Question: What factors limit woody plant recruitment in a mosaic landscape where former agricultural lands are dominated by the invasive tree Ligustrum lucidum (Oleaceae)?

Location: Subtropical northwestern Argentina.

Methods: In secondary forest patches, we measured (1) tree, shrub and liana abundance in different size classes; (2) seed rain of Ligustrum and two native trees and (3) topographic, soil and light variables. We used spatial autoregressive models to test for effects of Ligustrum dominance and environment on native plant abundance in each size class. We used multiple regression on resemblance matrices to quantify the relative importance of spatial (e.g. dispersal) and environmental effects on native species composition.

Results: Native tree abundance in the smallest size class was unrelated to Ligustrum canopy dominance, while native tree abundance in larger size classes and native liana abundance were negatively correlated with Ligustrum dominance. Native species composition was both environmentally and spatially structured, suggesting that some species are dispersal limited. Seed rain was spatially correlated with conspecific basal area for one of two native species, but not for Ligustrum.

Conclusions: Native tree recruitment appears to be limited primarily by sapling mortality in patches dominated by the invasive Ligustrum. Ligustrum does not appear to be dispersal limited in our study area and is likely to continue spreading. Invaded patches may persist for hundreds of years.

Nomenclature: Zuloaga & Morrone (1996, 1999).

Abbreviations: AR = Auto-regressive; MRM = Multiple regression on resemblance matrices; OLS = Ordinary least squares; PC = principal component; SC = size class; TSI = Terrain shape index.

Questions: 1. How do physiography, flooding regime, landscape pattern, land-cover history, and local soil conditions influence the presence, community structure and abundance of overstorey trees? 2. Can broad-scale factors explain variation in the floodplain forest community, or are locally measured soil conditions necessary?

Location: Floodplain of the lower 370 km of the Wisconsin River, Wisconsin, USA.

Methods: Floodplain forest was sampled in 10 m × 20 m plots (n = 405) during summers of 1999 and 2000 in six 12- to 15-km reaches.

Results: Species observed most frequently were Fraxinus pennsylvanica, Acer saccharinum and Ulmus americana. Physiography (e.g. geographic province) and indicators of flooding regime (e.g. relative elevation and distance from main channel) were consistently important in predicting occurrence, community composition, and abundance of trees. Correspondence analysis revealed that flood-tolerant and intolerant species segregated along the primary axis, and late-successional species segregated from flood-tolerant species along the secondary axis. Current landscape configuration only influenced species presence or abundance in forests that developed during recent decades. Land-cover history was important for tree species presence and for the abundance of late-successional species. Comparison of statistical models developed with and without soils data suggested that broad-scale factors such as geographic province generally performed well.

Conclusions: Physiography and indicators of flood regime are particularly useful for explaining floodplain forest structure and composition in floodplains with a relatively high proportion of natural cover types.

Nomenclature: Gleason & Cronquist (1991).

Question: How to refine simulations based on a global vegetation model in order to apply it to regional scale?

Location: Europe from 35° N to 71° N and 25° W to 70° E.

Methods: Geographical ranges of European plants were georeferenced and used with monthly mean climatic data (diurnal temperature ranges, ground frost frequencies, precipitation, relative humidity, rain frequencies, amount of sunshine hours and temperature) and growing degree days to infer climatic boundaries for 320 taxa. We performed a discriminant analysis to define their potential geographic ranges. Hierarchical clustering was computed on potential ranges.

Results: Clustering provided 25 Bioclimatic Affinity Groups (BAG) of plants consisting of 13 tree, seven shrub and five herb groups. These BAGs are characterized by different geographical ranges and climatic tolerances and requirements.

Conclusion: The use of monthly data instead of annual values improved the prediction of potential distribution ranges and highlighted the importance of climate seasonality for defining the plant groups with accuracy. The BAGs are detailed enough to provide finer reconstructions and simulations of the vegetation at the regional scale.

Nomenclature: Moore et al. (1991); Punt et al. (1994).

Abbreviations: BAG = Bioclimatic Affinity Group; PFT = Plant Functional Type; CARAIB = CARbon Assimilation In the Biosphere.

Question: Predictive vegetation modelling relies on the use of environmental variables, which are usually derived from a base data set with some level of error, and this error is propagated to any subsequently derived environmental variables. The question for this study is: What is the level of error and uncertainty in environmental variables based on the error propagated from a Digital Elevation Model (DEM) and how does it vary for both direct and indirect variables?

