Question: What is the relative importance of area- and edge-effects on woody seedling diversity in old Afromontane forest fragments?

Location: Mistbelt Afromontane forests, KwaZulu-Natal midlands, South Africa.

Methods: Woody seedling abundance and species richness in 590 1-m² plots were sampled at the forest edge (< 10 m from the edge) and interior in 31 old (> 60 a) Afromontane forest fragments (0.05 – 328.5 ha) with closed edges in an ancient grassland matrix.

Results: Unlike young (< 20 a) Amazonian fragments, there was no edge- or area-effect on sample plot seedling density and species richness, although these increased significantly with increasing herb cover (less disturbance). Seedling density, but not species richness, declined significantly with herbivory of seedlings, regardless of forest size or plot location. Seedling community composition and richness did not differ significantly between the edge and interior of forests across the range of forest sizes (i.e. no edge-effect). Community composition was nested with small forests retaining a subset of the seedling flora of larger forests. Overall, cumulative seedling species richness increased with forest area (i.e. area-effect).

Conclusions: Holocene climatic extinction filtering events and area-dependent species relaxation have potentially selected for tree species with convergent life histories adapted to local fragmentation-effects. Stable environmental conditions at old edges in these naturally fragmented forests cause similar regeneration conditions and seedling species composition between edge and interior. Consequently, seedling density and species richness are controlled more by response to gradients of local disturbance (habitat area, herb cover, herbivory) than by proximity to the edge. Large patches (> 50 ha) with intact edges had the highest tree seedling diversity and are a conservation priority. Although small patches contain no unique species they preserve landscape processes, have conservation value, and require protection. Conservation principles derived from recently created Amazonian fragments and that emphasize edge-effects, require critical evaluation for application to old Afromontane patches.

Nomenclature: Arnold & de Wet (1993).

Question: Is quantile regression an appropriate statistical approach to estimate the response of fen species to single environmental factors?

Background: Data sets in vegetation field studies are often characterized by a large number of zeros and they are generally incomplete in respect to the factors which possibly influence plant species distribution. Thus, it is problematic to relate plant species abundance to single environmental factors by the ordinary least squares regression technique of the conditional mean.

Location: Riparian herbaceous fen in central Jutland (Denmark).

Methods: Semi-parametric quantile regression was used to estimate the response of 18 plant species to six environmental factors, 95% regression quantiles were chosen to reduce the impact of multiple unmeasured factors on the regression analyses. Results of 95% quantile regression and ordinary least squares regression were compared.

Results: The standard regression of the conditional mean underestimated the rates of change of species cover due to the selected factor in comparison to 95% regression quantiles. The fitted response curves indicated a general broad tolerance of the studied fen species to different flooding durations but a narrower range concerning groundwater amplitude. The cover of all species was related to soil exchangeable phosphate and base-richness. A relationship between soil exchangeable potassium and species cover was only found for 11 species.

Conclusion: Considering the characteristics of data sets in vegetation science, non-linear quantile regression is a useful method for gradient analyses.

Nomenclature: Wisskirchen & Haeupler (1998).

Question: Do, in a semi-arid gypsum environment, neighbours condition the spatial patterns of emergence, survival and height of Helianthemum squamatum seedlings ?

Location: Vicinity of Chinchón, province of Madrid, Spain (40°11′N, 3° 35′W, 550 m a.s.l.)

Methods: We evaluated the effects of neighbours on the survival and growth of naturally emerging Helianthemum seedlings in a semi-arid area during a two-year period. We followed a two-fold approach based on the use of neighbour models for seedling survival and growth and spatial point pattern analyses for seedling emergence, taking into account the germination date.

Results: Seedlings appeared clumped in the vicinity of mature Helianthemum individuals. The neighbour models fitted showed that interactions with neighbours were extremely important for the survival and growth of Helianthemum seedlings. These models also suggest that the effects of neighbours on these variables vary with changes in spatial scale and in the abiotic conditions. Some species exerted negative or positive effects on Helianthemum seedlings only at certain spatial scales, and others exerted negative or positive effects at earlier stages of seedling development, but none later and vice versa.

