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Growing domestic and international ethanol demand is expected to result in increased sugarcane cultivation in Brazil. Sugarcane expansion currently results in land-use changes mainly in the Cerrado and Atlantic Forest biomes, two severely threatened biodiversity hotspots. This study quantifies potential biodiversity impacts of increased ethanol demand in Brazil in a spatially explicit manner. We project changes in potential total, threatened, endemic, and range-restricted mammals’ species richness up to 2030. Decreased potential species richness due to increased ethanol demand in 2030 was projected for about 19,000 km2 in the Cerrado, 17,000 km2 in the Atlantic Forest, and 7000 km2 in the Pantanal. In the Cerrado and Atlantic Forest, the biodiversity impacts of sugarcane expansion were mainly due to direct land-use change; in the Pantanal, they were largely due to indirect land-use change. The biodiversity impact of increased ethanol demand was projected to be smaller than the impact of other drivers of land-use change. This study provides a first indication of biodiversity impacts related to increased ethanol production in Brazil, which is useful for policy makers and ethanol producers aiming to mitigate impacts. Future research should assess the impact of potential mitigation options, such as nature protection, agroforestry, or agricultural intensification.
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Article
Biodiversity Impacts of Increased Ethanol Production
in Brazil
A.S. Duden 1, * , P.A. Verweij 1, A.P.C. Faaij 2, D. Baisero 3,4, C. Rondinini 3
and F. van der Hilst 1
1Copernicus Institute of Sustainable Development, Group Energy & Resources, Utrecht University,
Princetonlaan 8a, 3584 CB Utrecht, The Netherlands; P.A.Verweij@uu.nl (P.A.V.);
F.vanderHilst@uu.nl (F.v.d.H.)
2
Center for Energy and Environmental Sciences, Faculty of Science and Engineering, University of Groningen,
Nijenborgh 6, P.O. Box 221, 9700 AE Groningen, The Netherlands; A.P.C.Faaij@rug.nl
3Global Mammal Assessment Program, Department of Biology and Biotechnologies,
Sapienza Universitàdi Roma
, Viale dell’Universit
à
32, 00185 Roma, Italy; daniele.baisero@gmail.com (D.B.);
carlo.rondinini@uniroma1.it (C.R.)
4Key Biodiversity Area Secretariat and Wildlife Conservation Society, c/o RSPB,
David Attenborough Building, Pembroke Street, Cambridge CB2 3QZ, UK
*Correspondence: A.S.Duden@uu.nl; Tel.: +31-(0)30-253-6967
Received: 12 November 2019; Accepted: 19 December 2019; Published: 3 January 2020


Abstract:
Growing domestic and international ethanol demand is expected to result in increased
sugarcane cultivation in Brazil. Sugarcane expansion currently results in land-use changes mainly in
the Cerrado and Atlantic Forest biomes, two severely threatened biodiversity hotspots. This study
quantifies potential biodiversity impacts of increased ethanol demand in Brazil in a spatially explicit
manner. We project changes in potential total, threatened, endemic, and range-restricted mammals’
species richness up to 2030. Decreased potential species richness due to increased ethanol demand in
2030 was projected for about 19,000 km
2
in the Cerrado, 17,000 km
2
in the Atlantic Forest, and 7000 km
2
in the Pantanal. In the Cerrado and Atlantic Forest, the biodiversity impacts of sugarcane expansion
were mainly due to direct land-use change; in the Pantanal, they were largely due to indirect land-use
change. The biodiversity impact of increased ethanol demand was projected to be smaller than the
impact of other drivers of land-use change. This study provides a first indication of biodiversity
impacts related to increased ethanol production in Brazil, which is useful for policy makers and
ethanol producers aiming to mitigate impacts. Future research should assess the impact of potential
mitigation options, such as nature protection, agroforestry, or agricultural intensification.
Keywords:
species richness; bioenergy; sugar cane; land-use modeling; mammals; biodiversity;
bioethanol; land-use change; habitat modeling; biofuel
1. Introduction
Brazil is one of the major producers and exporters of agricultural and forestry products in the
world [
1
,
2
]. Crop production in Brazil is dominated by the production of sugarcane (30% of net
production value), soybean (29%), and corn (4%) [
2
], and the area dedicated to the cultivation of
these crops has increased rapidly over the last 30 years [
2
]. Brazil currently has the largest area of
sugarcane cultivation in the world [
2
], and is the second largest producer of ethanol [
3
]. The historical
increase in sugarcane cultivation is primarily the result of Brazilian policies focused on stimulating the
production of sugarcane-based ethanol in order to increase energy security, promote rural development,
and decrease the dependency on fossil fuels [
4
]. Due to growing domestic and international demand,
Brazilian ethanol production is expected to increase from 33.3 billion liters in 2018/2019 [
5
] up to
Land 2020,9, 12; doi:10.3390/land9010012 www.mdpi.com/journal/land
Land 2020,9, 12 2 of 17
54.2 billion liters in 2030 [
6
] based on the global outlook of International Energy Agency (IEA) and
Organization for Economic Cooperation and Development (OECD) [7].
Today, sugarcane is grown mainly in the Cerrado and Atlantic Forest biome in the southeast of
Brazil, and has recently also expanded further into the northwestern part of the Cerrado biome [
4
,
8
].
The projected increase in ethanol production is expected to require additional land for sugarcane
cultivation, resulting in changes in land use, which are both direct, when sugarcane replaces an area
previously occupied by another land use, or indirect, when the expansion of sugarcane induces changes
in land use elsewhere [
6
,
9
11
]. About 35,000 km
2
of sugarcane expansion is projected to occur between
2012 and 2030, mostly in the Cerrado and Atlantic Forest biomes [
6
,
11
]. Sugarcane is expected to replace
mainly cropland and natural grassland [
6
,
10
], as well as shrubland [
11
]. Additionally, the projected
expansion of sugarcane is expected to result in indirect land-use changes aecting an area ranging
from about 20,000 [
6
,
11
] to 78,000 km
2
[
10
]. The three biomes where direct and indirect land-use
change related to sugarcane expansion were projected were the Cerrado, Amazon, and Atlantic Forest
biomes [
6
,
10
,
11
], resulting in loss of forest [
6
,
10
] as well as other natural vegetation [
6
,
11
]. The Cerrado
and Atlantic Forest biomes are both biodiversity hotspots [12] under severe threat [13,14].
Brazil is recognized as one of the most biodiverse countries in the world [
15
,
16
] and contains about
15% to 20% of all species worldwide [
17
]. Mammal species are vulnerable to habitat loss, and Brazil has
been identified as one of the top 10 of countries with a high projected mammal decline by 2050 [
17
,
18
].
The tropical and semi-deciduous Atlantic Forest is home to a large number of endemic species [
15
].
Currently, only about 12% of the original vegetation cover of Atlantic Forest is left, and the remaining
forest areas are highly fragmented [
19
,
20
]. The Cerrado biome consists of savannah vegetation and
contains a large share of endemic plant and vertebrate species [
21
,
22
]. Only about 2% of the Cerrado
biome is legally protected [
23
,
24
], and over 40% of its original extent has been converted to agricultural
use [
13
,
23
,
25
]. Almost 70% of the Amazon rainforest falls within the borders of Brazil [
26
], and the
Brazilian Amazon forest has lost 18% of its original extent and has recently shown the highest levels of
deforestation in a decade [
27
]. For a quarter of all Amazonian mammal species, further deforestation
of the Amazon is projected to result in the loss of over 40% of their range by 2050 [28].
