Coenostate descriptors and spatial dependence in ...

COMMUNITY ECOLOGY 5(1): 105-114, 2004 1585-8553/$20.00 © Akadémiai Kiadó, Budapest

Coenostate descriptors and spatial dependence in vegetation – derived variables in monitoring forest dynamics and assembly rules G. Campetella1, R. Canullo1 and S. Bartha2 1

Department of Botany and Ecology, University of Camerino, via Pontoni 5, 62032 Camerino, Italy E-mail: [email protected], [email protected] 2 Institute of Ecology and Botany of the Hungarian Academy of Sciences, H-2163 Vácrátót, Hungary E-mail: [email protected] Keywords: Competitive dominance, Disturbance, Diversity, Dynamic tendencies, Forest herb layer, Information theory, Spatial scale. Abstract: Long term vegetation monitoring provides valuable information on spatio-temporal patterns in plant communities that could be analysed to detect spatial relationship changes among species and to interpret dynamic tendencies and assembly rules in non-equilibrium phytocoenoses. In studies of this kind, one should take into account recent ecological theories emphasizing the scale dependence of vegetation; in particular, fine-scale spatial patterns of vegetation are important constraints in the genesis and maintenance of diversity. The information theory models of Juhász-Nagy offer an appropriate tool for describing the relationship between diversity and multispecies spatial dependence in vegetation. Diversity (florula diversity) and spatial dependence (associatum) are calculated for a series of increasing plot sizes (spatial scaling). The plot sizes at which the two coenostate descriptors reach the maximum information represent the characteristic scales that should be considered as optimal plot sizes in monitoring data collection. Moreover, this methodology enables us to study non-equilibrium dynamics and assembly rules in a more effective way. Diversity and spatial dependence are related, but the power and direction of this relationship change according to environmental characteristics, vegetation type and successional context. The demonstrated correspondence between dominant pattern-generating mechanisms and the related trajectories in abstract coenostate spaces (florula diversity and associatum maximum values), obtained by exploratory simulation studies, can improve interpretation of dynamic state and vegetation tendencies and can support a better inference about the relative role of different background mechanisms. We present some results obtained using this methodology with field data from the forest of Bia³owieza National Park (Poland). In particular, we compared the herb layer spatial patterns of dynamically contiguous regeneration phases of the same phytocoenosis. Sampling was performed by recording the presence of plant species in 10 cm x 10cm contiguous microquadrats arranged in 150 m long circular transects. Field data were analysed with the same information theory methods as the ones applied to simulated data. Results show that assemblages of plant individuals are less diverse and more associated in primary than in regenerating stands, suggesting, in both situations, competitive dominance and disturbance as the main ecological mechanisms. Thus, the method was proven effective in distinguishing slightly different dynamical processes. Nomenclature: Tutin et al. (1993).

Introduction

rameters (the fixed size and arrangements of permanent plots).

Monitoring is fundamental in recognizing and evaluating vegetation dynamics induced by environmental changes or management. Monitoring calls for an operational methodology that is objective, repeatable, sensitive, and also applicable in comparative studies. All kinds of vegetation attributes are useful for detecting some changes over time. However, these attributes greatly differ in sensitivity. Repeated vegetation maps or repeated measurements in permanent plots are generally used in vegetation monitoring, but they have limitations because of their fixed syntaxonomical units or fixed scaling pa-

Spatial pattern is a more detailed, therefore a more effective indicator of vegetation changes than average attributes because with pattern analyses we are able to represent the fine-scale spatial variability and dependence of populations (Newbery and Proctor 1984, Peterson and Pickett 1990, Tilman 1993, Huston and De Angelis 1994, Moreno-Casasola and Vásquez 1999, Pélissier and Goreaud 2001). Recent studies emphasized the importance of spatial patterns in vegetation dynamics. In this respect, spatial pattern is more than a detailed vegetation indicator. Within-community spatial variability and depend-

