Download SOCIAL SCIENCES: Sustainability Science Projected land

1
Classification: SOCIAL SCIENCES: Sustainability Science
Projected land-use change impacts on ecosystem services in the U.S.
Joshua Lawler, University of Washington
David Lewis, Oregon State University
Erik Nelson, Bowdoin College
Andrew J. Plantinga, University of California, Santa Barbara
Stephen Polasky, University of Minnesota
John Withey, Florida International University
Dave Helmers, University of Wisconsin
Sebastián Martinuzzi, University of Wisconsin
Derric Pennington, World Wildlife Fund
Volker Radeloff, University of Wisconsin
Key words: land-use change, ecosystem services, carbon, food production, timber, biodiversity,
econometric model
Author contributions: J.L, D.L., E.N., A.P., S.P., J.W., and V.R. designed research; E.N., A.P.,
J.W., D.P., D.H., and S.M. performed research; J.L, D.L., E.N., A.P., S.P., J.W., and V.R.
analyzed data; and J.L, D.L., E.N., A.P., S.P., J.W., S.M., and V.R. wrote the paper.
The authors declare no conflict of interest.
2
Abstract
Providing food, timber, biofuels, housing, and other goods and services, while maintaining
ecosystem functions and biodiversity that underpin their sustainable supply, is among the most
important challenges facing humanity. Because land-use change can greatly alter the provision of
ecosystem services, understanding the drivers of land-use change and how policies can alter
land-use change will be critical to meeting this challenge. Here, we show that differences in the
underlying drivers of land-use change can have large impacts on projected land-use with
cascading effects on the provision of ecosystem services. We explore the potential effects of
projected land-use trends on carbon storage, food and timber production, and wildlife habitat
across the contiguous U.S. We forecast land-use trends for two baseline economic scenarios and
three policy alternatives designed to encourage forest cover, natural landscapes, and reduce
urban expansion. Projected land-use changes for 2051 result in increases in urban land, losses of
pasture and rangelands, and depending on the baseline scenario, either large increases or
decreases in croplands. In turn, these land-use changes result in increases in carbon storage,
timber production, food production (from increased yields), and >10% decreases in habitat for
many species. The three policy scenarios modify land-use changes but could not reverse these
powerful underlying trends. For example, even with strict policy to contain urban expansion,
urban land expands by 29%. Policy interventions need to be aggressive to significantly alter
underlying land-use change trends and shift the trajectory of ecosystem service provision.
3
Introduction
Land-use change can greatly alter the provision of ecosystem services. Globally, the
conversion of native grasslands, forests, and wetlands into croplands, tree plantations, and
developed areas has led to vast increases in production of food, timber, housing, and other
commodities but has come at a cost of reductions in the majority of ecosystem services and
declining biodiversity (MA 2005). How to meet the demand for food, timber, biofuels, housing,
and other goods while maintaining ecosystem functions and biodiversity that underpin the
sustainable supply of ecosystem services is one of the great challenges of our time.
Although recent land-use change in the U.S. has not been as rapid as in the tropics, it has
been significant. In general, the area of croplands has decreased and forests and urban areas have
expanded since World War II (Nickerson et al. 2011). For example, forest lands in the
contiguous U.S. expanded by 5.7 million acres between 1982 and 2007. However, basic
estimates of net land-use change often hide more complex dynamics. More than 30 million acres
transitioned into or out of forest (USDA 2009) during the period when net expansion was 5.7
million acres. Such transitions alter landscape patterns and ecosystem functions, both of which
affect the provision of ecosystem services.
We use an econometric model to predict spatially explicit land-use change across the
contiguous U.S. from 2001 to 2051. The model estimates the probability of conversion among
major land-use categories (cropland, pasture, forest, range, and urban) based on observations of
past land-use change, characteristics of land parcels, and economic returns, while accounting for
endogenous feedbacks from the policies into commodity prices. See the SI Text for definitions of
the land-use categories used in this study. A key advantage of this approach is that it allows us to
simulate the effects of future policies that modify the relative returns to different land uses.
We integrate land-use change analysis with models of provision of specific ecosystem
services: carbon storage, food production, timber production, and the habitats of 194 terrestrial
vertebrate species selected for their ecological and cultural importance or sensitivity, including
amphibians, influential species (e.g., top predators, keystone species, and ecosystem engineers),
game species, and at-risk birds. We use a broad definition of ecosystem services (the goods and
services provided by nature that are of value to people) to include both agricultural production,
4
which includes both natural and human-made inputs, and habitat provision for wildlife, which
may or may not be directly valued by people. We use the coupled econometric land-use and
ecosystem service models to explore the effects of incentive and land-use regulation policies that
affect land-use patterns and ecosystem service provision.
We explore the potential impacts of land-use change under two alternative reference
scenarios and three alternative land-use policy scenarios (Table 1). The first reference scenario
(1990s Trend) assumes continuation of exogenous factors driving land use (e.g., high
development pressures) during a five-year period from 1992 to 1997. The second reference
scenario (High Crop Demand) increases the price of agricultural commodities relative to the
1990s Trend with concomitant pressures to expand agricultural lands, which more closely
resembles the five-year period from 2007 to 2012. The two scenarios allow us to gauge the
sensitivity of our results to different assumptions about the underlying drivers of land-use
change. Scenarios are used in place of a formal treatment of parameter and data uncertainty
because of the wide range of potential outcomes given the large spatial and temporal scale of the
modeling exercise. We also analyze how three alternative land-use policy scenarios would shift
land use and the provision of ecosystem services relative to the reference scenarios: i) Forest
Incentives - incentives for afforestation and reduced deforestation similar to carbon sequestration
incentives (e.g., Lubowski et al. 2006), ii) Natural Habitats - incentives for conservation of forest
and range to prevent conversion to crop land, pasture, or urban, and iii) Urban Containment prohibition on urban land expansion in all non-metropolitan counties to concentrate urban
expansion in existing metropolitan areas. For all scenarios, land-use changes were only simulated
for privately-owned land from 2001 to 2051; land use on public land was kept constant over this
period. Clearly, we cannot anticipate all of the market forces that will influence land use over the
next four decades, such as the emergence of new technologies and shifts in societal preferences.
Our primary goal is to explore the effects of land-use policies relative to a given reference case
rather than to predict future land use. Unanticipated events that affect relative returns will
influence future land use under both the reference and policy scenarios, making predictions about
the difference between scenarios less uncertain than prediction of future land use itself, reducing
concerns about the long time horizon used in this study.
Results
5
Our model projects substantial land-use change between 2001 and 2051 under both the
1990s Trend and the High Crop Demand scenarios (Fig. 1 and 2) with rapid urban growth (Fig.
