We performed a computer assisted experiment to test the accuracy of different ratio scales in estimating vegetation cover. Sixteen subjects estimated the cover level of artificial vegetation patterns displayed on the screen for various levels of resolution (from presence/absence to 100 different states, each measured on the ratio scale). We found that estimation error is minimum when the range of cover is divided into ten equal parts. Finer resolution gives less precise estimation since subjects tend to divide cover level into ten or at most twenty intervals in their mind.
We elaborated and tested a novel operative framework for sampling and analysing fine-scale pattern of plant composition and biomass. We combined presence/absence sampling of plant species with non-destructive biomass estimation. In an open perennial sand grassland, we used 46 m long circular transects consisting of 0. 05 m by 0. 05 m adjoining elementary sampling units. This arrangement allows us to scale across a range of 0. 05 to 20 m. For measuring aboveground green biomass, we applied digital camera sensitive to red and near infrared parts of light spectrum, and we calculated normalised differential vegetation index (NDVI). We used information statistics proposed by Juhász-Nagy to study the association between spatial patterns of production and species composition. Since information statistical functions applied require binary data, we transformed NDVI data into one or several binary variables. We found that not only dominant species but subordinate gap species were also associated to high biomass, although the strength of association varied across scales. Most of the significant associations were detected at fine scales, from 0. 05 to 0. 25 m. At the scales commensurable with quadrat sizes usually applied in grasslands, i. e., from 0. 5 to 2. 0 m, we could hardly find any significant associations between species composition and biomass. We concluded that the novel methods applied proved reliable for studying fine-scale relationships between species composition and biomass.
Authors:G. Ónodi, V. Altbäcker, R. Aszalós, Z. Botta-Dukát, I. Hahn, and M. Kertész
We studied the long-term impact of wildfire on the vegetation dynamics of sand grasslands in a forest-steppe vegetation mosaic in Central Hungary (Kiskunság). Long-term permanent quadrat monitoring was carried out from 1997 to 2008. We sampled the forest-steppe mosaic both in burnt and unburnt areas in 100 patches altogether using 1 m × 1 m quadrats. The effect of fire and precipitation on vegetation dynamics was characterized by patch type transitions between years. Patch types were defined by means of Cocktail method. Nine patch types of sand grasslands were altogether identified. The least productive patch types, bare soil and cryptogam dominance, did not occur in the burnt patches, while annual dominated patch type appeared only in burnt patches. The frequencies of patch type changes were significantly higher in burnt patches than in unburnt ones, independently on time after fire. All the eight patch types found in the unburnt patches proved permanent, while in the burnt patches only four of seven were so. The relative frequency of patch type changes did not correlate with precipitation in the vegetation period in the unburnt patches, while positively correlated in the burnt patches. It was concluded that the long-term difference in grassland dynamics between the unburnt and burnt patches, i.e., the excess of the patch type transitions in the burnt grasslands, is due to increased drought sensitivity of the grassland, which is the consequence of the elimination of the woody component of the forest-steppe vegetation.