Universidade Federal do Acre
Floristics and Economic Botany of Acre, Brazil
Florística e Botânica Econômica do Acre, Brasil
A digital base map for studying the Neotropical flora
Nat Bletter1, John Janovec2,
Berry Brosi2, and Douglas C. Daly2.
1 Plant Sciences Dept., Graduate Center, City University of New York and The New York Botanical Garden, Bronx, NY 10458-5126.
2 The New York Botanical Garden, Bronx, NY 10458-5126.
Despite advances in GIS and remote-sensing technology and software, to date most systematists and other botanists working on the Neotropical flora, particularly on the monographic series Flora Neotropica, have used hard-copy maps. These maps make it possible to see basic distribution patterns, but they are highly inaccurate, and the fact that the data are not compiled in digital form means that it is difficult or impossible to retrieve the metadata (i.e., the collection data and attributes of the specimen(s) associated with each point on the map), select and combine distribution data sets for various organisms, perform spatial statistics on the distributions, or overlay species distributions onto maps of soils, climate, and other environmental variables. In an effort to help modernize Neotropical plant studies and make GIS more accessible to botanists, we have developed a digital base map of the Americas with multiple registered map layers that can be superimposed in any combination and can be used to easily create digital distribution maps from collection lists for dissemination and analysis. This base map, freely available to botanists and systematists, was made using ArcView® GIS. Several of the layers were derived from Environmental Systems Research Institute (ESRI) data sets (ArcWorld®, ArcAtlas®, Digital Chart of the World®). For a basic version of this system we obtained additional data sets on vegetation and soils (Woods Hole Research Center), elevation (U.S. Geological Survey), and EcoRegions (World Wildlife Fund); for our own projects, we have added layers from a number of additional sources and more appropriate in scale for Amazonia and the state of Acre, Brazil. The layers are being expanded upon as new data sets become available, and we are actively seeking additional sets from other sources, including geographical institutes in individual Neotropical countries. This system makes it possible to carry out a number of rather elegant analyses and visualizations, including the following: (1) plot the distributions of species and use the overlays to easily visualize coincidence of distribution patterns with geographic and environmental features; (2) map values for morphological characters onto the distribution points in order to examine character variation over the range of the species (e.g., plotting leaf size to see latitudinal or longitudinal patterns); (3) quantitatively analyze spatial statistics (e.g., examining the relationship between fruit size and rainfall); and (4) vastly increase the agility and versatility of historical biogeography techniques. Though any botanist adept at using GIS theoretically could conduct these analyses, the Americas Base Map assembles a disparate set of high-quality, botanically relevant environmental and geographic data in one place and provides instructions that obviate the need for deep expertise in GIS. This dramatically improves the access of botanists to relevant geospatial data, especially in the developing world, and should serve to expand the use of GIS in plant biogeography. We anticipate that this multifaceted approach to mapping species distributions will facilitate the work of systematists and floristicians, and that it will help Neotropical plant geography to progress from conjecture to testable hypotheses.
The Americas Base Map may be utilized by any botanist affiliated with a non-profit institution and who has access to ArcView® or ArcGIS 8®. It is available (with instructions for its use) on a CD or in electronic form via the following Web page: http://www.nybg.org/bsci/digital_maps; note that it does not include the regional data sets.
KEYWORDS: Acre, Amazonia, Americas, biogeography, Geographical Information Systems, GIS, mapping, Neotropics, spatial analysis, phytogeography, systematics.
