Questions: 1. To what extent does light availability differ among fen plant communities? 2. To what extent does light coincide with productivity and moisture gradients? 3. Does light act as an important environmental filter in natural and transformed riparian landscapes?

Location: Current data from the Biebrza Valley, NE Poland; literature data from the Třeboň area, Czech Republic and four sites in the western and southern Netherlands.

Methods: Relative light intensity (RLI) was measured in vertical profiles, next to vegetation relevés accompanied by measurements of above-ground biomass, summer groundwater level, N and P content in vegetation, pH and soil redox potential. Data derived from literature included profiles of RLI, biomass and vegetation records. Relationships between RLI and biomass and between species distribution, RLI and other variables were examined by regression analysis and CCA. Four traits were analysed: ability to spread clonally, seed weight, maximum height of adults and time of commencement of flowering.

Results: RLI at ground level varied from < 1% in reed beds and tall herb meadows to > 60% in sedge-moss communities and litter meadows. RLI was largely determined by the standing crop and explained a large part of variation in species occurrence. The combinations of analysed functional traits were constrained by the communities' light profiles.

Conclusion: Light availability is related more closely to site fertility than to hydrological regime. This confirms that hydrological regime and productivity should be analysed separately with regard to their effect on species distribution in wetlands. Limited light availability seems the major environmental control of the distribution of low growing and late flowering species.

Nomenclature: Mirek et al. (1995) for species; Schaminée et al. (1996–1999) for Dutch and Czech and Pałczyński (1984) for Polish plant communities.

Abbreviations: LAI = Leaf area index; RLI = Relative light intensity.

Question: What are the changes associated with the recent invasion by the non-native legume, Cytisus scoparius?

Location: Subalpine vegetation (1500 m a.s.l.) in Australia.

Methods: We used multivariate techniques and regression analyses to assess vegetation and environmental changes across six study sites. Vegetation and environmental variables were investigated at three different stages of invasion: (1) recent invasion (8–10 yr), (2) mature invasion (15–16 yr) and (3) long-term invasion (25 yr).

Results: Substantial changes in floristic composition and species richness were evident after 15 yr and these changes became more pronounced after 25 yr. Changes due to invasion were associated with a dramatic loss of native species or a reduction in their abundance. No ‘new species’ were evident under invaded stands. Forbs were most affected by the establishment of C. scoparius, although all growth forms responded negatively. Dense canopy shading and an increasingly dense, homogeneous litter layer in the understorey as a result of C. scoparius were strong environmental drivers of vegetation change. Greenhouse studies confirmed the importance of these processes on the germination and growth of two native species.

Conclusions: This study highlights the potential for C. scoparius to alter both vegetation and environmental processes in the subalpine region.

Nomenclature: Ross (2000).

Question: How much is the probability distribution of Fagus crenata forests predicted to change under a climate change scenario by the 2090s, and what are the potential impacts on these forests? What are the main factors inducing such changes?

Location: The major islands of Japan.

Methods: A predictive distribution model was developed with four climatic factors (summer precipitation, PRS; winter precipitation, PRW; minimum temperature of the coldest month, TMC; and warmth index, WI) and five non-climatic factors (topography, surface geology, soil, slope aspect and inclination). A climate change scenario was applied to the model.

Results: Areas with high probability (> 0.5) were predicted to decrease by 91 %, retreating from the southwest, shrinking in central regions, and expanding northeastwards beyond their current northern limits. A vulnerability index (the reciprocal of the predicted probability) suggests that Kyushu, Shikoku, the Pacific Ocean side of Honshu and southwest Hokkaido will have high numbers of many vulnerable F. crenata forests. The forests with high negative sensitivity indices (the difference between simulated probabilities of occurrence under current and predicted climates) mainly occur in southwest Hokkaido and the Sea of Japan side of northern Honshu.

Conclusion: F. crenata forest distributions may retreat from some islands due to a high WI. The predicted northeastward shift in northern Hokkaido is associated with increased TMC and PRS. High vulnerability and negative sensitivity of the forests in southern Hokkaido are due to increased WI.

