The national responsibility approach to setting conservation priorities—Recommendations for its use

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The national responsibility approach to setting conservation priorities—Recommendations for its use Dirk S. Schmeller a,d,e,∗ , Douglas Evans b , Yu-Pin Lin c,1 , Klaus Henle a a

UFZ – Helmholtz Centre for Environmental Research, Department of Conservation Biology, Permoserstr. 15, 04318 Leipzig, Germany European Topic Centre on Biological Diversity, 57 rue Cuvier, 75231 Paris cedex 05, France Department of Bioenvironmental Systems Engineering, National Taiwan University, Taiwan d Université de Toulouse, UPS, INPT, EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), 118 route de Narbonne, 31062 Toulouse, France e CNRS, EcoLab, 31062 Toulouse, France b c

a r t i c l e

i n f o

Article history: Received 21 August 2013 Received in revised form 21 February 2014 Accepted 7 March 2014 Keywords: Policy support Biodiversity monitoring Prioritization Resource allocations Conservation efficiency

a b s t r a c t The implementation of conservation strategies for species and habitats is frequently hampered by the availability of the necessary resources. These should be prioritized and focused on those species and habitats most in need, but also in regard to the importance of their distribution in a certain region, country or other administrative unit. In that perspective, the concept of national responsibilities (NR) is a recently developed tool to support priority setting. It captures the impact of the loss of a particular species or habitat within the focal region (usually a country) may have on the global persistence of that species or habitat type. Although the method consists of a few simple steps and is not very demanding in regard to data availability per species and habitat type, it is still impossible to determine NRs for all species and habitats. Here, we focus on the difficulties in determining NRs due to missing distribution data, varying interpretations of definitions especially in respect to habitat types, and differences in data formats and maps using European examples of these data limitations and sources of bias. These include artificially enlarged distribution areas resulting from grid cells being reported more than once, gridded shapefiles stretching into the sea or into other biogeographic regions, and differences in the size and the shape of grid cells and hence the resolution of maps. While focusing on European examples, these sources of bias are also relevant for conservation efforts on a global scale. Our analysis stresses the importance of quickly improving data quality, availability and comparability to render conservation more efficient. We give policy relevant examples on how the NR approach can be applied, e.g. how to help attributing budgets to poorer countries, on which species and habitats to focus limited monitoring resources, and how to consider newly emerging diseases. Generally, our analyses suggests (i) to develop clear global data standards, (ii) to regularly assess data to keep up with advances in data handling, and (iii) to use downscaling approaches for biodiversity data to a higher resolution for reducing the impact of bias to a negligible level together with improving the overall quality of distribution data for conservation purposes. © 2014 Elsevier GmbH. All rights reserved.

Introduction Despite numerous legal commitments, resources for conservation remain scarce and require a prioritization of conservation efforts. A variety of approaches to setting priorities have been developed for both species and habitats (reviewed in Schmeller,

∗ Corresponding author at: UFZ – Helmholtz Centre for Environmental Research, Department of Conservation Biology, Permoserstr. 15, 04318 Leipzig, Germany. Tel.: +49 1626769238. E-mail address: [email protected] (D.S. Schmeller). 1 Address: No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan.

Gruber, Budrys, et al. 2008). So far, priority areas of conservation importance have been defined using the concept of biological hotspots for large biomes (Mittermeier, Myers, Thomsen, Da Fonseca, & Olivieri 1998). At finer geographical scales, red lists are the most commonly used tool for conservation assessment. The resulting threat status is often taken as a measurement for conservation priorities. However, red lists may, at best, be a suboptimal tool for setting conservation priorities in a country or region as the threat status does not always reflect actual conservation needs (Eaton et al. 2005; Gärdenfors 2000, 2001; Mehlman, Rosenberg, Wells, & Robertson 2004), especially if the entire distributions and threats to species and habitats are considered. In addition, the majority of red lists target species although a limited number of