A pesar dos avanços em SIG e na tecnologia e software para sensoriamento remoto, até o momento, a maioria dos sistematas e outros botânicos que pesquisam a flora neotropical, particularmente, aqueles que contribuem para a série monográfica Flora Neotropica, ainda elaboram mapas no papel. Estes mapas possibilitam examinar padrões básicos de distribuição, mas são pouco precisos, e o fato dos dados não serem compilados em forma digital, difículta ou impossibilita resgatar os "metadados" (i.e., os dados associados com as coleções para cada ponto no mapa), bem como selecionar e integrar conjuntos de dados de distribuição, ou mesmo sobrepor mapas de distribuição em outros mapas de solos, clima e outros parâmetros ambientais. Buscando ajudar a modernizar os estudos da flora neotropical e facilitar para botânicos o acesso ao SIG, elaborou-se um mapa de base digital das Américas, com camadas múltiplas de mapas que podem ser sobrepostos em qualquer ordem ou conjunto e facilmente utilizados para criar mapas digitais de distribuição para divulgação e análise. Este mapa de base, disponível sem custos para botânicos e sistematas, foi elaborado usando ArcView® GIS (ou ArcGIS 8®). Várias das camadas foram derivadas de conjuntos de dados da Environmental Systems Research Institute-ESRI (ArcWorld®, ArcAtlas®, Digital Chart of the World®). Para uma versão básica deste sistema, obteve-se mais conjuntos de dados sobre vegetação e solos (Woods Hole Research Center), topografia (U.S. Geological Survey) e Ecorregiões (World Wildlife Fund); sendo que para o nosso projeto, obteve-se camadas adicionais de outras fontes, em escalas mais apropriadas para a Amazônia e para o Estado do Acre no Brasil. Pretende-se acrescentar mais camadas à medida que novos dados tornam-se disponíveis, procurando em outras fontes, como institutos geográficos de alguns países neotropicais. Este sistema possibilita implementar análises e visualizações elegantes, inclusive: (1) plotar as distribuições de espécies e utilizar as camadas para visualizar facilmente a coincidência de padrões de distribuição com padrões de feições ambientais; (2) mapear a variação em caráteres morfológicos com pontos de distribuição (e.g., plotando tamanho de folha vs. latitude ou longitude) para detectar padrões geográficos; (3) analisar estatísticas espaciais (e.g., relacionamento entre tamanho de fruto e pluviosidade); e (4) aumentar a agilidade e a versatilidade das técnicas de biogeografia histórica. Embora qualquer botânico bem treinado em SIG teoricamente poderia executar estas análises, o Americas Base Map une um espectro diverso de dados ambientais e geográficos de alta qualidade e relevantes para a botânica, bem como fornece instruções que eliminam a necessidade de cada botânico tornar-se um profissional em SIG. Isto melhora o acesso dos botânicos a dados geo-espaciais relevantes, sobretudo no mundo em esenvolvimento, servindo para expandir o uso de SIG na fitogeografia. Esta estratégia para mapear distribuições de espécies facilitará a pesquisa tanto na sistemática quanto na florística, ajudando a fitogeografia neotropical a progredir de suposições para hipóteses testáveis.
The Americas Base Map may be utilized by any botanist affiliated with
a non-profit institution and who has access to ArcView® or ArcGIS 8®.
It is available with instructions for its use on a CD or in electronic
form via the following Web page: http://www.nybg.org/bsci/digital_maps
(note that it does not include the regional data sets).
O Americas Base Map pode ser utilizado por qualquer botânico associado a uma instituição não comercial e com acesso a ArcView® ou ArcGIS 8®. O mapa é oferecido com instruções para seu uso em forma de CD ou forma eletrônica, através da seguinte página no Web: http://www.nybg.org/bsci/digital_maps (N.B. Esta versão não inclui os conjuntos de dados regionais).
PALAVRAS-CHAVE: Acre, Amazônia, Américas, análise espacial, biogeografia, mapeamento, Neotrópicos, fitogeografia, SIG, Sistemas de Informações Geográficas, sistemática.
Geographic information systems (GIS) -- computer programs for evaluating and analyzing spatial patterns -- have become dramatically more accessible to biologists in recent years in the sense that they are becoming increasingly affordable and user-friendly. The use of GIS has been brought into the mainstream of a number of biological arenas, including aquatic botany (e.g., Caloz & Collet, 1997; Seddon & al., 2000; Li & al., 2002), plant pathology (e.g., Wu & al., 2001), conservation (e.g., Olson et. al., 2001; Hamann & al., 2000), and zoology (e.g., Kidd & Ritchie, 2000; Noonan, 1999). Unfortunately, plant systematics has tended to lag behind, and this has impeded not only scientific progress but also the ability of botanists to contribute effectively to urgent conservation efforts.
