Biodiversity and Conservation (2007) 16:525–545 DOI 10.1007/s10531-006-6733-1
Springer 2006
Is vascular plant species diversity a predictor of bryophyte species diversity in Mediterranean forests? ALESSANDRO CHIARUCCI*, FRANCESCA D’AURIA and ILARIA BONINI Dipartimento di Scienze Ambientali ‘‘G. Sarfatti’’, Universita Di Siena, Via P.A. Mattioli 4, Siena 53100, Italy; *Author for correspondence (e-mail:
[email protected]; fax: +39-0577-232896) Received 2 May 2005; accepted in revised form 5 January 2006
Key words: Bryophytes, Conservation, Reservation sets, Reserve site selection, Taxon congruence, Taxon surrogacy, Vascular plants, Woody plants Abstract. This study aimed to (i) investigate the congruence among the species composition and diversity of bryophytes and vascular plants in forests; (ii) test if site prioritization for conservation aims by the maximization of the pooled number of vascular plant species is effective to maximize the pooled number of bryophyte species. The study was performed in six forests in Tuscany, Italy. Four-hundred and twenty vascular plant species (61 of which were woody) and 128 bryophyte species were recorded in 109 plots. Despite the good predictive value of the compositional patterns of both woody plants and total vascular with respect to the compositional pattern of bryophytes, the species richness of the latter was only marginally related to the species richness of the former two. Bryophyte rare species were not spatially related to rare plant species and neither coincided with the sites of highest plant species richness. The species accumulation curves of bryophytes behaved differently with respect to those of woody plants or total vascular plants. Reserve selection analysis based on the maximization of the pooled species richness of either woody plants or total vascular plants were not effective in maximizing the pooled species richness of bryophytes. This study indicates that species diversity of vascular plants is not likely to be a good indicator of the bryophyte species diversity in Mediterranean forests.
Introduction Forest ecosystems in the Mediterranean area are floristically extremely diversified. Tuscany, central Italy, hosts a wide variety of forest ecosystems, from the coastal Mediterranean ‘‘macchia’’, dominated by sclerophyllous evergreen species, to the high-mountain forests (up to 1600–1800 m) dominated by beech (Bernetti 1987). Forests cover more than half of the region’s surface, an area greater than one million hectares. About 120,000 ha of forest belongs to the Regional Administration of Tuscany and other 100,000 ha to other public administrations. In Tuscany, forests have always been considered a fundamental resource, especially for timber production and soil protection. However, consideration of forest ecosystems as environmental and landscape resources has developed in recent years. Tuscany was one of the first regions of Italy to establish a monitoring program of forests, within the ‘‘Scheme for the
526 protection of forests against atmospheric pollution’’ (EU Regulation 3528/86 and followings). Originally the monitoring network aimed to collect information exclusively on tree health, but carbon stock and biodiversity were later included in the program (Bartolozzi et al. 2002). Presently, these forests are undergoing continuous monitoring, in which biodiversity is a fundamental part of the program. During the first stage, biodiversity monitoring was based on six intensive permanent plots (Chiarucci et al. 2001), but it was then decided to collect floristic information representative of larger areas, i.e. whole forests (Chiarucci and Bonini 2005). In the Mediterranean area, bryophytes are one of the least investigated components of forest ecosystems and almost no quantitative survey exists for Tuscany. Although many papers have investigated the species composition of specific sites and/or habitats, to date there have not been any attempts to identify quantitative patterns of bryophyte diversity in forests. One method of estimating species diversity of less studied groups is by assuming ‘‘taxon surrogacy’’ (Ryti 1992; Garson et al. 2002), i.e. estimating the species richness of a given taxon based on the species richness of another taxon. Taxon surrogacy is based on the assumption of cross-taxon congruence in spatial patterns of species richness (Prendergast et al. 1993; Howard et al. 1998). Taxon surrogacy is interesting from both the evolutionary and ecological points of view and it might be used to assess spatial and temporal patterns of biodiversity for taxonomic groups which are difficult and or expensive to sample (Williams 1999; Virolainen et al. 2000). Vascular plants are an attractive group to use as surrogates for estimating the diversity of other groups as they constitute the bulk of the primary producers, reflect environmental conditions, provide physical habitat for other organisms, and are relatively easy to sample (Ryti 1992; Pharo et al. 2000). Vascular plants have been reported as good surrogates for identifying reserve areas for invertebrates (e.g. Panzer and Schwartz 1998) and birds (e.g. Tardif and DesGranges 1998). Only a few papers have discussed the use of vascular plants as surrogate taxon to evaluate the species diversity of bryophytes at different spatial scales in Australian ecosystems (Pharo et al. 1999; Pharo et al. 2000). This study aimed to: (i) investigate the congruence among the species composition and diversity of bryophytes and vascular plants in forest ecosystems at different scales; (ii) test if site prioritization based on maximizing the pooled number of vascular plant species is effective in maximizing the pooled number of bryophyte species.