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red lists have been published for plant communities, habitat types and ecosystems (e.g. Rodwell, Janssen, Gubbay, & Schaminée 2013) particularly in Europe, where 19 countries have now developed non-species based red lists (e.g. Molnár et al. 2007; Riecken 2006; Traxler et al. 2005). These lists use a variety of habitat typologies and assessment criteria and do not follow the recently published IUCN criteria for red listing of ecosystems (Keith et al. 2013), which still lacks an agreed typology. Further, from a subsidiary pointof-view it is more desirable to focus national conservation efforts on species and habitat types located in the respective country or administrative unit. The national responsibility approach captures that need and could help decision makers to use limited conservation resources more effectively and complements the concept of red lists (Schmeller, Gruber, Bauch, et al. 2008, see examples below). The concept of national responsibility is based on the fact that different parts of the range of a species or habitat make different contributions to its overall viability and persistence. For example, areas where a particular species is abundant are usually small and rare, with the result that some parts of the distribution range of a species are more important than others for the global conservation of a species. National responsibility encompasses the notion of the importance of a region for the conservation of biodiversity in respect to its irreplaceability (Brooks et al. 2006). National responsibility serves as a proxy for measuring the probability of global persistence, when the distribution area in the focal area (e.g. nation or region) is lost. Therefore, determination of national responsibility helps to emphasize international conservation obligations that may not be obvious at a local level or by only using national red lists. That also includes identifying gaps in distribution data, the need to standardize data and to develop conservation strategies. The determination of national responsibilities and conservation priorities, the latter yielded by combining national responsibilities and red list status (Schmeller & Henle 2008), could increase the efficiency of the decision making process. Several countries have developed methods to assess national responsibility for the conservation of species but they are not comparable across countries (reviewed in Schmeller, Gruber, Budrys, et al. 2008). Although the national responsibility approach (Schmeller, Gruber, Bauch, et al. 2008; Schmeller, Gruber, Budrys, et al. 2008; Schmeller and Henle 2008; Schnittler, Ludwig, Pretscher, & Boye 1994) only requires a few steps and is not very data demanding, it is still impossible to determine national responsibilities for all species and habitats, neither in Europe (the main focus of this study) nor on other continents, where data gaps may even be more important (Skidmore, Williams, Walters, & Costello 2013). Here, we focus on the difficulties to determine national responsibilities in Europe due to missing distribution data for a large number of species and habitat types, varying interpretations of definitions, especially in respect to habitat types, and differences in data formats and map resolutions. We highlight some basic data shortcomings and aim to assist current efforts to construct a European (Hoffmann et al. 2014) and a global biodiversity observation network (Scholes et al. 2012). We will also show several examples on how to employ the national responsibility approach with respect to different conservation issues to assist decision makers, stakeholders in biodiversity monitoring, and policy makers.

The National responsibility method The method to determine national responsibilities comprises three decision steps. Firstly, the assessment unit (species, subspecies, habitat type, ecosystem) is defined based on the underlying concepts and definitions chosen by the user (see Schmeller, Gruber, Bauch, et al. 2008); secondly, the current distribution pattern of a species or habitat is determined, meaning its range within and

