TY - JOUR
T1 - Assessing Atlantic cloud forest extent and protection status in southeastern Brazil
AU - Pompeu, Patrícia Vieira
AU - Fontes, Marco Aurélio Leite
AU - Mulligan, Mark
AU - Bueno, Inácio Thomaz
AU - de Siqueira, Marinez Ferreira
AU - Júnior, Fausto Weimar Acerbi
AU - Kamino, Luciana Hiromi Yoshino
AU - Waterloo, Maarten J.
AU - Bruijnzeel, L.A.
PY - 2018/4/18
Y1 - 2018/4/18
N2 - Abstract This study aims to map the spatial distribution of Atlantic cloud forest and assess its protection status in the Serra da Mantiqueira, southeastern Brazil, using a combination of predictive distribution modelling and remote sensing techniques. The potential distribution of cloud forests in the Serra da Mantiqueira was predicted using a combination of three algorithms for different environmental variables, including climatic, hydrometeorological, a topographic variable and a fog-related variable. After estimating the potential cloud forest distribution, remote sensing mapping techniques were used to approximate actual cloud forest area. Four land-use classes were distinguished: cloud forest, plantation forest, a ‘high-altitude complex’, and ‘other covers’. Actual mapped cloud forest areas were compared with locations of existing protected areas to assess the status of regional cloud forest protection. Predicted cloud forest distribution was excellent, with conditions above 1500 m.a.s.l. generally the most suitable for cloud forest occurrence. Actual cloud forest occurrence mapped with remote sensing imagery was 52% of the predicted potential area with differences likely due to past forest loss and the presence of non-forest (‘high-altitude complex’) vegetation. Much of the mapped cloud forest area is under nominal protection, with most areas falling into the ‘Protected Area with Sustainable Use of Natural Resources’ category. The combined use of predictive distribution modelling and remotely sensed observations successfully mapped cloud forest extent in the study area. The results reinforce the need to assign high conservation priority to the Serra da Mantiqueira as a whole and to create a core area with full protection status.
AB - Abstract This study aims to map the spatial distribution of Atlantic cloud forest and assess its protection status in the Serra da Mantiqueira, southeastern Brazil, using a combination of predictive distribution modelling and remote sensing techniques. The potential distribution of cloud forests in the Serra da Mantiqueira was predicted using a combination of three algorithms for different environmental variables, including climatic, hydrometeorological, a topographic variable and a fog-related variable. After estimating the potential cloud forest distribution, remote sensing mapping techniques were used to approximate actual cloud forest area. Four land-use classes were distinguished: cloud forest, plantation forest, a ‘high-altitude complex’, and ‘other covers’. Actual mapped cloud forest areas were compared with locations of existing protected areas to assess the status of regional cloud forest protection. Predicted cloud forest distribution was excellent, with conditions above 1500 m.a.s.l. generally the most suitable for cloud forest occurrence. Actual cloud forest occurrence mapped with remote sensing imagery was 52% of the predicted potential area with differences likely due to past forest loss and the presence of non-forest (‘high-altitude complex’) vegetation. Much of the mapped cloud forest area is under nominal protection, with most areas falling into the ‘Protected Area with Sustainable Use of Natural Resources’ category. The combined use of predictive distribution modelling and remotely sensed observations successfully mapped cloud forest extent in the study area. The results reinforce the need to assign high conservation priority to the Serra da Mantiqueira as a whole and to create a core area with full protection status.
KW - Brazilian cloud forest
KW - cloud forest biogeography
KW - species distribution modelling
KW - tropical montane forest
U2 - 10.1016/j.jnc.2018.04.003
DO - 10.1016/j.jnc.2018.04.003
M3 - Article
SN - 1617-1381
JO - Journal For Nature Conservation
JF - Journal For Nature Conservation
ER -