Development and Parameterization of a General Forest Gap Dynamics Simulator for the North-Eastern Mediterranean Basin (Greek Forest Species)

ecological modelling 204 (2007) 439–456

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Development and parameterization of a general forest gap dynamics simulator for the North-eastern Mediterranean Basin (GREek FOrest Species)

Nikolaos M. Fyllas a,∗, Oliver L. Phillips b, William E. Kunin c, Yiannis G. Matsinos a, Andreas I. Troumbis a a Biodiversity Conservation Laboratory, Department of Environment, University of the Aegean, Lesvos, Greece b Center for Biodiversity and Conservation, School of Geography, University of Leeds, UK c Center for Biodiversity and Conservation, School of Biology, University of Leeds, UK article info abstract

Article history: This paper describes the development of a model, able to simulate the dynamics of typi- Received 23 February 2006 cal mountainous Mediterranean ecosystems, following the forest gap dynamics framework. Received in revised form The model has been adapted to the bioclimatic conditions and species traits of the North- 26 January 2007 eastern part of the Mediterranean Basin, based on forest inventories and climate data from Accepted 1 February 2007 Greece. With GREFOS (GREek FOrest Species), we tried to develop a generalized forest sim- Published on line 26 March 2007 ulator able to both perform realistically in the mountainous Mediterranean climatic zone, and to identify transitional zones with the lower elevation Mediterranean vegetation pro- Keywords: file. GREFOS follows the structure and “evolution” of the ForClim model, which was initially Forest gap model developed for Temperate Central European forests. A life history strategy parameter, which Mountainous Mediterranean forests affects (under a functional group type approach) the regeneration and mortality pathways Plant functional types of the species included in the model, has been incorporated. In addition, a simplified fire Greece submodel was also embodied. For all species included in the model, we have computed the whole set of essential parameters used in forest gap models. Simulation exercises were car- ried out in two geographical areas with district site characteristics (Krania and Parnassos), where quantitative and qualitative field data were available, respectively. In both cases an altitudinal gradient exists and vegetation changes from a Mediterranean to a mountainous Mediterranean profile. The model produces realistic outputs despite its generality, while areas dominated by Mediterranean sclerophyllous species are successfully identified. As a final simulation exercise, for the second area of study, which comprises a natural reserve, we used GREFOS to explore scenarios of changes in the fire frequency. Following these scenarios pioneer pine species seem able to enhance their abundance, at both the upper distributional limit of typical Mediterranean forest communities and the lower limit of more Temperate oriented vegetative patterns. © 2007 Elsevier B.V. All rights reserved.

1. Introduction

Over the last decades, many forest growth models, based on the gap-phase dynamics hypothesis (Watt, 1947)have ∗ Corresponding author. been developed to analyze sustainable forest management E-mail address: [email protected] (N.M. Fyllas). practices (Botkin et al., 1972; Shugart, 1984) and to predict for- 0304-3800/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2007.02.006 440 ecological modelling 204 (2007) 439–456

est vegetation dynamics across climatic gradients (Bugmann, dicting the general forest patterns found at mountainous 1996) and under the influence of changing climatic patterns areas of the Eastern part of the Mediterranean Basin. GRE- (Solomon, 1986; Prentice et al., 1991, 1993; Miller and Urban, FOS (GREek FOrest Species) follows the structure of Bugmann’s 1999). (1994, 1996a) ForClim model. Although the gap dynamics Forest gap models simulate long-term vegetation dynam- framework corresponds to typical forest structure (but see ics, by mimicking physiological mechanisms that express the Coffin and Laurenroth, 1990; Peters, 2002), one of our main effect of abiotic and biotic conditions on growth, through concerns is to develop a generalized simulator, able to iden- simple response functions. These simplifying assumptions, tify Mediterranean to mountainous Mediterranean transition increased the applicability of forest gap models and led to the zones, derived by bioclimatic limitations and/or fire recur- development of a significant number of simulators for differ- rence patterns. The geomorphology of Greece presents such ent ecosystems (Shugart et al., 1992). The trade-off between types of bioclimatic gradients and vegetation patterns, which simplicity and modeling accuracy of physiological mecha- enable the calibration and validation of such type of mod- nisms, still remains a point of discussion among ecosystem els. Taking into account the critiques concerning the forest modelers (Pitelka et al., 2001; Reynolds et al., 2001). The incor- gap dynamics scheme, we have replaced and added ecosys- poration of simplified techniques proved to be essential for the tem processes considered significant for the representation of acceptability of the gap dynamics models as (1) they became a such systems. Thus, we have (a) parametrized a wider species starting point for many ecosystem modeling approaches and pool including both typical Temperate and typical Mediter- (2) dealt with physiological problems that had not been widely ranean forest tree species (52 species), (b) fitted a modified investigated (e.g. precise effects of some abiotic conditions on drought response function, (c) based the mortality and regen- growth) through species classification into common response eration process on a life-history strategies classification and groups. The latter assumption hints at a functional view of (d) added a simple fire submodel. The outputs of the model ecosystem structure and processes (Shugart, 1997). have been validated for two regions of interest (Krania and Par- Gap models are characterized by a “Gleasonian” view nassos), while we used GREFOS to explore scenarios of altered of succession, thus presenting a “cyclic vegetation change” fire occurrence regime at the second study area, which com- (Bugmann, 1996a). They simulate the succession of mixed- prise a natural reserve. aged and mixed-species forest patches, by following the life of each individual (Shugart, 1984). The individualistic approach to succession harmonizes with the individual-based nature 2. Materials and methods of this group of models. The structure of gap dynamics models are described elsewhere (Shugart, 1984; Prentice and 2.1. Forest dynamics of the mountainous Leemans, 1990; Botkin, 1993). Traditional gap models simulate Mediterranean zone the processes of forest succession in unitary forest patches. Basic ecological processes such as recruitment of new indi- A lot of work has been done, regarding the complex vegeta- viduals, growth and mortality are considered explicitly, and tion mosaic found at areas surrounding the Mediterranean critical reviews have been presented regarding the advan- Basin, with focus on the classification of the established for- tages and disadvantages of the assumptions undertaken at est types (Quezel, 1977; Debazac, 1983). The erratic distribution each life stage (Bugmann, 2001). Such reviews (Fischlin et al., of the abundant species and their assemblages emerges from 1995; Loehle and LeBlanc, 1996) provide important critiques the climatic and topographic heterogeneity (Bradbury, 1981), about their ability to predict realistic forest composition, espe- in addition with the long-standing human management (Le cially under varying climatic patterns. These critiques point Houerou, 1981; Barbero et al., 1990) of the region. Phytosoci- out the significance of the climatic inputs and the role of ological (Quezel, 1981) and bioclimatic studies (Daget, 1977; realized niche as driving forces of the early gap dynamics Nahal, 1981), have underlined the ecological value of Mediter- simulators. Furthermore, they underline issues regarding the ranean vegetation, in the context of both enhanced species implementation of these simulators for different types of diversity (Cowling et al., 1996) and “model ecosystems”, where forests ecosystems. However, the relative simple rationale the interaction of natural and anthropogenic factors could be of gap dynamics models and the continuant re-examination explored (Lavorel et al., 1998). of their basic compartments—submodels, provide obvious Forests found above the Mediterranean sclerophyllous advantages regarding the diffusion of controlled scientific evergreen zone, present a significant species diversity information to policy makers (Pace, 2003). (Debazac, 1983). The diversity of species’ structural and Despite the wide acceptance and applicability of this type functional traits, in addition with the heterogenous micro- of models, a gap simulator has not yet been developed for environmental conditions could lead to discrete forest the mountainous Mediterranean forests of the Eastern part dominance patterns. Frequently in such regions, the bound- of the Basin. We will hereafter use the term “mountainous aries between vegetation types are not sharp (Blasi et al., 1999). Mediterranean zone” to refer to the Upper Mediterranean Most of the deciduous broadleaved species found in this belt and the Mediterranean mountain forests (sensu Quezel, 1977). (e.g. Quercus frainetto, Quercus cerris, Quercus pubescens, Castanea We believe that a gap model is an appropriate and “easy to sativa), are usually considered as mid or late successional, use” management tool especially when dealing with ecosys- following the classical view of vegetation change in Mediter- tems rich in life strategy variation, but poor in available ranean forests (Barbero et al., 1998). Near the Mediterranean data (Pausas, 1999a). The aim of this paper is to present the Sea, most of these species are found in their southernmost development of a gap dynamics simulator capable of pre- geographical range and their distribution is controlled by ecological modelling 204 (2007) 439–456 441