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ence of populations put constraints on the dynamics, i.e., they affect the rate and direction of vegetation changes and offer important details about the controlling mechanisms (Juhász-Nagy and Podani 1983, Watkins and Wilson 1992, Wilson 1994, Bartha et al. 1998, Czárán 1998). This feature has important methodological implications for scaling (Hogeweg 2002). The dynamic consequences of large-scale spatial patterns and the related scaling issues are well recognized in landscape ecology (Openshaw 1984, Fotheringham and Wong 1991, Jelinski and Wu 1996). The importance of fine-scale spatio-temporal dynamics is also emphasized (van der Maarel 1996) but, until now, only a few studies have actually applied spatial pattern analyses for monitoring vegetation changes. A major objective of quantitative plant ecology is simplification together with summarization (especially in vegetation monitoring). This implies that vegetation data reflecting complex spatio-temporal phenomena should be presented in an easily understandable and meaningful format. The spatial patterns of many individuals belonging to different species involve too much information to be processed in real time; almost inevitably, computerized statistical methods are necessary to reduce the dimensionality and complexity of the system to a manageable level (Bartha et al. 1998). In this respect, a possible approach must deal with the following conditions: (1) The variables representing the pattern should be relevant features, such as the spatial proximity of the individuals or species combinations; this can be useful for example, in exploring how spatial patterns affect processes resulting in the coexistence of the species that constitute the community. (2) The choice of coenological descriptors should be based on a solid and coherent theory that offers a complete formal description of coenological state at any spatial scale. (3) This theory must lead to a direct syndynamical reference which can link coenological structure to community dynamics. Most methods for representing spatial dependence of populations were borrowed from other disciplines. Because these methods (e.g., geostatistics) were originally developed for univariate patterns, they have important limitations (Podani et al. 1993). Juhász-Nagy’s models (Juhász-Nagy 1976, 1984, 1993) have the important advantage of being developed in the framework of vegetation analysis, i.e. directly for representing complex multivariate patterns. Juhász-Nagy developed a whole family of models that represent the various features of communi-

ties. This is why we call them ‘coenological models’ and the related attributes as ‘coenological attributes’. The inherent property of these functions is that they change with spatial scale. Therefore, they should be detected at a series of increasing sampling unit sizes (Podani et al. 1993). The plot size at which Juhász-Nagy’s models reach the maximum information (given in bits) represents characteristic scales of the vegetation that should be considered as optimal plot size in monitoring data collection (Campetella et al. 1999, Campetella and Canullo 2001). A previous study revealed that the maximum values of these models provide appropriate coenostate descriptors for representing the spatiotemporal processes of vegetation (Juhász-Nagy and Podani 1983). In the present paper, we explore the utility of JuhászNagy’s models in monitoring the changing characteristics of the fine-scale spatial patterns of the herb layer vegetation in forests. In this respect, the vegetation of the Bia³owieza National Park, which contains well preserved forest ecosystems (Falinski 1986), represents an excellent study area. Because of the slow rate of forest succession, rather than compare permanent records, we analyse the spatial patterns of two representative successional phases based on the concept of space-for-time substitution. In order to design future permanent records, we intend to detect the pattern development trends and the magnitude of changes.

Methods Juhász-Nagy’s information theory methods In detecting spatial organization of a given system’s components, two basic aspects of plant behaviour seem particularly useful for inferring possible assembly rules:

• Measurement of the species coexistence: this can be assessed by capturing the coexistence relations through observing species combinations and measuring the scale effect upon these relations (which have to do with spatial diversity or spatial variability).

• Estimation of the deviation of coexistence relationships of plant populations from their total independence (random expectation) at different spatial scales (total interlocal dependence of species). Information theory models developed by Juhász-Nagy (1976, 1984, 1993) represent a coherent conceptual framework for addressing these two fundamental issues simultaneously. He developed a family of coenological descriptors to measure various aspects of spatial diversity and dependence. Moreover, the functions are meaningful only considering their values in changing scale (realized

Derived variables in monitoring forest dynamics

as a series of increasing sampling unit sizes). The details of these models were described in several contributions, but a recapitulation here seems useful in presenting this methodology in the context of the vegetation monitoring. Florula diversity Florula diversity is the frequency distribution of species combinations within the community detected as a function of spatial resolution. Assume that the community in question is composed of s species, A, B, ..., S, and is sampled with m sampling units of size j. A particular sampling unit (especially if it is not too large) rarely catches all of the species. The particular species combination (a small flora, a subset of s) found in a particular sampling unit is called ‘florula‘. The essence of the approach is that we count the number of times a given species combination occurs in the sample. Let fkj be the number of occurrences of the kth species combination while pkj = fkj/m is the relative frequency of this combination. z = 2s is the number of possible combinations of s species (including the empty florula). Then, the florula diversity (FD) of the community (at this scale point j) is estimated as the Shannon-entropy for the set of species combinations detected all over the study area: z