Past expansion and intensification of agriculture have been identified as the main drivers of
species decline, and with ongoing agricultural expansion, biodiversity impacts are expected to become
exacerbated in the future [
29
]. Conversion of native vegetation to agriculture is one of the main drivers
of habitat loss in the tropics [
30
,
31
]. Expansion of sugarcane leads directly or indirectly to habitat
loss [
6
], and has therefore been linked to impacts on biodiversity [
32
]. A number of studies have
projected substantial future biodiversity impacts of agricultural expansion in Brazil. For example,
studies assessing the projected eects of dierent scenarios of agricultural expansion pinpoint the
Cerrado and Atlantic Forest as regions with large potential range contractions for a number of bird
species [
33
] and non-flying mammals [
34
] by 2050, and potentially resulting in local extinction of 140 to
191 bird and mammal species [
35
] as well as 480 endemic plant species [
21
]. However, it remains
unclear which proportion of these impacts can be attributed specifically to an increase in ethanol
demand and subsequent sugarcane expansion, and how these biodiversity impacts vary over dierent
regions in Brazil. Despite the strong projected increase in sugarcane production, the historical impact of
agricultural expansion in Brazil, and the importance of Brazil in global biodiversity conservation, it has
been noted that there is a paucity of studies that assess the potential impact of sugarcane expansion
on biodiversity in Brazil [
8
]. The impact of ethanol-driven sugarcane expansion on biodiversity has
therefore been identified as a priority for future research [4].
Biodiversity within a land-use type was found to vary spatially due to spatial heterogeneity in
biophysical conditions [
36
], which implies that a conversion of one land-use type to another may
result in dierent impacts on biodiversity, depending on the location. Therefore, it is important to
assess potential biodiversity impacts in a spatially explicit manner. The aim of this study was to assess
the potential impacts of ethanol-driven sugarcane expansion on biodiversity in Brazil. To this end,
we performed a spatially explicit assessment of changes in potential mammal species richness following
Land 2020,9, 12 3 of 17
scenarios of land-use change up to 2030. We focused on total, threatened, endemic, and range-restricted
mammal species, and included the whole of Brazil in order to assess the biodiversity impacts of both
direct and indirect land-use changes related to increased ethanol demand.
2. Materials and Methods
2.1. General Approach
This study combined land-use change projections for Brazil from previous studies [
6
,
11
]
with species distributions derived from habitat suitability data [
37
] in order to assess impacts of
ethanol-driven land-use change in Brazil on potential mammal species richness.
A spatial modelling approach developed in an earlier study [
36
] was applied to determine potential
mammal species richness in 2012 and 2030 for a reference and an ethanol scenario. In the ethanol
scenario, demand for ethanol was assumed to increase to meet global ethanol mandates from
24 ×109L
in 2012 to 54
×
10
9
L in 2030, while in the reference scenario, ethanol demand remained static at the
2013 level. Both scenarios also included changes in demand for other agricultural and silvicultural
commodities. In this analysis, we combined land-use projections (at the 5-km resolution) and species
habitat suitability maps (at the 300-m resolution) to create maps of projected species richness index
(at the 5-km resolution) (Figure 1). Habitat suitability maps for all individual Brazilian mammal species
were summed to produce a map of potential mammal species richness in Brazil (Figure 1).
Land 2020, 9, x FOR PEER REVIEW 5 of 17
In this study, biodiversity assessments were based on habitat suitability data for terrestrial
mammal species [37]. Habitat suitability data is available for 95% of all terrestrial mammal species at
a global scale, covering 5027 species in total [37]. The maps of species habitat suitability used in this
study had a resolution of 300 m, and distinguished three habitat suitability classes: Highly suitable,
moderately suitable, and not suitable [37]. Only highly suitable habitat, defined as a primary habitat
that can sustain the species, was included as a suitable habitat in this study, in line with Visconti et
al. [18]. These habitat suitability maps, developed by Rondinini et al. [37] and used as input data in
this study, provide habitat suitability per species based on information on habitat suitability from the
IUCN Red List [40] on species’ range, species minimum and maximum elevation, preferred habitat
types (forest, shrub land, grassland, bare land, and artificial), required proximity to water bodies, and
tolerance of human disturbance [37], as well as spatial data on land cover (based on Globcover
version 2.3 [54]), water bodies, and elevation.
2.5. Spatial Analysis of Land Use and Biodiversity
We assessed potential biodiversity impacts of increased ethanol demand using a spatial
neighborhood analysis described in an earlier study [36], which combines land-use data with
potential species richness data to create projections of biodiversity impact under different scenarios
of land-use change. Potential species richness was defined as the sum of all species for which a highly
suitable potential habitat occurs in a cell. A map of total potential mammal species richness was
created by adding up the habitat suitability maps of all mammal species in Brazil. In a similar way,
maps of threatened, endemic, and range-restricted mammal species richness were created by adding
up suitability maps of these subsets of species. This method was developed specifically to quantify
the impact of land-use change on biodiversity and was applied previously to a different case study
[36].
Figure 1. Overview of the approach applied in this study. Rectangles represent maps, stacked
rectangles represent sets of maps. Text inside the rectangles describes the type of map, text above and
below the arrow describes the analysis step. Text below the rectangles provides the resolution of the
map.
The classification of habitat suitability was based on several factors, including landcover as
defined by Globcover v2.3 [54]. To do this, we used the link between habitat suitability and land use
to make projections about future potential species richness based on projections of land-use change.
To this end, we determined location- and land-use-specific average values of potential species
richness, and applied these to the projected land-use map. Because our aim was to create projections
of potential species richness based on the PLUC land-use scenarios, the Globcover land cover types
were aligned to PLUC land-use types (see the Supplementary Materials for a detailed description of
the reclassification). This resulted in the following land-use types being included in the analysis;
‘Urban’, ‘water’, ‘abandoned land and bare soil’, ‘forest’, ‘shrubs’, ‘grass’, ‘crops’, and ‘sugarcane’.
The land-use category water contains open water (unsuitable for most terrestrial species) and
Figure 1.
Overview of the approach applied in this study. Rectangles represent maps, stacked rectangles
represent sets of maps. Text inside the rectangles describes the type of map, text above and below the
arrow describes the analysis step. Text below the rectangles provides the resolution of the map.
We determined potential species richness for total, threatened, endemic, and range-restricted
mammal species. Potential species richness in 2012 was compared to potential species richness in 2030
in the reference scenario to determine biodiversity impacts due to increased demand for agricultural
and silvicultural commodities other than ethanol. Potential species richness in 2030 in the ethanol
scenario was compared to potential species richness in 2030 for the reference scenario in order to
quantify the impact of increased ethanol demand on biodiversity.
2.2. Biodiversity Indicators
Species richness is a frequently used indicator for biodiversity status [
12
,
38
,
39
]. In this study,
we assessed biodiversity impact expressed as changes in potential species richness of mammals.
We focused on the species richness of mammals because habitat loss and degradation have impacted
mammal communities in such a way that globally, 25% of all mammal species are currently threatened
with extinction [
18
,
40
,
41
]. Mammals are also one of the most intensively studied taxa [
37
], for which
spatial habitat data is available globally at a high resolution [
37
]. Furthermore, mammal species
Land 2020,9, 12 4 of 17
can modify vegetation structure and thereby influence species diversity and composition in other
taxonomic groups [
42
]. Land-use changes may aect dierent mammal species in dierent ways.
In this study, we used the following potential mammal species richness indicators: Total species
richness, threatened species richness, endemic species richness, and restricted-range species richness.