Methods Sampling sites Six forest estates (hereafter ‘‘forests’’) owned and managed by the regional administration of Tuscany were chosen as study areas (Figure 1). Each forest is
527
Foreste Pistoiesi #
Pisa
Firenze #
Foreste Casentinesi
Colline Livornesi
Siena #
Farma Merse
Macchia dellaMagona Bandite diScarlino Montioni
Belagaio Madonna delle Querce
0
50 km
Figure 1. The locations of the six investigated forests owned and managed by the regional administration of Tuscany.
formed by contiguous or non-contiguous woodlands located in neighbouring areas and managed as a single unit. These forests cover a total of 37,240 ha, and range in size from 2098 to 10,311 ha (see Table 1 for summary); they are located from the lowlands of the coastline to the Apennine mountains and host different plant communities, ranging from the evergreen Mediterranean forests dominated by Quercus ilex, along the coastline and the lower hillsides, to the Fagus sylvatica and Abies alba forests of mountain sites. Conifer plantations are present in all the forests. A stratified random sample of sites was selected from the sites used for the Forest Inventory of Tuscany (IFT). The number of sampling sites was selected in accordance with an existing network established for the monitoring of tree crown conditions (111 sites), with the number proportional to the true forest surface of each area, i.e. excluding open patches therein. The IFT was based on points placed on a 400 · 400 m regular grid used for photo interpretation of forest types; a subset of them was used for measurement of tree composition and structure. Two of the selected sites were not sampled since they were just burned.
10311
5468
2205
7294
271.6 (224.8–328.0) 120.8 (76.4–191.1) 74.0 (50.4–108.7) 105.9 (72.6–154.4) 37.8 (29.4–48.6) 63.2 (49.1–81.3)
Stem densitya (stem ha1)
1.8 ± 0.3
1.7 ± 0.3
1.1 ± 0.3
0.8 ± 0.3
1.4 ± 0.3
1.3 ± 0.3
Basal areab (m2 ha1)
Fagus sylvatica, Abies alba
Fagus sylvatica, Abies alba, Quercus cerris, Pinus nigra
Fraxinus ornus, Arbutus unedo, Pinus pinaster, Quercus cerris, Castanea sativa Quercus pubescens, Quercus cerris, Quercus ilex
Fraxinus ornus, Quercus ilex, Quercus pubescens, Arbutus unedo, Quercus cerris. Quercus ilex, Pinus halepensis, Pinus pinea
Dominant tree speciesc
b
The values reported are means ± 90% confidence intervals calculated after log-transformation to fit normality. The values reported are means ± 90% confidence intervals. c Defined as those species with a frequency ‡ 25% of the plots and at least 5% of total basal area, in order of decreasing frequency.
a
Foreste Pistoiesi (FP)
Foreste Casentinesi (FC)
Madonna delle Querce (MQ)
Farma-Merse e Belagaio (FM)
9865
180 (24–416) 204 (40–594) 351 (154–572) 544 (205–1077) 1091 (585–1656) 1188 (631–1891)
Bandite di Follonica, Macchia della Magona, Montoni (BF) Colline Livornesi (CL) 2098
Surface (ha)
Elevation (weighted mean and range)
Forest
Table 1. Location and basic features of the six investigated forests.
