycling to evaluate temporal changes in ecosystem nutrient dynamics of managed and non-

managed forest stands. The model was specifically designed to asses differences between two contrasting site-quality Pinus

sylvestris L. stands in the western Pyrenees (Navarre, Spain) managed under two thinning intensities. The model describes the

main nutrient fluxes in the stand: litterfall, decomposition, retranslocation, root uptake and management type, and it was

parameterized and verified with 3 years of field data. After model verification we examined the effects of thinning intensity,

thinning frequency and harvesting method (whole-tree versus stem removal) on potential nutrient losses. The results suggest that

in this heterogeneous region, sustainability of forestry practices is strongly site dependent. N and P were particularly sensitive to

overexploitation and in no case could whole-tree removal be recommended as it may have a strong negative effect on nutrient

reserves. In relation to previous nutrient cycling models, our model offers a satisfactory compromise between simplicity,

biological realism and predictability, and it proved to be a useful tool to predict short-term changes in nutrient reserves as well as

to evaluate possible negative effects of applying current thinning prescriptions on long-term sustainability of managed forests in

the western Pyrenees.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Thinning; Pinus sylvestris L.; Scots pine; Navarre; Whole-tree removal; Forest harvesting; Nitrogen; Phosphorus; Mediterranean

pine forests

* Corresponding author. Tel.: +34 948 169 115;

fax: +34 948 168 930.

E-mail address: federico.castillo@unavarra.es (F.J. Castillo).

0378-1127/$ – see front matter # 2005 Elsevier B.V. All rights reserved

doi:10.1016/j.foreco.2005.03.042

1. Introduction

To achieve ecosystem and economic sustainability

a forest practice must achieve three different goals: (1)

to be economically profitable and to perpetuate forest

.

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228210

cover, (2) to preserve ecosystem structure (e.g. for

biodiversity values) and (3) to preserve ecosystem

function (e.g. nutrient cycling) (Zavala and Oria,

1995; Sverdrup and Svensson, 2002). Among other

effects, thinning reduces stand biomass, nutrient

contents, litterfall (Klemmedson et al., 1990; Har-

rington and Edwards, 1999) and can alter decom-

position rates (Piene and Van Cleve, 1978; Perez-

Batallon et al., 1998). Continuous long-term studies of

the effect of management practices on nutrient budgets

are expensive and time consuming. As a compliment

to long-term experiments, simulation models are

useful tools that allow us to extrapolate observed

short-term changes in nutrient dynamics to longer

time scales (Landsberg, 2003). Experimental studies

and modelling are complementary approaches within

an adaptive management context that can identify

essential mechanisms controlling short- and long-term

processes that are critical for maintaining ecosystem

structure and function (Kirschbaum, 1999; Grigal,

2000). Once identified, a field-monitoring program

can be designed that maximizes the efficiency of

sampling and laboratory analyses, which are neces-

sary to confirm that management is sustainable.

Models of nutrient cycling are of particular

importance for evaluation of sustainability of forest

practices. Typically these models include feedbacks

among litterfall, retranslocation, tree growth, root

uptake and decomposition while aboveground pro-

cesses are directly related to tree growth. A number of

modelling approaches have been used to describe

decomposition and root uptake processes. These

models differ both in structure and applicability for

forest management. The first decomposition models

were correlational (Andersson et al., 2000) and were

based on statistical relationships among the different

variables involved (Olson, 1963; Swift et al., 1979). A

second generation of models explicitly considered

different fractions of soil organic matter characterized

by chemical composition and decomposition rates

(CENTURY, Parton et al., 1987). These models are

currently used for forest research and management as

well as modules within biosphere carbon cycle

simulators (e.g. FORSANA, Grote et al., 1998;

FORECAST, Kimmins et al., 1999; CenW, Kirsch-

baum, 1999; EFIMOD 2, Komarov et al., 2003;

ForNBM, Zhu et al., 2003). Although they provide

adequate biological realism, these models tend to be

very complex. This adds uncertainty to the causes

underlying their final predictions and weakens their

heuristic value (Andersson et al., 2000). Finally,

analytical models, such as the ‘‘Q’’ model (Agren and

Bosatta, 1996), provide useful theoretical insights but

they often omit too much critical detail (Kimmins,

2004) or are based on parameters difficult to estimate

under field conditions. Therefore, the applicability of

these simpler models for forest management is often

quite limited (Battaglia and Sands, 1998). From a

management perspective the most adequate model can

be defined as the simplest one among those that meet

the users needs both in terms of resolution and

precision (Battaglia and Sands, 1998; Botkin, 2001;

Landsberg, 2003). Based on this idea, in this study, we

have developed a relatively simple model that can be

easily parameterized with field data and that allows us

to quantify and compare the sustainability of different

thinning regimes and harvesting methods for Pinus

sylvestris L. stands of contrasting site quality. The

model has been developed in response to current forest

management needs in Navarre (Spain), and to our

knowledge is the first biologically informed model

developed in Spain resulting from the cooperation

between the university system, the public adminis-

tration and a forestry enterprise. Data and model

validation were performed on two contrasting experi-

mental forests located in the western Pyrenees which

represent the two extremes of a site quality gradient.