drought (Brewer et al., 2002). Among these deciduous tree this study was to evaluate the fluxes of the biogeochemi- species, ecophysiological experiments have underlined dif- cal elements during short time periods, rather than identify ferences in their ability to tolerate drought (Corcuera et al., long-term vegetation changes. On the other hand a few mod- 2002; Manes et al., 2006; Raftoyannis et al., 2006). In the same els have been developed focusing primarily on the dynamics belt, evergreen sclerophyllous species (e.g. Quercus coccifera, of typical low elevation Mediterranean ecosystems (Pausas, Quercus ilex, Pistacia lentiscus) are found usually as shrubs at 1998; Mouillot et al., 2001; Zavala and Bravo de la Parra, the understory, defining a transitional zone between the ever- 2005). Although these simulators refer to a discrete biocli- green and deciduous foliage profile (Debazac, 1983; Blasi et matic zone, some common ecological processes and species al., 1999). Species belonging to the sclerophyllous evergreen strategies could be identified. Taking into account the “vital group present a significant drought tolerance (Turner, 1994; attributes” framework (Moore and Noble, 1990), Mouillot et al. Corcuera et al., 2002), a relative shade tolerance at least dur- (2001) developed a spatial process-based simulator of mixed ing the establishment stage (Sanchez-Gomez et al., 2006) and a species—lifeforms dynamics. In this model, spatial processes high resprouting ability (Malanson and Trabaud, 1988; Espelta such as seed dispersal and water fluxes are explicitly consid- et al., 1999), that favors their dominance under frequent ered. Furthermore, a detailed consideration of the interaction disturbance regime (Pausas, 1999b). Lastly, the species pool between life history traits and fire disturbances is taken into of Mediterranean mountains, usually includes highly plastic account. The implementation of the gap-dynamics framework pine species (e.g. Pinus nigra and Pinus sylvestris), which are at Mediterranean ecosystems is rare. Main reasons for that considered the pioneer elements of the successional series. are the poor representation of the early stages of succession However, these light demanding, drought tolerant species and the lack of age data from growth rings for resprouting usually form self-maintained stands or “pine climax” forma- shrubs (Pausas, 1999a; Mouillot et al., 2001). Malanson and tions in unproductive sites, where mid and late succession O’Leary (1995) created a gap dynamics model for the coastal species are not able to dominate (Barbero et al., 1998). At sage chaparral boundary of California, using the methodol- higher altitudes, where Temperate oriented conifers (e.g. Abies ogy of forest gap models and adding a foliage cover measure. cephalonica and Abies borisii-regis) and broadleaved species Zavala (1999) has called into question the ability of gap mod- (Fagus spp.) are found, vegetation change could be approached els to simulate the vegetation dynamics of Mediterranean following the typical forest gap dynamics frame. systems, particularly during the early successional stages. At the ecophysiological level, information related to mech- In order to increase the accuracy of the establishment and anisms controlling vegetation dynamics is available, but has recruitment processes, he used logistic functions to simulate not yet been integrated into a mechanistic simulation model, the species saplings mortality rate in relation to water and particularly for the mountainous areas of the North-eastern light availability. Later on, Zavala and Zea (2004) developed a part of the Mediterranean Basin. For example it is established non-gap, spatial model of forest dynamics where they inves- that water availability is able to control the distribution of tigated the interaction of Pinus halepensis and Q. ilex. Their important species like Quercus spp. at a macro-scale (Brewer et model was calibrated with experimental data while included al., 2002), as well as their abundance and recruitment vigor at factors such as disturbance regime, species-specific disper- micro-environmental gradients in the mountainous Mediter- sal ability and spatial heterogeneity. Pausas (1998) developed ranean climatic zone (Pigott and Pigott, 1993; Martinez-Vilalta BROLLA, a non-spatially explicit gap dynamics simulator, and Pinol, 2002; Kunstler et al., 2005). The topographic extend using plant functional types rather than individual species, of typical evergreen species could be explained by the dou- and tested vegetation dynamics across fire gradients (Pausas, ble stress theory, where both drought and frost pressures 1999b). Apart from the short-term, process oriented FOREST- are the basic factors shaping their distribution (Mitrakos, BGC model, none of the simulators mentioned above explores 1980, 1982; Terradas and Savel, 1992). Although in many explicitly the dynamics of the mountainous Mediterranean of the mountain forests of the Mediterranean region fire zone, while in general the simulated species pool does not is not considered an important factor, it is not completely accounts the significant tree species diversity found at the absent (Figueiral and Carcaillet, 2005; Vernet, 2006). Further- Eastern part of the Mediterranean Basin. more, fire events could affect vegetation dynamics, leading Most of the key ecological processes identified as impor- to discrete dominance patterns controlled by the local envi- tant in mountainous Mediterranean forests (Section 2.1), are ronmental conditions and/or the local species pool (Gracia taken into consideration in the ForClim model (Bugmann, et al., 2002). All the above underline the significance of fac- 1996a). However, a fine tuning initial step is essential before tors as drought, frost and in some cases fire, as well as applying forest gap models to a specific geographic area their interaction with species specific traits, to the dynam- (Porte and Bartelink, 2002), while in our case an extension ics of forests found above the typical Mediterranean climatic of the model’s species pool is necessary. For example, the zone. water-stress derived reduction of the optimal growth for typical Mediterranean species is not taken into account in 2.2. Simulating the dynamics of the mountainous ForClim. Thus, we have created six new curves, expressing Mediterranean zone species-specific tolerance/avoidance to drought stress, based on experimental (Percival and Sheriffs, 2002) and bioclimatic One of the few simulators that explored the ecological pro- (Athanasiadis, 1984) data. In addition, all the species sim- cesses of the mountainous Mediterranean forest zone is a ulated in GREFOS, have been assigned to a specific frost calibrated version of the FOREST-BGC model (Running and tolerance threshold, in order to include the role of mean min- Coughlan, 1988; Chiesi et al., 2002). However, the purpose of imum winter temperature, as a filter enabling (or not) the 442 ecological modelling 204 (2007) 439–456

Table 1 – Basic processes and modiﬁcations applied in GREFOS Process Method Modiﬁcation References

