Integrating Habitat Status, Human Population Pressure, and Protection Status into 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 , Atlantic Forest, Mediterranean Basin, Caribbean Islands, Caucasus, and Indo-Burma are the hottest hotspots and that , the Philippines, and 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

Integraci´on del Estatus del H´abitat, la Presi´on de la Poblaci´on Humana y el Estatus de Protecci´on a la Definici´on de Prioridades de Conservaci´on Resumen: La definicion´ de prioridades es un componente esencial de la conservacion´ de la biodiversidad. Los m´etodos actuales para identificacion´ de prioridades para la conservacion´ se han centrado casi por completo en factores biologicos.´ Sugerimos un nuevo m´etodo de clasificacion´ relativa para identificar areas´ de conservacion´ prioritarias que integra tanto aspectos biologicos´ como sociales. Se basa en los siguientes criterios: el estatus del habitat,´ la presion´ de la poblacion´ humana, los esfuerzos humanos para proteger el habitat´ y el numero´ de especies end´emicas de plantas y vertebrados. Utilizamos este m´etodo para clasificar a 25 sitios de importancia para la conservacion,´ 17 pa´ıses megadiversos y los sitios de importancia para la conservacion´ en cada pa´ıs megadiverso. Utilizamos conjuntos de datos consistentes, georeferenciados y de banda multiple´ as´ı como herramientas de percepcion´ remota anal´ıtica y de sistemas de informacion´ geografica´ para cuantificar el estatus del habitat,´ la presion´ de la poblacion´ humana y el estatus de proteccion.´ La clasificacion´ sugiere que las Filipinas, el Bosque Atlantico,´ la Cuenca del Mediterraneo,´ las Islas Caribenas,˜ el Caucaso´ e India-Burma son los sitios de importancia mas´ importantes y que China, las Filipinas e India son los pa´ıses megadiverosos mas´ importantes. La gran variacion´ en t´erminos de habitat,´ areas´ protegidas y presion´ de la poblacion´ entre

††email [email protected] Current address: Colorado Division of Wildlife, 6060 Broadway, Denver, CO 80216, U.S.A. P§aper submitted September 8, 2003; revised manuscript accepted December 30, 2004.

1273 Conservation Biology 1273–1285 C 2005 Society for Conservation Biology " DOI: 10.1111/j.1523-1739.2005.00225.x 1274 Integrating Social Factors into Priority Setting Shi et al. los sitios de importancia, los pa´ıses megadiversos y los sitios de importancia en un pa´ıs sugiere la necesidad de pol´ıticas de conservacion´ espec´ıficas para los sitios de importancia y los pa´ıses.

Palabras Clave: estatus del h´abitat, estatus de protecci´on, pa´ıses megadiversos, presi´on humana, SIG, sitios de importancia para la conservaci´on

Introduction (1997) found a linear programming algorithm to be the optimal means for identifying areas to set aside as re- The importance of the ecological, social, economic, cul- serves. Results of a study by Margules and Pressey (2000) tural, and aesthetic values of biodiversity has been widely suggest six stages for conservation planning and include recognized (Pimm et al. 1995; Mittermerier et al. 1999). minimum-size area, complementarity, and minimal previ- Biodiversity is being significantly reduced by human activ- ous disturbance as the key determining factors for set- ities, however, and habitat destruction is the main threat ting conservation goals. Karieva and Marvier (2003) em- (Dompka 1996; Mittermerier et al. 1999; Thompson & phasized the need for incorporating a measure of the ef- Jones 1999). Some researchers estimate that the clearing fectiveness of past conservation efforts in conservation of half the world’s remaining forests would eliminate 85% priority setting. Rodrigues et al. (2004) used gap analy- of all the species they contain (Wilcove et al. 1998; Pimm sis to evaluate the effectiveness of the global protected &Raven 2000). Over the last two decades, the signifi- area network. Finally, Reid (1998) found that the value of cance of this threat has led to a growing awareness of hotspots may be more limited at smaller scales but that at the importance of biodiversity and habitat conservation. large geographic scales hotspots prove to contain useful Conservation experts have focused on identifying con- information for conservation planning. servation areas of prime importance as one of the keys to In almost all these studies, the indicators for setting pri- conserving the planet’s disappearing species, genes, and orities for conservation focus on plant or animal species. ecosystems (Olson & Dinerstein 1998; Stattersfield et al. Parameters include the total number and density of en- 1998; Prendergast et al. 1999; UNEP 1999). Their work demic plant and vertebrate species, gap species, or cov- has resulted in the identification of 17 megadiverse coun- ered species; the minimum area needed for adequate con- tries (McNeedly et al. 1990; Mittermeier et al. 1997) and servation; the state of connectivity; and the existence of 25 biodiversity hotspots (Mittermeier et al. 1999; Myers complementarity. These are all necessary, but together et al. 2000). they are still not adequate as a basis on which to set biodi- In 1995 more than 1.1 billion people were living in versity priorities. Some studies emphasize the importance biodiversity hotspots. The annual population growth rate of other biological and social aspects for biodiversity con- of 1.8% in these hotspots (1995–2000) was substantially servation. These include the status of habitat (Scott et al. higher than the annual global population growth rate of 1993; Kautz & Cox 2001), human population pressure 1.3% (Cincotta et al. 2000). Growing human populations, (Cincotta et al. 2000; Sanderson et al. 2002; Liu et al. owing to their increased demand for land, material prod- 2003), and human efforts to protect habitat (Karieva & ucts, and development projects, threaten natural habitats. Marvier 2003). None of the studies, however, integrates The most serious consequences of further habitat loss biological and social aspects in the criteria for setting occur in hotspot areas (Brooks et al. 2002). In addition, priorities for biodiversity conservation. In addition, past hotspots are high in species endemism and low in pris- studies also suffer from problems related to the reliabil- tine vegetation. Hence, various scholars, including Myers ity of data sources and from variability in the precision et al. (2000) and Pimm et al. (2001), have called for im- and accuracy of data (Myers et al. 2000). Similarly, these mediate steps to conserve these hotspots (Myers 1990; studies treated hotspots and megadiverse countries as in- SEPA 1998). dependent physical identities, which may not be an opti- The annual amount of financial resources available for mal approach because many hotspots are located within conservation—about US$6 billion ( James et al. 1999)—is megadiverse countries. small relative to the geographical area that requires bio- On the basis of these aforementioned issues, our diversity conservation (Weitzman 1998; GEF 2001). This goal was to develop a quantitative assessment of habi- financial resource scarcity has resulted in a variety of stud- tat (closed forest and other vegetation), protected areas, ies that focus on setting priorities for biodiversity con- and human population pressures in the 25 hotspots and servation. Those conducted by Mittermeier et al. (1999), 17 megadiverse countries. Using this quantitative assess- Myers et al. (2000), Brummitt and Lucghadha (2003), and ment, we developed a method of relative ranking for Ovadia (2003) prioritized hotspots (the hottest hotspots) conservation priority setting and compared the priority- based on the number of endemic species and their num- setting results of the 25 hotspots with the results from ex- ber area ratio. After comparing five algorithms, Csuti et al. isting macrolevel studies. We also extended the analysis