Aspurz is a highly productive Mediterranean forest

with relatively fast decomposition rates. Garde in turn

exhibits a more continental climate and has a lower

production and slower decomposition rates (Blanco

et al., 2003a,b). P. sylvestris L. forests have been

extensively studied in northern Europe (e.g. Berg and

Lousier, 2000; Agren and Knecht, 2001, among

others). Results from these studies, however, may not

be directly applicable to the Iberian P. sylvestris

forests (the southern and western distribution limits

for this species) where ecological factors and

processes can be qualitatively different from those

operating in northern forests. For example, thinning

has been shown to reduce decomposition rates in the

western Pyrenees forests (Blanco et al., 2003b), while

the opposite has been found at higher latitudes

(Edmonds, 1990). This, among other observations,

points out the need to develop simple biologically

based models of nutrient cycling specifically suited for

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228 211

Mediterranean forests that can assist us in the

evaluation of sustainability of current forest manage-

ment practices (e.g. Harmon, 2001; Verbug and

Johnson, 2001). Our modelling approach is based

on the simplification strategy proposed by Tiktak and

Van Grinsven (1995). These authors propose to

minimize the dynamic feedback between geochemical

processes and stand growth using independent

submodels, to omit or aggregate short time scale

processes (daily and seasonal) and to pool compart-

ments, simplifying and aggregating process descrip-

tions. The main objectives of our work are: (1) to

assess whether site quality influences sustainability of

thinning practices in western Pyrenees forests, (2) to

forecast long-term changes of nutrient pools in two

contrasting site-quality stands as a function of

harvesting method (whole-tree versus log removal),

thinning intensity and rotation length (3) and to

evaluate how predicted nutrient losses for different

management scenarios, including those currently

recommended in P. sylvestris forests in Spain,

influence stand nutrient balance and therefore, long-

term sustainability of thinning practices.

2. Materials and methods

2.1. Study sites

The model was tailored to describe nutrient cycling

of P. sylvestris stands in the western Pyrenees (Spain).

The model was parameterized with data from two

contrasting experimental sites, representing the two

extremes of a site quality gradient throughout this

region. The lower elevation site (Aspurz, 4284203100N,

18804000W) is one of the most productive P. sylvestris

forests in Spain. The stand has developed on a Dystric

Cambisol, with mean A horizon organic C of 7.0%.

Site mean altitude is 642 m asl with mean annual

precipitation and mean annual temperature

912 mm year�1 and 12 8C, respectively. The site is

an even-aged P. sylvestris stand resulting from strip-

like clear-cutting carried out in the mid-1960s. Mean

stand age is 37 years and stand density is

3555 trees ha�1, with a dominant height of 14 m

and a mean dbh of 24.9 cm. The higher elevation site

(Garde, 4284805000N, 085203000W) is an example of a

low-production P. sylvestris forest in Spain. The stand

is located on a Haplic Alisol, with mean A horizon

organic C of 6.3% at a mean altitude of 1335 m asl.

Mean annual precipitation is 1268 mm year�1, and

mean annual temperature is 8.2 8C. Stand structure is

even-aged resulting from clear-cutting during the

early sixties. Mean stand age is 32 years, with a

density of 3500 trees ha�1, a dominant height of

15.2 m and a mean dbh of 25.0 cm. Fagus sylvatica L.

saplings are relatively important on a percentage

cover basis in Aspurz but not in Garde. Bedrock for

both sites consists of flysch of sandstone and

limestones.

2.2. Experimental design

Data were gathered across nine rectangular

(30 m � 40 m) plots per location. Silvicultural treat-

ments were implemented by the ‘Departamento del

Medio Ambiente, Gobierno de Navarra’ according to

the guidelines of the International Union of Forestry

Research Organizations (IUFRO). The experimental

design consisted of three treatments with three

replicates per location: treatment 1 (P0) control with

no thinning; treatment 2 (P20) moderate low-thinning

(20% of basal area removed according to future tree

selection method; felled trees were mainly canopy

suppressed but included dominant or codominant trees

with malformed stems); treatment 3 (P30) heavy low-

thinning (30% of basal area removed using the future

tree selection method as in P20). To avoid edge effects,

the silvicultural treatments were applied within a 5–

10 m strip adjacent to each plot.

2.3. Model structure, parameterization and data

gathering

Our main objective was to estimate changes in N, P,

K, Ca and Mg contents in the major nutrient pools. The

model was designed to reach a satisfactory compro-

mise between mathematical complexity and biologi-

cal realism (e.g. Landsberg, 2003) and the number of

parameters were kept as low as possible. Fig. 1 shows

the main fluxes and pools considered in the model and

Fig. 2 shows model inputs and outputs. Tree to soil

feedbacks are integrated into the empirical growth

function (Tiktak and Van Grinsven, 1995; Agren and

Knecht, 2001). Summer drought in these two locations

is only moderate and hydrological processes were not

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228212

Fig. 1. Model fluxes and nutrient pools. Nd: needle demand; Bd: branch demand; Sd: stem demand; Rd: root demand; Ab: root uptake; Re:

retranslocation; Wl: branch litterfall; Nl: needle litterfall; Wm: branch mineralization; Nm: needle mineralization; Rm: root mineralization.

explicitly simulated. Thus, model results are restricted

to non-water limited forests (Verbug and Johnson,

2001). All combined processes were simulated with 1-

year time-steps with program language STELLA

Fig. 2. Information fluxes followed to evaluate sustainability of thinning

model outputs in bold letters.

Research 5.1.1 (High Performance Systems Inc.,

1997), which is particularly suited for modelling

ecosystem level processes (Costanza and Gottlieb,

1998).

practices with our model. Required inputs are shown in italics and

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228 213

Stand growth functions were simulated with

SILVES (Del Rıo and Montero, 2001) which simulates

diameter distributions and thinning of P. sylvestris

stands in Spain and calculates stand growth as a

function of site index, stand basal area and tree

density. Original parameter values were modified

based on growth and yield tables developed for this

species in Navarre (Puertas, 2003) (data provided by

‘‘Departamento de Medio Ambiente, Gobierno de

Navarra’’). Equations describing stem growth were:

Mass (Mg ha�1) = 495.348 ln (age, years) �1473.729 (R2 = 0.99, P < 0.001) for Aspurz, and mass