528
529 Data collection In each site, once located with a high precision GPS, a 20 · 20 m plot was delimited. The plot was divided into four 10 · 10 m quadrants. The following data were collected for vascular plants: (i) DBH of plants for individuals with DBH ‡ 3 cm and (ii) total list of species within the quadrant/plot. In each plot, bryophytes were sampled in 24 bryoplots whose positions were determined using four trees with DBH ‡ 12 cm, selected by a stratified random design within each plot (one in each quadrant). On each tree, two bryoplots were positioned on the trunk at 130 cm of height and two at the trunk base. For each position (130 cm and trunk base), one bryoplot was positioned on the northern face and one on the southern face of the trunk. Two additional bryoplots were located on the ground at 50 cm from the trunk base, one on the northern side and one on the southern site. These ground bryoplots were used to assess the bryophyte component on the ground as influenced by the tree presence. Each bryoplot was 10 · 20 cm. All the mosses and liverworts present within each bryoplot were identified at the species level directly in the field or later in the laboratory. Species richness at the plot scale is here defined as the pooled number of bryophyte species recorded in the 24 bryoplots within each plot. In order to prepare the lists of species, the nomenclature of all specimens was standardized according to Pignatti (1982) for vascular plants, Aleffi and Schumacker (1995) for liverworts and Cortini Pedrotti (2001) for mosses. In the present paper, species richness at the plot scale is discussed in relation to three groups of plants: (i) woody plants, (ii) total vascular plants and (iii) bryophytes (liverworts and mosses).
Data analysis Compositional patterns The main compositional patterns of the three groups were extracted using DCA (Detrended Correspondence Analysis). The use of the compositional patterns of woody plants and total vascular plants, as predictors of the compositional pattern of bryophytes was tested by linear, logarithmic and quadratic regressions of the plot scores for the 109 plots along the first DCA axis using bryophyte species data with respect to the correspondent plot scores obtained by using species data on woody plants and total vascular plants, and deemed significant if p £ 0.05. The Mantel test was also used to test the null hypothesis of no relationship between the dissimilarity matrix of bryophytes with respect to those of woody plants and total vascular plants. Species Richness and rarity The total number of species recorded in each plot was used for each group as indicator of species richness at the local scale. For each plot, rarity was
530 analysed by two different indicators, each calculated for woody plants, total vascular plants and bryophytes: (i) the number of unique species (i.e. occurring in only that site, Colwell and Coddington 1994); (ii) a Rarity Index, calculated according to the formula (1): P j Ijk N Fj =N R¼ ð1Þ Sk where Ijk is the incidence of species j in site k, Fj is total number of sites containing species j, N is the total number of sites and Sk is the number of species in the site k (Palmer et al. 2002). This index scales between zero (no infrequent species) and one (only infrequent species). Linear, logarithmic and quadratic regression were used to test if species richness and rarity indicators of woody plants and total vascular plants could be used to predict the same indicators of bryophytes, and deemed significant if p £ 0.05. Rarefaction curves We analysed how the species rarefaction curve calculated for the three groups were congruent within the same forest. Plot-based rarefaction curves were calculated as the mean of 10,000 accumulation curves obtained with different random sequence of plots (Sanders 1968; Gotelli and Colwell 2001), separately for woody plants, total vascular plants and bryophytes. The ratio between the species rarefaction curve obtained for bryophytes and those calculated for woody plants and vascular plants was then calculated. If bryophytes had the same rarefaction patterns of woody plant species or vascular plant species, these ratios were expected to be horizontal. Reserve selection analysis and taxon surrogacy We used an Integer Linear Programming (ILP) approach, as described by Rodrigues et al. (2000), to find the combinations of sites that maximized the number of species. A ‘reservation set’ was defined as the combination of sites which maximized the pooled number of species for that number of sites. The prioritization of sites to maximize the pooled species richness using richnessbased algorithms (Csuti et al. 1997; Polasky et