Biological Conservation 147 (2012) 3–12Contents lists available at SciVerse ScienceDirect Biological Conservation journal homepage: www.elsevier.com/locate/biocon Determining the ecological value of landscapes beyond protected areas K.J. Willis a,b,⇑, E.S. Jeffers a, C. Tovar a, P.R. Long a, N. Caithness c, M.G.D. Smit d, R. Hagemann d, C. Collin-Hansen d, J. Weissenberger d a Biodiversity Institute, Oxford Martin School, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK Department of Biology, University of Bergen, Allégaten 41, N-5007 Bergen, Norway c Oxford e-Research Centre, University of Oxford, Oxford OX1 3QG, UK d Statoil ASA, Forusbeen 50, 4035 Stavanger, Norway b a r t i c l e i n f o a b s t r a c t Whilst there are a number of mapping methods available for determining important areas for conservation within protected areas, there are few tools available for assessing the ecological value of landscapes that are ‘beyond the reserves’. A systematic tool for determining the ecological value of landscapes outside of protected areas could be relevant to any development that results in a parcel of land being transformed from its ‘natural’ state to an alternative state (e.g., industrial, agricultural). Speciﬁcally what is needed is a method to determine which landscapes beyond protected areas are important for the ecological processes that they support and the threatened and vulnerable species that they contain. This paper presents the results of a project to develop a method for mapping ecologically important landscapes beyond protected areas; a Local Ecological Footprinting Tool (LEFT). The method uses existing globally available web-based databases and models to provide an ecological score based on ﬁve key ecological features (biodiversity, vulnerability, fragmentation, connectivity and resilience) for every 300 m parcel within a given region. The end product is a map indicating ecological value across the landscape. We demonstrate the potential of this method through its application to three study regions in Canada, Algeria and the Russian Federation. The primary audience of this tool are those practitioners involved in planning the location of any landscape scale industrial/business or urban (e.g., new town) facility outside of protected areas. It provides a pre-planning tool, for use before undertaking a more costly ﬁeld-based environmental impact assessment, and quickly highlights areas of high ecological value to avoid in the location of facilities. Ó 2011 Elsevier Ltd. All rights reserved. Article history: Received 18 April 2011 Received in revised form 30 October 2011 Accepted 2 November 2011 Available online 28 January 2012 Keywords: Biodiversity valuation Connectivity Ecological footprint Fragmentation Threatened species Resilience 1. Introduction Protected areas have long been the mainstay of biodiversity conservation with 12% of land currently under some form of protection (Jenkins and Joppa, 2009) and a commitment to increase this to 15–20% by 2020 (Stokstad, 2010). There are a number of excellent tools available for mapping conservation priorities within these protected landscapes (e.g., C-Plan, Pressey et al., 2009; Marxan, Ball et al., 2009; and Zonation, Moilanen, 2007). However, users of the outputs of systematic conservation planning tools have traditionally treated land outside of the protected area network as ‘‘scorched earth’’, i.e., as providing no beneﬁt to biodiversity conservation (Edwards et al., 2010). Landscapes beyond protected areas are increasingly being recognised as important for providing ecological and evolutionary processes essential for the long-term persistence of biodiversity ⇑ Corresponding author at: Biodiversity Institute, Oxford Martin School, Department of Zoology, University of Oxford, Oxford OX1 3PS, UK. Tel.: +44 1865 281321. E-mail address: [email protected]
(K.J. Willis). 0006-3207/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2011.11.001 both within and beyond protected areas (Bengtsson et al., 2003; Carroll et al., 2010; Chazdon et al., 2009; Mathur and Sinha, 2008). Key biotic and abiotic features of these landscapes include their role in the provision of corridors between reserves (e.g., through waterways, wetlands, Edwards et al., 2010) and as refuges for species given future range-shifts resulting from climate change (e.g., Carroll et al., 2010). It is also recognised that many threatened and protected species have signiﬁcant populations outside of protected areas (IUCN, 2009) and that the majority of species migration routes occur beyond protected areas (Riede, 2004). Consideration is also needed of the landscape scale features that are important to maintain resilient ecosystems (Bengtsson et al., 2003; Klein et al., 2009). These biotic and abiotic features have numerous different spatial conﬁgurations across the landscape. All landscapes outside of the protected areas are not, therefore, equal in terms of ecological value. This point is particularly relevant when considering placement of business facilities, industrial operations and even the building of solar and wind farms; where can they be built that will have least ecological impact? There are currently very few tools available for mapping conservation we set out to devise a method that would have a simple user input. There are many different metrics to consider at varying spatial and temporal scales. NatureServe www.html). 1994). large mammal migration paths and/or methods to incorporate areas that appear to be more resilient to environmental perturbations because of the combination of biotic/abiotic features that they contain (Klein et al. connectivity and resilience. Willis et al. Recently. that effective planning for ecological processes requires a multi-criteria assessment approach that incorporates all of these important measures of ecological integrity (Regan et al.1.5 km). however. 