(Mg ha�1) = 308.595 ln (age, years) � 902.013 (R2 =

0.99, P < 0.001) for Garde. Stem, needle and branch

mass were predicted from stem mass through the

following allometric equations determined from field

data: needles (kg) = 0.0258 � stem (kg) + 0.9428

(R2 = 0.66, P < 0.001); branches (kg) = 0.0992 � stem

stem (kg) + 1.2482 (R2 = 0.74, P < 0.001). To esti-

mate needle, branch and stem biomass before thinning,

trees were classified into five dbh classes. Then, the

modal tree per dbh class was felled for each plot, for a

total of five trees per plot. After determining the

component biomass for felled trees, total stand above-

ground biomass was calculated by multiplying the

biomass of each dbh class by the number of trees in that

class. To determine nutrient amounts in needles,

branches and stems, we multiplied tissue concentra-

tions by the appropriate component. Root biomass was

not estimated in the field, so we assumed that root

nutrient content represent a constant fraction of the

aboveground tree nutrients contents as did Margolis

et al. (1995), and used above/belowground ratios

(Table 1) reported by Malkonen (1974) and Litton et al.

(2003). A similar assumption has been made in more

complex forest models such as HYBRID (Friend et al.,

1993) and C_CHANGE (Beets et al., 1999).

Litterfall in litter traps was collected monthly from

April 2000 to October 2002 in both forests (9 litter traps

of 0.29 m2 per plot � 3 treatments � 3 replicates � 2

sites � 31 months = 5022 samples). Samples were

separated in the laboratory into six components

(needles, branches, fruits, bark, other pine organs and

other litter) dried at 72 8C and weighed. For comparison

with the simulation only two litterfall fractions were

used: (1) needles and (2), a combined branches, bark

and fruit (referred to below as the woody litter fraction).

Litterfall inputs from tree species other than P. sylvestris

and from understory plant species were small,

particularly in Garde, and therefore, they have not

been included in the model. Leaf litterfall and wood

biomass has been assumed to be a fraction of total

aboveground leaf and stemwood biomass, respectively

(Kimmins et al., 1999; Kirschbaum, 1999; Komarov

et al., 2003). Litterfall values for our two experimental

sites (Table 1) were similar to mean litterfall values of

pine forests reported by Agren (1983), and to those used

in models developed by Agren and Knecht (2001).

Green and senescent needles were collected bimonthly

from December 2001 until October 2002. Three

branches per plot at 5 m above forest floor were cut

every sampling date, and green needles were separated

into three cohorts. A total of 972 green samples were

collected during this study (3 branches per plot � 3

cohorts � 3 treatments � 3 replicates � 2 sites � 6

sampling dates). Mean retranslocation efficiency for

all cohorts together was calculated following Aerts et al.

(1999) as: % retranslocation = 100 � (mean concen-

tration in green needles � mean concentration in

senescent needles)/mean concentration in green nee-

dles. Change in nutrient content of the green needles

after retranslocation was calculated by multiplying

retranslocation percentage by total needle nutrient

content. We assumed that retranslocation from roots

and woody litter fractions was negligible (Verbug and

Johnson, 2001). To calculate the uptake of nutrients by

the roots we assumed that the uptake equalled nutrients

requirements by trees (Cole and Rapp, 1981; Waring

and Running, 2001), which were provided in part by

nutrient retranslocation from senescing needles. To

estimate tree nutrient requirements we added up the

amount of nutrients used for needle, branch, stem and

root growth, plus losses through litterfall of branches

and needles, plus belowground transfers of dead roots

and root exudates to the soil.

Decomposition rates for needles were obtained

from 50 litterbags (2 mm mesh size, 10 g of needles)

per plot placed in the stands in November 2000. Three

bags were collected for mass loss measurement,

monthly for the first three months and then bimonthly

(from December 2000 to October 2002). A total of 702

samples were collected in this study (3 bags per

plot � 3 treatments � 3 replicates � 2 sites � 13

sampling dates). Decomposition of needle and woody

litter were simulated assuming that both fractions

consisted of a single type of material and exhibited

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Table 1

Parameter values used in the model and source by nutrient and experimental site

Parameter N P K Ca Mg Units Reference

A G A G A G A G A G

Concentration

Green needles 1.65 1.22 0.11 0.12 0.74 0.78 0.35 0.45 0.11 0. % Experimental

Dry needles 0.87 0.83 0.05 0.07 0.28 0.33 0.58 0.67 0.08 0. % Blanco et al. (2003a)

Branches 0.69 0.48 0.06 0.09 0.58 0.48 0.24 0.36 0.09 0. % Blanco et al. (2003a)

Stems 0.09 0.04 0.01 0.01 0.09 0.06 0.15 0.10 0.03 0. % Experimental

Soil 2225 2450 20 19 63 125 707 1614 89 164 ppm Experimental

Decomposition rate

Needles 0.517 0.299 0.517 0.299 0.517 0.299 0.517 0.299 0.517 0. 9 years�1 Blanco et al. (2003b)

Branches 0.028 0.028 0.028 0.028 0.028 0.028 0.028 0.028 0.028 0. 8 years�1 Agren and Bosatta (1996)

Roots 5 5 5 5 5 5 5 5 5 5 % year�1 Malkonen (1974)

Litterfall fraction

Needles 39 38 39 38 39 38 39 38 39 38 % year�1 Experimental

Branches 6 8 6 8 6 8 6 8 6 8 % year�1 Experimental

Atmospheric deposition 9.2 9.2 0.1 0.1 39.7 39.7 24.5 24.5 8.9 8. kg ha�1 year�1 Ministerio de Medio

Ambiente (2003)

Weathering 0.0 0.0 0.2 0.2 2.0 2.0 25.0 25.0 4.0 4. kg ha�1 year�1 Kimmins (2004), Fisher

and Binkley (2000)

Ratio above/below-ground biomass 0.19 0.19 0.19 0.19 0.24 0.24 0.19 0.19 0.21 0. % Malkonen (1974)

Initial aboveground nutrient pool 502 292 41 37 392 284 383 285 89 74 kg ha�1 Experimental

Initial soil nutrient pool 9758 12119 79 69 276 390 3134 7695 427 604 kg ha�1 Experimental

A: Aspurz and G: Garde. After the first thinning mean total tree biomass was 243,850 kg ha�1 (P0), 181,404 kg ha�1 (P20) and 164,86 kg ha�1 (P30) in Aspurz, and 218,396 kg ha�1

(P0), 158,265 kg ha�1 (P20) and 135,813 kg ha�1 (P30) in Garde (see treatment description in Section 2.2).