across biogeographic and environmental regions as an approximation of its adaptability to different environmental conditions (Schmeller, Gruber, Bauch, et al. 2008; Schmeller, Maier, Bauch, Evans, & Henle 2012). The third step determines the importance of the distribution of the defined assessment unit within a focal area as compared to the total distribution in a reference area, determining the expected and observed distribution. This step allows an adaptation to different geographic scales (see Schmeller, Gruber, Bauch, et al. 2008; Schmeller, Maier, et al. 2012). The distribution pattern and the expected value of occurrence together reflect the importance of a focal area for the global persistence of the defined assessment unit (Schmeller, Gruber, Bauch, et al. 2008; Schmeller, Maier, et al. 2012). Several difficulties occur at each of these steps, which we will show and discuss in detail below (Fig. 1) after showing some examples of the use of the national responsibility approach. Examples of the use of the national responsibility approach The national responsibility approach has the potential to be widely applied to a wide range of conservation related issues. It could be used on a global level to determine where additional capacities are required to adequately monitor biodiversity. With a global list of national responsibilities two (non-exclusive) types of countries can be identified: countries with many species and habitats of high to very high national responsibilities, and countries with a high number of data deficient species. Such a global list could be used by the United Nations Environment Programme (UNEP) or the intergovernmental platform for biodiversity and ecosystem services (IPBES) to determine needs for capacity building in biodiversity monitoring and could also serve the Group of Earth Observations Biodiversity Observation Network (GEO BON) to focus efforts of capacity building on regions with the most important needs. Where these countries do not have sufficient resources, additional budgets from international aid could be allocated to these countries so that national monitoring programs can be set up. The national responsibility approach may also be useful to direct conservation and monitoring efforts in regard to diseases and pathogens. We have seen an increasing number of virulent infectious diseases in natural populations and managed landscapes over the last two decades. Severe die-offs and even species extinctions have been observed in the wild (Walker et al. 2010). To use the national responsibility approach in regard to conservation actions linked to emerging pathogens, decision makers could determine which of their very high to high responsibility species and habitats are impacted by an emerging pathogen. Those species and habitats should then be monitored closely and population survival probabilities determined. In case of unspecific infections of a pathogen potentially able to infect a whole group (Fisher, Henk, et al. 2012; Fisher, Schmidt, et al. 2012), as e.g. the amphibian pathogen Batrachochytrium dendrobatidis (Fisher, Garner, & Walker 2009) or the white-nose syndrome of bats (Blehert et al. 2009), conservation action plans for those species in the species group with very high to high responsibilities should be developed to understand and mitigate potential adverse effects. The national responsibility approach may also be used e.g. in the determination of monitoring needs to analyze the impact of genetically modified crops. The area for commercial cultivation of genetically modified organisms (GMO) is ever increasing and has raised controversial debates on the potential adverse effects (Nickson & Head 1999; Schmeller and Henle 2008; Sharpe 1999). Public acceptance is low and effects of genetically modified crops on biodiversity cannot be excluded (Myhr & Traavik 2002). The national responsibility approach can be applied when

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Fig. 1. The assessment steps of national responsibilities and sources of bias.

selecting which species and habitats to monitor. Firstly, a decision maker would determine the national responsibility for habitats/ecosystems and species for the country in which genetically modified crops will be introduced. Secondly, one would check, if any of the very high, high or data deficient national responsibility categories apply to species and habitats in whose distribution area the genetically modified crops will be introduced. Monitoring efforts would then be directed to these species and habitats to analyze potential adverse effects (Schmeller and Henle 2008). The national responsibility approach may further be used to evaluate the effectiveness of current conservation networks in protecting species of high to very high national responsibility. While such an evaluation has been done based on the red list status of species for the European Natura 2000 network of protected habitats (Trochet & Schmeller 2013), using red listed species and habitats will miss issues of data availability in a focal country and on species and habitats of community interest, which are not red listed. To use the national responsibility approach to improve existing conservation networks, one would use the distribution maps of high to very high responsibility species and habitats in software for reserve site selection such as Zonation (Moilanen 2007) or Marxan (Beger et al. 2010). These tools will create maps of sites with high conservation value based on the species and habitat distribution areas inputted. The result of the software could then be compared to the existing conservation network to define important spatial conservation gaps in the current conservation networks. Thematic resolution of data - Classifications, definitions and distribution data While the definition of species is scientifically well accepted, despite numerous species concepts and differences regarding the status of particular taxa, there are several different habitat or ecosystem classifications in use (e.g. HELCOM habitat classification, Ramsar Classification System for Wetland Types; CORINE: Coordinated Information on the Environment). However, in order to determine national responsibilities for all habitat groups, a unified classification including all habitat types occurring within the focal geographic area is required. International classifications, which are not restricted to specific habitat groups and covering a wider geographic range, have been published for Europe and the Palearctic, but do not exist for a global scale. The CORINE programme (Devillers, Devillers-Terschuren, & Ledant 1991; Moss & Wyatt 1994) has produced classifications of land cover and of biotopes. CORINE Land Cover uses remote sensing to regularly produce land cover maps (1990, 2000 and 2006) using a typology with 44 units (CEC 1994), whilst the CORINE biotopes classification distinguishes habitats at a finer scale (Devillers et al. 1991; Moss & Wyatt 1994). The classification used for CORINE Land Cover is not sufficiently detailed to be of use for assessing