Conservation Biology Volume 19, No. 4, August 2005 Shi et al. Integrating Social Factors into Priority Setting 1275 of priority setting to the 17 megadiverse countries and over 10-day compositing periods. Bands 1 and 2 (red and the hotspots located within them. 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 Methods composites with a rule of minimum band 1 (red band) value (Waller & Zhu 1999). In the temporal composit- ing process, the maximum NDVI algorithm tends to re- We used a recent comprehensive, georeferenced, and tain large off-nadir pixels in the backscatter direction (Qi multiband data set, collected through the Advanced Very et al. 1993; Cihlar et al. 1994), resulting in varying pixel High Resolution Radiometer (AVHRR) and obtained from sizes and additional noise introduced into spectral bands. the National Oceanic and Atmospheric Administration Visual examination of the source data showed that large (NOAA). Various analytical tools for remote sensing and backscatter view angles were associated with high red geographic information systems (GIS) tools were used and infrared red (IR) reflectance in forest land. As a partial for digital data processing and analyses, such as Imagine measure to correct for this bidirectional effect, we used (ERDAS, Atlanta, Georgia) and the Environment for Vi- the minimum red band-compositing rule to better pre- sualizing Images (Research System, Boulder, Colorado). serve pixels near nadir or in fore-scatter direction and to Most multiple layer overlay analysis was done in the Grid maintain the integrity of patterns of forest lands. The final module of ArcGIS (ESRI, Redlands, California). Raster and image data consisted of 12 monthly composites of the first vector data layers were in an Interrupted Goode Homolo- two AVHRR bands (red and infrared) and the NDVI band sine Projection (a global equal area projection), and all for each of five major land masses: Africa, and raster data sets had a cell size of 1000 m. Digital data tropical Asia, Eurasia (Europe and temperate/subtropical analyses included estimation of habitats (closed forests Asia), North America, and South America. and other vegetation), human population pressure, and protection status. Combination Model with Linear Mixture Modeling Estimation of Habitats and Scaling of NDVI The recent International Geosphere–Biosphere Program To estimate the fraction of forest cover, we used a com- (IGBP) Data and Information System’s global land-cover bination of linear mixture modeling and scaling of NDVI mapping (Loveland et al. 2000) provides a basis for esti- and the visible (red) band based on pixel positions along mating the size of various habitats, including closed for- the near-IR band. In the combined model, pixels were est, other vegetation, and areas with no vegetation cover. modeled depending on their relative positions along the Forest classes in the IGBP database are not adequate for IR band axis. Pixels with low IR reflectance contain new direct conversion to the vegetation classes in our study. forests in burned areas, woody wetlands, and other dark We used the standard definitions of closed forest (canopy land cover such as shadow or water, and these pixels were density 40%) and open forest (canopy density between best scaled with their NDVI values, which are insensitive 10% and≥ 40%) (FAO 1999) and mapped six vegetation to illumination variance (Holben et al. 1986). In the red-IR classes: closed forest, open forest, other wooded land, space, the distribution of NDVI values for green vegeta- grassland, unvegetated land, and water. The description tion varies between the diagonal line (NDVI as 0) and the of the four classes in the IGBP database, other than closed boundary near the IR band axis (NDVI as 1). In our data and open forest, was compatible with our requirements, set, NDVI values varied between 0.3 and 0.8. Hence, for so these classes were derived from the IGBP database. pixels with low IR reflectance we used scaling that was Our method for closed and open forest-cover mapping set flexibly between different forest cover types. For the consisted of the following: temporal image compositing, details of this combined model (the three methods), refer estimating the percentage of closed and open forest cover to Zhu and Waller (2003). with a combination model of linear mixture modeling and Even with the use of these three methods in the mix- NDVI scaling, linking the resulting percent forest cover ture analysis, significant regional variations in climate, to- to the IGBP classes, and validation. pography, and forest types required the use of geographic stratification to ensure that the same canopy definitions could be mapped across varying regional conditions. Geo- Temporal Image Compositing graphic stratification for each continent was based on dig- Source data used for the forest-cover mapping were drawn itized lines following combinations of ecoregions, phys- from AVHRR NDVI composites produced for February iography, vegetation types, and imagery conditions. 1995–January 1996. The temporal composites, consist- The three methods in the combined model for for- ing of five calibrated AVHRR bands and an NDVI band, est canopy mapping were applied to each monthly were computed using the protocol of maximum NDVI AVHRR/NDVI composite. To provide the results least af- value (Holben et al. 1986; Eidenshink & Faundeen 1994) fected by the atmosphere, the final percentage of forest