2. would use existing globally available databases..J. These areas were selected to represent a wide variety of eco-regions at different geographical locations and varying data availabilities.. Underlying assumptions associated with this method include: (1) that it should be based on multiple valuation factors. 2°280 44. Currently the mainstays of ecological assessment by businesses for landscapes beyond protected areas are site Biodiversity Action Plans and Environmental Impact Assessments (EIA) (Slootweg. 1991) and irreplaceability (Pressey et al. 2009). Identiﬁcation of data sources and models to determine ecological criteria Through a series of workshops with practitioners and a detailed literature review.worldwildlife.org https://www.org/science/ ecoregions/item1847. these measures are based on observed biological patterns of conservation ‘assets’ or ‘actors’ but lack information on the ecological processes that support ecosystem functioning (Bennett et al. there exist tools such as iBat for business (iBatforbusiness.319400 W). The Yamal Peninsula Russia study area is located within the Yamal-Gydan Tundra ecoregion.org/ science/ecoregions/item1847. 111°270 13. Current land use is dominated by nomadic pastoralism. where perennial vegetation and agriculture is present.org/science/ecoregions/item1847.html).200 E). for example.ibatforbusiness. and/or require a high level of data knowledge and input. This paper describes the multi-criteria metrics and databases that we analysed and the algorithms developed to create a methodology to assess the ecological value of parcels of land across a landscape for any location in the world. Despite the extreme conditions. this park is about 300 km to the southeast of the study area. Many studies indicate that a more fragmented patch of landscape. Oases are also present in the landscape. There is also often a consideration of the contiguous nature of the habitat supporting the species.org/action_site_bap.org). The Alberta site is located within the mid-continental Canadian forest eco-region.5 km. rarity (Smith and Theberge. ﬁve criteria were determined as being of primary importance to the ecological valuation of a landscape. Whilst these assessments yield an estimate of the species present in an area proposed for development. the sparse vegetation is highly adapted to the hyper-arid climate via the persistence of the seed bank that allows for masting during high rainfall years. The Yamal’skiy marine protected area (UNEPWCMC. which is characterised by continuous mid-boreal mixed coniferous and deciduous forest across a variable topography with discontinuous permafrost cover (http://www. 2010).org/science/ecoregions/item1847. (2) make use of free spatial data available for almost any location worldwide. The Algeria site is located within the Saharan Desert eco-region which is hyper-arid. has minimal perennial vegetation cover and low species richness and endemism. Study areas Three study areas were chosen to determine an ecological valuation of the landscape: one in Alberta.g. they do not consider the overall footprint of the development upon important ecological processes on the landscape. taxonomic uniqueness (Vane Wright et al. which is a lowland tundra situated upon a thick layer of permafrost (http://www. Those that are available tend to be region or country-speciﬁc (e. 1986). which is also designated as a World Heritage Site and UNESCO Biosphere Reserve (UNEP-WCMC.4 K. rivers. It then describes results from testing this methodology for three case study regions in Canada. these plans are based on detailed site surveys carried out by a team of consultants. 2010) overlays the study area and extends to the south for 300 km. namely: biodiversity.worldwildlife. These provide the framework by which companies endeavour to minimise the impacts of their activities on ecosystems and habitats and ensure that planned development activities are in line with regional and national Biodiversity Action Plans (http:// www.. There are 70 mammalian. ﬂyways. Additionally. Canada (55°490 5. These criteria represent both the current ecological properties of . fragmentation. models and algorithms to produce mapped output at a spatial scale relevant to most landscape scale planning decisions (<0.. Vegetation cover in the northern part of the peninsula is dominated by mosses and lichens with a few shrubs and grasses that support larger herds of reindeer during the summer months. ideally <0.html).1600 N. 2009. has less ecological value than a large undisturbed patch (for recent review see Tjørve.naturserve. Crow Lake.html).worldwildlife. combined with well-established models and algorithms could be used to provide a baseline of ecological information for any site in the world. Typically. Methods 3.759400 E) and one on the Yamal Peninsula in Russia (72°180 47. 2010). Current land use is dominated by nomadic pastoralism. All these studies agree. Current land uses in the eco-region include forestry and oil and gas development. Edwards et al. 2010). vulnerability. rather the EIA often gives an indication of necessary mitigation measures to minimise the damage that will be caused by the development. it contains a relatively large number of species that are adapted to this high stress environment (http://www.. The aim of this project therefore was to devise a simple and quick.businessandbiodiversity.worldwildlife. The focus here was to determine what freely available spatial data from the internet. 