1d, 2a) and loss of rangelands and pasture (Fig. 1b, 1e, 2a). Urban growth is projected to be
greatest near existing major metropolitan areas. The losses in rangeland and pasture match
location of these systems with pasture loss primarily in the eastern half of the country and
rangeland loss primarily in the western US. Forest land showed modest increases overall but had
a complex pattern of gains and losses (Fig. 1c, 2a).
Comparing the projections for the two reference scenarios clearly demonstrates the
importance of underlying drivers of land-use change (Fig 1a, 2a). In the High Crop Demand
scenario cropland is projected to have a large increase (28.2 million ha) compared to a loss of
cropland under the 1990s Trend scenario (-11.2 million ha). The increase in cropland in the High
Crop Demand scenario comes at the expense of larger declines in pasture (30.5 million ha versus
15.0 million ha) and range (31.2 million ha versus 19.6 million ha) and smaller increases in
forest (7.3 million ha versus 16.3 million ha) and urban (26.2 million ha versus 29.5 million ha)
relative to the 1990s Trend scenario.
We project a large increase in food production under both scenarios—a 50% increase in
kilocalories under the 1990s Trend, and a doubling under the High Crop Demand scenario (Fig.
2b). These increases are roughly in line with estimated increases in global food demand between
2000 and 2050 of 70% (Alexandratos and Bruinsma 2012) or doubling (Tilman et al. 2011).
Increases in food production are driven by increases in crop yield (which we assume increase by
6% every five years) and changes in agriculture area. Taking out the increase due to yield
improvements, we predict a 12% increase in food production due to land-use change alone under
the High Crop Demand scenario and a 16% decline in food production due to land-use change
alone under the 1990s Trend scenario.
Both land-use change scenarios result in overall increases in carbon storage and timber
production (Fig. 2c, 2d). Carbon stored in biomass increases by 1.1 billion Mg (6%) under the
1990s Trend scenario. The gain in carbon stored is only half as large under the High Crop
Demand scenario (556 million Mg, 3%). Carbon stored in soil increases slightly under the 1990s
Trend (121 million Mg) but decreases under the High Crop Demand scenario (-306 million Mg).
Both changes are small relative to the total stock of soil carbon (Fig. 2c).
6
Habitat for the four groups of species we modeled showed overall declines under both
land-use change scenarios. Overall, 47 out of 194 species are projected to lose more than 10% of
their habitat under the 1990s Trend scenario, 137 experience little change, and only 10
experience gains of more than 10%. We see a similar pattern in the High Crop Demand scenario
(43 species lose more than 10%, 146 experience little change, 5 gain more than 10%). On
average, species do somewhat better in the High Crop Demand scenario compared to the 1990s
Trend (Wilcoxon signed-rank test, V = 5337, N = 194, P < 0.001, median difference = 1.6%).
The four groups of species (amphibians, influential species, game species, and at-risk birds)
responded in broadly similar ways to the two future scenarios with some minor differences
across groups (Fig. 2e, 2f, 2g, 2h). Of the four groups, the at-risk birds are projected to be the
most sensitive to land-use change. Roughly 1/3 of these species are projected to lose more than
10% of their habitat (median habitat loss for at-risk birds = -6.8% for 1990s Trend and -8.8% for
High Crop Demand scenarios, Fig. 2h). Even in this group, however, a few species are projected
to gain more than 10% of their habitat area under the two scenarios.
We also consider how alternative policy scenarios affect the underlying trends in land-use
change and the resulting provision of ecosystem services. We find that policy scenarios produce
similar changes relative to baseline regardless of which of the two reference scenarios (1990s
Trend or High Crop Demand) used. Therefore, we only present policy results relative to the
1990s Trend scenario (see SI Text for the comparison with the High Crop Demand scenario).
Each of the three policy alternatives (Forest Incentives, Natural Habitats, and Urban
Containment) predict substantial land-use change relative to the 1990s Trend scenario (Fig. 3 and
4). The Forest Incentives policy produces an additional 30.6 million ha of forest land (a 14%
increase relative to baseline), which occurs largely at the expense of rangeland and cropland and
to a lesser degree pasture (Fig. 3a, 4a). The largest increases in forest land are east of the 100th
meridian in areas with large amounts of land currently in agriculture. Most of the increase in
forest area is the result of afforestation and, thus, would require large government expenditures
on subsidies to landowners (approximately $7.5 billion per year). The Natural Habitats policy
results in an increase in rangeland (12.4 million ha, a 5% increase relative to baseline) at the
expense of crops and pasture, but virtually no change in forest land despite there being a tax on
land leaving forest (Fig. 3b, 4a). In contrast to the Forest Incentives policy, the Natural Habitats
policy generate tax receipts for the government of approximately $1.8 billion per year. The
7
Urban Containment policy reduces the amount of urban growth (17.3 million ha, a 24%
reduction relative to baseline) and results in slight increases in the other land-use types (Fig 3c,
4a). The Urban Containment policy is the only one of the three policies that alters the expansion
of urban land in a meaningful way.
Relative to changes under the 1990s Trend scenario, the three policies result in modest
changes in food production, and more substantial changes in carbon storage, timber production,
and habitat. The Forest Incentives policy has the largest positive effect on biomass carbon (1.7
billion Mg increase relative to baseline, 8%), and timber production (235 million cf relative to
baseline, 18%). The Forest Incentives policy reduces food production by 10% (1.93 x 1014 kcal)
compared to the 1990s Trend scenario. The Urban Containment policy results in modest
increases in biomass carbon storage (2%), timber production (5%), and food production (4%),
relative to the 1990s Trend values. By contrast, the Natural Habitats policy has relatively small
negative effects on all three of these services.
The Natural Habitats policy has the greatest positive effect on habitat of any policy
scenario with 31% of the species (61 of 194) gaining at least 10% in habitat area by 2051,
compared to 13% of species under the Forest Incentives policy, and 16% under Urban
Containment policy. Compared to the 1990s Trend scenario, the Natural Habitats policy
produces gains of at least 10% in habitat area for 25% more amphibians, 36% more at-risk birds,
15% more game species, and 23% more influential species (Fig. 4e-h). Both Forest Incentives
and Urban Containment policies also result in more species gaining than losing at least 10% in
habitat area, but the positive changes were not as great as under the Natural Habitats scenario.
Under all three policies, amphibians and at-risk birds have more species that gained 10% of
habitat and fewer species that lost 10% of habitat relative to the baseline (Fig 4e, 4h). For
influential species and game species, there is relatively little increase in species gaining at least
10% of habitat relative to the baseline under the Forest Incentives and Urban Containment
scenarios (Fig 4f, 4g).
