Land use biodiversity impacts embodied in international food trade

Global Environmental Change 38 (2016) 195–204

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Global Environmental Change journal homepage: www.elsevier.com/locate/gloenvcha

Land use biodiversity impacts embodied in international food trade Abhishek Chaudharya,* , Thomas Kastnerb a b

Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland Institute of Social Ecology Vienna, Alpen-Adria Universität Klagenfurt, Schottenfeldgasse 29, Wien, Graz, A-1070 Vienna, Austria

A R T I C L E I N F O

Article history: Received 20 December 2015 Received in revised form 14 March 2016 Accepted 23 March 2016 Available online xxx Keywords: Biodiversity Agriculture Global trade Land use Sustainability Consumption

A B S T R A C T

Agricultural land use to meet the demands of a growing population, changing diets, lifestyles and biofuel production is a significant driver of biodiversity loss. Globally applicable methods are needed to assess biodiversity impacts hidden in internationally traded food items. We used the countryside species area relationship (SAR) model to estimate the mammals, birds, amphibians and reptiles species lost (i.e. species ‘committed to extinction’) due to agricultural land use within each of the 804 terrestrial ecoregion. These species lost estimates were combined with high spatial resolution global maps of crop yields to calculate species lost per ton for 170 crops in 184 countries. Finally, the impacts per ton were linked with the bilateral trade data of crop products between producing and consuming countries from FAO, to calculate the land use biodiversity impacts embodied in international crop trade and consumption. We found that 83% of total species loss is incurred due to agriculture land use devoted for domestic consumption whereas 17% is due to export production. Exports from Indonesia to USA and China embody highest impacts (20 species lost at the regional level each). In general, industrialized countries with high per capita GDP tend to be major net importers of biodiversity impacts from developing tropical countries. Results show that embodied land area is not a good proxy for embodied biodiversity impacts in trade flows, as crops occupying little global area such as sugarcane, palm oil, rubber and coffee have disproportionately high biodiversity impacts. ã 2016 Elsevier Ltd. All rights reserved.

1. Introduction Terrestrial biodiversity fulfills important functions such as pollination, pest control, nutrient cycling and its loss has economic as well as human health implications (Cardinale et al., 2012; Hooper et al., 2012). The conversion of natural forests has negatively affected the flows of many important ecosystem services, such as carbon storage, water filtration, and habitat provision for biodiversity (MEA, 2005; Foley et al., 2005). The International Union for Conservation of Nature (IUCN) red list shows that 322 species of vertebrates have gone extinct since 1500, and approximately 41% of amphibians, 26% of mammals, 13% of birds,
40% of described invertebrate species and
30% of plant species are considered threatened with extinction (IUCN, 2014). It has been estimated that agricultural activities negatively impact 53% of threatened terrestrial species (Tanentzap et al., 2015). Overall, the current rate of extinction is about 100 times the background extinction rate (Ceballos et al., 2015).

* Corresponding author. E-mail addresses: [email protected], [email protected] (A. Chaudhary). http://dx.doi.org/10.1016/j.gloenvcha.2016.03.013 0959-3780/ ã 2016 Elsevier Ltd. All rights reserved.

Several international agreements aimed at reducing the current rate of biodiversity loss have failed to meet their targets (Tittensor et al., 2014). In addition to traditional measures such as setting aside areas for species conservation, novel policies aimed at directly addressing the human drivers of biodiversity loss (e.g. consumption patterns) are required (Lenzen et al., 2012; Tanentzap et al., 2015). It is thus important to identify the hotspots of agriculture driven biodiversity loss and global food trade flows embodying high biodiversity impacts. As the world’s economies are becoming increasingly interconnected, international trade flows of biomass products have been increasing (Erb et al., 2009). It is important to inform the consumers regarding the environmental impacts ‘hidden’ in products they consume. Environmentally extended input output analysis is a common tool to assess the impact of traded commodity in the country of origin and upstream supply chains (Wiedmann et al., 2011). Biophysical accounting methods have also been applied to trace the origin of consumed products and to assess embodied natural resource inputs such as land or water use in international supply chains (MacDonald et al., 2015; Kastner et al., 2014a). Assessments of carbon emissions embodied in world trade have also been carried out (Peters et al., 2011), but similar analyses of biodiversity impacts are rare.

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The study by Lenzen et al. (2012) is the first and to this date only attempt at quantifying biodiversity loss embodied in global trade of goods and services. They assessed the species threats caused by production of different commodities and combined it with economic multi-region input output (MRIO) tables to evaluate the trade of these threats between countries. Their results showed that industrialized countries are the main importers of threats that occur outside their borders, mainly in tropical developing countries. However, a similar analysis linking biophysical accounting trade databases (e.g. Kastner et al., 2014a) with biodiversity loss at a global scale is still lacking. Alternative globally applicable methods and metrics to quantify biodiversity loss are needed to test the robustness of results obtained by Lenzen et al. (2012).

countryside SAR model. Terrestrial ecoregions were chosen as spatial units because they contain distinct communities of species, and their boundaries approximate the original extent of natural ecosystems prior to major land use change (Olson et al., 2001). The countryside SAR thus predicted the fraction of species lost compared to those occurring naturally prior to human intervention in that ecoregion. The total predicted species loss due to land use was then allocated to individual land use types based on the area occupied by them within the ecoregion and the affinity of taxa to them to derive so called characterization factors (CFs) i.e. the factors indicating biodiversity damage caused by unit area of a particular land use in a particular region.