Conclusions: We suggest that the observed patterns are mainly influenced by small-scale modifications in soil conditions and microclimate created by neighbours, which change in time and space.

Nomenclature: Castroviejo (1986–2004).

Abbreviations: GLM = Generalized linear model.

Question: Are leaf dry matter content, specific leaf area and leaf life span relevant plant traits to discriminate the fertility gradient in species-rich natural grasslands? In other words, is species ranking conserved when nitrogen availability or growing periods change?

Location: Toulouse Research Centre, France; 150 m a.s.l.

Methods: Fifteen grasses and nine dicotyledons were sown in pure stands in a random block design with three replicates. Each species was cultivated at two levels of nitrogen supply, limiting and non-limiting for growth, with three replications per nitrogen level. Leaf traits were measured across both levels of nitrogen supply and growing periods over the year.

Results: Leaf dry matter content values separated the species into three life-form classes (grasses, rosette forbs and upright forbs, P < 0.001). This was not the case for specific leaf area and leaf life span. The three leaf traits were variable across growing periods and nitrogen levels, but the ranking of species was conserved over N-levels and growth periods. Furthermore leaf dry matter content was always less variable than the other leaf traits.

Conclusion: We conclude that leaf dry matter content measured only on grasses could be used as an indicator to describe the N-richness of the habitat where native herbaceous vegetation develops.

Abbreviations: CV = Coefficient of variation; INN = Index of nitrogen nutrition; LDMC = Leaf dry matter content; LLS = Leaf life span; LSD = Least significant difference; RGR = Relative growth rate; SLA = Specific leaf area.

Questions: 1. To what extent is biomass production of a Caucasian alpine tundra plant community limited by soil nitrogen and/or phosphorus? 2. Can the foliar N:P ratio predict the nutrient limitation pattern of alpine vascular plant communities?

Location: Lichen-rich tundra on Mt. Malaya Khatipara in the NW Caucasus, Russia (43°27′ N, 41°42′ E; alt. 2800 m a.s.l.).

Methods: We conducted a 4-year fertilization experiment (N, P, N P, lime additions and irrigation) on the alpine tundra in the northwestern Caucasus, Russia. We determined responses of biomass, tissue nutrient concentrations and nutrient pools of the above-ground component of the plant community.

Results: Total plant community biomass did not respond to fertilization. However, lichen biomass strongly decreased in response to the N- and N P treatments, whereas vascular plant biomass increased in response to the N- and even more to the N P treatment, but not to P or lime addition or irrigation. P-concentrations in vascular plant species were very low, but their biomass production was not principally P-limited, suggesting adaptation to low soil P-availability. The N-limitation of vascular plant biomass production in the community, which in lowlands usually occurs at N:P ratios below 16, could not be predicted from the mean foliar N:P mass ratio in the control (N:P = 29).

Conclusions: This Caucasian alpine plant community is an example of N- and P-co-limitation of vascular plant biomass production, with N being the principal and P the secondary limiting nutrient. Critical N:P ratios as determined for lowland communities are not applicable here.

Nomenclature: Vorob'eva & Onipchenko (2001).

Questions: Are artificial neural networks useful for the automatic assignment of species composition records from vegetation plots to a priori established classes (vegetation units)? Is the assignment more accurate (1) if the classes are defined by numerical classification rather than by expert-based classification; (2) if the training data set is selected to include plots that are richer in diagnostic species of particular classes?

Material: Species composition records (relevés) from 4186 plots of Czech grasslands.

Methods: Plots were classified into 11 phytosociological alliances (expert classification) and into 11 clusters derived from numerical cluster analysis. Some plots were used for training the classifiers, which were the multi-layer perceptrons (MLP; a type of artificial neural network). Other plots were used for testing the performance of these classifiers. Plots used for training were selected (1) randomly; (2) according to higher representation of diagnostic species of particular classes.

Results: Different MLP classifiers correctly classified 77–83% of plots to the classes of the expert classification and 70–78% to the classes of the numerical classification. The better result in the former case was mainly due to two classes in the expert classification, which were well recognized by the classifiers and at the same time contained a large proportion of the plots of the entire data set. Correct classification of the plots belonging to these large classes resulted in a good overall performance of the classifiers. After training with randomly chosen plots, the classifiers produced better results than after training with plots that contained more diagnostic species. This indicates that the biased selection of the training plots disables the classifiers to recognize the entire variation within the classes and results in errors when new plots are to be classified.

