Hierarchical and spatially aggregated plant cover data

Effect of nitrogen and glyphosate on the plant community composition in a simulated field margin ecosystem: Model-based ordination of pin-point cover data

The effect of nitrogen and glyphosate on the plant community composition was investigated in a simulated field margin ecosystem. The plant community composition was inferred from pin-point cover data using a model-based ordination method that is suited for modelling pin-point cover data. The mean structure of the ordination model is analogous to a standard linear model, which enabled us to estimate the mean effects of nitrogen and glyphosate and their interaction in the two-dimensional ordination space. There were significant effects of both nitrogen and glyphosate on the plant community composition and overall species diversity. The effects of nitrogen and glyphosate on the plant community composition differed significantly. Furthermore, the estimated combined effects of nitrogen and glyphosate indicated that nitrogen and glyphosate enforced the effect of each other on the plant community composition by synergistic interactions. Addition of nitrogen and glyphosate was found to favor a plant community that was dominated by perennial grasses, and there was a tendency for glyphosate to select for plant communities in which annual plants were more frequent. The results suggest that using the notion of plant functional types and specific knowledge of the degree of glyphosate tolerance may be effective for predicting the effect of glyphosate on the community composition.

State-space modeling of the dynamics of temporal plant cover using visually determined class data

A lot of vegetation-related data have been collected as an ordered plant cover class that can be determined visually. However, they are difficult to analyze numerically as they are in an ordinal scale and have uncertainty in their classification. Here, I constructed a state-space model to estimate unobserved plant cover proportions (ranging from zero to one) from such cover class data. The model assumed that the data were measured longitudinally, so that the autocorrelations in the time-series could be utilized to estimate the unobserved cover proportion. The model also assumed that the quadrats where the data were collected were arranged sequentially, so that the spatial autocorrelations also could be utilized to estimate the proportion. Assuming a beta distribution as the probability distribution of the cover proportion, the model was implemented with a regularized incomplete beta function, which is the cumulative density function of the beta distribution. A simulated dataset and real datasets, with one-dimensional spatial structure and longitudinal survey, were fit to the model, and the parameters were estimated using the Markov chain Monte Carlo method. Then, the validity was examined using posterior predictive checks. As a result of the fitting, the Markov chain successfully converged to the stationary distribution, and the posterior predictive checks did not show large discrepancies. For the simulated dataset, the estimated values were close to the values used for the data generation. The estimated values for the real datasets also seemed to be reasonable. These results suggest that the proposed state-space model was able to successfully estimate the unobserved cover proportion. The present model is applicable to similar types of plant cover class data, and has the possibility to be expanded, for example, to incorporate a two-dimensional spatial structure and/or zero-inflation.

Probability distribution of groundcover for runoff prediction in rangeland in the Burnett–Mary Region, Queensland

Considering the degree of spatial and temporal variation of groundcover in grazing land, it is desirable to use a simple and robust model to represent the spatial variation in cover in order to quantify its effect on runoff and soil loss. The purpose of the study was to test whether a two-parameter beta (β) distribution could be used to characterise cover variation in space at the sub-catchment scale. Twenty sub-catchments (area range 35.8–231km2) in the Burnett–Mary region, Queensland, were randomly selected. Thirty raster layers of groundcover at 30-m resolution were prepared for these 20 sub-catchments with the average cover for the 30 layers ranging from 24% to 91%. Three methods were used to test the appropriateness of the β distribution for characterising the cover variation in space: (i) visual goodness-of-fit assessment and Kolmogorov–Smirnov (K-S) test; (ii) the fractional area with cover ≤53%; and (iii) estimated runoff amount for a given rainfall amount for the area with cover ≤53%. The K-S test on 30×100 samples of groundcover showed that the hypothesis of β distribution for groundcover could not be rejected at P=0.05 for 97.5% of the cases. A comparison of the observed and β distributions in terms of the fractional area with cover ≤53% showed that the discrepancy was ≤8% for the 30 layers considered. A comparison in terms of the estimated runoff showed that results using the observed cover distribution and the β distribution were highly correlated (R2 range 0.91–0.98; Nash–Sutcliffe efficiency measure range 0.88–0.99). The mean absolute error of estimated runoff ranged from 0.98 to 8.10mm and the error relative to the mean was 4–16%. The results indicated that the two-parameter β distribution can be adequately used to characterise the spatial variation of cover and to evaluate the effect of cover on runoff for these predominantly grazing catchments.

Is Erica tetralix abundance on wet heathlands controlled by nitrogen deposition or soil acidification?

Erica tetralix is the key species on NW European wet heathlands, where it is often found to be the dominating plant species. Consequently, it is of considerable concern that the species has decreased significantly in cover from 28% to 18% over a six-year period. In order to understand the underlying causes, a structural equation modeling (SEM) approach was applied on ecological data from 1130 wet heathland plots. Both atmospheric N deposition and soil acidification were included in the SEM. The most important causal effect revealed by the SEM was a significant negative effect of N deposition on the cover of E. tetralix, whereas soil acidity tended to have a negative effect of relatively less importance. There was no significant effect of N deposition on soil pH, which indicates that there are no major indirect effects of N deposition on the cover of E. tetralix mediated by soil acidification.