Cover estimation versus density counting in species-rich pasture under different grazing intensities

Two methods for monitoring of grassland vegetation were compared: visual estimation of plant cover (C) and plant densities counting (D). C and D were performed in monthly intervals for three vegetation growing seasons after imposing different grazing regimes on abandoned grassland in 1998. Species scores obtained from paired redundancy analyses (RDA) of C and D data were compared and Spearman's rank correlations were used to show if the two methods give comparable results. Results of C and D were highly correlated in the first two growing seasons only. In the third season, correlation was substantially lower as the sward structure was more heterogeneous due to creation of differently defoliated patches especially under extensive grazing. Presence of the same plant species with different habit in frequently and in infrequently grazed patches, reduced significance of Spearman's rank correlations. Cover estimation can fully substitute plant density counting in grassland with lower proportion of frequently and infrequently grazed patches only, but caution should be used when comparing different management regimes in long term analyses.     

Comparing the natural variation of oribatid mite communities with their changes associated with anthropogenic disturbance

Several organism communities serve as ecological and environmental indicators to detect changes in human-impacted habitats. However, the composition of indicator communities may vary because of natural variation in addition to the changes associated with human disturbances. This meta-analysis compared the natural variation of oribatid mite assemblages, a good indicator model group in soil ecosystems, with their deviations associated with disturbance using diversity and dissimilarity indices and three human disturbance types. Literature data were collected about oribatid mite assemblages from natural and disturbed habitats. Human disturbances consisted of agriculture, heavy metal pollution and forest management. Biodiversity indices (Shannon and Berger-Parker) and dissimilarity indices (Jaccard and Bray-Curtis) were calculated among natural habitats and between disturbed and control habitats at the species and genus level. We considered oribatid mite assemblages as effective community-level indicators when the methods separated the differences of assemblages between disturbed and control habitats from their varieties among natural habitats. In addition, the study analysed the correlation between these indices and environmental variables of the study sites. Oribatid mite assemblages performed high indication strength with Bray-Curtis index for agricultural disturbances since dissimilarity values between disturbed and control habitats were as high as between different natural habitats and higher than among the same types of natural habitats. Genus-level values showed similar results to the species level. This approach may be useful to test the effectiveness of other indicator groups and methods.     

A synthesis of variability in nearshore Alaskan marine populations

One of the primary goals of any monitoring program is to detect anthropogenic changes; however natural variability can be so high that it prevents detection of human-induced effects. This project synthesized existing data to identify patterns of temporal variation within nearshore marine habitats in the Gulf of Alaska and was motivated by the need for monitoring in this environment vulnerable to oil spills, such as the Exxon Valdez. I collected 786 time series that were greater than 2 years in length from unimpacted (control) sites for 226 species. Temporal variability (CV) varied widely from 1% to 447%, averaged 89%, and was not significantly different among marine populations of birds, mammals, fish, algae and invertebrates. Temporal variability of invertebrates and algae were not significantly different when abundance was measured as biomass, percent cover or density. Both invertebrates and algae showed higher variability in low intertidal habitat compared to high intertidal habitat. For invertebrates, populations on bedrock substrates had greater temporal variability than those on cobble or soft sediment substrates, while there was not a significant difference for algae on either cobble or bedrock substrates. Many of these results are surprising, given differences in movement patterns, survey methods, life histories, and so forth. These results suggest that no single group, measure or habitat will be better or worse for monitoring and detecting change. Understanding variability is a difficult task, but until we tackle it, we will likely not understand or have predictive capabilities in ecological populations and communities.     

Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

The Spatial-Scale Effect of an Atmospheric Environmental Impact Assessment in Regional Strategic Environmental Assessment (R-SEA)

When evaluating the atmospheric environment in regional strategic environment assessment (R-SEA), the variation and choice of the spatial scale have a substantial influence on the conclusions of the assessment. In this study, we used numerical simulation to investigate the spatial-scale effect. Two varying spatial extents and two varying spatial details of pollutant emission data (emission inventories in this case) were provided for numerical modeling, and output distributions of atmospheric pollutants at different air pollution levels were compared. The results show that the resolution and spatial range of data collection do indeed influence the atmospheric prediction and assessment results in R-SEA. The spatial-scale effect is more significant under the air pollution condition than under excellent and good air quality conditions. A comparison of varying spatial extents of emission inventory shows that narrowing the prediction area to a local scale is more conducive to identifying the impact of local pollution sources. A comparison of varying spatial details of emission inventory indicates that a higher resolution is favorable for identifying local high concentrations of pollutants and their locations.     