Abbreviations: CCSR/NIES = Centre for Climate System Research / National Institute for Environmental Studies; DWS = Deviance-weighted score; GCM = Global Climate Model; IPCC = Intergovernmental Panel on Climate Change; JMA = Japan Meteorological Agency; MER = Misclassification error rate; NSNE = National Survey on the Natural Environment; PRS = Summer precipitation; PRW = Winter precipitation; TMC = Minimum temperatuire coldest month; WI = Warmth index.

Nomenclature: Ohwi & Kitagawa (1992).

Question: In the same landscape context – at a desert grassland-shrubland transition zone, how does subdominant plant abundance vary in microsites around dominant grasses and shrubs?

Location: Sevilleta LTER, New Mexico, USA (34°21′ N; 106°53′ W; 1650 m a.s.l.).

Methods: We compared the distribution of subdominant plants in canopy, canopy edge and interspace microsites around individual shrubs (Larrea tridentata) and grasses (Bouteloua eriopoda) at a transition zone that has been encroached by shrubs within the past 50 - 100 a. Plots of variable size according to microsite type and dominant plant size were sampled.

Results: Subdominant abundance was higher in microsites around L. tridentata shrubs than in microsites around B. eriopoda. Furthermore, differences in species abundance and composition were higher among microsites around grasses than among microsites around shrubs. The distribution of subdominants was mostly explained by their phenological characteristics, which indicates the importance of temporal variation in resources to their persistence.

Conclusions: This study of coexistence patterns around dominants revealed ecological contrasts between two dominant life forms, but other factors (such as disturbances) have to be taken into consideration to evaluate landscape-scale diversity.

Nomenclature: Anon. (1999).

Question: Predictive models in plant ecology usually deal with single species or community types. Little effort has so far been made to predict the species composition of a community explicitly. The modelling approach presented here provides a conceptual framework on how to achieve this by combining habitat models for a large number of species to an additive community model. Our approach is exemplified by Nardus stricta communities (acidophilous, low-productive grassland).

Location: Large areas of Germany, 0-2040 m a.s.l.

Methods: Logistic regression is applied for individual species models which are subsequently combined for an explicit prediction of species composition. Several parameters reflecting soil, management and climatic conditions serve as predictor variables. For validation, bootstrap and jackknife resampling procedures are used as well as ordination techniques (DCA, CCA).

Results: We calculated significant models for 138 individual species. The predictions of species composition and species richness yield good agreements with the observed data. DCA and CCA results show that the community model preserves the main patterns in floristic space.

Conclusions: Our approach of predicting species composition is an effective tool that can be applied in nature conservation, e.g. to assess the effects of different site conditions and alternative management scenarios on species composition and richness.

Abbreviations: AUC = Area under curve; CCR = Correct classification rate; GAM = Generalized additive model; GLM = Generalized linear model, ROC = Receiver operating characteristic.

Nomenclature: Ehrendorfer (1973); Frahm & Frey (1983)

Question: What are the correlations between the degree of drought stress and temperature, and the adoption of specific adaptive strategies by plants in the Mediterranean region?

Location: 602 sites across the Mediterranean region.

Method: We considered 12 plant morphological and phenological traits, and measured their abundance at the sites as trait scores obtained from pollen percentages. We conducted stepwise regression analyses of trait scores as a function of plant available moisture (α) and winter temperature (MTCO).

Results: Patterns in the abundance for the plant traits we considered are clearly determined by α, MTCO or a combination of both. In addition, trends in leaf size, texture, thickness, pubescence and aromatic leaves and other plant level traits such as thorniness and aphylly, vary according to the life form (tree, shrub, forb), the leaf type (broad, needle) and phenology (evergreen, summer-green).

Conclusions: Despite conducting this study based on pollen data we have identified ecologically plausible trends in the abundance of traits along climatic gradients. Plant traits other than the usual life form, leaf type and leaf phenology carry strong climatic signals. Generally, combinations of plant traits are more climatically diagnostic than individual traits. The qualitative and quantitative relationships between plant traits and climate parameters established here will help to provide an improved basis for modelling the impact of climate changes on vegetation and form a starting point for a global analysis of pollen-climate relationships.

Abbreviations: α = ratio of actual to equilibrium evapotranspiration; EPD = European pollen database; MTCO = Mean temperature of the coldest month; SLA = Specific leaf area.