Systematic and floristic work are tremendously labor-intensive and involve inordinate amounts of clerical work and other unskilled labor, particularly for mapping species distributions, but after a number of years of systematists talking and hearing about it, there was still no common-access digital base map for systematic and floristic work on the Neotropical flora as of mid-2002. The Flora Neotropica monograph series is the central focus of efforts to fully document the flora of the New World tropics, but incredibly, the distribution maps in almost all recently published monographs were still generated by hand, usually by rubbing black dots showing very rough locations on old hard-copy base maps with some misplaced rivers. Worse still, these maps constitute a dead end, that is, it is difficult or impossible to retrieve the metadata (i.e., the collection data and attributes of the specimen(s) associated with each point on the map), select and combine distribution data sets, or overlay species distributions onto maps of soils, climate, and other environmental variables.
The researcher's efficiency and productivity can be far greater using GIS now that (s)he needs to enter specimen-related data only once and from there import selected fields into a number of software packages that can be used to generate lists of exsiccatae, perform morphometric analyses, and create keys. Some packages, such as Linnaeus® (ETI Information Services Ltd.) can perform several of these functions. Significantly, these data can also be linked to geography in a number of ways through GIS, which allows the user to quickly produce maps in a way that permits immediate recovery of the data behind each dot on the map and, more importantly, in a way that readily lends itself to revision, qualitative and quantitative analysis, hypothesis testing, and integration with data from other organisms. Specifically regarding distribution maps, accurate data sets can make it possible to formulate and test hypotheses to explain and/or predict distributions, through examining layers and (more rigorously) calculating statistics on the data behind the layers; this is part of spatial modeling. This is more in line with standard scientific method than most previous floristic and botanical work.
While any botanist with access to GIS can produce digital maps with these advantages, many have been impeded by the lack of quality geographic and environmental data on which to base their maps. Commercial data sets are often prohibitively expensive, and while some data sets are free on the Internet, it can be very time-consuming to locate data of interest, download it, transform or configure it to alleviate compatibility issues, and then combine it with other data sets.
These limitations to acquiring data are troublesome enough for researchers in the developed world, but are especially daunting in the developing world, where all too frequently lack of funds and computer infrastructure, often slower Internet access, and sometimes language barriers can make data acquisition nearly impossible.
An issue that is often overlooked is how absurdly and unnecessarily inefficient it is for every botanist to retrieve and adapt the same geospatial data -- "re-inventing the wheel." Too much time is spent on the mechanics and too little on the actual content of the research.
The Americas Base Map
Fig. 1. A simple base map of the Neotropics made using Americas Base Map data in ArcView 3.2®.
In response to these needs and deficiencies, we have developed a standardized, digitized base map of the Neotropics that will be made freely available for investigators to map species distributions directly from databases as well as refine the interpretation of distribution patterns by using digitized overlays of river systems, topography, soils, and other data (Fig. 1). The Environmental Systems Research Institute, Inc. (ESRI) generously donated access to data from their commercial data products ArcAtlas®, ArcWorld®, and Digital Chart of the World®. Using ESRI's ArcView® software, we defined a base map and selected overlays from these sources, containing different degrees of detail for work on different scales (Fig. 2). Additional overlays were added by adapting data sets from other sources including elevation from the U.S. Geological Survey, vegetation and soil maps from the Woods Hole Research Center (e.g., Stone & al., 1994), and the Eco-Regions of the World Wildlife Fund (WWF)(Fig. 3; see Olson & al., 2001). For our work in Brazilian Amazonia and specifically in the state of Acre, we are using a number of data sets prepared respectively by the Instituto Socioambiental after the 2000 Workshop in Macapá (Ministério do Meio Ambiente, 2001; see Appendix II) and by the Acre State Zoning Project in Brazil (Governo do Estado do Acre, 2000; see Appendix III). We have made the necessary data transformations such that all data are immediately accessible in ArcView®, with no additional work necessary. Researchers using ESRI's ArcView 3.2® or ArcGIS 8® can import the data quickly and easily into that program. We are also soliciting data from many other sources, including geographical institutes in individual countries, and will continue to add useful data sets as they come available.