1.1. Assessing land use impacts on biodiversity

This study extends the analysis carried out by Chaudhary et al. (2015) by combining the CFs (in units—species lost/m2) per ecoregion for agricultural land with high-resolution maps of harvested area and crop yield for individual crops to derive the impacts per unit mass produced (species lost per ton) for 170 crops in 184 countries for four vertebrate taxa. We then obtain 170 matrices containing mass of each of the crop traded between different countries for the year 2011 using FAOSTAT trade database (FAOSTAT, 2015; Kastner et al., 2014a). Finally, the newly calculated impacts per ton at crop level are combined with these trade matrices to assess the biodiversity impacts embodied in international food trade and consumption.

Biodiversity loss due to land use has been studied at different spatial scales  local, regional and global. The estimates of local biodiversity loss are typically obtained from plot-scale biodiversity monitoring studies, comparing species richness between the disturbed site (e.g. agricultural land) and the natural, undisturbed habitat (reference site) of the same region (Gibson et al., 2011). Such spatial comparisons assume that human intervention have caused the biodiversity differences between otherwise similar sites (Newbold et al., 2015). In order to predict regional and global biodiversity loss due to land use, the models describing speciesarea relationships (SARs) are often employed. Traditionally the classic SAR model, defining species richness as a power function, S = cAz, (where A is the area, S is the number of species, and c and z are model parameters), has been used to predict species extinction following habitat loss in a region (Brooks et al., 2002). However it assumes that the areas converted to agriculture or used for forestry are totally hostile to biodiversity, thereby overestimating extinctions (Koh and Ghazoul, 2010). There is a growing recognition that human-modified habitats also play important role in biodiversity conservation (Karp et al., 2012). Alternative models that account for habitat heterogeneity have been proposed to assess patterns of species richness in multihabitat landscapes such as the matrix SAR model (Koh and Ghazoul, 2010) or countryside SAR model (Pereira and Daily, 2006; Pereira et al., 2014). Unlike classic or matrix SAR, the countryside SAR model recognizes the fact that species adapted to human-modified habitats also survive in the absence of natural habitat. The countryside SAR model has recently been proven to perform better than both matrix and classic SAR in predicting species extinction from habitat loss in heterogeneous, human modified landscapes (Pereira et al., 2014; Proença and Pereira, 2013; Guilherme and Pereira, 2013). Using the current extent of land use, the countryside SAR predicts the final, equilibrium level of species extinctions per ecoregion taking levels prior to human intervention as baseline but does not inform on the timing of extinctions. In other words, SARs provide an estimate of species ‘committed to extinction’ (Wearn et al., 2012) rather than species immediately going extinct. The term “extinction debt” has been coined to refer to future biodiversity losses due to past habitat destruction that have yet to be realized because of time delays in extinction. During this time delay it is possible to take conservation measures (e.g. restoring habitat) to safeguard the persistence of biodiversity that is otherwise committed to extinction (see Wearn et al., 2012). Recently, Chaudhary et al. (2015) calculated species lost due to total land use within each of the 804 terrestrial ecoregions for four taxa (mammals, birds, amphibians and reptiles) using the

1.2. Objective and scope

2. Materials and methods We here briefly summarize the methodology to calculate the species loss and how to link these estimates to crop products trade and consumption (see Chaudhary et al., 2015; Kastner et al., 2011, 2014a for full details). For each taxon g, countryside SAR predicts the number of species lost ðSlost Þ caused by all (cumulative) land use within an ecoregion j as a function of the number of species Sorg;j occurring in the original natural habitat area Aorg;j as presented in Eq. (1). Anew;j is the remaining natural habitat area in the region, hg;i;j is the affinity of taxa g to the land use type i (annual crops, permanent crops, pasture, urban, extensive forestry, intensive forestry), Ai;j is the area of individual land use type i in the ecoregion and zj (z-value) is the SAR exponent: 0 1z j Xn Anew;j þ h  Ai;j i¼1 g;i;j @ A ð1Þ Slost;g;j ¼ Sorg;g;j  Sorg;g;j  Aorg;j hg;i;j is a function of the z-value and the relative local species richness of the taxa in land use type i to that in natural forest of the same region (Pereira and Daily, 2006; Pereira et al., 2014). The value of hg;i;j for natural habitat is 1 and decreases till zero as land use becomes increasingly hostile for the taxon (Pereira et al., 2014). The area estimates per ecoregion (Anew;j ; Aorg;j and Ai;j ) were obtained from global land use maps (Ellis and Ramankutty, 2008), species richness per ecoregion (Sorg;g;j ) from WWF database (WildFinder, 2006), z-values (zj ) from Drakare et al. (2006) and the taxa affinities ðhg;i;j Þ from global literature review (Chaudhary et al., 2015). More details on model parameters and their sources are listed in supplementary information. 2.1. Allocating the total species loss to each land use type The species loss due to total land use in an ecoregion j is allocated to each land use type i according to their relative area share in the current human modified area and the taxa affinity to these land use types in the region through an allocation factor ðai;j Þ.