Regeneration Life history strategies Discrete densities for each Lhs Kazanis and Arianoutsou (2004) type Growth Moore’s growth Tree-shrubs life form with Moore (1989) and Bugmann (1994) equation averaged maximum height Mortality (a) FORCLIM’s intrinsic, Facultative resprouters 40% Solomon (1986), Bugmann (1994), (b) FORENA’s stress sprouting ability. Obligate Pausas (1999) and Vesk and related mortality, (c) fire resprouters 80% sprouting Westoby (2004) and/or browsing events ability Light competition FORCLIM’s foliage CO, DB, EB types Bugmann (1994) and C. Gracia and J. allometric parameters Vayreda (personal communication) Drought effect Modified Calibration for six drought Bugmann and Cramer (1998) evapotranspiration tolerance—avoidance classes model Dominance Individual’s basal area to Neighborhood’s basal area ratio process of regeneration. The two modified processes harmo- and Cramer, 1998; Bugmann and Solomon, 2000; Risch et al., nize with the “principles” derived from the widely accepted 2005). It is also a forest simulator with tree species more double stress theory (Mitrakos, 1980). similar to the species appearing in some mountain forest Functional groups of plant species have already been ecosystems in Greece, with climate similar to Central Europe. incorporated into the forest gap dynamics frame (Smith and GREFOS consists of three basic submodels: (a) Climate, (b) Soil Huston, 1989; Coffin and Urban, 1993). Some of the applied and (c) Tree, and three satellite submodels: (a) Light, (b) Neigh- classifications include bioclimatic indices (Bugmann, 1996b), borhood and (c) Fire (Fig. 1). The abiotic conditions (submodels while others focus on disturbance related traits (Pausas, Climate and Soil) are computed on a monthly time step and 1999b). In GREFOS a hybrid approach is followed, similar to their output is aggregated to annual values (Annual Variables). Mouillot et al., 2001, where specific bioclimatic parameters These annual values are used for the calculation of species- are maintained at the species level (e.g. growing degree days, level response functions in the Tree submodel, where the frost resistance, allometric variables, etc.), while the regen- birth, growth and death of each individual are explicitly sim- eration and mortality processes are linked to a life history ulated. Competition for light is calculated in the whole gap classification (Coffin and Urban, 1993; Acevedo et al., 1996). It through the satellite Light submodel. The position of each indi- should be noted that the derived functional types correspond vidual in the plot (x, y) is made explicit, in order to weight to response traits, i.e. to the response of a group of species the competition for belowground resources through a simple under a given environmental change (Lavorel and Garnier, 2002).

2.3. Programming platform

After reviewing the techniques implemented in previous modeling approaches, we have developed GREFOS, using the SIMILE modeling environment (Muetzelfeldt and Massheder, 2003). SIMILE is a “visual modeling environment” characterized by a declarative modeling technology. The visual representation of SIMILE’s code helps the researcher both to develop and present the conceptual model and its interactions, and to focus on the realistic description of the ecological processes involved, rather than the programming code. GREFOS is based on the structure of the ForClim model (Bugmann, 1996a; Risch et al., 2005). The modiﬁcations of the mainstream forest gap dynamics scheme, implemented in GREFOS are summarized in Table 1.

3. Description of the model

As already mentioned, the structure of GREFOS is similar to Bugmann (1996a) ForClim model. We selected the structure of ForClim, as it is a general forest gap dynamics model characterized by a continual “evolution” of both its general rationale and submodels (Fischlin et al., 1995; Bugmann, 1996a; Bugmann Fig. 1 – Overview of the GREFOS structure. ecological modelling 204 (2007) 439–456 443

Table2–Overview of GREFOS variables discussed in the text Factor or variable Abbreviation Unit or range or reference

Climate and soil submodels Monthly drought days mdrd days Monthly growing degree days mgdd days Annual drought days anndrd days Annual growing degree days anngdd days Species minimum growing degree days mingdd days ◦ Average monthly temperature tempm C Winter minimum temperature winmtemp ◦C Drought index d.i. –

Total monthly precipitation precm mm Evaporative demand Demand mm Evapotranspiration Evap transp mm Soil water supply Supply mm Available soil water aw mm

Tree and satellite submodels

Diameter at breast height (dbh) Di cm 2 Basal area of the ith individual BAi m Temperature response function TRF 0–1 Light response function LRF 0–1 Drought response function DRF 0–1 Dominance effect Dom effect 0–1 Modiﬁed geometric mean of the response functions f(e) 0–1 Available light AL %

Foliage allometric parameters C1, C2, A1, A2 Modiﬁed from ForClim Foliage area Fol Area m2 Foliage weight Fol Weight kg Shading leaf area SLA m2/m2 Shading leaf area index SLAI m2/m2

LRF’s coefﬁcients ai As in Urban (1993)

DRF’s coefficients di Fitted Drought tolerance class dtc Fitted Light filter for establishment Lf Boolean Heat requirement filter DDf Boolean Winter minimum temperature filter Wif Boolean Life history strategy Lhs – Maximum number of new saplings per Lhs type max sap –

Number of new saplings for species s nnss – density–dominance function (Neighborhood submodel), and to • anndrd (annual drought days) input for DRF (drought enable the resprouting process. Light response and dominance response function). effect are functions acting at the individual level. Fire events • winmtemp (winter mean minimum temperature) input for are simulated in a simple manner (Fire submodel), and when FrostFilter (Frost Filter). occurring each individual is assigned to an additional “mortality pathway” according to the life history strategy group it These variables serve as input parameters to the Tree belongs to. One could characterize GREFOS as a 2D spatially submodel’s response functions and regeneration filters. Mod- explicit gap model, as every individual has a specific set of ifications from the original ForClim algorithms include the Cartesian coordinates thought its foliage vertical profile is not calculation of (1) a “mediterranized” winter mean mini- considered. An overview of the simulator is presented in Fig. 1, mum temperature and (2) an annual number of drought while Table 2 summarizes the variables discussed in the text. days. The computation of the amount of monthly growing degree 3.1. Climate and soil submodels days is simplified to:

= { . − , } The Climate submodel operates on a monthly time step. mgdd max 30 5(tempm 5) 0 (1) Monthly time series of temperature and precipitation, or correlated values of them could be used as an input. The output where mgdd the monthly sum of growing degree days and of this submodel consists of three variables: tempm the average monthly temperature. The sum of 12 con- secutive mgdd gives the annual degree days (anngdd). This is probably a poorer approximation than the one used in ForClim, • anngdd (annual growing degree days) input for TRF (tem- but the same set of equations is used in the calculation of the perature response function). thermal limits for each species (see section parameterization 444 ecological modelling 204 (2007) 439–456

of woody species), in order to have a common “computation where LRF is the light response function, TRF the tempera- basis”. ture response function, DRF the drought response function, Mean minimum winter temperature is correlated with Dom effect is the density–dominance effect. average monthly temperature. The analysis of 42 time Competition for light resources is enabled within the Light series from weather station widespread in Greece, illustrated submodel. Available light is calculated according to Beer’s a good correlation between average monthly tempera- extinction law as a function of leaf area index (LAI). In For- ture of the coldest month and both mean minimum Clim, Bugmann (1994) introduced two equations to calculate 2 (=−4.56 + 1.12 tempm, r = 0.952) and absolute minimum tem- the foliage area from foliage dry weight. 2 perature (=−13.2 + 1.42 tempm, r = 0.944). C A modified soil water balance submodel proposed by = 2 Fol Area C Fol Weight (5) Bugmann and Cramer (1998) is incorporated in GREFOS.A 1 minor modification has been made with the computation of drought days. Drought days are defined as the days when the while Fol Weight is given by: demand for water resources (of the whole plot), is greater than A = C A D 2 the water that is lost through evapotranspiration. Firstly the Fol Weight 1 1 (6) monthly drought index is calculated with the equation: The advantages of these equations are, that they separate Demand − Evap transp coniferous from deciduous species and define five classes that d.i. = (2) Demand relate dbh and foliage weight. For common species between ForClim and GREFOS, the same parameterization is followed. where the monthly evapotranspiration value (Evap transp) is In order to include the evergreen broadleaved woody species given by the “lesser of a supply and a demand function” as abundant in Mediterranean ecosystems and mountainous shown in (Bugmann and Cramer, 1998). Then the monthly d.i. transitional zones, a third class-type (EB) has been developed, is used to approximate the amount of the current month’s as these species tend to have a different dry to wet ratio of drought days, as a coefficient multiplied with a mean number leaves, crown density and hence shading ability. So we have of days per month. modified the S-type parameters initially used in ForClim to:

