The EUNIS (European Union Nature Information System) habitat classification system aims to provide a common European reference set of habitat types within a hierarchical classification, and to cover all terrestrial, freshwater and marine habitats of Europe. The classification facilitates reporting of habitat data in a comparable manner, for use in nature conservation (e.g. inventories, monitoring and assessments), habitat mapping and environmental management. For the marine environment the importance of a univocal habitat classification system is confirmed by the fact that many European initiatives, aimed at marine mapping, assessment and reporting, are increasingly using EUNIS habitat categories and respective codes. For this reason substantial efforts have been made to include information on marine benthic habitats from different regions, aiming to provide a comprehensive geographical coverage of European seas. However, there still remain many concerns on its applicability as only a small fraction of Europe's seas are fully mapped and increasing knowledge and application raise further issues to be resolved. This paper presents an overview of the main discussion and conclusions of a workshop, organised by the MeshAtlantic project, focusing upon the experience in using the EUNIS habitats classification across different countries and seas, together with case studies. The aims of the meeting were to: (i) bring together scientists with experience in the use of the EUNIS marine classification and representatives from the European Environment Agency (EEA); (ii) agree on enhancements to EUNIS that ensure an improved representation of the European marine habitats; and (iii) establish practices that make marine habitat maps produced by scientists more consistent with the needs of managers and decision-makers. During the workshop challenges for the future development of EUNIS were identified, which have been classified into five categories: (1) structure and hierarchy; (2) biology; (3) terminology; (4) mapping; and (5) future development. The workshop ended with a declaration from the attendees, with recommendations to the EEA and European Topic Centre on Biological Diversity, to take into account the outputs of the workshop, which identify weaknesses in the current classification and include proposals for its modification, and to devise a process to further develop the marine component of the EUNIS habitat classification.
International audienceAimThe species–area relationship (SAR) is increasingly being used to set conservation targets for habitat types when designing protected area networks. This approach is transparent and scientifically defensible, but there has been little research on how it is affected by data quality and quantity.LocationEnglish Channel.MethodsWe used a macrobenthic dataset containing 1314 sampling points and assigned each point to its associated habitat type. We then used the SAR-based approach and tested whether this was influenced by changes in (i) the number of sampling points used to generate estimates of total species richness for each habitat type; (ii) the nonparametric estimator used to calculate species richness; and (iii) the level of habitat classification employed. We then compared our results with targets from a similar national-level study that is currently being used to identify Marine Conservation Zones in the UK.ResultsWe found that targets were affected by all of the tested factors. Sample size had the greatest impact, with specific habitat targets increasing by up to 45% when sample size increased from 50 to 300. We also found that results based on the Bootstrap estimator of species richness, which is the most widely used for setting targets, were more influenced by sample size than the other tested estimators. Finally, we found that targets were higher when using broader habitat classification levels or a larger study region. However, this could also be a sample size effect because these larger habitat areas generally contained more sampling points.Main conclusionsHabitat targets based on the SAR can be strongly influenced by sample size, choice of richness estimator and the level of habitat classification. Whilst setting habitat targets using best-available data should play a key role in conservation planning, further research is needed to develop methods that better account for sampling effort
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