Dataset Information

Drivers of tropical forest loss between 2008 and 2019.

ABSTRACT: During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform ( www.geo-wiki.org ) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public.

SUBMITTER: Laso Bayas JC

PROVIDER: S-EPMC8976004 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Drivers of tropical forest loss between 2008 and 2019.

Laso Bayas Juan Carlos JC See Linda L Georgieva Ivelina I Schepaschenko Dmitry D Danylo Olga O Dürauer Martina M Bartl Hedwig H Hofhansl Florian F Zadorozhniuk Roman R Burianchuk Maksym M Sirbu Flavius F Magori Brigitte B Blyshchyk Kateryna K Blyshchyk Volodymyr V Rabia Ahmed Harb AH Pawe Chandra Kant CK Su Yuan-Fong YF Ahmed Merajuddin M Panging Kripal K Melnyk Oleksandr O Vasylyshyn Olesia O Vasylyshyn Roman R Bilous Andrii A Bilous Svitlana S Das Krishna K Prestele Reinhard R Pérez-Hoyos Ana A Bungnamei Khangsembou K Lashchenko Andrii A Lakyda Maryna M Lakyda Ivan I Serediuk Oleksandr O Domashovets Galyna G Yurchuk Yuriy Y Koper Michèle M Fritz Steffen S

Scientific data 20220401 1

During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across th ...[more]

PMID: 35365661

Similar Datasets

Project description:Using data from 50 long-term permanent plots from across Venezuelan forests in northern South America, we explored large-scale patterns of stem turnover, aboveground biomass (AGB) and woody productivity (AGWP), and the relationships between them and with potential climatic drivers. We used principal component analysis coupled with generalized least squares models to analyze the relationship between climate, forest structure and stem dynamics. Two major axes associated with orthogonal temperature and moisture gradients effectively described more than 90% of the environmental variability in the dataset. Average turnover was 1.91 ± 0.10% year-1 with mortality and recruitment being almost identical, and close to average rates for other mature tropical forests. Turnover rates were significantly different among regions (p < 0.001), with the lowland forests in Western alluvial plains being the most dynamic, and Guiana Shield forests showing the lowest turnover rates. We found a weak positive relationship between AGB and AGWP, with Guiana Shield forests having the highest values for both variables (204.8 ± 14.3 Mg C ha-1 and 3.27 ± 0.27 Mg C ha-1 year-1 respectively), but AGB was much more strongly and negatively related to stem turnover. Our data suggest that moisture is a key driver of turnover, with longer dry seasons favoring greater rates of tree turnover and thus lower biomass, having important implications in the context of climate change, given the increases in drought frequency in many tropical forests. Regional variation in AGWP among Venezuelan forests strongly reflects the effects of climate, with greatest woody productivity where both precipitation and temperatures are high. Overall, forests in wet, low elevation sites and with slow turnover stored the greatest amounts of biomass. Although faster stand dynamics are closely associated with lower carbon storage, stem-level turnover rates and woody productivity did not show any correlation, indicating that stem dynamics and carbon dynamics are largely decoupled from one another.

Project description:Landscape-scale deforestation poses a major threat to global biodiversity, not only because it limits habitat availability, but also because it can drive the degradation of the remaining habitat. However, the multiple pathways by which deforestation directly and indirectly affects wildlife remain poorly understood, especially for elusive forest-dependent species such as arboreal mammals. Using structural equation models, we assessed the direct and indirect effects of landscape forest loss on arboreal mammal assemblages in the Lacandona rainforest, Mexico. We placed camera traps in 100 canopy trees, and assessed the direct effect of forest cover and their indirect effects via changes in tree basal area and canopy openness on the abundance and diversity (i.e., species richness and exponential of Shannon entropy) of arboreal mammals. We found that forest loss had negative indirect effects on mammal richness through the increase of tree canopy openness. This could be related to the fact that canopy openness is usually inversely related to resource availability and canopy connectivity for arboreal mammals. Furthermore, independently of forest loss, the abundance and richness of arboreal mammals was positively related to tree basal area, which is typically higher in old-growth forests. Thus, our findings suggest that arboreal mammals generally prefer old-growth vegetation with relatively low canopy openness and high tree basal area. However, unexpectedly, forest loss was directly and positively related to the abundance and richness of mammals, probably due to a crowding effect, a reasonable possibility given the relatively short history (~40 years) of deforestation in the study region. Conversely, the Shannon diversity was not affected by the predictors we evaluated, suggesting that rare mammals (not the common species) are the ones most affected by these changes. All in all, our findings emphasize that conservation measures ought to focus on increasing forest cover in the landscape, and preventing the loss of large trees in the remaining forest patches.

Dataset Information

Drivers of tropical forest loss between 2008 and 2019.

Publications

Drivers of tropical forest loss between 2008 and 2019.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets