How good is GLASOD?

The Global Assessment of Soil Degradation (GLASOD) has been the most influential global appraisal of land quality in terms of environmental policy. However, its expert judgments were never tested for their consistency and could not be reproduced at unvisited sites, while the relationship between the GLASOD assessments of land degradation and the social and economic impact of that degradation remains unclear. Yet, other methodologies that could respond to urgent calls for an updated assessment of the global environmental quality are not operational or, at best, in progress. Therefore, we evaluate the reliability and social relevance of the GLASOD approach and assess its candidacy for new global environmental assessments. The study concentrates on the African continent, capitalizing on new GIS data to delineate and define the characteristics of GLASOD map units. Consistency is tested by comparing expert judgments on soil degradation hazard for similar combinations of biophysical conditions and land use. Reproducibility is evaluated by estimating an ordered logit model that relates the qualitative land degradation classes to easily available information on explanatory variables, the results of which can be used to assess the land degradation at unvisited sites. Finally, a cross-sectional analysis investigates the relation between GLASOD assessments and crop production data at sub-national scale and its association with the prevalence of malnutrition. The GLASOD assessments prove to be only moderately consistent and hardly reproducible, while the counter-intuitive trend with crop production reveals the complexity of the production-degradation relationship. It appears that increasing prevalence of malnutrition coincides with poor agro-productive conditions and highly degraded land. The GLASOD approach can be improved by resolving the differences in conceptualization among experts and by defining the boundaries of the ordered classes in the same units as independent, quantitative land degradation data.     

Estimating the variance in before-after studies

Problem

To simplify the computation of the variance in before-after studies, it is generally assumed that the observed crash data for each entity (or observation) are Poisson distributed. Given the characteristics of this distribution, the observed value (x_i) for each entity is implicitly made equal to its variance. However, the variance should be estimated using the conditional properties of this observed value (defined as a random variable), that is, f(x_i|μ_i), since the mean of the observed value is in fact unknown.

Method

Parametric and non-parametric bootstrap methods were investigated to evaluate the conditional assumption using simulated and observed data.

Results

The results of this study show that observed data should not be used as a substitute for the variance, even if the entities are assumed to be Poisson distributed. Consequently, the estimated variance for the parameters under study in traditional before-after studies is likely to be underestimated.

Conclusions

The proposed methods offer more accurate approaches for estimating the variance in before-after studies.     

Monitoring waterbird abundance in wetlands: The importance of controlling results for variation in water depth

Wetland use by waterbirds is highly dependent on water depth, and depth requirements generally vary among species. Furthermore, water depth within wetlands often varies greatly over time due to unpredictable hydrological events, making comparisons of waterbird abundance among wetlands difficult as effects of habitat variables and water depth are confounded. Species-specific relationships between bird abundance and water depth necessarily are non-linear; thus, we developed a methodology to correct waterbird abundance for variation in water depth, based on the non-parametric regression of these two variables. Accordingly, we used the difference between observed and predicted abundances from non-parametric regression (analogous to parametric residuals) as an estimate of bird abundance at equivalent water depths. We scaled this difference to levels of observed and predicted abundances using the formula: ((observed − predicted abundance)/(observed + predicted abundance)) × 100. This estimate also corresponds to the observed:predicted abundance ratio, which allows easy interpretation of results. We illustrated this methodology using two hypothetical species that differed in water depth and wetland preferences. Comparisons of wetlands, using both observed and relative corrected abundances, indicated that relative corrected abundance adequately separates the effect of water depth from the effect of wetlands.     

Benzene, toluene, ethyl benzene and xylenes in ambient air and Pinus sylvestris L. needles: a comparative study between Belgium, Hungary and Latvia

Concentrations of benzene, toluene, ethyl benzene and xylenes (BTEX) in ambient air and in 1 yr old Pinus sylvestris pine needles were monitored along a busy road, petrol station and rural area of Belgium, Hungary and Latvia in a 1 yr period. To test P. sylvestris as a possible biomonitor for the BTEX concentrations, samples were taken in the four seasons. As the distribution of data was not normal, the level of pollution on different sites and seasons was compared and evaluated by non-parametric tests. The measured air concentrations did not differ significantly from one season to another throughout the year. There were, however, differences between sampling places. The C₂-alkylbenzene and toluene concentrations in the needles were similar in the autumn/winter and spring/summer periods but a significant decrease in their concentration was observed in every place between winter and spring. This effect was less obvious for toluene.     

塔里木河流域水质变化趋势分析

应用季节性肯达尔非参数检验方法,对塔里木河干流及三大源流１９９２－１９９８年间的水质变化趋势进行了分析检验,客观、定量地描述了塔里木河的水质变化动态,对于进一步掌握塔里木河的水质变化有着重要意义。