12

10

09

03

29

02

9

0

21

8

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228 215

negative exponential weight losses over time (Olson,

1963). We considered different needles cohorts, which

fell down in different years and did not change either

their chemical composition or their decomposition

rate through the decomposition process. Nutrients

released from decomposing litter were estimated as

Mt ¼ M0 e�kt

where Mt is mass remnant at time t, M0 the initial mass

and k is the decomposition rate (Olson, 1963). To

estimate the amount of organic matter decomposition

at a given time t (OMDt), we estimated variation in

soil organic matter as the balance between inputs

from litterfall and losses from decomposition accor-

ding to:

OMDt ¼ OMlitterfallt � OMdecompositiont

This is

OMdecomposedt ¼ OMlitterfallt � OMDt

¼ OMlitterfallt � ðOMt � OMt�1Þ

where OMt is the organic matter content in decom-

posing litter. Litter is composed of a number of

cohorts generated at different times. At a given time

organic matter is the sum of the remnant fraction in

each of these cohorts, which can be estimated as a

sum of negative exponential curves (see a CEN-

TURY-based simplification by Bolker et al., 1998).

Thus, the amount of organic matter that is lost at

any given time from decomposing litter was esti-

mated as

OMdecomposedt ¼OMlitterfallt

��Xx¼t

x¼0

ðOMlitterfallx�e�kxÞ

�Xx¼t

x¼0

ðOMlitterfallx�1�e�kðx�1ÞÞ�

To calculate the decomposition of woody materials

we used the rates reported by Agren and Bosatta

(1996) (Table 1), following the same procedure as for

needles. To simplify the model we assumed that

nutrients and organic matter were lost at the same rate

over the decomposition process (Andersson et al.,

2000; Chertov et al., 2001; Potter et al., 2001). By

expressing organic matter changes through time as a

sum of first order exponential curves, we can quantify

the sensitivity of the model to organic matter changes

and make a better use of the data available (Bolker

et al., 1998). As for roots, and given that they were not

directly studied, we kept the decomposition process as

simple as possible. Therefore, dead roots and roots

exudates were pooled together and decomposition rate

was estimated as a fixed percentage of total root

biomass for all the soil profile following Malkonen

(1974) (Table 1)

Soil B horizon samples were collected once in

November 1999. To better characterize the upper A

horizon we collected soil samples 5 cm deep

bimonthly in all plots from April 2001 to April

2003. Soil and leaves (green and senescent) samples

were ground with an electric mill. N (soil and leaves)

was analyzed using the Kjeldhal method (Harwitte,

1980). Soil and foliar P were analyzed following Bray

and Kurtz (1945) and the phosphomolibdo-vanadate

method (MAFF, 1986), respectively. For both soil and

leaves samples, Ca and Mg were analyzed by atomic

absorption spectrophotometry and K by flame photo-

metry. In the model, all soil horizons were pooled

together excluding branches and needles in the forests

floor. To calculate initial forest floor total nutrient

content from which to initiate all simulations, we ran

the model for 30 years starting from a soil with no

organic layer. Values for different litter fractions at the

end of the simulation were used as the initial values on

posterior runs of the model (Makipaa et al., 1998;

Kimmins et al., 1999).

Simulation of leaching losses of N, Ca and Mg was

based on simulated available quantity of these

nutrients less the amounts taken up by the trees. This

approach is also used in BIOME-BGC (Hunt and

Running, 1992), Q (Rolff and Agren, 1999), CenW

(Kirschbaum, 1999), FORECAST (Kimmins et al.,

1999) and NuCSS (Verbug and Johnson, 2001). In the

case of P and K, it was assumed that nutrients not used

by trees were inmobilized in the soil (Cooke, 1981).

Atmospheric inputs data (wet and dry deposition) used

in the model were taken from a nearby site (<40 km

distant) by the Spanish Forest Protection Service

(Ministerio de Medio Ambiente, Madrid, 2003); the

values were similar to mean deposition rates in non-

polluted areas in Europe. Given that both experimental

sites have similar parent material and that distance

between them is less than 40 km, we assumed similar

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228216

atmospheric inputs and geological weathering for both

locations. Forest soil weathering rates for this type of

bedrock have been reported in Global data base sets

elaborated by Kimmins (2004) and Fisher and Binkley

(2000).

2.4. Model analysis: verification and sensitivity

analysis

We verified our model in several ways. Predicted

versus observed values for short-term responses to

thinning were evaluated for the 3 years following

thinning. We simulated changes in nutrient contents in

the control plots after thinning, and then compared

model estimates with data from the thinned plots at

both locations. A more detailed deviation analysis

between observed and simulated data was carried out

for N as this nutrient is the most commonly limiting

nutrient in forests (e.g. Parton et al., 1996; Gilmanov

et al., 1997). The mean absolute deviation (Dabs) was

calculated as:

Dabs ¼1

n�Xn

i¼1

jXestðtiÞ � XobsðtiÞj

where Xest and Xobs are estimated and observed values

for a given variable, and ti (i = 1, . . ., n) represents time

in years. Maximum N was defined as the maximun

observed value of a particular flux or pool of N in the

experimental plots. We compared for each type of N

flux or pool the percentage of the maximum N value

accounted by the Dabs with the percentage of the

maximum N value accounted by the standard error

of the observed data.