national responsibilities. For example, only three types of natural forests are distinguished in comparison to over 800 in CORINE biotopes. The CORINE biotopes classification is sufficiently detailed but at this fine thematic resolution there is limited corresponding data on distribution of the habitat types at a European scale, although good data is available for some regions (e.g. Catalonia). A wide variety of habitat classifications are currently in use around the world including national, regional and international systems but so far no single classification has established itself as a global standard. Within Europe, different national classifications are used (e.g. Germany; Riecken 2006; Hungary; Fekete, Molnar, & Horvath 1997; see also below), mainly due to differences in the floristic (phytosociological) classifications of vegetation on ´ & Ewald which they are based (Becking 1957; Dengler, Chytry, 2008; Jennings, Faber-Langendoen, Loucks, Peet, & Roberts 2009; Tichy´ 2002). Even within a single classification, habitats have both an inherent variability and variation in how they are interpreted from country to country, and sometimes between regions in the same country (Evans 2010). Habitat definitions also differ among non-EU European countries (European Environment Agency (EEA) 2006; Hall, Krausman, & Morrison 1997) and among international organizations such as the United Nations Food and Agriculture Organization (FAO), the Convention on Biological Diversity (CBD), and the United Nations Framework Convention on Climate Change (UNFCCC) (Schoene, Killmann, von Lüpke, & Loyche 2007). For forests, for example, height, tree density, area, and species composition play a major role. In the German biotope classification, which was used to compile the red list of German habitats (Riecken 2006), deciduous forests was divided in several sub-classes according to local edaphic characteristics, water dependency, site elevation, or species composition. Hungary (Fekete et al. 1997; Molnár et al. 2007), and Austria (Essl, Egger, Ellmauer, & Aigner 2002; Traxler et al. 2005) used yet different habitat classifications. Habitat surveys using national habitat classifications, hence, provide distribution information which is coherent only within national boundaries or even within smaller administrative units (Winter & Seif 2011). To overcome these problems, the European Nature Information System (EUNIS; eunis.eea.europa.eu/; Davies, Moss, & Hill 2004) aims to provide a comprehensive classification for European habitats, including a framework of descriptions using habitat parameters and presents information on habitats including descriptions and cross-references with other classification systems. EUNIS gives some distribution data based on protected sites from which the habitat has been reported, but remains insufficient to determine national responsibility for most habitats. As EUNIS can be linked to syntaxa, it should be possible to use vegetation databases such as the recently created European Vegetation Archive (http://euroveg.org/eva-database) to give distribution data for EUNIS habitats, and a study to assess the feasibility of this approach is underway.

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Annexes I and II of the EU Habitats Directive lists habitats and species for which Sites of Community Interest (SCI) must be designated as part of the NATURA 2000 network (Evans 2012). There is also an obligation under Articles 11 and 17 of the Directive for Member States to monitor and report on the conservation status of these species and habitats using an agreed format, which includes distribution maps. Although Annex I is not a habitat classification (the habitats come from several classifications, see Bunce et al. 2010; Evans 2006, 2010), it does include a wide range of European natural and semi-natural habitats. Reports from the 2nd Article 17 reporting period (2001–2006) of European Member States are available (ETC/BD EIONET webtool http://bd.eionet.europa.eu/article17/habitatsprogress; EEA 2009), including assessments of the conservation status of habitats and their distribution area. The national distribution maps are in a variety of formats, but a harmonized set of distribution maps based on a 10 km × 10 km grid has been produced by the ETC/BD. At present the Article 17 database represents the most extensive dataset for habitat distribution and conservation status in the European Union. Although there are many problems associated with the interpretation of the habitats listed in Annex I (Evans 2010, 2012), which can result in unevenness between Member States, basic distribution data gathered under the same framework across the EU25 is available. As such, the lack of an accepted international habitat classification introduces enormous difficulties to create a global biodiversity observation system, as the monitoring aims will be difficult to define. Such a lack then introduces biases and incompatibilities in the datasets and limits the application of international approaches, such as the national responsibility approach. For species, the issue is less important, as distribution data are available for red-listed species or species of community interest, either in atlases or from databases, such as the Global Biodiversity Information Facility (GBIF, http://www.gbif.org/) is available due to scientifically accepted species concepts. Biogeographic concepts In the national responsibility approach, the distribution pattern of the assessment unit is determined using a biogeographic map to determine how widely the assessment unit is distributed across different biogeographic regions. That way, the distribution pattern may serve as an approximation of the ability of a habitat or species to cope with a variety of environmental conditions and threat factors (e.g. McIntyre & Wiens 1999; Wiens, Schooley, & Weeks 1997). There are currently a range of biogeographic maps available, including the Indicative European Map of Biogeographical Regions (ETC/BD 2006; EEA 2006), and global maps, including Udvardy’s biogeographic provinces (Udvardy 1975), the World Wide Fund for Nature’s (WWF) ecoregions (Olson et al. 2001), and the environmental zones of Metzger and colleagues (Metzger, Brus, et al. 2013; Metzger, Bunce, et al. 2013). All of these biogeographic maps can be used with the national responsibility approach, but the most objective is the one published by Metzger and colleagues (Metzger, Brus, et al. 2013; Metzger, Bunce, et al. 2013) and we recommend its use for the national responsibility approach (Schmeller, Maier, et al. 2012). Spatial resolution of data For defining the international importance of an assessment unit’s distribution area in a focal area, the national responsibility method determines the expected value of occurrence (OVexp ) in the focal area (e.g. a country). The expected distribution probability (DPexp ) is then compared to the observed distribution probability (DPobs ), following the suggestion of Keller and Bollmann (Keller