Conservation Biology Volume 19, No. 4, August 2005 1276 Integrating Social Factors into Priority Setting Shi et al. cover for a continent was determined over the course of Estimation of Human Population Pressure the year (February 1995–January 1996) on the basis of maximum monthly forest cover value achieved, regard- We analyzed human population pressure with the ge- less of the method chosen. The maximum forest com- ographically referenced population database of the positing was compared to average forest canopy cover UNEP/Global Resource Information Database (GRID), the over the course of the year. We examined areas of high global vegetation map, and the original boundary grid of ratio to prevent overestimating from anomalous data. The the hotspots. We estimated the number of people living two forest classes—closed forest and open forests—were in closed forest areas and in the other vegetation areas then derived on the basis of their definitions. for each hotspot and each megadiverse country. The es- timation was done by overlaying different grid layers. We Adapting the IGBP Classes to the Vegetation Classification used the population pressure classification system sug- gested by Singh et al. (2001), which groups population In the four remaining vegetation classes, the two grassland pressure into three categories: (1) low, <25 people/km2; and water classes were obvious. The “other wooded land” (2) medium, 25–100 people/km2; and (3) high, >100 peo- class consisted primarily of open or closed shrub land ple/km2. 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, A Method for Relative Ranking of Conservation Areas ice and snow, and cropland from the IGBP database. To Four essential components of a relative ranking method derive these four vegetation classes, the IGBP classes were are the dimensions of comparison, indicators for each di- used as the baseline data continent by continent. The re- mension, a scoring method, and the weights assigned to finement methods to fit IGBP classes to the definitions different dimensions and indicators. As outlined in the were similar to the methods used in producing the IGBP introduction, there are four critical dimensions of biodi- classes. Class mergers and splits were aided by ancillary versity conservation for a given geographical area: (1) the data sets such as ecoregions and digital elevation mod- number of endemic plant and vertebrate species in the els. Spectral reclustering and user-defined polygon splits area (Mittermeier et al. 1999; Myers et al. 2000); (2) the (Loveland et al. 1999) were also used. In areas of disagree- status of habitat in the area (Scott et al. 1993; Kautz & Cox ment, the two initially defined forest classes took priority. 2001), which can be represented by the extent of closed Finally, the mapping of these four vegetation classes was forests and other vegetation in the area; (3) the human merged with the mapping of the two forest classes for population pressure on the habitat (Cincotta et al. 2000; each continent to produce a global vegetation map. Sanderson et al. 2002; Liu et al. 2003) in the area; and The boundary grid showing the original extent of (4) the human efforts to protect the habitat and endemic hotspots, obtained from the Conservation International species in the area (Karieva & Marvier 2003). We included Database (CI 2001), was combined with the global vege- all four dimensions in our method. tation map to quantify closed forest and other vegetation The first two dimensions, endemic species and habi- in the hotspots. The same exercise was repeated for the tat, have two natural subdimensions: endemic plants and 17 megadiverse countries and for the hotspots in these endemic vertebrates and closed forest and other vege- 17 countries. Political boundary data were taken from the tation, respectively. The two other dimensions (human U.S. National Imagery and Mapping Agency’s Vector Map population pressure and protection efforts) required cat- Level 0 series. egorization in two subdimensions because of the two cat- egories of habitat. Hence, we needed a total of eight indi- Estimation of Protection Status of Habitats cators, two for each dimension: (1) number of endemic The World Commission on Protected Areas (WCPA) plants, (2) number of endemic vertebrates, (3) percentage and the UNEP World Conservation Monitoring Center of other vegetation in the total land area, (4) percentage (WCMC) provided the protected-areas georeferenced of closed forest in the total land area, (5) percentage of database. The database included the legal designation, other vegetation area that is under high population pres- name, IUCN management category, area, location (poly- sure, (6) percentage of closed forest that is under high gons), and the year of establishment of some 20,000 sites. population pressure, (7) percentage of other vegetation To calculate the protected-area status of the closed forests that is protected, and (8) percentage of closed forest that and other vegetation in the biodiversity hotspots, we over- is protected. lapped the protected-area grid, the closed forest and other In our method of relative ranking, we calculated the vegetation distribution grid prepared in the first step, and mean value of each indicator for the 25 hotspots and the the grid of the original boundaries of the hotspots. The 17 megadiverse countries separately. For each of the 25 same exercise was repeated for the 17 megadiverse coun- hotspots, the value of each indicator for a given hotspot tries and for the hotspots within the megadiverse coun- was compared with the mean value of the indicator for tries. the 25 hotspots. In this comparison, if the value of an

