Plant and animal endemism in the eastern Andean slope: challenges to conservation
- Jennifer J Swenson10, 1Email author,
- Bruce E Young1,
- Stephan Beck2,
- Pat Comer1,
- Jesús H Córdova3,
- Jessica Dyson1, 11,
- Dirk Embert4,
- Filomeno Encarnación5,
- Wanderley Ferreira6,
- Irma Franke3,
- Dennis Grossman1, 12,
- Pilar Hernandez1, 13,
- Sebastian K Herzog7,
- Carmen Josse1,
- Gonzalo Navarro6,
- Víctor Pacheco3,
- Bruce A Stein1, 14,
- Martín Timaná1, 15,
- Antonio Tovar8,
- Carolina Tovar8,
- Julieta Vargas9 and
- Carlos M Zambrana-Torrelio2, 16
© Swenson et al; licensee BioMed Central Ltd. 2012
Received: 19 September 2011
Accepted: 27 January 2012
Published: 27 January 2012
The Andes-Amazon basin of Peru and Bolivia is one of the most data-poor, biologically rich, and rapidly changing areas of the world. Conservation scientists agree that this area hosts extremely high endemism, perhaps the highest in the world, yet we know little about the geographic distributions of these species and ecosystems within country boundaries. To address this need, we have developed conservation data on endemic biodiversity (~800 species of birds, mammals, amphibians, and plants) and terrestrial ecological systems (~90; groups of vegetation communities resulting from the action of ecological processes, substrates, and/or environmental gradients) with which we conduct a fine scale conservation prioritization across the Amazon watershed of Peru and Bolivia. We modelled the geographic distributions of 435 endemic plants and all 347 endemic vertebrate species, from existing museum and herbaria specimens at a regional conservation practitioner's scale (1:250,000-1:1,000,000), based on the best available tools and geographic data. We mapped ecological systems, endemic species concentrations, and irreplaceable areas with respect to national level protected areas.
We found that sizes of endemic species distributions ranged widely (< 20 km2 to > 200,000 km2) across the study area. Bird and mammal endemic species richness was greatest within a narrow 2500-3000 m elevation band along the length of the Andes Mountains. Endemic amphibian richness was highest at 1000-1500 m elevation and concentrated in the southern half of the study area. Geographical distribution of plant endemism was highly taxon-dependent. Irreplaceable areas, defined as locations with the highest number of species with narrow ranges, overlapped slightly with areas of high endemism, yet generally exhibited unique patterns across the study area by species group. We found that many endemic species and ecological systems are lacking national-level protection; a third of endemic species have distributions completely outside of national protected areas. Protected areas cover only 20% of areas of high endemism and 20% of irreplaceable areas. Almost 40% of the 91 ecological systems are in serious need of protection (= < 2% of their ranges protected).
We identify for the first time, areas of high endemic species concentrations and high irreplaceability that have only been roughly indicated in the past at the continental scale. We conclude that new complementary protected areas are needed to safeguard these endemics and ecosystems. An expansion in protected areas will be challenged by geographically isolated micro-endemics, varied endemic patterns among taxa, increasing deforestation, resource extraction, and changes in climate. Relying on pre-existing collections, publically accessible datasets and tools, this working framework is exportable to other regions plagued by incomplete conservation data.
Global prioritization areas themselves are typically too large to protect in their entirety (e.g. the Andean 'hotspot' sensu, covers an area over four times the size of Germany and crosses over seven Andean countries), and are not practical nor intended for use in national or departmental planning. For many data-poor countries however, global datasets such as these are the only consistent estimates of biodiversity that are available. Effective on-the-ground conservation efforts and decisions require planning and biodiversity information at a much finer scale .
Endemic species are restricted to a particular geographic area-occurring nowhere else-and are important components in most global conservation prioritizations. A focus on endemic species richness can provide unique information about biodiversity patterns [3, 15] compared to all-encompassing species richness that is dominated by generalist (non-endemic) species , which are typically the lowest priority for conservation. Areas high in endemism are especially valuable because they may represent areas of high past speciation in evolutionary hotspots . The forces that create areas of high species endemism and richness are still not well understood, which is one argument for their preservation for further study . Another reason for preservation is that these areas may function as species refugia during future climate changes, as they may have in the past. Globally, areas of high endemism are currently underrepresented by the protected area network .
The Andes region of South America harbours one of the largest assemblages of endemic plant and animal species and is one of the most biodiverse and threatened areas of the world [1–5]. Explanations for such a concentration of endemics include past climate shifts, geotectonic events, modern ecological interactions, and limited dispersal. This area was historically isolated from the lowlands by the Andean uplift, which created a complex mosaic of high mountains and deep inter-Andean valleys. Researchers generally agree that this ancient uplift and isolation were important drivers in speciation, resulting in high concentrations of endemic birds [18–22], mammals , and plants [24–27]. Analyses of Andean amphibians are limited but indicate similar drivers of environmental divergence [28–30] and colonization from different regions . Recent climatic stability influenced by topography has created ideal conditions for high biodiversity (very humid areas) and endemism (dissected topography creating isolated dry valleys) .
Despite the agreement among scientists about the origins and existence of the extremely high endemic diversity of this region, it remains scientifically understudied . We have very limited knowledge of current patterns of Andean species distributions and diversity within this globally prioritized area . National-level efforts to prioritize conservation in Peru and Bolivia have previously explored gaps in protected area coverage, but have been hindered by the limited information available on species status and distribution [34, 35]. The information available is primarily of bird diversity patterns rather than other taxon groups [36–40]. Yet even the most recent endemism studies of birds were delimited by a 1/4° grid (~28 × 28 km) as the unit of analysis [36, 37]. Studies of the spatial pattern of Andean endemic mammal richness are lacking, possibly due to unstable taxonomy and incomplete knowledge about distributions . A worldwide distributional analysis at a coarse scale with a 1° grid (~111 × 111-km) showed a relative concentration of endemic mammal species along the east side of the Andes in Peru and northern Bolivia . As well, a regional study in Peru corroborated this pattern . We are unaware of spatially explicit analyses of amphibian endemic patterns, although several authors have suggested that higher concentrations of endemics should be found in montane regions [43–45]. Knowledge of endemic plants in this region varies widely by taxonomic group. Analyses of a few better-known groups suggest peaks of diversity and endemism in the eastern Andes [17, 46–49]. Vegetation and land cover maps of this region have variable coarse spatial and classification detail; different regions employ distinct classification schemes and methods that make joining maps along borders difficult.