Table 1 Area per biogeographic region and country from the gridded distribution for the habitat type 9160 Sub-Atlantic and medio-European oak or oak-hornbeam forests of the Carpinion betuli. Country codes are: AT, Austria; BE, Belgium; DE, Germany; DK, Denmark; ES, Spain; FR, France; GB, Great Britain; IT, Italy; LT, Lithuania; LU, Luxembourg; LV, Latvia; NL, Netherlands; PL, Poland; PT, Portugal; SE, Sweden. Country

Biogeographic region

Reported grid cell area (km2 )

AT AT BE BE DE DE DK DK ES ES ES ES FR FR GB IT IT IT LT LU LV NL PL PT PT SE SE

Alpine Continental Atlantic Continental Atlantic Continental Atlantic Continental Alpine Atlantic Marine Atlantic Mediterranean Atlantic Continental Atlantic Alpine Continental Mediterranean Boreal Continental Boreal Atlantic Continental Atlantic Mediterranean Boreal Continental

4327 12,924 13,300 15,300 54,340 153,697 1000 22,600 700 5300 400 800 15,100 119,000 13,400 1300 4000 500 21,600 2400 73,900 7300 37,000 1000 700 56,300 14,300

Total sum of reported grid cells

652,488

& Bollmann 2001, 2004). DPexp is the ratio of the total distribution area of the assessment unit to the size of the reference area, while DPobs is the ratio of the distribution range of the assessment unit in a focal region (country) to the total size of the focal region. If the latter value is more than double the expected distribution probability, the expected value of occurrence of a habitat in the focal area is high, whereas below it is classified as being low (DPobs > 2 * DPexp ⇒ OVexp = High; DPobs < 2 * DPexp ⇒ OVexp = Low). For this third step, distribution data should be as detailed as possible and comparable between different countries. Several sources of bias need to be considered when applying the national responsibility approach, including different data formats, and the spatial resolution of data. Some of these biases include artificially enlarged total distribution areas due to grid cells being counted more than once. Especially at national borders and borders of biogeographic regions, grid cells can overlap (Fig. 2 and Table 1). Not correcting for the artificial enlargement of distribution areas due to double reporting of grid cells will increase the total area of distribution and will lead to an underestimation of national responsibilities. A bias may also result from the differences in the size and the shape of grid cells and hence the resolution of maps (Figs. 3 and 4 and Table 2). Such a bias can be seen as unnaturally smooth borders for some habitat distributions reported under Article 17 by France, Hungary, and the Spanish Mediterranean Islands, differences in grid cell sizes in Finland, and in reporting the presence or absence of a habitat in Latvia (Fig. 3). If a species, ecosystem or habitat is reported covering large areas within a country even though the actual distribution area is smaller, the determined national responsibility will be higher than appropriate. Furthermore if any country overestimates the distribution area, other countries, which in reality hold large parts of the distribution area, will be attributed national responsibilities which are too low. The other way round,