Conclusions: MLP is suitable for assigning vegetation plots to already established classes. Unlike some other methods of supervised classification, it performs well even in communities that are poor in diagnostic species. However, the method does not provide clear assignment keys that could be used for class identification in field surveys. It is therefore more appropriate in applications that aim at a reliable class assignment rather than understanding the assignment rules.

Abbreviations: MLP = Multi-layer perceptron.

Question: Can patterns of species frequency in an old-field be explained within the context of a metapopulation model? Are the patterns observed related to time, spatial scale, disturbance, and nutrient availability?

Location: Upland and lowland old-fields in Illinois, USA.

Method: Species richness was recorded annually for seven years following plowing of an upland and lowland old-field subject to crossed fertilizer and disturbance treatments (mowing and rototilling). Species occupancy distributions were assessed with respect to the numbers of core and satellite species.

Results: In both fields, species richness became higher in disturbed plots than in undisturbed plots over time, and decreased in fertilized plots irrespective of time. A bimodal pattern of species richness consistent with the Core-satellite species (CSS) hypothesis occurred in the initial seed bank and through the course of early succession. The identity of native and exotic core species (those present in > 90% of blocks) changed with time. Some core species from the seed bank became core species in the vegetation, albeit after several years. At the scale of individual plots, a bimodal fit consistent with the CSS hypothesis applied only in year 1 and rarely thereafter.

Conclusions: The CSS hypothesis provides a metapopulation perspective for understanding patterns of species richness but requires the assessment of spatial and temporal scaling effects. Regional processes (e.g. propagule availability) at the largest scale have the greatest impact influencing community structure during early secondary succession. Local processes (e.g., disturbance and soil nutrients) are more important at smaller scales and place constraints on species establishment and community structure of both native and exotic species. Under the highest intensity of disturbance, exotic species may be able to use resources unavailable to, or unused by, native species.

Nomenclature: Mohlenbrock (2002).

Question: Does canopy tree regeneration response to different large disturbances vary with soil drainage?

Location: Old-growth conifer (Dacrydium and Dacrycarpus), angiosperm (Nothofagus and Weinmannia) rain forest, Mount Harata, South Island, New Zealand.

Methods: Trees were aged (1056 cores) to reconstruct stand history in 20 (0.12 - 0.2 ha) plots with different underlying drainage. Spatial analyses of an additional 805 tree ages collected from two (0.3 – 0.7 ha) plots were conducted to detect patchiness for five canopy tree species. Microsite preferences for trees and saplings were determined.

Results: There were clear differences in species regeneration patterns on soils with different drainage. Conifer recruitment occurred infrequently in even-aged patches (> 1000 m²) and only on poorly drained soils. Periodic Nothofagus fusca and N. menziesii recruitment occurred more frequently in different sized canopy openings on all soils. Weinmannia recruitment was more continuous on all soils reflecting their greater relative shade-tolerance. Distinct periods of recruitment that occurred in the last 400 years matched known large disturbances in the region. These events affected species differently as soil drainage varied. Following earthquakes, both conifers and N. menziesii regenerated on poorly drained soils, while Nothofagus species and Weinmannia regenerated on well-drained soils. However, Dacrydium failed to regenerate after patchy storm damage in the wetter forest interior; instead faster-growing N. fusca captured elevated microsites caused by uprooting.

Conclusions: Underlying drainage influenced species composition, while variation in the impacts of large disturbance regulated relative species abundances on different soils.

Question: Does interspecific variation in leaf phenology among grassland species help to explain the differences in species' performance under contrasting disturbance regimes.

Location: Merishausen, northern Switzerland.

Methods: Seasonal variations in leaf production and mortality were assessed for three species of nutrient-poor limestone grasslands: Brachypodium pinnatum, Bromus erectus and Salvia pratensis; each of these species tends to become dominant under a contrasting form of management. Their phenological characteristics were compared with their performance in plots differently managed for 21 years: (1) mowing in July; (2) mowing in October; (3) controlled burning in February; and (4) no biomass removal.

