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Downscaling land-use data to provide global 30″ estimates of five land-use classes.

Hoskins AJ, Bush A, Gilmore J, Harwood T, Hudson LN, Ware C, Williams KJ, Ferrier S - Ecol Evol (2016)

Bottom Line: The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land-use mapping.Coarse-scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R (2): 0.68 ± 0.19).Additional validation of the downscaled cropping layer with the geoWiki layer showed an R (2) improvement of 0.12 compared with the Land-use Harmonization data.

View Article: PubMed Central - PubMed

Affiliation: CSIRO Land and Water Canberra ACT 2601 Australia.

ABSTRACT
Land-use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land-use mapping. Assessments of land-use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land-use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land-use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km(2)) estimates of five land-use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio-realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine-grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land-use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio-realms produced global fine-grained layers from the 2005 time step of the Land-use Harmonization dataset. Coarse-scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R (2): 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land-use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R (2) improvement of 0.12 compared with the Land-use Harmonization data. The downscaling method presented here produced the first global land-use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the reporting of land-use impacts on biodiversity at a finer resolution than previously possible. Furthermore, the general method presented here could be useful to others wishing to downscale similarly constrained coarse-resolution data for other environmental variables.

No MeSH data available.


Related in: MedlinePlus

Relative operating characteristic curves for comparisons of the PREDICTS validation dataset and each of the five different land‐use classes for the downscaled data (solid gray line) and the Land‐Use Harmonization dataset (dashed gray line). (A) Cropping land‐use. (B) Pasture land‐use. (C) Primary habitat. (D) Secondary Habitat. (E) Urban land‐use.
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ece32104-fig-0007: Relative operating characteristic curves for comparisons of the PREDICTS validation dataset and each of the five different land‐use classes for the downscaled data (solid gray line) and the Land‐Use Harmonization dataset (dashed gray line). (A) Cropping land‐use. (B) Pasture land‐use. (C) Primary habitat. (D) Secondary Habitat. (E) Urban land‐use.

Mentions: Our downscaled datasets achieved a significant improvement in AUC compared with the LUH datasets at PREDICTS sites for all five land uses (P < 0.0001 in all cases; Table S3). The AUC value increased between 0.03 and 0.12 depending on the land‐use class. Discrimination was good to very good (AUC > 0.7) for all downscaled predictions (AUC range 0.73–0.98; Fig. 7) with the exception of secondary habitat which showed poor discrimination between PREDICTS sites classed as secondary (AUC = 0.59).


Downscaling land-use data to provide global 30″ estimates of five land-use classes.

Hoskins AJ, Bush A, Gilmore J, Harwood T, Hudson LN, Ware C, Williams KJ, Ferrier S - Ecol Evol (2016)

Relative operating characteristic curves for comparisons of the PREDICTS validation dataset and each of the five different land‐use classes for the downscaled data (solid gray line) and the Land‐Use Harmonization dataset (dashed gray line). (A) Cropping land‐use. (B) Pasture land‐use. (C) Primary habitat. (D) Secondary Habitat. (E) Urban land‐use.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC4814442&req=5

ece32104-fig-0007: Relative operating characteristic curves for comparisons of the PREDICTS validation dataset and each of the five different land‐use classes for the downscaled data (solid gray line) and the Land‐Use Harmonization dataset (dashed gray line). (A) Cropping land‐use. (B) Pasture land‐use. (C) Primary habitat. (D) Secondary Habitat. (E) Urban land‐use.
Mentions: Our downscaled datasets achieved a significant improvement in AUC compared with the LUH datasets at PREDICTS sites for all five land uses (P < 0.0001 in all cases; Table S3). The AUC value increased between 0.03 and 0.12 depending on the land‐use class. Discrimination was good to very good (AUC > 0.7) for all downscaled predictions (AUC range 0.73–0.98; Fig. 7) with the exception of secondary habitat which showed poor discrimination between PREDICTS sites classed as secondary (AUC = 0.59).

Bottom Line: The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land-use mapping.Coarse-scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R (2): 0.68 ± 0.19).Additional validation of the downscaled cropping layer with the geoWiki layer showed an R (2) improvement of 0.12 compared with the Land-use Harmonization data.

View Article: PubMed Central - PubMed

Affiliation: CSIRO Land and Water Canberra ACT 2601 Australia.

ABSTRACT
Land-use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land-use mapping. Assessments of land-use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land-use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land-use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km(2)) estimates of five land-use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio-realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine-grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land-use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio-realms produced global fine-grained layers from the 2005 time step of the Land-use Harmonization dataset. Coarse-scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R (2): 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land-use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R (2) improvement of 0.12 compared with the Land-use Harmonization data. The downscaling method presented here produced the first global land-use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the reporting of land-use impacts on biodiversity at a finer resolution than previously possible. Furthermore, the general method presented here could be useful to others wishing to downscale similarly constrained coarse-resolution data for other environmental variables.

No MeSH data available.


Related in: MedlinePlus