Integrating Habitat Status, Human Population Pressure, and Protection Status into Biodiversity Conservation Priority Setting

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1 Integrating Habitat Status, Human Population Pressure, and Protection Status into Biodiversity Conservation Priority Setting HUA SHI, ASHIBINDU SINGH, SHASHI KANT, ZHILIANG ZHU, AND ERIC WALLER Faculty of Forestry, University of Toronto, 33 Willcocks Street Toronto, ON M5S 3B3, Canada Division of Early Warning and Assessment North America, UN Environmental Programme, U.S. Geological Survey/EROS Data Center, Sioux Falls, SD 57198, U.S.A. U.S. Geological Survey/EROS Data Center, Sioux Falls, SD 57198, U.S.A. Abstract: Priority setting is an essential component of biodiversity conservation. Existing methods to identify priority areas for conservation have focused almost entirely on biological factors. We suggest a new relative ranking method for identifying priority conservation areas that integrates both biological and social aspects. It is based on the following criteria: the habitat s status, human population pressure, human efforts to protect habitat, and number of endemic plant and vertebrate species. We used this method to rank 25 hotspots, 17 megadiverse countries, and the hotspots within each megadiverse country. We used consistent, comprehensive, georeferenced, and multiband data sets and analytical remote sensing and geographic information system tools to quantify habitat status, human population pressure, and protection status. The ranking suggests that the Philippines, Atlantic Forest, Mediterranean Basin, Caribbean Islands, Caucasus, and Indo-Burma are the hottest hotspots and that China, the Philippines, and India are the hottest megadiverse countries. The great variation in terms of habitat, protected areas, and population pressure among the hotspots, the megadiverse countries, and the hotspots within the same country suggests the need for hotspot- and country-specific conservation policies. Key Words: biodiversity hotspots, GIS, habitat status, human pressure, megadiverse countries, protection status Integración del Estatus del Hábitat, la Presión de la Población Humana y el Estatus de Protección a la Definición de Prioridades de Conservación Resumen: La definiciónde prioridadeses uncomponenteesencial dela conservación de la biodiversidad. Los métodos actuales para identificación de prioridades para la conservación se han centrado casi por completo en factores biológicos. Sugerimos un nuevo método de clasificación relativa para identificar áreas de conservación prioritarias que integra tanto aspectos biológicos como sociales. Se basa en los siguientes criterios: el estatus del hábitat, la presión de la población humana, los esfuerzos humanos para proteger el hábitat y el número de especies endémicas de plantas y vertebrados. Utilizamos este método para clasificar a 25 sitios de importancia para la conservación, 17 países megadiversos y los sitios de importancia para la conservación en cada país megadiverso. Utilizamos conjuntos de datos consistentes, georeferenciados y de banda múltiple así como herramientas de percepción remota analítica y de sistemas de información geográfica para cuantificar el estatus del hábitat, la presión de la población humana y el estatus de protección. La clasificación sugiere que las Filipinas, el Bosque Atlántico, la Cuenca del Mediterráneo, las Islas Caribeñas, el Cáucaso e India-Burma son los sitios de importancia más importantes y que China, las Filipinas e India son los países megadiverosos más importantes. La gran variación en términos de hábitat, áreas protegidas y presión de la población entre h.shi@utoronto.ca Current address: Colorado Division of Wildlife, 6060 Broadway, Denver, CO 80216, U.S.A. Paper submitted September 8, 2003; revised manuscript accepted December 30, C 2005 Society for DOI: /j x 1273

2 1274 Integrating Social Factors into Priority Setting Shi et al. los sitios de importancia, los países megadiversos y los sitios de importancia en un país sugiere la necesidad de políticas de conservación específicas para los sitios de importancia y los países. Palabras Clave: estatus del hábitat, estatus de protección, países megadiversos, presión humana, SIG, sitios de importancia para la conservación Introduction The importance of the ecological, social, economic, cultural, and aesthetic values of biodiversity has been widely recognized (Pimm et al. 1995; Mittermerier et al. 1999). Biodiversity is being significantly reduced by human activities, however, and habitat destruction is the main threat (Dompka 1996; Mittermerier et al. 1999; Thompson & Jones 1999). Some researchers estimate that the clearing of half the world s remaining forests would eliminate 85% of all the species they contain (Wilcove et al. 1998; Pimm & Raven 2000). Over the last two decades, the significance of this threat has led to a growing awareness of the importance of biodiversity and habitat conservation. Conservation experts have focused on identifying conservation areas of prime importance as one of the keys to conserving the planet s disappearing species, genes, and ecosystems (Olson & Dinerstein 1998; Stattersfield et al. 1998; Prendergast et al. 1999; UNEP 1999). Their work has resulted in the identification of 17 megadiverse countries (McNeedly et al. 1990; Mittermeier et al. 1997) and 25 biodiversity hotspots (Mittermeier et al. 1999; Myers et al. 2000). In 1995 more than 1.1 billion people were living in biodiversity hotspots. The annual population growth rate of 1.8% in these hotspots ( ) was substantially higher than the annual global population growth rate of 1.3% (Cincotta et al. 2000). Growing human populations, owing to their increased demand for land, material products, and development projects, threaten natural habitats. The most serious consequences of further habitat loss occur in hotspot areas (Brooks et al. 2002). In addition, hotspots are high in species endemism and low in pristine vegetation. Hence, various scholars, including Myers et al. (2000) and Pimm et al. (2001), have called for immediate steps to conserve these hotspots (Myers 1990; SEPA 1998). The annual amount of financial resources available for conservation about US$6 billion ( James et al. 1999) is small relative to the geographical area that requires biodiversity conservation (Weitzman 1998; GEF 2001). This financial resource scarcity has resulted in a variety of studies that focus on setting priorities for biodiversity conservation. Those conducted by Mittermeier et