Question: How should species cover be weighted when calculating average indicator values of vegetation relevés?

Location: The Netherlands.

Method: Various weighting methods were statistically investigated with 188 relevés from The Netherlands for which accurate groundwater levels were available. For each method the correlation between average Ellenberg indicator value for moisture and mean spring groundwater level was calculated. A permutation test on correlation coefficients revealed whether differences between methods were significant or not.

Results: Optimization of a general weighting function did not produce a significantly higher correlation than disregarding cover and calculating the average as the arithmetical mean of indicator values. Giving a higher weight to species at both ends of the indicator scale and using indifferent species as indicators of mediocre conditions did improve the correlation significantly. Weighting species proportionate to their cover yielded a significantly lower correlation than the correlation obtained with the method that disregards cover. A significantly lower correlation was also established when taking into account the fact that cover is related to the growth strategy of species.

Abbreviations: F_m = Site mean Ellenberg indicator value for moisture; MSL = Mean spring groundwater level.

Question: The optimal use of the point intercept method (PIM) for efficient estimation of plant biomass has not been addressed although PIM is a commonly used method in vegetation analysis. In this study we compare results achieved using PIM at a range of efforts, we assess a method for calculating these results that are new with PIM and we provide a formula for planning the optimal use of PIM.

Location: Northern Norway.

Methods: We collected intercept data at a range of efforts, i.e. from one to 100 pins per 0.25 m² plots, on three plant growth forms in a mountain meadow. After collection of intercept data we clipped and weighed the plant biomass. The relationship between intercept frequency and weighed biomass (b) was estimated using both a weighted linear regression model (WLR) and an ordinary linear regression model (OLR). The accuracy of the estimate of biomass achieved by PIM at different efforts was assessed by running computer simulations at different pin densities.

Results: The relationship between intercept frequency and weighed biomass (b) was far better estimated using WLR compared to the normally used OLR. Efforts above 10 pins per 0.25 m² plot had a negligible effect on the accuracy of the estimate of biomass achieved by PIM whereas the number of plots had a strong effect. Moreover, for a given level of accuracy, the required number of plots varied depending on plant growth form. We achieved similar results to that of the computer simulations when applying our WLR based formula.

Conclusion: This study shows that PIM can be applied more efficiently than was done in previous studies for the purpose of plant biomass estimation, where several plots should be analysed but at considerably less effort per plot. Moreover, WLR rather than OLR should be applied when estimating biomass from intercept frequency. The formula we have deduced is a useful tool for planning plant biomass analysis with PIM.

Nomenclature: Lid & Lid (1994).

Abbreviations: BM = Biomass; CV = Coefficient of variation; IF = Intercept frequency; PIM = Point intercept method; OLR = Ordinary linear regression model; WLR = weighted linear regression model.

Questions: How does the seed bank respond to different types of tree-fall gaps and seasonal variations? How does the soil seed bank influence recovery of the standing vegetation in the mature forest and tree-fall gaps?

Location: 1800 - 2020 m a.s.l., Quercus-Pinus forest, Baja California Sur, Mexico.

Methods: Seed size, species composition and germination were estimated under different environmental conditions during dry and rainy seasons: a mature forest plot and gaps created by dead standing trees, snapped-off trees and uprooted trees. The soil seed bank was investigated using direct propagule emergence under laboratory conditions, from soil cores obtained during both seasons.

Results: 21 species, 20 genera and 14 families constitute the seed bank of this forest community. Fabaceae, Asteraceae, Euphorbiaceae and Lamiaceae were the most frequently represented families in the seed bank. Floristic composition and species richness varied according to the different modes of tree death. Species composition of seed banks and standing vegetation had very low similarity coefficients and were statistically different. Seed bank sizes varied between 164 and 362 ind.m–2 in the mature forest plot for the dry and rainy seasons, respectively, while soil seed bank sizes for gaps ranged between 23–208 ind.m–2 for the dry season and between 81–282 ind.m–2 for the rainy season.

Conclusions: Seed bank sizes and germination response were always higher in the rainy season under all the environmental conditions analysed. Results suggest that timing responses to gap formation of the soil seed bank could be more delayed in this temperate forest than expected.