Fig. 2: A more detailed map of the Brazilian state of Acre with municipalities and major rivers of the Southwest Amazon.
Fig. 3: World Wildlife Fund eco-regions for the Neotropics.
The Americas Base Map can be utilized by any botanist affiliated with a non-profit institution and who has access to ArcView® or ArcGis 8®. Until further notice, information about obtaining the Americas Base Map and instructions for its use can be found on the Internet at http://sciweb.nybg.org/digital_maps; it is available on a CD or in electronic form through a Web page accessible through the preceding address (note that it does not include the regional data sets). A set of instructions for adding point data from botanical collections, setting the scale and boundaries, selecting overlays, creating legends, etc. is also available. The system accepts point data (i.e., a table with collection information including geographic coordinates, collection number, etc.) in .dbf (d-base) or tab-delimited text formats. Many common database and spreadsheet programs (e.g., Microsoft Excel®) can save files in .dbf format; almost all database/spreadsheet programs will save files as tab-delimited text. Once the base map is available on the Web, botanists will be able to download the specific data sets they need, and the data will be updated such that users will have access to the latest and most accurate data sets.
Applications in systematic research
The simplest application of the GIS base maps is generating species distribution maps. The collection data are entered in a table that can then be easily read into the GIS application and the collection points displayed over single or multiple environmental or geographic layers of the map. Once input into a GIS, the metadata (collector, habitat, description, etc.) can easily be called up for any given point or set of points (Fig. 4).
Fig. 4: Distribution of Mabea pohliana Müll. Arg. mapped over yearly precipitation with a callout showing all the metadata for one collection point.
The correlation of a species distribution with environmental factors such as elevation or rainfall can then be examined qualitatively by simultaneously displaying both the species collection points and the environmental factor map layer (Figs. 4, 5), but the GIS software also makes it possible to examine this quantitatively via statistical spatial correlations of the species distribution with these environmental factors (e.g., Noonan, 1999). Environmental data from the Americas Base Map can be joined to a systematist’iacute;iacute;s collection data to produce a spreadsheet or database with both collection and environmental data. In this process, each species collection point is "punched through," i.e., all the environmental characters at that location are found, and these characters are added to the character table for the group under study (in ArcView 3.2, this is done through the Geoprocessing Wizard as a "spatial join"). The resulting joined data can be used to statistically test hypotheses about species distributions across environmental gradients using multivariate analysis, or, for more advanced users, in spatial modeling. To date, however, most spatial environmental data in the Americas Base Map and most other generally available data sets are not of high enough resolution to be useful in this application. It is possible to obtain higher-resolution data sets from regional government offices and NGOs that make these applications possible (see next section).
Fig. 5: Distribution of Heliconia scarlatina Abalo & G.L. Morales mapped on elevation, showing the species' Andean affinities and preference for mid-range elevations.
More complex models of species distributions can be created using any combination of map layers. This may involve pre-defined values such as elevation or rainfall, combined with buffers usually derived from additional detailed knowledge about the species in question. For example, a species may be known to occur within a certain distance of flood zones (a buffer), within a range of average annual temperature (pre-defined), and within a certain range of ratios between clay and sand in the soil, and these and other data can be made into a hypothetical model of the species range. As an example, Randrianasolo et al. (2002) analyzed the distribution of Anacardiaceae in Madagascar using GAP analysis, in which the management status of plant communities, vertebrate species and vertebrate species richness is evaluated via GIS overlay of biological distribution data on a map of existing biological reserves (California GAP Analysis, 2002).