2009). 3. There is one protected area. The options to relocate a facility due to impact on ecological processes at this point in the decision making process are also extremely limited. In addition. At the landscape scale probably the most common measures adopted to date are indices of species richness. They also tend to occur once a decision has been made regarding where to develop. Algeria and Russia. How to measure ecological value is complex. 2007) in order to secure both the arenas and the actors (Beier and Brost. The nearest protected area to the study area is the Tassilin’Ajjer National Park. Beyond the oases. which is 40 km to the west of the study area (UNEP-WCMC.519400 N. yet effective method for determination of important ecological features and processes on landscapes beyond protected areas. 2010). several studies that have attempted to include additional factors in conservation planning that prioritise according to the ‘dynamic’ features of the landscape including wetlands. one in central Algeria (27°190 33. 72°260 58.2400 N. / Biological Conservation 147 (2012) 3–12 priorities outside of reserves. 90 avian and 100 reptilian species known to reside within the Saharan Desert eco-region (http://www. and (3) be at a scale that is relevant to the extent of most development concessions.html). 2010)..org/) which facilitate access to accurate and up-to-date biodiversity information but this again focuses on key biodiversity areas and legally protected areas and produces mapped output at a spatial scale ($20 km) that is often too coarse for most landscape planning decisions.. However. It also contains fossil records but these were removed from the analyses. led by MEDIAS-France. there is no minimum requirement for the number of species occurrences. 2002. (2005). Ferrier and Guisan. therefore. (2009). Biodiversity For most regions in the world there will rarely be enough detailed species data to obtain a clear picture of the biodiversity. Species richness is then estimated as the sum of species for which habitat suitability exceeds a given threshold. Factor Biodiversity Vulnerability Fragmentation Connectivity Resilience a b c d e f g h Deﬁnition Compositional turnover with respect to environmental covariates The number of threatened species present The size of the vegetation patch River.) at 10 for inclusion of each group in the modelling in order to be able to robustly parameterise a GDM for that group. GDM predictions of compositional dissimilarity for every possible pair of sites within the study area were made in the statistical software package R using the Table 1 Factors to be included in a local ecological footprint tool for assessing ecological integrity across a landscape and the available data source.. Willis et al.gbif. one picture element in a raster or remotely sensed image) for elevation (topography) to 10 km sized pixels for soil characteristics. . 2004) as contained in the study area and to remove any duplicate records of species occurrence in one location.g. (2001).. temperature seasonality. Zhao et al. the study area is divided into 300 m sized pixels and the other data layers are superimposed upon this in order to calculate values for each of the ecological indicators. plants. Ideally we would use species richness as well as compositional turnover to estimate diversity across a landscape using a approach such as ecological niche factor analysis (Hirzel et al.2. This method predicts habitat suitability for individual species based on occurrence records and environmental covariates. birds. precipitation seasonality Annual mean temperature..J. Since GDMs evaluate assemblages of species. corridors). total annual precipitation. Riede et al. In order to determine a measure of species occurrence in the study area. Given how important a knowledge of the vegetation cover is for calculation of many of the variables (biodiversity. mammals and reptiles Plants a b c Predictor variables Distance to water bodies. 2002. lake and wetland features. The GBIF Data Portal currently provides access to more than 300 million records of species occurrence worldwide. hydrosheds) were obtained from other global databases (see Table 2). (2008) Hijmans et al.. to estimate species richness using such models requires more than around 30 records per species (Zaniewski et al. threatened species) and the key features important for supporting ecosystem functions (e. 700 km Â 700 km) around our study areas for constructing the GDM. BIOCLIM. fragmentation.. Data for the abiotic variables in study area (climate. and it was these data which were used to generate predicted compositional dissimilarity for our study area. precipitation seasonality. The number of migratory species present The ability to sustain high rates of net primary productivity in areas of low precipitation Data source GBIFa.c soil water holding capacity Global Lakes and Wetlands Database and HYDROSHEDS. To determine the ﬁnest spatial resolution possible for determining an ecological value of a landscape we examined the range of readily available. These data were then used in a Generalised Dissimilarity Model to determine the compositional turnover in the study area which is the rate of change in species composition with respect to the environmental variables (Ferrier et al. connectivity (migration routes. The biodiversity data were ﬁltered to retain only those occurrences recorded in locations within the same eco-regions (Olson et al. we set a minimum number of species per biological group (e. HYDROSHEDSc and Global Registry of Migratory Speciesg MODIS/TERRA NPP Yearly L4 Global 1 km SIN Grid V004h and BIOCLIM Global Biodiversity Information Facility (http://www. We found that there are very few regions in the world that have appropriate densities of GBIF records to allow this approach. 