Statistical models for evaluating suspected artefacts in long-term environmental monitoring data

Long-term water quality monitoring is of high value for environmental management as well as for research. Artificial level shifts in time series due to method improvements, flaws in laboratory practices or changes in laboratory are a common limitation for analysis, which, however, are often ignored. Statistical estimation of such artefacts is complicated by the simultaneous existence of trends, seasonal variation and effects of other influencing factors, such as weather conditions. Here, we investigate the performance of generalised additive mixed models (GAMM) to simultaneously identify one or more artefacts associated with artificial level shifts, longitudinal effects related to temporal trends and seasonal variation, as well as to model the serial correlation structure of the data. In the same model, it is possible to estimate separate residual variances for different periods so as to identify if artefacts not only influence the mean level but also the dispersion of a series. Even with an appropriate statistical methodology, it is difficult to quantify artificial level shifts and make appropriate adjustments to the time series. The underlying temporal structure of the series is especially important. As long as there is no prominent underlying trend in the series, the shift estimates are rather stable and show less variation. If an artificial shift occurs during a slower downward or upward tendency, it is difficult to separate these two effects and shift estimates can be both biased and have large variation. In the case of a change in method or laboratory, we show that conducting the analyses with both methods in parallel strongly improves estimates of artefact effects on the time series, even if certain problems remain. Due to the difficulties of estimating artificial level shifts, posterior adjustment is problematic and can lead to time series that no longer can be used for trend analysis or other analysis based on the longitudinal structure of the series. Before carrying out a change in analytic method or laboratory, it should be considered if this is absolutely necessary. If changes cannot be avoided, the analysis of the two methods considered, or the two laboratories contracted, should be run in parallel for a considerable period of time so as to enable a good assessment of changes introduced to the data series.     

Monitoring of atmospheric nitrogen dioxide by long-path pulsed differential optical absorption spectroscopy using two different light paths

Measurements of the local distribution of atmospheric nitrogen dioxide (NO(2)) by long-path pulsed differential optical absorption spectroscopy (LP-PDOAS) in Tokyo during August 2008 are presented. Two LP-PDOAS systems simultaneously measured average NO(2) temporal mixing ratios along two different paths from a single observation point. Two flashing aviation obstruction lights, located 7.0 km north and 6.3 km east from the observation point, were used as light sources, allowing spatiotemporal variations of NO(2) in Tokyo to be inferred. The LP-PDOAS data were compared with ground-based data measured using chemiluminescence. Surface wind data indicated that large inhomogeneities were present in the spatial NO(2) distributions under southerly wind conditions, while northerly wind conditions displayed greater homogeneity between the two systems. The higher correlation in the NO(2) mixing ratio between the two LP-PDOAS systems was observed under northerly wind conditions with a correlation factor R(2) = 0.88. We demonstrated that the combined deployment of two LP-PDOAS systems oriented in different directions provides detailed information on the spatial distribution of NO(2).     

M12半自动净化法与手工净化法应用于飞灰中二噁英类检测比较

研究比较了M12半自动净化法与手工净化法应用于二噁英检测的效果。结果显示,采用高分辨气相色谱-高分辨质谱法(HRGC-HRMS)检测飞灰样品中的二噁英,手工净化法的回收率要优于M12半自动净化法的回收率,但M12半自动净化法除2种单体外,其余单体回收率均满足对二噁英的分析要求,且2种净化方法所得二噁英的总毒性当量结果相近,证明手工净化法与HRGC-HRMS联用检测飞灰中二噁英的效果较好,但M12半自动净化法也能够满足HRGC-HRMS检测二噁英的净化要求。应用酶联免疫法检测飞灰样品中二噁英的总毒性量,当样品中二噁英浓度较高时,M12半自动净化法和手工净化法净化测得结果与基准值一致性较好;当样品中二噁英浓度较低时,手工净化法检测结果与基准值差别较大,表明酶联免疫法更适合用M12半自动净化法进行净化。     

Using similarity measures in benthic impact assessments

Eleven similarity measures were used to assess the impacts of clam digging on the infauna of one intertidal mud-flat. When the similarity matrices were clustered using a divisive polythetic algorithm two different conclusions were possible from the dendograms produced. Dendograms generated by 3 coefficients indicated digging had an impact on areas dug more than once, while the 8 others did not. In addition, existing data from an impact analysis of a power plant effluent were re-analyzed using 5 different coefficients. In this case, three of the five coefficients gave results which indicated that the benthic community at the effluent pipe was different from that present at the control. Two coefficients gave the opposite conclusion — the populations were not different.These results indicate that the objectivity of cluster analysis, as it has come to be used in impact studies, is only apparent, not real. Too many subjective choices are made in selecting algorithms and methods of interpretation. Objective criteria need to be developed for the choices made, based on the intrinsic and ecological properties of the quantitative methods. Some criteria that have been developed are discussed, and used to determine which coefficients are best suited for the two data sets analyzed. To reduce the number of subjective choices made an analysis of variance of the similarity matrix is presented as an alternative to clustering.     

Behaviour of different lichen species as biomonitors of air pollution by PAHs in natural ecosystems

Six different species of lichens (Parmelia sulcata Tayl., Evernia prunastri (L.) Ach., Ramalina farinacea, Pseudevernia furfuracea (L.) Zopf., Usnea sp. and Lobaria pulmonaria (Schreb.) Hoffm.) were collected in two mountain valleys in Central Pyrenees: the Aspe and Aragon valleys. Two multivariate techniques have been applied with different purposes, ANOVA and Discriminant Analysis (DA), to evaluate the data. The PAHs spatial distribution was studied in the three more abundant and widespread species in the area: P. sulcata, E. prunastri (L.) Ach. and R. farinacea in terms of total PAHs, PAHs related to the combustion processes and toxicity. Different behaviour of each lichen species to trap PAHs was found, being P. sulcata the best one to monitor the most persistent PAHs of pyrogenic origin and E. prunastri the most appropriate to provide information about pyrogenic and petrogenic PAHs. Traffic was the most relevant influence in PAHs bioaccumulation in lichen species.