3.2. Tree submodel • CO for conifers. • DB for deciduous broadleaved. The Tree submodel operates on an annual time step and sim- • EB for evergreen broadleaved. ulates the establishment, growth and death of the individuals of each species. For every tree species the standard physio- The evaluation of species S-type parameters is presented logical parameters used in gap models have to be provided. in the parameterization section. In GREFOS a new parameter named Lhs (life history strategy) The available light (AL) for each individual is then calcu- has been introduced. Lhs is linked to both the recruitment lated by the following equation: and mortality algorithms. Furthermore, the introduction of the Neighborhood submodel simplistically mimics the competi- AL = exp(−k × SLA) (7) tion for belowground resources (Zavala and Bravo de la Parra, 2005). This submodel compares the basal area of the target where k is the light extinction coefficient (k = 0.4) and SLA the individual with the sum of the basal areas of the competitors shading leaf area index. Light response function is then: in a circular neighborhood. In total 52 tree species have been parameterized but not all of them are included in the simula- = a − −a − a tion exercises reported here. The default value of the gap size LRF 1(1 exp( 2(AL 3)) (8) is set to 750 m2. In the following paragraphs we discuss the functioning of the basic compartments of the model. with ai the coefficients used in both ZELIG (Urban, 1993) and DRYADES (Mailly et al., 2000). The parabolic function used in the early gap models (Botkin 3.2.1. Growth et al., 1972) for the computation of the temperature response As in most forest gap models, the basic parameter of GREFOS is function (TRF) has been criticized as invalid (Loehle and the diameter at breast height (dbh, 130 cm) of the individuals LeBlanc, 1996), especially for simulators that explicitly incor- in the plot. The concept of a “realized growth” deriving from porate a drought response function. The right portion of the the reduction of the optimal diameter growth from response parabola could be considered as a proxy of the reduction in functions is followed as well. Optimal diameter increment (for tree growth when temperature and water availability inter- a specific species) follows the equation proposed by Moore act, if the model does not incorporate a specific algorithm (1989). A reduction of this value is achieved by multiplying for simulating drought (Bugmann and Solomon, 2000). Bug- with f(e), a modified geometric mean of all response functions mann and Solomon used an asymptotic TRF that does not (Bugmann, 1996a). In GREFOS we include Dom effect in the require a maximum growing degree days limit and does not computation of f(e), thus: minimize the growth of species at their upper thermal limits. A similar TRF is applied in GREFOS, assuming a reduction of f e = 4 × × × ( ) LRF TRF DRF Dom effect (4) the growth rate by 25% relative to its optimum, when anngdd ecological modelling 204 (2007) 439–456 445

• Seeder (SED), species that regenerate only by recruitment of Table 3 – Coefficients for drought response function used in GREFOS new seedlings (P. nigra, P. sylvestris, A. cephalonica, A. borisii- regis, etc.). Drought d coefficient d coefficient 1 2 • Fire seeder (FRS), species with recruitment triggered by fire tolerance class (P. halepensis and P. brutia). 1 −0.01982 −0.00028 • Facultative resprouter (FCR), primarily regenerate by recruit- − − 2 0.00474 0.00041 ment but resprout as well (C. sativa, Fagus spp., Carpinus spp., 3 −0.00030 −0.00024 Ulmus spp., etc.). 4 +0.00210 −0.00015 • 5 +0.00407 −0.00011 Obligate resprouter (OBR), population persistence by 6 +0.00481 −0.00007 resprouting and a relative low number of recruited seedlings (Quercus spp., Pistacia spp., Arbutus spp., etc.).

This maximum number of saplings is limited by environ- equals mingdd + 1000: mental suitability, assuming that the regeneration process

q − follows much the same restrictions as the growth process: TRF = max[1 − e (mingdd anngdd), 0] (9)

nnss = max sap(Lf × DDf × Wif × DRF) (11) where q is constant coefﬁcient (q = 0.0013).

Drought has to be explicitly simulated in models of moun- where nnss is the number of new saplings for species s per tainous Mediterranean vegetation dynamics. The drought year, Lf the light flag for establishment (Bugmann, 1996a), DDf response function is used to calculate the effect of drought (degree days flag) the heat requirement filter for establishment stress on various woody species. Annual drought days are used (Botkin et al., 1972), Wif the winter minimum temperature flag as an input for this function. We fitted six drought tolerance for establishment (Bugmann, 1996a) and DRF is the drought curves of the form: response function. Lf compares the species-specific light threshold with the 2 available light at the forest floor and enables or blocks the DRF = max[1 + d1 anndrd + d2 anndrd , 0] (10) saplings’ establishment. DDf uses as a threshold the species- specific minimum value of annual degree days. If the annual based on (1) the reduction of photochemical efficiency follow- degree days are fewer than the species-specific minimum ing drought (Percival and Sheriffs, 2002) and (2) the number degree days, then the regeneration process is blocked. Wif of drought days that specific forest communities in Greece functions in the same way by comparing a species-specific can tolerate (Athanasiadis, 1984). The coefficients used in the minimum-frost temperature with the mean minimum tem- new DRF are given in Table 3. We note that there is no dis- perature of the previous winter season. The filters mentioned crimination between species that are characterized as drought above are common in most forest gap models and in ForClim. avoiders or drought tolerants. All species are classified accord- In Mediterranean ecosystems water availability is a determin- ing to the literature review of their ability to withstand severe ing factor of the recruitment density of most species (Keeley, drought conditions, regardless of the strategy followed, in one 1992; Lloret et al., 2004), as it is controlling both seed and of the six tolerance classes. seedling mortality. Similar evidence has been reported for the mountainous Mediterranean climatic zone (Pigott and Pigott, 3.2.2. Birth and establishment 1993; Gracia et al., 2002). In order to incorporate drought stress In the majority of forest gap simulators, regeneration is simu- effects on the regeneration process, the same response func- lated as an aggregated process, without taking into account tion used in growth is multiplied by the maximum number of differences in seed production, dispersal, storage and ger- saplings, assuming a similar behavior for these processes. mination between species (Price et al., 2001). In addition an The maximum number of new saplings per Lhs type is set unlimited source of seeds and seedlings is assumed. In ecosys- as follow: Seeders have a density of 0.5 new individual per tems where the abundance of resprouting species and the role square meter. Fire seeders increase their new sapling density of fire are significant, the use of such methods could under- after a fire event to 1 m−2. Facultative resprouters have sapling estimate the dynamics of regeneration (Pausas, 1999b). For density of 0.2 m−2 and Obligate resprouters of 0.1 m−2, assum- example, recruitment of some species such as P.halepensis, has ing that as resprouting efficiency increases, the resources to be triggered by fire events while others such as Quercus spp. diverted for recruitment decrease (Chapin et al., 1990; Bond do not (mainly) base their population persistence on sapling and Midgley, 2001). These values have been selected after a recruitment and are not (usually) destroyed by a fire event, as review of the natural regeneration literature of these ecosys- they are able to resprout (Malanson and Trabaud, 1988; Pausas, tems (see Section 3.3). 1999a; Bond and Midgley, 2001). In GREFOS a regeneration process based on a life history 3.2.3. Mortality strategy (Lhs parameter) classification has been implemented. The simulation of the mortality processes in gap models Following the functional group type framework proposed by has attracted substantial criticism (Loehle and LeBlanc, 1996; Kazanis and Arianoutsou (2004), each species corresponds to Keane et al., 2001). In GREFOS mortality is simulated as an an Lhs type, characterized by a maximum regeneration den- aggregation of three processes. These are (a) intrinsic mor- sity. This classification has been modified to: tality, (b) stress-related mortality and (c) fire mortality. As in 446 ecological modelling 204 (2007) 439–456