al. 2001) may be done using optimization algorithms, which consider all the possible combinations for any number of sites, or heuristic (‘greedy’) algorithms. Although greedy algorithms are reported to provide near-optimal solutions (Csuti et al. 1997; Moore et al. 2003), optimization algorithms based on Integer Linear Programming (ILP), may find better solutions (Pressey et al. 1993; Church et al. 1996; Rodrigues et al. 2000; Rodrigues and Gaston 2002). The software C-Plex (ILOG 1999) was used to find, for any given number of sites, all the possible solutions resulting in the same maximum pooled number of species (multiple optima) for the three groups. These combinations were considered as group-specific reservation sets. We then used the reservation sets optimized for woody plant species and total vascular plant species to calculate the pooled number of bryophytes
531 species ‘captured’ by them. The performance in capturing bryophyte species richness of these reservation sets was tested against the pooled number of bryophyte species obtained in 10,000 random combinations of plots (Sanders 1968; Gotelli and Colwell 2001) and those obtained by bryophyte-specific reserve sets. Performance was measured as: X Nlv P% ¼ ð2Þ 10000 Where: P% = Performance index (as %) of a combination of n plots; Nlv = number of random combinations of n plots resulting in a pooled number of bryophyte species lower than that obtained with the taxon-surrogacy approach. Species richness values falling in the upper decile (i.e. P% ‡ 0.9) were considered to be significantly higher than the random or bryophyte-specific reserve sets. Results Compositional patterns The compositional pattern of bryophytes species was highly congruent with those of woody plants and total vascular plants, as demonstrated by the relatively high predictive value of the first axes of the DCA ordination obtained with woody plants and total vascular plants with respect to those obtained with bryophytes (Figure 2a, b). The similarity in species composition of bryophytes with respect to woody plants and total vascular plants was confirmed by the Mantel test (p < 0.001 in both cases).
Species richness and rarity The pooled species richness of the 109 plots resulted in a total of 61 woody plant species, 420 vascular plant species and 128 bryophyte species. Species richness per plot was highly variable: 1–14 species for woody plants; 3–69 species for vascular plants and 0–22 species for bryophytes (note that in contrast to the woody and total species richness, bryophyte species richness was not the total species richness for the 400 m2 plot but the pooled species richness obtained from the 24 bryoplots). Woody and vascular plant species richness showed a limited capability of predicting bryophyte species richness at the plot scale. In fact, bryophyte species richness within each plot was significantly related to woody plant species richness and vascular plant species richness (Figure 3a, b), but the amount of explained variance was relatively low in both cases. Neither the number of singletons nor the rarity index of bryophytes were significantly related (p £ 0.05) to number of singletons or the rarity index of
532
Bryophytes - DCA 1
(a)450 400
y = 0.4234x + 53.004
350
R2 = 0.716; P < 0.001
300 250 200 150 100 50 0 0
200 400 Woody Plants - DCA 1
600
(b) 450 y = 0.4419x + 40.243
400
R2 = 0.750;P < 0.001 Bryophytes - DCA 1
350 300 250 200 150 100 50 0 0
100
200
300
400
500
600
700
Vascular Plants - DCA 1 Figure 2. Relationship between the scores of the 96 plots (13 plots did not have any bryophyte species) along the first DCA axis obtained with the species composition of bryophytes with respect to the scores of the first DCA axis obtained with the species composition of woody plants (a) and total vascular plants (b).
woody plant species and total vascular plant species, indicating that the sites hosting the rare species of bryophytes were not the same in which the rare species of woody plants or total vascular plants were recorded. Similarly, the number of singletons and the rarity index of bryophytes were not related (p £ 0.05) to the species richness of woody plants or total vascular plant species.
533 (a) Bryophyte species richness
30 y = 0.6246x + 5.2712
25
R2 = 0.1187; p 100 3 12
12
>100 1 19
15
>100 23 20
18
>100 2 43
21
>100 25 >100
24
1 >100
27
>100 >100
30
>100 >100
33
>100 3
36
4
39
>100
42
>100
45
>100
48
>100
51
>100
54
>100
57
Table 2. Number of possible solutions for the reservation sets based on the optimization of the maximum pooled species richness of woody plants, total vascular plants and bryophytes (only combinations for steps of 3 plots are reported for simplicity).
