Suchergebnisse

The remarkable biodiversity of the Brazilian Amazon is poorly documented and threatened by deforestation. When undocumented areas become deforested, in addition to losing the fauna and flora, we lose the opportunity to know which unique species had occupied a habitat. Here we quantify such knowledge loss by calculating how much of the Brazilian Amazon has been deforested and will likely be deforested until 2050 without having its tree flora sufficiently documented. To this end, we analysed 399 147 digital specimens of nearly 6000 tree species in relation to official deforestation statistics and future deforestation scenarios. We find that by 2017, 30% of all the localities where tree specimens had been collected were mostly deforested. Some 300 000 km (12%; 485 25 × 25 km grid cells) of the Brazilian Amazon had been deforested by 2017, without having a single tree specimen recorded. An additional 250 000–900 000 km of severely under-collected rainforest will likely become deforested by 2050. If future tree sampling is to cover this area, sampling effort has to increase two- to six-fold. Nearly 255 000 km or 7% of rainforest in the Brazilian Amazon is easily accessible but does yet but remain under-collected. Our study highlights how progressing deforestation increases the risk of losing undocumented species of a hyper-diverse tree flora. ; This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES; Finance Code 001; PPG-DIBICT fellowship to BU and PNPD fellowship to JVS), the Brazilian National Council for Scientific and Technological Development (CNPq research project (#448688/2014-0) and post-doctoral fellowship to JS (#434391/2016-6)). RAC is currently funded by the Helsinki Inst. for Sustainability Science (HELSUS) and the Univ. of Helsinki. RJL and ACMM are supported by CNPq (#309879/2019-1 and #309980/2018-6). This study also benefited from financial support from 'GBIF's 2016 Young Researchers' Award' and the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Action (grant agreement #843234; project: TAXON-TIME).