FD j = ∑ pkj log pkj . k =1

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Associatum Pairwise spatial association is often used to describe the spatial dependence between two species (Kershaw 1964, Greig-Smith 1964, Bartha and Kertész 1998). The term ‘associatum’ (AS) refers to the multispecies generalization of this relationship. Assume a hypothetical random community without spatial constraints limiting the local co-occurence of species. In this idealized community, the probability of a local species combination (i.e., florula) can be calculated directly from the abundances of species (for details, see Bartha et al. 1998). This idealized plant community will have a theoretical maximum florula diversity, while spatial constraints will result in relatively less diverse communities. The difference between the two diversity estimates (i.e., between the expected theoretical maximum diversity and the actual diversity found the field) offers a simple way to calculate the overall spatial dependence of species (Juhász-Nagy 1984, Juhász-Nagy and Podani 1983): ASj = expected FDj – actual FDj . Since associatum is larger for a more diverse community, we calculated a standardized version called ‘relative associatum‘ (RAS) based on the ratio of associatum and florula diversity: RASj = ASj/FDj . State-space representation of vegetation structure

FD is thus a special application of Shannon diversity, otherwise extensively used to express species/individual diversity of communities (Pielou 1975, Magurran 1988). In this context, however, it is defined on the frequency distribution of observed (realized) species combinations within the sample, rather than on the frequency distribution of species abundances themselves. By changing the sampling unit size j, florula diversity can be expressed in the function of spatial scale (i.e., calculated for samples recorded with a series of different sampling unit sizes). For very small sampling units, florula diversity tends to be very small, because the units are either empty, or contain only a single species. These combinations are not ‘attractive’ ecologically, and there is very little uncertainty regarding the contents of a random plot. Extremely large plot sizes, on the other hand, will tend to include all or almost all species. Again, such combinations are not very interesting, and the uncertainty is low as well. Between these extremes, florula diversity takes at least one maximum value, that is, at a sampling unit size where the most diverse species combinations were captured by the sampling procedure.

Compared to the alternative methods for multivariate spatial pattern analyses (e.g., Lepš 1990, Dale and Zbigniewich 1995, Lande 1996, Wagner 2003), Juhász-Nagy models are very simple to calculate. This simplicity comes from the additivity of information theory models. At the same time, these models offer the opportunity to decompose the overall community measures in order to see the contribution of particular species or species combinations (Juhász-Nagy 1984, 1993). These models were successfully applied in describing simulated spatio-temporal dynamics of vegetation as well (Bartha et al. 1998); the authors used the Juhász-Nagy models to develop a state-space representation of vegetation. The coenological state (community organization) of a particular vegetation stand is depicted as a point in a two-dimensional coenostate space. Florula diversity and associatum are used as the axes of the state-space representing the structural complexity of local coexistence (FD) and the overall spatial dependence (AS) of species in the communities. Because FD and AS change with the spatial resolution (i.e., sampling unit sizes) and usually have unimodal

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Figure 1. a: Structure of the state-space based on the dominant pattern-generating mechanisms. I niche differentiation; II competitive dominance; III competitive dominance + disturbance; IV.a environmental heterogeneity; IV.b strong environmental heterogeneity; V prohibited zone. b: Direction of local transformations of an actual vegetation state due to the effect of a specific pattern-generating mechanism (from Bartha et al. 1998).

maximum curves, only their maximum values (FDmax and ASmax) and the related scales are used here for the simplified state-space representations. Using explorative simulation studies, the authors found correspondence between the dominant pattern-generating mechanisms and the resulting trajectories in the coenostate spaces, and thus could hypothesize about the direction of local transformations of an actual vegetation state due to the effect of a specific pattern-generating mechanism (Figure 1, cf. Bartha et al. 1998). The simulations were run with different input parameters, initialising several virtual successions with a maximum of 10 species and different competitive relationships, propagulum availability, demographic structure, disturbance regimes and spatial heterogeneity of the environment. The simulated trajectories were classified according to the areas they run through in the state-space, thus revealing correspondence between clusters of trajectories and the types of pattern generating mechanisms. Figure 1a summarizes these details and shows a theoretical bound (diagonal) introducing a “prohibited zone” - V. Figure 1b describes the direction of local transformations of an actual vegetation state due to the effect of pattern generating forces. The structure of the coenostate space includes the following areas: (I) Niche differentiation: the simulations consider the intraspecific competition stronger than the interspecific one, in a homogeneous environment; the consequent freedom for species combinations will give final steps with maximum florula diversity (FD) and minimum spatial dependence (AS).