al. (1999), Myers et al. (2000), Brummitt and Lucghadha (2003), and Ovadia (2003) prioritized hotspots (the hottest hotspots) based on the number of endemic species and their number area ratio. After comparing five algorithms, Csuti et al. (1997) found a linear programming algorithm to be the optimal means for identifying areas to set aside as reserves. Results of a study by Margules and Pressey (2000) suggest six stages for conservation planning and include minimum-size area, complementarity, and minimal previous disturbance as the key determining factors for setting conservation goals. Karieva and Marvier (2003) emphasized the need for incorporating a measure of the effectiveness of past conservation efforts in conservation priority setting. Rodrigues et al. (2004) used gap analysis to evaluate the effectiveness of the global protected area network. Finally, Reid (1998) found that the value of hotspots may be more limited at smaller scales but that at large geographic scales hotspots prove to contain useful information for conservation planning. In almost all these studies, the indicators for setting priorities for conservation focus on plant or animal species. Parameters include the total number and density of endemic plant and vertebrate species, gap species, or covered species; the minimum area needed for adequate conservation; the state of connectivity; and the existence of complementarity. These are all necessary, but together they are still not adequate as a basis on which to set biodiversity priorities. Some studies emphasize the importance of other biological and social aspects for biodiversity conservation. These include the status of habitat (Scott et al. 1993; Kautz & Cox 2001), human population pressure (Cincotta et al. 2000; Sanderson et al. 2002; Liu et al. 2003), and human efforts to protect habitat (Karieva & Marvier 2003). None of the studies, however, integrates biological and social aspects in the criteria for setting priorities for biodiversity conservation. In addition, past studies also suffer from problems related to the reliability of data sources and from variability in the precision and accuracy of data (Myers et al. 2000). Similarly, these studies treated hotspots and megadiverse countries as independent physical identities, which may not be an optimal approach because many hotspots are located within megadiverse countries. On the basis of these aforementioned issues, our goal was to develop a quantitative assessment of habitat (closed forest and other vegetation), protected areas, and human population pressures in the 25 hotspots and 17 megadiverse countries. Using this quantitative assessment, we developed a method of relative ranking for conservation priority setting and compared the prioritysetting results of the 25 hotspots with the results from existing macrolevel studies. We also extended the analysis

3 Shi et al. Integrating Social Factors into Priority Setting 1275 of priority setting to the 17 megadiverse countries and the hotspots located within them. Methods We used a recent comprehensive, georeferenced, and multiband data set, collected through the Advanced Very High Resolution Radiometer (AVHRR) and obtained from the National Oceanic and Atmospheric Administration (NOAA). Various analytical tools for remote sensing and geographic information systems (GIS) tools were used for digital data processing and analyses, such as Imagine (ERDAS, Atlanta, Georgia) and the Environment for Visualizing Images (Research System, Boulder, Colorado). Most multiple layer overlay analysis was done in the Grid module of ArcGIS (ESRI, Redlands, California). Raster and vector data layers were in an Interrupted Goode Homolosine Projection (a global equal area projection), and all raster data sets had a cell size of 1000 m. Digital data analyses included estimation of habitats (closed forests and other vegetation), human population pressure, and protection status. Estimation of Habitats The recent International Geosphere Biosphere Program (IGBP) Data and Information System s global land-cover mapping (Loveland et al. 2000) provides a basis for estimating the size of various habitats, including closed forest, other vegetation, and areas with no vegetation cover. Forest classes in the IGBP database are not adequate for direct conversion to the vegetation classes in our study. We used the standard definitions of closed forest (canopy density 40%) and open forest (canopy density between 10% and 40%) (FAO 1999) and mapped six vegetation classes: closed forest, open forest, other wooded land, grassland, unvegetated land, and water. The description of the four classes in the IGBP database, other than closed and open forest, was compatible with our requirements, so these classes were derived from the IGBP database. Our method for closed and open forest-cover mapping consisted of the following: temporal image compositing, estimating the percentage of closed and open forest cover with a combination model of linear mixture modeling and NDVI scaling, linking the resulting percent forest cover to the IGBP classes, and validation. Temporal Image Compositing Source data used for the forest-cover mapping were drawn from AVHRR NDVI composites produced for February 1995 January The temporal composites, consisting of five calibrated AVHRR bands and an NDVI band, were computed using the protocol of maximum NDVI value (Holben et al. 1986; Eidenshink & Faundeen 1994) over 10-day compositing periods. Bands 1 and 2 (red and infrared) were further corrected for atmospheric ozone and Rayleigh scattering effects. Bands 1 and 2 and NDVI of the 10-day composites were processed into monthly composites with a rule of minimum band 1 (red band) value (Waller & Zhu 1999). In the temporal compositing process, the maximum NDVI algorithm tends to retain large off-nadir pixels in the backscatter direction (Qi et al. 1993; Cihlar et al. 1994), resulting in varying pixel sizes and additional noise introduced into spectral bands. Visual examination of the source data showed that large backscatter view angles were associated with high red and infrared red (IR) reflectance in forest land. As a partial measure to correct for this bidirectional effect, we used the minimum red band-compositing rule to better preserve pixels near nadir or in fore-scatter direction and to maintain the integrity of patterns of forest lands. The final image data consisted of 12 monthly composites of the first two AVHRR bands (red and infrared) and the NDVI band for each of five major land masses: Africa, Australia and