most gap models the maximum species age-related mortal- the annual growth. The incorporation of the root–shoot ratio ity (intrinsic) assumes that only 2% of the population reaches in this equation could probably increase its realism, but is not that age, and is simulated using an exponential survivorship included in the simulations reported here. curve (Botkin, 1993; Bugmann, 2001). Stress related mortality assumes that when a tree is under stress conditions it experi- 3.3. Parameterization of species simulated by GREFOS ences an increased probability of death, thus a stochastic extra mortality process is being activated. This algorithm expresses 3.3.1. Size, age and site requirements the hypothesis that in order for a plant to survive it must be The morphological data of each species were gathered able to grow, using as an indicator the growth achieved the last from independent sources, through a literature review 2 years (Hawkes, 2000). In most gap models there is a standard (Moulopoulos, 1970; Dafis, 1986). The “final” maximum size threshold defining low growth. We apply a species-specific and age of each species is set to the average of the reported threshold, changing with tree’s size to 10% (Solomon, 1986) values. For the species found mainly in the understory, in of the optimal growth it could achieve for the current size. the form of tall shrubs with a potential tree form (Arbutus We thus avoid the elimination of low growth rate species that andrachne, Arbutus unedo, Juniperus oxycedrus, Phillyrea latifolia, would die in conventional forest gap models with fixed mini- Pistacia lentiscus and Quercus coccifera), a maximum size greater mum growth thresholds. Furthermore, individuals of species than the one usually found (but still within growth limits) able to resprout (facultative seeders and obligate resprouters) is used. Site requirements for each species, regarding light are replaced by new individuals, if they are in the mature and water (Dafis, 1986) were transformed to shade (young and phase (10% and 5% of their maximum age, respectively), and adult) and drought tolerance relative classes (very intolerant the light requirements are fulfilled. Lastly, the fire submodel to very tolerant). triggers fire related mortality. When a fire event occurs, all The usage of an asymptotic temperature response func- seeders and fire seeders die, while the basal area of all faculta- tion requires as an input only the minimum growing degree tive seeders and resprouters is reset to a random initial value. days for each species. This species-specific parameter has Then a random number is selected, assigning a probability been calculated with two methods. In the first, growing degree 30% for facultative seeders to resprout and 80% for resprouters days surfaces have been created for the Balkan Peninsula (Espelta et al., 1999; Pausas, 1999b; Vesk and Westoby, 2004). (using monthly mean temperatures, threshold 5 ◦C) and over- laid with species distribution maps. The multiplication of 3.2.4. Light competition these two layers (growing degree days × species presence or This subroutine mimics the light competition process. As sug- absence) approximates the mingdd parameter. In the second gested in the forest gap dynamics scheme, in each patch the method we used the altitude limits of species presence (Strid highest tree is the “shader” of all other trees, and all individ- and Tan, 1997) to validate the minimum growing degree days, uals are depressed (regarding light resources) by bigger trees based again on site-specific monthly mean temperatures. The in the plot. In order to implement this process a state variable lowest value of these two methods is used as the species’ rel height is defined. This variable compares the heights of all minimum heat requirement. individuals in the plot and triggers the appropriate processes to calculate the shading leaf area for every individual (SLAI). The light response function for every individual is computed 3.3.2. Foliage area and shading ability of species: based as described above. on ForClim, S-type parameters In ForClim, Bugmann (1994, 1996a) applied Eqs. (5) and (6) to 3.2.5. Dominance effect calculate the foliage area from the foliage dry weight. The The majority of forest gap models do not take into account advantages of these equations are that they separate conifer- the distance between individuals. In some models a threshold ous from deciduous species and define classes of relationship of carrying capacity is assumed, while the stand is consid- between dbh and foliage weight (while other models use a ered horizontally homogenous (Shugart, 1984). The subroutine single power function). For species appearing both in ForClim Dominance effect makes a simple consideration of the density and GREFOS the same parameters have been used. In order to effect for every individual, by enabling a reduction of the opti- include the evergreen broadleaved woody species appearing mal growth. This is done by relating the ratio of the basal area in the study sites, we have introduced and parameterized a of the target individual to the total basal area in a circular third class-type, indexed as EB. neighborhood with radius equal to 0.1 of the gap’s side: Coniferous and deciduous species are classified as in For- Clim, where C1CO = 0.45, C2CO =6, C1DB = 0.35, C2DB = 12 and ranging A1, A2 values. The dry to wet ratio for EB was chosen BAi Dom Effi = min , 1 (12) with a constant C1 = 0.40 and a leaf area per leaf weight C2 =9, n−1 j j=1 BA assuming that leaf biomass of evergreen species is interme- diate between deciduous and coniferous species (Hilbert and where Dom Eff is the dominance effect, BAi the basal area of Canadell, 1995). This hierarchical scale agrees with data from n−1 j the target individual and j=1 BA is the sum of all other indi- ecophysiological studies for both adults (Larcher, 2003) and viduals BAs in the neighborhood. This function assumes that seedlings (Cornelissen et al., 1996; Antunez et al., 2001). The individuals with greater basal area than the target tree are A1 and A2 parameters are fitted with foliage allometric equa- better competitors for water and nutrients. This parameter is tions for evergreen Q. ilex provided by CREAF (C. Gracia and J. multiplied with the usual response functions in order to obtain Vayreda, personal communication, 2005). ecological modelling 204 (2007) 439–456 447