Conservation Biology Volume 19, No. 4, August 2005 Shi et al. Integrating Social Factors into Priority Setting 1277 indicator for a given hotspot for items 3, 4, 7, and 8 was Two hotspots, Polynesia-Micronesia and the Succulent Ka- less than its mean value, the indicator was scored as 1; roo, had no closed forests, whereas the remaining 13 otherwise, it was scored as 0. Similarly, for the indicators hotspots had < 25% of their area under closed forests of items 1, 2, 5, and 6, if the value of an indicator of a (Table 1). given hotspot was greater than its mean value, the indi- The protection status of most of the 25 hotspots was < cator was scored 1; otherwise, it was scored as 0. On the 10% of the total geographical area, 20% of the closed for- basis of each indicator’s score for a given hotspot, the total est, and 12% of other vegetation cover. The hotspot with score was calculated for each hotspot, and the hotspots the highest proportion of protected area was the Califor- were ranked on the basis of the total score. The same pro- nia Floristic Province, with 38.6% of the total area, 65.8% cedure was used for the relative ranking of megadiverse of the closed forest, and 23.5% of the other vegetation countries. In this case, however, the value of each indi- cover under protection (Table 1). cator for a given country was compared with the mean In 2000, about 22% of the world’s population lived in value of the indicator for the 17 megadiverse countries. and around the hotspots. In these hotspots, 3.5% of the Thus, this ranking method provided a relative ranking population lived in and around the closed forest and 6.5% of the hotspots (and megadiverse countries) among the in and around other vegetation (Fig. 2). In 15 hotspots, the group of hotspots (and megadiverse countries) and not percentage of the population living in closed forest areas an absolute ranking. was higher than the world average (12%). In 8 hotspots, As far as weights for different dimensions and indica- more than 20% of the population lived in closed forest. tors are concerned, at this stage, we did not have any With 44%, the Mountains of South-Central China had the field-tested criteria for assigning actual weights. Conse- highest percentage of its population living in closed for- quently, we started by assigning equal weights to each di- est. Succulent Karoo and Polynesia-Micronesia had no mension and indicator. On the other hand, there is strong closed forest and hence no population in this category. merit to assigning a higher weight to closed forest habi- In 17 hotspots, the percentage of the population in areas tat compared with other vegetation, but we did not know under other vegetation cover was above the global aver- the difference in weights for these two categories of habi- ageof 26%. Five hotspots had a population of more than tats. Hence, we conducted two sensitivity analyses: the 50% in other vegetation cover areas; the highest was 76% weights of 1.1 and 1.2 to closed forest and the associated in Brazilian Cerrado and the lowest was 4.3% in Polynesia- indicators (high population pressure on closed forest and Micronesia. The population concentration was higher in protection status of closed forest) and the weights of 0.9 areas with a greater percentage of closed forest and other and 0.8 to other vegetation cover and the associated indi- vegetation cover. cators, respectively. In 2000, 10.7% of closed forest areas and 13% of areas with other vegetation cover were subject to high popula- tion pressure in the hotspots (Fig. 3). By isolating popula- Results tion 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 Habitat, Human Population, and Protection Status and Sri Lanka, Polynesia-Micronesia, and the Philippines of 25 Hotspots and Caribbean Islands hotspots. Almost all closed forest Vegetation cover occupied 66.8% of the land area in the 25 and other vegetation cover in New Caledonia, Southwest hotspots. This vegetation cover consisted of 25.5% closed Australia, and Cerrado were free from high population forest, 41.3% other vegetation, and 33.2% unvegetated pressure. land and water. Designated protected areas covered only about 8.3% of the hotspot area and 15.9% and 6% of the Habitat, Human Population, and Protection Status of closed forest and the other vegetation areas, respectively Megadiverse Countries (Table 1). Vegetation covered more than 50% of the total area in Closed forests and other vegetation cover in the 17 18 hotspots and between 25% and 50% of the total area in megadiverse countries occupied an average of 25.2% and six hotspots. Only one hotspot had < 25% of the total area 32.3% of total land area, respectively (Table 2). Most of under vegetation cover (Fig. 1). The Mountains of South- the countries had more than 50% of their land area under Central China had the highest vegetation cover (89.2%), vegetation cover except Australia, India, the Philippines, and in four other hotspots vegetation covered more than China, , and the United States (Fig. 1). Ten 80% of the total area. The hotspot with lowest vegetation countries had more than 30% of their land area under cover was Succulent Karoo, with only 14%. closed forest. Six countries had < 25% of their land in Two hotspots, Mesoamerica and Wallacea, had more closed forest cover. had the highest percent- than 50% of the area under closed forests. Closed forests ageof its land covered in other vegetation (about 75%), occupied 25–50% of the area in eight hotspots (Table 1). and India had the lowest (about 18.4%) in this category.

Conservation Biology Volume 19, No. 4, August 2005 1278 Integrating Social Factors into Priority Setting Shi et al. c analysis indicator scores, the total score was imension and indicator. If the value e b nd protection status of closed forest) and essure in closed forest, endemic vertebrates, ess than its mean value, then the indicator was species (%) a Area Area under high Global Land under human population endemic Sensitive area (%) protection (%) pressure (%) etation forest vegetation forest vegetation forest vertebrates plants (rank) (rank) (rank) other closed other closed other closed score 1 score 2 score 3 g ve 191,828 50.78 10.51 20.80 7.65 2.36 2.13 0.5 0.4 2 (7) 2.2 (15) 2.4 (15) c 597,673 74.64 14.13 0.75 3.13 2.55 1.34 3.2 2.8 5 (4) 5.1 (6) 5.2 (6) . 2 f 100/km 254,730 29.69 17.19 9.60 22.90 85.59 71.08 0.7 1.3 4 (5) 4 (5) 4 (5) e > ) 247,423 31.19 30.02 5.23 16.24 45.36 37.51 2.3 2.9 6 (3) 5.8 (5) 5.6 (5) 2 d 1,480,400 56.81 7.20 0.63 5.07 19.97 12.67 2.7 2.1 7 (2) 7.1 (2) 7.2 (2) d 2,273,303 30.51 34.21 10.19 14.05 22.07 13.84 2.3 1.9 6 (3) 6 (4) 6 (4) 280,545 18.93 24.70 4.79 4.49 62.49 43.28 1.9 1.9 8 (1) 8 (1) 8 (1) 1,475,121 22.66 42.58 10.85 18.50 21.99 7.38 5 2.6 4 (5) 3.8 (11) 3.6 (11) e f f Vegetation cover, closed forest, protected status, human population pressure, and relative ranks for 25 biodiversity hotspots. leading hottest hotspots on the list of Myers et al., in which appear all five factors in the top 10 listings for each factor. conducted two sensitivity analyses: weights of 1.1 and 1.2 to closed forest and associated indicators (high population pressure on closed forest a ynesia-Micronesia, 1,404 57.12 0.00 0.62 0.00 61.03 0.00 1.1 0.8 4 (5) 4 (10) 4 (10) e l erage, 15,629,989 41.29 25.47 6.01 15.88 13.05 10.67 1.76 1.4 opical Andes, 1,396,569 41.54 16.97 11.21 25.75 4.78 6.06 6.7 5.7 3 (6) 3.1 (12) 3.2 (12) allacea, 317,927 16.82 51.27 10.49 18.07 21.52 8.02 0.5 1.9 3 (6) 2.8 (14) 2.6 (14) estern Ghats & Sri Lanka, Hottest hotspots in Myers et al. (2000) that appear at least four times in the top 10 listings for each factor. Human population density Data from Myers et al. (2000). Number of global endemic vertebrates 27,298; number of global endemic plants 30,000. Th Hottest hotspots in Myers et al. (2000) that appear at least three times in the top 10 listings for each factor. We Hotspot, area (km Madagascar & Indian Ocean Islands, Table 1. Atlantic Forest, Av Tr W W Po Southwest Australia, 306,903Succulent Karoo, 102,840Sundaland, 19.53 14.14 7.11 0.00 9.69 1.77 11.56 0.00 1.59 0.00 0.68 0.00 1.4 0.6 0.4 3 (6) 0.2 3.1 (12) 4 (5) 3.2 (12) 4 (10) 4 (10) Brazilian Cerrado, 1,830,910California Floristic Province, 351,568Cape Floristic Region, 74,771Caribbean Islands, 22.20 75.06 38.32 46.96 13.86 23.48 3.48 65.84 5.35 20.06 14.47 1.86 7.61 0.27 0.00 0.7 0.06 4.88 4.13 1.5 0.3 2 (7) 1.9 0.4 1.8 (17) 3 1.6 (6) (17) 0.2 3.1 (12) 4 (5) 3.2 (12) 4.1 (9) 4.2 (9) Guinean Forests of West Africa,Indo-Burma, 879,511 52.08 31.87 3.71 5.34 20.37 13.35 0.8 1 4 (5) 4 (10) 4 (10) Caucasus, 556,246Central Chile, 289,627Choco-Darien-Western , 223,984Eastern Arc Mountains & Coastal Forests, 30.56 34.98 9.11 20.36 30.23 14.76 21.06 13.20 15.95 5.41 2.11 11.29 17.94 7.01 0.8 6.02 20.87 1.5 20.44 0.00 5 (4) 0.5 0.5 5 (7) 0.2 0.2 5 (7) 6 3 (3) (6) 2.9 (13) 6 (4) 2.8 (13) 6 (4) Mediterranean Basin, 520,177Mesoamerica, 1,144,270Mountains of S. Central China,New 557,260 Caledonia, 17,301New Zealand, 257,698Philippines, 55.50 34.91 33.72 4.98 21.60 3.39 50.84 2.72 37.51 7.36 54.68 21.31 4.95 4.07 27.68 4.06 14.07 25.89 0.85 8.02 5.96 13.44 37.41 4.53 19.77 7.89 1.2 4.3 0.00 1.61 1.7 0.7 0.00 0.9 1.00 2 (7) 7 4.2 (2) 0.9 0.6 2 5 (16) 7 (4) (3) 0.3 4.8 2 (8) (16) 0.5 7 (3) 4 4.6 (5) (8) 0 (8) 4 (10) 0 (18) 4 (10) 0 (18) the weights of 0.9of and the 0.8, indicators respectively, to (other otherscored vegetation as cover, vegetation closed cover 1; forest, and otherwise, protection associated itand status was indicators. endemic of plants), zero. Score other Similarly, if and vegetation for the cover, rankcalculated the value and are for other of protection each based an four status hotspot, indicator indicators on of and was (high equal closed the greater weights population than hotspots forest) given pressure its was were in mean ranked to l on value, other each the the vegetation d indicator basis cover, high was of population scored the as pr total 1; score. otherwise, it was zero. On the basis of th a b c d e f