tropical Asia, Eurasia (Europe and temperate/subtropical Asia), North America, and South America. Combination Model with Linear Mixture Modeling and Scaling of NDVI To estimate the fraction of forest cover, we used a combination of linear mixture modeling and scaling of NDVI and the visible (red) band based on pixel positions along the near-ir band. In the combined model, pixels were modeled depending on their relative positions along the IR band axis. Pixels with low IR reflectance contain new forests in burned areas, woody wetlands, and other dark land cover such as shadow or water, and these pixels were best scaled with their NDVI values, which are insensitive to illumination variance ( Holben et al. 1986). In the red-ir space, the distribution of NDVI values for green vegetation varies between the diagonal line (NDVI as 0) and the boundary near the IR band axis (NDVI as 1). In our data set, NDVI values varied between 0.3 and 0.8. Hence, for pixels with low IR reflectance we used scaling that was set flexibly between different forest cover types. For the details of this combined model (the three methods), refer to Zhu and Waller (2003). Even with the use of these three methods in the mixture analysis, significant regional variations in climate, topography, and forest types required the use of geographic stratification to ensure that the same canopy definitions could be mapped across varying regional conditions. Geographic stratification for each continent was based on digitized lines following combinations of ecoregions, physiography, vegetation types, and imagery conditions. The three methods in the combined model for forest canopy mapping were applied to each monthly AVHRR/NDVI composite. To provide the results least affected by the atmosphere, the final percentage of forest

4 1276 Integrating Social Factors into Priority Setting Shi et al. cover for a continent was determined over the course of the year (February 1995 January 1996) on the basis of maximum monthly forest cover value achieved, regardless of the method chosen. The maximum forest compositing was compared to average forest canopy cover over the course of the year. We examined areas of high ratio to prevent overestimating from anomalous data. The two forest classes closed forest and open forests were then derived on the basis of their definitions. Adapting the IGBP Classes to the Vegetation Classification In the four remaining vegetation classes, the two grassland and water classes were obvious. The other wooded land class consisted primarily of open or closed shrub land cover in tropical and subtropical regions and low-density tree cover in northern boreal zones near the polar region. The unvegetated-land class contained mostly barren land, ice and snow, and cropland from the IGBP database. To derive these four vegetation classes, the IGBP classes were used as the baseline data continent by continent. The refinement methods to fit IGBP classes to the definitions were similar to the methods used in producing the IGBP classes. Class mergers and splits were aided by ancillary data sets such as ecoregions and digital elevation models. Spectral reclustering and user-defined polygon splits (Loveland et al. 1999) were also used. In areas of disagreement, the two initially defined forest classes took priority. Finally, the mapping of these four vegetation classes was merged with the mapping of the two forest classes for each continent to produce a global vegetation map. The boundary grid showing the original extent of hotspots, obtained from the Conservation International Database (CI 2001), was combined with the global vegetation map to quantify closed forest and other vegetation in the hotspots. The same exercise was repeated for the 17 megadiverse countries and for the hotspots in these 17 countries. Political boundary data were taken from the U.S. National Imagery and Mapping Agency s Vector Map Level 0 series. Estimation of Protection Status of Habitats The World Commission on Protected Areas ( WCPA) and the UNEP World Conservation Monitoring Center ( WCMC) provided the protected-areas georeferenced database. The database included the legal designation, name, IUCN management category, area, location ( polygons), and the year of establishment of some 20,000 sites. To calculate the protected-area status of the closed forests and other vegetation in the biodiversity hotspots, we overlapped the protected-area grid, the closed forest and other vegetation distribution grid prepared in the first step, and the grid of the original boundaries of the hotspots. The same exercise was repeated for the 17 megadiverse countries and for the hotspots within the megadiverse countries. Estimation of Human Population Pressure We analyzed human population pressure with the geographically referenced population database of the UNEP/Global Resource Information Database (GRID), the global vegetation map, and the original boundary grid of the hotspots. We estimated the number of people living in closed forest areas and in the other vegetation areas for each hotspot and each megadiverse country. The estimation was done by overlaying different grid layers. We used the population pressure classification system suggested by Singh et al. (2001), which groups population pressure into three categories: (1) low, <25 people/km 2 ; (2) medium, people/km 2 ; and (3) high, >100 people/km 2. A Method for Relative Ranking of Conservation Areas Four essential components of a relative ranking method are the dimensions of comparison, indicators for each dimension, a scoring method, and the weights assigned to different dimensions and indicators. As outlined in the introduction, there are four critical dimensions of biodiversity conservation for a given geographical area: (1) the number of endemic plant and vertebrate species in the area (Mittermeier et al. 1999; Myers et al. 2000); (2) the status of habitat in the area (Scott et al. 1993; Kautz & Cox 2001), which can be represented by the extent of closed forests and other vegetation in the area; (3) the human population pressure on the habitat (Cincotta et al. 2000; Sanderson et al. 2002; Liu et al. 2003) in the area; and (4) the human efforts to protect the habitat and endemic species in the area (Karieva & Marvier 2003). We included all four dimensions in our method. The first two dimensions, endemic species and habitat, have two natural subdimensions: endemic plants and endemic vertebrates and closed forest and other vegetation, respectively. The two other dimensions (human population pressure and protection efforts) required categorization in two subdimensions because of the two categories of habitat. Hence, we needed a total of eight indicators, two for each dimension: (1) number of endemic plants, (2) number of endemic vertebrates, (3) percentage of other vegetation in the total land area, (4) percentage of closed forest in the total land area, (5) percentage of other vegetation area that is under high population pressure, (6) percentage of closed forest that is under high population pressure, (7) percentage of other vegetation that is protected, and (8) percentage of closed forest that is protected. In our method of relative ranking, we calculated the mean value of each indicator for the 25 hotspots and the 17 megadiverse countries separately. For each of the 25 hotspots, the value of each indicator for a given hotspot was compared with the mean value of the indicator for the 25 hotspots. In this comparison, if the value of an