(II) Competitive dominance: a competitive hierarchy between species or the dominance of a single competitor species provides a pattern with low FD and AS. (III) Competitive dominance and disturbance: here a competitive exclusion may be modified by local disturbances if there is a trade-off between the competitive abilities of the species and their success in tolerating disturbances. It produces a steady-state of high-medium FD and low-medium AS. (IV) Spatial heterogeneity: in this case a structured environment (patches), invariant in time, is presupposed. Due to the best adaptation to the local environment, each patch supports a certain species combination. The result is a medium-low FD combined with medium-high AS depending on the patches’ selectivity level: when the latter is lower the AS also will be reduced, while AS will assume larger values in the case of high heterogeneity. The areas of maximum values (characteristic areas) can be used to depict an alternative state-space representation without any theoretical limitation. It is complementary to the FDmax - ASmax state-space, and the coordinates of a stand are the sampling unit sizes at which the maxima of florula diversity (CAFD) and associatum (CAAS) were detected (more details in Bartha et al. 1998). The demonstrated correspondence of dominant pattern-generating mechanisms and the relative trajectories


in abstract coenostate spaces can facilitate interpretation of dynamic state and tendencies of real vegetation and offers a better inference about the relative role of different background mechanisms. Study area and field sampling The Bia³owieza primeval forest (Northeast Poland), represents the only forest area in temperate Europe where large tracts of natural forest communities have been conserved. The study site was located in the Bia³owieza National Park, a strictly protected area since 1921. Preserved since 15th century as a royal hunting ground, this area maintained through the centuries its status as a complex of forests. The ‘big forest’ occupies an area of 1250 km2 on the boundary between Poland and Belarus (Figure 2). The prevailing substrates are old post-glacial formations and the climate shows evident continental characters: mean annual temperature is 6.8 °C, ranging from 5.1 to 8.8 °C, and the mean annual precipitation is 641 mm, ranging from 426 to 940 mm (Olszewski 1986). The predominant plant community is the oak-limehornbeam forest (Tilio-Carpinetum), occurring in three closely related variants, typicum, caricetosum and stachyetosum. Other plant communities are coniferous forests, thermophilous oak forests and spruce forests on peatland (Falinski and Matuszkiewicz 1963, Falinski 1986). The best-preserved primeval forest communities exhibit spontaneous primary vegetation with many species and layers and different-aged stands. The study was carried out in the south part of the protected area, in a system of three clear-cut parallel belts within the Tilio-Carpinetum community. The felled belts were created in 1910 (that is, before the establishment of the National Park)

Figure 2. Location of the study area.

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each of 1076 m length and 107 m width, separated by bands (remnants) of primary forest of the same width (Falinski 1988). The old-growth forest belts are dominated by Tilia cordata and Carpinus betulus with a considerable participation of spruce. Its vertical structure is highly differentiated with maximal age of the trees reaching 150-250 years. It undergoes fluctuation processes under natural disturbances. The alternated belts, formerly characterized by a birch-aspen stand with a large participation of Salix caprea, are undergoing a regeneration process. Under the dominant tree layer of remnant pioneer anemochoric species (Populus tremula and Betula pendula), a younger dense understory of primary components (Tilia cordata, Carpinus betulus, Picea abies) has developed, while a self-thinning of pioneer species is observed. The herb layer in the regenerating stands is close to the specific composition of Tilio-Carpinetum communities, still influenced by soil moisture deficit due to the different litter composition and enhanced irradiance, as compared with the herb layer under the primary stand. As the alternate belts are contiguous, a certain edge-effect is to be considered. Considering all these facts, the two forest stands are at slightly different phases of the same regeneration process, one of which in terminal phase, due to inner mechanisms (Faliñski et al. 1988). In this belt system, the field layer of vegetation (0-2 m height) was sampled along four spatially independent elliptical transects, two of them in the regenerating stands and other two in the old-growth forest remnants, each composed of 1500 contiguous quadrats measuring 10 cm x 10 cm (as sampling units). The presence/absence of the species (rooting and non-rooting) in each sampling unit was recorded. Sampling was performed in June 1999, because at this time one is more likely to find the best species composition for each site. Springtime was not considered because of the masking effect of an abundant appearance of geophytes, which renders high similarity between the two kinds of stands (Bobiec 1994). Further computerized sampling was performed from the field data in spatial series (Podani 1984) i.e., with a series of gradually increasing sampling unit sizes (16 steps, from 0.1 to 134 m), and the resulting 16 sxm (species by sampling units) binary matrices were analysed using information theory models (Juhász-Nagy 1984, Juhász-Nagy and Podani 1983). JNP-model 2.0 software (Bartha et al. 1998) was used to perform the computerized sampling and the related analyses.