A. Chaudhary, T. Kastner / Global Environmental Change 38 (2016) 195–204 6 X ai;j = 1. i¼1   Ai;j  1  hi;j ai;j ¼ X6   A  1  hi;j i¼1 i;j

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2.4. Total biodiversity damage due to food consumption per country

Note that

ð2Þ

Combining Eqs. (1) and (2), the characterization factors (CFs) for each taxa g, in an ecoregion j are then calculated as: CF regional;i;j;g ¼

Slost;g;j  ai;j Ai;j

ð3Þ

CFs in equation 3 thus give an estimate of regional species loss (i.e. species ‘committed to extinction’ at the regional level) per m2 of six different land use types in each of the 804 terrestrial ecoregion (Chaudhary et al., 2015). 2.2. Country-specific biodiversity impacts per ton of crop production We obtained the harvested area and annual production (Pc;p in tons) of each of 170 crops at a 5 min by 5 min pixel level from Monfreda et al. (2008) (which follows the FAOSTAT crop classification system). Pfister et al. (2011) adjusted this crop area per pixel for multiple cropping, using length of growing season estimates of each crop in different agro-ecological zones. We used this adjusted area occupied by a crop c per pixel p (denoted as Ac;p hereafter) for further analysis. For each taxon g, the characterization factors per ecoregion (CF g;j ) for annual and permanent crops (Eq. (3)) were taken from Chaudhary et al. (2015) and it was assumed that CF value is the same for all pixels p occurring within an ecoregion j (i.e. CF g;p ¼ CF g;f 8 p 2 j). The impact per ton of each crop (Ic;k;g ) was then obtained by dividing the total impact caused by each crop in each country k with its total production: Xn CF g;p  Ac;p p¼1 ð4Þ Ic;k;g ¼ Xn P p¼1 c;p Here n is the total number of pixels within the country k and CF g;p is the characterization factor for taxa g in pixel p (units—species lost per m2). Ac;p and Pc;p are the area (in m2) and production (in tons) of crop c in pixel p. This resulted in a total of 170  184 = 31,280 Ic;k;g values.

Biodiversity impacts caused by food consumption in each country consist of impacts due to use of domestic land plus impacts occurring outside its borders from imported items, minus the exported impacts. For impacts occurring inside a country we multiplied its crop production (in tons) with impacts per ton of that crop in that country (Ic;k;g , Eq. (4). For imported impacts, the crop mass imported was multiplied with corresponding impacts per ton for that crop  country combination. 2.5. Embodied regional species loss The biodiversity damage associated with each trade flow is calculated by multiplying the mass traded of each crop from one country to other with newly calculated impacts (species lost per ton, Eq. (4)) for that combination of crop and country. This gives the species loss embodied in individual trade of each crop from exporting to importing country. 2.6. Characterization factors (CFs) for global species loss CFs in Eq. (3) give an estimate of regional species loss per m2 of agriculture land use in ecoregion j. However it does not tell if the extinction occurs in that region only or if it is a global extinction. If a species is endemic to a region, its loss will mean permanent global extinction. For example consider Region 1 that hosts just the range edges of 10 species as compared to a Region 2 which hosts 10 endemic species (i.e. 100% of their habitat range). Following a human land use intervention, if both regions are made totally unsuitable for these 10 species, the actual biodiversity damage will be more severe in Region 2, as it results in global loss of the 10 species. While avoiding high regional species loss is necessary to ensure resilience of ecosystem services of the region (Hooper et al., 2012), preventing global extinctions is also important in order to preserve genetic diversity of life on Earth (Mace et al., 2003). In order to get an estimate of global (permanent) species extinctions, we replaced species richness (Sorg;g;j in Eq. (1)) with number of endemic species per ecoregion (Send;g;j ) and calculated another set of CFs, hereafter referred to as global CFs. Global species loss per ton were calculated using equations 2–4. Finally, we also quantified the embodied global species loss in international trade flows. 3. Results

2.3. Crop trade We first obtained bilateral trade linkages between 184 countries for 450 agricultural commodities (in metric tons) from FAOSTAT for the year 2011 (for details see Kastner et al., 2014a). Each processed food and livestock item was first converted into primary crop equivalents (total of 170 crops) based on factors calculated as the ratio of dry matter content of the processed product and the dry matter content of the primary product (for details see Kastner et al., 2014a). Next, using the approach proposed by Kastner et al. (2011), we link the (apparent) consumption of each crop product (including crop product feed embodied in animal product trade) to the actual country of crop production, eliminating trade links with countries where only processing takes place. For instance, if Swiss chocolate, made with cocoa beans originating from Ecuador is exported to China, our trade matrix will show the link between consumption in China and cocoa cultivation in Ecuador. This resulted in 170 matrices indicating for each of the 184 countries the country of production for the consumed crops (in tons of primary equivalents; including the amount of domestic production for domestic consumption).