3. http://data.e.. Ó ESA GlobCover Project. Output from this model highlights areas that are more different to their neighbours than other areas within the landscape in terms of their species assemblage and thus provides a measure of heterogeneity per unit area. Harmonized World Soil Database. BIOCLIMd IUCN Red Liste GLOBCOVERf Global Lakes and Wetlands Databaseb. habitat integrity.b total annual precipitation. however. 2002). It is therefore necessary to model predictive diversity across the landscape using a combination of point species occurrences and environmental variables to predict diversity on landscape (Hirzel et al. birds. Table 2 Environmental covariates used in generalised dissimilarity modelling per biological group. etc. / Biological Conservation 147 (2012) 3–12 5 the landscape (e. soils.g. temperature seasonality.K. Lehner and Döll (2004) Lehner et al.a annual mean temperature. resilience). 2006). 2009.gbif.org).. % nitrogen in soil. wetlands. However.. Biodiversity data were obtained for a larger area (i. we used the data contained in the Global Biodiversity Information Facility database (GBIF.org/). and resilience.. we selected vegetation cover at 300 m pixel size resolution (GLOBCOVER. global data were available at every 90 m sized pixel (i. Ferrier et al..e.. Ó ESA GlobCover Project. 2002). 2002). IUCN. biodiversity. Biological group Amphibians.g. spatially continuous geographic data. Data and models (described below) were then assimilated to calculate these criteria and an algorithm developed to sum the criteria into an overall dimensionless indicator of ecological value to enable a mapped output (see Table 1). led by MEDIAS-France) as the base layer for the LEFT. 3.groms. we reclassiﬁed the GLOBCOVER vegetation categories into the following groups: closed forest. Pixels which were situated within a riverine corridor or wetland were given a higher ecological score.. the actual maximum value is dependent on the availability of the data for each study site. we used total NPP in the year 2005 since this was the year in which the GLOBCOVER data was created.7. 3.000 have spatial data (http:// www. To determine the pixels that supported river corridors and/or wetlands (and the pixels immediately adjacent to these) we used data available in the HYDROSHEDS 15-arc-second river network database (hydrosheds. The output was transferred back into a GIS platform for visualisation. Prior to running FRAGSTATS. It therefore provides a large number of polygons of migratory ranges and distributions of species that migrate across national boundaries (Riede and Kunz. To ensure that NPP data were consistent with the vegetation cover data. Pixels demonstrating higher levels of compositional dissimilarity were treated as having a higher ecological value. Larger areas of continuous. . We therefore include a calculation to prioritize pixels that support migratory processes. bare areas.3. The 2010 IUCN Red List of Threatened Species has assessments for $56. All bare areas. EN. or NT) terrestrial vertebrate within each study area (birds. Ideally a measure of resilience to environmental disturbances (e. therefore.. While it is possible to ﬁlter or weight species by their threat category. the highest resilience) was assigned to pixels that were in both the upper quartile of NPP and the lower quartile of rainfall. similar vegetation cover were assumed to have a higher ecological value.000 species globally. all other pixels were given a value of zero (i. The maximum ecological value for any pixel was therefore six given that there are two complementary measures of connectivity. we retained all globally threatened species to maintain the breadth of species potentially present in our study areas. Willis et al..e.. burning.de) and (ii) waterways and wetlands.. lakes and agricultural areas were assigned a value of zero. GROMS currently contains a list of 2880 migratory vertebrate species in digital format and digital maps for 545 of these species. Values of annualized net primary productivity (NPP. 2009) per vegetation type (determined by GLOBCOVER) were overlaid with data of the typical precipitation of the driest quarter (WORLDCLIM bio17. VU. However. is habitat integrity. we retained the highest value of compositional dissimilarity per pixel in order to report the highest potential value of compositional dissimilarity which could occur in that pixel. the higher its functionality (greater diversity.4. more pollinators. 2009). Summary ecological value In order to ascribe a dimensionless ecological value to each 300 m pixel that incorporates all ﬁve measures described above.. 2002) was used to deﬁne a patch using the rule of eight pixels (orthogonal and diagonal adjacency) and to estimate the natural logarithm of the area of each patch (ha). The resulting index highlights the pixels containing the most resilient patches of vegetation based on the ability to retain productivity despite low rainfall.org/). non-natural and bare areas were automatically set a value of zero for patch size. Resilience Ecological resilience is the capacity of a system to undergo disturbance and maintain its functions and controls (Holling. Folke et al. we standardised the values of each factor to an index between zero and one. mammals. Range polygons of globally threatened (CR. the lowest value) for resilience. Hijmans et al.wwf) (Lehner et al. We calculated patch size according to similar vegetation type based on the classes in the