Location: Kioloa region, New South Wales, Australia

Methods: The level of error in a DEM is assessed and used to develop an error model for analysing error propagation to derived environmental variables. We tested both indirect (elevation, slope, aspect, topographic position) and direct (average air temperature, net solar radiation, and topographic wetness index) variables for their robustness to propagated error from the DEM.

Results: It is shown that the direct environmental variable net solar radiation is less affected by error in the DEM than the indirect variables aspect and slope, but that regional conditions such as slope steepness and cloudiness can influence this outcome. However, the indirect environmental variable topographic position was less affected by error in the DEM than topographic wetness index. Interestingly, the results disagreed with the current assumption that indirect variables are necessarily less sensitive to propagated error because they are less derived.

Conclusions: The results indicate that variables exhibit both systematic bias and instability under uncertainty. There is a clear need to consider the sensitivity of variables to error in their base data sets in addition to the question of whether to use direct or indirect variables.

Abbreviations: AML = Arc/INFO Macro Language; DEM = Digital Elevation Model; GPS = Global Positioning System; HDOP = Horizontal Dilution of Precision; TWI = Topographic Wetness Index; VDOP = Vertical Dilution of Precision.

Questions: How long may it take for desert perennial vegetation to recover from prolonged human disturbance and how do different plant community variables (i.e. diversity, density and cover) change during the recovery process?

Location: Sonoran Desert, Arizona, USA.

Methods: Since protection from grazing from 1907 onwards, plant diversity, density and cover of perennial species were monitored intermittently on ten 10 m × 10 m permanent plots on Tumamoc Hill, Tucson, Arizona, USA.

Results: The study shows an exceptionally slow recovery of perennial vegetation from prolonged heavy grazing and other human impacts. Since protection, overall species richness and habitat heterogeneity at the study site continued to increase until the 1960s when diversity, density and cover had been stabilized. During the same period, overall plant density and cover also increased. Species turnover increased gradually with time but no significant relation between any of the three community variables and precipitation or Palmer Drought Severity Index (PDSI) was detected.

Conclusions: It took more than 50 yr for the perennial vegetation to recover from prolonged human disturbance. The increases in plant species richness, density, and cover of the perennial vegetation were mostly due to the increase of herbaceous species, especially palatable species. The lack of a clear relationship between environment (e.g. precipitation) and community variables suggests that site history and plant life history must be taken into account in examining the nature of vegetation recovery processes after disturbance.

Abbreviation: CV = Coefficient of variation; PDSI = Palmer Drought Severity Index.

Nomenclature: Lehr (1978).

Question: Previous interpretations of the variance plot of paired quadrat variance method (PQV) have been incomplete. The objective of this study was to clarify the interpretation of PQV, and to shed additional light on how different quadrat variance methods can be used, in concert, to measure scale in transect data.

Methods: We used artificial and real data to examine how the PQV method elucidates spatial pattern. Two-term local quadrat variance (TTLQV) and new local variance (NLV) methods, together with their three-term counterparts, were also applied to the same data sets, and the results from all methods were compared.

Results: When the mean gap size equalled the mean patch size along a transect, the first peak of the variance of PQV, NLV and TTLQV corresponded with the gap size (or patch size). However, if the mean gap size and patch size were unequal, the variance plot of PQV displayed a flat-topped plateau, in which the first inflection represented the mean size of the smaller phase and the second inflection represented the mean size of the larger phase; TTLQV showed a clear peak and NLV displayed a distinct first peak while the second inflection was dampened. The results also indicated than the three-term versions of quadrat variance methods did not consistently outperform their two-term counterparts, and often confused the interpretation of scale.

Conclusions: The quadrat variance methods associated with the patch-gap measurements were able to efficiently detect not only the size of patches, but also the size of gaps.

Abbreviations: NLV = New local variance; PQV = Paired quadrat variance; RPQV = random paired quadrat variance TQV = Triplet quadrat variance; TTLQV = Two-term local quadrat variance; 3TLQV = Three-term local quadrat variance; 3TNLV = Three-term new local variance.

Question: What is the nature and relative importance of compositional gradients within- and between fens?

Location: Iowa, USA.

