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SUPPLEMENTAL ON-LINE MATERIALS

For Land-Use Practices Have Negative, Global Scale Effects on Ecosystem Services and

Human Welfare by Foley et al.

Box S1. Land Use Practices and the Loss of Biodiversity. The current rate of species

extinction on Earth is orders of magnitude faster than expected background (i.e., without

human influence) rates (S4). It is estimated that ~800 plant and animal species are now

extinct in the wild (S5), and approximately half of the global flora is now threatened (S6).

The local extirpation of species, which affect local ecosystem dynamics and services, may

be twice as prevalent as global extinctions (S6-S8).

Land use practices have caused large losses of biodiversity, defined here as the number

and relative abundance of species that occur naturally in that biome (S9). Land use

changes ecosystems via habitat loss, modification and fragmentation, soil and water

degradation, reduction of water supply, exploitation of native species and introduction of

non-native species (S10-11). These changes often favor generalists and fast-growing

species, at the expense of native and rare organisms. Freshwater species are also

particularly vulnerable to land-use impacts (S7). Tropical moist forest conversion to

agriculture has caused the most extensive and direct reductions of biodiversity, by

deliberately removing the species-rich native forests and replacing them with simpler, less

diverse ecosystems.

Future land use will likely cause increasing rates of global species extinctions and local

biodiversity loss during the next century, mostly because agricultural land use is expected

to become both more extensive and more intensive (S12-13). One possible outcome is that

tropical forest biodiversity hotspots may lose 18-40% of their eukaryotic species over the

next few centuries, depending on how well the hotspots are protected between now and

2100 (S14). Using scenarios of future land use and climate, Sala et al. (S9) found that land

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use will cause the biggest share of biodiversity loss by 2100, especially in tropical,

Mediterranean and grassland regions.

Box S2. What are Ecosystem Goods and Services? Ecosystem goods and services

refer to the essential products (e.g., food, fiber, freshwater), vital environmental processes

(e.g., pollination, flood control, water purification, climate regulation), cultural and

aesthetic benefits (e.g., recreation and tourism, heritage, serenity and inspiration), and

preservation of options (e.g., genetic and species diversity for future use) provided by the

biosphere. The term has received considerable attention in recent years (S1-S3) and is the

focus of the recent international Millennium Ecosystem Assessment (S2).

Figure S1A-G. Global Patterns of Agriculture. Today, nearly 16-18 million km2

(roughly the size of South America) are in some form of cultivation, while another 33-35

million km2 (roughly the size of Africa) are used for pastures and rangeland (S15-16). Here

we illustrate the global patterns of croplands (adapted from S15) across each region of the

planet.

Figure S2A. Diminishing Returns in U.S. Agriculture? The average annual maize

productivity in the United States has risen from 1.6 T ha-1 in the 1930s to 8.6 T ha-1 in

2001, primarily as a result of large increases in nitrogen fertilizer use. However, the

average rate of annual productivity gains for the Corn Belt region has declined from 3.4%

yr-1 (124.5 kg ha-1 yr-1) in the 1960s to 0.78% yr-1 (49.2 kg ha-1 yr-1) in the 1990s (ref. S17).

It has been suggested that as corn yields approach their upper limit defined by yield

potential, it becomes increasingly difficult for farmers to overcome the complex

interactions of soil nutrients, weather, and disease that inhibit productivity (S18-19).

Figure S2B. Effects of Changing Fertilizer Application on Maize Yield and Nitrate

Leaching in the Upper Mississippi Basin. A process-based agricultural ecosystem model

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(S20) has estimated changes in maize yield and nitrate leaching for farms in the Upper

Mississippi drainage basin, in response to variations in fertilizer application rates (S21).

These results show how increasing fertilizer applications can result in low to moderate

increases in crop yield but potentially generate moderate to large increases in nitrate

leaching. Balancing the benefits (increased crop yield) and environmental costs (increased

nitrate leaching, among others) of farming practices may depend on recognizing such non-

linear relationships.

Figure S3A. Global Water Withdrawals for Agricultural, Domestic and Industrial

Demands. Geographic patterns of global water withdrawals estimated for the year 2000

(S22) show high levels of water use in large urban areas (including cities in the Eastern

U.S., Western Europe and China) and from irrigation across many major food-producing

regions, such as the Indogangenic Plain, eastern China, central Europe, the Nile River, and

throughout the western United States. (Adapted from ref. S22)

Figure S3B. Global Water Withdrawals Compared to Long-Term Average Renewable

Water Supply. Global water withdrawals for the year 2000 are compared to estimates of

the long-term average renewable water supply (S22), as estimated by a water balance model

and global climate data from 1901 to 1995. The ratio of total water withdrawals to long-

term average renewable water supply (an indicator of relative water stress) is presented for

each major river basin of the globe. Values that approach one suggest that basins could

experience persistent water shortages, as a result of annual withdrawal amounts exceeding

the average rate of renewable water supply. (Adapted from ref. S22)

Figure S2C. Global Water Withdrawals Compared to Renewable Water Supply of

~10% Driest Years. Here global water withdrawals (for 2000) are compared to the

renewable water supply estimated for the driest 10 years between 1901 and 1995 (S22). In

this comparison, several regions exhibit increased vulnerability to water shortages,

including: the southwest U.S., portions of Western Europe, northern Africa, the Rio

Colorado Basin in Argentina, northeast Brazil, southern Africa, and much of Australia.