We used potential threatened, endemic, and range-restricted species richness as indicators because
these species are considered conservation priorities due to vulnerability and high extinction risk.
Threatened species are considered to be already at risk of extinction, and are therefore considered
a conservation priority [
43
]. Endemic species are those species whose distribution is restricted to a
unique geographical area [
44
] and are considered conservation priorities because of their uniqueness
and high risk of extinction [
44
,
45
]. Species with restricted ranges are considered a conservation priority
because they are more vulnerable to human impact in comparison to species with large ranges [46].
We selected all mammal species whose potential habitat overlapped partly or entirely with Brazil,
which was the case for 610 species (Table 1; for a list of species, see the Supplementary Materials).
Threatened mammal species were defined as ‘critically endangered’, ‘endangered’, or ‘vulnerable’
according to the International Union for Conservation of Nature (IUCN) Red List of Threatened
Species [
40
] or the Brazilian National List of Threatened Fauna Species (Lista Nacional Ofical de
Esp
é
cies da Fauna Ameaçadas de Extinç
ã
o [
47
]). Endemic species were defined as having 100% of
the potential habitat within Brazil, in line with previous research [
15
]. Restricted range species were
identified as those species with a total potential habitat area smaller than 50,000 km
2
, in accordance with
earlier studies [
48
,
49
]. Based on our definition, there can be an overlap in the number of threatened,
endemic, and range-restricted species. This means that a certain species can, for instance, be categorized
as both threatened and range restricted.
Table 1.
Numbers of mammal species included in this study; total number of species, threatened
species, endemic species, and range-restricted species in Brazil, as derived from habitat suitability
data [37].
Species Group Indicator Number of Species
All mammal species 610
Threatened mammal species 107
Endemic mammal species 93
Restricted-range mammal species 120
2.3. Land-Use Projections
The projected land-use changes in Brazil following scenarios of future ethanol demand and
developments in demand for other agricultural an silvicultural commodities were taken from previous
studies [
6
,
11
]. We compared two scenarios: An ethanol scenario and a hypothetical reference scenario.
The ethanol scenario assumed fulfillment of current and planned ethanol mandates worldwide, which
results in Brazilian ethanol production more than doubling from 24
×
10
9
L in 2012 to 54
×
10
9
L in
2030 [
11
]. The reference scenario assumed static ethanol demand at the level of 2013 (27
×
10
9
L) up to
2030, but in this scenario, the area of sugarcane still increases due to increased demand for sugar [
11
].
Both scenarios also assumed increased production of crops, livestock, and wood products by 2030 [
6
].
Trends of agricultural and silvicultural production were based on the Shared Socio-economic reference
Pathway SSP2 (a ‘Middle of the Road’ scenario), which projects socio-economic developments up to
2100 in line with historical trends [50].
The land-use projections were based on a combination of the global computable general equilibrium
(CGE) model MAGNET (Modular Applied GeNeral Equilibrium Tool) with the PLUC (PC Raster
Land Use Change) model, which were run for the period 2012 to 2030. MAGNET was used to model
the development in the demand and supply of dierent commodities, including ethanol, on a global
scale [
51
]. It provided projections of the amount of land in Brazil needed to meet increased demand
for ethanol up to 2030, as well as that of other agricultural and silvicultural commodities [
6
,
11
].
Land 2020,9, 12 5 of 17
Projected land requirements between 2012 and 2030 for crops, livestock production, forest plantations,
and sugarcane were then spatially allocated using the PLUC model [
52
,
53
]. PLUC was developed
to project land-use change based on temporal developments in demand for various land uses and
spatially explicit suitability of land for various land uses [
52
]. PLUC produced annual maps of
projected land use in Brazil between 2012 and 2030 at a 25-km
2
resolution for the following land-use
types: Grass and shrubs, natural forest, planted forest, crops (excluding sugarcane), sugar cane,
planted pasture, rangeland, abandoned agricultural land, urban, water, and bare soil [11].
The largest share of projected land-use change between 2012 and 2030 in both scenarios consists of
cropland expansion (excluding sugarcane, see the Supplementary Materials for an overview of land-use
changes), which increases by about 320,000 km
2
between 2012 and 2030 in both scenarios. This occurs
predominantly in the Cerrado biome, at the expense of natural forest, rangeland, and grass/shrubland.
Sugarcane expansion also occurs mainly in this region, mostly replacing cropland [
6
]. Sugarcane area
increases by 1400 km
2
in the reference situation and 36,775 km
2
in the ethanol scenario. In addition, the
ethanol scenario projects 20,150 km
2
of the indirect land-use change scenario as a result of increased
ethanol demand (Supplementary Materials).
2.4. Species Habitat Suitability
In this study, biodiversity assessments were based on habitat suitability data for terrestrial mammal
species [
37
]. Habitat suitability data is available for 95% of all terrestrial mammal species at a global
scale, covering 5027 species in total [
37
]. The maps of species habitat suitability used in this study had
a resolution of 300 m, and distinguished three habitat suitability classes: Highly suitable, moderately
suitable, and not suitable [
37
]. Only highly suitable habitat, defined as a primary habitat that can
sustain the species, was included as a suitable habitat in this study, in line with Visconti et al. [
18
].
These habitat suitability maps, developed by Rondinini et al. [
37
] and used as input data in this study,
provide habitat suitability per species based on information on habitat suitability from the IUCN Red
List [
40
] on species’ range, species minimum and maximum elevation, preferred habitat types (forest,
shrub land, grassland, bare land, and artificial), required proximity to water bodies, and tolerance of
human disturbance [
37
], as well as spatial data on land cover (based on Globcover version 2.3 [
54
]),
water bodies, and elevation.
2.5. Spatial Analysis of Land Use and Biodiversity
We assessed potential biodiversity impacts of increased ethanol demand using a spatial
neighborhood analysis described in an earlier study [
36
], which combines land-use data with potential
species richness data to create projections of biodiversity impact under dierent scenarios of land-use
change. Potential species richness was defined as the sum of all species for which a highly suitable
potential habitat occurs in a cell. A map of total potential mammal species richness was created by
adding up the habitat suitability maps of all mammal species in Brazil. In a similar way, maps of
threatened, endemic, and range-restricted mammal species richness were created by adding up
suitability maps of these subsets of species. This method was developed specifically to quantify the
impact of land-use change on biodiversity and was applied previously to a dierent case study [36].
The classification of habitat suitability was based on several factors, including landcover as
defined by Globcover v2.3 [
54
]. To do this, we used the link between habitat suitability and land
use to make projections about future potential species richness based on projections of land-use
change. To this end, we determined location- and land-use-specific average values of potential species
richness, and applied these to the projected land-use map. Because our aim was to create projections of
potential species richness based on the PLUC land-use scenarios, the Globcover land cover types were
aligned to PLUC land-use types (see the Supplementary Materials for a detailed description of the
reclassification). This resulted in the following land-use types being included in the analysis; ‘Urban’,
‘water’, ‘abandoned land and bare soil’, ‘forest’, ‘shrubs’, ‘grass’, ‘crops’, and ‘sugarcane’. The land-use
category water contains open water (unsuitable for most terrestrial species) and shorelines. Spatially
Land 2020,9, 12 6 of 17
variable and land-use specific potential species richness values were then determined by creating a
Boolean map of each land-use type, and multiplying these maps with the potential species richness
maps, thereby creating a land-use-specific potential species richness map. This provides a map with
species richness for each land use, only for the locations where this land use occurs.