GLOBCOVER dataset. lakes and wetlands) and resilience measures were already in binary integer values and thus required no conversion. We used the GROMS maps in the same way as the IUCN polygons: we calculated the number of migratory species ranges which intersect each pixel and those pixels that had the highest number of migration routes occurring across them were deemed to be of higher ecological value.e. 3. Areas characterised by human activities. After standardisation. To determine climatic resilience we adopted the approach used by Klein et al.. 2007) and the results were imported into MATLAB where a distance weighted average (using the inverse square distance measure) of all pair-wise combinations was calculated per site. 3. then applied the following rules for assessing the resilience value of each pixel: a value of 1 (i.6 K. Pixels with more globally threatened species potentially present were treated here as having a higher ecological value. open forest. water/snow/ice. Vulnerability In order to assess vulnerability of an area in terms of the potential loss of important species if the area was damaged. lakes. 2004). 3. however. 1973. forest/shrub/grass mosaic.5 was assigned to pixels that were in the upper quartile of productivity and the second lowest quartile of rainfall. Resilience is therefore an important ecological feature of any landscape and areas that can maintain resilience despite climate/environmental disturbance are of high ecological signiﬁcance.iucnredlist. 2008) and the Global Lakes and Wetlands database GLWD3 wetland classiﬁcation (Lehner and Döll.. A key feature to try and retain on any landscape. soil erosion) should also be calculated. as yet no global datasets are yet available that can be harnessed to produce a relative measure of resilience to such environmental disturbances across space. 2005) to identify patterns across space in the level of productivity of each vegetation type given spatial variations in rainfall.). In general the greater the patch size.g. 2001). of which about 28. connectivity (rivers.5. The biodiversity.6.J.. Zhao et al. Where multiple biological groups were modelled. Two complimentary factors that represent migration processes across the landscape were included namely (i) migration routes as identiﬁed in the Global Register of Migratory Species GROMS (www. This was achieved by setting the minimum value to zero and dividing the individual values per pixel by the maximum value of each measure. we summed the value of each factor together to provide an overall indication of ecological value per pixel according to the formula: Summary ecological value = Biodiversity + Vulnerability + Fragmentation + Connectivity + Resilience. 2004). Fragmentation There is a vast ecological literature demonstrating the ecological importance of habitat integrity and ‘intactness’ and the impact of fragmentation on biodiversity (see Fahrig (2003) for review). Connectivity Connectivity across a landscape either through riverine corridors and/or other migratory routes is essential to any ecologically functioning landscape. We calculated quartiles for the precipitation and NPP data per vegetation type within the study area. reptiles and amphibians) were obtained and overlain in order to count the number of globally threatened terrestrial vertebrates potentially present in each pixel. we used the IUCN Red List of Threatened Species (IUCN. (2009) that focuses on the ability of a parcel of land to maintain productivity – a fundamental aspect of ecosystem functioning – despite relatively high water stress. The software FRAGSTATS (McGarigal et al. / Biological Conservation 147 (2012) 3–12 GDM package (Ferrier et al. a value of 0. greater complexity in food webs etc. Algeria The Globcover map for the study region is presented in Fig. which is associated in space with the boundary between unconsolidated bare areas (i. we manually downloaded all of the spatial data and projected it into the Universal Transverse Mercator (UTM) coordinate system. within the study area there are predicted to be atleast 59 internationally migratory species and the number in any location ranges from 52–59 (Supplementary data Fig. 5. data from the IUCN Red Data list of Threatened species indicates that there three different globally threatened species predicted to occur within the study Fig. 3. results from FRAGSTATS reveals that there is a wide range in vegetation patch size in this study area from less than 1 ha to over 1000 ha (Supplementary data Fig. with large areas of continuous boreal forest and with some smaller areas of wetland along the western edge of the study area. Using this data in combination with the environmental co-variates (Table 2) the GDM output demonstrates that predicted biodiversity is the highest in the northwestern portion of the study area. Canada The Globcover map for the study region is presented in Fig.2. The summary ecological valuation map is presented in Fig. 1a). In terms of the vulnerability measure. plants (n = 1). 1. vulnerability. sand dunes) and consolidated bare areas (Supplementary data Fig. 1. This demonstrates that the highest possible ecological value for this study area is 6 (values assigned for biodiversity. Output from GBIF indicates that in this region there are point occurrences for birds (n = 282 species).K. reptiles (n = 3) or amphibians (n = 0). 