@InProceedings{MottaBragSilvChri:2017:AvDeMo,
author = "Motta, Alline Zagnoli Villela and Braga, Sollano Rabelo and Silva,
Nathalia Drummond Marques da and Christofaro, Cristiano",
title = "Avalia{\c{c}}{\~a}o do desempenho de modelos de
distribui{\c{c}}{\~a}o potencial da esp{\'e}cie Wunderlichia
azulenzis",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "6874--6881",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "Potential distribution models, when allowing the occurrence
mapping of species, can be a powerful tool for conservation of
natural resources programs. The objective of this study is to
evaluate the performance of many modeling algorithms utilizing
distribution data of the Wunderlichia azulenzis species. The
species is listed in the Ministry of Environment''s National list
of endangered species of flora in the Caatinga biome. Two groups
of algorithms, classified according to two types of entry data
(presence and absence), were evaluated using the Area Under the
Curve - AUC. From the registered occurrences for the species on
database Global Biodiversity Information Facility GBIF, and
utilizing six temperature and precipitation variables selected
from the Worldclim project, species distribution maps were
created. Six different algorithms were used to create the
distribution maps of the species. The Mahalanobis Distance (0,978)
and the Random Forest (0,0993) algorithms presented the greatest
AUC values among its respective groups, while the Bioclim (0,931)
and General Linear Model - GLM (0,807) algorithms presented the
lowest values. The algorithms that are a part of the group of
models that use only presence registers (Bioclim, Domain and
Mahalanobis Distance) were considered efficient.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "59861",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSMDMR",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMDMR",
targetfile = "59861.pdf",
type = "Monitoramento e modelagem ambiental",
urlaccessdate = "27 abr. 2024"
}