A spatial neighborhood analysis was carried out to calculate the potential species richness for each
cell in the study area at a 25-km
2
resolution. This was done by calculating the average species richness
in the cell’s neighborhood for a particular land-use type. We used four dierent window sizes in the
calculation: 15
×
15 km, 85
×
85 km, 395
×
395 km, and finally, the whole of Brazil. These window
sizes were applied sequentially: If there was a value for the specific land-use type within the smallest
window, this window was applied; if not, a larger-sized window was selected (see the Supplementary
Materials for a detailed description of the window sizes). For the majority of cells (75%), the 15
×
15 km
window was applied because a cell with the same land-use type was found within this window size.
Potential species richness maps for 2012 and 2030 (both for the ethanol and the reference scenario) were
created for all species groups. Potential species richness in 2012 and 2030 for each cell was expressed as
the land-use-specific window-averaged number of mammal species with suitable habitat in the direct
neighborhood of each cell, from here on referred to as ‘species richness index’ or SRI. The projected
SRI map for 2012 was compared to the 2030 map for the reference scenario to assess changes in SRI
due to changes in the demand for agricultural and silvicultural commodities (besides ethanol) during
this period. Finally, SRI in 2030 for the reference scenario was compared to SRI in 2030 for the ethanol
scenario to assess the impact of ethanol-driven sugarcane expansion on biodiversity.
3. Results
3.1. Potential Species Richness in 2012
Total potential mammal SRI in 2012 ranged from 0 to 150, depending on the location. In line
with an earlier study based on the same potential species range data [
37
], some hotspots of SRI
were identified; SRI was highest in the Amazon region, particularly in the western part (Figure 2),
and was slightly lower at the edges of the Amazon rainforest, in the so-called ‘arc of deforestation’,
where forested land mainly borders pasture area. Total SRI was also relatively high in remnants of
the Atlantic Forest. SRI was relatively low in the Pampas biome and at the southern edge of the
Cerrado region. These areas are dominated by pasture and cropland. Threatened mammal SRI showed
a spatial pattern similar to that of total SRI but also had high values in the north-eastern Cerrado
region, where more remnant natural vegetation remains. Endemic SRI was highest in the Amazon and
eastern Cerrado. Range-restricted SRI was highest along the coastal areas of the Atlantic Forest region,
where most forest remnants of Atlantic Forest remain. See the Supplementary Materials for SRI maps
for threatened, endemic, and range-restricted species in 2012.
We determined ranges and median values of SRI per land-use type. The median value of the total
SRI of forest in Brazil was about three to six times higher compared to the median values for the other
land uses (Figure 3). Median total SRI was lowest in sugarcane, urban land, bare land, and cropland.
Shrubland, grassland, and water (including shorelines) had slightly higher median values for total SRI.
Forest showed the largest variation in total SRI; this can be due to the fact that it includes dierent
forest types, such as Amazonian rainforest and Atlantic Forest, but also plantation forest. Urban area
showed the smallest variation. As most land-use types showed a wide range in SRI values, a transition
from one land use to another could result either in positive or negative changes, depending on the
local SRI values of each land-use type.
Land 2020,9, 12 7 of 17
Land 2020, 9, x FOR PEER REVIEW 7 of 17
Figure 2. Total mammal species richness index (SRI) in Brazil in 2012 at a 25-km
2
resolution. Black
lines show biome boundaries, abbreviations show biome names; AM = Amazon, CA = Caatinga, CE
= Cerrado, PN = Pantanal, AF = Atlantic Forest, PM = Pampas.
We determined ranges and median values of SRI per land-use type. The median value of the
total SRI of forest in Brazil was about three to six times higher compared to the median values for the
other land uses (Figure 3). Median total SRI was lowest in sugarcane, urban land, bare land, and
cropland. Shrubland, grassland, and water (including shorelines) had slightly higher median values
for total SRI. Forest showed the largest variation in total SRI; this can be due to the fact that it includes
different forest types, such as Amazonian rainforest and Atlantic Forest, but also plantation forest.
Urban area showed the smallest variation. As most land-use types showed a wide range in SRI values,
a transition from one land use to another could result either in positive or negative changes,
depending on the local SRI values of each land-use type.
Figure 2.
Total mammal species richness index (SRI) in Brazil in 2012 at a 25-km
2
resolution.
Black lines show biome boundaries, abbreviations show biome names; AM =Amazon,
CA =Caatinga
,
CE =Cerrado, PN =Pantanal, AF =Atlantic Forest, PM =Pampas.
Land 2020, 9, x FOR PEER REVIEW 8 of 17
Figure 3. Species richness index (SRI) per land-use type in Brazil for 2012. Boxplots show median
values (middle horizontal bar) and first (upper limit) and third quartile (lower limit) values, while
vertical lines indicate maximum and minimum values. Bare = abandoned land and bare soil.
3.2. Projected Changes in Species Richness Index between 2012 and 2030 for the Reference Scenario
Between 2012 and 2030 in the reference scenario, estimated changes in total mammal SRI in
Brazil ranged from +115 to 114 (Figure 4). The largest area of SRI changes occurs in the Cerrado
biome (Figure 5), including both increases and decreases in SRI. In the Cerrado, reductions in SRI are
mainly caused by crop expansion at the expense of grassland, shrubland, abandoned land, and bare
soil and forest (see Supplementary Materials for an overview of SRI changes per land-use change),
with most changes resulting in a reduction of SRI of over 50% (Figure 5). These areas, however,
generally have low SRI values and the absolute changes in SRI are often small (Supplementary
Materials). In some areas in the Cerrado, crop expansion results in a small increase in SRI, for example
when cropland expands over sugarcane and grassland. In the Amazon region, SRI was reduced
mainly due to the projected conversion from forest to grassland (Supplementary Materials). Increases
in SRI occur when sugarcane and abandoned land and bare soil are converted into cropland. In the
Pantanal and Atlantic Forest biomes, the area with increased total SRI was slightly larger than the
area with decreases. The magnitude of positive and negative changes was relatively high in the
Amazon biome, where 37% of changes resulted in a loss of total SRI over 50% to 100%. A number of
land-use changes, including crop expansion over abandoned land and bare soil and expansion of
forest over cropland and grassland, resulted in positive SRI shifts in the Pantanal and Atlantic Forest
biomes. For example, forest expansion over cropland resulted in an average increase in total SRI of
8.6 in the Atlantic Forest biome (Supplementary Materials). The intensity (in % change) of changes in
SRI were relatively large for range-restricted species compared to the other indicators, with over
20,000 km
2
in the Amazon and over 35,000 km
2
in the Cerrado projected to lose all range-restricted
species. The intensity of changes for endemic and threatened SRI was similar to the magnitude of
change for the total SRI but was higher for threatened and endemic species in the Atlantic Forest
biome. The intensity of change was higher for range-restricted species.
Figure 3.
Species richness index (SRI) per land-use type in Brazil for 2012. Boxplots show median values
(middle horizontal bar) and first (upper limit) and third quartile (lower limit) values, while vertical
lines indicate maximum and minimum values. Bare =abandoned land and bare soil.
Land 2020,9, 12 8 of 17
3.2. Projected Changes in Species Richness Index between 2012 and 2030 for the Reference Scenario
Between 2012 and 2030 in the reference scenario, estimated changes in total mammal SRI in Brazil
ranged from +115 to
114 (Figure 4). The largest area of SRI changes occurs in the Cerrado biome
(Figure 5), including both increases and decreases in SRI. In the Cerrado, reductions in SRI are mainly
caused by crop expansion at the expense of grassland, shrubland, abandoned land, and bare soil and
forest (see Supplementary Materials for an overview of SRI changes per land-use change), with most
changes resulting in a reduction of SRI of over 50% (Figure 5). These areas, however, generally have
low SRI values and the absolute changes in SRI are often small (Supplementary Materials). In some
areas in the Cerrado, crop expansion results in a small increase in SRI, for example when cropland
expands over sugarcane and grassland. In the Amazon region, SRI was reduced mainly due to the
projected conversion from forest to grassland (Supplementary Materials). Increases in SRI occur
when sugarcane and abandoned land and bare soil are converted into cropland. In the Pantanal
and Atlantic Forest biomes, the area with increased total SRI was slightly larger than the area with
decreases. The magnitude of positive and negative changes was relatively high in the Amazon biome,
where 37% of changes resulted in a loss of total SRI over 50% to 100%. A number of land-use changes,
including crop expansion over abandoned land and bare soil and expansion of forest over cropland
and grassland, resulted in positive SRI shifts in the Pantanal and Atlantic Forest biomes. For example,
forest expansion over cropland resulted in an average increase in total SRI of 8.6 in the Atlantic Forest
biome (Supplementary Materials). The intensity (in % change) of changes in SRI were relatively large
for range-restricted species compared to the other indicators, with over 20,000 km
2
in the Amazon and
over 35,000 km
2
in the Cerrado projected to lose all range-restricted species. The intensity of changes
for endemic and threatened SRI was similar to the magnitude of change for the total SRI but was higher
for threatened and endemic species in the Atlantic Forest biome. The intensity of change was higher
for range-restricted species.
Land 2020, 9, x FOR PEER REVIEW 9 of 17
Figure 4. Change in total mammal species richness index (SRI) between 2012 and 2030 (reference
scenario). Black lines show biome boundaries, biome names: AM = Amazon, CA = Caatinga, CE =
Cerrado, PN = Pantanal, AF = Atlantic Forest, PM = Pampas. Grey areas indicate areas with no change
in Species Richness Index (SRI).
Figure 5. Area (in km
2
) in which changes in the mammal species richness index (SRI) occur between
2012 and 2030 (reference scenario) per biome for total SRI (Tot), threatened SRI (Thr), endemic SRI
(End), and range-restricted SRI (Rrs). Decreases in SRI are in red, increases are in green, and darker
colors represent a larger percent change in SRI.
3.3. Projected Differences in Species Richness Index in 2030 between the Ethanol and Reference Scenario
Figure 4.
Change in total mammal species richness index (SRI) between 2012 and 2030 (reference
scenario). Black lines show biome boundaries, biome names: AM =Amazon,
CA =Caatinga
,
CE =Cerrado
, PN =Pantanal, AF =Atlantic Forest, PM =Pampas. Grey areas indicate areas with no
change in Species Richness Index (SRI).
Land 2020,9, 12 9 of 17
Land 2020, 9, x FOR PEER REVIEW 9 of 17
Figure 4. Change in total mammal species richness index (SRI) between 2012 and 2030 (reference
scenario). Black lines show biome boundaries, biome names: AM = Amazon, CA = Caatinga, CE =
Cerrado, PN = Pantanal, AF = Atlantic Forest, PM = Pampas. Grey areas indicate areas with no change
in Species Richness Index (SRI).
Figure 5. Area (in km
2
) in which changes in the mammal species richness index (SRI) occur between
2012 and 2030 (reference scenario) per biome for total SRI (Tot), threatened SRI (Thr), endemic SRI
(End), and range-restricted SRI (Rrs). Decreases in SRI are in red, increases are in green, and darker
colors represent a larger percent change in SRI.
3.3. Projected Differences in Species Richness Index in 2030 between the Ethanol and Reference Scenario
Figure 5.
Area (in km
2
) in which changes in the mammal species richness index (SRI) occur between
2012 and 2030 (reference scenario) per biome for total SRI (Tot), threatened SRI (Thr), endemic SRI
(End), and range-restricted SRI (Rrs). Decreases in SRI are in red, increases are in green, and darker
colors represent a larger percent change in SRI.
3.3. Projected Dierences in Species Richness Index in 2030 between the Ethanol and Reference Scenario
Dierences in SRI between the ethanol scenario and the reference scenario were found
predominantly in the Cerrado, Atlantic Forest, and Pantanal biomes, and range from –111 to +103
(Figure 6). The ethanol scenario was projected to have a total SRI 50% lower than the reference scenario
in over 2500 km
2
in the Cerrado, and over 3500 km
2
in the Atlantic Forest biome (Figure 7). In the
Atlantic Forest and Cerrado, lower total SRI in the ethanol scenario was caused by sugarcane replacing
forest, grassland, and abandoned land and bare soil. Lower total SRI in the ethanol scenario compared
to the reference in the Pantanal occurred where cropland replaces shrubland and forest. Positive shifts
also occurred in the Atlantic Forest, which is due to the expansion of forest and grassland, as well
as the expansion of sugarcane over cropland. Even though relative dierences in total SRI between
cropland and sugarcane can be large, absolute SRI dierences between crop and sugarcane were
small in most regions (Supplementary Materials). A shift from cropland to sugarcane in the ethanol
scenario (compared to the reference) resulted in higher SRI in the ethanol scenario in most biomes.
In the Pantanal and Amazon biome, however, it resulted in a strong decrease in SRI (Supplementary
Materials), because the average total SRI value of cropland in these biomes was about twice as high as
in the other biomes (Supplementary Materials). Impacts in the Atlantic Forest and Cerrado region
were mostly caused by direct land-use change while impacts in the Amazon, Caatinga, Pantanal,
and Pampas were mainly the result of indirect land-use change (see the Supplementary Materials for an
overview of SRI changes for direct and indirect land-use change). As a result, changes in biodiversity
due to sugarcane expansion should be seen in the context of changes in biodiversity occurring due to
other drivers of land-use change. When comparing the ethanol and the reference scenario in 2030,
direct land-use change (sugarcane expansion) was responsible for 38% of all land-use transitions,
and was responsible for 23% of the decline in total SRI losses (Supplementary Materials). Forest loss
(indirect land-use change) constituted 16% of all land-use transitions but was responsible for 27% of
all total SRI losses. Between 2012 and 2030, gross sugarcane expansion constitutes about 50,000 km
2
in the ethanol scenario, compared to about 20,000 km
2
in the reference scenario (Supplementary
Materials). Summarizing, sugarcane expansion was projected to result both in relatively small positive
and negative shifts in SRI, depending on the location. Indirect land-use changes, however, consist of
Land 2020,9, 12 10 of 17
a range of land-use transitions and has a larger impact. Especially, the loss of forest and shrub land
resulted in SRI declines.
Land 2020, 9, x FOR PEER REVIEW 10 of 17
Differences in SRI between the ethanol scenario and the reference scenario were found
predominantly in the Cerrado, Atlantic Forest, and Pantanal biomes, and range from –111 to +103
(Figure 6). The ethanol scenario was projected to have a total SRI 50% lower than the reference
scenario in over 2500 km
2
in the Cerrado, and over 3500 km
2
in the Atlantic Forest biome (Figure 7).
In the Atlantic Forest and Cerrado, lower total SRI in the ethanol scenario was caused by sugarcane
replacing forest, grassland, and abandoned land and bare soil. Lower total SRI in the ethanol scenario
compared to the reference in the Pantanal occurred where cropland replaces shrubland and forest.