The development of computer-aided models to predict species distributions presents an opportunity to develop distribution information at the scale necessary for in-country conservation planning [50, 51]. With the goal of producing relatively fine resolution species and ecosystem data within a repeatable framework of methods, we created geographic distributions of endemic birds, mammals, amphibians, plants, and mapped their ecosystems on the eastern slope of the Andes in Peru and Bolivia at a scale applicable to conservation planning (1 km2 grid, less than < 1/60°, 1:250,000 - 1:1,000,000). This multiple taxon approach enables a broader characterization of diversity, given that one taxonomic group or species is not always representative of other taxa [15, 52, 53]. By geographically integrating this data, we identify areas of high endemic concentrations and irreplaceable areas (greatest number of narrowly distributed endemics) across the study area . We characterize the ecological systems where endemic species reside and perform a gap analysis to identify species ranges, endemic concentrations and ecological systems currently located outside of established national-level protected areas. In addition to pinpointing candidate areas for future protection efforts, the results highlight several challenges to conservation in the region.
In addition to the following descriptions of endemic distribution modelling, mapping of ecological systems and geographical analysis of all the overlapping datasets, the Supporting Information Additional Files 1, 2, 3, 4, 5, 6, contain further method details.
Endemic Species and Locality Data
Summary of endemic species groups and modeled ranges
Total number localities
Median number records per species
No. data sources collaborating Institutions
Number Maxent models formed
Median distributional area, (km2)
For each of the 782 species of endemic plants and animals, we compiled locality records from an exhaustive search of specimen records in 81 local and international natural history collections and herbaria, published records, and for birds and mammals only, observational data. Specimen searches were carried out 2004-2006 with Peruvian, Bolivian and international institutions, individuals, and from published sources (see Additional File 5). The majority of specimens were collected in the 1990's and 2000's, yet dates ranged wider for published sources that we validated with national gazetteers of collecting locations . The oldest localities for example, were collected for mammal species in the early part of this century . Because many specimen labels did not include global positioning system-based coordinates for the collecting locations, we identified the most reliable localities based on their described location and geo-referenced them using standardized methods , and additional resources such as consultation with the collector, and geographic gazetteers (e.g.). To further assure the creation of an accurate locality database, we then asked taxonomic specialists familiar with the species and geography to review mapped localities to ensure the creation of an accurate locality database. We buffered the study area by 100 km for the endemic species data gathering and modelling to avoid edge effects.
Predictive Distribution Modelling
Environmental predictors and data sources for species distribution modelling
Mean annual temperature, mean temperature diurnal range, isothermality, precipitation of wettest and driest month, precipitation seasonality
Worldclim, (Hijmans et al. 2005. www.worldclim.org), 1-km resolution
Shuttle Radar Topography Mission digital elevation data provided by CGIAR (http://srtm.csi.cgiar.org/) resampled to 1-km resolution
Degree of slope (maximum rate of change in elevation from each pixel to its 8 neighbors) derived from the SRTM digital elevation data
Expresses the relative position of each pixel on a hillslope (e.g. ridge, valley, toe slope). Using methods of Zimmermann (2000) on the SRTM digital elevation data with three neighborhood windows of 3x3, 6x6 and 9x9
Percent tree cover
MODIS global vegetation continuous fields sourced from http://glcf.umiacs.umd.edu/data/modis/vcf/data.shtml (Hansen et al. 2003) 1-km resolution, and summarized within 3- and 5- km moving windows
Enhanced Vegetation Index (EVI)
Principal component 1
Principal component 2
MODIS vegetation indices 16-Day data product sourced from the NASA EOS data gateway; Principal component analysis of 3 years of 16-day composites. MODIS EVI data summarized within 5 km moving window
There are drawbacks to predictive distribution modelling-for example, models may overestimate species' geographic ranges [62, 63]-as well as advantages, such as reducing the effect of uneven collecting efforts . Nonetheless, distribution modelling is arguably the best approach at present when reliable locality and environment data are available . We chose Maximum Entropy ("Maxent") , a statistical mechanics approach, as our modelling algorithm because of its documented success at modelling species with limited locality data, a common problem when working with endemic species [65, 67–69]. To ensure that Maxent was best suited to modeling distributions of Andean species, we compared the success of Maxent and two new promising methods: Mahalanobis Typicalities (a method adopted from remote sensing analyses), and Random Forests (a model averaging approach to classification and regression trees). We found that Maxent produced more consistent predictions across varying climatic conditions for 16 species . Two to seven taxonomic specialists reviewed each model output to determine thresholds to convert continuous predictions into presence-absence maps based on known areas of absence, and to remove areas of known over-prediction (i.e., where the species was known not to occur). Specialist review is especially necessary when modelling with small sets of locality data [52, 67, 70]. For species known from a single or very few localities, we ran "rule-based" models (instead of Maxent) consisting of the geographic intersection of known ranges in elevation and other environmental variables such as temperature and precipitation.
Areas of Endemism and Irreplaceability
Traditionally, ecologists have overlain distribution maps of species to identify areas of high endemism or species richness . We followed this approach to identify areas of high endemism for each vertebrate and plant group. To identify discrete areas of high endemism we chose an arbitrary threshold value of two-thirds the maximum number of overlapping species for each group and compared these patterns with previous studies, where they exist. This simple threshold could be changed depending on the desire to be more or less inclusive in identifying areas of high endemism.
To highlight areas harbouring species with very restricted ranges, and therefore of potentially greater conservation significance, we created maps of summed irreplaceability for each group using the C-Plan Software . Summed irreplaceability is the likelihood that a given analysis unit should be protected to achieve a specified conservation target for the study area . We used 10-km2 analysis pixels and defined 25 of these pixels for each species as a conservation "target". If a given species was found present in < 25 of the 10-km2 pixels, we set the target as the number of pixels in which the species occurs. For each species, irreplaceability for each pixel ranges from 0 to 1. Low values of irreplaceability indicate that for a species there are many other (replaceable) sites that may be conserved (in other words that a species occurs in many pixels), whereas high values indicate there are very few sites available (irreplaceable) because the species have very narrow ranges. The final irreplaceability number is the result of summing irreplaceability values for all species occurring at each location, thereby emphasizing the locations with the higher number of narrow-range endemics.
To complement the endemic species information, we produced a detailed map of natural vegetation types at a scale of 1:250,000 (25 ha minimum mapping unit). We applied a hemisphere-wide vegetation classification system  that is the terrestrial classification employed as a standard in North America in U.S. federal mapping projects [73, 74] and an emerging standard in Latin America . The classification relies on the concept of terrestrial ecological systems , which are groups of vegetation communities that tend to co-occur in landscapes as a result of the action of common ecological processes, substrates, and/or environmental gradients. The ecological system classification allows for effective integrated vegetation mapping, at desired levels of thematic detail, permitting planners to prioritize across borders and across large regions. The species distribution models did not use this map as a predictor variable, thus the map provides an independent characterization of areas where endemics reside. In addition to analysing protection gaps and representativeness of the systems, we examined the overlap between ecological systems and areas of high endemism. Our goal was to identify if any systems were disproportionally represented in endemic areas compared to their distributions across the study area.