To determine the sensitivity of the output variables to

variation in input parameter values, we varied each

parameter input 10% when the trees reached 100

years, and then assessed the changes shown by

aboveground nutrient content of tree biomass (stems + -

branches + needles), decomposing plant debris (woody

litter + needles) and the totals for all soil horizons

(soil + woody litter + needles). The percentage change

in the output variables was partitioned into percentage

variation due to each parameter, obtaining a sensitivity

percentage. Thus, a sensitivity value higher than 100%

indicates that the model amplifies parameter changes

while a value lower than 100% indicates that model

internal dynamics reduce them (Kirschbaum, 1999).

2.5. Management scenarios

To simulate different thinning treatments we

imposed different combinations of thinning intensity

(percentage removal of basal area), thinning frequency

(number of years between two successive thinnings)

and removal types (stems or whole-trees). In addition,

we have analyzed current management prescriptions

for these forests in Spain (Dıez and Fernandez-Golfın,

1997; Del Rıo and Montero, 2001) and specifically

those adapted to Navarre (Puertas, 2003). According

to these prescriptions the exploitation cycle may be

reduced to 80 years in high quality stands. Thinning

initiates when stands are 20 or 25 years old, and

continues every 10 years removing 30% of the total

basal area when timber extracted exceeds 50 m3 ha�1

(economic criteria). This prescription was simulated

for Aspurz. A low intensity thinning in low quality

stands, however, is not profitable and production

cycles can be as long as 120 years, with a thinning

program that removes 20% of the total basal area every

15 years. This was the thinning program simulated for

Garde. We established a 100-year production cycle for

both localities.

3. Results

3.1. Model analysis: verification and sensitivity

analysis

There was a strong correlation between predicted

and observed values for all nutrients inspected

(Table 2). Dabs of N removed within the stems was

3.9 kg N ha�1, representing 5% of the maximum

observed value in this pool (89.1 kg N ha�1). This

percentage was similar to the result of the maximum

observed N pool accounted for by the standard error of

the observed values (6%). For N in branches, the Dabs

percentage (6%) was slightly lower than the percentage

for the observations error (7%). For N in needles, the

Dabs percentage (12%) was similar to the observational

error percentage (9%). Dabs for N in needle litterfall

(1.3 kg N ha�1) was within the precision range of the

measurements and its percentage (5%) was almost

equal to the observational error percentage (4%).

Finally, for woody litterfall (10.1 kg N ha�1), the Dabs

percentage (11%) was bigger than the observational

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228 217

Table 2

Results of verification regressions (R2) of observed data vs. simulated data for nutrient amount (kg ha�1) in five different fluxes

Nutrient Extracted stems Extracted branches Extracted needles Leaf litterfall Woody litterfall

N 0.832** 0.866** 0.898** 0.725* 0.760**

P 0.821** 0.836** 0.900** 0.715* 0.761**

K 0.840** 0.862** 0.888** 0.720* 0.755**

Ca 0.831** 0.871** 0.891** 0.716* 0.761**

Mg 0.812** 0.860** 0.890** 0.723* 0.765**

* P < 0.01.** P < 0.001.

error percentage (6%). Sensitivity analysis showed that

the model behaved similarly in both localities,

diminishing the changes for all the parameters (less

than 100% of variation for all variables, see Section 2).

The most sensitive variable was N content in decom-

posing leaf litter (needles + woody litter), as it varied

55%inAspurzwhen the litterfall fractionparameterwas

changed. For other parameters sensitivity values were

lower than those for the N content in decomposing litter.

3.2. Changes in nutrient pools over time

Woody litter was the main pool for N in the control

stands as its low decomposition rates favoured

accumulation, particularly in Garde (Olson’s k was

42% lower than in Aspurz; Blanco et al., 2003b) (Figs. 3

and 4). On the contrary, stems were the pool with the

lowest N contents in the control stands. A total of

1819 kg N ha�1 had accumulated in Aspurz at the end

of the simulation, which represented 55% more than

that accumulated in Garde (1177 kg N ha�1). The

simulated thinning cycle caused a 60 and a 27% total N

reduction in Aspurz and Garde, respectively (Figs. 3

and 4). The most and the least sensitive pools were

needles and woody litter, respectively. These differ-

ences caused changes in the relative importance of each

pool, particularly for needles, which ranked first before

thinning, and second after thinning in both sites. For P,

the branches were the most important pool in the control

stands with 39 kg P ha�1 in Aspurz and 44 kg P ha�1 in

Garde at the end of the simulation. The relative

importance of woody litter increased over time, and

became the second most important pool in both sites at

the end of the simulation. Total aboveground content of

P was 145 and 131 kg ha�1 in Aspurz and Garde,

respectively. The distribution of P among pools

changed dramatically after applying the recommended

management practices (Figs. 3 and 4). Thus, total P

content in leaf litter increased 69% and 37% in Aspurz

and Garde, respectively, while total aboveground

content of P decreased 19% and 21% in Aspurz and

Garde, respectively.

Unlike P and N, K content in the thinning plots was

smallest in the decomposing pools (leaf litter and

woody litter), while in the control plots the pools with

the highest K content were stems and branches. At the

end of the simulation, the stems had 322 kg K ha�1 in

Aspurz and 296 kg K ha�1 in Garde. Total K content

was similar in both forests (913 kg kg K ha�1 in Aspurz

and 834 kg K ha�1 in Garde; Figs. 3 and 4). The

decomposing woody litter was the most important pool

for K in Aspurz after thinning, but in Garde the stems

and the branches were the most important pool before

and after thinning (Figs. 3 and 4). In addition, only 39

and 73% of K remained in Aspurz and in Garde,

respectively, at the end of the simulation. The response

of Ca reflected its structural role in stems, being the pool

with the highest contents. In the reference plots, Ca

accumulation in the stems was 780 kg ha�1 in Aspurz

and 468 kg ha�1 in Garde. After thinning, total Ca

showed a 52% reduction, but in Garde it only

diminished 2%. Finally, differences between sites were

higher for Mg than for Ca (Figs. 3 and 4). Decomposing

leaf litter was the most important pool (170 kg Mg ha�1

at the end of the simulation) in Aspurz baseline plots. In

Garde, however, stems were the most important pool

with 124 kg Mg ha�1. Thinning caused little changes in

the relative importance of Mg among pools in both sites,

although the total content of Mg decreased 68% in

Aspurz and 27% in Garde.