Positive shifts also occurred in the Atlantic Forest, which is due to the expansion of forest and
grassland, as well as the expansion of sugarcane over cropland. Even though relative differences in
total SRI between cropland and sugarcane can be large, absolute SRI differences between crop and
sugarcane were small in most regions (Supplementary Materials). A shift from cropland to sugarcane
in the ethanol scenario (compared to the reference) resulted in higher SRI in the ethanol scenario in
most biomes. In the Pantanal and Amazon biome, however, it resulted in a strong decrease in SRI
(Supplementary Materials), because the average total SRI value of cropland in these biomes was
about twice as high as in the other biomes (Supplementary Materials). Impacts in the Atlantic Forest
and Cerrado region were mostly caused by direct land-use change while impacts in the Amazon,
Caatinga, Pantanal, and Pampas were mainly the result of indirect land-use change (see the
Supplementary Materials for an overview of SRI changes for direct and indirect land-use change). As
a result, changes in biodiversity due to sugarcane expansion should be seen in the context of changes
in biodiversity occurring due to other drivers of land-use change. When comparing the ethanol and
the reference scenario in 2030, direct land-use change (sugarcane expansion) was responsible for 38%
of all land-use transitions, and was responsible for 23% of the decline in total SRI losses
(Supplementary Materials). Forest loss (indirect land-use change) constituted 16% of all land-use
transitions but was responsible for 27% of all total SRI losses. Between 2012 and 2030, gross sugarcane
expansion constitutes about 50,000 km
2
in the ethanol scenario, compared to about 20,000 km
2
in the
reference scenario (Supplementary Materials). Summarizing, sugarcane expansion was projected to
result both in relatively small positive and negative shifts in SRI, depending on the location. Indirect
land-use changes, however, consist of a range of land-use transitions and has a larger impact.
Especially, the loss of forest and shrub land resulted in SRI declines.
Figure 6.
Dierence in total species richness index (SRI) at a 25-km
2
resolution between the ethanol
scenario and the reference situation in 2030. Black lines show biome boundaries, abbreviations show
biome names; AM =Amazon, CA =Caatinga, CE =Cerrado, PN =Pantanal, AF =Atlantic Forest,
PM =Pampas.
Land 2020, 9, x FOR PEER REVIEW 11 of 17
Figure 6. Difference in total species richness index (SRI) at a 25-km
2
resolution between the ethanol
scenario and the reference situation in 2030. Black lines show biome boundaries, abbreviations show
biome names; AM = Amazon, CA = Caatinga, CE = Cerrado, PN = Pantanal, AF = Atlantic Forest, PM
= Pampas.
Figure 7. Area in which differences in species richness index (SRI) occur in 2030 between the ethanol
scenario and the reference, per biome for total SRI (Tot), threatened SRI (Thr), endemic SRI (End), and
range-restricted SRI (Rrs). Negative changes are in red, positive changes are in green, and darker
colors represent a larger percent change in SRI.
4. Discussion
The method applied in this study allows for a spatial and quantitative assessment of biodiversity
impacts of land-use change for large groups of species over a large area, and the comparison of
different scenarios provides a way to identify impacts due to specific drivers of land-use change. The
application of this method to assess biodiversity impacts of increased ethanol demand in Brazil has
provided a first indication of potential biodiversity risks and opportunities of ethanol-driven
sugarcane expansion. We found that in the absence of increased ethanol demand, Brazil was
projected to experience a range of SRI changes due to land-use change caused by increased demand
for other agricultural and silvicultural commodities. Biodiversity impacts, both in terms of area and
intensity of change, were highest in the Cerrado biome, where cropland is expected to expand, and
in the Amazon area, where grassland is expected to replace forest. In the ethanol scenario, expansion
of sugarcane was predominantly projected to result in loss of SRI in the Cerrado and Atlantic Forest
biomes while crop expansion was expected to result in both positive and negative SRI impacts in the
Pantanal region. SRI impacts due to sugarcane expansion were generally small, as it mostly replaces
cropland and pasture. Impacts of indirect land-use change showed a large range of potential impacts,
highlighting the need to include indirect land-use changes in biodiversity impact assessments. Forest
loss led to relatively large losses in SRI, impacts were particularly high when forest was converted to
cropland or sugarcane. However, compared to biodiversity impacts due to other land-use changes,
biodiversity impacts of sugarcane expansion are relatively small. This implies that other drivers of
land-use change need to be addressed as well in order to reduce negative impacts on biodiversity in
the future.
Uncertainties in input maps of habitat suitability and land use of Globcover and PLUC influence
the results of our analysis. Therefore, our projections of potential species richness should be
Figure 7.
Area in which dierences in species richness index (SRI) occur in 2030 between the ethanol
scenario and the reference, per biome for total SRI (Tot), threatened SRI (Thr), endemic SRI (End),
and range-restricted SRI (Rrs). Negative changes are in red, positive changes are in green, and darker
colors represent a larger percent change in SRI.
Land 2020,9, 12 11 of 17
4. Discussion
The method applied in this study allows for a spatial and quantitative assessment of biodiversity
impacts of land-use change for large groups of species over a large area, and the comparison of
dierent scenarios provides a way to identify impacts due to specific drivers of land-use change.
The application of this method to assess biodiversity impacts of increased ethanol demand in Brazil
has provided a first indication of potential biodiversity risks and opportunities of ethanol-driven
sugarcane expansion. We found that in the absence of increased ethanol demand, Brazil was projected
to experience a range of SRI changes due to land-use change caused by increased demand for other
agricultural and silvicultural commodities. Biodiversity impacts, both in terms of area and intensity of
change, were highest in the Cerrado biome, where cropland is expected to expand, and in the Amazon
area, where grassland is expected to replace forest. In the ethanol scenario, expansion of sugarcane
was predominantly projected to result in loss of SRI in the Cerrado and Atlantic Forest biomes while
crop expansion was expected to result in both positive and negative SRI impacts in the Pantanal region.
SRI impacts due to sugarcane expansion were generally small, as it mostly replaces cropland and
pasture. Impacts of indirect land-use change showed a large range of potential impacts, highlighting
the need to include indirect land-use changes in biodiversity impact assessments. Forest loss led to
relatively large losses in SRI, impacts were particularly high when forest was converted to cropland or
sugarcane. However, compared to biodiversity impacts due to other land-use changes, biodiversity
impacts of sugarcane expansion are relatively small. This implies that other drivers of land-use change
need to be addressed as well in order to reduce negative impacts on biodiversity in the future.
Uncertainties in input maps of habitat suitability and land use of Globcover and PLUC influence
the results of our analysis. Therefore, our projections of potential species richness should be considered
as indicators of trends and areas of interest, rather than robust projections of future biodiversity.
Habitat suitability maps contain uncertainty due to the variable extent of knowledge of habitat
suitability per region, habitat type, or species [
37
]. Furthermore, this study did not take into account the
landscape composition or connectivity while landscape configuration has been shown to influence local
species richness [
55
,
56
]. When compared to point locality data for a subset of species, species habitat
maps were able to accurately predict about 77% of species occurrences [
37
]. Validation of the PLUC
land-use projections for Brazil has shown that at aggregated spatial levels (250
×
250 km
2
and for
the whole of Brazil), PLUC has relatively low uncertainty (coecient of variation, or cv, of 0.91) at
allocating direct land-use change, but the precise location of indirect land-use change remains highly
uncertain (cv of 1.61) [
11
]. Projections of land-use transitions towards sugarcane, cropland, and pasture
were shown to be relatively accurate in validation of the PLUC model while transitions towards
rangeland and planted forest were more uncertain [11].
