@InProceedings{CostaAmarVale:2006:PrMoMe,
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",
year = "2006",
organization = "Congresso da Sociedade Bot{\^a}nica de S{\~a}o Paulo.",
keywords = "spatial distribution modeling, OpenModeller, GIS, Coccocypselum.",
abstract = "Predictive habitat modeling, i.e. the use of a statistical model
to predict the locations of suitable habitat for a given species
has become 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 modeling 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 carry 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 on
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 methods could
contribute to select priority biodiversity sites for possible
conservation.",
conference-location = "Piracicaba, S{\~a}o Paulo",
targetfile = "550.pdf",
urlaccessdate = "27 abr. 2024"
}