536
537
(a) 70 Pooled number of species
60 50 40 30 20 10 0 0
10
20
30 40 50 60 70 80 Number of selected plots
90
100 110
0
10
20
30 40 50 60 70 80 Number of selected plots
90
100 110
0
10
20
30 40 50 60 70 80 Number of selected plots
90 100 110
(b)450 Pooled number of species
400 350 300 250 200 150 100 50 0
(c) 140 Pooled number of species
120 100 80 60 40 20 0
Figure 6. Number of species obtained with specifically optimized reserve sets (filled circles) for woody plant species (a), vascular plant species (b) and bryophytes (c) compared with their respective rarefaction curves (open circles ± 1 SD). For bryophytes the number of species obtained with the reserve sets optimized for woody plant species (bold line) or total vascular plant species (thin line) is also given.
538 numbers of possible combinations (Table 2) and needed of 36 plots (2 different combinations) to obtain a set of sites including all species (Figure 6c). The pooled bryophyte species richness for reservation sets optimized for woody plant species or vascular plant species richness were lower than those specifically optimized for bryophyte species richness, and only slightly higher than the mean plus one standard deviation of the reservation set based on random combinations of sites (Figure 6c). The reservation sets optimized for woody plants resulted in extremely variable values of bryophyte pooled species richness. With respect to the specifically optimized reservation sets, a proportion from 42.1% to 59.3%, for the lower values, and from 53.9% to 96.6%, for the higher values, of bryophyte species was found in these reservation sets (Figure 7a). Most of the higher values showed a performance index ‡0.9 but none of the lower values showed a performance index ‡0.9. This indicates that, for any number of selected plots, some of the reserve sets based on woody plant species richness were also effective for optimizing bryophyte species richness while others were not. The reservation sets obtained by optimizing total vascular plant species richness performed more homogeneously in terms of pooled bryophyte species richness (Figure 7b); with respect to the specifically optimized reservation sets, a proportion from 36.4% to 84.4% (for both the lower and the higher values) of bryophyte species was found in these reservation sets (Figure 7b). Also in this case, only some of the reservation sets optimized for all vascular plants obtained a performance index ‡0.9 in optimizing bryophyte species. This indicates that the reservation sets optimized for total vascular plants do not always optimize bryophyte species.
Discussion Compositional patterns Our analyses showed that the main compositional pattern of bryophytes was highly congruent with those of woody plants and total vascular plants, suggesting that changes in species composition of bryophytes reflect those of woody plants and total vascular plants. This suggests that patterns in woody plant or total vascular plant species composition may be used to infer the main compositional patterns of bryophytes, at least at the plot scale within Mediterranean forest ecosystems. Nekola and White (1999) found that, in North American spruce-fir forests, the rate of similarity decay with increasing distance was 1.5–1.9 times higher for vascular plants than for bryophytes, indicating that the species composition of vascular plants changed more rapidly than that of bryophytes along environmental gradients. Pharo et al. (1999) found significant correlations between the patterns of species turnover of bryophytes and different groupings of vascular plants (fern, overstory species, understory species, all vascular plants), though none of the correlations were particularly strong. In addition, Saetersdal et al. (2004) found that changes in
539
(a) 100 % of max # of bryophyte species
90 80 70 60 50 40 30 20 10 0 0
5
10 15 Number of Plots
20
25
(b) 100 % of max # of bryophyte species
90 80 70 60 50 40 30 20 10 0 0
10
20 30 40 Number of Plots
50
60
Figure 7. Proportion of bryophyte species richness (expressed as % of the optimized maximum) resulting in the reserve sets obtained by maximizing the number of woody plant species (a) and the number of vascular plant species (b). In each graph, the upper and lower curves denote respectively the maximum and the minimum proportion of species obtained with different combinations of n sites. The asterisks denote that the obtained values of bryophyte species richness have a performance index higher than 0.9.
species composition of vascular plants were reflected in comparable degrees of change in the species composition of bryophytes as well as of other taxonomic groups, including lichens, spiders, carabids, staphylinids, snails and polypore fungi. On the other hand, Negi and Gadgil (2002) reported a reduced concordance between the species turnover patterns of vascular plants and bryophytes in different habitat types in India.