Systematists have at their hands a wealth of quantitative data that can be used to analyze the geographical distribution and variation of morphological patterns of the species they study. Traditionally, this has been done in the univariate or bivariate graphing modes of modern statistical or graphing programs (e.g., Borchsenius 1999). Prior to computer technology, these graphs were drawn by hand. The graphic output is usually presented as a scatter plot, bar graph, box-whisker plot, or line graph, depending on the pattern being displayed.
Through the GIS framework, a new set of tools and techniques is available for displaying quantitative patterns of morphological variation in a geospatial framework. Within ArcView and many other GIS programs, individual points can be displayed as different sizes, shapes, and colors based on their relative quantitative values. In ArcView, this is easily accomplished by simply changing the character settings for the geographic theme such that the points are defined by a "Unique Value" or graduated color.
An additional set of techniques, called "raster" in GIS parlance, uses a regular grid of pixels instead of arbitrarily placed points, lines, or polygons (i.e., vector data). While raster data are more memory-intensive because the computer must store null data even for grid points where no data has been collected (vector data are stored only for the locations where they have been collected), the simple relational geometry between grid cells permits faster computation and more complex analyses. Each pixel potentially contains an associated quantitative value; these values can be displayed as color gradients or three-dimensional height fields by the GIS program.
In ArcView, raster analysis is accomplished through the add-on module Spatial Analyst. This technique allows individual quantitative variables (i.e., leaf length, fruit length, etc.) to be analyzed, processed, visualized, and presented as continuously changing gradients, rather than solid polygons or a series of points. Using processes called interpolation and neighborhood analysis, values are estimated for the pixels in between sample points. These are particularly important techniques for botanists, because we usually start out with specimen points that can have a great deal of empty space between them. The estimated values are presented as a color gradient, thus depicting morphological or environmental gradients evenly across space, instead of only at sporadic points. The display provides a quantitative depiction of the distribution of the values, thus providing a method for visualizing and presenting the overall variation of these values in geospace.
As an example, Figure 6 shows overall patterns of variation in four
quantitative morphological characters for Compsoneura capitellata
(A.DC.) Warb. (Myristicaceae). In this case, values were interpolated,
i.e., added to grid cells across the map based on Euclidean distance values
between points. As an alternative, neighborhood statistics (as they are
termed in ArcView) comprise a similar type of grid interpolation that involves
calculating estimated values at a fixed radius from sample points; as it
does not put data in areas the map far from where a species is known to
occur, neighborhood statistics can be better than interpolation techniques
in some cases.
Fig. 6: Morphological variation patterns in Compsoneura capitellata, with the darker areas of the grid representing larger values for the given characters. A. Trunk diameter. B. Tree height. C. Leaf length. D. Leaf width.
The system can also be used as a way to investigate multivariate morphometric patterns on the map. Figure 7 presents a morphometric analysis of two Compsoneura species (Myristicaceae), showing how the results of statistical analyses can be mapped using GIS, with quantitative values expressed as map attributes, in this case a color continuum. Compsoneura mexicana (Hemsl.) Janovec and C. sprucei (A.DC.) Warb. are members of a species complex recently investigated by Janovec & Harrison (2002). Principal components analysis (PCA) was conducted on 17 morphological characters, and the scores show the position of individual specimens in the overall framework of variation, as well as what and how characters contribute to the overall variation. For the map, the values along principal components axis one (see inset), represented in shades of gray, were plotted on to the Americas Base Map to demonstrate the differences between the two species. The specimen PCA scores were turned into a grid, and neighborhood statistics was used to represent the grid cells on the map, surrounding the points that represent specimens. As can be seen on the inset, the PCA values for Axis 1 are rather different for the two species; even so, combining the PCA graphs with mapping provides more visualization of the multivariate patterns. In other cases, such a map can be used to investigate the multivariate patterns in the geospace.