Nomenclature: Wiggins (1980).

Question: Is there a critical depth of burial by sand beyond which species and communities fail to recover, and does repeated incremental burial have a greater impact than a single large deposition?

Location: The machair on the calcareous sand dunes on South Uist, in the Outer Hebrides of Scotland, UK.

Methods: Eight turves were collected from each of four machair sub-community types. After acclimatization in an unheated polythene tunnel, they were buried with sterilized machair shell sand, either by one single burial to 5 cm or by five applications of 1 cm of sand at approximately seven-week intervals. Species response was recorded on five occasions.

Results: Within machair sub-communities, burial by sand reduced the abundance (local rooted frequency) of plants more than it reduced species richness. Intermittent burial was more damaging than a single burial event. Those species with the highest pre-burial frequencies tended to dominate recovery in the sub-community as a whole. Species occurring across all four sub-community types exhibited varying responses to community burial between the differing types. Samples from slack sub-communities had distinctly different response characteristics from those of foredunes and unploughed and three-year fallow dune grassland.

Conclusions: The perennial life-form of many machair species has been evolutionarily selected for and dominates throughout the machair habitat. Account needs to be taken of competitive interaction between species in relation to burial response. The results of the investigation show that the hypotheses of Gilbertson et al. and Kent et al. on ‘machair stratification’ require refinement in that frequency of shallow burial can be as important as overall burial depth.

Nomenclature: Stace (1997); Dobson (1992) for lichens; Watson (1981) for mosses and liverworts.

Question: Have recent increases in temperature caused a decline in arctic-alpine plants at the southern margin of their range?

Location: Above tree line; Glacier National Park, Montana, western USA.

Methods: We monitored the abundance of seven arctic-alpine vascular plants at or near the southern limits of their ranges at three sites in Glacier National Park, Montana from 1989 through 2002. In addition we recorded canopy cover of all plant species in sample plots once at the beginning and again at the end of the study.

Results: Mean summer temperature during this period averaged 0.6 °C higher than the previous four decades. Results of ordinations with non-metric multidimensional scaling suggested that vegetation moved toward the dry end of a moisture gradient at two sites during the course of the study. At the same time four of the peripheral arctic-alpine indicator species demonstrated 31–65% declines in abundance, while none increased.

Conclusions: We cannot rigorously infer causality from our descriptive study; however, changes in both indicator species and the vegetation matrix were consistent with predictions of climate-induced extirpation of high-elevation species and the northern migration of floras. Our results also suggest that species responded to the decade of warming individualistically with little relationship to growth form.

Nomenclature: Lesica (2002) for vascular plants; Esslinger & Egan (1995) for lichens; Anderson et al. (1990) for mosses.

Question: How do tree seedlings differ in their responses to drought and fire under contrasting light conditions in a tropical seasonal forest?

Location: Mae Klong Watershed Research Station, 100–900 m a.s.l, Kanchanaburi Province, western Thailand.

Method: Seedlings of six trees, Dipterocarpus alatus, D. turbinatus, Shorea siamensis, Pterocarpus macrocarpus, Xylia xylocarpa var. kerrii and Sterculia macrophylla, were planted in a gap and under the closed canopy. For each light condition, we applied (1) continuous watering during the dry season (W); (2) ground fire during the dry season (F); (3) no watering/no fire (intact, I). Seedling survival and growth were followed.

Results: Survival and growth rate were greater in the gap than under the closed canopy for all species, most dramatically for S. siamensis and P. macrocarpus. Dipterocarpus alatus and D. turbinatus had relatively high survival under the closed canopy, and watering during the dry season resulted in significantly higher survival rates for these two species. Watering during the dry season resulted in higher growth rates for five species. All seedlings of D. alatus and D. turbinatus failed to re-sprout and died after fire. The survival rates during the dry season and after the fire treatment were higher for the seedlings grown in the canopy gap than in the shade for S. siamensis, P. macrocarpus, X. xylocarpa var. kerrii and S. macrophylla. The seedlings of these species in the canopy gap had higher allocation to below-ground parts than those under the closed canopy, which may support the ability to sprout after fire.

Conclusions: The light conditions during the rainy season greatly affect seedling survival and resistance to fire during the subsequent dry season. Our results suggest differentiation among species in terms of seedling adaptations to shade, drought and fire.