3.1. Country-specific biodiversity impacts per ton of crop production Table S1 presents the regional biodiversity impacts per ton (Eq. (4)) for each crop and country combination. As not every crop is grown in every country, the impacts per ton for a total of 8458 crop  country combinations are calculated. Highest impacts are observed for cropland use in tropical regions, followed by temperate regions and lowest for boreal regions. For all four taxa, the impacts for a particular cropland use varies over six orders of magnitude (
104 to 1011 species lost/ton) depending upon the country. For example, one ton of wheat cultivation in Guatemala results in 2.69  106 mammal species lost as compared to 2.90  108 in Estonia (Table S1). 3.2. Hotspots of biodiversity loss due to agricultural land use The impacts per ton were multiplied by volumes of current crop production (in tons) in each country to identify which crop causes high land-use impacts in each country (Table S2). This enables identifying the hotspots of biodiversity loss due to global

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cropland use plus imported impacts minus the impacts associated with exported crop items). As expected, populous, biodiversity rich nations like China, India, Brazil, Indonesia, Mexico, Nigeria, Philippines and Thailand with large agricultural area occupy top ranks in terms of total biodiversity impacts due to their consumption (Fig. 1a, Table S3). As domestic land use is used for producing commodities for domestic consumption as well as exports, we decoupled these impacts. Fig. 1b shows that in terms of exported impacts—Indonesia, Thailand, India and Malaysia, rank very high for all taxa. Cropland use for producing export items results in a total of 156, 65, 63 and 62 species lost in these countries, respectively. The exported impacts differ in terms of taxa, e.g. exports from Mexico embody significant amphibian species loss, exports from Australia high reptile species loss and exports from USA, Viet Nam and Argentina are causing significant birds species loss (Table S3). Next, Fig. 1c shows that imports into the USA and China embody highest species lost. It is interesting to see that even countries with smaller populations such as Japan, Germany, South Korea, UK, France, and Italy—all cause high biodiversity loss abroad owing to their high per capita consumption and import levels. We found that in total 83% of total regional species loss (4747 species) is incurred due to land use devoted for domestic

agricultural land use. Wheat, rice and maize land use contributed to 2220 species lost (40% of global agricultural land use impacts). This was expected because together these three crops occupy
40% of global cropland. However, the crops such as sugarcane, palm oil, coconut, cassava, rubber, and coffee are responsible for surprisingly high land use driven species loss (together accounting for just below 23% of global impacts) considering the fact that together they only occupy less than 10% of global cropland. Globally, 70% of agricultural land use biodiversity impacts are accounted for by the 13 crops (Table S2). There exist important regional differences in terms of impacts. For example, while wheat in Canada and Russia occupy large areas, their contribution to global biodiversity loss is meagre owing to low characterization factors (Eq. (3)) and thus low impacts per ton (Eq. (4), Table S1). Land use for rice, coconut, rubber and palm oil production in the South-east Asian countries Indonesia, Malaysia and Philippines were found to contribute the most to biodiversity loss among all crop-country combinations (see Table S2 for the full list). 3.3. Total biodiversity damage due to food consumption per country Fig. 1a shows the top 10 countries with highest biodiversity impacts due to food consumption (i.e. impacts due to domestic

a) Consumpon impacts 0

100

200

20

40

300

400

500

600

700

800

900

120

140

160

180

India Indonesia China Philippines Viet Nam Brazil Mexico Nigeria USA Myanmar

b) Exported impacts 0

60

80

100

Indonesia Thailand India Australia Malaysia Viet Nam USA Brazil Sri Lanka Ecuador

c) Imported impacts 0

20

40

60

80

100

120

140

USA China Japan Germany India South Korea Indonesia Russia Italy France Fig. 1. Top ranking countries for biodiversity impacts due to consumption, exports and imports. Unit: number of species lost (regional species loss estimate). See Table S3 for full list.

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Table 1 Top-ranking net importers and net exporters of biodiversity impacts. Group I (‘importers’) consist of nations where biodiversity impacts rest more abroad than domestically and whose exports embody little impacts. Group II (‘traders’) countries are that both import and export biodiversity impacts. Countries in group III (‘exporters’), export significant impacts but import little from abroad. Group IV (‘domestic-oriented’) comprises of countries with relatively little engagement in global biodiversity trade. Unit: number of species lost (regional species loss estimate). GDP per capita values are in US$ for the year 2011 (IMF, 2011). See Table S4 for full list. Country

Total Consumption impacts

Of which

Exported impacts

Domestic

Imported

Imports–exports

China USA Japan Germany South Korea Russia Italy U.K France Saudi Arabia

489 181 78 46 32 59 47 33 27 17

382 66 20 3 2 33 21 11 4 1

107 115 58 43 29 26 26 22 23 16

13 46 0 2 0 6 6 3 5 0

94 68 58 41 29 20 20 19 19 16

Indonesia Thailand Australia Malaysia Ecuador Brazil Sri Lanka Viet Nam India Cameroon