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Results The field data collected in each transect were used to calculate the JNP functions at increasing spatial scale. The coenological descriptors were observed to be strictly dependent on sampling unit size. Figure 3 illustrates the results on the number of species combinations (NSC) reported in both kinds of forest stands. This simple variable is useful in characterizing at first glance the overall compositional behaviour of species. The maximum number of species combinations appears at about 0.7 m sampling unit length in the primary forest stand, while in the regenerating stands it is reduced to 0.5 m. The analysis included 11 species seen with a frequency >3% in all transects. All the possible combinations (i.e. 211= 2048) were not realized in the field. In fact, a maximum of only 439 combinations was observed in a regenerating stand (B3). The minimum number of species combinations occurred around 73.5 m in all transects. At this scale, every frequent species occurred in every sampling unit. The NSC does not distinguish between rare and common species combinations: the estimate may be the same in a sample

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with a balanced distribution of species combinations and in another dominated by a single combination. Florula diversity (FD), instead, permits detection of such a difference. The maximum FD value (Figure 3) is higher in regenerating forests (around 8.15 bits, as compared to 7.25 bits in primary forest), while the sampling unit size at which the maximum FD was detected is higher in the old-growth stands (around 1 m compared to 0.7 m in the regenerating systems). All these estimations converge to indicate more freedom for species combinations in the regenerating stands and more constraints on species coexistence in the primary forest remnants. The descriptor of multi-species spatial dependence (AS) shows similar relationship (Figure 4). Higher maximum values of spatial dependence were found in the primary forest stand (3.05 bits compared to 2.13 bits in the regenerating forest). Here, the scale of maximum information appeared around 3.15 m, more than twice that of the regenerating forests. Because the absolute associatum values might be affected by the varying florula diversity,

Figure 3. Results of multispecies coenological descriptors (number of species combinations and florula diversity) from field data collected in the transects. Black circle and triangle: old-growth primary forest stands; empty circle and triangle: regenerating forest stands. Each coenostate model was calculated with increasing sampling unit sizes, as plotted in the figure.


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Figure 4. Effect of scale on the spatial dependence of species (associatum), in the old-growth primary forests (black triangle and circle) and regenerating forest (empty triangle and circle).

Figure 5. Effect of scale on the relative associatum of species in primary and regenerating forest stands. Symbols as in Figure 4.

we calculated the relative associatum (standardized by florula diversity) as well. The pattern of relative associatum (Figure 5) confirmed that the overall spatial dependence among species is stronger in the primary forest (0.5 compared to 0.33 in the regenerating forest). The scales of maximum relative associatum ranged between 2.7 and 6.7 m. Table 1 summarizes the results discussed above including the maximum values of the models (bits) and the related maximum scales (length of sampling unit, m). State-space representation, based on maximum values of florula diversity and associatum, summarizes the results obtained from each transect of old-growth primary and regenerating stands (Figure 6). Here the different dynamic state of the two communities is underlined by the

positions of points: lower compositional diversity (FD) and higher spatial dependence (AS) in primary stands are evident features. Comparing our results with those of simulation studies by Bartha et al. (1998), namely, the structure of the FDmax – ASmax state-space (Figure 1a), we have seen that both communities can be attributed to the same dominant pattern III (competitive dominance + disturbance), where the process of competitive exclusion might be modified by local disturbances if there is a trade-off between the competitive abilities of the species and their capacity to tolerate disturbances or to regenerate after disturbance. State-space representation based on the sampling unit size at which the maxima of florula diversity (CAFD) and

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Table 1. Maximum values and maximum scales of the most important information theory models characterising the vegetation pattern of the understory of old-growth and regenerating forest stands.

Figure 6. Representation in abstract space of primary old-growth and regenerating stands. (a) State-space representation based on maximum values of florula diversity and associatum (FDmax – ASmax); (b) State-space representations based on quadrat size at which the maxima of florula diversity and associatum were detected (CAFD - CAAS).

associatum (CAAS) were detected, depicts an alternative plant community structure. The results can be interpreted to show increased heterogeneity towards the old-growth primary stands, while the forest in regeneration shows a less structured understory.