5 Shi et al. Integrating Social Factors into Priority Setting 1277 indicator for a given hotspot for items 3, 4, 7, and 8 was less than its mean value, the indicator was scored as 1; otherwise, it was scored as 0. Similarly, for the indicators of items 1, 2, 5, and 6, if the value of an indicator of a given hotspot was greater than its mean value, the indicator was scored 1; otherwise, it was scored as 0. On the basis of each indicator s score for a given hotspot, the total score was calculated for each hotspot, and the hotspots were ranked on the basis of the total score. The same procedure was used for the relative ranking of megadiverse countries. In this case, however, the value of each indicator for a given country was compared with the mean value of the indicator for the 17 megadiverse countries. Thus, this ranking method provided a relative ranking of the hotspots (and megadiverse countries) among the group of hotspots (and megadiverse countries) and not an absolute ranking. As far as weights for different dimensions and indicators are concerned, at this stage, we did not have any field-tested criteria for assigning actual weights. Consequently, we started by assigning equal weights to each dimension and indicator. On the other hand, there is strong merit to assigning a higher weight to closed forest habitat compared with other vegetation, but we did not know the difference in weights for these two categories of habitats. Hence, we conducted two sensitivity analyses: the weights of 1.1 and 1.2 to closed forest and the associated indicators (high population pressure on closed forest and protection status of closed forest) and the weights of 0.9 and 0.8 to other vegetation cover and the associated indicators, respectively. Results Habitat, Human Population, and Protection Status of 25 Hotspots Vegetation cover occupied 66.8% of the land area in the 25 hotspots. This vegetation cover consisted of 25.5% closed forest, 41.3% other vegetation, and 33.2% unvegetated land and water. Designated protected areas covered only about 8.3% of the hotspot area and 15.9% and 6% of the closed forest and the other vegetation areas, respectively (Table 1). Vegetation covered more than 50% of the total area in 18 hotspots and between 25% and 50% of the total area in six hotspots. Only one hotspot had < 25% of the total area under vegetation cover (Fig. 1). The Mountains of South- Central China had the highest vegetation cover (89.2%), and in four other hotspots vegetation covered more than 80% of the total area. The hotspot with lowest vegetation cover was Succulent Karoo, with only 14%. Two hotspots, Mesoamerica and Wallacea, had more than 50% of the area under closed forests. Closed forests occupied 25 50% of the area in eight hotspots (Table 1). Two hotspots, Polynesia-Micronesia and the Succulent Karoo, had no closed forests, whereas the remaining 13 hotspots had < 25% of their area under closed forests (Table 1). The protection status of most of the 25 hotspots was < 10% of the total geographical area, 20% of the closed forest, and 12% of other vegetation cover. The hotspot with the highest proportion of protected area was the California Floristic Province, with 38.6% of the total area, 65.8% of the closed forest, and 23.5% of the other vegetation cover under protection (Table 1). In 2000, about 22% of the world s population lived in and around the hotspots. In these hotspots, 3.5% of the population lived in and around the closed forest and 6.5% in and around other vegetation (Fig. 2). In 15 hotspots, the percentage of the population living in closed forest areas was higher than the world average (12%). In 8 hotspots, more than 20% of the population lived in closed forest. With 44%, the Mountains of South-Central China had the highest percentage of its population living in closed forest. Succulent Karoo and Polynesia-Micronesia had no closed forest and hence no population in this category. In 17 hotspots, the percentage of the population in areas under other vegetation cover was above the global average of 26%. Five hotspots had a population of more than 50% in other vegetation cover areas; the highest was 76% in Brazilian Cerrado and the lowest was 4.3% in Polynesia- Micronesia. The population concentration was higher in areas with a greater percentage of closed forest and other vegetation cover. In 2000, 10.7% of closed forest areas and 13% of areas with other vegetation cover were subject to high population pressure in the hotspots (Fig. 3). By isolating population pressure in closed forest and other vegetation cover from other factors, we identified four hotspots most at risk from high human population pressure: Western Ghats and Sri Lanka, Polynesia-Micronesia, and the Philippines and Caribbean Islands hotspots. Almost all closed forest and other vegetation cover in New Caledonia, Southwest Australia, and Cerrado were free from high population pressure. Habitat, Human Population, and Protection Status of Megadiverse Countries Closed forests and other vegetation cover in the 17 megadiverse countries occupied an average of 25.2% and 32.3% of total land area, respectively (Table 2). Most of the countries had more than 50% of their land area under vegetation cover except Australia, India, the Philippines, China, South Africa, and the United States (Fig. 1). Ten countries had more than 30% of their land area under closed forest. Six countries had < 25% of their land in closed forest cover. Madagascar had the highest percentage of its land covered in other vegetation (about 75%), and India had the lowest (about 18.4%) in this category.