529 129 36 70 42 222 73 224 785 46

503 123 31 50 41 213 70 208 751 45

27 6 6 20 1 9 3 17 34 2

156 65 62 62 36 44 36 49 63 25

129 58 57 42 35 35 34 32 29 23

consumption whereas 17% due to export production (a total of 969 species). 3.4. Synthetic typologies of biodiversity trade Table 1 below shows the ranking of countries in terms of net biodiversity impacts imported (= imported  exported impacts). Countries that export more impacts than they import are net biodiversity exporters, and vice versa. Following the approach by MacDonald et al. (2015), we also divided the countries into four major groups depending upon the relative role they play in agricultural globalization and the trade of biodiversity impacts (see Table 1, Table S4). In the first group are those where the majority of biodiversity impacts rest abroad rather than domestically and whose exports embody little impacts. This includes small countries with negligible arable land (e.g. Singapore, Saudi Arabia, Hong

Group

GDP/capita

II II I I I II I I I I

6091 51749 46720 41863 22590 14037 33072 39093 39772 25136

II III III II III III III II IV III

3557 5480 67556 10432 5425 11340 2923 1755 1489 1167

Kong etc.) and European industrialized countries such as Germany, France, Austria, Netherlands, Belgium, Sweden, Norway, Finland and Denmark where 80–99% of impacts occur through imported items. For example, total German food consumption was estimated to result in 46 species lost, of which 43 are from imported food items (Table 1). The second group consists of nations that both import and export biodiversity impacts. These countries include USA, Canada, Malaysia, Spain, China (Table S4) who often trade oil crops or other high-value commodities with staple crops. For example, Canadian imports embody 15 species lost (mainly through rubber imports from Indonesia and coffee from Mexico, Colombia, and Guatemala) and its exports cause 19 species loss domestically (mainly through rapeseed and wheat destined for China, Japan and USA). In group III lie countries exporting significant biodiversity impacts but importing little impacts from abroad. For example,

Table 2 Top-ranking bilateral international trade flows in terms of embodied biodiversity impacts, major crops causing the impact and their ranking in terms of embodied cropland area (see Table S5 and S6 for full list). Unit: number of species lost (regional species loss estimate). Impacts in

Driven by

Mammals

Birds

Amphibians

Reptiles

Total

Major causes

Rank area

Indonesia Indonesia Mexico Indonesia Thailand Malaysia Indonesia Ecuador Viet Nam India USA Australia Australia Brazil USA Guatemala Indonesia Viet Nam Indonesia Sri Lanka

USA China USA India China China Japan USA China China China Indonesia Japan China Mexico USA Germany Indonesia S. Korea Russia

7 8 7 7 8 7 5 5 4 3 2 1 1 2 2 2 2 3 2 2

5 5 4 4 4 4 3 3 3 4 5 4 4 1 4 1 2 2 1 1

2 2 2 2 1 2 1 2 1 0 1 0 0 2 0 1 1 0 1 1

6 6 5 5 3 3 4 3 1 1 1 2 2 1 1 2 2 1 2 2

20 20 19 18 16 15 13 13 10 9 9 8 7 7 7 6 6 6 6 6

Rubber, cocoa, coffee Palm oil, rubber Coffee, vegetables, fruits Palm oil, cashew, nuts Rubber, cassava, fruits Palm oil, rubber Rubber, coffee, cocoa Cocoa beans, coffee Cassava, rubber, rice Cotton, castor, rapeseed Soybean, cotton Wheat Wheat, barley Soybean Wheat, soybean, sorghum Coffee, bananas Palm oil, rubber, coffee Rice Rubber, coconut, palm oil Tea

23 26 32 22 15 46 54 189 108 9 1 11 10 2 3 243 125 154 156 966

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exports from Thailand are responsible for
35% of total species lost domestically while imports account for just 5% of their total consumption impacts. At the extreme end of this group are Australia, Argentina, and Paraguay whose exports actually embody more species loss than incurred due to land used for their domestic consumption (Table S4). Group IV comprises of countries that have relatively little engagement in global biodiversity trade. Here both the exports and imports embody little impacts and the local consumption is mainly met through crops grown domestically. These domestic-oriented countries lie exclusively in SE Asia (e.g. Myanmar, Nepal), Africa (e.g. Nigeria, Tanzania) and Caribbean (e.g. Haiti, Jamaica). In general, industrialized countries with high per capita GDP tend to be major net importers of biodiversity impacts, while many developing tropical countries suffer habitat degradation and consequent biodiversity loss for the sake of producing crop items for exports. Average GDP per capita for group I countries stood at 31,000$ as compared to 11,200$ (group II); 9258$ (group III) and 3700$ for group IV (IMF, 2011). In terms of imported impacts per capita, 35 out of top 40 countries belonged to group I signifying their high consumption levels (see Table S4). While India, Indonesia and China were top

3 countries in terms of total consumption impacts (Fig. 1a), they rank way lower at 79, 20 and 136th position respectively in terms of per capita consumption impacts. On the other hand, Luxembourg ranked 146th when total impacts were considered compared to 22nd position in terms of per capita impacts. Biodiverse Central American or Caribbean countries such as Belize, Suriname, Panama, Jamaica and Haiti suffering high species loss and with small populations come at top with respect to per capita impacts (Table S4). 3.5. Embodied impacts in bilateral trade links Table 2 shows the top 20 trade flows in terms of embodied regional species loss (see Table S5 for full list) along with the major crops causing biodiversity damage in the exporting country. Exports from Indonesia and Mexico to USA embody highest impacts (20 and 19 species lost respectively). Exports from Brazil, Thailand, Malaysia and Indonesia to China also cause high species loss. For amphibian species loss, exports from Brazil to China show the highest values. For reptiles, exports from Australia to Japan and Indonesia were identified as having significant impacts.