3.3. Potential nutrient losses due to the thinning

and tree removal method

Potential nutrient losses are defined as the sum of

nutrients extracted due to tree removal and nutrients

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228218

Fig. 3. Mass evolution of N, P, K, Ca and Mg in every pool along stand life in Aspurz. Left column is a non-managed forest and right column is a

simulation under recommended thinning prescription (thinning of 30% basal area every 10 years, stem removal).

lixiviation when mineralization rates exceed root

absorption. Accumulated potential losses resulting

from simulations in Aspurz averaged 345 kg N ha�1

when only tree stems were removed, and 635 kg N ha�1

for whole-tree removal. These values were lower in

Garde: 269 and 523 kg N ha�1, respectively. An

increase in thinning intensity up to 20% resulted in

higher potential losses particularly for whole-tree

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228 219

Fig. 4. Mass evolution of N, P, K, Ca and Mg in every pool along stand life in Garde. Left column is a non-managed forest and right column is a

simulation under recommended thinning prescriptions (thinning of 20% basal area every 15 years, stem removal).

removal (Fig. 5). The rate of increase diminished after a

thinning intensity of approximately 20%. Phosphorus

mean potential losses for stem removal equalled

18 kg P ha�1 in Aspurz and 10 kg P ha�1 in Garde,

and considerably increased if the whole tree was

removed (41 kg P ha�1 in Aspurz and 42 kg P ha�1 in

Garde). Maximum losses in Garde occurred for a range

of thinning intensities between 20 and 30%, while in

Aspurz, nutrient losses increased and reached a plateau

after 10% of the basal area was removed (Fig. 6). Mean

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228220

Fig. 5. Potential N losses caused by management (thinning + lixiviation) in the two experimental sites, with stem (top) and whole-tree (bottom)

removal as a function of thinning intensity and rotation. The point labelled by a solid dot represents recommended thinning intensity and

frequency. The horizontal solid line represents accumulated external N inputs by deposition and mineral weathering.

potassium losses in Aspurz equalled 163 kg K ha�1 for

stem-only extraction and 400 kg K ha�1 for whole-tree

removal and 161 and 370 kg K ha�1 in Garde,

respectively. Maximum potassium losses in Garde

were found for thinning intensities around 30% (whole-

tree removal). These trends were not as pronounced in

the Aspurz plots (Fig. 7). For calcium, differences

between the two types of tree removal were not as

dramatic 423 and 510 kg Ca ha�1 in Aspurz, and 276

and 340 kg Ca ha�1 in Garde for stem and whole-tree

removal, respectively. Loss rates increased dramati-

cally with thinning intensity up to 20% and were less

pronounced after 20% to decrease afterwards (Fig. 8).

Similar trends were described for magnesium with

average potential losses of 87 and 138 kg Mg ha�1

(Aspurz) and 83 and 114 kg Mg ha�1 (Garde), respec-

tively, for stem and whole-tree removal (Fig. 9).

Overall, a strong interaction between silvicultural

treatments and sites was observed. For each nutrient,

thinning resulted in a broader range of variation of

potential nutrient losses (difference between max-

imum and minimum losses) in Garde when compared

to Aspurz. This variation was also affected differen-

tially by tree removal procedure in each site, being

more pronounced in Garde than in Aspurz. In

particular, whole-tree removal amplified this variation

in Garde but ameliorated it in Aspurz. With respect to

thinning frequency, an increment from 5 to 25 years

resulted in losses of around 50% for each nutrient in

both sites (Figs. 5–9).

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228 221

Fig. 6. Potential P losses caused by management (thinning) in the two experimental sites, with stem (top) and whole-tree (bottom) removal as a

function of thinning intensity and rotation. The point labelled by a solid dot represents recommended thinning intensity and frequency. The

horizontal solid line represents accumulated external P inputs by deposition and mineral weathering.

4. Discussion

Despite its simplicity, our model was in a good

agreement with the observations over a short-time

frame (Table 2). Also, low model sensitivity suggests

that model structure is robust with respect to error

propagation of initial estimates (Chertov et al., 2001).

4.1. Factors influencing sustainability of forestry

practices

Site quality at each location was the most important

factor influencing sustainability of forest manage-

ment. Potential N losses in Garde, a site with poorer

site productivity and N stem content, were signifi-

cantly lower than those found in Aspurz (Fig. 5).

Similar patterns were observed for Ca and Mg. For P

and K, however, lower outputs were compensated by

larger nutrient concentrations found in Garde trees.

Thus, potential nutrient P and K losses were similar in

both sites. Comparable results have been found by

Morris et al. (1997) who pointed out that sustainability

of forestry practices may critically depend on site

ecological features such as decomposition rate (in our

study higher in Aspurz than in Garde, see Blanco et al.,

2003b). Potential lixiviation rates for N, Ca and Mg

were estimated as the difference between mineraliza-

tion release and root uptake. Thus, higher mineraliza-

tion rates may result in higher potential losses. Also,

an increment in lixiviated nutrients after thinning has

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228222

Fig. 7. Potential K losses by management (thinning + lixiviation) in the two experimental sites, with stem (top) or whole-tree (bottom) removal

as a function of thinning intensity and rotation. The point labelled by a solid dot represents recommended thinning intensity and frequency.