Applications to floristics and conservation in southwestern Amazonia
The potential of floristic work to guide conservation and development policy now hinges on harnessing GIS and linked statistical functions, so another impetus for the Americas Base Map project is specific to our conservation and floristic work in southwestern Amazonia, specifically in the state of Acre, Brazil. Note that while we have secured more data sets for Amazonia and Acre than for any other (see Appendix II, III), we are continuing to add data for all Neotropical areas.
Southwestern Amazonia as a whole, and some discrete areas it contains, are considered conservation priorities (e.g., Olson & al., 1996; Capobianco & al., 2001; Ministério do Meio Ambiente, 2001). The government of Acre is engaged in what has become an extended state ecological-economic zoning project, or ZEE (e.g., Governo do Estado do Acre, 2000), and a convênio (partnership) between the New York Botanical Garden (NYBG) and the Universidade Federal do Acre has been able to contribute directly to the creation of new protected areas and the defense of existing conservation units through the ZEE. The ZEE now works mostly with digitized thematic maps, so conservation-related analyses and arguments that can interact with state agencies in this medium will be immensely more effective than analyses and arguments without digital geographic information and presentations.
Beyond the activities of our own convênio and the Acre state zoning project, this region is a major focus of a number of other groups active in research, development, and conservation:
This is where the flexibility of the Americas Base Map to absorb data sets on scales ranging from the Western Hemisphere to areas smaller than one political department in Peru is of fundamental importance. For example, we have acquired a number of data sets from the Acre state zoning project ZEE (Governo do Estado do Acre, 2000; see Appendix III). These data sets, now formatted and incorporated into our expanded version of the Americas Base Map, can provide information on a landscape scale for each location, relevant to diverse studies such as characterization of vegetation cover, quantitative forest inventories, and management of forest resources.
We will also be using the system to map the known distributions of all the species of plants recorded from southwestern Amazonia, and to identify and explain biogeographic patterns; the results will determine whether (as we hypothesize) the SW Amazon is a distinct phytogeographical unit, and will help justify the conservation importance of the region and of specific areas within it. The data collection necessary for this project theoretically would involve locating and data-basing the geographic coordinates for the thousands of herbarium collections involved, most of which lack geographic coordinates on their labels -- an effort that would involve tens of thousands of labor hours. To circumvent this obstacle, we will map species distributions outside of Acre at the level of municipality. Coarse distribution maps can be generated by highlighting the polygons corresponding to each political subdivision when a species has been collected there.
More significantly, GIS and the Americas Base Map can aid and accelerate techniques used in historical biogeography (e.g., Crisci & al., in press). ArcView® streamlines the chore of scoring the occurrence of species in a set of defined geographic areas because it is relatively easy to circumscribe sets of areas by drawing a polygons on the base map for each area and generate the necessary matrix of taxa occurrence vs. area using the Link function. The base map enhances the researcher's versatility in testing biogeographic hypotheses, because it is also possible to derive sets of areas from existing layers of the map (e.g., Ecoregions, topography, precipitation regimes).
Limitations and next steps
As in any scientific endeavor, the results obtained using the Americas Base Map can be only as strong as the data on which they are based. Any modeling using GIS should be appropriate to the scale of the data on which it is based; while much of our data for geographic features such as river systems and political boundaries tend to have workable resolution on a regional scale, this is less true of the environmental data (e.g., soils and precipitation) included in the Americas Base Map. For example, when we mapped the distribution of Tillandsia paraensis Mez (Bromeliaceae) on a soils map, the distribution points for this known white-sand habitat specialist did not map onto sandy soils because the resolution of the soils data set we currently use is inadequate for the purpose. Similarly, using a hemisphere-wide precipitation map in modeling a tree species endemic to the Osa Peninsula in Costa Rica is unlikely to give useful results.
