Coarse Spatial Data Research Articles

Corroboration and contradictions in global biodiversity indicators

Biodiversity indicators measure progress toward global biodiversity goals, including the Kunming-Montreal Global Biodiversity Framework of the UN Convention on Biological Diversity. A suite of indicators is typically needed to capture the complexity of biodiversity. For the suite to be effective, it needs to capture the important aspects of the system, without over-representing some at the expense of others. It is therefore important to identify redundancies and contradictions between indicators within a suite. Comparing indicators derived from independent input data can also serve as a validation of indicator accuracy, or alternatively, identify unexpected behaviour and potential flaws or incompatibilities in data or construction. We examined the relationships between eight widely adopted global biodiversity indicators, all proposed for use in the monitoring framework of the Kunming-Montreal global biodiversity framework. We focused on the relationships between indicators driven by landuse data and those driven by species observations, testing for pairwise and multivariate correlations between indicator values for ecoregions. We used hierarchical clustering to identify spatial patterns in relationships between landuse and species response indicators. We found no strong correlation between landuse and species-response indicators in ecoregions with high landuse. Reasons for disagreement among indicators may include inaccurate, or coarse spatial data and mismatched baselines. We did find strong correlations between indicators sharing input data. We suggest that indicators selected for the Kunming-Montreal framework be systematically reviewed for correlations and recommend that suites of indicators should consider the independence of input data sources to minimise the risk of using correlated or biased measures of conservation progress.

Historical U.S. Cropland Areas and the Potential for Bioenergy Production on Abandoned Croplands

Agriculture is historically a dominant form of global environmental degradation, and the potential for increased future degradation may be driven by growing demand for food and biofuels. While these impacts have been explored using global gridded maps of croplands, such maps are based on relatively coarse spatial data. Here, we apply high-resolution cropland inventories for the conterminous U.S. with a land-use model to develop historical gridded cropland areas for the years 1850-2000 and year 2000 abandoned cropland maps. While the historical cropland maps are consistent with generally accepted land-use trends, our U.S. abandoned cropland estimates of 68 Mha are as much as 70% larger than previous gridded estimates due to a reduction in aggregation effects. Renewed cultivation on the subset of abandoned croplands that have not become forests or urban lands represents one approach to mitigating the future expansion of agriculture. Potential bioenergy production from these abandoned lands using a wide range of biomass yields and conversion efficiencies has an upper-limit of 5-30% of the current U.S. primary energy demand or 4-30% of the current U.S. liquid fuel demand.