Low Taxonomic Resolution Research Articles

Population assessments of soil fauna: General criteria for the planning of sampling schemes

This investigation aims at providing quantitative criteria for the planning of sampling schemes for the field census of soil invertebrates. In total, 400 data sets from the literature comprising field counts of 120 species of soil macro- and mesofauna from 10 000 samples are analyzed with respect to spatial variance. The data exhibited a consistent dependence of variance on the mean and on the degree of taxonomic resolution. Analyses of field data and simulations were combined to quantify (1) the probability of recording rare species, (2) the confidence limits for estimates of population density, and (3) the ability to discriminate between density estimates from different sites. Nomograms and equations are provided for practical use. Population data with low taxonomic resolution, i.e. data based on functional or taxonomic groups, proved to exhibit a considerably higher statistical quality than data on single species. The consequences for a biological characterization of soils are briefly discussed.

Some Cases of Buttercup Poisoning in Cattle

Networks of sensors could be used to evolve biomonitoring for environmental and biodiversity conservation subsidies in agriculture away from the ‘obligation of means’, where farmers receive financial support for those practices that have been put in place, to one of an ‘obligation of results’ where subsidies are paid when the wanted environmental benefits of a practice are observed. The sensor technologies that have been developed to date fall into a relatively narrow set of classes, based upon optical, sound and radio-magnetic waves, and these have predominantly used active technologies that emit and receive signals. We believe that passive sensors, which only receive signals for display and analysis, and that detect across a larger set of signal classes would have clear benefits of simplicity, robustness and cost for biomonitoring in agricultural landscapes. The use of passive sensors would allow non-scientist actors, such as farmers and citizens living in agricultural landscapes, to conduct monitoring of biodiversity and for pest and disease decision-making, promoting inclusion in the process. Feedback from citizens to farmers, where the monitoring shows a positive effect of the practices adopted, would also work positively, increasing farmer well-being and further improving uptake of biomonitoring. The downsides of passive sensors, which include poor signal quality and low taxonomic resolution, could in part be overcome by a combination of high uptake, by ensuring that the sensors are employed at high replication and have broad coverage of agricultural landscape habitat types, and by developing back-office computing systems that can appropriately process, analyse and curate the data collected. These systems would facilitate a joined-up approach to agricultural landscapes because they could both combine biomonitoring across different agricultural habitats and provide metrics that could be linked to the activities of individual farmers. Such metrics could include derived variables, formed from measurements from multiple sensor types and remote-sensing, such as Essential Biodiversity Variables that are being developed for global biomonitoring of the environment, biodiversity and ecosystem services. This would bring together the biomonitoring of agricultural landscapes and larger, global-scale biomonitoring initiatives.