− 3.3.3. Maximum recruitment density and life history of 1 saplings m 2 for the FRS class was selected as a reasonable strategy parameters value. In order to assign a recruitment vigor (at the sapling stage) Most of the deciduous broadleaved tree species found in to each Lhs type, a literature review has been combined Greece have been classified in the facultative resprouter (FCR) with our unpublished data, from two forest ecosystems: (1) class. An exception was made to the genus Quercus, which in a vegetation transition zone on Lesvos island (North- species were set as obligate resprouters (OBR). In forest of Cen- eastern Greece) and (2) in a more Temperate oriented tral Europe Fagus sylvatica, seedlings density could reach up to − region of Vikos Aoos (North-western Greece). In the first 7.5 m 2 (mean height 30 cm) (Modry et al., 2004), while in Spain − − study area, a mixed P. brutia–P. nigra ssp. palassiana for- (Rozas, 2003) saplings densities between 0.02 m 2 to 0.1 m 2 est, with an evergreen understory has been censured. In are reported. Recruitment patterns of Fagus orientalis (another the second area, measurements took place in a mixed Q. beech subspecies found in Greece) present similar densities frainetto–Q. cerris forest. We acknowledge issues regarding site- (Sagheb-Talebi and Schotz,´ 2002). As mentioned above, in this − specific bioclimatic conditions that could affect the density Lhs class a maximum sapling density of 0.2 m 2 is used. of newly arrived individuals, but this parameterization is Species assigned to the (OBR) life history type (Quercus spp. used to provide a rational input in our general forest gap and most of the sclerophyllous evergreen species), are set simulator. to a maximum sapling density of 0.1 individuals per square − Species classified in the Seeder group (SED), are assigned meter. In Spain mean densities of 259 saplings ha 1 (in holm − a maximum regeneration density of 0.5 individual per square oak stands) and 575 saplings ha 1 (in Aleppo pine stands) are meter. In Greece measurements in black pine (P. nigra) forests, reported for the evergreen Q. ilex (Retana et al., 1999), while report (maximum) values of regeneration density up to 6 m−2 Q. rubur can recruit up to 0.0675 new individuals per square (sum of seedlings and saplings) (Apatsidis, 1977). For the same meter in open canopies (Rozas, 2003). In Greece in a mixed Q. stands the density of individuals with age greater that 5 years frainetto–Q. cerris forest, we have counted a maximum num- and height until 1 m reached 3.62 individuals per square meter. ber of 0.18 and 0.11 individuals per square meter (of height Vergos (1979) recorded in the same region saplings density 100–200 cm), respectively. However, seedlings emergence of up to 1.94 m−2 (100–200 cm). On the island of Lesvos, data resprouting evergreen plants like Ph. latifolia is affected by from an unburned mixed P. nigra ssp. palassiana–P. brutia for- water availability (Lloret et al., 2004). Under normal condi- − est, report maximum seedlings (10–100 cm) density 0.74 m−2 tions the initial recruitment density of 2 m 2 is significantly and maximum saplings (100–200 cm) density 0.17 m−2 (N. Fyl- reduced when water is excluded (Lloret et al., 2004). In this las and P. Dimitrakopoulos, unpublished data). In a mixed A. study approximately half of the recruits survived after the sec- cephalonica–A. borisii-regis forest, Moulopoulos (1956) created ond year. Rey and Alcantara (2000) found densities of recruits gaps by cutting down groups of dominant trees and monitored (greater that 1-year old) ranging from 0.06 to 0.61 for Olea the dynamics of regeneration for site quality, aspect and slope europea. Observed saplings densities on the island of Lesvos gradients for 15 years. Maximum recruitment density ranged (N. Fyllas and P. Dimitrakopoulos, unpublished data) for Pi. from 6 m−2 (southern aspects) to 14 m−2 (northern aspects). lentiscus and Q. coccifera are near 0.11 and 0.17 individuals per These substantial densities could be highly reduced after the square meter, respectively. Considering the seedling densi- self-thinning process. For example in A. alba stands in Poland, ties reported in the literature review, the slow growth rates the initial maximum value of 6.8 m−2 falls down to 0.059 m−2 that sclerophyllous shrub-trees illustrate, and the Lhs frame- in the size class of 50–130 cm (Dobrowolska, 1998), or even work incorporated in GREFOS, a maximum sapling density of − 0.02 m−2 in Slovenia (Diaci, 2002). The value (0.5 m−2) assigned 0.1 m 2 was assigned to the OBR type. to the max sap for the SED class is probably too high considering values used in other models [0.006, (Bugmann, 1996a)], 4. Simulation experiments but it represents a reasonable estimation for the basic species concerned (P. nigra and Abies spp.). Measurements of new saplings density for P. halepensis We assessed the predictive ability of GREFOS in a quanti- in the absence of fire and outside the canopy report 0.0025 tatively and qualitatively way. Quantitative tests involved (age >3 years) (Nathan et al., 2000) to 0.2 m−2 (seedlings and comparisons of field observations with the model’s output for saplings) in natural regenerating stands (Retana et al., 2002). specific geographical points. On the other hand, qualitative In mature unburned forests in Israel, the average regen- comparisons explored the ability of GREFOS to change realis- eration density was 0.0312 m−2 (Arianoutsou and Ne’eman, tically the projected ecosystem type, across wide altitudinal 2000). Sixty months after fire, observed increases in saplings gradients and vegetation transition zones. In all simulation densities range from 3.6 to 3.9 m−2 when competing or not exercises the model was run 100 times and the mean output with Cistus species, respectively (De las Heras et al., 2002). values were considered to describe the dynamics of vegeta- In Greece (island of Thasos), P. brutia saplings density ranges tion. from 2 to 0.5 m−2 60 months after fire (Spanos et al., 2000), while at more favorable site conditions (Northern Greece) 4.1. Quantitative tests it can reach to 2–4 and 1.7–2.4 saplings m−2 10 years after fire (Tsitsoni et al., 2004). Considering that P. halepensis or P. The quantitative tests compared plot data with model predic- brutia seedlings usually reach the height of 1 m (becoming tions for both monospecific and mixed-species communities. saplings under the forest gap models logic) 10 years after fire Plot data reported in Vergos (1979) were obtained at Krania ◦ ◦ (Thanos, 1999; Thanos and Doussi, 2000), a maximum number (21 11 E39 51 N) in the prefecture of Grevena, North-western 448 ecological modelling 204 (2007) 439–456

Table 4 – Classiﬁcation of soils on the base of their water holding capacity at the area of Krania, North-western Greece (Apatsidis, 1977) Soil type Depth (cm) Available Aspect Slope water (mm) (%)

30 47 S, SW, SE >40 36 63 S, W, E <40 Dry 65 78 N >40 120 93 NW, NE <40

>120 108 NW, NE <40 >120 123 Sockets 0 Wet >120 138 N, E <40 >120 155 – 0

Greece. The elevation ranges from 800 to 1800 m above sea level (a.s.l.) and the climate is characterized as cold subhumid. The mean annual temperature and mean annual precipitation are 10.7 ± 1.4 ◦C and 882 ± 228 mm, respectively, measured at the weather station of Krania–Grevena (Papoulias, 1973). Apatsidis (1977), while studying the regeneration dynamics of P. nigra, classified the soil types of this region according to their ability to hold water into eight groups (Table 4). In the forests of Krania the dominant species are P. nigra, Q. frainetto and A. borisii-regis while Alnus glutinosa, Platanus orientalis, Acer pseudoplatanus, Populus tremula, Ostrya carpinifolia, and Fraxinus ornus are also present (Vergos, 1979). Data from both monospecific (P. nigra) and diverse plots were collected by Vergos (1979) to identify the structural types that describe the successional pathways of these forests. Three sequences of succession were specified as: (a) pure self- Fig. 2 – (a and b) One random, single run for pure Pinus 2 replacing P. nigra stands, (b) P. nigra stands being replaced nigra stands with GREFOS, basal area (m /ha) and by Q. frainetto at mesic plots and (c) P. nigra stands changing population density outputs vs. plot data at an altitudinal to A. borisii-regis at less dry areas of higher altitude (Vergos, gradient at Krania. Point values represent field observations 1979). In this study population densities and basal area per from Vergos (1979). species for the upper, middle and lower stratum are reported. However, the altitude of the plots is not explicitly mentioned. In our simulations, these measured stand data were compared with our model’s output. GREFOS performance was for the first two development stages, but its projections are tested for both pure P. nigra and diverse (all abundant species) better for the rest. Regarding population densities, a good pre- stands. diction of the observed values and trends is achieved. The cyclic change in vegetation patterns (Watt, 1947) is bound 4.1.1. Monocultures together with the rationale of forest gap dynamics models. In this first set of quantitative tests, basal area and population A single iteration of the model mimics a potential history of densities outputs are compared with the observed plot data one patch. By “aggregating the histories” of many patches for pure P. nigra stands. As there is no specific reference to the a mean “stable transect” is produced, which could be con- altitude of each stand, the model was run at three different sidered closer to the climax view of succession (Bugmann, altitudes 800, 950 (weather station) and 1100 m a.s.l, consid- 1996a). However, if we duplicate the plot data by adding the ering a precipitation lapse rate equal to 0.8 mm m−1 year−1 age of the regeneration phase to the original stands’ age, thus (Karapiperis and Katsoulis, 1969). The age of each stand was creating two or three vegetation cycles, and plot the output approximated by equations relating mean dbh with stand age of the model for 1000 years, Fig. 3(a) and (b) is obtained. In (Vergos, 1979). Available water was set to 120 mm and fire fre- the case of P. nigra self-maintained stands in the region of quency to zero. The plot data refer to (1) juvenile, (2) optimum, Krania, the length of this phase is approximately 700 years (3), mature, (4) old and (5) regeneration stages. The mean basal (two rotation cycles), a value similar to other reported in lit- area for each stage was 38.37, 47.49, 62.41 and 32.54 m2/ha, erature (Bugmann and Pfister, 2000; Gritti et al., 2006). This respectively. phenomenon could be interpreted by the so-called “spin-up” In Fig. 2(a) and (b) the progress of basal area and popula- period, which characterizes forest gap dynamics simulators tion density as projected by one single run of the model, are (Bugmann, 1994). After this transient period much better plotted against the measured field data, at three discrete alti- agreements of single model runs with the reported data are tudes. GREFOS seems to underestimate the total basal area achieved. ecological modelling 204 (2007) 439–456 449