To create the ecological systems map, we incorporated existing vegetation maps where possible, and with in-country mapping teams of local field and botanical experts; we applied one cohesive classification system across the two countries. The mapping relied on field work, visual interpretation of Landsat TM and ETM+ satellite images in the Peruvian lowlands and areas of Bolivia, and spatial modelling and image classification for upland areas in Peru. Though more advanced mapping methods exist (e.g., ), we found our methods to be appropriate for these landscapes and the limited data availability, as well as more accessible to the in-country mapping teams. For ecological system characterization as well as accuracy assessment, we developed a rapid field survey protocol for more than 2000 points across the study area using spatial optimization to identify candidate clusters of points. Field observations and aerial transects of high-resolution digital photos of remote and inaccessible areas provided the basis for map validation and accuracy assessment. Details of the mapping methods, classification system and accuracy assessment can be found in Additional File 1.
We conducted a gap analysis (sensu) by examining the representation of terrestrial ecological systems, species distributions, and areas of high endemism and irreplaceability with respect to existing national-level protected areas. We included all designated nationally administered areas corresponding to World Conservation Union (IUCN) categories I-VI (IUCN 1994), as well as those that have not yet been scored against the IUCN criteria. This covered national parks, communal reserves, protected forests, integrated management areas, and other national sanctuaries. Rather than limiting our analysis to those areas with IUCN categories reflecting the strictest levels of protection, we took an inclusive approach, recognizing that in this region effective protection can vary in any category. We used digital maps of protected area boundaries from 2007 provided by our in-country collaborators as they were more current than the World Database of Protected Areas WDPA  at the time. National level protected area boundaries have not changed in the region at the time of publication of this article; however improvements have been made to the WDPA information. Regional protected areas have experienced shifts in jurisdiction, area, and level of protection. While including regional protected areas in this analysis would be advantageous, information on protection levels and boundaries of regional areas is incomplete in some areas and inconsistent across country borders.
The datasets and individual species maps for most of the analyses described here are publically accessible (in both graphic and geospatial format) on the project website (http://www.natureserve.org/andesamazon). The supporting Additional Files 1, 2, 3, 4, 5, 6, contain supplementary results in detail.
Areas of Endemism and Irreplaceability
Ecological systems that overlap vertebrate endemic areas and irreplaceable areas.
Percent of endemic area covered by system
Percent of irreplaceable areas covered by system
Percent of study area covered by system
Percent of system range that is protected
Montane pluvial forest of the Yungas
Lower montane pluvial forest and palm grove of the Yungas
Montane humid pluviseasonal forest of the Yungas
Upper montane pluvial forest of the Yungas
Upper montane pluviseasonal forest of the Yungas
Low montane subhumid pluviseasonal forest of the southern Yungas
Lower montane humid pluviseasonal forest of the Yungas
Southwestern Amazon subandean evergreen forest
High-Andean and upper montane pluvial grassland and shrubland of the Yungas
Western Amazon subandean evergreen forest
Southwestern Amazon piedmont forest
Southwestern Amazon subandean evergreen seasonal forest
Western Amazon semideciduous azonal forest
Lower montane humid pluviseasonal forest of the Yungas
Lower montane pluvial forest of the Condor Mountain Range
Coverage of endemic species ranges by national-level protected areas; IUCN l - VI (IUCN, 1994)
Percent range in IUCN I-VI
51 to 75
26 to 50
10 to 25
Total number of species
Terrestrial ecological systems having less than 2% protection in the study area
Percent of study Area
Area protected (ha)
Complex of non-alkaline savannas of the Beni transitional to the Cerrado
Cerrado complex of the northern Beni
Western Amazon semideciduous azonal forest
Complex of non-alkaline savannas of the Beni
Central-south Amazon Palm dominated forest
Chiquitania and Beni seasonally flooded herbaceous oligotrophic savanna
Beni seasonally flooded palm grove and savanna of the alkaline flatlands
Chiquitania and Beni "Cerradão"
Beni seasonally flooded herbaceous mesotrophic savanna
Montane interandean xeric forest and shrubland of the Yungas
Interandean xeric scrub of the Yungas
Beni and Chiquitania open hydrophytic savanna
Lower montane xeric forest and shrubland of the northern Yungas
Beni mixed-water riparian vegetation and forests complex
Northern Yungas dry submontane complex
Cerrado hydrophytic savannah with termite mounds
Chiquitania and Beni semideciduous subhumid forest
Beni clear and dark-water riparian forests and vegetation complex
Central-south Amazon ridges lithomorphic scrub
Northern Yungas dry montane and submontane complex
Yungas ridge pluviseasonal forest
Montane lithomorphic vegetation of the Yungas
Western Beni seasonally flooded thorn forest of the alkaline flatlands
Upper montane pluvial Polylepis forest of the Yungas
Several areas of endemism and irreplaceability without current national-level protected status are worth highlighting (Figure 7). In northern Peru, areas near the cities of Iquitos and Tarapoto host unique concentrations of endemic plants. The Tarapoto region also has a large irreplaceable area for amphibians. The Carpish Hills in the Department of Huanuco host many endemic plants (Acanthaceae, Aquifoliaceae and Fuchsia) and are highly irreplaceable for endemic birds (up to 32 ranges overlap) but are completely unprotected. The Cordillera de Vilcabamba is a major area of endemism for birds, mammals and plants (Fuchsia). It also constitutes the largest cohesive irreplaceable area for birds and mammals in the study area, and is highly irreplaceable for some plants. Currently the Cordillera of Vilcabamba has only one protected area, the Machu Picchu Historical Sanctuary, which covers just 326 km2, and is highly impacted by tourism. The north-eastern corner of the Department of Puno has numerous endemic birds and mammals and is also unprotected. However, many of the ranges of these species extend into Bolivia where they are protected in Madidi National Park.
In Bolivia, the cordilleras near La Paz have high levels of bird, mammal and plant endemism (8 of the 13 plant groups analysed), and scored as highly irreplaceable for endemic mammals and plants. Most of these cordilleras are not protected, although a small area that is irreplaceable for amphibians coincides with the 608-km2 Cotapata National Park (Figure 5). In central Bolivia, unprotected endemic areas for birds, mammals, and amphibians occur in the Cordillera de Cocapata-Tiraque and Cochabamba Department, between protected areas.