6 1278 Integrating Social Factors into Priority Setting Shi et al. Table 1. Vegetation cover, closed forest, protected status, human population pressure, and relative ranks for 25 biodiversity hotspots. Area Area under high Global Land under human population endemic Sensitive area (%) protection (%) pressure (%) a species (%) b analysis c Hotspot, area (km 2 ) other closed other closed other closed score 1 score 2 score 3 vegetation forest vegetation forest vegetation forest vertebrates plants (rank) (rank) (rank) Atlantic Forest, d 1,480, (2) 7.1 (2) 7.2 (2) Brazilian Cerrado, 1,830, (6) 3.1 (12) 3.2 (12) California Floristic Province, 351, (7) 1.8 (17) 1.6 (17) Cape Floristic Region, 74, (5) 4.1 (9) 4.2 (9) Caribbean Islands, d 247, (3) 5.8 (5) 5.6 (5) Caucasus, 556, (3) 6 (4) 6 (4) Central Chile, 289, (6) 2.9 (13) 2.8 (13) Choco-Darien-Western Ecuador, 223, (4) 5 (7) 5 (7) Eastern Arc Mountains & Coastal Forests, c 191, (7) 2.2 (15) 2.4 (15) Guinean Forests of West Africa, 879, (5) 4 (10) 4 (10) Indo-Burma, e 2,273, (3) 6 (4) 6 (4) Madagascar & Indian Ocean Islands, f 597, (4) 5.1 (6) 5.2 (6) Mediterranean Basin, 520, (2) 7 (3) 7 (3) Mesoamerica, 1,144, (4) 4.8 (8) 4.6 (8) Mountains of S. Central China, 557, (7) 2 (16) 2 (16) New Caledonia, 17, (5) 4 (10) 4 (10) New Zealand, 257, (8) 0 (18) 0 (18) Philippines, f 280, (1) 8 (1) 8 (1) Polynesia-Micronesia, 1, (5) 4 (10) 4 (10) Southwest Australia, 306, (6) 3.1 (12) 3.2 (12) Succulent Karoo, 102, (5) 4 (10) 4 (10) Sundaland, f 1,475, (5) 3.8 (11) 3.6 (11) Tropical Andes, 1,396, (6) 3.1 (12) 3.2 (12) Wallacea, 317, (6) 2.8 (14) 2.6 (14) Western Ghats & Sri Lanka, e 254, (5) 4 (5) 4 (5) Average, 15,629, a Human population density > 100/km 2. b Data from Myers et al. (2000). Number of global endemic vertebrates 27,298; number of global endemic plants 30,000. c We conducted two sensitivity analyses: weights of 1.1 and 1.2 to closed forest and associated indicators (high population pressure on closed forest and protection status of closed forest) and the weights of 0.9 and 0.8, respectively, to other vegetation cover and associated indicators. Score and rank are based on equal weights given to each dimension and indicator. If the value of the indicators (other vegetation cover, closed forest, protection status of other vegetation cover, and protection status of closed forest) was less than its mean value, then the indicator was scored as 1; otherwise, it was zero. Similarly, for the other four indicators (high population pressure in other vegetation cover, high population pressure in closed forest, endemic vertebrates, and endemic plants), if the value of an indicator was greater than its mean value, the indicator was scored as 1; otherwise, it was zero. On the basis of the indicator scores, the total score was calculated for each hotspot, and the hotspots were ranked on the basis of the total score. d Hottest hotspots in Myers et al. (2000) that appear at least four times in the top 10 listings for each factor. e Hottest hotspots in Myers et al. (2000) that appear at least three times in the top 10 listings for each factor. f The leading hottest hotspots on the list of Myers et al., in which appear all five factors in the top 10 listings for each factor.

7 Shi et al. Integrating Social Factors into Priority Setting 1279 Figure 1. Closed forest and other vegetation cover in 25 biodiversity hotspots and 17 megadiverse countries. Megadiverse countries: A, United States; B, Mexico; C, Colombia; D, Ecuador; E, Peru; F, Venezuela; G, Brazil; H, Democratic Republic of the Congo (Zaire); I, South Africa; J, Madagascar; K, India; L, China; M, Malaysia; N, Philippines; O, Indonesia; P, Papua New Guinea; Q, Australia (Mittermeier et al. 1997). Twenty-five biodiversity hotspots: 1, California Floristic Province; 2, Mesoamerica; 3, Chocó-Darién Western Ecuador; 4, Tropical Andes; 5, Central Chile; 6, Caribbean; 7, Brazilian Cerrado; 8, Atlantic Forest Region; 9, Mediterranean Basin; 10, Guinean Forests of West Africa; 11, Succulent Karoo; 12, Caucasus; 13, Eastern Arc Mountains & Coastal Forests of Tanzania & Kenya; 14, Cape Floristic Province; 15, Indo-Burma; 16, Western Ghats & Sri Lanka; 17, Madagascar & Indian Ocean Islands; 18, Mountains of South-Central China; 19, Sundaland; 20, Phlippines; 21, Wallacea; 22, Southwest Australia; 23, Polynesia-Micronesia; 24, New Caledonia; 25, New Zealand (Myers et al. 2000). About 20.1% of closed forest and 7.6% of areas under the cover of other vegetation had been accorded formal protection status in the megadiverse countries. Countries with the most protected area in these categories were Venezuela, the United States, Colombia, and Indonesia. China, Madagascar, Mexico, and South Africa had poor protection status. Some 3.38 billion people, or more than half of the world s population of 6.09 billion ( WRI 2000), were concentrated in the 17 megadiverse countries. China and India had the most, with more than 2 billion people. In 5 countries, more than 20% of the total population lived in and around closed forest. In 8 countries, half or more of the total population lived in and around areas covered by other vegetation. Compared with the world average, the 17 megadiverse countries had on average a higher percentage of closed-forest areas with high population pressures but a lower percentage of closed-forest areas with low and medium population pressures. At the national level, 8 countries had a higher percentage of closed forest areas under high population pressure than the global average of 5.1%. The results for areas covered by other vegetation were similar. Compared with the world average, an average of 17 megadiverse countries had a higher percentage of areas with other vegetation subject to high population pressures but a lower percentage of these areas where population pressures were low and medium. At the national level, the percentage of areas under other vegetation cover with high population pressures was higher than the global average (8.3%) in only 7 megadiverse countries. Habitat, Human Population, and Protection Status in the Hotspots within 17 Megadiverse Countries The 17 megadiverse countries include 60% of the geographic area of the 25 hotspots (Table 3). Hotspots covered the total geographical areas of two countries Madagascar and Malaysia whereas the Democratic Republic of Congo and Papua New Guinea had no hotspots. More than 70% of the geographical area of the Philippines, Ecuador, and Indonesia were covered by hotspots, whereas hotspots covered < 10% of the geographical area of the United States, Australia, Venezuela, and China.