Methods: 506 0.5 m × 0.5 m quadrats were sampled from 31 fens across a 550 km extent. Presence/absence of all vascular plant taxa, plus the non-vascular genera Sphagnum and Chara, and values for 24 environmental variables were noted. Global Non-Metric Multidimensional Scaling and Monte Carlo tests were used to describe compositional variation and identify significant environmental co-variables. Model-based cluster analysis was used to identify the optimal number of groups supported by the data, while k-means clustering was used to assign each quadrat to a group. The number of occurrences (and frequency) of each species within each group was calculated. Two-dimensional 95% Gaussian confidence intervals, ANOVA, correlation coefficient homogeneity tests, log-linear modelling, and Fisher's exact tests were used to document patterns of compositional change.

Results: Two stable axes of variation were identified: the first being most closely correlated with soil pH, Mg, Ca, P, S, vegetation height, surface and −10 cm soil temperature, site area, perimeter, perimeter/area ratio, growing season, and air temperature, with the second being most correlated to soil moisture, N, disturbance level, % organic matter, hummock height, N-S coordinate, and precipitation. Individual sites harboured between 20–47% of total compositional variation, with 28% of Axis 1 and 55% of Axis 2 scores being contained within-sites. Five compositional regions were identified that differed in the proportion of calciphile and hydrophile species. Compositional groups differed significantly between geologic types.

Conclusions: While the principal axis of variation (corresponding to the rich-poor fen gradient) is present largely between sites, the second axis (corresponding to water level) is largely repeated within sites. Documentation and protection of vegetation patterns and species diversity within Iowa fens will thus require consideration of multiple sites across the landscape.

Nomenclature: Nekola (1994).

Question: What was the change in diversity of urban synantropic vegetation in a medium-sized Central European city during the period of increasing urbanization (1960s–1990s)?

Location: The city of Plzeň, an industrial centre of the western part of the Czech Republic.

Methods: Sampling of various types of synanthropic vegetation, conducted in the 1960s, was repeated by using the same methods in the 1990s. This yielded 959 relevés, of which 623 were made in the 1960s and 336 in the 1990s. The relevés were assigned to the following phytosociological classes: Chenopodietea, Artemisietea vulgaris, Galio-Urticetea, Agropyretea repentis and Plantaginetea majoris. Total number of vascular plant species, evenness index J, number of alien species (classified into archaeophytes and neophytes), and mean Ellenberg indicator values for light, temperature, continentality, moisture, soil reaction, and nutrients were obtained for each relevé.

Results: From 1960s to 1990s, there was a significant decrease of species richness and diversity in synanthropic vegetation. The proportion of archaeophytes decreased in most vegetation types, indicating the contribution of this group of species, often confined to specific rural-like habitats, to the observed impoverishment of ruderal vegetation. The proportion of neophytes did not change between the two periods. Comparison between 1960s and 1990s indicated a decrease in light, temperature, moisture, soil reaction and nutrient indicator values in some vegetation types. In both periods, Artemisieta, Galio-Urticetea and Chenopodietea formed a distinct group harbouring more species than Agropyretea and Plantaginetea. Neophytes, i.e. recently introduced species, were most represented in the early successional annual vegetation of Chenopodietea, rather than in perennial vegetation of the other classes.

Conclusions: Synanthropic vegetation of Plzeň exhibited a general trend of decrease in species diversity.

Nomenclature: Ehrendorfer (1973) for species; Moravec et al. (1995) for phytosociological units.

Question: Is the relation between productivity and species richness due to an increase in plant size and hence a reduced plant density?

Location: Glasshouse experiment.

Methods: Productivity was manipulated with fertilizer and irrigation in a microcosm experiment. The ‘sampling effect’ was removed using rarefaction to a common density of individual plants per pot.

Results: Fertility increased community biomass towards an asymptotic maximum, and reduced the light passing through plant canopies towards an asymptotic minimum. As biomass increased, so did species richness. However, this did not seem to be a direct effect of productivity on species richness, but rather one mediated by plant density, since: (1) the richness/density relation was stronger than the richness/biomass one; (2) adding biomass to the richness/density regression did not increase its predictivity; (3) the richness/biomass relation was removed by rarefaction to 200 individuals per pot. It is therefore concluded that the richness/biomass relation observed was due to the sampling effect. Rarefaction to a small number of plants gave a quite different trend: lower richness at high biomass. This seems to be due to an increased number of subordinate species at high community biomass, and a more uneven distribution of abundance.