Accumulated K inputs are 2836 kg ha�1 in Aspurz and 2627 kg ha�1 in Garde (beyond figure limits).

been evidenced in experimental studies by Baeumler

and Zech (1998).

In addition to the site, tree removal procedure (stem

versus whole-tree) had the second impact on sustain-

ability. For all nutrients and sites, whole-tree removal

resulted in significantly higher potential losses with

respect to traditional on-site tree processing, allowing

nutrients within leaves, twigs and branches to recycle.

Increments in potential nutrient losses due to whole-

tree removal were particularly critical for P and K and

are mostly located in branches and needles. Nutrient

losses were less for Ca and Mg, which are primarily

found in the stems. According to the model results,

these increments turn out to be critical for the long-

term sustainability of current thinning practices. For

example, in Aspurz, N inputs compensate for N losses

for a range of thinning regimes but this balance

reverses under whole-tree removal operations. This

leads to important reductions in nutrient reserves that

may compromise future forest productivity. Similar

results have been found by Morris et al. (1997) and

Rolff and Agren (1999). Our model is conservative

with respect to other factors such as competition of

understory plants that may amplify this reduction

(Jacobson et al., 2000).

The third determinant of sustainability was thin-

ning intensity. Potential nutrient losses increased

dramatically between unmanaged and managed stands

for all sites and nutrients. Thinning effects on nutrient

losses increased up to a maximum for a basal area

removal of approximately 20%, and decreased after-

wards. The effect of thinning intensity interacted with

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228 223

Fig. 8. Potential Ca losses by management (thinning + lixiviation) in the two experimental sites, with stem (top) or whole-tree (bottom)

extraction as a function of thinning intensity and rotation. The point labelled by a solid dot represents recommended thinning intensity and

frequency. Accumulated Ca inputs are 3366 kg ha�1 in Aspurz and 3069 kg ha�1 in Garde (beyond figure limits).

site. In Garde, maximum P and K losses were found

under thinning intensities around 30% and tended to

decrease at higher intensities. These variations,

however, were significantly smoother in Aspurz

(Figs. 6 and 7). At intermediate thinning intensities,

the increment in standing crop due to increased growth

of released trees, compensates for the decrease in

production derived from lower tree density. Hence, it

may be possible to maintain or even to increase wood

extraction relative to that of lower intensity thinning

regimes. At higher thinning intensities, however, a

point is reached in which reduction in tree density is so

severe than growth increments of remnant trees do not

compensate for the density reduction and, thus wood

extracted in each cycle tends to decrease. On the other

hand, decrease in decomposition rates in response to

thinning in Aspurz (Blanco et al., 2003b) results in a

decrease in mineralization rates and likely a reduction

in lixiviation. Nutrient losses due to extraction were

relatively higher than those caused by an increase in

lixiviation and were therefore in agreement with

previous studies (Grigal, 2000; Zhu et al., 2003;

Kimmins, 2004). Johnson and Todd (1987) also

showed that this difference had significant effects for

N and P concentrations in forests in Tennessee (USA).

As these authors suggest it is unlikely that lixiviation

alone results in a decrease in productivity, even

during late successional stages when losses due to

lixiviation equal ecosystem inputs (Vitousek and

Reiners, 1975).

Finally, according to model results, sustainability

of the thinning regime is influenced by its frequency or

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228224

Fig. 9. Potential Mg losses by management (thinning + lixiviation) in the two experimental sites, with stem (top) or whole-tree (bottom)

extraction, depending on thinning intensity and frequency. The point labelled by a solid dot represents recommended thinning intensity and

frequency. Accumulated Mg inputs are 877 kg ha�1 in Aspurz and 813 kg ha�1 in Garde (beyond figure limits).

rotation cycle, which determines the average number

of years between two consecutive forest operations.

The shorter the cycle, the larger the number of

interventions and thus, the larger potential losses of

nutrients. This trend can be observed for all the

nutrients considered (Figs. 5–9), and interacts with

thinning intensity, so its effect is relatively larger for

low-thinning intensities. This reflects forest capability

to recover initial biomass after moderate interven-

tions. The remnant trees can restore or even surpass

the previous standing crop density, providing that

operational lag is long enough (Thornley and Cannell,

2000). However, if thinning intensity is greater than

30% stand ability to restore its initial biomass

decreases (Montero et al., 1999). In this case, a

longer interval between interventions does not allow

for stand recovery and the relative impact of thinning

frequency decreases. Short rotations also may increase

the time of soil exposure and low forest cover

(increasing the period over which mineralization rates

are greater than tree demand) therefore, increasing

lixiviation. Rolff and Agren (1999) predicted a

decrease in productivity for shorter time intervals

between interventions, while Seely et al. (2002)

observed that shorter rotations can result in a

substantial decrease in forest nutrient reserves in the

long-term. Our modelling results suggest that thinning

frequency effects on nutrient reserves are not as

critical as those of the tree removal procedure and

thinning intensity. This does not imply, however, that

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228 225

this aspect of forest management is not critical for

other components of sustainability.

4.2. Silvicultural implications

All situations that compromise ecosystem nutrient

recharge capability should be avoided as they will

result in a gradual decrease of nutrient reserves and

productivity (Kimmins, 2004). Nevertheless, it is

possible, to attain a level of exploitation that allows to

sustain long-term forest productivity or even to induce

a recovery of nutrient reserves (Morris et al., 1997).

Our modelling study suggests that P reserves can be

particularly sensitive to overexploitation (Fig. 6). This

is in agreement with previous experimental studies

conducted at our site, which suggest P limitations

(Blanco et al., unpublished data) and indicate the need

to pay more attention to phosphorus cycling under

different site and thinning regimes, as well as its

effects on site productivity and community composi-

tion. The strong variability across sites regarding their

response to thinning suggests that results should not be

extrapolated from one site to another. Chiefly, site

dependency of tree growth rates result in quantitative

and qualitative differential responses in nutrient

dynamics (Figs. 3 and 4). Thus, forestry prescriptions

for P. sylvestris in the Central and Iberian ranges in

Spain (Del Rıo and Montero, 2001) may result in

nutrient reserve dynamics close to recharge capability.

