Vegetation and chemical plant and soil data from 18 terrestrial non-forested natural habitat types have been collected each year since 2004 (Table 1). Presently, cover data of plant species measured by pinpoint (16 grid points in 0.5 m x 0.5 m frame) and plant frequency data (5 m circles) exist for more than 50,000 plots; the measuring of chemical properties in plant, soil, and water samples has been less intense. Presently there are more than 12,000 registrations of carbon content and 57,000 measurements of pH in topsoil. Soils are always samples from the top 5 cm. On heathland habitats the thickness of the mor layer is measured. All data are georeferenced and can be accessed at http://www.naturdata.dk/ The data comprises approximately 1,200 sites both within and outside of Natura 2000 areas. At each site the measurements were made at 20-60 randomly positioned plots. 70% of the plots have been revisited each year, whereas 30 % have only been visited once. Each year a total number of additional 5,000 sample plots and registrations will be performed. The protocols for the different measurement types (in Danish) may be downloaded from http://www.dmu.dk/fileadmin/Attachments/TAN1_106_01_FDCNY1.pdf. This report describes the available content in the vegetation-plot database NATURDATA.DK (GIVD ID EU-DK-001).Keywords: moss; nitrogen; pin point; plant cover; plant frequency; soil analysis.
GIVD Database ID: EU-DK-001Last
In order to predict the exposure of hedgerows and other neighboring biotopes to pesticides from field-spray applications, an existing Gaussian atmospheric dispersion and deposition model was developed to model the changes in droplet size due to evaporation affecting the deposition velocity. The Gaussian tilting plume principle was applied inside the stayed track. The model was developed on one set of field experiments using a flat-fan nozzle and validated against another set of field experiments using an air-induction nozzle. The vertical spray-drift profile was measured using hair curlers at increasing distances. The vertical concentration profile downwind has a maximum just above the ground in our observations and calculations. The model accounts for the meteorological conditions, droplet ejection velocity and size spectrum. Model validation led to an R 2 value of 0.78, and 91% of the calculated drift values were within a factor of four of the measurements.
Image-based methods for species identification offer cost-efficient solutions for biomonitoring. This is particularly relevant for invertebrate studies, where bulk samples often represent insurmountable workloads for sorting, identifying, and counting individual specimens. On the other hand, image-based classification using deep learning tools have strict requirements for the amount of training data, which is often a limiting factor. Here, we examine how classification accuracy increases with the amount of training data using the BIODISCOVER imaging system constructed for image-based classification and biomass estimation of invertebrate specimens. We use a balanced dataset of 60 specimens of each of 16 taxa of freshwater macroinvertebrates to systematically quantify how classification performance of a convolutional neural network (CNN) increases for individual taxa and the overall community as the number of specimens used for training is increased. We show a striking 99.2% classification accuracy when the CNN (EfficientNet-B6) is trained on 50 specimens of each taxon, and also how the lower classification accuracy of models trained on less data is particularly evident for morphologically similar species placed within the same taxonomic order. Even with as little as 15 specimens used for training, classification accuracy reached 97%. Our results add to a recent body of literature showing the huge potential of image-based methods and deep learning for specimen-based research, and furthermore offers a perspective to future automatized approaches for deriving ecological data from bulk arthropod samples.
A new species has recently invaded coastal dune ecosystems in North West Europe. The native and expansive inland grass, Deschampsia flexuosa, progressively dominating inland heaths, has recently invaded coastal dunes in Denmark, occasionally even as a dominant species. A total of 222 coastal locations with 5,000 random sample plots have been investigated. These findings are in contrast to historical records, and D. flexuosa has never been considered belonging to coastal dune ecosystems. The occurrence of the typical inland grass in the coastal dunes is a strong indication of increase in nutrient level and that human influences may cause a native species to be invasive in new ecosystems. This could be a radical example of change in species composition due to a long lasting exceedance of critical load of nitrogen. The investigation also showed a general increase in cover of the most dominant species.
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