Conclusion: The Competitive Exclusion and No-Interaction hypotheses have been seen as alternatives. We suggest that they can operate simultaneously.

Questions: How does the time interval between subsequent stand-replacing fire events affect post-fire understorey cover and composition following the recent event? How important is fire interval relative to broad- or local-scale environmental variability in structuring post-fire understorey communities?

Location: Subalpine plateaus of Yellowstone National Park (USA) that burned in 1988.

Methods: In 2000, we sampled understorey cover and Pinus contorta density in pairs of 12-yr old stands at 25 locations. In each pair, the previous fire interval was either short (7–100 yr) or long (100–395 yr). We analysed variation in understorey species richness, total cover, and cover of functional groups both between site pairs (using paired t-tests) and across sites that experienced the short fire intervals (using regression and ordination). We regressed three principal components to assess the relative importance of disturbance and broad or local environmental variability on post-fire understorey cover and richness.

Results: Between paired plots, annuals were less abundant and fire-intolerant species (mostly slow-growing shrubs) were more abundant following long intervals between prior fires. However, mean total cover and richness did not vary between paired interval classes. Across a gradient of fire intervals ranging from 7–100 yr, total cover, species richness, and the cover of annuals and nitrogen-fixing species all declined while the abundance of shrubs and fire-intolerant species increased. The few exotics showed no response to fire interval. Across all sites, broad-scale variability related to elevation influenced total cover and richness more than fire interval.

Conclusions: Significant variation in fire intervals had only minor effects on post-fire understorey communities following the 1988 fires in Yellowstone National Park.

Nomenclature: Dorn (1992).

Abbreviations: YNP = Yellowstone National Park.

Questions: Does a reduced nutrient load in open water increase species richness and the importance of regional and local site characteristics for species abundance and spatial distribution? Can we build lake-specific models of macrophyte abundance and distribution based on site characteristics in order to prepare a cost-efficient framework for future surveys?

Location: Lake Constance, 47°39′ N, 9°18′ E.

Methods: Generalized additive models (GAMs) were used to predict the potential distributions of eight species and overall species richness. Submersed macrophyte distribution in 1993 was compared with corresponding data from 1978, when eutrophication was at its maximum.

Results: Spatial predictions for eight species and overall species richness were relatively accurate and independent of water chemistry. Depth was confirmed as a main predictor of species distribution, while effective fetch distance was retained in many models. Mineralogical variables of sediment composition represent allogenic and autogenic sediment sources and their east-west gradient in Lake Constance corresponded to east-west gradients of species distribution and richness. GAMs appeared more efficient than generalized linear models (GLMs) for modelling species responses to environmental gradients.

Conclusions: Reduced trophic status increases species richness and the importance of regional and local site characteristics for species abundance and distribution. Our models represent a spatio-temporal framework for future lake monitoring purposes and allow the development of effective monitoring; this could be generalized for many ecosystem types and would be particularly efficient for large lakes such as Lake Constance.

Abbreviations: C_org = Organic carbon content; EFD = Effective fetch distance; GAM = Generalized Additive Model; GLM = Generalized Linear Model; GRASP = Generalized regression analyses and spatial predictions; MGS = Mean grain size; ROC = Receiving Operating Characteristic; ROC_cv = Cross validated ROC; R_tot = Species richness.

Question: Do warm season (i.e. mainly C₄) grass species exhibit a higher water status than winter-growing competitors, (mainly C₃ plants) and is this part of their competition ability?

Location: Flooding Pampa grassland at Balcarce, eastern Argentina.

Methods: Predawn leaf water potential was measured in five species: (1) three cool-season species: Festuca arundinacea, Lolium multiflorum and Stipa neesiana, and (2) two warm-season species: Sporobolus indicus and Paspalum dilatatum, throughout a summer season. The sensitivity of leaf growth to low water status was investigated in a greenhouse experiment where plants were grown in pots at three levels of water deficit.

Results: The field study revealed strong differences of water status between the five species likely indicating large contrasts in soil water availability, irrespective of their photosynthetic metabolism. By contrast, the response of leaf elongation rate to predawn water potential was similar in all species studied. Species to which water was less available did not compensate with a lesser sensitivity to water status.

Conclusion: Competition for water may play an important and specific role within natural semi-grasslands of these regions, independently of the metabolic pathway of grasses.