Our results, at a conservation practitioner's scale, identify geographic areas in the eastern slopes of the Peruvian and Bolivian Andes with high concentrations of endemic species, areas with high irreplaceability, gaps in protection for both species and ecosystems, and ecological systems where these endemic species reside. Our focus on a variety of vertebrate and plant groups underlines the variation in spatial distribution patterns among different taxa. The geographical extents and levels of current protection of the ranges of species, endemic areas, irreplaceable areas, and key ecological systems also vary widely.
Mapping species distributions is inherently limited in terms of a true representation of biodiversity. As a one dimensional map of potential habitat based on climate, elevation and vegetation, the distribution modelling omits species interactions such as predation and competition, effect of human edges along habitat, and the effects of climate change [63, 81]. However it is a large step forward for this region where current conservation analyses are obliged to rely upon generalized hand-drawn maps of species ranges, or species lists for very large multi-country geographical units (e.g. Hotspots or Ecoregions) that were not intended nor appropriate for regional or landscape level applications . Our mapping of ecological systems, for example, identified ~90 ecological systems; the same area is covered by parts of 12 ecoregions (sensu).
The locations of high endemism (Figure 4) agree with past studies for taxa that have been examined previously, yet earlier studies were conducted with much less data availability and at much coarser spatial resolution. The high levels of endemic bird richness found in the northern part of the study area are consistent with previous work [36, 40, 83]. However, our study revealed previously unrecognized areas of bird endemism in Peru: the southern Huánuco region, the western Cordillera de Vilcabamba, and the region along the Río Mapacho-Yavero east of Cuzco (Figure 4, 7; see  for details). This study is the first to reveal detailed patterns of endemic species for mammals and amphibians (see  for location descriptions), and therefore few comparisons with past studies can be made. However the areas of high endemic mammal richness in Peru corroborate the one regional study of similar scope  and the mid-elevation concentration of endemic amphibians coincides with the less spatially explicit suggestions of  and . Centres of plant endemism varied among groups and families, yet the pattern for one group (Ericaceae) did correspond to a previous study . Other existing analyses use such coarse resolution (e.g., the 1°×1° Flora Neotropica grid ) that comparisons are too general to be meaningful. For most plant groups, this study is the first to assess spatial patterns of endemism in the eastern Andean basin of Peru and Bolivia.
Despite the increased level of detail in spatial scale that our dataset provides, continued work needs to focus on refining these biodiversity data to even finer spatial scales (e.g. 1:100,000) and higher levels of accuracy. The dataset and analyses we have produced are tied to the time of specimen collections and to the quality of available data. As more specimen locations are collected in the future with increasingly accurate locational and elevational information (using a precise global positioning system), distribution models could be re-run and models validated. Geographical collection bias, a problem for presence-only distribution models could be addressed in future modelling efforts by the selection of pseudo-absence data having similar bias as the presence data . More precise geographical climate data could refine the spatial resolution of model predictions; there will be an increasing prevalence of 'downscaled' geographical climate data thanks to higher spatial resolution digital elevation models (SRTM and ASTER). However the overall limitation is the lack of adequate meteorological stations in the region. Other layers that would be useful to incorporate upon their refinement would be a characterization of soils or geology. We successfully modelled all endemic vertebrates yet, additional models of plant species distributions should be realized. Considering there are over 5000 endemic plant species in the country of Peru (of which approximately 3200 fall within the altitudinal range of our study area) , our 435 species represents a small fraction of endemics to Peru and/or Bolivia in the Amazon watershed.
Our country wide analysis could be refined to department scale using land tenure information and local to regional protected areas and resource concessions. Current maps of forest deforestation and degradation would aid in calculating the remnant ranges for each species as well as ecological systems. Further analysis could be made in terms of the complementarity of species assemblages and their relationship to ecological systems and levels of protection, whose results could further guide priorities. However, the greater battle for biodiversity conservation lies in managing elements beyond our datasets and analyses, as described below.
The geographical patterns of endemism, irreplaceability, and ecosystems revealed here pose several challenges for conservation planning in the region (Figure 7). The most obvious challenge is the geographic configuration of the locations of endemic or irreplaceable areas. Although we mapped only a small subset of the biodiversity that occurs in the region, we found striking geographic differences in endemic species concentrations across taxonomic groups. The difficulty of using surrogates of one species group for another has been recognized [15, 52, 53], and our findings underscore the need for a large portfolio of protected areas and other protection mechanisms to conserve diverse elements of biodiversity.
Second, the gap analysis demonstrates that many areas where concentrations of endemic species occur remain unprotected today. Considering ongoing threats in the region from infrastructure development , oil extraction , gold mining [90, 91], illicit crops , and the continually advancing agricultural fronts, more carefully situated protected areas and novel land use regulation strategies will be necessary to safeguard substantial amounts of biodiversity.
Third, although we use protected area coverage to evaluate conservation coverage, we acknowledge that protection status does not necessary translate into actual protection on the ground. Indeed, resource extraction and degradation is continuing in many legally protected lands in the study area . Nevertheless, these reserves have the potential to protect important segments of endemic and irreplaceable areas, suggesting that strengthening the capacity of relevant authorities to improve protection is an important and continuing challenge.
Fourth, large reserves will probably be insufficient to maintain all biodiversity. Although large reserves often provide the best means for maintaining well-functioning ecosystems , the pattern of endemism we document, in which microendemic species are scattered across the landscape and not always concentrated geographically, will require multi-pronged conservation efforts. Restricted-range species that occur far from the major areas of endemism or irreplaceability, such as the two primates in the Bolivian Beni, would benefit from a wider network of smaller reserves, perhaps established by departmental, provincial, or municipal governments or private entities. Current trends toward the decentralization of responsibility for natural resource management to provincial governments may provide a useful institutional context for the establishment of some of these smaller, but nonetheless critical reserves .
Our finding that highly endemic areas disproportionally occupy a handful ecological systems presents yet a fifth challenge. Ecological systems characterize broad, integrated units of biodiversity and can be used as a coarse filter for conservation. While maintaining representation of all systems in landscape-level protection plans , planners may need to balance the need to protect endemic species with the need for a representative sample of ecosystem type and function as well as other targets such as endangered species or carbon sequestration. On the other hand, these particular ecological systems could be considered surrogates for areas of high endemism. The systems are advantageously close together in the Yungas region, are relatively limited in extent (totalling 7% of the study area), and have individual ranges that are < 35% protected.