1b). Results 5. . This area is marked by a dense river network and both small and large wetlands are associated with these rivers (Supplementary data Fig. 1f). shrubland and grasslands. especially in the east where there are numerous wetlands.e. Willis et al. fragmentation. In terms of connectivity. GBIF species occurrences for the Algeria site indicate that only bird species can be used in the GDM modelling (n = 27) since there were not enough data for mammals (n = 7). mammals (n = 18 species) amphibians (n = 10 species) and plants (n = 545 species). Data handling and display For the study areas described above. Canada study area. We set the study area at 100 km Â 100 km and each 300 m pixel within this area was evaluated for its ecological value using the algorithm described above. The greatest number of international migratory species appears to be concentrated in the north central part of the large continuous patch of closed needle-leaved forest. 2. Alberta. This large patch is surrounded by much smaller patches of a mosaic of forest. The highest values (darkest grey-scale) appear to be consistent with the river boundaries. 2a). The largest continuous patch consisting of closed needle-leaved deciduous and evergreen forest occupies the central region of the study area. 5.. A desert region with sparse vegetation cover except for that present on intermittent wetlands (oases) in the centre of the area. Output from the GDM modelling using the bird data combined with environmental covariates (Table 2) indicated highest beta diversity in the central and southwestern portions of the study region. In terms of habitat fragmentation. connectivity (due to migratory species plus the landscape features that support migration) and resilience. / Biological Conservation 147 (2012) 3–12 7 4. Other areas of high resilience are scattered around the study area. 1c). the summary ecological value for each pixel was plotted on a ﬁnal map layer ArcGIS 10 to display differences in value across the landscape.1. Once calculated. The IUCN threatened species list indicates that the study area contains two globally threatened species both of which potentially occur across the whole study area (Supplementary data Fig. 1d). in pixels that contain closed needle-leaved evergreen forest and lakes (Supplementary data Fig.J. 1e). The resilience measure indicated that the areas of highest resilience are in the north-west corner of the landscape (Supplementary data Fig. Results from the fragmentation calculation using FRAGSTATS was good and indicated variation in patch sizes ranging from <1 ha to 3200 ha (Supplementary data Fig. there was little spatial difference in their migratory ranges. 2e).) region. The predicted range of these species appears to be concentrated in the northwestern region of the study area. the maximum value of the summary ecological value for this site is 5. The summary ecological value for the Algerian study area is presented in Fig. thus the only areas subject to the fragmentation assessment is the tiny patch of vegetation in the centre of the study area (Supplementary data Fig. (Supplementary data Figs.. (d) connectivity due to migratory species. 2d). there is little to no vegetation cover within the study area. This is due to the extreme aridity of the Saharan Desert and the resulting high variability in vegetation cover over each year resulting in no consistency between the NPP data and vegetation cover. 2c). 4) while there are small patches of higher ecological value at the sites of the intermittent wetlands (in the northeast and southwest) and around the few patches of vegetation cover (central area). 3f) indicated a patchy distribution of resilient vegetation across the landscape. (c) fragmentation. / Biological Conservation 147 (2012) 3–12 Fig. 3c). Output from GROMS indicates that there are at least eight internationally migrating species that are expected to utilise the habitat around the Algerian study area (Supplementary data Fig. Another region of high ecological value is the northern boundary of the study area. In terms of a vulnerability calculation. These layers are overlaid to determine the (g) summary value of each pixel within the Canadian study area (see also Supplementary data Fig. This vegetation cover is so sparse that the maximum patch size is about 0. Results from this map indicate the areas of highest ecological value are along the coastline and along the river and wetland networks (Fig. 2f). 2b). 6) for the Russian study area is therefore based on 5 of the possible 6 layers: vulnerability.. therefore.e. . The summary ecological value map (Fig. 5. The resulting map indicates that the majority of the study area has low relative ecological value (Fig. Despite the hyper-aridity of the area. lakes and wetlands. As this region is part of the Saharan desert. oases) within the study area that may provide support for the migratory bird species that are known to occur in the Tasslin’Ajjer National Park and these pixels are detected in the connectivity layer (Supplementary data Fig. fragmentation.8 K. (e) connectivity from rivers.e. All three occur in the northern portion of the study area. 1a–f. This area is surrounded by largely continuous desert land cover. two connectivity measures and resilience (i. vulnerability. the maximum value is ﬁve).3. In the northern part of the study area there are also numerous patches of regularly ﬂooded vegetation and open forest. fragmentation and both indicators of connectivity.03 ha. there are intermittent wetlands (i. It was not possible to assess the differences in resilience of vegetation across space for this area (Supplementary data Fig. Measures of ecological value for the Canadian study area including: (a) beta-diversity. including both deciduous and evergreen forest. which is desert habitat and far away from human settlements that tend to concentrate around the wetland areas. a landscape that is predominantly covered by grasslands and shrublands and interspersed with small water bodies. 