Fig. 4 – (a and b) Mean basal area (m2/ha) and population density output (100 iterations) for the full species pool at an altitude of 800 m a.s.l. Point values represent field observations at the beginning of secondary succession Fig. 3 – (a and b) One random, single run for pure P. nigra from stands in the area of Krania (Vergos, 1979). stands with GREFOS, basal area (m2/ha) and population density outputs vs. plot data for three vegetation cycles, at Krania 950 m a.s.l. lated structural stand parameters are within the acceptable ranges. At northern oriented (20◦ slope), moist stands (available 4.1.2. Full species pool water equals to 150 mm) of a higher altitude (950 m), the model Following the successional pathways documented by Vergos suggests that P. nigra is replaced by the less-drought toler- (1979), the model’s outputs have been tested with available ant fir A. borisii-regis, as documented in the study of Vergos stand data of the first stages of secondary succession. For (1979). Again GREFOS produces realistic outputs regarding both the pine to oak vegetation transition, the site conditions the species basal area and population density, when com- were set to an altitude of 800 m and to a relatively dry soil pared with the field observations (Fig. 5(a) and (b)). In this site, profile, of 70 mm maximum available water. Southern ori- a more accurate prediction of the species population densi- entation of 20◦ slope was selected (Apatsidis, 1977), while ties is achieved. Both P. nigra and A. borisii-regis are classified at the same time we enabled GREFOS to “pick” from all into the seeder Lhs group, in contrast with the Q. frainetto, abundant species (full species pool). Under these conditions which is considered as an obligate resprouter species. Thus the forest dynamics lead to the replacement of P. nigra with the implemented mortality functions seem to underestimate the moderately drought-tolerant species Q. frainetto. The model’s loss of resprouting individuals, underlying a need of a more output is plotted against field observations in Fig. 4(a) and detailed consideration of the specific process. However, at both (b). There is a good agreement between the simulated and altitudes the general patterns of vegetation change are well observed standing biomass (expressed as basal area). Further- represented, thus we could stay that the simulator accounts more, the model projects realistic population densities for well for the range of successional trends observed in the area P. nigra, but slightly underestimated the respective densities of Krania, a typical mountainous Mediterranean climatic zone. for Q. frainetto. Considering that there is a good agreement of the oak species simulated basal area with the reported 4.2. Qualitative tests field values, GREFOS projects fewer, bigger individuals at the specific successional stage. This could be attributed to the The predictive ability of GREFOS has been tested against qual- general mortality functions, which are not calibrated with site- itative data from the Parnassos natural reserve, located in the specific survival data (Bigler and Bugmann, 2004). However, central continental part of Greece (38◦40,22◦35). The aim of in this typical Pine to Oak successional series the simu- this set of simulations is to test the ability of the model in tran- 450 ecological modelling 204 (2007) 439–456

from the altitude of 600 m on northern aspects and much higher (1000–1200 m) on southern aspects, covering most of the reserve’s areas. P. nigra is also abundant on this mountain usually in mixed stands with A. cephalonica, from 750 to 1500 m above sea level. At lower altitudes and until 1000 m a.s.l., Q. coccifera dominated shrublands represent the second most abundant “forest type”. Human practices, mainly through browsing, have increased the abundance of the vig- orously resprouting kermes oak (Q. coccifera) in the region (NAGREF, 1996). The simulator has been applied to this altitudinal gradient and presented fairly realistic outputs regarding the distribution of the potential vegetation (Fig. 6). At southern oriented stands, evergreen sclerophyllous species (mainly Q. coccifera) are the dominant elements of vegetation. This holds true for both the 850 m (Fig. 6(a)) and 950 m (Fig. 6(b)) sites, suggest- ing that under a bioclimatic sense, only very drought tolerant species are able to establish and maintain populations at these quite unproductive conditions. Under these conditions, in the absence of a fire recurrence pattern, P.nigra is abundant at very low portions. As reported in the study of NAGREF (1996), the stands found at the northern aspects of the reserve are dominated by A. cephalonica. GREFOS realistically projects such type of forest types, at both the altitude of 950 and 1000 m a.s.l. (Fig. 6(c) and (d)). The deep foliage stratum developed by this shade tolerant species, excludes individuals of the rest species 2 Fig. 5 – (a and b) Mean basal area (m /ha) and population pool to enter the stands, leading to pure A. cephalonica pop- density output (100 iterations) for the full species pool at an ulations. The presence of P. nigra is slightly more significant altitude of 950 m a.s.l. Point values represent field at lower elevation. These qualitative outputs agree with the observations at the beginning of secondary succession observations of vegetation at the reserve, where the endemic from stands in the area of Krania (Vergos, 1979). fir is the most abundant species.

4.3. Scenarios of shifting fire frequency sitional vegetation zones, where the profile of vegetation alters from evergreen sclerophyllous to more Temperate oriented Global-change induced climatic changes could alter the fire assemblages. On the mountain of Parnassos, at an altitude regime in many forest ecosystems (Dale et al., 2001). An of 650 m a.s.l. (Desfina weather station) the mean annual tem- expected change in the fire patterns would probably be the perature is 15.1 ± 1.7 ◦C, the annual amount of precipitation shift of fire frequency. In order to partially account for the is 571 ± 140 mm, while the precipitation lapse rate has been effect of an increased fire frequency in the Parnassos nat- calculated to 0.8 mm m−1 year−1 (NAGREF, 1996). Mean tem- ural reserve, a simplistic scenario of fire cycle is applied. perature and precipitation data are available for a weather We thus assume a stochastic occurrence of one fire event station at a higher altitude (1300 m a.s.l.), while timeseries of per 100 years, to act as an additional disturbance process at precipitation do exist for the altitude of 960 m (Amfissa). For stands found at an altitude of 950 m a.s.l. Both southwards the mid altitude (960 m), temperature data were computed by and northwards oriented stands are simulated, where cur- interpolating values from the low and high altitude weather rently sclerophyllous and coniferous evergreen species are stations, using a typical temperature lapse rate. In this reserve established, respectively. This simulation exercise aims to the profile of vegetation alters, following the altitudinal gra- investigate the behavior of the model, rather than studying dient, from typical Mediterranean (evergreen broadleaved) the interaction of fire frequency and vegetation patters in to Oro-Mediterranean (pure or mixed A. cephalonica forests) forest ecosystems. Such detailed modeling experiments are (Table 5). Stands of the endemic fir species could be found discussed elsewhere (Pausas, 1999b), although referring to a

Table5–Topographic proﬁle and vegetation patterns at the natural reserve of Parnassos, Central Greece Altitude (m) Aspect Slope (%) Soil type Available water (mm) Forest type

880 South 35 Claystone 50 Sclerophyllous evergreen 960 South 33 Claystone 50 Sclerophyllous evergreen 980 North 65 Limestone 40 Abies cephalonica

Source: NAGREF (1996). ecological modelling 204 (2007) 439–456 451

Fig. 7 – Potential vegetation at the mid altitude (950 m a.s.l.) of the Parnassos natural reserve, in southward (a) and northward (b) oriented aspects, under scenarios of altered ﬁre regime.