Conservation Biology Volume 19, No. 4, August 2005 Shi et al. Integrating Social Factors into Priority Setting 1279

Figure1. Closed forest and other vegetation cover in 25 biodiversity hotspots and 17 megadiverse countries. Megadiverse countries: A, United States; B, ; C, ; D, Ecuador; E, ; F, ; G, ; H, Democratic Republic of the Congo (Zaire); I, South Africa; J, Madagascar; K, India; L, China; M, ; N, Philippines; O, ; P, ; Q, Australia (Mittermeier et al. 1997). Twenty-five biodiversity hotspots: 1, California Floristic Province; 2, Mesoamerica; 3, Choco-Dari´ ´en 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 &; 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 by other vegetation were similar. Compared with the the cover of other vegetation had been accorded formal world average, an average of 17 megadiverse countries protection status in the megadiverse countries. Countries had a higher percentage of areas with other vegetation with the most protected area in these categories were subject to high population pressures but a lower percent- Venezuela, the United States, Colombia, and Indonesia. ageof these areas where population pressures were low China, Madagascar, Mexico, and South Africa had poor and medium. At the national level, the percentage of ar- protection status. eas under other vegetation cover with high population Some 3.38 billion people, or more than half of the pressures was higher than the global average (8.3%) in world’s population of 6.09 billion (WRI 2000), were con- only 7 megadiverse countries. centrated in the 17 megadiverse countries. China and India had the most, with more than 2 billion people. Habitat, Human Population, and Protection Status in the In 5 countries, more than 20% of the total population lived in and around closed forest. In 8 countries, half or Hotspots within 17 Megadiverse Countries more of the total population lived in and around areas The 17 megadiverse countries include 60% of the ge- covered by other vegetation. Compared with the world ographic area of the 25 hotspots (Table 3). Hotspots average, the 17 megadiverse countries had on average a covered the total geographical areas of two countries– higher percentage of closed-forest areas with high popu- Madagascar and Malaysia–whereas the Democratic Re- lation pressures but a lower percentage of closed-forest public of Congo and Papua New Guinea had no hotspots. areas with low and medium population pressures. At the More than 70% of the geographical area of the Philip- national level, 8 countries had a higher percentage of pines, Ecuador, and Indonesia were covered by hotspots, closed forest areas under high population pressure than whereas hotspots covered < 10% of the geographical the global average of 5.1%. The results for areas covered area of the United States, Australia, Venezuela, and China.

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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).