Fig. 2. (a) Total biodiversity impacts imported by the United States from different countries and, (b) total impacts exported by Indonesia to other countries. Unit: number of species lost (regional species loss estimate).

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3 global extinctions (rank 32nd) compared to 106 regional extinctions (rank 13th). We found that in total 81% of total global species loss (514 species) is incurred due to land use devoted for domestic consumption whereas 19% due to export production (a total of 117 species). Overall, the synthetic typologies as listed in Table 1 above changed little when global extinctions are considered. Note that SAR gives an estimate of total species loss within a region but doesn’t tell which species are lost. Therefore with our approach, we are not able to tell how many species in the regional estimate are lost in all the regions they occur in. As a result, the estimated global extinctions are a conservative estimate as they consider only species strictly endemic to individual ecoregions.

The main crops causing high impact of these bilateral links are also shown in Table 2. Exports of rubber from Indonesia to USA alone is responsible for a total of 14 species lost at the regional level, while oil palm exports from Indonesia, Malaysia to India, China and Pakistan also embody high species loss. Rubber exports from Indonesia, Thailand and Malaysia to China, USA and Japan also cause high impacts on biodiversity. In addition, land used for coffee and cocoa exports from the Central American countries Mexico, Colombia, Ecuador to the USA are identified as causing high species loss. Also, tea exports from Sri Lanka to Russia were identified as having high impact on amphibians and reptiles (full list in Table S6). It is interesting to note that the embodied cropland use area between countries is not a good proxy for embodied biodiversity impacts. As shown in Table 2, in terms of embodied land, exports from Ecuador to USA rank 189 (
0.28 million ha) but in terms of species loss, it ranks 8th among all international trade flows. Similarly, agricultural goods exports from Canada to USA embody
3.6 million ha land (5th highest) but this trade flow ranks 36th in terms of embodied total species loss (Table S6). The international trade in biodiversity impacts can be visualized using global maps. Fig. 2 below illustrates the flows of embodied biodiversity impacts for two countries: imports to the USA and exports from Indonesia. US imports are responsible for a total of 115 species lost abroad mainly in Mexico and Indonesia but also in Ecuador, Colombia, Guatemala and Costa Rica (Fig. 2a). In Indonesia 156 species are lost due to land use devoted to export production—mainly destined for USA, China and India followed by Japan, Germany, South Korea and Malaysia (Fig. 2b).

4. Discussion 4.1. Methodological aspects The study is first to quantify the land use driven biodiversity impacts embodied in internationally traded crop items by combining ecological models with biophysical trade flows. We used countryside SAR model along with high-resolution spatial information on global crop area and production to calculate regional species extinctions per unit mass of each crop produced in each country. These impacts were linked with international food trade and consumption data to identify trade flows embodying high biodiversity damage. Note that SARs provide an estimate of species ‘committed to extinction’ (Wearn et al., 2012) rather than species immediately going extinct. This implies that not all the calculated extinctions have already taken place and the producing countries can still act towards preventing a part of them. Additionally, we also estimated the embodied global extinctions by considering only endemic species which are unique to each ecoregion and thus highly vulnerable to any future habitat loss. This study corroborates the findings of earlier researchers (Lenzen et al., 2012) and shows that imported agricultural goods are causing significant loss of biodiversity in the country of origin and highlighted the major crops in different countries causing high biodiversity extinctions (Table S2). This information is useful for producing countries to identify the hotspots of species loss within their borders and perhaps become a starting point for further indepth investigations aimed at designing specific mitigation measures. For example, many of the crops responsible for high damage have below par yields (tons/ha) owing to technological or other factors in the country of production (Mueller et al., 2012; Pradhan et al., 2015). If yields were raised, some of the existing agricultural area could be abandoned and left for regenerating, thereby benefitting local biodiversity. This would need, however, accompanying measures to ensure that higher yields do not fuel overall production (and area) increases due to increased

3.6. Global species loss Table 3 shows the global (=endemic) species loss embodied in bilateral crop item trade flows between different countries (see Table S7 for full list). It can be seen that the ranking of several trade flows changed compared to regional species loss. For example, exports from Sri Lanka to Russia and from the Philippines to the USA now rank 3rd and 8th respectively in terms of embodied global species extinctions in contrast to ranks 20 and 31 when embodied regional extinctions were considered. Conversely, exports from USA to Mexico ranked 15th in terms of regional species loss but appear only at position 288 in the global loss rankings (Table S8). Table S7 shows the total global species loss caused by food consumption in each country along with global extinctions exported and imported. Here also the ranking of countries changed: for example, Haiti now ranks 8th in terms of total consumption impacts (19 global species extinctions) while it ranked 29th when the regional species loss was considered (Table S7). On the other hand, Bangladesh’s consumption results in

Table 3 Top-10 international supply chains in terms of embodied global (=endemic) biodiversity impacts and comparison with embodied regional species loss ranking. See Table S8 for all flows. Unit: number of species lost (global species loss estimate). Impacts suffered in