A final challenge is continued climate change. We know that because of climate change, the ranges of many species will shift across the landscape and possibly out of protected areas [96, 97]. Evidence is accumulating that along the Andean slope, species shifts are already occurring [98, 99]. Yet the variation in projections of future South American climate makes assessment of the effects on species' distributions difficult . The steep elevation (and therefore climate) gradients in the Andes, where most endemic species are located, suggest that such displacements may take place over relatively small distances. Extinctions are most likely in species inhabiting the highest-elevation habitats, which occur above our study area . Nevertheless, planners should consider adding upslope buffers to conservation areas designated using current distributions of endemic species, and future research could model these species distributions under future climate scenarios.
To complement the further creation and effective management of protected areas, other alternative approaches, which will result in the maintenance of key ecosystems, should expand and continue. These approaches include, strategic conservation on private lands and brokering conservation agreements with private companies, effective land use planning and possibly carbon accounting at the regional government level for both public and private lands, and payments for ecosystem services (e.g. water provision, ecotourism recreation, carbon storage through forests: Reducing Emissions from Deforestation and forest Degradation, REDD). However priority areas for ecosystem services concessions may not necessarily overlap with priorities for biodiversity conservation (e.g.).
We believe these spatial datasets provide a substantive base upon which to make decisions and move forward for further protection. The approach to developing these datasets described here, relying on existing environmental data sources, data in natural history collections, and in-country expertise to identify endemic species distributions, concentrations and gaps in protection across national borders is applicable to many regions of the world where survey efforts are incomplete. Our results demonstrate that even under these conditions, conservationists can develop spatial datasets for multiple taxonomic groups at a scale useful to guide planning.
We are deeply indebted to the Gordon and Betty Moore Foundation for financial support and technical guidance during the project. The project would not be possible without the generous data sharing, opinions, advice and participation of our long list of collaborators, found at http://www.natureserve.org/aboutUs/latinamerica/col_institutions.jsp. Funds from NatureServe, Duke University's Nicholas School of Environment, and USAID's Emerging Pandemic Threats PREDICT Program supported the publication of this manuscript. The datasets for most of the maps and analyses described here are accessible in both graphic and GIS format at http://www.natureserve.org/andesamazon.
We greatly appreciate the critiques of the anonymous reviewers and Nigel Pitman that helped shape and improve this manuscript.
- Myers N, Mittermeier RA, Mittermeie CG, da Fonseca GAB, Kent J: Biodiversity hotspots for conservation priorities. Nature. 2000, 403: 853-858. 10.1038/35002501.View ArticlePubMedGoogle Scholar
- Rodrigues ASL, Akcakaya HR, Andelman SJ, et al: Global gap analysis: Priority regions for expanding the global protected-area network. BioScience. 2004, 54: 1092-1100. 10.1641/0006-3568(2004)054[1092:GGAPRF]2.0.CO;2.View ArticleGoogle Scholar
- Orme CDL, Davies RG, Burgess M, et al: Global hotspots of species richness are not congruent with endemism or threat. Nature. 2005, 436: 1016-1019. 10.1038/nature03850.View ArticlePubMedGoogle Scholar
- Brooks TM, Mittermeier RA, da Fonseca GAB, Gerlach J, Hoffmann M, Lamoreux JF, Mittermeier CG, Pilgrim JD, Rodrigues ASL: Global biodiversity conservation priorities. Science. 2006, 31: 58-61.View ArticleGoogle Scholar
- Ceballos G, Ehrlich PR: Global mammal distributions, biodiversity hotspots, and conservation. Proceedings of the National Academy of Sciences. 2006, 103: 19374-19379. 10.1073/pnas.0609334103.View ArticleGoogle Scholar
- Olson DM, Dinerstein E: The global 200: priority ecoregions for global conservation. Annals of the Missouri Botanical Garden. 2002, 89: 199-224. 10.2307/3298564.View ArticleGoogle Scholar
- IUCN: Guidelines for protected areas management categories. Cambridge, UK and Gland, Switzerland: International Union for Conservation of Nature. 1994Google Scholar
- Patterson BD, Ceballos G, Sechrest W, Tognelli MF, Brooks T, Luna L, Ortega P, Salazar I, Young BE: Digital distribution maps of the mammals of the Western Hemisphere. Edited by: NatureServe. 2007, Arlington, V.A.: NatureServe, 2.0Google Scholar
- Ridgely RS, Allnutt TF, Brooks T, McNicol DK, Mehlman DW, Young BE, Zook JR: Digital distribution maps of the birds of the Western Hemisphere. Edited by: NatureServe. 2007, Arlington, VA, 2.1Google Scholar
- Schipper J, Chanson JS, False Chiozza: The status of the world's land and marine mammals: diversity, threat, and knowledge. Science. 2008, 322: 225-230. 10.1126/science.1165115.View ArticlePubMedGoogle Scholar
- Londoño-Murcia MC, Tellez-Valdés O, Sánchez-Cordero V: Environmental heterogeneity of World Wildlife Fund for Nature ecoregions and implications for conservation in Neotropical biodiversity hotspots. Environmental Conservation. 2010, 37 (2): 116-127. 10.1017/S0376892910000391.View ArticleGoogle Scholar
- Freitag S, Nicholls AO, Van Jaarsveld AS: Nature reserve selection in the Transvaal, South Africa: what data should we be using?. Biodiversity and Conservation. 1996, 5: 685-698. 10.1007/BF00051781.View ArticleGoogle Scholar
- Hurlbert AH, White EP: Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications. Ecology Letters. 2005, 8: 319-327. 10.1111/j.1461-0248.2005.00726.x.View ArticleGoogle Scholar
- Ferrier S: Mapping spatial pattern in biodiversity for regional conservation planning: where to from here?. Systematic Biology. 2002, 51: 331-363. 10.1080/10635150252899806.View ArticlePubMedGoogle Scholar