Discussion
Land-use change is a major driver of change in the spatial pattern and overall provision
of ecosystem services. Our results demonstrate that differences in the underlying drivers of land-
8
use change, such as changes in future crop prices, can have large impacts on projected land-use
change with cascading effects on the provision of ecosystem services. We find that projected
land-use changes by 2051 will likely enhance the provision of some ecosystem services. For
example, carbon sequestration and timber harvests are projected to increase due to expansion of
forest land under our baseline scenarios. On the other hand, almost one-quarter of modeled
species (47 out of 194 species in the 1990s Trend scenario) are projected to lose greater than
10% of their habitat by 2051; only a few species are projected to gain more than 10% of their
habitat.
During the 1990s, low agricultural prices generated low returns to agriculture relative to
returns to other land uses driving land out of agriculture and into forest and urban land. The shift
out of agricultural land toward forest land increases the amount of carbon storage in biomass and
timber production, and generates a modest gain in carbon stored in soil. Despite land moving out
of agriculture, food production increases under the 1990s Trend scenario due to increases in crop
yields. We assume a 6% increase in yield every 5 years, which generates a 79% increase in
yields between 2001 and 2051. This productivity gain is below the increase in major crops
during the previous 50-year period (USDA-NASS 2012) but consistent with projections showing
positive but declining growth in U.S. agricultural productivity (Alston et al. 2010, Nelson 2013).
This predicted increase in yields could be overly optimistic if yield growth is linear rather than
exponential (Baker et al. 2013) or if climate change has significantly negative impact on yields
(Schlenker and Roberts 2009, Lobell et al. 2011). We find that assumptions regarding trends in
yields have more impact on food production than do changes in cropland area.
The High Crop Demand scenario assumes a dramatic exogenous increase in crop prices
(160% by 2051 relative to 2001 prices), which generates a large increase in net returns to
cropland. Under this scenario croplands are projected to expand 22% by 2051 relative to 2001.
The relatively inelastic response of cropland area to crop-price increases (a 22% increase in
cropland area versus 160% increase in crop prices) is due in part to endogenous changes in the
returns to other land uses. As more land moves into crops land in the other uses becomes more
scarce. Increased scarcity raises the value of land in other uses and reduces the incentive to shift
land into crops. Incorporating changes in prices and relative returns across different sectors
generates negative feedbacks that tend to dampen the effect of changes in demand in any one
9
sector on overall land use. These results illustrate the importance of including all major types of
land use and accounting for market interactions among them.
In both reference scenarios, the overall net change in land use hides important local landuse changes that can be important for determining the provision of ecosystem services. For
example, even though forest land increased on net, there are areas where forests are projected to
contract as well as areas projected to expand rather than there being uniform expansion across
the country (SI Figs. 1-3).
Local changes in land use are particularly important for determining habitat provision.
Under both reference scenarios a significant proportion of species (approximately 25%) lose
more than 10% of their habitat. At-risk bird species fare relatively poorly with approximately
one-third of these species losing more than 10% of their habitat. Only a small number of species
are projected to gain more than 10% of their habitat. The result that species overall fare slightly
better under the High Crop Demand baseline compared to 1990s Trend is driven by influential
species and game species (Fig. 2f, 2g), which are much more likely than amphibians and at-risk
birds to use croplands as habitat. Overall, however, the majority of the 194 species we consider
in this study are not projected to gain or lose more than 10% of their prime habitat area by 2051
(Fig. 2e-h) under either reference scenario. This result may be due in part to the fact that many
species in our study are associated with forest or rangeland habitat types, and these habitat types
do not suffer large percentage losses (Fig. 2a). In addition, our calculation of habitat area
includes habitat on public lands, which we assume will not change, thereby moderating overall
impacts on habitat. For the species in this study, the median percentage of habitat on public lands
is 27%. There are few species in our analysis that are threatened or endangered: 170 out of the
194 species in this analysis ranked as species of “least concern” by IUCN standards. The species
we analyzed generally have relatively large ranges and are less likely to experience large
percentage changes in habitat area. Species with smaller ranges could be more heavily impacted
by the land-use changes. In fact, we find that at-risk birds show greater vulnerability than other
species groups―and even if median losses of -6.8% (1990s Trend) to -8.8% (High Crop
Demand) over 50 years does not appear significant, that rate represents greater median losses
than Breeding Bird Survey population trends from 1966-2009 (median of -2.2% for 29 of our 47
at-risk species with calculable trends, range = -5.0 to -0.7%) as well as 40-year Christmas Bird
10
Count trends (median of -3.4% for 17 of our 47 species, range = -10.1 to 15.8%), as compiled by
the American Bird Conservancy (2012).
Our results show that the adoption of policies can influence land-use changes and
increase the expected provision of at least some ecosystem services relative to baseline scenarios.
Forest land increases by over 30 million ha under the Forest Incentives policy, the largest change
relative to the baseline under any of the three policies. This increase in forest land leads to
significant increases in timber production (18%) and biomass carbon (8%), relative to the 1990s
Trend scenario. As a result of endogenous price changes, however, the increases in forest land
cause increased scarcity of other types of land, which drive up returns in other land uses. We find
that the increased crop prices create financial incentive to crop areas that are in other land uses
under the 1990s Trends reference scenario meaning that the gains in total carbon storage
resulting from forest expansion are reduced by decreases in soil and biomass carbon from the
conversion of pasture and range to cropland. The Forest Incentives policy also leads to some
improvement in species conservation (26 species gained greater than 10% of habitat and 26
species lost greater than 10% of habitat) but a decline in food production relative to the 1990s
Trend scenario.
The Natural Habitats policy is the most successful policy for improving outcomes for
provision of habitat but result in lower provision of other ecosystem services. The Natural
Habitats policy has the greatest positive impact on rangelands and hence the group of at-risk
birds, which has a larger proportion of range specialists (12 out of 47) than other species groups.
This policy, however, reduces crop production and leads to small reductions in carbon storage
and timber production relative to the 1990s Trend scenario. Though the aim of the Natural
Habitats policy is to preserve forests and grasslands, it actually has a negligible effect on forest
area. The Natural Habitats policy uses a tax to discourage deforestation. Such an approach has
little impact when there is a limited amount of baseline deforestation as is the case for the 1990s
Trend scenario. By contrast, the payments provided under the Forest Incentives policy for
establishing new forests has a large effect because there is a large amount of land in agricultural
uses that can be converted to forest. As a result, there are larger changes in ecosystem services
under the Forest Incentives policy than under the Natural Habitats policy. Our finding that
positive incentives are more effective than penalties on land-use change depends critically on
11
trends in baseline conditions and may not be applicable to other regions, such as tropical
countries with high deforestation rates.