Driven by

Global species loss

Trade flow rank for regional species loss

Ecuador Indonesia Sri Lanka Brazil Indonesia Mexico Malaysia Philippines Indonesia Indonesia

USA USA Russia China China USA China USA Japan India

3 2 2 2 2 2 2 2 2 2

8 1 20 14 2 3 6 31 7 4

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profitability of agriculture (Rudel et al., 2009). Our results also allow for comparing similar crop items from different countries (Table S1). Countries experiencing high rates of biodiversity loss and looking to expand their cropland area may avoid future extinctions by considering importing the corresponding crops grown in countries with low impacts per ton. On the consumption side, the results are relevant to countries such as China, USA, Germany, Japan (Table 1) to help identify the most damaging imported items such as rubber, soybeans, palm oil, coffee and their origins (Tables 2 and 3). For example, China, importing soybean for use as livestock feed, could encourage its population to adopt change from meat towards more environmentally benign dietary options, thereby helping to reduce the biodiversity damage occurring in exporting tropical countries such as Brazil. Here a nutritional assessment of traded food items will be useful to guide such policy changes (D’Odorico et al., 2014). Using human-edible crops to feed animals has been shown to be an inefficient way to provide calories to humans (Cassidy et al., 2013). 4.2. Comparison of results with existing studies Our approach provides an alternative to that of Lenzen et al. (2012) who used an economic MRIO model to explore embodied biodiversity impacts in global supply chains. In the MRIO approach impacts are connected to final consumption based on monetary links between economic sectors of countries or world regions. In contrast, we apply purely biophysical accounting methods based on bilateral trade links of crops and products processed from them. Studies have shown different results based on whether MRIO or purely biophysical approaches are used to determine the land area associated with traded crop items (Kastner et al., 2014b; Bruckner et al., 2015) with both approaches having their pros and cons. Our results indicate that China is a net cropland and biodiversity impact importer (Table 1). In contrast, recent MRIO studies based on monetary trade data predict that China is a net exporter of embodied cropland (Weinzettel et al., 2013; Yu et al., 2013) and also a net exporter of biodiversity impacts (354 threats imported vs. 434 exported, Lenzen et al., 2012). Reasons for these disparities include, on the one hand, the coarse sector aggregation of current MRIO models that lump together crops with very different economic value compared to the product level resolution of purely biophysical accounts. On the other hand, the purely biophysical approach used by us does not take into account flows associated with more complex supply chains (e.g., biomass used in the production of cars). Recent review by Bruckner et al. (2015) concluded that biophysical accounting such as employed in this study is more appropriate for land footprint analysis of food products that typically undergo only a few processing steps, as well as when aiming at a detailed product resolution. MRIO accounting as used by Lenzen et al. (2012) on the other hand is more suited for the analysis of land flows embodied in non-food land-based products such as wood products, paper, biofuels, textiles, and leather. In this study, we calculated species extinctions in different ecoregions due to total land use (equation-1) and then allocated the total species loss to agriculture land use in that region according to the species affinity to them (Eq. (2)). However, apart from agriculture land use impacts on biodiversity through habitat loss/degradation, crop production also causes biodiversity damage through other environmental pathways. For example, fertilizer run-off from fields leads to eutrophication of rivers affecting aquatic biodiversity (MacDonald et al., 2012). Irrigation water use might lead to water scarcity in the region (Hoekstra and Mekonnen, 2012), while pollution from machinery use on farm and during processing, transport of crops can also negatively affect flora and fauna. It was beyond the scope of this study to model

these pathways and quantify the resulting species loss. Hence our results likely underestimate the biodiversity impacts traded. Projects such as world food LCA database (Peano et al., 2012) aim to gather detailed environmental emissions data for several food items and thus will be useful in future to assess complete impacts associated with them. In this regard, the estimates by Lenzen et al. (2012) are more complete as they also account for other threats caused during different stages of food production. 4.3. Limitations and data gaps The input data used to calculate species extinctions through SAR model come with uncertainties and limitations that should be considered when interpreting the results. For example, the species affinity estimates (hg;i;j , Eq. (1)) were derived from empirical data from global literature review (see Chaudhary et al., 2015). As more plot-scale local biodiversity monitoring data becomes available (e.g. PREDICTS database, Hudson et al., 2014), these estimates can be updated and accuracy of results can be improved. While the crop trade data was obtained from FAOSTAT for the year 2011, the yield and harvested area maps used to derive impacts per ton (Eq. (4)) were based on the year 2000 available from Monfreda et al., 2008. Yields of some crops might have increased (or decreased) over last decade. Thus we might have over- or underestimated some of the traded impacts but currently these maps are the best available source for high resolution impact assessment of global cropland use. Next, our trade and consumption data only covers the crop portion of livestock feed but does not include data on pasture land use associated with livestock products, implying that biodiversity impacts due to livestock grazing are not accounted for. Additionally, our account does not include areas planted to fodder crops such as alfalfa or clover (Kastner et al., 2014a). Limitation of using species richness loss as an indicator of biodiversity damage as in our study is that complex changes in composition and community structure that are commonly caused by human land use are not accounted for. Biodiversity indicators that compare compositional (e.g., Sørensen’s similarity index, Sørensen, 1948) or population (mean species abundance, Alkemade et al., 2009) changes in the species community between a reference and agricultural land use, could reveal further insights and identify additional/different trade flows embodying high impacts. However, monitoring data required for such indicators is rarely available on a global scale for multiple taxa. Future studies should explore these alternative measures of biodiversity. Further, owing to the lack of species richness per ecoregion data in the IUCN and WWF databases, we could not calculate the impacts on invertebrates, fungi and bacteria that contribute to several ecological services and together make up
80% of global terrestrial species. 5. Conclusions Our results are potentially useful to both, decision makers in countries currently importing or exporting biodiversity impacts. Whether the responsibility for reducing the impacts lies with producing or consuming countries is a subject of debate (Liu, 2015). Producers control production methods and exert the impacts but consumer demand and lifestyle drives production, so that responsibility is shared between them (Lenzen et al., 2007). While some countries are totally reliant on imports because of land and resource constraints (e.g. Hong Kong, Saudi Arabia etc.), countries such as Germany, France could theoretically reduce the displaced impacts abroad by devoting more of their domestic resources to local crop production (Fader et al., 2013). Reducing the