Results: The species-specific phenological patterns of leaf production and leaf mortality are associated with the abundance of the three species under the different management regimes. B. erectus, with relatively short-lived leaves and leaf production late in the season dominates plots mown annually in June; it has almost disappeared from plots with winter burning. B. pinnatum, with production maxima of the long-lived leaves early in the season, does not tolerate June mowing but is most abundant in plots burnt in winter when the species has no living leaves. S. pratensis, a species with long-lived leaves but fast senescence of all the leaves in autumn, dominates plots mown in October. In unmown plots, all species are equally abundant.

Conclusions: The seasonal pattern of leaf production and mortality strongly influence biomass and nutrient loss due to the management, and the growth that can be realized between the disturbances. A species may become dominant if it ‘fits’ into the particular management regime, whereas a mismatch between phenological pattern and disturbance regime leads to its elimination from the community.

Nomenclature: Hess et al. (1976–1980).

Questions: 1. Does resource use efficiency increase with increased species richness in conifer forests? 2. Do patterns found in resource use support niche differentiation/complementarity between species, or is any increase indicative of a selection effect?

Location: All data were collected from upper montane (2200–2600 m a.s.l) conifer forests of the Desolation Wilderness in the central California Sierra Nevada, USA.

Methods: We established 281 plots of varying levels of conifer richness throughout the wilderness area. Within each plot we used hemispherical photos to measure canopy closure and LAI, total soil carbon and nitrogen from the A-horizon, and stand basal area. We used linear regression and ANOVA to analyse the relationship between stand species richness and resource availability.

Results: We found no correlation of either soil nitrogen or carbon with stand biomass. Nor did soil nitrogen and carbon levels change with species richness. Canopy closure increased with species richness but also varied significantly between pure stands of different species. Pure Pinus monticola stands had the lowest canopy closure, Tsuga mertensiana stands the highest. Composition explained more canopy cover variation than did species richness. We found evidence supporting both the sampling effect and niche differentiation models at different stages of stand development.

Conclusions: During initial stages of stand development, the interaction between the shade-intolerant Pinus species and shade-tolerant Abies magnifica and T. mertensiana followed the niche differentiation model, but switched to the sampling effect model during the competitive-exclusion stage. In contrast, interaction between A. magnifica and T. mertensiana followed the niche differentiation model.

Abbreviations: CC = Mean canopy closure; LAI = Leaf area index.

Nomenclature: Hickman (1993).

Questions: Does post-fire plant succession in boreal bogs vary microtopographically and are successional patterns reproducible among similar microtopographic features? Does succession preserve microtopography post-fire?

Location: Boreal bog peatlands near Sinkhole Lake and Athabasca, Alberta, Canada.

Methods: We assessed microtopographic variation in post-fire plant community succession through stratigraphic macrofossil analysis of bog soil cores collected from high (hummock) and low (hollow) positions. We conducted vegetation surveys and collected soil cores from ten hummocks and hollows in each bog. Pre-fire microtopographic status was inferred based on floral composition and compared to current microtopography.

Results: Hollow vegetation was more variable than hummocks, both in present composition and post-fire succession. The successional trajectory of current hummocks was relatively uniform, showing relatively rapid shifts to Sphagnum fuscum dominance, but varied greatly in hollows. Hollows, although compositionally variable, were typically perpetuated following fire, while hummocks had an approximately equal chance of being perpetuated or becoming hollows.

Conclusions: Greater compositional variability at lower microtopographic positions, both spatially and temporally, is most likely due to the ability of hollows to support a wider range of species and greater susceptibility to severe disturbance. Likewise, spatial variability in fire severity appears to be responsible for perpetuation or change in microtopographic status, favouring the creation of hollows over maintenance of hummocks.

Abbreviations: Ath = Athabasca; DSE = Distance from set elevation; Hol = Hollow; Hum = Hummock; MTF = Microtopographic feature; NMDS = Non-metric dimensional scaling; RCD = Relative charcoal distance; R = ANOSIM test statistic; SL = Sinkhole Lake.