The Urban Containment policy is the only policy to have an appreciable effect on
urbanization; the total area of urban land is 24% lower compared to the 1990s Trend scenario.
Increases in the net returns to non-urban uses are unlikely to slow the rate of urban development
because returns to urban development in areas of high demand for development dwarf returns to
agricultural and forest lands. Urban expansion is only slightly lower under the High Crop
Demand scenario—which involves substantial increases in net returns to cropland —than in the
1990s Trend scenario. This finding suggests that aggressive price-based policies would be
needed to significantly limit urbanization. In the absence of this, regulatory approaches, such as
zoning and urban growth boundaries, may be the only feasible approach. Even so, the Urban
Containment policy, which is a much more restrictive policy than is likely ever to be
implemented, has only modest positive effects on the provision of ecosystem services relative to
the baseline. Because urban land makes up a small percentage of overall land area even large
percentage changes in urban land do not make significant contributions to the overall supply of
ecosystem services at the national scale of our analysis.
We find that policies designed to improve certain ecosystem service objectives are not
necessarily beneficial for the provision of other services. The afforestation payments in the
Forest Incentives policy have sizable positive effects on biomass carbon and timber production,
but come at the expense of food production and fewer gains in habitat area for some species,
especially influential species and game species. Likewise, penalties to preserve existing forest
and rangeland in the Natural Habitats scenario increase habitat for some species, but come at the
expense of food production, timber production, and carbon storage. The Urban Containment
policy limits the growth of urban lands, but produces minimal positive effects on food, timber,
carbon, and habitat for three of our four wildlife groups (influential species, game species, and
at-risk birds). Although all policies generate relatively more habitat for most species, there are
still species that lost >10% of their prime habitat under each of the three policy scenarios. No
policy we examine is uniformly good for all ecosystem services and the conservation of all
groups of species. There appear to be inevitable tradeoffs among services (Polasky et al. 2008).
12
Such tradeoffs can make it difficult to provide clear policy advice. Providing evidence on
the change in overall net benefits when some ecosystem services increase and others decrease
requires taking the analysis a step further by either pricing ecosystem services and applying
benefit-cost analysis, or using some form of multi-objective decision analysis (NRC 2005,
Bateman et al. 2011, Newton et al. 2012, Polasky and Binder 2012). Pricing ecosystem services
would allow comparison of the value of changes to each ecosystem services in a common
monetary metric and a summary statement of overall change in net value. While some ecosystem
services are readily expressed in a common monetary metric of value (e.g., crop and timber
production values), other ecosystem services are not (e.g., existence value of an endangered
species). Approaches to valuation of ecosystem services have been outlined elsewhere (e.g.,
NRC 2005, Polasky and Segerson 2009) and applied to at least some services to illustrate how to
rank alternatives (e.g., Nelson et al. 2009, Bateman et al. 2013). Given the controversial nature of
some estimates and our primary focus on the links from policy to land-use change, and from
land-use change to provision of services, we chose not to report all outcomes in monetary terms
or use multi-objective decision analysis here. In addition, there are other benefits and costs not
included in our analysis that should be considered in any ranking of alternative policies. For
example, the Urban Containment policy restricts potentially valuable urban development and
may thereby reduce net benefits from land use.
In focusing on land-use change, we find that it is difficult for policies to overcome
powerful trends originating from market fundamentals or the overall structure of government
programs that shape land-use change. For example, urban land is projected to increase by 26.2 to
29.5 million ha (63-71%) from 2001 to 2051 under baseline conditions. Even under a policy of
urban containment, one that is probably stronger than could ever realistically be put into practice,
the contiguous U.S. still experiences a 12.2 million hectare expansion in urban area (i.e., the
policy only prevents 58.5% of the baseline expansion in urban area). The modest effects of the
urban containment policy largely reflect the fact that most of the urban growth occurs in
metropolitan counties with or without the policy. Price feedbacks limit the effects of the other
policies. A policy that subsidizes one use, such as the Forest Incentives policy, indirectly raises
the returns to the other uses, thereby creating negative feedbacks that limit the effectiveness of
the policy. In a few cases, we find that policies brought about larger changes in the provision of
13
ecosystem services and habitat for species relative to the baseline scenario than are caused by
changes under the baseline scenario itself, but mostly the reverse was true.
In this paper, we analyze the consequences of land-use change between major categories
of land use (crop land, pasture, range, forest, urban development). We do not consider variation
of land use within these major categories. An important restriction in our approach is that we do
not include analysis of changes in land management. Land management is likely to respond to
changes in relative prices and to biophysical restrictions. For example, we would expect more
intensive farming practices that would raise yields in response to higher agricultural prices
(Golub and Hertel 2012). Similarly, new cropland and pasture west of the Mississippi River
would likely require irrigation. Inclusion of responses on the intensive margin would likely
reduce land use conversions and resulting changes in ecosystem service provision between
alternative scenarios. However, because there are also impacts on ecosystem services from
changes in management intensity (e.g., more irrigation can impact water quality on the
landscape), the overall impact of inclusion of changes in the intensive margin on ecosystem
services is less clear.
Our model produces results at a 1-ha resolution. However, it is important to note that
these results are the product of land-cover data sampled at 1-ha resolution, county level returns
data, and soils data at an intermediate resolution. Thus, the interpretation of our results is
constrained by the coarser county level data. See SI text and Table S1 for details on the land-use
change model.
Our research contributes to a large existing literature on land-use change and ecosystem
services (MA 2005, U.K. NEA 2011) in two significant ways. First, we predict land-use change
and its impact on ecosystem service and habitat provision with illustrative tax, subsidy, and
zoning policies that mimic the policy instruments that could be implemented by policy makers.
We model how landowners will likely react to policy-driven changes that cause changes in
relative prices and relative returns to different land uses. Our work builds from econometric
analysis of landowner decisions based on relative returns (Lubowski et al. 2006). An advantage
of the econometric approach is that it can capture actual landowner behavior (e.g., rigidities in
land-use transitions) not represented in sectoral optimization models (e.g., the Forest and
Agricultural Sector Model described in Alig et al., 1998). Recently, Radeloff et al. (2012)
14
applied this approach to the scale of the contiguous U.S. Here we extend the approach of
Radeloff et al. (2012) to show how predicted land-use change under various polices translates
into changes in ecosystem service provision. In similar work, Busch et al. (2012) use observed
land-use change data to build an econometric model that relates relative prices to land-use
change across a country, in this case Indonesia, at the grid-cell level. These authors use the
model to look back in time to estimate how much deforestation would have been avoided in
Indonesia from 2000 to 2005 if a REDD policy existed.