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volume of imported commodities that cause high species loss and raising consumers’ awareness of the biodiversity damage caused by these products they buy can help induce sustainable consumption patterns. Brazil’s soy moratorium (Gibbs et al., 2015a) is a good example of how demand-side interventions can contribute to reducing deforestation or adoption of environmentally benign production methods in the exporting countries. Many multinational traders, processers and retailers who buy beef or soy products have agreed to stop sourcing from farms established on recently cleared forests (Gibbs et al., 2015b). By quantifying biodiversity impacts hidden in crop products, our results help identify high damage products from different countries and could also be useful for product labelling and certification schemes. Existing carbon footprint labeling schemes (Cohen and Vandenbergh 2012; Tan et al., 2014) should be extended in future to also include biodiversity impacts of the product (Lenzen et al., 2012). Future studies should extend or complement our assessment linking biodiversity impacts to consumption, through, for instance, employing alternative measures of biodiversity loss, including other land use types and land-based products, or using more spatially-refined, sub-national trade and consumption accounting approaches (Godar et al., 2015). Further insights from such assessments would be highly valuable for devising demand-side strategies to reduce current rates of biodiversity loss. Acknowledgment TK acknowledges funding by the European Research Council Starting Grant LUISE (263522). AC was funded within the National Research Programme “Resource Wood” (NRP 66) by the Swiss National Science Foundation (project no. 136612). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j. gloenvcha.2016.03.013. References Alkemade, R., van Oorschot, M., Miles, L., Nellemann, C., Bakkenes, M., Ten Brink, B., 2009. GLOBIO3: a framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 12 (3), 374–390. Brooks, T.M., Mittermeier, R.A., Mittermeier, C.G., Da Fonseca, G.A., Rylands, A.B., Konstant, W.R., Hilton-Taylor, C., 2002. Habitat loss and extinction in the hotspots of biodiversity. Conserv. Biol. 16 (4), 909–923. Bruckner, M., Fischer, G., Tramberend, S., Giljum, S., 2015. Measuring telecouplings in the global land system: a review and comparative evaluation of land footprint accounting methods. Ecol. Econ. 114, 11–21. Cardinale, B.J., Duffy, J.E., Gonzalez, A., Hooper, D.U., Perrings, C., Venail, P., Naeem, S., 2012. Biodiversity loss and its impact on humanity. Nature 486 (7401), 59–67. Cassidy, E.S., West, P.C., Gerber, J.S., Foley, J.a., 2013. Redefining agricultural yields: from tonnes to people nourished per hectare. Environ. Res. Lett. 8, 034015. Ceballos, G., Ehrlich, P.R., Barnosky, A.D., García, A., Pringle, R.M., Palmer, T.M., 2015. Accelerated modern human–induced species losses: entering the sixth mass extinction. Science advances 1 (5), e1400253. Chaudhary, A., Verones, F., de Baan, L., Hellweg, S., 2015. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49 (16), 9987–9995. Cohen, M.A., Vandenbergh, M.P., 2012. The potential role of carbon labeling in a green economy. Energy Econ. 34, S53–S63. D’Odorico, P., Carr, J.a., Laio, F., Ridolfi, L., Vandoni, S., 2014. Feeding humanity through global food trade. Earth’s Future 2, 458–469. doi:http://dx.doi.org/ 10.1002/2014ef000250. Drakare, S., Lennon, J., Hillebrand, H., 2006. The imprint of the geographical, evolutionary and ecological context on species-area relationships. Ecol. Lett. 9 (2), 215–227. Ellis, E., Ramankutty, N., 2008. Putting people in the map: anthropogenic biomes of the world. Front. Ecol. Environ. 6 (8), 439–447. Erb, K.-H., Krausmann, F., Lucht, W., Haberl, H., 2009. Embodied HANPP: mapping the spatial disconnect between global biomass production and consumption. Ecol. Econ. 69, 328–334. doi:http://dx.doi.org/10.1016/j.ecolecon.2009.06.025.

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