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Eaton M.A.,The Center for Conservation Science | Burns F.,The Center for Conservation Science | Isaac N.J.B.,UK Center for Ecology and Hydrology | Gregory R.D.,The Center for Conservation Science | And 12 more authors.
Biodiversity | Year: 2015

We describe the development of two complementary priority species indicators (PSIs) to help the UK to report progress towards Aichi target 12 on the status of known threatened species. Based on species identified as national conservation priorities, the indicators present average changes in (i) 213 species for which trends in relative abundance are available from structured monitoring schemes, and (ii) 179 species for which trends in frequency of occurrence were modelled from data sets of unstructured biological records. Both indicators show substantial declines in priority species since 1970, of 67% and 40%, respectively, although the rate of decline in the relative abundance-based PSI may have lessened over the last five years (2007–2012). We discuss the biases and weaknesses of the indicators at present, and put forward suggestions as how these may be addressed, including through the development of a third PSI. © 2015 Biodiversity Conservancy International.


Eaton M.A.,The Center for Conservation Science | Burns F.,The Center for Conservation Science | Isaac N.J.B.,UK Center for Ecology and Hydrology | Gregory R.D.,The Center for Conservation Science | And 11 more authors.
Biodiversity | Year: 2015

We describe the development of two complementary priority species indicators (PSIs) to help the UK to report progress towards Aichi target 12 on the status of known threatened species. Based on species identified as national conservation priorities, the indicators present average changes in (i) 213 species for which trends in relative abundance are available from structured monitoring schemes, and (ii) 179 species for which trends in frequency of occurrence were modelled from data sets of unstructured biological records. Both indicators show substantial declines in priority species since 1970, of 67% and 40%, respectively, although the rate of decline in the relative abundance-based PSI may have lessened over the last five years (2007–2012). We discuss the biases and weaknesses of the indicators at present, and put forward suggestions as how these may be addressed, including through the development of a third PSI. © 2015 Biodiversity Conservancy International


Avitabile V.,Wageningen University | Herold M.,Wageningen University | Heuvelink G.B.M.,Wageningen University | Lewis S.L.,University of Leeds | And 33 more authors.
Global Change Biology | Year: 2016

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha-1 vs. 21 and 28 Mg ha-1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets. © 2016 John Wiley & Sons Ltd.

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