Other simulations of grid cell-level land-use change over large landscapes have used a
combination of basic economic theory, agent-based models, and ad hoc rules to predict overall
expected land-use change and to spatially distribute the change to particular grid cells. For
example, as an input into the Millennium Ecosystem Assessment, Alcamo et al. (1998) used a
series of global energy-use policies, technological change rates, and economic and demographic
growth assumptions to generate overall expected global land-use change in 2100, and an
algorithm to spatially allocate this expected land-use change to specific places on the ground.
Nelson et al. (2010), Polasky et al. (2011), Swetnam et al. (2012), among others, have used
scenarios and ad hoc projections of land-use change to produce spatially explicit predictions of
service provision. Bierwagen et al. (2010) use U.S. Census Bureau population projection
methodologies, climate change scenarios, housing demand models, and gravity models to
allocate housing development across the conterminous U.S. to 2100. In these papers, a mixture
of expert opinion and observed trends in land-use change were used to create rules for future
land-use changes.
In other ecosystem service assessments, local landscape experts have been asked to
predict or envision how land use on a landscape might change (e.g., Castella et al. 2005, Nelson
et al. 2009, Goldstein et al. 2012). Finally, our model results can be compared to other relatively
fine-grained model projections of regional land-use change scenarios (e.g. Sohl and Sayler 2008,
Sohl et al. 2007, 2010) and agent-based modeling approaches and deterministic housing growth
models to map urban, suburban, and exurban expansion (e.g., Theobald 2005, Bierwagen et al.
2010).
Second, we combine an endogenous price modeling approach that represents major land
uses (agriculture, forestry, urban development) with detailed local-scale analysis of land-use
15
change important for determining the provision of ecosystem services. Most endogenous price
modeling approaches generate results at aggregate regional scales. For example, Hertel et al.
(2010) use a general equilibrium model to explore global land-use impacts of an expansion in
U.S. ethanol production. They model global land-use change and environmental impacts across
18 agro-ecological zones. Beach et al. (2012) and Baker et al. (2013) use the partial equilibrium
model Forest and Agricultural Sector Optimization Model (Alig et al. 1998) to model regional
land-use change across the U.S. in response to biofuel policy or growth in agricultural
productivity. On the other hand, many detailed local-scale analyses suitable for analysis of
provision of ecosystem service do not incorporate price feedbacks resulting from induced
changes in land use (e.g., Goldstein et al. 2012, Nelson et al. 2013, Polasky et al. 2011). Localscale analyses, however, often are able to explicitly represent spatial processes affecting land-use
decisions as well as the provision of ecosystem services (e.g., Irwin and Bockstael 2002, Polasky
et al. 2008, Lewis et al. 2011). An important extension of our work is the development of a
national land-use model that includes local spatial processes.
In our analyses, changes in land use and productivity are not a function of expected
climate change. Haim et al. (2011) found that land-use choices will not be greatly affected by
climate change between 2001 and 2051 across the US. However, it is fairly clear that land-use
productivity and vegetative cover will be affected by climate change (Schlenker and Roberts
2009, Lobell et al. 2011). There is also evidence to suggest that many tree species will migrate
and forest productivity in the U.S. will change due to climate change. For example, Kirilenko
and Sedjo (2007) suggest that productivity in many forests will increase as a result of CO2
fertilization. We do not account for the fertilization effect in our study, which would raise our
estimates of timber production and carbon sequestration in forests. However, increases in
productivity due to fertilization could be offset by climate change factors such as increasing
frequency of forest fires (Westerling et al. 2006). Finally, species ranges will change as the
climate changes with many species shifting their distributions to track suitable climates (Lawler
et al. 2009). Incorporating climate-change impacts is an important next step in providing more
realistic future projections.
We have also simplified carbon sequestration dynamics by using a steady-state analysis.
In the analysis of carbon storage (and timber production) we assume that the carbon stored in
16
vegetation and soil is in steady-state equilibrium on the landscape in 2001 and that the carbon
storage between 2001 and 2051 only changes when land use changes. Therefore, increases in
carbon stock on maturing forests, including public lands, are not included in this analysis.
Further, we assume that carbon stocks in land that changes between 2001 and 2051 reaches its
steady-state storage by 2051. This approach precludes examining the time path of the carbon
stock over the period 2001 to 2051. To the extent that not all carbon adjustments to land-use
change have been made by 2051, our carbon estimates may overestimate their actual 2051
values. Finally, not all carbon pools are accounted for in our model, including woody biomass in
shrub land and root biomass in grasslands. Carbon losses from land use change from perennial
grasses to annual crops may be substantial. Despite simplifying carbon sequestration dynamics,
our estimate of future carbon storage in biomass and soil on private lands across the contiguous
U.S. by 2051 is similar to a U.S. Forest Service estimate (Birdsey and Heath 1995).
Our work combines county and sub-county-level data, which incorporate local
heterogeneity and land-use patterns important for determining the provision of many ecosystem
services, with national-scale analyses, which are important for determining market prices and for
analysis of policies that are important drivers of land-use change. This combination of detailed
local analyses with broad national coverage allows us to predict how ecosystem services will be
impacted by changes in market prices or government policies. One can use such analyses to gain
insight into how various policies or changes in underlying trends in drivers of land use are likely
to affect the provision of ecosystem services. One of the main lessons that emerges from our
analysis is that there are powerful underlying trends that will drive land-use change, as illustrated
by the two baseline scenarios that we examined. Land-use patterns can be affected by policy
interventions, but such interventions will need to be aggressive to significantly alter underlying
land-use change trends.
Materials and Methods
Our analysis consists of two major parts: projections of future land use based on an
econometric model, and an assessment of the implications of future land-use change on select
17
ecosystem services. We discuss both parts briefly here. Details of both analyses are provided in
the SI Text.
