Without robust and unbiased systems for monitoring, changes in natural systems will remain enigmatic for policy makers, leaving them without a clear idea of the consequences of any environmental policies they might adopt. Generally, biodiversity-monitoring activities are not integrated or evaluated across any large geographic region. The EuMon project conducted the first large-scale evaluation of monitoring practices in Europe through an on-line questionnaire and is reporting on the results of this survey. In September 2007 the EuMon project had documented 395 monitoring schemes for species, which represents a total annual cost of about 4 million euro, involving more than 46,000 persons devoting over 148,000 person-days/year to biodiversity-monitoring activities. Here we focused on the analysis of variations of monitoring practices across a set of taxonomic groups (birds, amphibians and reptiles, mammals, butterflies, plants, and other insects) and across 5 European countries (France, Germany, Hungary, Lithuania, and Poland). Our results suggest that the overall sampling effort of a scheme is linked with the proportion of volunteers involved in that scheme. Because precision is a function of the number of monitored sites and the number of sites is maximized by volunteer involvement, our results do not support the common belief that volunteer-based schemes are too noisy to be informative. Just the opposite, we believe volunteer-based schemes provide relatively reliable data, with state-of-the-art survey designs or data-analysis methods, and consequently can yield unbiased results. Quality of data collected by volunteers is more likely determined by survey design, analytical methodology, and communication skills within the schemes rather than by volunteer involvement per se.

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Monitoring of biodiversity at the level of habitats is becoming increasingly common. Here we describe current practices in habitat monitoring based on 150 schemes in Europe. Most schemes were initiated after 1990 in response to EU nature directives or habitat management/restoration actions, with funding mostly from European or national sources. Schemes usually monitor both the spatial distribution and the quality of the habitats, and they frequently collect data on environmental parameters and potential causes

The monitoring of biodiversity at the level of habitats is becoming widespread in Europe and elsewhere as countries establish national habitat monitoring systems and various organisations initiate regional and local schemes. Parallel to this growth, it is increasingly important to address biodiversity changes on large spatial (e.g. continental) and temporal (e.g. decade-long) scales, which requires the integration of currently ongoing monitoring efforts. Here we review habitat monitoring and develop a framework for integrating data or activities across habitat monitoring schemes. We first identify three basic properties of monitoring activities: spatial aspect (explicitly spatial vs. non-spatial), documentation of spatial variation (field mapping vs. remote sensing) and coverage of habitats (all habitats or specific habitats in an area), and six classes of monitoring schemes based on these properties. Then we explore tasks essential for integrating schemes both within and across the major classes. Finally, we evaluate the need and potential for integration of currently existing schemes by drawing on data collected on European habitat monitoring in the EuMon project. Our results suggest a dire need for integration if we are to measure biodiversity changes across large spatial and temporal scales regarding the 2010 target and beyond. We also make recommendations for an integrated pan-European habitat monitoring scheme. Such a scheme should be based on remote sensing to record changes in land cover and habitat types over large scales, with complementary field mapping using unified methodology to provide ground truthing and to monitor small-scale changes, at least in habitat types of conservation importance.

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A constant and controlled level of emission of carbon and other gases into the atmosphere is a pre-condition for preventing global warming and an essential issue for a sustainable world. Fires in the natural environment are phenomena that extensively increase the level of greenhouse emissions and disturb the normal functioning of natural ecosystems. Therefore, estimating the risk of fire outbreaks and fire prevention are the first steps in reducing the damage caused by fire. In this study, we build predictive models to estimate the risk of fire outbreaks in Slovenia, using data from a GIS, Remote Sensing imagery and the weather prediction model ALADIN.The study is carried out on three datasets, from three regions: one for the Kras region, one for the coastal region and one for continental Slovenia. On these datasets, we apply both classical statistical approaches and state-of-the-art data mining algorithms, such as ensembles of decision trees, in order to obtain predictive models of fire outbreaks.Responsible editor: Katharina Morik, Kanishka Bhaduri and Hillol Kargupta. This paper has its origins in a project report ) and a short conference paper (Stojanova et al. 2006) that introduced the problem of forest fire prediction in Slovenia, using GIS, RS and meteorological data. However, this paper significantly extends and upgrades the work presented there. In particular: We consider a wider set of data mining techniques, from single classifiers to ensembles; We present a comparison of the predictive performance in terms of several frequently used evaluation measures for classification; We present an example of the results obtained from the modeling task in the form of decision rules, explain and interpret their meaning; We generate geographical maps and compare them with other fire prediction models (e.g., FWI fire risk danger maps) provided by other services.

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