@InProceedings{CostaAmarVale:2006:ExWiRu,
author = "Costa, Cristina Bestetti and Amaral, Silvana and Valeriano, Dalton
M.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = ". Presence-only modeling method for predicting species
distribution and species richness: an example with the widespread
Rubiaceae genus Coccocypselum",
year = "2006",
organization = "Congresso da Sociedade Bot{\^a}nica de S{\~a}o Paulo, 16.",
keywords = "Rubiaceae, Coccocypselum, spatial distribution modeling, GARP,
OpenModeller, GIS.",
abstract = "Predictive habitat modelling, i.e. the use of a statistical model
to predict the locations of suitable habitat for a given species
became very popular in recent years. Predictive models represent
an important tool to better understand the factors that control
species distributions. Many of these models have been developed in
temperate areas. However, it is poorly sampled in tropical
regions, where the highest biodiversity areas remain and models
would be of major value. In the best of cases, primary inventory
data exist as georeferenced coordinates from localities where
specimens have been collected. There is rarely data indicating
absence or abundance of species. However, most of the current
modelling approaches need the existence of both presence and
absence data, and many of them are based only on biological
tolerance to climate. The botanical collections are difficult to
use for the assessment of plant diversity first because they are
geographically biased, favoring more easily accessed areas, and
second because of the taxonomic correctness of the names of the
specimens. The information present at the taxonomic studies, made
always by a specialist during a long period, can carries
sufficient collections to estimate species distributions. Also,
the specialist can guarantee the taxonomic information in the
database. This work presents a contribution of the taxonomic
revision of the neotropical genus Coccocypselum P. Br. (Rubiaceae)
for the species distribution modeling purpose, focusing in
richness pattern and conservation status of the group. Using the
Genetic Algorithm for Rule-Set Prediction (GARP) inside a spatial
distribution modeling library (openModeller), nine brazilian
species of Coccocypselum were modeled. Climate and topographical
data defined the potential niche, and together with the
Coccocypselum data occurrence were manipulated in Terraview, a GIS
database structure. To produce the Coccocypselums species-richness
map for the Brazilian territory all the known locality collections
were used to calculate the most significant sites. The final
modeled distributions were then used to improve the
species-richness map summarizing the contents of modeling process
throughout an analysis of average of occurrences. These modeled
distribution maps for all species evidenced the sites with higher
Coccocypselum richness, compensating the geographically biased
limitations, usually presented in the traditional approach.
Superposing the official conservation unities, the conservation
status of Coccocypselum was discussed. The current approach can be
used to explore the options and demonstrate the role that
botanical collection data can play in building richness and
distribution maps. With additional data, these results could
contribute to select priority biodiversity sites for possible
conservation. .",
conference-location = "Piracicaba, S{\~a}o Paulo",
conference-year = "18 - 21 de setembro de 2006",
targetfile = "Resumo_Congresso_SBSP_S.doc",
urlaccessdate = "21 maio 2024"
}