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Fig. 2. Distribution of habitat 9160 Sub-Atlantic and medio-European oak or oak-hornbeam forests of the Carpinion betuli in Germany. The distribution shows double counting of grid cells falling into the Atlantic bioregion (blue) and the Continental bioregion (green), as well as cells partly covering ocean and hence unsuitable area along the Baltic coastline. The total sum of the reported grid cell area of habitat ‘9160 Sub-Atlantic and medio-European oak or oak-hornbeam forests of the Carpinion betuli’ of Annex I is 652,488 km2 , while the dissolved grid area is 582,200 km2 , a reduction in the total distribution area of almost 10% (see also Table 1).

if a country does not report existing distribution areas on its territory, the country will not be attributed the necessary national responsibility, while the other countries’ national responsibilities would be overestimated (Fig. 4). A solution of the problem is the development of clear data standards on a global scale, downscaling approaches to improve the resolution of current distribution data and closer collaboration of different national bodies along country borders (for the European Union see Evans & Arvela 2011). Regular assessments of data, however, need to take in consideration advances in data computation and new revisions of data standards are necessary, especially on a global level to serve global initiatives and processes optimally. Edge effects due to target size Gridded shapefiles stretching into sea area or into other biogeographic regions may lead to biases, especially in case of small target areas (Figs. 1 and 2). Thus, some parts of the distribution areas of habitats or species cannot directly be designated to a biogeographic region. These “undefined” areas can either be completely neglected

and cut off, as they do not harbor terrestrial species or habitats, or they could be regarded as an own biogeographic region, especially when the assessment unit occurs in the intertidal zone. They may also be allocated to the respective region on the continent. While such overlapping and inaccurate grid cells at coastlines are easily detectable, inaccurate overlaps at inland borders between biogeographic regions are much more difficult to observe (Fig. 2). A solution is to allocate the undefined area to the adjacent continental region, eliminating obvious mistakes of terrestrial habitats or species occurring at sea. Any of these corrective measures will have an impact on the total distribution area of the assessed taxonomic unit. They may therefore change the expected occurrence value and also the classification to a local, regional or wide distribution pattern. The importance for such errors to determining national responsibilities varies with the extent of the distribution area – for relatively widespread species and habitats the errors will be a low proportion and probably unimportant, but for a small distribution area, with few occupied grid cells, the error will be considerable and may lead to an overestimation of the distribution area and invalid determination of the distribution pattern (a local species

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Table 2 Examples of biases found within spatial data from Article 17 reports for the period 2001–2006. Habitat code

Habitat name

Comment

9010

Western taiga

Finland and Latvia covered completely

9020

Larger grid cells used in Finland

9030 9060 9070

Fennoscandian hemiboreal natural old broad-leaved deciduous forests (Quercus, Tilia, Acer, Fraxinus or Ulmus) rich in epiphytes Natural forests of primary succession stages of landupheaval coast Coniferous forests on, or connected to, glaciofluvial eskers Fennoscandian wooded pastures

9050

Fennoscandian herb-rich forests with Picea abies

9080

Fennoscandian deciduous swamp woods

Larger grid cells used in Finland, Habitat not considered present in Latvia due to different habitat interpretation Latvia covered completely, Larger grid cells used in Finland

9120

Atlantic acidophilous beech forests with Ilex and sometimes also Taxus in the shrublayer (Quercion robori-petraeae or Ilici-Fagenion) Subalpine and montane Pinus uncinata forests (*if on gypsum or limestone)

France smoothed grid shape

Sub-Atlantic and medio-European oak or oak-hornbeam forests of the Carpinion betuli Riparian mixed forests of Quercus robur, Ulmus laevis and Ulmus minor, Fraxinus excelsior or Fraxinus angustifolia, along the great rivers (Ulmenion minoris)

Latvia covered completely

9180 9190

Tilio-Acerion forests of slopes, screes and ravines Old acidophilous oak woods with Quercus robur on sandy plains