- Leroux SJ, Schmiegelow FKA: Biodiversity concordance and the importance of endemism. Conservation Biology. 2007, 21: 266-268. 10.1111/j.1523-1739.2006.00628.x.View ArticlePubMedGoogle Scholar
- Balmford A, Mace GM, Ginsberg JR: The challenges to conservation in a changing world: putting processes on the map. Conservation in a changing world. Edited by: G.M Mace AB, and J.R. Ginsberg. 1998, Cambridge: Cambridge Univ Press, 1-28.Google Scholar
- van der Werff H, Consiglio T: Distribution and conservation significance of endemic species of flowering plants in Peru. Biodiversity and Conservation. 2004, 13: 1699-1713.View ArticleGoogle Scholar
- Roy MS, Silva JMCD, Arctander P, Garcia-Moreno J, Fjeldså J: The speciation of South American and African birds in montane regions. Avian Molecular Evolution and Systematics. Edited by: Mindell DP. 1997, New York: Academic Press, 325-343.View ArticleGoogle Scholar
- Fjeldså J: Geographical patterns for relict and young species of birds in Africa and South America and implications for conservation priorities. Biodiversity and Conservation. 1994, 3: 207-226. 10.1007/BF00055939.View ArticleGoogle Scholar
- Roy MS, Torres-Mura JC, Hertel F: Molecular phylogeny and evolutionary history of the tit-tyrants (Aves: Tyrannidae). Molecular Phylogenetic Evolution. 1999, 11: 67-76. 10.1006/mpev.1998.0563.View ArticleGoogle Scholar
- García-Moreno J, Fjeldså J: Chronology and mode of speciation in the Andean avifauna. Bonner Zoological Monographs. 2000, 46: 25-46.Google Scholar
- Dingle C, Lovette IJ, Canaday C, Smith T: Elevational zonation and the phylogenetic relationships of the Henicorhina wood-wrens. Auk. 2006, 123: 119-134. 10.1642/0004-8038(2006)123[0119:EZATPR]2.0.CO;2.View ArticleGoogle Scholar
- Patton JL, Smith MF: MtDNA phylogeny of Andean mice: a test of diversification across ecological gradients. Evolution. 1992, 46: 174-183. 10.2307/2409812.View ArticleGoogle Scholar
- Young KR: Biogeographical paradigms useful for the study of tropical montane forests and their biota. Biodiversity and conservation of neotropical montane forests. Edited by: Churchill; SP, Balslev; H, Forero; E, Luteyn JL. New York. 1995, The New York Botanical GardenGoogle Scholar
- Young KR, Ulloa C, Luteyn JL, Knapp S: Plant evolution and endemism in Andean South America: an introduction. Botanical Review. 2002, 68: 4-21. 10.1663/0006-8101(2002)068[0004:PEAEIA]2.0.CO;2.View ArticleGoogle Scholar
- Hughes C, Eastwood R: Island radiation on a continental scale: Exceptional rates of plant diversification after uplift of the Andes. Proceedings of the National Academy of Sciences. 2006, 103: 10334-10339. 10.1073/pnas.0601928103.View ArticleGoogle Scholar
- Donoghue MJ: A phylogenetic perspective on the distribution of plant diversity. Proceedings of the National Academy of Sciences. 2008, 105: 11549-11555. 10.1073/pnas.0801962105.View ArticleGoogle Scholar
- Lynch JD, Duellman WE: The Eleutherodactylus of the Amazonian slopes of the Ecuadorian Andes (Anura: Leptdactylidae). Miscellaneous Publication of the University of Kansas Natural History Museum. 1980, 69: 1-86.Google Scholar
- Graham CH, Ron SR, Santos JC, Schneider CJ, Moritz C: Integrating phylogenetics and environmental niche models to explore speciation mechanisms in Dendrobatid frogs. Evolution. 2004, 58: 1781-1793.View ArticlePubMedGoogle Scholar
- Lynch JD: Origins of the high Andean herpetological fauna. Págs. High Altitude Tropical Biogeography. Edited by: Vuilleumier F, Monasterio M. 1986, Oxford: Oxford University Press, 478-499.Google Scholar
- Santos JC, Coloma LA, Summers K, Caldwell JP, Ree R, Cannatella DC: Amazonian amphibian diversity is primarily derived from late Miocene Andean lineages. PLOS Biol. 2009, 7: 0448-0461.View ArticleGoogle Scholar
- Killeen TJ, Douglas M, Consiglio T, Jørgensen PM, Mejia J: Dry spots and wet spots in the Andean hotspot. Journal of Biogeography. 2007, 34 (8): 1357-1373. 10.1111/j.1365-2699.2006.01682.x.View ArticleGoogle Scholar
- Hoorn C, Wesselingh FP, ter Steege H, Bermudez MA, Mora A, et al: Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science. 2010, 330: 927-931. 10.1126/science.1194585.View ArticlePubMedGoogle Scholar
- Bush M, Lovejoy T: Amazonian conservation: pushing the limits of biogeographical knowledge. Journal of Biogeography. 2007, 34: 1291-1293. 10.1111/j.1365-2699.2007.01758.x.View ArticleGoogle Scholar
- Rodriguez LO, Young KR: Biological diversity of Peru: determining priority areas for conservation. Ambio. 2000, 29: 329-337.View ArticleGoogle Scholar
- Fjeldså J, Alvarez MD, Lazcano JM, Leon B: Illicit crops and armed conflict as constraints on biodiversity conservation in the Andes region. Ambio. 2005, 34: 205-211.View ArticlePubMedGoogle Scholar
- Fjeldså J, Lambin E, Mertens B: Correlation between endemism and local ecoclimatic stability documented by comparing Andean bird distributions and remotely sensed land surface data. Ecography. 1999, 22: 63-78. 10.1111/j.1600-0587.1999.tb00455.x.View ArticleGoogle Scholar
- Hellmayr CE: Ueber neue und selteneVögel aus Südperu. Verh Ornithol Ges Bayern. 1912, 11: 159-163.Google Scholar
- Müller P: The dispersal centers of terrestrial vertebrates in the Neotropical realm. Biogeographica. 1973, 2: 1-244.Google Scholar
- Stattersfield AJ, Crosby MJ, Long AJ, Wege DC: Endemic bird areas of the world. 1998, Cambridge, UK.: BirdLife InternationalGoogle Scholar
- Pacheco V, Quintana HL, Hernandez PA, Paniagua L, Vargas J, Young BE: Mammals. Endemic species distributions on the east slope of the Andes in Peru and Bolivia. Edited by: Young BE. Arlington, Virginia: NatureServe. 2007, 40-45.Google Scholar
- Pacheco V: Mamíferos del Perú. Diversidad y conservación de los mamíferos neotropicales. Edited by: Ceballos; G, Simonetti J. Mexico City, Mexico. 2002, CONABIO-UNAM, 586-Google Scholar
- Doan TM, Arizábal W: Microgeographical variation in species composition of the herpetofaunal communities of Tambopata region, Peru. Biotropica. 2002, 34: 101-117.View ArticleGoogle Scholar