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Even though this comparison represents an extreme case, it does illustrate the susceptibility

of many of the regions over the globe to possible water scarcity from drought. (Adapted

from ref. S22)

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Table Legends

Table S1. Global Cropland Areas, By Continent. Adapted from ref. S15.

Table S2A. Estimated Changes in Temperature and Precipitation That Could Result

from Large-Scale Land Cover Change. A set of global climate model simulations (S23)

have demonstrated how large-scale land cover change removing an entire biome from the

surface of the planet, one at a time could affect the global climate system. Each biomes

potential influence on the annual average temperature (C) and precipitation (mm day-1) are

presented as differences (vegetation removal control simulations). The results are

summarized over the areas where vegetation was removed (devegetated), over all land

areas (all land) and the entire globe (global). Only gridcells with a statistically

significant change in temperature or precipitation (using a two-sided Students t-Test, at

95% confidence) are used in this analysis. (Adapted from ref. S23)

Table S2B. Summary of How Large-Scale Land Cover Change Could Affect Climate.

Global climate model simulations (S23) are analyzed to determine the primary mechanisms

by which large-scale land cover change could affect climate. Each simulation, wherein an

entire biome is removed from the planet, is ranked by how changed in albedo or

evapotranspiration (ET) affect the climate. The qualitative ranking is from strong to

moderate to weak and is based on the results from the whole suite of land cover change

simulations. A brief description highlights the important biophysical mechanisms and

climatic effects associated with each land-cover change simulation. (Adapted from ref.

S23)

Supplemental References

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S1. Daily, G.C. Nature's Services: Societal Dependence on Natural Ecosystems (Island Press,

Washington, D.C., 1997).

S2. Millennium Ecosystem Assessment. Ecosystems and Human Well- being: A Framework for

Assessment (Island Press, Washington, D.C., 2003).

S3. Palmer et al., Science 304, 1251-1252 (2004).

S4. Hanski, I., J. Clobert and W. Reid. in Global Biodiversity Assessment, Section 4, R. Barbault

and S. Sastrapradja, Eds., Cambridge Univ. Press, Cambridge (1995).

S5. IUCN Red List. http://www.redlist.org/ (2003).

S6. Pitman, N.C.A. and P.M. Jrgensen, Science 298, 989 (2002).

S7. Brook, B.W., N.S. Sodhi and P.K.L. Ng, Nature 424 420-423 (2003).

S8. Myers, N., et al., Nature 403, 853-858 (2000).

S9. Sala, O.E. et al., Science 287,1770-1774 (2000).

S10. Sala, O.E. in Global Biodiversity Assessment, Section 5, H.A. Mooney, J. Lubchenco, R.

Dirzo and O.E. Sala, Eds., Cambridge Univ. Press, Cambridge (1995)

S11. IPCC, Climate Change 1995: Impacts, Adaptations and Mitigation of Climate Change:

Scientific-Technical Analysis, edited by R.T. Watson, M.C. Zinyowera, R.H. Moss, and D.J.

Dokken, pp. 878, Cambridge University Press, Cambridge (1995).

S12. Tilman, D., et al., Science 292, 281-284 (2001).

S13. IPCC. IPCC Technical Paper V, H. Gitay, A. Surez, R.T. Watson and D.J. Dokken, Eds.,

Cambridge Univ. Press, Cambridge (2002).

S14. Pimm, S.L. and P. Raven., Nature 403, 843-845 (2000).

S15. Ramankutty, N. & J.A. Foley, Global Biogeochem. Cy. 13, 997-1027 (1999).

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S16. Asner, G.P., A.J. Elmore, L.P. Olander, R.E. Martin, & A.T. Harris, Ann. Rev. Environ.

Resources, 29 (2004).

S17. Kucharik, C.J. & Ramankutty, N. Earth Interactions, in press.

S18. Mann, C.C., Science 283, 310-314 (1999).

S19. Cassman, K.G., Dobermann, A., Walters, D.T. & Yang, H., Annu. Rev. Environ.

Resour. 28, 315-358 (2003).

S20. Donner, S.D., Kucharik, C.J. & Foley, J.A. Global Biogeochem. Cy. 18, GB1028,

doi:10.1029/2003GB002093 (2004).