8 1280 Integrating Social Factors into Priority Setting Shi et al. Figure 2. Percentage of human population living in and around closed forest and other vegetation cover in 25 biodiversity hotspots (1-25; see Fig.1 for names and locations). Figure 3. Human population pressure in closed forest and other vegetation cover in 25 biodiversity hotspots (1-25; see Fig. 1 for name and location).

9 Shi et al. Integrating Social Factors into Priority Setting 1281 Table 2. Vegetation cover, closed forest, protected status, human population pressure, and relative ranks for 17 megadiverse countries. Area Area under high Global Land under human population endemic Sensitive area protection (%) pressure (%) a species (%) b analysis c Country, other closed other closed other closed score 1 score 2 score 3 area (km 2 ) vegetation forest vegetation forest vegetation forest vertebrates plants (rank) (rank) (rank) Australia, 7,686, (3) Brazil, 8,500, (5) 3 (8) 3 (9) China, 9,402, (1) 7 (1) 7 (1) Colombia, 1,141, (8) 0 (11) 0 (12) D.R. of the Congo, 2,338, (6) 2 (9) 2 (10) Ecuador, 248, (4) 4 (6) 4 (7) India, 3,154, (2) 6 (2) 6 (2) Indonesia, 1,887, (4) 3.8 (7) 3.6 (8) Madagascar, 592, (3) 5.1 (3) 5.2 (4) Malaysia, 327, (4) 3.8 (7) 3.6 (8) Mexico, 1,953, (3) 4.9 (4) 4.8 (6) Papua New Guinea, 459, na 2 (6) 2 (9) 3 (9) Peru, 1,295, (6) 2 (9) 2 (10) Philippines, 288, (1) 7 (1) 7 (1) South Africa, 1,221, na 4 (4) 4.2 (5) 5.4 (3) United States, 9,406, (6) 2 (9) 2 (10) Venezuela, 914, (7) 1 (10) 1 (11) Average a Human population density > 100/km 2. b Data from WRI (2002). Number of global endemic vertebrates 27,298; the number of global endemic plants 30,000. c We conducted two sensitivity analyses: the weights of 1.1 and 1.2 to closed forest and the associated indicators (high population pressure on closed forest and protection status of closed forest) and the weights of 0.9 and 0.8, respectively to other vegetation cover and the associated indicators. The score and rank are based on the equal weights given to each dimension and indicator. If the value of the indicators (other vegetation cover, closed forest, protection status of other vegetation cover, and protection status of closed forest), was less than its mean value, then the indicator was scored as 1; otherwise it was zero. Similarly, for the other four indicators (high population pressure in other vegetation cover, high population pressure in closed forest, endemic vertebrates, and endemic plants), if the value of an indicator was greater than its mean value, then the indicator was scored as 1; otherwise it was zero. On the basis of the indicator scores, the total score was calculated for each hotspot, and the hotspots were ranked on the basis of the total score. Hotspots covered 10 40% of the total geographical area of 8 other countries. In 9 countries, closed forests covered more than 30% of each country s total area of hotspots; Mexico and Malaysia s hotspots had the highest proportion of closed forest area at 49.7% and 49.3%, respectively, whereas South Africa s hotspots had the lowest proportion of closed forest area (1.6%). The hotspots in these 17 megadiverse countries were covered with other vegetation in percentages ranging from 20% to 80%, except foraustralia and South Africa. An average of only 7.9% of the land in the 17 megadiverse countries hotspots was designated as protected area. The averages for the other categories were 5.3% of the area under other vegetation and 15.6% of the area under closed forest. Venezuela, with 66.1% of the hotspots, had the highest percentage of its hotspots protected, with 67.7% of the area under other vegetation and 65.5% of closed forest under protection. Ten of the megadiverse countries the Philippines, India, Venezuela, China, Ecuador, Colombia, Indonesia, the United States, Malaysia, and Mexico had high proportions of closed forest and other vegetation cover subject to high population pressure. The proportions of closed forest and other vegetation cover with high population pressure in the other countries were less than the global averages. Relative Rankings of 25 Hotspots and 17 Megadiverse Countries On the basis of the total score of eight indicators for each hotspot, we ranked and grouped the 25 hotspots into three categories: coolest hotspots (scores from 0 to 2), intermediate hotspots (scores from 3 to 5), and hottest hotspots (scores from 6 to 8) (Table 1). On the basis of equal weights for each indicator, the Philippines ranked first and New Zealand was last. In terms of grouping, 6 hotspots (the Philippines, Atlantic Forest, Mediterranean Basin, Caribbean Islands, Caucasus, and Indo-Burma) were the hottest, and 4 hotspots (New Zealand, the Eastern Arc Mountains & Coastal Forests, the California Floristic Province, and the Mountains of South- Central China) were the coolest. The assignment of different weights to the indicators associated with closed forest and other vegetation (1.1

10 1282 Integrating Social Factors into Priority Setting Shi et al. Table 3. Vegetation cover, closed forest, protected status, human population pressure, and relative ranks for 25 biodiversity hotspots in 17 megadiverse countries. Area under high Area under human population Global endemic Land area (%) protection (%) pressure (%) a species (%) b Sensitive analysis c other closed other closed other closed score 1 score 2 score 3 Country and hotspot vegetation forest vegetation forest vegetation forest vertebrate plants (rank) (rank) (rank) Brazil Atlantic Forest (1) 7.1 (1) 7.2 (1) Brazilian Cerrado (6) 2.2 (13) 2.4 (13) China Indo-Burma (2) 6 (3) 6 (3) Mountains of S. Central China (6) 2 (14) 2 (14) Colombia Choco-Darien-Western Ecuador (5) 3 (11) 3 (11) Tropical Andes (4) 4 (7) 4 (7) Ecuador Choco-Darien-Western Ecuador (2) 5.9 (4) 5.8 (4) Tropical Andes (5) 3.1 (10) 3.2 (10) India Indo-Burma (2) 6 (3) 6 (3) Western Ghats & Sri Lanka (4) 4 (7) 4 (7) Indonesia Sundaland (4) 3.8 (9) 3.6 (9) Wallacea (5) 2.8 (12) 2.6 (12) Malaysia Indo-Burma (1) 7.1 (1) 7.2 (1) Sundaland (3) 4.9 (6) 4.8 (6) Mexico California Floristic Province (2) 6.1 (2) 6.2 (2) Mesoamerica (4) 3.9 (8) 3.8 (8) Peru Choco-Darien- Western Ecuador (5) 3 (11) 3 (11) Tropical Andes (5) 3.1 (10) 3.2 (10) South Africa Cape Floristic Region (5) 3.1 (10) 3.2 (10) Succulent Karoo (4) 4 (7) 4 (7) United States California