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Gymnocladus assamicus is a critically endangered tree species endemic to northeastern India. Local inhabitants traditionally used this species for a variety of purposes. However, rapid population declines led to the species being considered extinct, until fieldwork in 2004 to 2007 identified 14 discrete populations of 1 to 7 trees each. To overcome constraints on field surveys imposed by the region's remoteness and rugged terrain, we targeted areas of further field inventories by estimating the potential distribution of the species. Ecological niche modeling enabled us to identify 26 sites which the model predicted to be highly suitable for the species' occurrence. We conducted rapid field surveys at 14 of the most accessible of these predicted sites. New populations were discovered at 5 of the 14 surveyed sites. In the remaining 12 less accessible sites, we interviewed residents from nearby villages and obtained indirect evidence of populations at 5 additional sites, which remain to be confirmed by direct field observations. This study demonstrates the utility of niche modeling as a tool for locating new populations of rare and endangered species. Our results will enhance ongoing efforts towards in situ conservation of this endangered species.

Most quantitative methods for identifying conservation-priority areas require more detailed knowledge about the extent and distribution of biodiversity than is currently available. Accelerated and irreversible losses of biodiversity call for the development of alternative methods to identify priority sites for biodiversity inventory and protection. We focused on the state of Arunachal Pradesh, a biodiversity-rich region in northeast India. We used a geographic information system and spatially explicit modeling to examine the correlation of land-cover and land-use patterns with biogeophysical characteristics and to project future patterns of land-use change. In 1988, 70% of Arunachal Pradesh was covered by forest. We project that 50% of the state's 1988 forest will be lost by 2021, based on anticipated growth of the human population and resulting resource use. Of the total simulated deforestation, 76% occurs in areas that have no legal state protection. We developed a map of threats to biodiversity that divides areas that were forested in 1988 into four categories: (1) susceptible to future deforestation and currently unprotected; (2) susceptible to future deforestation but currently within the protected-area network; (3) not susceptible to future deforestation and protected; and (4) neither susceptible to future deforestation nor currently protected. We make the following recommendations based on our analyses. Areas in category 1 should be a high priority for biodiversity inventory and conservation action. Areas in category 2 should have rigid enforcement of protection. Areas in category 3 are locations of relatively low priority for enforcement. Areas in category 4 that have a high conservation potential are politically the easiest to include in the protected-area network and should be protected before they become targets of future land-use change. Reserve forests-forests managed by the state forest department for a variety of purposes, including selective logging for timber harvesting-are predominantly located in areas susceptible to land-use change and are prime candidates for upgrading of protection status. Identificación de Areas Prioritarias de Conservación en los Trópicos: un Modelo Enfocado al Cambio en el Uso del SueloResumen: La mayoría de los métodos cuantitativos para identificar áreas prioritarias de conservación requieren de conocimiento más detallado que el que actualmente se tiene acerca de la magnitud y distribución de la biodiversidad. La pérdida acelerada e irreversible de biodiversidad exige el desarrollo de métodos alternativos para identificar sitios prioritarios para inventariar y proteger la biodiversidad. Enfocamos el estado de Arunachal Pradesh, una región rica en biodiversidad del noreste de India. Utilizamos SIG y modelos espacialmente explícitos para examinar la correlación de superficie y patrones de uso del suelo con características biogeofísicas y para proyectar patrones futuros de cambio en el uso del suelo. En 1988, el 70% de Arunachal Pradesh estaba cubierto por bosques. Proyect...

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