Data/hora: 23/06/2024 10:24:14

Biblioteca(s): 	Embrapa Pantanal.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	A - 1	
Data corrente: 	07/12/2021	
Data da última atualização: 	07/12/2021	
Autoria: 	PERAZZI, P. R.; PASSAMANI, M.; THIELEN, D.; PADOVANI, C. R.; ALMONACID, M. A. A.	
Afiliação: 	PAOLO RAMONI PERAZZI, Universidade Federal de Lavras; MARCELO PASSAMANI, Universidade Federal de Lavras; DIRK THIELEN, University of the Andes; CARLOS ROBERTO PADOVANI, CPAP; MARCO AURELIO ARIZAPANA ALMONACID, University of Huanta, Ayacucho, Peru.	
Título: 	BrazilClim: The overcoming of limitations of pre-existing bioclimate data.	
Ano de publicação: 	2021	
Fonte/Imprenta: 	International Journal of Climatology, p. 1-15, 2021.	
Idioma: 	Português	
Conteúdo: 	Abstract: Species distribution modelling has become instrumental in assessing the influence of environmental conditions on the occurrence or abundance of taxa. The set of environmental layers used for this purpose is a crucial aspect, for which different climate-based (bioclimatic) datasets have been recently developed. These bioclimatic variables result from combinations of precipitation and temperatures surfaces. Here, we explored both the performance and possibility of improving some of the currently available bioclimatic databases, through an evaluation of the precipitation and temperatures surfaces used to generate them. For this purpose, we used a combination of statistic and graphic approaches. We focused on Brazil, not only due to its natural megadiversity, but also due to its continental size and orographic heterogeneity: an excellent ground for refining methods replicable elsewhere. We found a better match between the climatic data measured on-field and Tropical Rainfall Measuring Mission (TRMM 3B43 v7) in the case of precipitation, and the surfaces provided by the National Oceanic and Atmospheric Administration (NOAA) in the case of temperatures, sources uncommonly used for species niche modelling. We gauge-calibrated the best performing surfaces using machine-learning algorithms and generated corrected surfaces that allowed us to create BrazilClim: a database of bioclimatic variables, based on improved primary surfaces, which will result in more assertive predicted distributions and more actual pictures of the species' ecological requirements for megadiverse Brazil, an approach replicable elsewhere. All primary and bioclimatic surfaces generated for this study may be freely downloaded.	
Thesagro: 	Bioclimatologia; Ecologia.	
Thesaurus Nal: 	Climatic factors; Ecology.	
Categoria do assunto: 	P Recursos Naturais, Ciências Ambientais e da Terra	
Registro original: 	Embrapa Pantanal (CPAP)	
Exemplares: 	CPAP - (60705-1) - UPC - AP - DD -  -  -  - DIS