S21. Donner, S.D. and C.J. Kucharik. Global Biogeochem. Cy 17:

doi:10.1029/2001GB001808 (2003).

S22. Helkowski, J. M.S. Thesis. (Environmental Monitoring Program, University of Wisconsin

Madison, Madison, WI, 2004).

S23. Snyder, P.K, C. Delire, J.A. Foley, Clim. Dynam. 23, 279-302 (2004).

Region cropland area total land area cropland percentage

North America 2.90 x 106 km2 21.28 x 106 km2 13.6%

South America 2.01 x 106 km2 17.62 x 106 km2 11.4%

Africa 2.35 x 106 km2 29.71 x 106 km2 7.9%

Eurasia 10.00 x 106 km2 50.75 x 106 km2 19.7%

Australia-Pacic 0.74 x 106 km2 10.72 x 106 km2 6.9%

Global 18.00 x 106 km2 130.09 x 106 km2 *

(* not including ice covered regions)13.8% *

(* not including ice covered regions)

Table S1. Global Cropland Areas, By Continent. Adapted from ref. S4.

Biome Removed T devegetated region Tall land T global P devegetated region Pall land P globaltropical forest 1.18C 0.71C 0.24C -1.34 mm day-1 -0.55 mm day-1 -0.11 mm day-1

boreal forest -2.75C -1.58C -0.77C -0.27 mm day-1 -0.11 mm day-1 0.00 mm day-1

temperate forest -1.07C -0.46C -0.22C -0.49 mm day-1 -0.18 mm day-1 -0.06 mm day-1

savanna 0.87C 0.37C 0.12C -1.10 mm day-1 -0.61 mm day-1 -0.19 mm day-1

grassland / steppe 0.75C 0.20C 0.05C -0.41 mm day-1 -0.31 mm day-1 -0.12 mm day-1

shrubland / tundra 0.32C 0.04C -0.01C -0.35 mm day-1 -0.31 mm day-1 -0.08 mm day-1

Table S2A. Estimated Changes in Temperature and Precipitation That Could Result from Large-Scale Land Cover Change. A set of globalclimate model simulations (S23) have demonstrated how large-scale land cover change removing an entire biome from the surface ofthe planet, one at a time could affect the global climate system. Each biomes potential inuence on the annual average temperature(C) and precipitation (mm day1) are presented as differences (vegetation removal control simulations). The results are summarizedover the areas where vegetation was removed (devegetated), over all land areas (all land) and the entire globe (global). Onlygridcells with a statistically signicant change in temperature or precipitation (using a two-sided Students t-Test, at 95% condence) areused in this analysis. (Adapted from ref. S23)

Biome Removed albedo effects ET effects description

tropical forest moderate strongModerate surface albedo increase causes a decrease in surface net radiation. Severe reduction in ET along with reduction in surface energy limits latent cooling and surface warms considerably. Near-surface specic humidity and precipitationreduced. Cloud cover decreases.

boreal forest strong weakLarge surface albedo increase causes a large decrease in net radiation and the surface cools. Reduction in ET limits near-surface moisture and precipitation only during summer growing season. Surface cooling leads to a reduction in planetaryboundary layer height and a large increase in cloud cover.

temperate forest moderate moderateIncrease in albedo decreases net radiation during all seasons. Winter and spring increase causes a cooling, while summer and fall increase causes a warming when combined with moderate reduction in ET. Decrease in ET reduces near-surfacemoisture and precipitation (mostly in summer).

savanna moderate strong Moderate surface albedo increase causes a decrease in surface net radiation. Large reduction in ET limits latent cooling and surface warms. Near-surface humidity and precipitation severely reduced. Cloud cover decreases.

grassland / steppe moderate moderateModerate surface albedo increase causes a reduction in net radiation. In winter and spring, this cools the surface while in summer it warms the surface when combined with a moderate reduction in ET. Grassland region of central US most affected by ET reduction (warming). Steppe regions of Asia mostly affected by albedo increase (cooling).

shrubland / tundra moderate moderate / weakModerate surface albedo increase causes a reduction in net radiation over all regions. Albedo increase in tundra regionscauses cooling; albedo increase in shrubland regions of Australia, combined with reduced ET, causes warming during austral summer and cooling in austral winter. Weak reduction in ET over tundra regions.

Table S2B. Summary of How Large-Scale Land Cover Change Could Affect Climate. Global climate model simulations (S23) areanalyzed to determine the primary mechanisms by which large-scale land cover change could affect climate. Each simulation, whereinan entire biome is removed from the planet, is ranked by how changed in albedo or evapotranspiration (ET) affect the climate. Thequalitative ranking is from strong to moderate to weak and is based on the results from the whole suite of land cover change simulations.A brief description highlights the important biophysical mechanisms and climatic effects associated with each land-coverchange simulation. (Adapted from ref. S23)