Our approach also introduced new uncertainty in the analysis. The habitat suitability maps
used in this study are, amongst other maps, based on land cover as classified by Globcover 2.3 [
54
].
This map is based on remote sensing data [
57
], and its accuracy has been shown to be 67.5%, based on
expert validation [
54
]. Some classes, such as bare soil, cropland, closed broadleaved evergreen forest,
and water bodies, were classified more accurately while urban areas, sparse vegetation, and herbaceous
vegetation were found to be more prone to misclassification [
54
]. Aligning land-use classification used
in Globcover with the land-use classification in PLUC may have added to the uncertainty. Because the
Globcover map contains mosaic land-use types, some assumptions had to be made about the proportion
of each PLUC land-use type within these categories.
As shown by our results, SRI values per land-use type show a wide range, and therefore partly
overlap. This is due to spatial variation in the potential species richness and the large size of our study
area. SRI in cropland diers from one area to the other, for example, due to dierences in elevation
or proximity to water but also due to heterogeneity within the land-use type due to dierent crop
or management types. For this reason, a specific land-use transition could result in an increase or
a decrease in SRI, depending on the location. For example, the replacement of cropland by sugar
cane resulted in both positive and negative changes in SRI. This highlights the importance of a spatial
Land 2020,9, 12 12 of 17
assessment. Future research using this method would be aided by biodiversity data based on more
detailed land-use categorization, and more detailed projections of land-use change. We were unable
to separate natural grassland from pasture because the Globcover land-use map did not make a
distinction between the two types. The availability of maps of managed pasture could reduce the
uncertainty of the analysis.
Species richness as an indicator for biodiversity status is simple to calculate and easy to
interpret [
58
], and has been shown to correlate to ecosystem function and resilience [
59
63
]. However,
species richness has also received criticism regarding its ability to reflect the concept of biodiversity and
provide information on the conservation priority of areas [
64
]. An assessment of the number of species
does not take into account species composition (beta-diversity) and therefore provides no information
on functional diversity, endemism, or rarity [
65
]. We find that the mean mammal species richness index
varies little between most non-forest land-use types. We expect, however, that the species composition
varies considerably across these dierent land-use types. A change from shrubland to cropland may
therefore result only in a small reduction of the species richness index while potentially all original
shrub-dwelling species are replaced by species tolerant to agricultural lands. This failure to report
information on species composition is significant, because community structure and diversity are
related to the delivery of ecosystem services [
64
,
66
]. In this study, we alleviated this shortcoming by
including subsets of species as an indicator, including threatened, endemic, and range-restricted species,
which provide some information on the conservation priority of species assemblies. Our study provides
projected trends in potential species richness, which require long-term monitoring for verification.
We therefore recommend future research to provide field measurements of biodiversity in a range of
land-use types, including agricultural lands, such as sugarcane fields.
The focus on potential species richness allowed us to carry out a spatially explicit analysis based
on high resolution habitat data [
37
] for a large number of species. The use of potential habitat data to
assess potential species richness avoids problems of sampling eort and observation chances that may
skew presence/absence data between dierent species or regions. However, the presence of suitable
habitat is no guarantee of the actual presence of the species that may use this habitat [
67
]. Activities,
such as hunting, may aect the presence of species within suitable habitat [
68
]. While not explicitly
included in the analysis, this aspect was indirectly included in the analysis due to the fact that ‘human
disturbance’ was included as a factor in the habitat suitability maps. The habitat data on which this
analysis was based is only available for mammals. It has been shown that dierent taxonomic groups
respond dierently to (projected) land-use changes [
36
,
69
]. Therefore, results based on species richness
for mammals cannot be used as proxy for overall biodiversity.
The results presented here therefore provide first indications of potential areas with high
biodiversity loss. However, further research is necessary to assess how other taxonomic groups
may be aected by projected land-use changes. As a next step, it will be interesting to apply the method
to dierent mitigation scenarios, such as policy measures that prohibit the transition of forest land, or
scenarios that include strategies for nature-inclusive agriculture and agroforestry. Forests were found
to have a higher species richness index than the other land-use types. Therefore, in order to reduce the
negative impacts of sugarcane expansion on biodiversity, loss of forest should be avoided. Furthermore,
to reduce impacts on endemic species, loss of other natural land-use types containing high numbers of
endemic species, such as shrubland in the Cerrado biome, should also be prevented. The protection
of natural areas can reduce the encroachment of land-use transitions into natural areas, but current
protections levels may not be sucient to avoid impacts; for example, only 2% of the Cerrado biome
is legally protected. Biodiversity losses on a landscape scale may furthermore be reduced by eorts
to increase biodiversity values of agricultural land-use types. Biodiversity values of cropland could,
for instance, be increased by implementing agroforestry practices [
70
], which could provide suitable
habitat for a number of species while also maintaining economically viable yields [
71
]. More intensive
agriculture and livestock production could also reduce the extent of projected indirect land-use changes
but also reduce the species richness values of agricultural land. These trade-os are at the heart of the
Land 2020,9, 12 13 of 17
land-sparing versus land-sharing debate. More detailed data on species habitat suitability and the
spatial distribution of land management would allow for the assessment of these types of mitigation
measures. Research has shown that a combination of mitigation strategies (including land conservation
policies, yield improvements, and shifts to second generation ethanol production) could reduce the
loss of natural vegetation by up to 96% [
6
]. The quantification of the eect of such mitigation policies
and land management scenarios on the biodiversity impact of sugarcane expansion will be the focus of
further research.
5. Conclusions
This study provides the first quantification of spatial patterns of ethanol-driven biodiversity
impacts in Brazil. We applied a method developed specifically to assess spatial trends in biodiversity
impacts due to land-use change.
We found that:
Increased demand for ethanol (from 24
×
10
9
L in 2012 to 54
×
10
9
L in 2030) resulted in large
areas of negative changes in potential species richness in the Cerrado (close to 20,000 km
2
),
Atlantic Forest (over 15,000 km2), and Pantanal (almost 7000 km2) biomes.
In the Cerrado and Atlantic Forest, about 14% of these changes resulted in losses of total SRI of
over 50%. These impacts were mainly due to the direct eects of sugarcane expansion while in the
Pantanal, this was due to indirect land-use change.
These impacts should be viewed in the context of land-use change resulting from increased
demand for other agricultural products, which, in the absence of increased ethanol demand,
resulted in substantially larger areas with projected biodiversity losses and gains, particularly in
the Cerrado (over 200,000 km
2
, of which over 120,000 km
2
negative changes) and the Amazon
(close to 100,000 km2, of which over 70,000 km2negative changes) biomes.
Loss of forest and shrubland resulted in the largest losses of potential species richness.