In particular, for P reserves application of these

prescriptions may result in values above the sustain-

ability threshold in the most productive site, and on the

verge of this threshold in the least productive site. On

the high quality site these practices may result in N

losses below inputs, but for P reserves the balance

results in significant net losses suggesting the need to

reduce thinning intensity or rotation cycles. Whole-

tree extraction is not advised in any of our sites as it

resulted into unsustainable P losses for all thinning

regimes considered. This practice would only be

justified if applied along with N–P fertilization as

proposed by Rolff and Agren (1999), although its costs

are likely not viable in this region.

Finally, other forest interventions such as fine wood

extraction to decrease fire risk, slash crushing to

accelerate decomposition or the use of litter for

gathering game or domestic range could have very

different impact on natural versus managed forests.

This may be especially true when decomposing

material is the most important forest nutrient reserve,

which among other factors, depends on the nutrient

considered, locality and management practice. In this

context, in order to ameliorate CO2 emissions, using

biomass such as leaves and branches as a substitute for

fossil fuels has been claimed to be more beneficial

from social and economic perspectives than seques-

tering the carbon in forests (Hall, 1997). However, the

results from this study suggest caution to favour this

practice without proper evaluation as forest sustain-

ability may be negatively affected by removing the

whole tree from the forest.

4.3. Model limitations and further work

Firstly, we have only considered nutrient cycling

associated with P. sylvestris while understory effects

that can have an influence on nutrient retention and

reduce lixiviation have been ignored. Secondly, we

have oversimplified root description and despite low

model sensitivity, the decomposition of dead roots and

exudates should be adequately described as they can

constitute an important soil biomass input (Beets et al.,

1999). Thirdly, our experimental data do not indicate

effects of thinning on decomposition rates at Garde

after 2 years of study but show a lowering effect at

Aspurz, possibly due to alterations of the decomposer

community (Blanco et al., 2003b). We have tentatively

explored how these latter results may affect nutrient

losses but we need further empirical studies to

understand in which direction these changes will take

place. For example, increments in incident radiation

and temperature, due to a reduction in tree density,

may stimulate mineralization and result in losses from

lixiviation higher than the ones described in our

model. Fourthly, if nutrient concentration in trees

declines with age (Kimmins, 2004), losses from wood

removal would be lower than those simulated. Fifth,

the external components of the nutrient cycle (runoff,

erosion, etc.) should be adequately described to assess

its relative contribution to other fluxes in our model.

Finally, simulated estimates of nutrient losses can be

misleading. For example, in unmanaged forests

nutrient outputs can be naturally high (e.g. Ca

in calcareous locations), or there may be managed

forests in which high-nutrient reserves allow for

more intense exploitation. Thus, it may be more

J.A. Blanco et al. / Forest Ecology and Management 213 (2005) 209–228226

suitable to establish relative comparisons among

alternate thinning regimes to rank them in terms of

potential risk. All these limiting factors caution

against our quantitative predictions. Despite these

limitations, our work shows how simple simulation

models, based on parameters which can be easily

obtained from standard forest ecosystem studies (litter

traps and decomposition bags, chemical analyses of

soil, needles, branches, wood) can be used as

diagnosis tools to compare the potential impact of

alternate interventions on long-term sustainability and

suggest critical processes that may not be intuitively

obvious. The main challenge to developing explana-

tory and yet useful models of ecosystem function from

a management point of view is to achieve a reasonable

balance between model complexity, parameter obser-

vability and biological realism. We feel that an

iterative approximation of experimental studies and

modelling within an adaptive management context is

the most promising direction.

5. Conclusions

It has been shown that suitability of recommended

forestry practices in such a heterogeneous region is

very site dependent. Firstly, we caution against

extrapolation from one site to another without a

specific impact evaluation. It is possible that many

studies from temperate P. sylvestris regions must be

interpreted with caution. Secondly, differences in

ecosystem function in nutrient cycling between a

Mediterranean climate (Aspurz) and a more con-

tinental climate (Garde), are in part overridden by

the impact of biomass extraction. However, faster

decomposition rates under Mediterranean conditions

can make these forests more sensitive to human

intervention. Thirdly, current recommendations

seem adequate for N, K, Ca and Mg but their

consequences on P reserves should be investigated in

more detail. Finally, the single most important

silvicultural factor in a thinning operation is the tree

harvesting method. For all the cases analyzed in this

study whole-tree removal should not be used as it

may compromise long-term sustainability. Results

from simple models of this sort for evaluation of

long-term effects on current silvicultural practices

must be evaluated for management effects on

regeneration, harvesting age or size as well as forest

operation costs to compile with current economic

demands and ecological sustainability of forest

resource management.

Acknowledgements

Juan A. Blanco was supported by a research grant

from CICYT (‘Ministerio de Ciencia y Tecnologıa’)

during his stay at the University of Alcala. We thank

‘‘Gobierno de Navarra, Departamento de Educacion y

Cultura’’ for financial support and ‘Departamento del

Medio Ambiente’ for experimental setting of silvi-

cultural treatments and financial support. In particular,

we acknowledge Fernando Puertas, Carmen Traver

and Ana Iriarte for assistance at several stages of this

work. We are grateful to the scientific forest network

GLOBIMED (http://www.globimed.net) for hosting

meetings where the cooperation between our uni-

versities was initiated. We are also grateful to Dr. J.P.

Kimmins, Tanya Seebacher and two anonymous

reviewers for their useful suggestions and comments

on the manuscript.

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