(Fig. 7(a)) and northwards (Fig. 7(b)) oriented stands are being invaded by the pioneer P. nigra. This quiet plastic pine species (Barbero et al., 1998) is known to be able to compete with even sclerophyllous evergreen species, at the lower limits of its distribution (Quezel, 1977). In our mid fire cycle scenarios, the available space created by such type of disturbances favors its relatively light demanding regeneration. It should be noted though, that the assumption of an unlimited stock of available saplings could lead to overestimation of the significance of this SED species, which has been observed to display some time lags at the recruitment process at xeric sites (Gracia et al., 2002). In both aspects the abundance of OBR species is enhanced, an output attributed to their resprouting ability. A characteristic example of this functional type behavior is presented at northern sites, where under no fire influence Q. coccifera is absent, in contrast with the simulated ability of kermes oak to establish and maintain a small number of Fig. 6 – (a–d) Potential vegetation in the Parnassos natural individuals under an assumed mid fire cycle. reserve, Central Greece, as simulated with GREFOS at the altitudinal gradient under study. 5. Discussion and concluding remarks typical Mediterranean climatic profile and species pool syn- With GREFOS we tried to mimic the majority of the key thesis. ecological properties and functions, considered important Under these scenarios, the vulnerability of these forests, when describing vegetation dynamics at the mountainous to global-change-induced fire frequency shifts have been Mediterranean zone and its lower altitude transitional zone explored. Following the outputs of the model, both southwards with sclerophyllous evergreen species. The behavior of the 452 ecological modelling 204 (2007) 439–456

model has been explored at two altitudinal gradients, where As argued in Pausas (1999a), the use of plant functional transition zones from typical Mediterranean to Temperate cli- types in vegetation dynamics simulators can deal (as well) mate appear. Quantitative and qualitative tests increase our with data availability issues. In GREFOS a functional view of the confidence of the simulator’s predictions, which despite its processes of regeneration and mortality is applied, through generality produces realistic outputs. In the following para- the Lhs parameter. Lhs is linked to the regeneration and mor- graphs some insights arising from the simulation exercises, tality algorithms and assigns discrete “means of persistence” in addition with some issues regarding the extension of the to the population of each functional type. The efficiency of forest gap dynamics rationale at mountainous Mediterranean the Lhs parameter is established when simulating the mixed forest ecosystems are discussed. (seeder–resprouter) stands of P. nigra–Q. frainetto at the lower A widespread vegetative pattern across the Mediterranean elevation site of Krania. In this case, both basal area and pop- Basin is the Pine–Oak woodland (Zavala et al., 2000). Follow- ulation density match the observed data for the early stages ing the climax view of succession these ecosystems could of succession and the general sequence of vegetation change. be considered as a transitional phase leading to oak dom- The concept of calibrating the number of offspring according inated forests. However, both palynological (Carrion et al., to species regeneration strategy has been used in other stud- 2001) and modeling studies (Zavala and Bravo de la Parra, ies (He and Mladenoff, 1999; Risch et al., 2005), which however 2005), underline the ability of pine species to persist at least emphasized on the species shade tolerance. In this study, a at the landscape level. This phenomenon has been explained different “functional grouping variable” was selected, which as a “competition–colonization” or “shade and drought tol- emphasizes (indirectly) on the allocation of resources, as a erance” trade-off (Zavala and Zea, 2004), in the case of P. means to deal with disturbances at the individual level, like halepensis–Q. ilex dominated ecosystems. Our simulation exer- fire or browsing. While testing the performance of the model, a cises focus on higher altitudes and consequently at a more large number of simulation exercises have been conducted. A Temperate oriented climatic profile. Thus, the Pine–Oak com- general observation suggests that the analogy between regen- petitive interaction is realized with different species (e.g. P. eration densities of the Lhs types, rather than their absolute nigra and Q. frainetto). Outputs from the mesic sites of Kra- values, affects the behavior of the simulator. nia, project that oak species is able to replace the pioneer The Dom effect algorithm provides a consideration of the pine, although explorative simulations with GREFOS (results dominance effect in individual defined cyclic areas. This func- not shown here) are projecting these species coexistence at tion serves as a measure of the carrying capacity, so we avoid drier soil profiles. Thus, a question to be answered is if the the usage of a limit to the maximum basal area or biomass trade-offs suggested by Zavala et al. (2000) could be extended value per site or ecosystem type. The rationale of the neigh- to the mountainous Mediterranean forests, and explain the borhood submodel is similar to the zone of influence approach wide distribution of pine species like P. nigra. Model sim- (Berger and Hildenbrandt, 2000) and is inclined to the symmet- ulations under an altered fire regime (Parnassos), present ric view of competition (proportional usage of resources based an increase in the abundance of P. nigra and thus suggest on the size of the competing individuals), in defining size hier- that this species may act as an invader (Richardson and archies (Bonan, 1988). Although the assumption of a constant Bond, 1991), at the upper limit of the Mediterranean and competing area is rather oversimplifying and more advanced the lower limit of the Temperate climatic zones. Although techniques could be implemented (Berger and Hildenbrandt, GREFOS does not accounts for the spatial nature of the pro- 2000), Dom effect approximates the effect of competition for cess of invasion (Higgins and Richardson, 1996), its output belowground resources, assuming that the root surface is pro- agree with that of Higgins and Richardson (1998), where the portional to the basal area of an individual (Zavala and Bravo P. nigra functional type (“U-pine”) presents a consistent abil- de la Parra, 2005). It is important to note that the power of the ity to invade various ecosystems (more efficiently forests). Dom effect function is greater at stages (particularly regener- However, Gracia et al. (2002), observed a post-fire delayed ation), where competition for light is not so significant and recruitment of P. nigra, in contrast with the consistent persis- the stand presents increased population density. In later ver- tence of a resprouting oak species (Q. faginea) in xeric sites sions of GREFOS, we are attempting to incorporate species in Spain. This time-lag led at a mid-term Pine–Oak coex- root to shoot ratio as a weighting factor of this subroutine. istence, underlying the importance of micro-environmental However, this procedure is not so straightforward and several conditions and pre-fire established vegetation in controlling hypotheses could be considered (Coomes and Grubb, 2000). On the post-fire dynamics. Thus, our results might overestimate the other hand, a “reversed” gap dynamics scheme has been the mid-term ability of P. nigra to persist and expand its successfully applied to semi-arid ecosystems, where individ- distribution range, as it assumes a constant availability of uals compete symmetrically for water resources (Coffin and saplings and ignores factors affecting its recruitment. Spa- Laurenroth, 1990). Coffin and Urban (1993) compared STEPPE tially explicit consideration of the regeneration process is and ZELIG, two gap dynamics models developed for grass- necessary to confidently extrapolate the output from forest land and forest ecosystems, respectively, and underlined the gap dynamics models (Ordonez et al., 2006). Following the importance of life history traits and mode of resource competi- expected bioclimatic changes under global change phenom- tion (symmetric belowground in the first case and asymmetric ena, experimental consideration of the factors affecting the aboveground in the second) in evaluating ecosystem-level regeneration process of P. nigra, and other widely distributed properties. In this study, the authors identified similarities and “U-type” pine species of the mountainous Mediterranean differences between the patterns of some ecosystem proper- zone, would be necessary to evaluate the dynamics of these ties. The underlined qualitative differences have been mainly ecosystems. attributed to the growth form and the area where individu- ecological modelling 204 (2007) 439–456 453

als compete for resources. In cases where a common growth the manuscript. Part of this study has been completed while equation could be used for discrete lifeforms, an aggregation N.F. was a Marie Curie fellow in the Centre for Biodiversity and of the above and belowground consideration of gap dynamics Conservation, University of Leeds (EVK2-CT-2000-57122). N.F. could enhance our ability to simulate vegetation dynamics, is partly funded by the N. Papadimitriou Foundation for Forest particularly in transient zones, where the mode of competi- Protection. tive interaction between different lifeforms probably change though temporal and spatial scales. The application of vegetation models at the regional scale references should provide potential users with more detailed information compared with large-scale general simulators (Sykes et al., 2001). Furthermore, as the computational power of Acevedo, M.F., Urban, D.L., Shugart, H.H., 1996. 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