Inger Auestad

Scale-dependence of vegetation-environment relationships in semi-natural grasslands

Abstract Questions: Which environmental and management factors determine plant species composition in semi-natural grasslands within a local study area? Are vegetation and explanatory factors scale-dependent? Location: Semi-natural grasslands in Lærdal, Sogn og Fjordane County, western Norway. Methods: We recorded plant species composition and explanatory variables in six grassland sites using a hierarchically nested sampling design with three levels: plots randomly placed within blocks selected within sites. We evaluated vegetation-environment relationships at all three levels by means of DCA ordination and split-plot GLM analyses. Results: The most important complex gradient determining variation in grassland species composition showed a broad-scale relationship with management. Soil moisture conditions were related to vegetation variation on block scale, whereas element concentrations in the soil were significantly related to variation in species composition on all spatial scales. Our results show that vegetation-environment relationships are dependent on the scale of observation. We suggest that scale-related (and therefore methodological) issues may explain the wide range of vegetation-environment relationships reported in the literature, for semi-natural grassland in particular but also for other ecosystems. Conclusions: Interpretation of the variation in species composition of semi-natural grasslands requires consideration of the spatial scales on which important environmental variables vary. Nomenclature: Lid & Lid (1994) except for Betula pubescens agg. (which includes B. pubescens and B. verrucosa) and Hieracium spp. (which includes all Hieracium species except H.umbellatum).

Road Verges: Potential Refuges for Declining Grassland Species Despite Remnant Vegetation Dynamics

Whether road verge vegetation can be manipulated to resemble traditionally managed grassland has been much debated. This short-term study compared management effects on road verge and pasture vegetation in western Norway. We quantified vegetation change and explored whether it occurred along underlying environmental gradients. We found management-related variation in species richness and vegetation physiognomy, but high resistance in species composition prevented directional changes in vegetation dynamics. Initial differences between the habitats indicated historical management effects on traditionally managed pastures and road verges. Given proper management, road verges may have a nature conservation potential. Moreover, their linear structure may enable fine-scale mosaic management that allows the coexistence of a wide range of grassland species.

Management history affects grassland seed bank build-up

Disturbance in the form of different management regimes affects established vegetation, but how the same management affects the corresponding seed banks is poorly understood. We used the seedling emergence method to investigate how present and previous management intensity impacts the dynamics of established vegetation and corresponding seed bank in six semi-natural grasslands (three pastures and three road verges) in W Norway. The increased management intensity reduced seed bank species richness but increased seedling density and the fraction of species with a persistent seed bank. Higher intensity also increased the components’ floristic similarity, probably through formation of gaps where seeds may germinate. Moreover, the seed bank responded in parallel with the established vegetation to underlying environmental variables as well as to management intensity. Management intensity hence impacted directly on many aspects of seed bank—established vegetation relationships, and controlled established vegetation partly through seed bank dynamics involving both temporal and spatial dispersal.

Advancing restoration ecology: A new approach to predict time to recovery

Handling Editor: Lars Brudvig Abstract 1. Species composition is a vital attribute of any ecosystem. Accordingly, ecological restoration often has the original, or “natural,” species composition as its target. However, we still lack adequate methods for predicting the expected time to compositional recovery in restoration studies. 2. We describe and explore a new, ordination regression-based approach (ORBA) for predicting time to recovery that allows both linear and asymptotic (logarithmic) relationships of compositional change with time. The approach uses distances between restored plots and reference plots along the successional gradient, represented by a vector in ordination space, to predict time to recovery. Thus, the approach rests on three requirements: (a) the general form of the relationship between compositional change and time must be known; (b) a sufficiently strong successional gradient must be present and adequately represented in a species compositional dataset; and (c) a restoration target must be specified. We tested the approach using data from a boreal old-growth forest that was followed for 18 years after experimental disturbance. Data from the first 9 years after disturbance were used to develop models, the subsequent 9 years for validation. 3. Rates of compositional recovery in the example dataset followed the general pattern of decrease with time since disturbance. Accordingly, linear models were too optimistic about the time to recovery, whereas the asymptotic models provided more precise predictions. 4. Synthesis and applications. Our results demonstrate that the new approach opens for reliable prediction of recovery rates and time to recovery using species compositional data. Moreover, it allows us to assess whether recovery proceeds in the desired direction and to quantitatively compare restoration speed, and hence effectiveness, between alternative management options.