Document Type
Article
Publication Date
12-15-2016
Abstract
The current unprecedented expansion of infrastructure promises to enhance human wellbeing but risks causing substantial harm to natural ecosystems and the benefits they provide for people. A framework for systematically and proactively identifying the likely benefits and costs of such developments is badly needed. Here, we develop and test at the subregional scale a recently proposed global scheme for comparing the potential gains from new roads for food production with their likely impact on biodiversity and ecosystem services. Working in the Greater Mekong—an exceptionally biodiverse subregion undergoing rapid development—we combined maps of isolation from urban centres, yield gaps, and the current area under 17 crops to estimate where and how far road development could in principle help to increase food production without the need for cropland expansion. We overlaid this information with maps summarising the importance of remaining habitats to terrestrial vertebrates and (as examples of major ecosystem services) to global and local climate regulation. This intersection revealed several largely converted yet relatively low-yielding areas (such as central, eastern, and northeastern Thailand and the Ayeyarwady Delta), where narrowing yield gaps by improving transport links has the potential to substantially increase food production at relatively limited environmental cost. Concentrating new roads and road improvements here while taking strong measures to prevent their spread into areas which are still extensively forested (such as northern Laos, western Yunnan, and southwestern Cambodia) could thus enhance rural livelihoods and regional food production while helping safeguard vital ecosystem services and globally significant biological diversity.
Recommended Citation
Balmford A, Chen H, Phalan B, Wang M, O'Connell C, Tayleur C, et al. (2016) Getting Road Expansion on the Right Track: A Framework for Smart Infrastructure Planning in the Mekong. PLoS Biol 14(12): e2000266. https://doi.org/10.1371/journal.pbio.2000266
S1 Fig. Food production gaps. Based on the Mueller et al. 2012 database [28] (a) and the IIASA/FAO 2012 database [40] (b). Gaps between current and attainable food production on existing cropland were estimated as the sum, across 17 (a) or 16 (b) major crops, of the product of each 0.0833o-cell’s yield gap (expressed in energy terms) and its harvested area for that crop [48]. Note that in subsequent analyses the values plotted here were re-scaled to a 0–1 scale, with equal-area deciles. Underlying data can be found in S1 Data. https://doi.org/10.1371/journal.pbio.2000266.s001 (EPS)
S2Fig.eps (1671 kB)
S2 Fig. Travel time. Surface shows travel time (in mins) to cities with >50,000 people (from [50]). Note that in subsequent analyses the values plotted here were re-scaled to a 0–1 scale, with equal-area deciles. We multiplied this measure of isolation by our production gap maps (S1 Fig) to generate an aggregate surface of the potential food production benefit of new or improved roads. Underlying data can be found in S1 Data. https://doi.org/10.1371/journal.pbio.2000266.s002 (EPS)
S3Fig.eps (1967 kB)
S3 Fig. Importance for terrestrial vertebrates. Calculated as the mean, across mammals, birds and amphibians, of the product of total inverse range size of all species present in each cell and its proportion of intact natural vegetation. Underlying data can be found in S1 Data. https://doi.org/10.1371/journal.pbio.2000266.s003 (EPS)
S4Fig.eps (2274 kB)
S4 Fig. Importance for storage of carbon. Map shows the estimated sum of above- and below-ground live carbon, and soil carbon, and assumes conversion of intact natural vegetation to agriculture results in the loss of all live carbon and a 10% gain in the carbon present in the upper 30cm of soil. Underlying data can be found in S1 Data. https://doi.org/10.1371/journal.pbio.2000266.s004 (EPS)
S5Fig.eps (1957 kB)
S5 Fig. Importance of natural vegetation for local climate regulation. Estimate based on an assessment of how land-cover change affects the loading of heat and moisture into the atmosphere [56]. Underlying data can be found in S1 Data. https://doi.org/10.1371/journal.pbio.2000266.s005 (EPS)
S6Fig.eps (693 kB)
S6 Fig. Existing road network. Density of existing Level 1, 2 and 3 roads, based on [59] and [60]. This helped our interpretation of variation in the potential costs and benefits of new roads (Fig 2) by identifying areas which were already relatively well-served by transport infrastructure. Underlying data can be found in S1 Data. https://doi.org/10.1371/journal.pbio.2000266.s006 (EPS)
S7Fig.eps (518 kB)
S7 Fig. Population density. Data from [62]. Note that in subsequent analyses the values plotted here were re-scaled to a 0–1 scale, with equal-area deciles. Underlying data can be found in S1 Data. https://doi.org/10.1371/journal.pbio.2000266.s007 (EPS)
S8Fig.tif (1607 kB)
S8 Fig. Benefits and costs of proposed roads. Mean values for the potential food production benefit of grid cells in a 10km buffer around each of 43 proposed new roads or road improvements, plotted against their mean potential environmental cost (a); and values for each grid cell adjacent to roads TH1 (b) and CMB2 (c). See Fig 2 and S2 Table for more details on road locations and characteristics. Underlying data can be found in S1 Data. https://doi.org/10.1371/journal.pbio.2000266.s008 (TIF)
S1Table.docx (15 kB)
S1 Table. The crops whose yield gaps we mapped, and their energy content. https://doi.org/10.1371/journal.pbio.2000266.s009 (DOCX)
S2Table.docx (24 kB)
S2 Table. Summary of 43 road proposals in the Greater Mekong Subregion. Road locations are from [61] and mapped in Fig 2. Mean values for potential food production benefit, environmental cost, travel time to the nearest city of >50,000 people [50], population density [62], and density of existing roads [59,60] are for a 10km buffer on either side of each road. https://doi.org/10.1371/journal.pbio.2000266.s010 (DOCX)
S1Data.xlsx (13 kB)
S1 Data. Summary of data sources. https://doi.org/10.1371/journal.pbio.2000266.s011 (XLSX)
S2Data.zip (9734 kB)
S2 Data. Underlying GIS files. https://doi.org/10.1371/journal.pbio.2000266.s012 (ZIP)
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Comments
This article was originally published in PLoS Biology, volume 14, issue 12, in 2016. https://doi.org/10.1371/journal.pbio.2000266