Loss of natural vegetation due to sugarcane expansion can be strongly reduced by mitigation
strategies, such as reducing the loss of natural vegetation (e.g., through forest protection), increasing
biodiversity values of agricultural land (e.g., through agroforestry), or reducing the extent of land-use
change (e.g., through agricultural intensification). Future research should focus on assessing potential
mitigation options to avoid biodiversity impacts of sugarcane expansion. The identification of locations
of potential biodiversity impacts provided in this study is useful for policy makers and ethanol
producers aiming to reduce or mitigate biodiversity impacts.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2073-445X/9/1/12/s1,
Figure S1. Area (km
2
) of projected land use and land-use changes in PLUC projections, Table S1: Sugarcane
expansion and other land-use transitions between 2012 and 2030 in the reference scenario, Table S2: Sugarcane
expansion and other land-use transitions between 2012 and 2030 in the ethanol scenario, Table S3: Reclassification
of Globcover land cover classes to PLUC land use projections, Table S4: Area (km
2
) and percentage of cells (%)
using the window sizes, Figure S2. Window size (in km) applied in the calculation of SRI in 2012 in the spatial
analysis, Figure S3. Species richness index in 2012 for threatened, endemic and range-restricted species in Brazil,
Figure S4. Species richness index (SRI) per ecoregion and land-use type in Brazil for 2012, Table S5. Summary
of the area of dierence in land use between the ethanol and the reference scenario and the total SRI losses
represented by each category, Table S6. Summary of the area of dierence in land use between the ethanol and
the reference scenario and the total SRI gains represented by each category, Table S7. Area (in km
2
) per land-use
transition, Table S8. Area (in km
2
) per land-use transition, Table S9. Species name, reference model number, and
their status as threatened, endemic or range-restricted, Figure S5. Dierence in species richness index in 2012 and
2030 for the reference scenario, Figure S6. Dierence in species richness index in 2030 between the ethanol and
the reference scenario, Table S10. Area (in km
2
) of changes in total SRI in dierent regions and dierent impact
categories caused by direct (dLUC) and by indirect (iLUC) LUC, and Figure S7. Location of direct (dLUC) and
indirect (iLUC) land use changes between the reference and ethanol scenario in 2030.
Author Contributions:
Formal analysis, A.S.D.; Funding acquisition, A.P.C.F.; Methodology, A.S.D.; Supervision,
P.A.V. and F.v.d.H.; Writing—original draft, A.S.D.; Writing—review & editing, A.S.D., P.A.V., A.P.C.F., D.B., C.R.
and F.v.d.H. All authors have read and agreed to the published version of the manuscript.
Land 2020,9, 12 14 of 17
Funding:
This work was carried out within the BE-Basic R&D Program, which was granted a FES subsidy from
the Dutch Ministry of Economic Aairs, Agriculture and Innovation (EL & I).
Acknowledgments:
The authors would like to thank Maarten Zeylmans van Emmichoven, Yoeri Kraak and Will
Zappa for their support in the data analysis.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
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... With vast expanses of arable land and favourable climatic conditions, Brazil has emerged as a global leader in ethanol production (Karp et al., 2021). Based on projections from the International Energy Agency (IEA) and Organisation for Economic Cooperation and Development (OECD), Brazilian ethanol production is anticipated to surge from 33.3 billion litres in 2018-2019 to 54.2 billion litres by 2030 (Duden et al., 2020;Van Der Hilst et al., 2018). This success has not only reduced Brazil's reliance on fossil fuels but has also significantly mitigated carbon emissions, with ethanol production saving over 50 million tonnes of CO 2 emissions per year. ...
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This chapter gives a detailed description of the concept of bioeconomy resources and the technologies and trends in the Global South region. It also includes the importance of the production of food, services, and materials in an eco-friendly manner inclusive of maintaining a sustainable environment for all. These resources include agriculture, forestry, and aquatic. Bioeconomy technologies in the Global South have found useful applications in renewable technologies, renewable energy, and biorefineries are useful in different disciplines such as microbiology, biochemistry, agriculture, biotechnology, bioresource management, energy, and ecology. Some factors influencing bioeconomy development like poverty, food security, biodiversity/climate change, affordable /clean energy, and water scarcity were discussed. Successful case studies in some countries of the Global South like India with the Biodiesel Project, Bangladesh with the Community-Based Aquaculture Projects, Brazil with the Sugarcane Ethanol Industry, and Costa Rica with the Sustainable Forestry Management were also documented. Based on the opportunities available, the chapter recommends that all countries desirous of economic growth and development should urgently key into bioeconomy resources for their advancement and environmental sustainability. Abstract This chapter gives a detailed description of the concept of bioeconomy resources and the technologies and trends in the Global South region. It also includes the importance of the production of food, services, and materials in an eco-friendly manner inclusive of maintaining a sustainable environment for all. These resources include agriculture, forestry, and aquatic. Bioeconomy technologies in the Global South have found useful applications in renewable technologies, renewable energy, and biorefineries are useful in different disciplines such as microbiology, biochemistry, agriculture, biotechnology, bioresource management , energy, and ecology. Some factors influencing bioeconomy development like poverty, food security, biodiversity/climate change, affordable /clean energy, and water scarcity were discussed. Successful case studies in some countries of the Global South like India with the Biodiesel Project, Bangladesh with the Community-Based Aquaculture Projects, Brazil with the Sugarcane Ethanol Industry, and Costa Rica with the Sustainable Forestry Management were also documented. Based on the opportunities available, the chapter recommends that all countries desirous of economic growth and development should urgently key into Bioeconomy resources for their advancement and environmental Sustainability
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... Tropical ecosystems provide essential services, such as nutrient cycling, carbon sequestration, and climate and water regulation (IPBES, 2019). Despite their environmental and socioeconomic importance, these ecosystems are currently threatened by overexploitation and accelerated deforestation associated with habitat conversion to crops, livestock, timber industry, and urban infrastructures (Duden et al., 2020;Laurance et al., 2009;Melo et al., 2013;Morris, 2010;Potapov et al., 2017;Seto et al., 2013). Concern that these alterations will cause a mass extinction in the near future makes it urgent to improve understanding of how the loss of tropical forests will affect biodiversity and associated functions (Alroy, 2017;Cardinale et al., 2012;Pillay et al., 2022). ...
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Tropical species richness is threatened by habitat degradation associated with land‐use conversion, yet the consequences for functional diversity remain little understood. Progress has been hindered by difficulties in obtaining comprehensive species‐level trait information to characterize entire assemblages and insufficient appreciation that increasing land‐cover heterogeneity potentially compensates for species loss. We examined the impacts of tropical deforestation associated with land‐use heterogeneity on bird species richness, functional redundancy, functional diversity, and associated components (i.e., alpha diversity, species dissimilarity, and interaction strength of the relationship between abundance and functional dissimilarity). We analyzed over 200 georeferenced bird assemblages in the Atlantic Forest of Brazil. We characterized the functional role of the species of each assemblage and modeled biodiversity metrics as a function of forest cover and land‐cover heterogeneity. Replacement of native Atlantic Forest with a mosaic of land uses (e.g., agriculture, pastures, and urbanization) reduced bird species richness in a nonrandom way. Core forest species, or species considered sensitive to edges, tended to be absent in communities in heterogenous environments. Overall, functional diversity and functional redundancy of bird species were not affected by forest loss. However, birds in highly heterogenous habitats were functionally distinct from birds in forest, suggesting a shift in community composition toward mosaic‐exclusive species led by land‐cover heterogeneity. Threatened species of the Atlantic Forest did not seem to tolerate degraded and heterogeneous environments; they remained primarily in areas with large forest tracts. Our results shed light on the complex effects of native forest transformation to mosaics of anthropogenic landscapes and emphasize the importance of considering the effects of deforestation and land‐use heterogeneity when assessing deforestation effects on Neotropical biodiversity.
... Reduced species richness in oil palm plantations in Malaysia has also been reported for 48%-60% of forest bird species (Azhar et al., 2011). Another example is the expansion cultivation of sugarcane in Brazil for bioethanol production causes loss of natural vegetation which is home to a large number of endemic species (Duden et al., 2020). ...
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