91E0

Alluvial forests with Alnus glutinosa and Fraxinus excelsior (Alno-Padion, Alnion incanae, Salicion albae)

Larger grid cells used in Finland, Latvia covered completely Larger grid cells used in Finland, France partly larger grid size (Poland and northern France grid seem to be smoothed), Latvia is completely covered, Larger grid cells used in Finland

91G0 91H0

Pannonic woods with Quercus petrae and Carpinus betulus Pannonian woods with Quercus pubescens

Hungary smooth grid borders, partly rougher grid

9320 9330

Olea and Ceratonia forests Quercus suber forests

Mallorca and Menorca “closed” grid France large closed grid

9430 9160 91F0

might become a regional species) and hence to an underestimation of the national responsibilities. Downscaling of data to a higher resolution would reduce the importance of the error to a negligible level and would improve the overall quality of distribution data for conservation purposes.

Fig. 3. Part of the distribution of habitat 9180 Tilio-Acerion forests of slopes, screes and ravines. Finland used larger grid cells than other EU Member States and Latvia indicated that this habitat type was found throughout the country. In Finland 50 km × 50 km grid cells were used for most habitats, whereas all other countries reported distribution areas for grid cells sized 10 km × 10 km (or compatible with a 10 km grid). This leads to an overestimation of Finnish national responsibilities as presumably the distribution areas do not cover each large grid cell evenly and a finer grid would reveal blank grid cells. This might also have been the case for some habitats in Portugal for which the country reported a distribution range but gave no information about distribution area presumably as information on habitat distribution was not available.

Recommendations In the light of continuing decline of natural habitats, on-going biodiversity loss (Hoffmann et al. 2010; Ricketts et al. 2005), and

Fig. 4. Distribution of habitat 6110 Rupicolous calcareous or basophilic grasslands of the Alysso-Sedion albi in France for 2001–2006 (gray) and 2007–2012 (red cross hatching). The GIS area in the Continental region of France has decreased from 72,700 km2 to 25,300 km2 although there has been no significant change in the distribution of the habitat. The proportion of the habitat in France would drop from 46 to 23%, assuming the reported distribution in other countries does not change. (Data reported by France under Article 17 of the Habitats Directive.)

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the CBD Aichi 2020 targets 5, 10 (Strategic Goal B: Reduce the direct pressures on biodiversity and promote sustainable use), and 17 (Strategic Goal E: Enhance implementation through participatory planning, knowledge management and capacity building) urgent action is clearly required. Within the European Union targets are set by the EU Biodiversity Strategy to 2020 while the principle legal instruments are the Birds and Habitats Directives. The latter require urgent full implementation. In non-EU countries the Council of Europe’s Bern Convention and its Emerald network require a similar action. This implementation should be based on standardized and harmonized data on abundance and distribution, depending on the question and conservation aim. Conservation prioritization in conserving habitats suffers from the lack of a globally and even regionally accepted habitat classification and the limited availability of distribution data across biomes. Similarly, for species some taxonomic problems persist, e.g. the taxonomic treatment used for the European red list for fish is controversial and not accepted by many specialists (Etheridge et al. 2012). The lack of clear classifications and taxonomic definitions impacts on the determination of national responsibilities in two ways. Firstly, it restricts its use to habitats and species with the same definition standard and comparability of national responsibilities of habitats and species with the same definition may not be coherent. Secondly, differing habitat classifications and species definitions and hence the different names for the same habitat or species, make it cumbersome to retrieve information on the total distribution area and has consequences for biodiversity monitoring of a habitat type and species per se (Mücher, Hennekens, Bunce, Schamine, & Schaepman 2009). If a habitat definition or the taxonomy of a species is unclear, monitoring data from different sources (e.g. national authorities responsible for nature conservation) may not be compatible. Generally, data for species are better and more coherent, but are biased toward birds, butterflies, mammals, vascular plants, amphibians and reptiles, while data for e.g. a large range of insect species, non-vascular plants and molluscs are incomplete and often dated (e.g. Klemetsen 2010). Therefore, at present the national responsibility approach cannot be applied to all species and may largely be limited to the comparatively small proportion of species covered by the IUCN red list database. Here, national efforts should be increased to better cover those species groups which are less popular with the public, but important for maintaining certain ecological services (e.g. pollination by insects) and therefore poorly covered by existing volunteer-based monitoring programs (Schmeller, Henle, Loyau, Besnard, & Henry 2012; van Swaay, Plate, & van Strien 2002). Our analysis has also shown that determining national responsibilities with the data currently available suffers from artificially enlarged distribution areas by grid cells being counted more than once, gridded shapefiles for terrestrial species and habitats stretching into the sea, across borders or into other biogeographic regions with important effects especially for small target areas, and differences in the size and the shape of grid cells and hence the resolution of maps. In the future, coherent distribution data, based on a consistent and complete habitat classification should be gathered and made available globally. To make advances with definitions and classifications and to fill monitoring gaps, especially in nonvascular plants, insects, fish and molluscs, will require action by national governments and international organizations and only when those gaps are filled it will become possible to evaluate the trends and status of biodiversity more clearly and to develop robust indicators, as needed for the work conducted by e.g. the CBD and IPBES. The national responsibility method could guide decision makers in regard to which species and habitats to target in their area of responsibility. The approach creates hierarchical lists of national