Incompatibilities of scale constitute a major reason why we are obtaining supplemental data sets and particularly for regional-scale sets such as those developed by WWF for the Southwest Amazonia EcoRegion (Olson & al., 2001) and for the southwestern Amazonian state of Acre, Brazil (Governo do Estado do Acre, 2000). It is inevitable that the spatial resolution available for a given set of parameters will vary greatly among regions, because while detailed soil data may be available for Acre, they may not be for neighboring Ucayali, Peru. Some data sets themselves are composites and contain different degrees of resolution; an example is the vegetation map prepared by the Woods Hole Research Center (Stone & al., 1994).
Users of the Americas Base Map must also be aware of different degrees of category resolution in the data sets, as distinct from spatial resolution. For example, one data set may map ten different soil types over a very detailed spatial grid (low character resolution and high spatial resolution), while another data set may map 20 different types but over a less detailed spatial grid (high character resolution but lower spatial resolution). While the levels of character and spatial resolution usually increase or decrease in tandem, this is not always the case.
In many cases, the Americas Base Map offers the advantage of having more than one data set available for testing hypotheses in a given geographical area, but the user should be aware of the origin of the data sets and be willing to critically examine the primary sources when choosing which data are appropriate for their analyses. We will continue to format and add new data sets, but users are encouraged to add other data to their own projects as well as submit new public-domain data to the Americas Base Map. In general, the resolution of data should match the spatial resolution of the analysis whenever possible.
In evaluating data and maps for potential use in a GIS project, one must be wary of the common pitfall that digital maps tend to be taken as 'truth' simply because they are in digital format. In fact, the environmental data in most digital maps is estimated over large spatial areas, as it would be impossible to measure a parameter such as rainfall at closely and evenly spaced intervals over the entire Western Hemisphere. Ground-truthing (i.e., confirming the values of digital map data in the field) is the best method of assessing the validity of a digital map for a particular area. If collection localities are accurately recorded, herbarium labels can be used as a quick surrogate for ground-truthing of vegetation type, elevation, soils, and water proximity recorded on the label against these layers in the base map at the collection location. If these do not match well, one should look more deeply into the sources of the maps.
The Americas Base Map should serve to jump-start the use of GIS in botanical investigations by putting a collection of free, relevant, high-quality formatted data into the hands of botanical researchers. We have already seen the utility of the Base Map at the New York Botanical Garden, where several systematists have put the data to use in plotting specimens and conducting basic analyses. We suspect that the utility of the data will be even greater in the developing world, where the price and/or inaccessibility of relevant data are serious barriers to the use of GIS by plant scientists.
While the data in the Americas Base Map is the highest quality that is readily available, users of the Base Map are responsible for determining whether or not the character/spatial resolution and the accuracy of the data for the region of interest match the needs of their analysis. Limitations of the data aside, it is our sincere belief that the Americas Base Map will stimulate botanists to put their data to work in the service of conservation, as well as begin to quantitatively test hypotheses on species distributions. We encourage the reader to utilize and contribute to this valuable scientific resource.
Partial support for development of the Americas Base Map was provided by The Tinker Foundation. For data sets and advice, we thank Charles Peters, Amanda Neill, Wayt Thomas, the Instituto do Meio Ambiente do Estado do Acre, Environmental Systems Research Institute-ESRI, Peter Schlesinger and the Woods Hole Research Center, Tom Allnutt and the World Wildlife Fund-WWF, Chelsea Specht (formerly a consultant with WWF), and Daisy Gomes P. da Silva and the Acre Zoneamento Ecológico-Econômico project.