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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 (km2)vegetation forest vegetation forest vegetation forest vertebrates plants (rank) (rank) (rank)

Australia, 7,686,399 33.40 4.62 4.84 12.80 0.32 1.20 5.1 4.7 5 (3) Brazil, 8,500,633 41.80 42.54 5.10 17.00 5.11 0.50 3.2 0.0 3 (5) 3 (8) 3 (9) China, 9,402,349 31.60 11.87 2.36 3.60 27.17 36.30 1.4 6.0 7 (1) 7 (1) 7 (1) Colombia, 1,141,159 38.60 45.51 16.08 25.00 7.30 2.40 1.6 0.5 0 (8) 0 (11) 0 (12) D.R. of the Congo, 2,338,145 42.70 49.70 7.32 8.40 2.95 0.50 0.5 0.4 2 (6) 2 (9) 2 (10) Ecuador, 248,557 32.30 51.18 8.97 17.97 15.70 7.50 1.3 1.3 4 (4) 4 (6) 4 (7) India, 3,154,408 18.40 12.03 4.52 10.80 77.22 43.10 1.5 1.7 6 (2) 6 (2) 6 (2) Indonesia, 1,887,482 20.20 49.14 15.67 20.10 14.52 5.40 3.8 5.8 4 (4) 3.8 (7) 3.6 (8) Madagascar, 592,474 75.00 14.16 0.72 3.10 2.36 0.80 2.2 2.2 5 (3) 5.1 (3) 5.2 (4) Malaysia, 327,949 27.50 49.02 6.35 10.80 30.34 5.80 0.7 1.2 4 (4) 3.8 (7) 3.6 (8) Mexico, 1,953,784 29.70 30.76 1.69 2.70 8.03 5.60 2.9 4.2 5 (3) 4.9 (4) 4.8 (6) Papua New Guinea, 459,291 20.60 70.59 14.27 9.40 0.97 0.30 1.3 na 2 (6) 2 (9) 3 (9) Peru, 1,295,548 29.30 45.78 8.44 8.70 2.35 0.00 1.5 1.8 2 (6) 2 (9) 2 (10) Philippines, 288,752 18.90 24.47 4.73 4.50 62.20 43.30 1.9 1.2 7 (1) 7 (1) 7 (1) South Africa, 1,221,600 44.20 1.41 2.65 0.90 8.87 11.20 0.7 na 4 (4) 4.2 (5) 5.4 (3) United States, 9,406,269 23.50 25.16 21.54 40.40 3.45 4.00 1.5 1.3 2 (6) 2 (9) 2 (10) Venezuela, 914,084 45.80 44.54 31.29 65.00 4.58 0.00 0.9 2.7 1 (7) 1 (10) 1 (11) Average 32.30 25.20 7.64 20.10 13.68 7.40 1.9 2.1 aHuman population density > 100/km2. bData from WRI (2002). Number of global endemic vertebrates 27,298; the number of global endemic plants 30,000. cWe 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 forest and other vegetation cover with high population 8 other countries. In 9 countries, closed forests covered pressure in the other countries were less than the global more than 30% of each country’s total area of hotspots; averages. Mexico and Malaysia’s hotspots had the highest propor- tion of closed forest area at 49.7% and 49.3%, respectively, Relative Rankings of 25 Hotspots and 17 Megadiverse whereas South Africa’s hotspots had the lowest propor- Countries tion of closed forest area (1.6%). The hotspots in these 17 megadiverse countries were covered with other veg- On the basis of the total score of eight indicators for etation in percentages ranging from 20% to 80%, except each hotspot, we ranked and grouped the 25 hotspots forAustralia and South Africa. An average of only 7.9% of into three categories: coolest hotspots (scores from 0 the land in the 17 megadiverse countries’ hotspots was to 2), intermediate hotspots (scores from 3 to 5), and designated as protected area. The averages for the other hottest hotspots (scores from 6 to 8) (Table 1). On the categories were 5.3% of the area under other vegetation basis of equal weights for each indicator, the Philip- and 15.6% of the area under closed forest. Venezuela, with pines ranked first and New Zealand was last. In terms 66.1% of the hotspots, had the highest percentage of its of grouping, 6 hotspots (the Philippines, Atlantic For- hotspots protected, with 67.7% of the area under other est, Mediterranean Basin, Caribbean Islands, Caucasus, vegetation and 65.5% of closed forest under protection. and Indo-Burma) were the hottest, and 4 hotspots (New Tenof the megadiverse countries—the Philippines, In- Zealand, the Eastern Arc Mountains & Coastal Forests, the dia, Venezuela, China, Ecuador, Colombia, Indonesia, the California Floristic Province, and the Mountains of South- United States, Malaysia, and Mexico—had high propor- Central China) were the coolest. tions of closed forest and other vegetation cover subject The assignment of different weights to the indicators to high population pressure. The proportions of closed associated with closed forest and other vegetation (1.1