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responsibility for species and habitats and decision makers could work down that list from very high to high to medium to basic responsibilities, depending on the availability of resources. All species and habitats with very high and high national responsibilities should be closely monitored following appropriate monitoring programs (Henry et al. 2008; Kull et al. 2008; Lengyel et al. 2008). The national responsibility approach also helps to identify gaps in currently available data for which new monitoring programs need to be set up with high priority. The category “data deficient” includes species and habitats for which we do not have sufficient distribution data. Without such data, an initial assessment of impacts on such species and habitats and hence their threat status is impossible. Decision makers may therefore use the national responsibility approach to determine how much resources need to be set aside to monitor those species and habitats to rapidly fill these data gaps. Conclusions Our analysis stresses the importance to overcome data issues to improve data quality, availability and comparability quickly to render conservation more efficient. For that, clear, globally applicable data standards are urgently needed (Pereira et al. 2013) as well as harmonized habitat classifications. For a fully comprehensive coverage of conservation needs, the national responsibilities for both species and habitats would also need to be determined in regard to topical prioritization (Henle et al. 2013). With the necessary data at hand, the national responsibility approach can be used to determine which species and habitats to monitor more closely in regard to different threats, to focus budgets on species and habitats where countries have high to very high responsibilities, and to help attributing monitoring budgets to poorer countries that have high responsibility for many species or habitats, but insufficient resources to closely monitor them. In summary, urgent actions to render the determination of national responsibilities useful are (i) the development of clear data standards, (ii) regular assessments of data, to take in consideration advances in data computation and new revisions of data standards, and (iii) data downscaling to a higher resolution to reduce the impact of bias to a negligible level and to improve the overall improvement of the quality of distribution data for conservation purposes. A global solution is required to facilitate globally acting processes and initiatives such as IPBES, the CBD, and GEO BON. Acknowledgements We are grateful to Jean-Baptiste Mihoub for valuable comments on the manuscript. This study was financed by SCALES (“Securing the Conservation of biodiversity across Administrative Levels and spatial, temporal, and Ecological Scales”; Henle et al. 2010), a large-scale collaborative research project funded by the European Commission under the 7th Framework Programme (contract no. 226852). References Becking, R. (1957). The Zürich-Montpellier school of phytosociology. The Botanical Review, 23, 411–488. Beger, M., Linke, S., Watts, M., Game, E., Treml, E., Ball, I., et al. (2010). Incorporating asymmetric connectivity into spatial decision making for conservation. Conservation Letters, 3, 359–368. Blehert, D. S., Hicks, A. C., Behr, M., Meteyer, C. U., Berlowski-Zier, B. M., Buckles, E. L., et al. (2009). Bat white-nose syndrome: An emerging fungal pathogen? Science, 323, 227. Brooks, T. M., Mittermeier, R. A., da Fonseca, G. A. B., Gerlach, J., Hoffmann, M., Lamoreux, J. F., et al. (2006). Global biodiversity conservation priorities. Science, 313, 58–61.

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