3d and 3e) The resilience measure (Supplementary data Fig. GROMs indicates that there are at least 21 migratory animals predicted to occur in the study area. 3b). (f) vegetation resilience. 5. 6). The largest patch is found in the southern part of the study area.J. It was not therefore possible to calculate a measure of betadiversity for this region. the IUCN Red data list for Threatened Species indicates that there are two globally threatened terrestrial vertebrates that are predicted to occur in the study area (Supplementary data Fig. 4 and is based on biodiversity. which is consistent with the highest values of connectivity and vulnerability. A measure of connectivity was also possible for this site. Willis et al. Russia The Globcover map for the Yamal peninsula study region is presented in Fig. 2. where the land cover is primarily consolidated bare areas (Supplementary data Fig. (b) vulnerability. Results from GBIF indicate a serious data gap of species occurrences for this region with only four mammal species and eight plant species recorded (Biodiversity Data from GBIF). lakes and wetlands. These layers are overlaid to determine the (g) summary ecological value of each pixel within the Algerian study area (see also Supplementary data Fig. On a global scale.) 6. 2a–f. Measures of ecological value for the Algerian study area including: (a) beta-diversity. there is now sufﬁcient information available in global databases to make some estimation of ecological value. some data gaps are inevitable. it would appear that even for data poor regions. Discussion For all three study areas examined. this study demonstrates that it is possible to obtain an estimation of the spatial distribution of some important ecological features across a landscape and at a relatively ﬁne spatial resolution (here at a 300 m pixel resolution). using globally available databases and existing models and algorithms.K.J. (b) vulnerability. 3. but using a multi-criteria metric approach such as described here. (d) connectivity due to migratory species. Willis et al. 4. Fig. / Biological Conservation 147 (2012) 3–12 9 Fig. Algeria study area. (f) vegetation resilience. (c) fragmentation. The poorest data-region from our . (e) connectivity from rivers. lakes and wetlands.. The difference . Fig. Willis et al. In addition to the data gaps however.10 K. (c) fragmentation. (b) vulnerability. These layers are overlaid to determine and (g) summary ecological value of each pixel within the Russian study area (see also Supplementary data Fig. three case-studies was northern Russia – but it was still possible to obtain spatial information sufﬁcient to map vulnerability. Russia study area. information that could have relevance to sighting of industrial facilities and highlight areas of high ecological risk. 2011). / Biological Conservation 147 (2012) 3–12 Fig. (e) connectivity from rivers. The main limitation is the coarse resolution of some of the input data.J. especially from the species distribution ranges (Palminteri et al. Measures of ecological value for the Russian study area including: (a) beta-diversity. (d) connectivity due to migratory species. 6. there are also sources of uncertainty associated with these global databases which need to be acknowledged from the outset. fragmentation and connectivity across this landscape. 3a–f). 5. (f) vegetation resilience. Yen..1016/j. (Edwards et al.g. Marxan. Angelstam.. J.2011. anywhere in the world. Such a trade-off approach is starting to be considered for some site facility location projects. something that is of much higher consideration outside of protected areas. To provide additional information for the Environmental Impact Assessment. Lake. Mackey. P.... The next steps in the LEFT development are to automate the methods described herein..R. M.. while preliminary checks are routinely conducted on GBIF data. 2007).F. However.. Oxford University Press.... Ambio 32.K.. Haslem. future work plans to include a comparative analysis of the output from the approach described herein with the output from high-resolution. Cheal. Quinn. G. Use of land facets to plan for climate change: conserving the arenas. A. A. and Zonation.A. J. However. To provide the ﬁrst stage of assessment in the determination of areas for Biodiversity Offsetting projects (Business and Biodiversity Offsets programme (BBOP. http://bbop.. is provides a way of visualising the ecological value of the whole EIA study area.F. 2010) and evolutionary.D. 2010.P. have) been used for systematic planning outside of protected areas. the data is not easily available. Some of the information obtained by this tool will not be routinely collected as part of the EIA and can therefore be used to provide additional sources of ecological information for a chosen development area. Conservation Biology 24.. there is a urgent need to devise methodologies that can map at a landscape scale. 2011) ’’a gap in market infrastructure that persists is lack of landscape-scale ecological monitoring – while site-level ecological monitoring is not uncommon. migratory. O’Hara..11. To assess long-term ecological impact of a development: therefore to assess ecological value of the landscape before.R.R. we are undertaking a ground-truthing and uncertainty estimation exercise by comparing the results of our LEFT methodology with those obtained from high-resolution ﬁeld datasets. 