Spatially Explicit Analysis of Biodiversity Loss Due to Global

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Spatially Explicit Analysis of Biodiversity Loss due to Global Agriculture, Pasture and Forest Land Use from a Producer and Consumer Perspective Abhishek Chaudhary, Stephan Pfister, and Stefanie Hellweg Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b06153 • Publication Date (Web): 25 Feb 2016 Downloaded from http://pubs.acs.org on February 28, 2016

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Environmental Science & Technology

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Spatially Explicit Analysis of Biodiversity Loss due to Global Agriculture, Pasture and Forest Land Use from a Producer and Consumer Perspective

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Abhishek Chaudhary1*, Stephan Pfister1, and Stefanie Hellweg1

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5 6 7

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ABSTRACT

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Anthropogenic land use to produce commodities for human consumption is the major driver of

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global biodiversity loss. Synergistic collaboration between producers and consumers in needed to

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halt this trend. In this study, we calculate species loss on 5 min × 5 min grid level and per

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country due to global agriculture, pasture and forestry by combining high-resolution land use

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data with countryside species area relationship for mammals, birds, amphibians, and reptiles.

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Results show that pasture was the primary driver of biodiversity loss in Madagascar, China and

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Brazil, while forest land use contributed the most to species loss in DR Congo and Indonesia.

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Combined with the yield data, we quantified the biodiversity impacts of 1 m3 of roundwood

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produced in 139 countries, concluding that tropical countries with low timber yield and a large

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presence of vulnerable species suffer the highest impact. We also calculated impacts per kg for

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160 crops grown in different countries and linked it with FAO food trade data to assess the

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biodiversity impacts embodied in Swiss food imports. We found that more than 95% of Swiss

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consumption impacts rest abroad with cocoa, coffee and palm oil imports being responsible for

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majority of damage.

Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland, (*Corresponding author phone: 41-44-6330254; fax: 41-44-6331061; e-mail: [email protected])

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ACS Paragon Plus Environment


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INTRODUCTION

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Global biodiversity faces unprecedented extinction crisis1 mainly owing to habitat loss caused by

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conversion of natural forests to anthropogenic land use.2 Growing population, increasing per

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capita consumption, a shift to meat-based diets and biofuel production are the leading factors

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responsible for this trend.3 More than 75% of the Earth’s ice-free land is already being affected

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by human activities.4 The FAO forecasts a ∼70% increase in global food demand from 2000 to

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2050, perhaps leading to further habitat loss to make way for additional cropland.5 In addition to

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deforestation for agriculture and pasture land use, forest exploitation for commercial purposes is

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another threat to biodiversity especially in the tropics.6

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International efforts aimed at halting rate of global biodiversity loss have failed to meet their

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targets.1,7 Apart from traditional measures such as setting aside areas rich in biodiversity for

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conservation purposes,8 novel policies aimed at directly addressing the human drivers of

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biodiversity loss (e.g. reducing the intensity of damaging land use types or changing

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consumption patterns) need to be implemented in parallel.9 Identification of hotspots and the

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land use types causing high biodiversity damage can help producer nations develop local

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strategies and control further damage. On the other hand, as the international trade of food and

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timber products continues to increase,10 informing the consumers regarding the environmental

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impacts ‘hidden’ behind the imported products and identifying commodities causing high

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damage in producing nations may induce sustainable consumption patterns and help design

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demand-side mitigation measures.

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Life cycle assessment (LCA) is increasingly used to evaluate the cradle to grave environmental

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impacts of products.11 The main advantage of LCA studies over traditional impact assessments is

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that it establishes a link between the final commodity (e.g. one kg of tea) and the associated


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biodiversity loss.12 However, within LCA, biodiversity impacts due to land use are often poorly

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quantified.13-15

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Land use impact assessment within LCA

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Traditional methods for land use impact assessment within LCA are often based on limited

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biodiversity datasets from specific world regions (such as Europe) or taxa (mainly vascular

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plants).16 Only recently, globally applicable and spatially differentiated methods have been made

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available. de Baan et al.17 were the first to provide local characterization factors (CFs, i.e. the

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factors indicating species loss caused by unit area of a particular land) for different taxa in

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different terrestrial biomes. However, local CFs do not inform regarding the contribution of land

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use towards potential irreversible, global extinction of rare and threatened species due to habitat

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loss/degradation. Avoiding global species extinctions is important to preserve the evolutionary

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and genetic diversity of life on Earth. For predicting regional and global species extinctions

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resulting from habitat loss, species area-relationships (SARs) have commonly been used in

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LCA.18,19

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Countryside SAR has recently been shown to perform better than other SAR models in

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predicting species extinction from habitat loss in a heterogeneous, human modified landscape

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(i.e. the countryside) and recognizes the fact that species adapted to human-modified habitats

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also survive in the absence of natural habitat.20 Recently Chaudhary et al.21 provided ecoregion

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specific regional CFs for six land use types using countryside SAR (e.g. regional species lost per

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m2 of annual crop) for different taxa. The global CFs were then calculated by weighting the