Floristic Province (6) 1.8 (15) 1.6 (15) Caribbean (4) 4 (7) 4 (7) Venezuela Caribbean (3) 5 (5) 5 (5) Tropical Andes (4) 3.9 (8) 3.8 (8) Average a Human population density > 100 people/km 2. b Data from WRI (2002). Number of global endemic vertebrates is 27,298; the number of global endemic plants is 30,000. c We conducted two sensitivity analyses: the weights of 1.1 and 1.2 to closed forest and the associated indicators (high population pressure on closed forest and protection status of closed forest) and the weights of 0.9 and 0.8, respectively to other vegetation cover and the associated indicators. The score and rank are based on the equal weights given to each dimension and indicator. If the value of the indicators (other vegetation cover, closed forest, protection status of other vegetation cover, and protection status of closed forest) was less than its mean value, then the indicator was scored as 1; otherwise, it was zero. Similarly, for the other four indicators (high population pressure in other vegetation cover, high population pressure in closed forest, endemic vertebrates, and endemic plants) if the value of an indicator was greater than its mean value, then the indicator was scored as 1; otherwise, it was zero. On the basis of the indicator scores, the total score was calculated for each hotspot, and the hotspots were ranked on the basis of the total score.

11 Shi et al. Integrating Social Factors into Priority Setting 1283 and 0.9 and 1.2 and 0.8 respectively) affected the ranks of some hotspots but did not change their membership in the different groups. For example, on the basis of equal weights, the Atlantic Forest and the Mediterranean Basin had equal score and rank, but when closed forest was assigned a higher weight, the Atlantic Forest ranked higher than the Mediterranean Basin but both hotspots maintained their grouping in the hottest category. A similar situation existed for the Caucasus and the Caribbean and Indo-Burma and the Caribbean. In sum, when slightly higher weights were assigned to indicators related to closed forest, the number of levels in the ranking increased, resulting in 18 ranks compared with 8 when the indicators were given equal weights. When only slightly higher weights (1.1 and 1.2) were given to indicators of closed forest, the three groupings of hotspots did not change. When a much higher weight (1.5) was assigned to indicators related to closed forests, however, Madagascar and the Indian Ocean Island moved to the hottest hotspot category. The same method was used for the 17 megadiverse countries (Table 2). On the basis of equal weights, China ranked first and Colombia last. In terms of grouping, 3 countries (China, the Philippines, and India) were the hottest and 6 countries (Democratic Republic of Congo, Papua New Guinea, Peru, the United States, Venezuela, and Colombia) were the coolest megadiverse countries. The outcome of the assignment of different weights to the indicators associated with closed forest gave similar, but not exactly the same, results as in the case of hotspots. For example, applying the relatively low weight of 1.2 to indicators related to closed forests moved Papua New Guinea from the coolest to the intermediate category. Similarly, these weights (1.1 and 1.2) did not have any effect on a country s ranking within the hottest megadiverse category, but the weight of 1.2 created seven distinguishable ranks in the category of the intermediate megadiverse countries, whereas assigning equal weights resulted in only three distinguishable ranks within the group. We also compared the hotspots within those megadiverse countries that have more than one hotspot (Table 3). For this comparison, however, numbers of endemic plants and vertebrate species in a country-specific area of the hotspot were not available, and we used the same numbers of endemic plants and vertebrate species for a portion of a hotspot as in a given country. In all the megadiverse countries except Peru, the total scores of two hotspots, for the case of equal weights, were different, and a change in the weights did not affect the ranking of the hotspots within the given country. In Peru, the two hotspots, Choco-Darien-Western Ecuador and Tropical Andes, had the same score of 3 for the case of equal weights, but for higher weights the closed-forestrelated indicators created a distinction between the two hotspots. Discussion and Conclusions Biodiversity conservation policies and practices are inherently social phenomena, but the conservation community continues to look to the biological sciences to design these policies and practices. Although biologists and practitioners, at least in recent years, have increasingly recognized that social factors are often the primary determinants of the success or failure of conservation efforts (Mascia et al. 2003), biological criteria continue to dominate the literature on priority-setting. Clearly, the biological sciences have the theoretical and analytical tools to identify rare and threatened species and ecosystems, but the failure to integrate social aspects in the priority setting process may lead to either partial success or total failure. The results of our proposed method of relative ranking, which incorporates biological and social aspects, provide interesting outcomes that should enrich conservation planners and practitioners understanding of priority setting for conservation areas. As far as we know, no other study has included all the four dimensions of biodiversity conservation that we incorporated here. Myers et al. (2000), Brummitt and Lughadha (2003), Myers and Mittermeier (2003), and Ovadia (2003) rank hotspots for conservation priority setting, but their ranking is based on the number and number-area ratio of endemic species. They also included remaining primary vegetation as the percentage of original extent, but the data are questionable. We could not find any explanation (reference year) for the original extent of primary vegetation in any of these papers, and the reliability of vegetation data for the period of hundreds or thousands of years ago is extremely limited. In addition, the method of identifying the hottest hotspots used in these studies hottest hotspots appear at least three times in the top 10 listings for each factor is highly subjective. The main issue