Econometric land use model and policy simulations
The land-use change model was parameterized using observed land-use changes between
1992 and 1997 at 844,000 sample points of the USDA NRI National Resource Inventory (USDA
2009). Pixel-level land-use change is explained by county-specific net returns to each land use
and each pixel’s soil type and starting land use (Lubowski et al. 2006). As such, our land-use
model accounts for spatial heterogeneity in the factors driving land-use decisions (e.g.,
differences among pixels in soil type), but does not explicitly model spatial processes such as the
effect that the land use of one parcel might have on land-use decisions made for neighboring
parcels. From the estimated econometric model we generated a land-use transition probability
matrix for the period 2001 to 2051 for each county-soil type combination. The transition
matrices account for movements of land among five NRI categories: crops, pasture, forest,
urban, and range (see Table SI 1), where range includes grasslands and shrublands, and urban
includes developed open space and low- to high-intensity urban lands. The econometric model
also includes endogenous feedbacks from land-use changes to net returns. By using endogenous
price feedbacks in our model we control for the impact that changes in the supply of a good can
have on market prices and net returns to land. For example, encouraging forest land at the
expense of crop production will reduce the supply of crops, thereby increasing crop prices and
the corresponding net returns to cropland, which will dampen the shift from cropland to forest.
The econometric model represents changes among land uses (the extensive margin) but does not
model changes in the intensity of uses (the intensive margin). As a way to partially remedy this
shortcoming, we assume an exogenous 6% increase in crop yields every five years. Allowing
land-use intensity to change endogenously would be an important extension of the current
approach.
The 2001 land-use map, the initial land-use map in our simulations, comes from the
National Land Cover Database (NLCD) (Homer et al. 2007). We re-sampled the original 30-m
resolution NLCD grid map to a 100-m resolution to give a more realistic size for average landuse change pixels (Radeloff et al., 2012). We then used the 50-year land-use transition matrices
18
with the re-sampled 2001 map to generate an expected pixel-level 2051 land-use map for the
contiguous U.S.
Ecosystem service models
We modeled soil carbon storage for all land uses. Additionally, for forest and urban
areas, we accounted for above- and below-ground biomass carbon storage, but not for other land
use types. To estimate forest biomass carbon, we made several simplifying assumptions. We
assumed that all privately-owned forests would be managed with even-aged rotations, that the
rotation length was determined by the Faustmann formula, and that all age classes were evenly
represented in the landscape. Forest biomass carbon was then assessed based on the Forest
Inventory and Analysis (FIA) estimates for forest types in each county and allometric curves of
tree growth (Smith et al. 2006). Soil carbon estimates to a soil depth of 30 cm for each land-use
type in a county were based on carbon stock estimates from Bliss et al. (2009). See the SI Text
for details on carbon modeling in biomass and soil.
We estimated kilocalorie production on private croplands in 2051 as a function of
observed 2001 yields and observed 2001 crop-planting patterns on the landscape (USDA-ARS
2011, Haim et al. 2011). We assumed a 6 % increase in yield every 5 years across the entire
nation and all crops (Haim et al. 2011). Such technological improvement is consistent with past
rates of yield increases (Haim et al. 2011). In addition, we modeled timber yield from private
forests based on average yield data from FIA and the rotation length that was estimated as part of
the biomass carbon assessment. We assumed no change in yield for timber.
To assess species responses to land-use change, we quantified the amount of change in
habitat area individually for 194 terrestrial vertebrate species, which were chosen for their
ecological or social importance: amphibians (because of their sensitivity to environmental
change), influential species (in terms of their ecological role, e.g. top predators, keystone species,
and ecosystem engineers), game species (because of their importance to hunters and land
managers), and at-risk birds (categorized by the American Bird Conservancy (2012) as
‘vulnerable’ or ‘potential concern’). We quantified habitat area for each species under current
and future land-use conditions, based on known information about the species’ geographic range
19
and habitat associations. For our analysis, we assumed that species’ geographic ranges and
habitat associations would be static despite the fact that climate change has the potential to alter
both. For birds, we used only portions of the range that were used for breeding or year-round
residency. Our species-habitat associations were based on a land-cover classification of
ecological systems (Comer et al. 2003), cross-walked to the land-use categories used in the
econometric model on a spatially explicit basis, in conjunction with expert opinion. Across the
contiguous U.S., for each species a pixel of current (2001) land use/land cover (LULC) was
scored a 1 if it was prime habitat and a 0 otherwise. For simulated future LULC we used the
land-use transition probability matrices for the contiguous U.S. generated by the econometric
land use model under each of our scenarios. The summation of the potential habitat values within
a species’ range in 2051, compared to the summed habitat value of current land cover, quantified
the impact of future land-use change on a given species. For each species, we compared the
projected change in habitat area resulting from each policy scenario, and summarized results by
four species groups (amphibians, influential species, game species, and at-risk birds). See SI text,
Tables S2-S4, and Dataset S1 for more details on the ecosystem service models.
Acknowledgements
We gratefully acknowledge support for this research from the National Science Foundation’s
Coupled Natural–Human Systems Program. We are also grateful for comments from three
anonymous reviewers, which greatly improved our manuscript.
References
Alcamo J., R. Leemans, and E. Kreileman (eds.) 1998. Global Change Scenarios of the 21st
Century: Results from the IMAGE 2.1 Model. Oxford: Pergamon.
Alexandratos, N., and J. Bruinsma. 2012. World agriculture towards 2030/2050: The 2012
Revision. ESA Working Paper No. 12-03. Rome: Food and Agriculture Organization.
Alig, R.J., D.M. Adams, and B.A. McCarl. 1998. Impacts of incorporating land exchanges
between forestry and agriculture in sector models. Journal of Agricultural and Applied
Economics 30(2): 389–401.
20
Alston, J., M. Anderson, J. James, and P. Pardey. 2010. Persistence Pays: U.S. Agricultural
Productivity growth and the Benefits from R&D Spending. New York: Springer.
American Bird Conservancy. 2012. List of the Birds of the United States with Conservation
Rankings. http://www.abcbirds.org/abcprograms/science/conservationchecklist/index.html
Baker, J. S., B.C. Murray, B.A. McCarl, S. Feng, and R. Johansson. 2013. Implications of
alternative agricultural productivity growth assumptions on land management, greenhouse gas
emissions, and mitigation potential. American Journal of Agricultural Economics 95(2): 435441.
Bateman, I.J. G.M. Mace, C. Fezzi, G. Atkinson, and K. Turner. 2011. Economic analysis for
ecosystem service assessments. Environmental and Resource Economics 48: 177-218.
Bateman, I.J., A.R. Harwood, G.M. Mace, R.T. Watson, D.J. Abson, B. Andrews, A. Binner, A.
Crowe, B.H. Day, S. Dugdale, C. Fezzi, J. Foden, D. Hadley, R. Haines-Young, M. Hulme, A.
Kontoleon, A.A. Lovett, P. Munday, U. Pascual, J. Paterson, G. Perino, A. Sen, G. Siriwardena,
D. van Soest, M. Termansen. 2013. Bringing ecosystem services into economic decisionmaking: Land use in the United Kingdom. Science 341: 45-50.
