Artigo

Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil

Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence prob...

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Autor principal: Figueiredo, Symone Maria de Melo
Outros Autores: Venticinque, Eduardo Martins, Figueiredo, Evandro Orfanó, Ferreira, Evandro José Linhares
Grau: Artigo
Idioma: English
Publicado em: Acta Amazonica 2020
Assuntos:
Modeling
Maxent
Forest Inventory
Modeflora
Amazon
Modelagem
Maxent
Inventário Florestal
Modeflora
Amazônia
Acesso em linha: https://repositorio.inpa.gov.br/handle/1/13456
id oai:repositorio:1-13456
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spelling oai:repositorio:1-13456 Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil Predição da distribuição de espécies florestais usando variáveis topográficas e de índice de vegetação no leste do Acre, Brasil Figueiredo, Symone Maria de Melo Venticinque, Eduardo Martins Figueiredo, Evandro Orfanó Ferreira, Evandro José Linhares Modeling Maxent Forest Inventory Modeflora Amazon Modelagem Maxent Inventário Florestal Modeflora Amazônia Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables wereelevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging. A modelagem de distribuição de espécie tem implicações fundamentais para o estudo da biodiversidade, tomada de decisão em conservação e para a compreensão dos requerimentos ecológicos das espécies. O objetivo deste trabalho foi avaliar se a utilização de inventários florestais pode melhorar a estimativa de probabilidade de ocorrência, identificar os limites da distribuição potencial e preferência de habitat de um grupo de espécies madeireiras. As variáveis ambientais preditoras foram: altitude, declividade, exposição, índice de vegetação por diferença normalizada (NDVI) e distância vertical à drenagem mais próxima (HAND). Para estimar a distribuição das espécies foi utilizado o método de máxima entropia (Maxent). Em comparação com uma distribuição aleatória, utilizando variáveis topográficas e de índice de vegetação, o método Maxent alcançou uma acurácia de 86%, em média, na distribuição geográfica predita das espécies estudadas. A altitude e o NDVI foram as variáveis mais importantes. Houve limitações na interpolação dos modelos para locais não amostrados e que estão fora do gradiente de altitude associado aos dados de ocorrência, em aproximadamente 7% da área da bacia. Ceiba pentandra (samaúma), Castilla ulei (caucho) e Hura crepitans (assacu) tem maior probabilidade de ocorrência em áreas próximas aos cursos de água. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) e Astronium lecointei (aroeira) podem ocorrer também em floresta de terra firme e solos bem drenados. Essa abordagem de modelagem tem potencial de aplicação para outras espécies tropicais ainda pouco estudadas, sobretudo aquelas que estão sobre pressão da atividade madeireira. 2020-04-24T15:25:09Z 2020-04-24T15:25:09Z 2015 Artigo https://repositorio.inpa.gov.br/handle/1/13456 10.1590/1809-4392201402834 en Volume 45, Número 2, Pags. 167-174 Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ application/pdf Acta Amazonica
institution Instituto Nacional de Pesquisas da Amazônia - Repositório Institucional
collection INPA-RI
language English
topic Modeling
Maxent
Forest Inventory
Modeflora
Amazon
Modelagem
Maxent
Inventário Florestal
Modeflora
Amazônia
spellingShingle Modeling
Maxent
Forest Inventory
Modeflora
Amazon
Modelagem
Maxent
Inventário Florestal
Modeflora
Amazônia
Figueiredo, Symone Maria de Melo
Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil
topic_facet Modeling
Maxent
Forest Inventory
Modeflora
Amazon
Modelagem
Maxent
Inventário Florestal
Modeflora
Amazônia
description Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables wereelevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging.
format Artigo
author Figueiredo, Symone Maria de Melo
author2 Venticinque, Eduardo Martins
Figueiredo, Evandro Orfanó
Ferreira, Evandro José Linhares
author2Str Venticinque, Eduardo Martins
Figueiredo, Evandro Orfanó
Ferreira, Evandro José Linhares
title Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil
title_short Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil
title_full Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil
title_fullStr Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil
title_full_unstemmed Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil
title_sort predicting the distribution of forest tree species using topographic variables and vegetation index in eastern acre, brazil
publisher Acta Amazonica
publishDate 2020
url https://repositorio.inpa.gov.br/handle/1/13456
_version_ 1787142244255399936
score 11.755432

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