- Duellman WE: Distribution patterns of amphibians in South America. Patterns of distribution of amphibians. Edited by: Duellman WE. Baltimore, MD. 1999, Johns Hopkins Univ Press, 255-328.Google Scholar
- Reichle S: Distribution and conservation status of Bolivian Amphibians. 2007, Germany: Rheinische Friedrich Wilhelms UniversitaetGoogle Scholar
- Kessler M: The elevational gradient of Andean plant endemism: varying influences of taxon-specific traits and topography at different taxonomic levels. Journal Biogeography. 2002, 29: 1159-1166. 10.1046/j.1365-2699.2002.00773.x.View ArticleGoogle Scholar
- Knapp S: Assessing patterns of plant endemism in Neotropical uplands. Botanical Review. 2002, 68: 22-37. 10.1663/0006-8101(2002)068[0022:APOPEI]2.0.CO;2.View ArticleGoogle Scholar
- León B, Young KR: Distribution of pteridophyte diversity and endemism in Peru. Pteridology in perspective. Edited by: Camus; JM, Gibby; M, Johns RJ. Kew, U.K. 1996, Royal Botanic Garden, 77-91.Google Scholar
- Luteyn JL: Diversity, adaptation, and endemism in Neotropical Ericaceae: biogeographical patterns in the Vaccinieae. Botanical Review. 2002, 68: 55-87. 10.1663/0006-8101(2002)068[0055:DAAEIN]2.0.CO;2.View ArticleGoogle Scholar
- Guisan A, Thuiller W: Predicting species distribution: offering more than simple habitat models. Ecology Letters. 2005, 8: 993-1009. 10.1111/j.1461-0248.2005.00792.x.View ArticleGoogle Scholar
- Guisan A, Zimmermann NE: Predictive habitat distribution models in ecology. Ecological Modelling. 2000, 135: 147-186. 10.1016/S0304-3800(00)00354-9.View ArticleGoogle Scholar
- Kremen C, Cameron A, Moilanen A, Phillips S, Beentje H, Dransfeld J, Fisher BL, Glaw F, Hijmans R, Lees D, et al: Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science. 2008, 320: 222-226. 10.1126/science.1155193.View ArticlePubMedGoogle Scholar
- Prendergast JR, Quinn RM, Lawton JH, Eversham BC, Gibbons DW: Rare species, the coincidence of diversity hotspots and conservation strategies. Nature. 1993, 365: 335-337. 10.1038/365335a0.View ArticleGoogle Scholar
- Ferrier S, Pressey RL, Barrett TW: A new predictor of the irreplaceability of areas for achieving a conservation goal, its application to real-world planning, and a research agenda for further refinement. Biological Conservation. 2000, 93: 303-325. 10.1016/S0006-3207(99)00149-4.View ArticleGoogle Scholar
- IUCN, Conservation International, NatureServe: Global amphibian assessment. 2006, IUCN (International Union for Conservation of Nature, Conservation International, NatureServe, Version 1.1Google Scholar
- Stephens L, Traylor ML: Ornithological gazetteer of Peru. Museum of Comparative Zoology. Edited by: University H. Boston, MA. 1983Google Scholar
- Thomas O: New mammals from Peru and Bolivia, with a list of those recorded from the Inambari River, upper Madre de Dios. Ann Mag Nat Hist. 1901, 7 (5): 148-153.Google Scholar
- MaNIS/HerpNet/ORNIS: Mammal Networked Information System: Georeferencing Guidelines. 2001Google Scholar
- Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A: Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology. 2005, 25: 1965-1978. 10.1002/joc.1276.View ArticleGoogle Scholar
- Farr TG: The Shuttle Radar Topography Mission. Rev Geophys. 2007, 45 (RG2004):Google Scholar
- Hansen M, DeFries R, Townshend JR, Carroll M, Dimiceli C, Sohlberg R: Vegetation Continuous Fields, MOD44B, 2001 Percent Tree Cover, Collection 3. Edited by: Univ Maryland CP. College Park, MD 2003.Google Scholar
- Graham CH, Ferrier S, Huettman F, Moritz C, Peterson AT: New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology and Evolution. 2004, 19: 497-503. 10.1016/j.tree.2004.07.006.View ArticlePubMedGoogle Scholar
- Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, Williams PH: Avoiding pitfalls of using species distribution models in conservation planning. Conservation Biology. 2003, 17: 1591-1600. 10.1111/j.1523-1739.2003.00233.x.View ArticleGoogle Scholar
- Nelson BW, Ferreira C, da Silva M, Kawasaki ML: Endemism centres, refugia and botanical collection density in Brazilian Amazonia. Nature. 1990, 345: 714-716. 10.1038/345714a0.View ArticleGoogle Scholar
- Elith J, Graham CH, Anderson RP, et al: Novel methods improve prediction of species' distributions from occurrence data. Ecography. 2006, 29: 129-151. 10.1111/j.2006.0906-7590.04596.x.View ArticleGoogle Scholar
- Phillips SJ, Anderson RP, Schapire RE: Maximum entropy modeling of species geographic distributions. Ecological Modelling. 2006, 190: 231-259. 10.1016/j.ecolmodel.2005.03.026.View ArticleGoogle Scholar
- Hernandez PA, Franke I, Herzog SK, Pacheco V, Paniagua L, Quintana HL, Soto HA, Swensen JJ, Tovar C, Valqui TH, et al: Predicting species distributions in poorly-studied landscapes. Biodiversity and Conservation. 2008, 17: 1353-1366. 10.1007/s10531-007-9314-z.View ArticleGoogle Scholar
- Hernandez PA, Graham CH, Master LL, Albert DL: The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography. 2006, 29: 773-785. 10.1111/j.0906-7590.2006.04700.x.View ArticleGoogle Scholar
- Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A: Predicting Species Distributions Working Group N: Effects of sample size on the performance of species distribution models. Diversity and Distributions. 2008, 14: 763-773. 10.1111/j.1472-4642.2008.00482.x.View ArticleGoogle Scholar
- Loiselle BA, Jørgensen PM, Consiglio T, Jiménez I, Blake JG, Lohmann LG, Montiel OM: Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?. Journal of Biogeography. 2008, 35: 105-116.Google Scholar
- Pressey RL, Watts M, Ridges M, Barrett T: C-Plan conservation planning software. User Manual. 2005, New South Wales, Australia: New South Wales Department of Environment and ConservationGoogle Scholar
- Josse C, Navarro G, Comer P, Evans R, Faber-Langendoen D, Fellows M, Kittel G, Menard S, Pyne M, Reid M, et al: Ecological systems of Latin America and the Caribbean: a working classification of terrestrial systems. 2003, Arlington, VA: NatureServeGoogle Scholar