List of themes (data layers) currently included in the Americas Base Map, with their native resolution and coverage
|Country borders||Vector||1:25,000,000||N. & S. America||ESRI/ArcWorld|
|Internal country borders||Vector||1:25,000,000||N. & S. America||ESRI/ArcWorld|
|Agriculture statistics||Polygon||1:25,000,000||N. & S. America||ESRI/ArcWorld|
|Country statistics||Polygon||1:25,000,000||N. & S. America||ESRI/ArcWorld|
|Roads and railroads||Vector||1:700,000 to 1:12,000,000||N. & S. America||ESRI/ArcAtlas|
|Protected areas||Polygon||>40,000 hectares||N. & S. America||ESRI/ArcAtlas|
|Small protected areas||Point||>40,000
no boundaries recorded
|N. & S. America||ESRI/ArcAtlas|
|Rivers||Vector||1:3,000,000||N. & S. America||ESRI/ArcWorld|
|Detailed Neotropical rivers||Vector||1:1,000,000||Neotropics||ESRI/Digital Chart of the World|
|Major rivers and lakes||Polygon||1:3,000,000||N. & S. America||ESRI/ArcWorld|
|Detailed Neotropical lakes||Polygon||1:1,000,000||Neotropics||ESRI/Digital Chart of the World|
|Water bodies||Polygon||n/aa||N. & S. America||ESRI/ArcAtlas|
|WWF Ecoregions||Polygon||~1:1,000,000b||N. & S. America||World Wildlife Fund|
|Vegetation types||Polygon||1:20,000,000||N. & S. America||ESRI/ArcAtlas|
|Grid||1 km||S. America||Woods Hole Research Center|
|Amazonian vegetation||Polygon||1:5,000,000||Brazilian Amazonia||Woods Hole Research Center|
|Amazonian soil types||Polygon||1:5,000,000||Brazilian Amazonia||Woods Hole Research Center|
|Soil types||Polygon||1:5,000,000 to 1:10,000,000||N. & S. America||ESRI/ArcAtlas|
|Land use||Polygon||1:15,000,000||N. & S. America||ESRI/ArcAtlas|
|Climatic regions||Polygon||1:15,000,000.||N. & S. America||ESRI/ArcAtlas|
|N. & S. America||ESRI/ArcAtlas|
|Yearly precipitation||Polygon||n/aa||N. & S. America||ESRI/ArcAtlas|
|Yearly temperature||Polygon||n/aa||N. & S. America||ESRI/ArcAtlas|
|Elevation||Grid||30 seconds (0.0083333º)||N. & S. America||United States Geological Survey|
a Several layers in the ESRI ArcAtlas data set do not give resolution or scale information in the documentation.
b The WWF states that their Ecoregions map is generally at
1:1,000,000 scale, but, because this map was derived from such variable
data from different sources, some parts of the map may be be at a higher
or lower scale than 1:1,000,000.
Abridged list of data layers prepared by the Instituto Socioambiental (ISA) for a synthesis of priority actions for Amazonia (Ministério do Meio Ambiente, 2001)
|Layer a||Final verson prepared by|
|State and international boundaries||ISA|
|Municipalities; municipal seats||IBGE b|
|Roads and rail lines||ISA|
|Seasonally flooded areas||ISA|
|Hydrological balance (precipation minus evapotranspiration)||WHRC c|
|"Phytophysiognomies" [~ vegetation types]||IBGE|
|State, federal conservation units||ISA|
|Superposition of state and federal reserves||ISA|
|Areas altered by human activities (includes deforestation in 1992, 1994, 1997)||IBGE|
|Forest fire locations||MMA e, EMBRAPA f|
|Fire risk||WHRC/IPAM g|
|Hydroelectric projects||Eletrobrás & ISA|
a most on a scale
b IBGE - Instituto Brasileiro de Geografia e Estatística
c WHRC - Woods Hole Research Center
d IMAZON - Instituto do Homem e Meio Ambiente da Amazõnia
e MMA - Ministério do Meio Ambiente
f EMBRAPA - Empresa Brasileira de Pesquisa Agropecuária
g IPAM - Instituto de Pesquisa Ambiental da Amazônia
h INCRA - Instituto Nacional de Colonização e Reforma Agrária
Abridged list of themes (data layers) prepared by the ZEE-Zoneamento Ecológico-Econômico do Estado do Acre (see Governo do Estado do Acre, 2000)
|Disputed Areas [land]|
|Fauna collection points/study sites|
| Rare SWAmazonian
|New records for Brazil|
| SW Amazonian
endemics (not rare)
|Major river basins|
|Vegetation types and occurrence of forest resources|
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