Question: The use of expert-based indicator values to estimate abiotic conditions from vegetation is widespread. However, recent research has shown that expert judgement may contain considerable bias and thereby introduces a large amount of uncertainty. Could expert based indicator values be replaced by indicator values based on field measurements?

Location: Europe.

Methods: We developed a method to estimate species response based on measured physical data, and a method to predict abiotic conditions from the vegetation composition using these responses. This method was tested for soil pH.

Results: We were able to estimate the pH response of 556 species of the Dutch flora. Ca. 20% of the responses were, at least, bimodal and many responses had a very wide range. The simplest method (‘raw mean’) yielded the best prediction of pH; the indicator value of a species is the mean of the soil pH values of the sites where it was observed. A list of all raw-mean estimates per species is given. The predicted pH of a new site is the mean of the indicator values of the present species. The estimated species responses were validated on independent Dutch and European data sets. Older successional stages seem to be predicted better than younger stages.

Conclusions: Our method performed better than the popular Ellenberg indicator system for the Dutch data set, while being just as easy to use, because it only needs a single value per species. We foresee that, when more data become available, our method has the potential to replace the Ellenberg system.

Abbreviation: RMSEP = Root mean squared error prediction.

Question: In floodplain forests, does frequent flooding allow for self-replacement of shade-intolerant tree species or do small canopy gap openings lead to replacement by shade-tolerant tree species?

Location: Cache River, Arkansas, US; 55 m a.s.l.

Methods: The species, diameter-at-breast height, and elevation of primary gap-maker trees were determined for new gaps from 1995–1998. The size and species of gap-filler trees were identified and placed into three classes: definitive, edge, or interior. Transition probabilities were determined.

Results: The dominant shade-intolerant species Quercus texana is being replaced primarily by the more shade-tolerant A. rubrum var. drummondii, Fraxinus spp. and Ulmus americana. Only 20 of 2767 gap fillers were Q. texana. Replacement probabilities are not constant across elevations, however, as the least shade-tolerant of the three most common species of definitive gap fillers, Fraxinus spp., occurred at lower elevations than A. rubrum var. drummondii, and U. americana.

Conclusions: The contention that frequent flooding would allow for self-replacement of shade-intolerant species was only partially supported. The small canopy gaps undoubtedly influenced canopy replacement processes.

Nomenclature: Anon. (USDA) 2005.

Question: The heterogeneous origin of the data in large phytosociological databases may seriously influence the results of their analysis. Therefore we propose some strategies for stratified resampling of such databases, which may improve the representativeness of the data. We also explore the effects of different resampling options on vegetation classification.

Methods: We used 6050 plot samples (relevés) of mesic grasslands from the Czech Republic. We stratified this database using (1) geographical stratification in a grid; (2) habitat stratification created by an overlay of digital maps in GIS; (3) habitat stratification with strata defined by traditional phytosociological associations; (4) habitat stratification by numerical classification and (5) habitat stratification by Ellenberg indicator values. Each time we resampled the database, taking equal numbers of relevés per stratum. We then carried out cluster analyses for the resampled data sets and compared the resulting classifications using a newly developed procedure.

Results: Random resampling of the initial data set and geographically stratified resampling resulted in similar classifications. By contrast, classifications of the resampled data sets that were based on habitat stratifications (2–5) differed from each other and from the initial data set. Stratification 2 resulted in classifications that strongly reflected environmental factors with a coarse grain of spatial heterogeneity (e.g. macroclimate), whereas stratification 5 resulted in classifications emphasizing fine-grained factors (e.g. soil nutrient status). Stratification 3 led to the most deviating results, possibly due to the subjective nature of the traditional phytosociological classifications.

Conclusions: Stratified resampling may increase the representativeness of phytosociological data sets, but different types of stratification may result in different classifications. No single resampling strategy is optimal or superior: the appropriate stratification method must be selected according to the objectives of specific studies.

Abbreviations: ASS = Phytosociological association; ELL = Ellenberg indicator values; GEO = Geographical stratification; GIS = Geographical information system; NUM = Numerical classification; RAN = Random resampling.