in question is the choice of the number of hotspots included in the top category. We question a method that selects the top 10; why not the top 5, 7, or 11? For example, by identifying the top 5 instead of the top 10, only 1 hotspot the Philippines would be left on the list based on the method proposed by Myers et al., whereas Sundaland, Madagascar, the Tropical Andes, the Caribbean Islands, and Mesoamerica would be on the list based on the method proposed by Brummitt and Lughadha. In our method, we compared each hotspot with the average of all the hotspots or the megadiverse countries, not with an arbitrary number, and we did not select a limited number of areas; rather we ranked all the areas. Hence, in addition to the inclusion of all four dimensions, our method is robust. Furthermore, we address the indeterminacy of weights through sensitivity analysis. Our results are also quite different from and more informative than the studies mentioned above. Four hotspots the Philippines, the Atlantic Forest, the Caribbean,

12 1284 Integrating Social Factors into Priority Setting Shi et al. and Indo-Burma are common to Myers et al., Brummitt and Lughadha, and our list of the hottest hotspots. Our list, however, includes the Mediterranean Basin and the Caucasus, which are on neither the Myers et al. nor the Brummitt and Lughadha lists. Similarly, Madagascar and Sundaland are on the lists of both Myers et al. and Brummitt and Lughadha, but they do not appear on our list. In fact, the Eastern Arc Mountains and Coastal Forest, one of the hottest hotspots according to Myers et al., is in the category of coolest hotspots on our list. The outcome of our comparative study indicates that depending exclusively on the number and the numberarea ratio of endemic species to identify priority conservation areas and excluding criteria related to habitat, human population pressure, and protection status can have serious consequences for conservation priority setting. On the basis of these outcomes, we suggest that conservation policies and strategies should use the following categories of hotspots: (1) high percentage of endemic species and plants, high population pressure, low closed forest and other vegetation cover, and low protection status (hottest hotspots); (2) high percentage of endemic species and plants, low protection status, population pressure not high (Madagascar and Indian Ocean Island); (3) high percentage of endemic species and plants, low habitat status, protection status above average (Sundaland and Tropical Andes); and (4) low percentage of endemic species and plants, low population pressure, and high vegetation cover, closed forests, and protection status (coolest hotspots). Based on this classification, class-specific and hotspot-specific conservation policies and strategies should be developed. For example, in the hotspots with a high percentage of forests and vegetation cover, the emphasis should be on developing effective protection-area strategies, whereas in the hotspots with a low percentage of forests and vegetation cover emphasis should be on restoration strategies. Similar categorization and approaches should be used for the megadiverse countries. In some countries, different hotspots have different features, whereas in other countries all the hotspots have the same features. For example, the California Floristic Province (Mexico) has percentages of closed forests and other vegetation cover below the respective mean values and percentages of high population pressure areas, in closed forests and vegetative areas, above the respective mean values. The Mesoamerica hotspot (Mexico) has percentages of vegetation cover and closed forests above the respective mean values and the percentages of high population pressure areas, in closed forests and other vegetative areas, below the respective mean values. Sundaland and Wallacea (Indonesia) have similar features. Hence, some countries in which different hotspots have diverse features will require hotspot-specific conservation policies and strategies, whereas countries with similar hotspots can have generic policies. In addition, many hotspots are spread over many countries, and in transboundary hotspots the contradictions in approach and conflicts of interest should be harmonized. The importance of the social aspects of biodiversity conservation and the contribution that social sciences can make to conservation efforts must be emphasized. We have provided a base for incorporating the four critical dimensions of biodiversity conservation in priority setting at the macrolevel as we did with the 25 hotspots and the megadiverse countries. In our analysis, we used equal weights for all four dimensions and conducted a sensitivity analysis to assign different weights to the indicators related to closed forests. The next step should be to identify actual weights for different dimensions and to conduct the analysis based on those weights. A holistic view is a necessity in biodiversity conservation priority setting. We suggest, therefore, that future studies should focus on such a holistic view and on the integration of social and biological aspects. Acknowledgments The views expressed in this publication are not necessarily those of the agencies cooperating in this project. The designations employed and the presentations do not imply the expression of any opinion whatsoever on the part of cooperating agencies concerning the legal status of any country, territory, city, or area or of its authorities or of the delineation of its frontiers or boundaries. We are grateful to UNEP, the U.S. National Aeronautics and Space Administration, and U.S. Geological Survey for financial support. We are grateful to T. Loveland and his team for generating and providing the IGBP global land-cover-distribution database ( We are extremely thankful to all reviewers and editors for their most valuable comments. Literature Cited Brooks, T. M., et al Habitat loss and extinction in hotspots of biodiversity. 16: Brummitt, N., and E. C. Lucghadha Biodiversity: where is hot and where is not. 17: CI (Conservation International) Biodiversity hotspots boundaries database. CI, Washington, D.C. Available from biodiversityhotspots.org/xp/hotspots/home/(accessed March 2001). Cihlar, J., D. 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