Conservation Biology Volume 19, No. 4, August 2005 1282 Integrating Social Factors into Priority Setting Shi et al. c Sensitive analysis otal score was calculated for each hotspot, and b untries. et each dimension and indicator. If the value of the orest and protection status of closed forest) and the osed forest, endemic vertebrates, and endemic plants) if the species (%) an its mean value, then the indicator was scored as 1; a Area under high Area under human population Global endemic . 2 Land area (%) protection (%) pressure (%) other closed other closed other closed score 1 score 2 score 3 100 people/km > Vegetation cover, closed forest, protected status, human population pressure, and relative ranks for 25 biodiversity hotspots in 17 megadiverse co opical Andesopical Andes 54.91 51.84 25.49 34.45 11.32opical Andes 11.61 17.14 15.67 15.22 11.23 8.35 66.82opical Andes 10.31 6.7 7.42 6.7 5.7 8.28 39.25 4 5.7 (4) 20.39 46.43 3 4 (5) (7) 1.52 67.71 3.1 (10) 4 (7) 0.00 3.2 65.53 (10) 6.7 16.51 25.35 5.7 0.9 3 (5) 3.1 (10) 2.7 3.2 (10) 4 (4) 3.9 (8) 3.8 (8) allacea 16.55 50.46 10.55 18.02 21.46 7.99 0.5 1.9 3 (5) 2.8 (12) 2.6 (12) estern Ghats & Sri Lanka 27.69 15.76 8.70 20.35 91.81 81.51 0.7 1.3 4 (4) 4 (7) 4 (7) conducted two sensitivity analyses: the weights of 1.1 and 1.2 to closed forest and the associated indicators (high population pressure on closed f erage 46.37 24.71 5.28 15.59 11.21 10.13 1.8 1.4 nezuela Atlantic ForestBrazilian CerradoIndo-BurmaMountains of S. Central ChinaChoco-Darien-Western EcuadorTr 57.17 38.25 75.08 57.17Choco-Darien-Western EcuadorTr 32.11 33.05 45.33Indo-Burma 13.84 5.57 29.88W 33.69 2.01Sundaland 9.76 5.32W 31.04 0.59Indo-Burma 2.82 3.74 12.63Sundaland 14.35 7.45 3.61California Floristic Province 28.69Mesoamerica 4.19 4.24 7.86 0.27 21.68 7.78Choco-Darien- Western EcuadorTr 54.39 30.53 21.11 31.96 6.68 17.73 7.41 0.06 17.88 45.59Cape Floristic Region 51.82Succulent Karoo 40.38 5.39 1.71 22.41 27.29California 1.2 Floristic 0.8 0.70 56.51 Province 1.5 2.7Caribbean 20.48 24.00 12.43 49.24 8.50 0.12 0.8Caribbean 47.01Tr 8.37 2.3 21.50 0.7 1.5 51.13 0.09 0.4 2.1 21.13 44.35 16.60 6.45 0.00 1.5 3.48 34.73 41.32 2 3 (6) (5) 2 7 1.9 2.91 (6) (1) 18.08 0.22 21.22 10.87 10.96 0.00 6 (2) 26.36 2.2 7.1 1.87 0.00 2 15.91 (13) (1) 3 (14) 6 (11) (2) 83.82 4.38 7.74 29.55 2.3 17.52 5.9 40.91 (4) 2.4 1.69 (13) 7.2 66.34 (1) 2 3 (14) (11) 0.00 0.00 9.93 6 68.49 10.29 (3) 5 5.8 (4) 5.61 0.7 0.00 1.9 90.50 0.00 31.35 0.8 4.93 6 5.35 2.3 (3) 5 6 (2) 0.00 2.6 3 7.55 0.00 43.86 4.16 1.5 1.7 1.9 6 4 (3) (4) 0.00 1.5 13.14 0.00 6 (2) 2.6 3 0.7 (5) 7 (1) 4.2 3.8 (9) 6 6.1 (3) (2) 10.61 0.00 5 0.7 (3) 1.4 7.1 (1) 3 (11) 3.6 (9) 4 0 (4) 6.2 (2) 4.9 (6) 1.5 0.00 7.2 2 (1) (6) 3 0 (11) 3.9 (8) 3 4.8 (5) (6) 1.8 (15) 0.9 3.8 1.4 (8) 4 (4) 3.1 (10) 1.6 (15) 4 3.2 (4) (10) 2.7 4 (7) 4 (7) 5 (3) 4 (7) 4 (7) 5 (5) 5 (5) lue 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, th eights 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 Human population density Data from WRI (2002). Number of global endemic vertebrates is 27,298; the number of global endemic plants is 30,000. We Country and hotspot vegetation forest vegetation forest vegetation forest vertebrate plants (rank) (rank) (rank) Table 3. Brazil China Colombia Ecuador India Indonesia Malaysia Mexico Peru South Africa United States Ve Av a b c w indicators (other vegetation cover, closed forest,otherwise, protection it status was of zero. other Similarly, forva vegetation the cover, and other protection four status indicators of (highthe closed population hotspots forest) pressure were was in ranked less on other th vegetation the cover, basis high of population the pressure in total cl score.

Conservation Biology Volume 19, No. 4, August 2005 Shi et al. Integrating Social Factors into Priority Setting 1283 and 0.9 and 1.2 and 0.8 respectively) affected the ranks Discussion and Conclusions of some hotspots but did not change their membership in the different groups. For example, on the basis of equal Biodiversity conservation policies and practices are in- weights, the Atlantic Forest and the Mediterranean Basin herently social phenomena, but the conservation com- had equal score and rank, but when closed forest was as- munity continues to look to the biological sciences to signed a higher weight, the Atlantic Forest ranked higher design these policies and practices. Although biologists than the Mediterranean Basin but both hotspots main- and practitioners, at least in recent years, have increas- tained their grouping in the hottest category. A similar ingly recognized that social factors are often the primary situation existed for the Caucasus and the Caribbean and determinants of the success or failure of conservation ef- Indo-Burma and the Caribbean. In sum, when slightly forts(Mascia et al. 2003), biological criteria continue to higher weights were assigned to indicators related to dominate the literature on priority-setting. Clearly, the bi- closed forest, the number of levels in the ranking in- ological sciences have the theoretical and analytical tools creased, resulting in 18 ranks compared with 8 when the to identify rare and threatened species and ecosystems, indicators were given equal weights. When only slightly but the failure to integrate social aspects in the prior- higher weights (1.1 and 1.2) were given to indicators of ity setting process may lead to either partial success or closed forest, the three groupings of hotspots did not total failure. The results of our proposed method of rel- change. When a much higher weight (1.5) was assigned to ative ranking, which incorporates biological and social indicators related to closed forests, however, Madagascar aspects, provide interesting outcomes that should enrich and the Indian Ocean Island moved to the hottest hotspot conservation planners and practitioners’ understanding category. of priority setting for conservation areas. The same method was used for the 17 megadiverse As far as we know, no other study has included all the countries (Table 2). On the basis of equal weights, China four dimensions of biodiversity conservation that we in- ranked first and Colombia last. In terms of grouping, 3 corporated here. Myers et al. (2000), Brummitt and Lu- countries (China, the Philippines, and India) were the ghadha (2003), Myers and Mittermeier (2003), and Ova- hottest and 6 countries (Democratic Republic of Congo, dia (2003) rank hotspots for conservation priority setting, Papua New Guinea, Peru, the United States, Venezuela, but their ranking is based on the number and number-area and Colombia) were the coolest megadiverse countries. ratio of endemic species. They also included remaining The outcome of the assignment of different weights to the primary vegetation as the percentage of original extent, indicators associated with closed forest gave similar, but but the data are questionable. We could not find any ex- not exactly the same, results as in the case of hotspots. For planation (reference year) for the “original extent” of pri- example, applying the relatively low weight of 1.2 to indi- mary vegetation in any of these papers, and the reliability cators related to closed forests moved Papua New Guinea of vegetation data for the period of hundreds or thou- from the coolest to the intermediate category. Similarly, sands of years ago is extremely limited. In addition, the these weights (1.1 and 1.2) did not have any effect on method of identifying the hottest hotspots used in these a country’s ranking within the hottest megadiverse cate- studies—hottest hotspots appear at least three times in gory, but the weight of 1.2 created seven distinguishable the top 10 listings for each factor—is highly subjective. ranks in the category of the intermediate megadiverse The main issue in question is the choice of the number countries, whereas assigning equal weights resulted in of hotspots included in the “top” category. We question only three distinguishable ranks within the group. a method that selects the top 10; why not the top 5, 7, We also compared the hotspots within those megadi- or 11? For example, by identifying the top 5 instead of verse countries that have more than one hotspot (Table the top 10, only 1 hotspot—the Philippines—would be 3). For this comparison, however, numbers of endemic left on the list based on the method proposed by Myers plants and vertebrate species in a country-specific area et al., whereas Sundaland, Madagascar, the Tropical An- of the hotspot were not available, and we used the same des, the Caribbean Islands, and Mesoamerica would be numbers of endemic plants and vertebrate species for on the list based on the method proposed by Brummitt a portion of a hotspot as in a given country. In all the and Lughadha. In our method, we compared each hotspot megadiverse countries except Peru, the total scores of with the average of all the hotspots or the megadiverse two hotspots, for the case of equal weights, were differ- countries, not with an arbitrary number, and we did not ent, and a change in the weights did not affect the rank- select a limited number of areas; rather we ranked all the ing of the hotspots within the given country. In Peru, areas. Hence, in addition to the inclusion of all four dimen- the two hotspots, Choco-Darien-Western Ecuador and sions, our method is robust. Furthermore, we address the Tropical Andes, had the same score of 3 for the case of indeterminacy of weights through sensitivity analysis. equal weights, but for higher weights the closed-forest- Our results are also quite different from and more in- related indicators created a distinction between the two formative than the studies mentioned above. Four hot- hotspots. spots—the Philippines, the Atlantic Forest, the Caribbean,