Nomenclature: Smitinand (1980).

Abbreviations: C = Closed canopy; DDF = Deciduous dipterocarp forest; DEF = Dry evergreen forest; Fi = Fire; Ga = Gap; In = Intact; MDF = Mixed deciduous forest; SOC = Standard overcast sky distribution; S:R = Shoot-root ratio; Wa = Watering.

Question: What changes in species composition and cover have occurred in chaparral as a function of fire history across an ecoregion?

Location: San Diego County, California, USA.

Methods: Stands in which 40 mid-elevation chaparral vegetation plots (each 400 m² in area) were located in the 1930s were resurveyed in 2001. We stratified the stands into Infrequently versus Frequently burned (0–1 versus 2 or more fires recorded in the 91-yr period), and Immature versus Mature (­31 yr versus >31 yr since last fire), resulting in four groups. Ten stands were randomly selected from each of these groups for survey.

Results: There were no major shifts in life form composition, e.g., live oak trees were not invading chaparral that had experienced little or no fire, nor were subshrubs or herbaceous species replacing shrubs in areas that had experienced more frequent fires. However, there was a notable increase in the frequency of the subshrub Eriogonum fasciculatum across all fire history groups. In the mature stands with infrequent fire, average cover of resprouting shrubs increased (from 72 to 91%) and cover of obligate seeding shrubs (species with fire-cued germination) decreased (from 21 to 6%) significantly. Mature stands with frequent fire showed a significant decrease in resprouter cover (from 87 to 80%) and increase in obligate seeders (from 10 to 16%).

Conclusions: While the tremendous changes in land use in southern California have been predicted to cause shifts in chaparral composition, these shifts are difficult to detect because species longevity and fire cycles are on the order of decades to a century. In this study, the expected trends could only be detected in groups that were mature at the time of the second survey.

Abbreviations : F = Facultative seeder; OS = Obligate seeder; R = Obligate resprouter; S = Seeder; VTM = Vegetation Type Map.

Nomenclature : Hickman (1993) and Simpson & Rebman (2001).

Question: How can we derive baseline/reference situations to evaluate the impact of global change on terrestrial ecosystem functioning?

Location: Main biomes (steppes to rain forests) of Argentina.

Methods: We used AVHRR/NOAA satellite data to characterize vegetation functioning. We used the seasonal dynamics of the Normalized Difference Vegetation Index (NDVI), a linear estimator of the fraction of the photosynthetic active radiation intercepted by vegetation (f_PAR), and the surface temperature (Ts), for the period 1981–1993. We extracted the following indices: NDVI integral (NDVI-I), NDVI relative range (R_rel), NDVI maximum value (V_max), date of maximum NDVI (D_max) and actual evapotranspiration.

Results: f_PAR varied from 2 to 80%, in relation to changes in net primary production (NPP) from 83 to 1700 g.m–2.yr–1. NDVI-I, Vmax and f_PAR had positive, curvilinear relationships to mean annual precipitation (MAP), NPP was linearly related to MAP. Tropical and subtropical biomes had a significantly lower seasonality (R_rel) than temperate ones. D_max was not correlated with the defined environmental gradients. Evapotranspiration ranged from 100 to 1100 mm.yr⁻¹. Interannual variability of NDVI attributes varied across the temperature and precipitation gradients.

Conclusions: Our results may be used to represent baseline conditions in evaluating the impact of land use changes across environmental gradients. The relationships between functional attributes and environmental variables provide a way to extrapolate ecological patterns from protected areas across modified habitats and to generate maps of ecosystem functioning.

Abbreviations: CV = Coefficient of variation; D_max = Date of maximum NDVI; e = Light use efficiency; Et = Evapotranspiration; f_PAR = Fraction of photosynthetic active radiation intercepted by vegetation; MAP = Mean annual precipitation; MAT = Mean annual air temperature; NDVI = Normalized difference vegetation index; NDVI-I = NDVI integral; NPP = Net primary production; PAL = Pathfinder AVHRR Land; PUE = Precipitation use efficiency; R_rel = NDVI relative range; Ts = Surface temperature; V_max = NDVI maximum value