540 It appears to be almost a general rule that species composition of bryophytes often changes in a more or less parallel manner with respect to that of vascular plants. However, this pattern and its magnitude are likely to be ecosystem and scale dependent and it is not possible to see a unique model of cross-taxon covariation in species composition with the data currently available.
Species richness and rarity Negi and Gadgil (2002) found that in different habitat types in the Chamoli district of Uttaranchal, Indian Garhwal Himalaya, the species richness of woody plants was significantly related to that of mosses, though not to liverworts. Ingerpuu et al. (2001) found a good correlation between bryophyte and vascular plant species richness in boreo-nemoral moist forests and mires, at the regional scale and the ten-stand scale and marginally non-significant correlations between them at the 1 ha stand scale. In a range of forest types in the costal lowlands of eastern Australia, Pharo et al. (1999) found that bryophyte species richness was well correlated with that of ferns and with that of overstory species, but not at all with total plant species richness. Saetersdal et al. (2004) found that bryophyte species richness was highly correlated with vascular plant species richness in 0.25 ha stands sampled in forest ecosystems of Norway. In our sample, the bryophyte species richness was found to be only weakly correlated with the woody plant or total vascular plant species richness, indicating that the species richness of these taxa cannot be used as a good surrogate for bryophyte species richness at the local, i.e. plot, scale. Bryophyte rarity did not show any apparent relation to the patterns of rarity or species richness of either woody plants or total vascular plants, confirming that vascular plant rarity and bryophyte species richness and rarity are not related at all, as suggested by the above cited studies. In addition any cross-taxon correlation between these taxa is likely to be dependent on the spatial scale, complicating the whole picture.
Rarefaction curves Gotelli and Colwell (2001) observed that the category–subcategory taxonomic ratios, frequently used in biogeography, suffer from sample-size dependence and thus should be analyzed by using ratios of the respective rarefaction curves. We adopted a similar approach – the ratio between the rarefaction curves of two different taxa from the same sites – to analyze the cross-taxon congruence in relation to the sampling intensity between bryophytes and woody plants or total vascular plants. As far as we know this is the first attempt to investigate this aspect of community ecology and conservation biology by using such an approach. The six forests investigated differed widely in this ratio, as well as in their rarefaction curves. This indicates that different
541 gradients of species accumulation operates in the six forests for the three groups considered and it is extremely difficult to use vascular plants – or woody plants – to estimate larger scale diversity of bryophytes. However, given the lack of examples of this type of analyses, further studies may reveal interesting patterns in the species rarefaction processes of cross-taxon congruence. This replication is particularly important given that the use of the slope of species accumulation curves as an index of b-diversity remains controversial (Scheiner 2003; Gray et al. 2004). However, it is evident that the greater the slope of the accumulation curve the higher the species dissimilarity among sampling sites is (Ricotta et al. 2002). The comparison of species accumulation curves or the ratio between taxa can reveal analogies or differences in this particular aspect of species diversity.