Discussion The analysis of multivariate spatial patterns (JNP models) of understory vegetation in the Bia³owieza forest, has highlighted important differences in degree of organization and assembly rules between undisturbed oldgrowth and regenerating forest communities. The information theory methods enabled quantification of some important vegetation complexity features by directly counting all species combinations at a given scale. Our results emphasize that vegetation is a complex spatial phenomenon and underline the importance of plot size, a fact supported by methodological studies (e.g., Greig-Smith 1979, Juhász-Nagy and Podani 1983, Podani 1984, Podani et al. 1993), but still not applied routinely in

vegetation analysis and monitoring. The scale of maximum values provides a very useful tool in such analysis and monitoring: in this study, the florula diversity index (FD) suggests an ideal sampling unit size of at least 0.1 m2 for investigating richness, specific composition and diversity of the forest floor; the estimation of spatial dependence of species (AS) indicates 0.315 m2 as the minimum sampling size to use in analyzing synusia, patchiness, functional groups, etc. The stands examined are slightly different, one in a phase of regeneration and the other in a terminal state (old growth primary forest), and the models are sensitive to reflect differences in multispecies patterns: the field layer of old-growth primary forest presents less spatial variability and more spatial dependence of species than in stands under regeneration. In the latter, a more homogeneous forest understory is depicted. The patterns that emerge in terms of the number of species combinations (NSC), and the relative scale of maximum values show that in the regenerating forests the


species have more freedom to make combinations in the space. In old-growth forest, the maximum value of species combinations is considerably lower; this is probably due to the spatial association of species and textural constraints. Such a difference in spatial organization is also confirmed by the results of compositional diversity (florula diversity). The disturbance produced by clear-cuts and wood exploitation introduced important changes in the regenerating ecosystems, modifying canopy cover, vertical structure, species compositions and habitat homogenisation, as a consequence of the intense disturbance of the forest field layer and soil cover (Faliñski et al. 1988). Consequently, clearly decreased mosaic diversity and enlarged homogeneous patches caused a more independent spatial distribution of species, with the result that the overall system demonstrated more diversified species combinations. Stronger spatial dependence (AS) among species was found in the undisturbed old-growth forest remnants. In these types of stands, it seems that differences in tree species composition and greater complexity in canopy structure determine an understory environment with more constraints. This suggests that in the old-growth primary stands a more patchy spatial variation is present: within each of the patches an on-going selection seems to be working in favour of the species combinations more adapted to the local conditions. The results about relative associatum (AS/FD) also confirm these patterns. These results underline a correspondence between dynamic state and the spatial organization of multispecies assemblage. In fact, comparison with the simulated statespace suggests that competitive dominance and disturbance are the main pattern-generating mechanisms, corresponding to a dynamic process of regeneration in both situations. The study-case of Bia³owieza, even if not representative on a territorial basis, provided an understanding of the processes underway in the forest field layer, which has been especially useful in testing the power of JuhászNagy models. Vegetation dynamic monitoring needs new operative approaches able to estimate the development of multispecies patterns along different successional steps. Monitoring must deal not only with changes in species abundance but also with changes in heterogeneity, scale, spatial dependence, and coexistence. The information theory models of Juhász-Nagy express ecological properties, thus their variation with scale trough time can indicate impor-

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tant changes in the system. Therefore, these models can be used successfully in vegetation monitoring. Information theory statistics, designed to explore multispecies patterns based on the presence/absence of data, offer a useful tool for depicting spatio-temporal vegetation patterns in abstract coenostate space. The advantages of this approach lie in its ability to integrate scale and basic coenological phenomena, such as diversity and spatial dependence, at a multivariate level. In monitoring vegetation, the use of permanent plots, even for detecting “on the spot” changes, is not always appropriate, because of the limited measurement scales (Haveman and van der Wijngaart 2003). Moreover, the optimal sampling unit size can vary as the process involved changes, so that it must be tested over time, in order to obtain the best estimate of a plant system’s coenological properties. As a secondary result of this study, the scales at which the derived variables reach their maxima can be used to determine the best minimum plot size for integrating long-term sampling designs. The scale indicated by florula diversity (and number of species combinations) can be useful when the aim is linked to monitoring diversity, richness, and specific composition; the scale suggested by associatum values would be best used to define a proper scale for monitoring synusial structure, patchiness, and assembly rules. Acknowledgements. This work was supported by grants of University Research Founds to G.C. and to R.C. and by a grant of the Hungarian National Science Foundation (OTKA) Nr. T032630 to S.B.

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