Beach, R.H., Y.W. Zhang, and B.A. McCarl. 2012. Modeling bioenergy, land use, and GHG
Emissions with FASOM GHG: Model overview and analysis of storage cost implications.
Climate Change Economics 3(3): 1-34.
Bierwagen, B.ritta G., D. M. Theobald, C. R. Pyke, A. Choate, P. Groth, J. V. Thomas, and P.
Morefield. 2010. National housing and impervious surface scenarios for integrated climate
impact assessments. Proceedings of the National Academy of Sciences 107: 20887-20892.
Birdsey, R. A., and L.S. Heath. 1995. Carbon changes in U.S. forests. In Productivity of
America’s Forests and Climate Change, L. A. Joyce (ed.), pp. 56–70. General Technical Report
RM-271. Fort Collins, CO: USDA Forest Service.
Bliss, N.B., S.W. Waltman, and L. West. 2009. Detailed mapping of soil organic carbon stocks
in the United States using SSURGO [abs.], in Fall Meeting, San Francisco, CA, Dec. 14-18,
2009, Eos Transactions, Suppl., v. 90, no. 52: Washington, D.C., American Geophysical Union,
p. B51F-0367.
Busch, J., R. N. Lubowski, F. Godoy, M. Steininger, A. A. Yusuf, K. Austin, J. Hewson, D.
Juhn, M. Farid, and F. Boltz. 2012. Structuring economic incentives to reduce emissions from
deforestation within Indonesia. Proceedings of the National Academy of Sciences 109 (4): 10621067.
Castella, J.C., T. Ngoc Trung, and S. Boissau. 2005. Participatory simulation of land-use changes
in the northern mountains of Vietnam: the combined use of an agent- based model, a role-playing
game, and a geographical information system. Ecology and Society 10 (1): 27.
21
Comer, P., D. Faber-Langendoen, R. Evans, S. Gawler, C. Josse, G. Kittel, S. Menard, M. Pyne,
M. Reid, K. Schulz, K. Snow, and J. Teague. 2003. Ecological Systems of the United States: A
Working Classification of U.S. Terrestrial Systems. Arlington, VA: NatureServe.
Goldstein, J.H., G. Caldarone, T. Kaeo Duarte, D. Ennaanay, N. Hannahs, G. Mendoza, S.
Polasky, S. Wolny, and G.C. Daily. 2012. Integrating ecosystem-service tradeoffs into land-use
decisions. Proceedings of the National Academy of Sciences 109(19): 7565-7570.
Golub, A.A. and T.W. Hertel. 2012. Modeling land-use change impacts of biofuels in the GTAPBIO framework. Climate Change Economics 3(3): 1250015.
Haim, D., R.J. Alig, A.J. Plantinga, and B. Sohngen. 2011. Climate change and future land use in
the United States: An economic approach. Climate Change Economics 2 (1): 27-51.
Hertel, T., A. Golub, A. Jones, M. O’Hare, R. Plevin and D. Kammen. 2010. Global land use and
greenhouse gas emissions impacts of US maize ethanol: Estimating market-mediated responses.
BioScience 60(3): 223-231.
Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, N. Herold, A. McKerrow, J.N.
VanDriel, and J. Wickham. 2007. Completion of the 2001 National Land Cover Database for the
Conterminous United States. Photogrammetric Engineering and Remote Sensing 73(4): 337-341.
Irwin, E.G., and N.E. Bockstael. 2002. Interacting agents, spatial externalities and the evolution
of residential land use patterns. Journal of Economic Geography 2(1): 31-54.
Kirilenko, A.P., and R.A. Sedjo. 2007. Climate change impacts on forestry. Proceedings of the
National Academy of Sciences 104(50): 19697-19702.
Lawler J.J., S.L. Shafer, D. White, P. Kareiva, E.P. Maurer, A.R. Blaustein, and P.J. Bartlein.
2009. Projected climate-induced faunal change in the western hemisphere. Ecology 90: 588-597.
Lewis, D.J., A.J. Plantinga, E. Nelson, and S. Polasky. 2011. The efficiency of voluntary
incentive policies for preventing biodiversity loss. Resource and Energy Economics 33: 192-211.
Lobell, D., W. Schlenker, and J. Costa-Roberts. 2011. Climate trends and global crop production
since 1980. Science 333: 616-620.
Lubowski, R.N., A.J. Plantinga, and R.N. Stavins. 2006. Land-use change and carbon sinks:
Econometric estimation of the carbon sequestration supply function. Journal of Environmental
Economics and Management 51(2): 135-152.
Mason, C.F., and A.J. Plantinga. 2013. The additionality problem with offsets: Optimal contracts
for carbon sequestration in forests. Journal of Environmental Economics and Management 66:114.
22
Manson, S.M., and T. Evans. 2007. Agent-based modeling of deforestation in southern Yucatán,
Mexico, and reforestation in the Midwest United States. Proceedings of the National Academy of
Sciences 104(52): 20678-20683.
Millennium Ecosystem Assessment (MA). 2005. Ecosystems and Human Well-Being.
Washington, DC: Island Press.
National Research Council (NRC). 2005. Valuing Ecosystem Service: Towards Better
Environmental Decision-making. Washington, DC: National Academies Press.
Nelson, E., G. Mendoza, J. Regetz, S. Polasky, H. Tallis, D.R. Cameron, K.M.A. Chan, G. Daily,
J. Goldstein, P. Kareiva, E. Lonsdorf, R. Naidoo, T.H. Ricketts, and M.R. Shaw. 2009. Modeling
multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at
landscape scales. Frontiers in Ecology and the Environment 7(1): 4–11.
Nelson, E., H. Sander, P. Hawthorne, M. Conte, D. Ennaanay, S. Manson, and S. Polasky. 2010.
Projecting global land use change and its affect on ecosystem service provision and biodiversity
with simple techniques. PLoS ONE 5(12): e14327.
Newton, A.C., K. Hodder, E. Cantarello, L. Perrella, J.C. Birch, J. Robins, S. Douglas, C.
Moody, and J. Cordingley. 2012. Cost–benefit analysis of ecological networks assessed through
spatial analysis of ecosystem services. Journal of Applied Ecology 49: 571–580.
Nickerson, C., R. Ebel, A. Borchers, and F. Carriazo. 2011. Major Land Uses in the United
States, 2007. Economic Research Service, Economic Information Bulletin 89. Washington, DC:
U.S. Department of Agriculture.
Polasky, S. and S. Binder. 2012. Valuing the environment for decision-making. Issues in Science
and Technology 28(4): 53-62.