Reserve selection analysis and taxon surrogacy Ingerpuu et al. (2001) concluded their study on vascular plants and bryophytes of boreo-nemoral forests and mires, stating that ‘the species richness between bryophytes and vascular plants is positively correlated on larger scales, and conservation of communities rich in species of one group maintains also the species richness of the other’. This is the logical basis for most conservation programmes using one (or a few) taxonomic groups as indicators for the selection of nature reserves. Our results indicate that the selection of reserve sets of sites by maximizing vascular plant species richness can only be partially effective in maximizing bryophyte species richness. When quantitatively checking the amount of bryophyte species captured in reserve networks based on vascular plants the results may be contradictory. Pharo et al. (2000) found that a network of sites that captured 90% of vascular plants also captured 87% of lichen species but only 75% of bryophytes species. In our survey, we found that the average proportion of bryophyte species captured in reservation sets based on woody plant species richness varied between 50.7% for the worst performing solutions and 84.9% for the best performing ones. The corresponding values obtained by reservation sets based on vascular plant species were 69.1% and 72.3%. However, especially for the reservation sets based on woody plants, the range of bryophyte species richness captured by different combination of plots was extremely high. Virolainen et al. (2000) found that, for small patches within old-growth forests in Finland and Sweden, complementary networks based on vascular plants efficiently captured a high proportion of species richness of other taxonomic groups, but bryophytes were not analysed. Saetersdal et al. (2004) extended the results of Virolainen et al. (2000) to forest ecosystems of Norway and also included in their survey bryophytes; in their analysis of the cumulative percentage of species of the different species groups captured as a function of cumulative percentage area from a complementary selection of sites based on
542 vascular plant species, the proportion of bryophyte species was constantly higher with respect to that of other taxonomic groups. Our work indicated that, although a relatively high proportion of bryophyte species, compared to random expectations, was captured by reserve sets based on vascular plant species, this proportion was seldom significantly different from that of random combinations of sites. Designing efficient networks of nature reserves based on maximizing the pooled species richness of a target taxon is computationally possible with present-day computers (Rodrigues and Gaston 2002; Rodrigues et al. 2004). This approach is becoming popular and it is likely that these methods will result in practical conservation applications in a near future. However, since the data are normally available only for a few taxonomic groups, this method requires a taxon surrogacy approach (Ryti 1992; Prendergast et al. 1993; Howard et al. 1998; Garson et al. 2002) which may not effectively allow for differences in basic components of community structure (e.g. within site or between site species richness). In forest ecosystems of Tuscany, the selection of sites which maximize the vascular plant species richness was not efficient in maximizing the bryophyte species richness. This may be due to the limited correlation of within-site species richness combined with the totally uncorrelated patterns of rare species. A similar result was found for fungi in reserve sets optimized for vascular plants in 25 forest stands of Tuscany (Chiarucci et al. 2005). Again in that case, fungal species composition was related to vascular plant species composition, though with lower explained variance, and there was no correlation between stand scale fungal and plant species richness (Chiarucci et al. 2005). Although the issue of the spatial scale certainly affects taxon-surrogacy approaches used in reserve selection this was not considered here. Two basic components of the spatial scale can affect the results: the scale at which the data are aggregated, the plots in this case or geographic cells in others, and the grain in which the data are really collected. Our bryophyte data were gathered by 24 small sampling units within a plot and almost certainly do not provide complete lists of species within the plot. However, it is almost impossible to have complete species lists for several large sampling units and the use of a large number of smaller sampling units is a necessity, especially for bryophytes and other difficult-to-sample taxa.
Conclusion The present study demonstrated that compositional gradients of woody plants and total vascular plants at the plot scale are well reflected by the compositional patterns of bryophytes in forest ecosystems of Tuscany, central Italy. A reduced predictive value of species richness of woody plants and total vascular plants was found for bryophyte species richness at the plot scale and an even lower performance was found in using plants for the optimization of reserve
543 site selection. In addition, bryophyte species rarity was uncorrelated with plant species rarity. This reduced performance of plant species richness as indicators of bryophyte species richness indicates that specific data on bryophyte species composition are needed when some interest is given to this group and that cross-taxon congruence is scarcely usable for detecting their patterns, at least in Mediterranean forest ecosystems.
Acknowledgements Paper No. 29 of the research project MONITO – (MONitoraggio Intensivo foreste TOscane), funded by the Regional Administration of Tuscany and the European Community on the Regulations EEC 2157/92 and 3528/86. The project was also supported by a grant of the University of Siena (PAR Progetti 2001) to the first author (AC). We also wish to acknowledge Alfonso Riva, Luisa Frati, Francesca Casini, Antonio Gabellini, Marco Cetoloni, Daniele Viciani, Federico Ghini, Manuela Boddi and Giacomo Nicoletti for collaboration during the sampling activities. A special thank is due to Duccio Rocchini for completely managing the GIS and GPS systems employed during the research. A very special thanks to the ever patient Barbara Anderson for attempting to correct the English.
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