Conservation Biology Volume 19, No. 4, August 2005 1284 Integrating Social Factors into Priority Setting Shi et al. and Indo-Burma—are common to Myers et al., Brummitt many hotspots are spread over many countries, and in and Lughadha, and our list of the hottest hotspots. Our transboundary hotspots the contradictions in approach list, however, includes the Mediterranean Basin and the and conflicts of interest should be harmonized. Caucasus, which are on neither the Myers et al. nor the The importance of the social aspects of biodiversity Brummitt and Lughadha lists. Similarly, Madagascar and conservation and the contribution that social sciences Sundaland are on the lists of both Myers et al. and Brum- can make to conservation efforts must be emphasized. mitt and Lughadha, but they do not appear on our list. In We have provided a base for incorporating the four crit- fact, the Eastern Arc Mountains and Coastal Forest, one ical dimensions of biodiversity conservation in priority of the hottest hotspots according to Myers et al., is in the setting at the macrolevel—as we did with the 25 hotspots category of coolest hotspots on our list. and the megadiverse countries. In our analysis, we used The outcome of our comparative study indicates that equal weights for all four dimensions and conducted a depending exclusively on the number and the number- sensitivity analysis to assign different weights to the indi- area ratio of endemic species to identify priority con- cators related to closed forests. The next step should be servation areas and excluding criteria related to habitat, to identify actual weights for different dimensions and to human population pressure, and protection status can conduct the analysis based on those weights. A holistic have serious consequences for conservation priority set- view is a necessity in biodiversity conservation priority ting. On the basis of these outcomes, we suggest that setting. We suggest, therefore, that future studies should conservation policies and strategies should use the fol- focus on such a holistic view and on the integration of lowing categories of hotspots: (1) high percentage of en- social and biological aspects. demic species and plants, high population pressure, low closed forest and other vegetation cover, and low pro- tection status (hottest hotspots); (2) high percentage of endemic species and plants, low protection status, popu- Acknowledgments lation pressure not high (Madagascar and Indian Ocean Is- land); (3) high percentage of endemic species and plants, The views expressed in this publication are not necessar- low habitat status, protection status above average (Sun- ily those of the agencies cooperating in this project. The daland and Tropical Andes); and (4) low percentage of designations employed and the presentations do not im- endemic species and plants, low population pressure, ply the expression of any opinion whatsoever on the part and high vegetation cover, closed forests, and protec- of cooperating agencies concerning the legal status of any tion status (coolest hotspots). Based on this classification, country, territory, city, or area or of its authorities or of the class-specific and hotspot-specific conservation policies delineation of its frontiers or boundaries. We are grateful and strategies should be developed. For example, in the to UNEP,the U.S. National Aeronautics and Space Admin- hotspots with a high percentage of forests and vegetation istration, and U.S. Geological Survey for financial support. cover, the emphasis should be on developing effective We are grateful to T. Loveland and his team for generat- protection-area strategies, whereas in the hotspots with a ing and providing the IGBP global land-cover-distribution low percentage of forests and vegetation cover emphasis database (http://edcdaac.usgs.gov/glcc/glcc.html). We are should be on restoration strategies. Similar categorization extremely thankful to all reviewers and editors for their and approaches should be used for the megadiverse coun- most valuable comments. tries. In some countries, different hotspots have different fea- Literature Cited tures, whereas in other countries all the hotspots have the same features. For example, the California Floristic Brooks, T. M., et al. 2002. Habitat loss and extinction in hotspots of Province (Mexico) has percentages of closed forests and biodiversity. Conservation Biology 16:909–923. Brummitt, N., and E. C. Lucghadha. 2003. Biodiversity: where is hot and other vegetation cover below the respective mean val- where is not. 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Conservation Biology Volume 19, No. 4, August 2005