Abbreviations: LER = Leaf elongation rate; FC = Field capacity; MWD = Moderate water deficit; SWD = Severe water deficit; ψ_D = Predawn leaf water potential

Nomenclature: Cabrera (1953).

Question: What is the influence of refuse dumps of leaf-cutting ants on seedling recruitment under contrasting moisture conditions in a semi-arid steppe?

Location: Northwestern Patagonia, Argentina.

Methods: In a greenhouse experiment, we monitored seedling recruitment in soil samples from refuse dumps of nests of the leaf-cutting ant Acromyrmex lobicornis and non-nest sites, under contrasting moisture conditions simulating wet and dry growing seasons.

Results: The mean number of seedling species and individuals were higher in wet than in dry plots, and higher in refuse dump plots than in non-nest soil plots. The positive effect of refuse dumps on seedling recruitment was greater under low moisture conditions. Both the accumulation of discarded seeds by leaf-cutting ants and the passive trapping of blowing-seeds seems not explain the increased number of seeds in refuse dumps. Conversely, refuse dumps have higher water retention capacity and nutrient content than adjacent non-nest soils, allowing the recruitment of a greater number of species and individual seedlings.

Conclusions: Nests of A. lobicornis may play an important role in plant recruitment in the study area, allowing a greater number of seedlings and species to be present, hence resulting in a more diverse community. Moreover, leaf-cutting ant nests may function as nurse elements, generating safe sites that enhance the performance of neighbouring seedlings mainly during the driest, stressful periods.

Nomenclature: Correa (1969–1998).

Many individual-based models of forest dynamics lack spatial complexity. Although, in certain cases, spatially simple models may not be substantially inferior to spatially complex models, advances in vegetation science indicate potential weaknesses, particularly the lack of consideration of propagule availability in horizontal space, and varying patch (or canopy gap) dimensions. Models with vertical and horizontal spatial complexity can address these issues, but, thus far, evidence that they outperform patch (or gap) models is limited. Comparison of projections from models that differ only in their spatial complexity is needed to address the effects of propagule availability in space, spatial pattern of canopy tree mortality, and spatial resolution.

A recent analysis published in this journal found different relationships between mean Ellenberg indicator values and environmental measurements in different vegetation types. The cause was stated as bias in mean Ellenberg values between relevés which in turn suggested to reflect a bias in individual Ellenberg values. We discuss two phenomena that could explain these results without the need to invoke bias in either individual or mean Ellenberg values. Firstly, slopes of linear regression lines underestimate true relationships when analyses involve explanatory variables measured with error. Secondly, syntaxon-specific distributions of Ellenberg values follow from the floristic definition of phytosociological units. Mean Ellenberg values per relevé therefore carry the stamp of their associated syntaxon even though associated abiotic conditions may vary between relevés. This will lead to variation in slopes and intercepts between vegetation types not because of bias in individual Ellenberg values but because of prescribed bias in the distribution of Ellenberg values between syntaxa. The residual variation in calibrations carried out across vegetation types is undoubtedly reduced by introducing vegetation type as a factor. However users should note that this is unlikely to reflect bias in individual Ellenberg values but is more likely to reflect error in environmental measurements as well the constraint imposed by phytosociological classification.

Smart & Scott (2004, this issue) criticized our paper (Wamelink et al. 2002) about the bias in average Ellenberg indicator values. Their main criticism concerns the method we used, regression analysis. They state the bias can be mimicked by the construction of an artificial data set and that regression analysis is not a suited tool to investigate underlying phenomena. Moreover they claim that the present bias is caused by the distribution of Ellenberg indicator values between syntaxa, instead of a bias in average Ellenberg indicator values per species. We show that their criticism of the use of regression analysis does not hold. We selected average Ellenberg values per vegetation group for several pH classes and applied an F-test to determine whether or not the vegetation groups within each pH class differed significantly from each other. This was the case for all tested classes (P < 0.001). Moreover we simulated an artificial data set, of which the F-test for varying measurement error could not explain the magnitude of the F-value we found earlier. This indicates that the bias we found in average Ellenberg indicator values cannot be explained by measurement errors or by regression to the mean. In the end, Smart & Scott, as we did, come to the conclusion that there is a bias present and that separate regression lines per vegetation type are necessary, but the debate remains open on whether or not this is caused by the bias in Ellenberg indicator values per species.

Abbreviation: Ellenberg IV = Ellenberg indicator value.