2009) therefore ground-truthing can also verify the classiﬁcation of vegetation and whether vegetation change has occurred since the MERIS data used in the GLOBCOVER classiﬁcation were acquired (i. 2009. Jones.e. Ecological processes: a key element in strategies for nature conservation. In order to obtain a quantitative measure of uncertainty. The tool would therefore be run before the EIA and be used as a guide to appropriate areas for more detailed on-ground surveys. Nystrom. (Klein et al. R.G. Koehn.biocon. / Biological Conservation 147 (2012) 3–12 11 between predicted distributions and actual occurrences of the species can only be properly detected by ground-truthing the data.F.. B.. A. 2005). Lunt. T. (Eds.001.W. we found that for all three case studies it was possible to obtain a measure of the ecological value . Bennett. Reserves. ground-truthed data. T.. Clarke.. Watts. P. at doi:10.... With an increasing requirement to map and value landscapes according to their ecosystem service provision.. The other question that needs to be addressed is how this new approach aligns with the other methodologies currently available. Ball et al.. 701–710. and often for landscapes where detailed data on species is far from complete..org/).P. Emanuelsson. It is also widely acknowledged that before this can happen.. Brost. Radford. Whilst they can (and in a few cases. Possingham. 192–199. 2009. 2009. Mac Nally. therefore. this methodology is such that additional data can be incorporated if and when they are available.. Willis et al. 2.). H.. it is inevitable that economic/ecological trade-offs will need to be considered more often (de Groot et al.. Furthermore.. In addition. C. ground-truthed datasets. resilience and dynamic landscapes.. R.25% (Bicheron et al.M.. Moilanen.Q. Bengtsson. T. K. D. M. much less compiled in a comprehensive way’’. Possingham. In particular there was a general lack of freely available remotely sensed environmental data in high latitude regions such as in the Yamal Peninsula. Folke. since the focus of such approaches is on ﬁnding the best location for conservation.. it can both compensate for the data gaps in some layers and also be used to support a wider variety of land-use decisions. P. Similarly in restoration projects.. which are available for a variety of costs but can provide high-resolution data. spatial decision support tools are now being developed to inform landscape-scale forest restoration such that multiple landscape functions (ecological and economic) are maximised (Gimona & van der Horst. 2009. New. Moberg. Recently several methodologies have also been proposed for the conservation of landscape features important to maintaining ecosystem processes (i. Russia. of the landscapes under investigation even if for some regions it was only possible to measure a few indicators. P. these measures and tools have been developed almost exclusively for use in strategic conservation planning exercises. Menkhorst. J. Supplementary data Supplementary data associated with this article can be found.L. their requirement for very high quality species/environmental data often means that they are of extremely limited use in data poor regions. What this methodology has also demonstrated is that there is a vast amount of global data available on the web to provide enough information to demonstrate differences in ecological value across the landscape for even the remotest of study areas.D.J.e. Newell. 2010).C.S. F.. Ecological Management & Restoration 10. The uncertainty associated with the GBIF points is related to the correct estimation of coordinates from the original source. Additionally. CPlan. not the actors. M. As a pre-planning tool to be used before any site locations are determined to highlight the most ecologically sensitive regions..E. J. In: Moilanen. during and post-development to determine ecological improvement or loss over time when combined with ﬁeld-based. since 2005). Beier. it is essential to ground-truth these species data where possible (Chapman. Oxford. The GLOBCOVER information has a known accuracy level of 79.. which will perform the analysis for any location in the world within a web-based tool and end-user platform. in the online version. Watson.. Ihse. I. 3... The approach we have presented in this paper is a ﬁrst attempt at providing a framework to do this – and by its use of a range of ecological indicators..forest-trends. Elmqvist. As mentioned in the introduction. Marxan and relatives Software for spatial conservation optimization. van Haaren and Fthenakis (2011) have developed a GIS-based site selection tool for locating windturbines on the landscape such that important bird areas are avoided. As recently stated in a review on the state of Biodiversity markets (Madsen et al. 2007). there is an extensive literature and an excellent number of tools now available to measure ecological value for the selection of priority areas for conservation (e. We envisage that this methodology has potential for use in four stages of planning and facility location in landscapes beyond protected areas as follows: 1. less attention is given to the ecological versus economic tradeoffs. Wilson. 389–396.N. B. Pressey et al. Use of this methodology to analyse the three study areas inevitably highlighted signiﬁcant differences in data availability across the globe despite our use of globally available datasets. Conclusions Using the approach described. To make full use of this approach for such regions may require accessing additional sources of remotely sensed data. the important ecological properties and functions of landscapes.. References Ball. 2003. Robinson. H.. for example. L.. Spatial Conservation Prioritization Quantitative Methods and Computational Tools. 4. U. Lumsden. M. I. 2009)). G.. Appendix A. 7. A. D.P. Community-level modelling. Possingham. 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