Research

Mapping Biodiversity in Central America

Building the GIS

The steps involved in building this GIS from the above two museum collection databases combined are described in some detail herewith. We believe that our experience may be instructive to others attempting to utilize museum collection data to establish a GIS for any organism.

All changes described below were made to newly created fields so as to retain the original information. We did not in any case examine the actual fish specimen stored in the museum (except the ones in the STRI collection, which were examined), so any errors in the original identification and or recording the data could not be corrected.

Step 1: Geo-referencing the records

Although most of the records had location information, in many cases, there was no latitude, longitude data available. This meant reading the textual location information and looking up the collection sites on a map.

This was a most tedious and time-consuming step. Some helpful procedures were:

Preparing a list of unique collection sites to look up.

Since most collection field trips would results in the collection of multiple individuals and species from the same location, one could reduce the total number of records to be georeferenced by grouping them according to collection site and location. (7951 unique locations in 40,000 records).

Using National Gazetteers to spot locations.

The national P267ry & Mapping Agency (NIMA) has an excellent resource to aid in this process. It is the Digital Interim Geographic Names (NIMA, 1997). This collection of two CD-ROMs provides names of geographic features in most countries of the world along with a basic classification and latitude and longitude of the feature. By converting these national gazettes into databases, it was possible to locate the features indicated in the textual location fields of the museum collection databases and come closer to the original point of collection.

Some difficulties that one must beware of are:

• Same name e.g. Panama, San Juan, San Pablo occur at more than one level of geographic organization an often in more than place.
• Historical Changes. E.g. building of the canal. The building of canals and dams often lead to the re-location of towns and villages to other areas. Errors in geo-location can result from ignoring the date of the collection and whether this was an earlier location or a more recent one.
• Insufficient information to locate reference. In some cases, information provided in the 'locale' or 'location' fields may simply be insufficient to locate the collection point with the degree of accuracy needed to make the point useable in biogeographic analyses.

Step 2: Filling out higher taxonomy information

We found the taxonomic information supplied with the data sets incomplete. We went through the data and filled out higher taxonomic categories (families, orders and classes).

Caveats:

Although we made every effort to identify each species by its most recent classification, our task was made more daunting by the state of knowledge of neotropical freshwater fish. Many genera and families are undergoing quite complete revisions while others have never been formally studied.

Step 3: Revising the taxonomic information

It is not possible to obtain species counts from the raw data set because of the following problems:

• Synonymies
• Multiple valid and invalid names
• Misspellings
• Misidentification

We therefore undertook to correctly identify the scientific name as best we could utilizing several resources (Eschmeyer, 1998;FishBase, 1997; Nelson, 1994; Berra, 1984), a couple of which are available as searchable digital databases on CD-ROM, a monograph on the fishes of Costa Rica (Bussing, 1998) and the expertise of local and visiting scientists (see acknowledgments). Since our lab has collected extensively in Panama and Costa Rica has been adequately described by Bussing (1998), these two countries posed less of problem. In addition, we used a couple of monographs on fishes of Panama (Meek & Hilderbrand, 1916; Loftin, 1965).

Our representation of Colombia is far from complete. Our calls were conservative so our estimates of biodiversity there are under-estimates at best.

Step 4: Identifying freshwater fish

Myers (1938, 1949, 1951) had developed a widely used classification of fishes in freshwater fish based on their tolerance to salt water. This was later modified by Darlington (1957) into the categories described belowThe focus of our study was Neotropical freshwater fish. To restrict our analysis to this group, we had to create fields that identified taxa as being primary (little salt tolerance and confined to freshwaters), secondary (usually confined to freshwater but their dispersal suggests that they could travel through salt waters) and peripherals (derived from marine ancestors). Since this information was not part of the original museum record, we had to key it in.

To do this, we used several reference works(Eschmeyer, 1998; FishBase, 1997;Nelson, 1994; Bussing, 1998) and local expertise).

Step 5: Collating the final data set

At the end of these steps, it was possible to perform a query on the database and obtain those records that met our criterion. These were:

• Found in LCA (all Costa Rican, Panamanian records and only those Colombian records that were found to the west of the Cordillera Occidental).
• Primary or Secondary fish species only
• Geographic information complete
• Taxonomy information complete.

The records not included in the analysis are described in the adjoining table. The collection points for these records are displayed on the accompanying map of lower Central America shown below.

Geographic coverages

Obtaining good quality geographic maps useable in ArcView was difficult. Panama has only just begun (past 2 -3 years) to generate digital maps suitable for use in GIS. We began by using the 1:1 million scale maps of Digital Chart of the World. We were subsequently able to obtain excellent quality maps of Panama from Geoinfo, S.A. - who generously supported our work by donating the use of their data products to the project. We have subsequently also received coverages for use in the project from CyberTech, S.A.

Obtaining river drainages was more difficult. No one had digitized the river drainages using hydrological models. We have been working from river drainage polygon coverage that I eyeballed using the rivers and contour coverages. I intend to use Spatial Analyst to generate better drainages utilizing the contour, spot elevation and country boundary coverages obtained from the above mentioned commercial sources. The maps of Costa Rica and Colombia used were those of Digital Chart of the World (1: 1 million).