Polasky, S., E. Nelson, J. Camm, B. Csuti, P. Fackler, E. Lonsdorf, C. Montgomery, D. White, J.
Arthur, B. Garber-Yonts, R. Haight, J. Kagan, A. Starfield, and C. Tobalske. 2008. Where to
put things? Spatial land management to sustain biodiversity and economic returns. Biological
Conservation 141(6): 1505-1524.
Polasky, S., E. Nelson, D. Pennington, and K. Johnson. 2011. The impact of land-use change on
ecosystem services, biodiversity and returns to landowners: a case study in the State of
Minnesota. Environmental and Resource Economics 48(2): 219-242.
Polasky, S., and K. Segerson. 2009. Integrating ecology and economics in the study of ecosystem
services: some lessons learned. Annual Review of Resource Economics 1: 409-434.
Radeloff, V.C., E. Nelson, A.J. Plantinga, D.J. Lewis, D. Helmers, J.J. Lawler, J.C. Withey, F.
Beaudry, S. Martinuzzi, V. Bustic, E. Lonsdorf, D. White, and S. Polasky. 2012. Economicbased projections of future land use in the conterminous United States under alternative policy
scenarios. Ecological Applications 22: 1036–1049.
23
Schlenker, W., and M.J. Roberts. 2009. Nonlinear temperature effects indicate severe damages to
U.S. crop yields under climate change. Proceedings of the National Academy of Sciences 106:
15594-15598.
Smith, J.E.; L.S. Heath, K.E. Skog, and R.A. Birdsey. 2006. Methods for Calculating Forest
Ecosystem and Harvested Carbon with Standard Estimates for Forest Types of the United States.
U.S. Forest Service General Technical Report NE-343. Newtown Square, PA: U.S. Department
of Agriculture.
Sohl, T., T.R. Loveland, B.M. Sleeter, K.L. Sayler, and C.A. Barnes. 2010. Addressing
foundational elements of regional land-use change modeling. Landscape Ecology 25: 233-247.
Sohl, T., and K.L. Sayler. 2008. Using the FORE-SCE model to project land-cover change in the
southeastern United States. Ecological Modeling 219: 49-65.
Sohl, T., K.L. Sayler, M.A. Drummond, and T.R. Loveland. 2007. The FORE-SCE model: A
practical approach for projecting land cover change using scenario-based modeling. Journal of
Land Use Science 2(2): 103-126.
Swetnam, R.D., B. Fisher, B.P. Mbilinyi, P.K.T. Munishi, S. Willcock, T. Ricketts, S.
Mwakalila, A. Balmford, N.D. Burgess, A.R. Marshall, and S.L. Lewis. 2011. Mapping socioeconomic scenarios of land cover change: A GIS method to enable ecosystem service modelling.
Journal of Environmental Management 92(3): 563-574.
Theobald, D.M. 2005. Landscape patterns of exurban growth in the USA from 1980 to 2020.
Ecology and Society 10(1): 32.
Tilman, D., C. Balzer, J. Hill, and B. Befort. 2011. Global food demand and the sustainable
intensification of agriculture. Proceedings of the National Academy of Sciences 108: 2026020264.
U.K. National Ecosystem Assessment (UK NEA). 2011. The UK National Ecosystem
Assessment: Technical Report. Cambridge, UK: UNEP-WCMC.
U.S. Department of Agriculture (USDA). 2009. Summary Report: 2007 National Resources
Inventory. Washington, DC: Natural Resources Conservation Service. Ames, IA:Center for
Survey Statistics and Methodology, Iowa State University.
U.S. Department of Agriculture, Agricultural Research Service. (USDA-ARS) 2011. USDA
National Nutrient Database for Standard Reference, Release 24.
http://www.ars.usda.gov/ba/bhnrc/ndl.
US Department of Agriculture, National Agricultural Statistics Service (USDA-NASS). 2012.
Quick Stats 2.0. http://nass.usda.gov/Quick_Stats/.
Westerling, A.L., H.G. Hidalgo, D.R. Cayan and T.W. Swetnam. 2006. Warming and earlier
24
spring increase Western U.S. forest wildfire activity. Science 313: 940-943.
25
Scenario
Description
Targeted Services
Alternative Reference Scenarios
1990s Trend
Continuation of land-use change
trends from 1992 to 1997
NA
High Crop
Demand
Land-use changes accounting for
10% increase in crop prices every
five years relative to the 1990s
Trend scenario
NA
Alternative Policy Scenarios
Forest Incentives
$100/acre payment per year for
land converted to forest; $100/acre
tax per year for land taken out of
forest
Timber production,
carbon storage, habitat
Natural Habitats
$100/acre tax per year on land
converted from forest or range to
crop land, pasture, or urban
Habitat
Urban
Containment
Prohibition on land conversion to
urban in non-metropolitan
counties.
Habitat, timber
production, carbon
storage, food production
Table 1: Description of Alternative Reference and Policy Scenarios
26
Figure Legends
Figure 1. Spatial patterns in land cover in 2001 and in 2051 under the two baseline scenarios,
1990s Trends and High Crop Demand, and changes between 2001 and 2051 under the two
baseline scenarios.
Figure 2. Projected changes between 2001 and 2051 under the two baseline scenarios for: a)
land cover, b) food production, c) carbon storage, d) timber production, and area of prime habitat
for different groups of wildlife species (e-h). The bars in figures (a) through (d) display the
difference between 2051 and 2001 with labels for changes greater than 1%. Bars in figures (e)
through (h) show the number of species in each of three categories: lose >10% of prime habitat
area, little/no change in prime habitat area (-10% to +10%), and gain >10% in prime habitat area.
In addition the median % change across species in each group, by baseline scenario, is shown in
figures (e) through (h).
Figure 3. Spatial patterns in land cover changes under the three conservation policy scenarios
(Forest Incentives, Natural Habitats, and Urban Containment) relative to projections based on the
1990s Trends baseline scenario.
Figure 4. Projected changes under the three conservation policy scenarios (Forest Incentives,
Natural Habitats, and Urban Containment) relative to projections based on the1990s Trends
scenario for: a) land cover, b) food production, c) carbon storage, d) timber production, and area
of prime habitat for different groups of wildlife species (e-h). The bars in figures (a) through (d)
display the difference between the policy scenarios and 1990s Trends projection as of 2051, with
labels for changes greater than 1%. Bars in figures (e) through (h) show the increase or decrease
in the number of species in the categories (defined in Figure 2) under each policy scenario
compared to 1990s Trends baseline scenario.
27
Figure 1
28
29
30
Figure 2
Figure 3
31
32
Figure 4