- Comer P, Faber-Langendoen D, Evans R, et al: Ecological systems of the United States: a working classification of U.S. terrestrial systems. 2003, Arlington, VA: NatureServeGoogle Scholar
- Comer P, Schulz K: Standardized ecological classification for meso-scale mapping in southwest United States. Rangeland Ecology Management. 2007, 60: 324-335. 10.2111/1551-5028(2007)60[324:SECFMM]2.0.CO;2.View ArticleGoogle Scholar
- Sayre R, Bow J, Josse C, Sotomayor L, Touval J: Terrestrial ecosystems of South America. North America land cover summit: a special issue of the Association of American Geographers. Edited by: Campbell JC, Jones KB, Smith JH. 2008, Washington, DC, 131-152.Google Scholar
- Lowry J, Ramsey RD, Thomas K, et al: Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework: a case study of the Southwest Regional Gap Analysis Project (SWReGAP). Remote Sensing of Environment. 2007, 108 (108): 59-73.View ArticleGoogle Scholar
- Scott MJ, Davis F, Cusuti B, Noss R, Butterfield B, Groves C, Anderson H, Caicco S, D'Erchia F, Edwards TC, et al: GAP analysis: a geographic approach to protection of biological diversity. Wildlife Monographs. 1993, 123: 1-41.Google Scholar
- IUCN, UNEP-WCMC: The World Database on Protected Areas (WDPA). 2010, Cambridge, UK: UNEP-WCMCGoogle Scholar
- Calle Josse: Ecological Systems of the Amazon Basin of Peru and Bolivia: Classification and Mapping. 2007, Arlington, Virginia: NatureServeGoogle Scholar
- Ellenberg H: Vegetationsstufen in perhumiden bis perariden Bereichen der tropischen Anden. Phytocoenologia. 1975, 368-387. 2Google Scholar
- Sieck M, Ibisch P, Moloney K, Jeltsch F: Current models broadly neglect specific needs of biodiversity conservation in protected areas under climate change. BMC Ecology. 2011, 11 (1): 12-10.1186/1472-6785-11-12.View ArticlePubMedPubMed CentralGoogle Scholar
- Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D'Amico JA, Itoua I, Strand HE, Morrison JC, et al: Terrestrial Ecoregions of the World: A New Map of Life on Earth. Bioscience. 2001, 51 (11): 933-938. 10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2.View ArticleGoogle Scholar
- Graves GR: Linearity of geographic range and its possible effect on the population structure of Andean birds. Auk. 1988, 105: 47-52.Google Scholar
- Young BE, Franke I, Hernandez PA, Herzog SK, Paniagua L, Soto A, Tovar C, Valqui T: Using spatial models to predict areas of endemism and gaps in the protection of Andean slope birds. Auk. 2009, 126: 554-565. 10.1525/auk.2009.08155.View ArticleGoogle Scholar
- Endemic species distributions on the east slope of the Andes in Peru and Bolivia. Edited by: Young BE. 2007, Arlington, VA: NatureServeGoogle Scholar
- Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S: Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications. 2009, 19 (1): 181-197. 10.1890/07-2153.1.View ArticlePubMedGoogle Scholar
- Leon B, Pitman N, Roque J: Introducción a las plantas endémicas del Perú. Rev Peru Biol. 2006, 13 (2): 9-25.Google Scholar
- Delgado C: Is the Interoceanic Highway exporting deforestation?. Master's thesis. 2008, Durham: Duke UniversityGoogle Scholar
- Finer M, Jenkins CN, Pimm SL, Keane B, Ross C: Oil and Gas Projects in the Western Amazon: Threats to Wilderness, Biodiversity, and Indigenous Peoples. PLoS ONE. 2008, 3 (8): e2932-10.1371/journal.pone.0002932.View ArticlePubMedPubMed CentralGoogle Scholar
- Fraser B: Peruvian gold rush threatens health and the environment. Environmental Science and Technology. 2009, 43: 7162-7164. 10.1021/es902347z.View ArticlePubMedGoogle Scholar
- Swenson JJ, Carter CE, Delgado CI, Domec JC: Gold mining in the Peruvian Amazon: global prices, deforestation, and mercury imports. PLoS One. 2010, 6 (4): e18875-View ArticleGoogle Scholar
- Killeen TJ, Calderon V, Soriana L, Quezeda B, Steininger MK, Harper G, Solorzano LA, Tucker TJ: Thirty years of land-cover change in Bolivia: exponential growth and no end in sight. Ambio. 2007, 36: 600-606. 10.1579/0044-7447(2007)36[600:TYOLCI]2.0.CO;2.View ArticlePubMedGoogle Scholar
- Margules CR, Pressey RL: Systematic conservation planning. Nature. 2000, 405: 243-253. 10.1038/35012251.View ArticlePubMedGoogle Scholar
- Ferroukhi L: Municipal forest management in Latin America. 2003, Bogor, Indonesia.: CIFOR and IDRCGoogle Scholar
- Groves CR, Jensen DB, Valutis LL, Redford KH, Shaffer ML, Scott JM, Baumgartner JV, Higgins JV, Beck MW, Anderson MG: Planning for biodiversity conservation: putting conservation science into practice. Bioscience. 2002, 52: 499-512. 10.1641/0006-3568(2002)052[0499:PFBCPC]2.0.CO;2.View ArticleGoogle Scholar
- Parmesan C: Ecological and evolutionary responses to recent climate change. Annual Review Ecology Systematics. 2006, 37: 637-669. 10.1146/annurev.ecolsys.37.091305.110100.View ArticleGoogle Scholar
- Williams P, Hannah L, Andelman S, Midgley G, Araújo M, Hughes G, Manne L, Martinez-Meyer E, Pearson R: Planning for climate change: identifying minimum dispersal corridors for the Cape Proteaceae. Conservation Biology. 2005, 19: 1063-1074. 10.1111/j.1523-1739.2005.00080.x.View ArticleGoogle Scholar
- Feeley KJ, Silman MR, Bush MB, Farfan W, Cabrera KG, Malhi Y, Meir P, Revilla NS, Quisiyupanqui MNR, Saatchi S: Upslope migration of Andean trees. Journal of Biogeography. 2011, 38 (4): 783-791. 10.1111/j.1365-2699.2010.02444.x.View ArticleGoogle Scholar
- Forero-Medina G, Terborgh J, Socolar SJ, Pimm SL: Elevational Ranges of Birds on a Tropical Montane Gradient Lag Behind Warming Temperatures. PLoSOne. 2011, 6 (12): e28535-View ArticleGoogle Scholar
- Magrin G, Gay García C, Cruz Choque D, Giménez JC, Moreno AR, Nagy GJ, Nobre C, Villamizar A: Latin America. Climate change 2007: impacts, adaptation and vulnerability. Climate change 2007: impacts, adaptation and vulnerability. Edited by: Parry; ML, Canziani; OF, Palutikof; JP, Linden; PJvd, Hanson CE. Cambridge, U.K. 2007, Cambridge Univ Press, 581-615.Google Scholar
- Chan KMA, Shaw MR, Cameron DR, Underwood EC, Daily GC: Conservation Planning for Ecosystem Services. PLOS Biol. 2006, 4 (11): e379.-View ArticlePubMedPubMed CentralGoogle Scholar
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