Use of native mosses as biomonitors of heavy metals and nitrogen deposition in the surroundings of two steel works

A biomonitoring survey using the moss species Hypnum cupressiforme Hedw. was conducted in the surroundings of two steel plants located in the North of Spain. Levels of V, Cr, Ni, Cu, Zn, As, Cd, Hg, Pb and N were determined. Very high concentrations in the areas of study were detected when compared to nearby unaffected regions. Similar trends were observed for all the elements in the differently orientated transects, showing an appreciable influence of the NW prevailing winds of the region in the dispersion of pollutants, as well as a clear decreasing gradient in the concentrations of metals in mosses within a distance of 1500 meters from the facilities. A differentiation between the elements emitted by the chimney as result of the industrial activity (V, Cr, Ni, Cu and As) and those with a high presence in steel slag deposits (Zn, Cd, Hg and Pb) was observed. The range of contamination was also established by means of the Contamination Factor, indicating a category 4 out of 6 categories, which shows the high levels reported in the areas of study. A different dynamic was registered for nitrogen regarding the rest of the heavy metals analysed except for Hg, probably due to the elevated volatility and mobility of both elements, as well as their high persistence in the atmosphere.     

Use of Descriptors of Ecosystem Functioning for Monitoring a National Park Network: A Remote Sensing Approach

Baseline assessments and monitoring of protected areas are essential for making management decisions, evaluating the effectiveness of management practices, and tracking the effects of global changes. For these purposes, the analysis of functional attributes of ecosystems (i.e., different aspects of the exchange of matter and energy) has advantages over the traditional use of structural attributes, like a quicker response to disturbances and the fact that they are easily monitored through remote sensing. In this study, we described the spatiotemporal patterns of different aspects of the ecosystem functioning of the Spanish national parks and their response to environmental changes between 1982 and 2006. To do so, we used the NOAA/AVHRR-GIMMS dataset of the Normalized Difference Vegetation Index (NDVI), a linear estimator of the fraction of photosynthetic active radiation intercepted by vegetation, which is the main control of carbon gains. Nearly all parks have significantly changed during the last 25 years: The radiation interception has increased, the contrast between the growing and nongrowing seasons has diminished, and the dates of maximum and minimum interception have advanced. Some parks concentrated more changes than others and the degree of change varied depending on their different environmental conditions, management, and conservation histories. Our approach identified reference conditions and temporal changes for different aspects of ecosystem functioning, which can be used for management purposes of protected areas in response to global changes.     

The development and application of remote sensing to monitor sand dune habitats

Sand dunes are complex systems that contain several habitats, often as mosaics or transitions between types. Several of these habitats are afforded protection under European Legislation and in the UK nationally within Special Areas of Conservation (SAC) and Sites of Special Scientific Interest (SSSI). Natural England has a statutory duty to report to Europe on the conservation status and condition of sand dunes; and is required to report to the UK Government on designated sites. To achieve this we have sought ways of capturing, analysing and interpreting data on the extent and location of sand dune habitats. This requires an ability to be able to obtain data over large areas of coastline in an efficient way. Natural England and Environment Agency Geomatics have worked collaboratively for over 16 years, sharing data and ecological knowledge. In 2012 work started to evaluate the use of remote sensing to map UK BAP and Annex I sand dune habitats. A methodology has now been developed and tested to map sand dune habitats. The key objective was to provide an operational tool that will help to map these habitats and understand change on sites around England. This has been achieved through analysis of LIDAR and Compact Airborne Spectrographic Imager (CASI) data using Object Orientated Image Analysis. Quality Control (QC) and accuracy assessments have shown this approach to be successful and 11 sites have been mapped to date. These techniques are providing a new approach to monitoring change in coastal vegetation communities and informing management of protected sites.     

How to evaluate models: Observed vs. predicted or predicted vs. observed?

A common and simple approach to evaluate models is to regress predicted vs. observed values (or vice versa) and compare slope and intercept parameters against the 1:1 line. However, based on a review of the literature it seems to be no consensus on which variable (predicted or observed) should be placed in each axis. Although some researchers think that it is identical, probably because r² is the same for both regressions, the intercept and the slope of each regression differ and, in turn, may change the result of the model evaluation. We present mathematical evidence showing that the regression of predicted (in the y-axis) vs. observed data (in the x-axis) (PO) to evaluate models is incorrect and should lead to an erroneous estimate of the slope and intercept. In other words, a spurious effect is added to the regression parameters when regressing PO values and comparing them against the 1:1 line. Observed (in the y-axis) vs. predicted (in the x-axis) (OP) regressions should be used instead. We also show in an example from the literature that both approaches produce significantly different results that may change the conclusions of the model evaluation.