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regional CFs with a ‘vulnerability score’ (VS) of that ecoregion.21

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Global scale land use impact assessment



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Many studies have assessed the land use impacts of individual crops or food items from

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particular countries using different methods. For example, Mattsson et al.22 assessed Swedish

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rapeseed, Brazilian soybean and Malaysian palm oil, de Baan et al.12 assessed impacts of tea,

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coffee and tobacco in East Africa, Chaudhary et al.21 assessed sugarcane, wheat, sugarbeet and

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maize from Brazil, France and USA. However, an explicit assessment of species loss due to all

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crops from all countries is lacking. Similarly, for impacts due to forest or pasture land use,

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studies have focused on individual countries, e.g. for Norway23 or Ghana24, while a global

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analysis is lacking. In order to assess the impacts of different land use types on biodiversity with

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global coverage, spatially differentiated land use inventory data need to be combined with

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regionalized characterization factors.25

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On the inventory side, global maps of agricultural crops and pasture land use have been compiled

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by Monfreda et al.26 at 5 × 5 arc minute grid level. For forest land use, global maps also at 5 arc

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minute grid level are available.27,28 The advantage of these maps is that they combine ‘land

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cover’ data from remote sensing with statistical data on human activities, and thereby provide

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information on ‘land use’ (e.g. forests for timber extraction) and not only land cover (e.g.

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coniferous forests - as given by Hansen et al.29). On the impact assessment side, the

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characterization factors (CFs) provided by Chaudhary et al.21 can be used to assess impacts of

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different land use classes such as arable or permanent crops on a global scale.

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Objectives & Scope

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The overall goal of manuscript is to provide a spatially-explicit analysis of land-use driven

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biodiversity loss from worldwide agriculture, pasture and forestry, using the recently published

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impact assessment method of Chaudhary et al.21in combination with high resolution land use

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maps. From a producer’s perspective, we identify the most damaging land use types causing high


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species loss for mammals, birds, amphibians and reptiles globally and also on a regional scale at

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5 arc minute resolution. We then aggregate the results to country level and in combination with

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crop and timber yield data, quantify species loss per kg of 160 global crops and per m3 of

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roundwood production. These results can be applied in combination with trade data to assess

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biodiversity impacts embodied in traded food, fibre and wood items and to determine

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biodiversity impacts from a consumer perspective. To illustrate how the newly calculated

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country-crop impact factors can be used to quantify the impacts imported and exported by

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different countries, we carry out a case-study on Swiss food consumption.

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MATERIALS AND METHODS

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Land use biodiversity impacts at 5 arc minute resolution

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We imported the global land occupation characterization factors (CFs) per ecoregion for the land

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use types - annual crops, permanent crops, pasture, extensive forest and intensive forest from

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Chaudhary et al.21. The global CFs were derived using countryside SAR and weighted with

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vulnerability score for each of the four vertebrate taxa (mammals, birds, amphibians and reptiles)

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in 804 terrestrial ecoregions. The global CFs are higher for ecoregions hosting more endemic and

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threatened species and provide an estimate of permanent (irreversible) species extinction caused

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by an unit area of land use.21 See supporting information-1 (SI-1) for more details and equations

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used for calculation of CFs.

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We combine these CFs with high-resolution (5 x 5 arc minute) land use maps of global

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agriculture26, pasture26 and forest27,28 land use to calculate biodiversity impacts per grid cell

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(pixel). It was assumed that value of characterization factors in each pixel, CFp, is the same for

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all pixels p situated within an ecoregion j (CFj). The biodiversity damage for each taxon g due to

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land use type i per grid cell (,, ) is given by:



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,, = ,,, × , 117

Equation 1

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Where , is the area occupied by land use type i in the pixel p (in m2·years). The global CFs

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( ,,, ) provide biodiversity damage in the units- global species equivalents lost per m2 of

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land use type i in pixel p (hereafter species eq. lost/m2, see Chaudhary et al.21 for full details).

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Therefore, equation-1 provides the biodiversity damage (,, ) caused by land use type i in

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units of species eq. lost·years for each taxonomic group g.21 Additionally, we also assessed the

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impacts using the taxa-aggregated CFs for each land use type per ecoregion provided by

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Chaudhary et al.21 that give the biodiversity impacts in the units- potentially disappeared fraction

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per m2 of land use (PDF/m2), which can be used in LCA studies (see SI-1 for details).21

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The pasture and agricultural land area inside each pixel was imported from Monfreda et al.26

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while the area occupied by forest land use per pixel was taken as the average of managed forest

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area provided by global land use maps ANTHROME28 and LADA27, both at 5 arc minute grid

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level.

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For global agricultural land use, the harvested area and yield maps are available for each of 160

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crops on a 5 min grid level from Monfreda et al.26 Pfister et al.30 adjusted this crop area per pixel

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for multiple cropping, using length of growing season estimates of each crop in different agro-

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ecological zones. We used these adjusted values for area occupied by a crop c in each pixel p

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(denoted as , hereafter). The biodiversity damage (,, ) per taxa g due to crop c is

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calculated by multiplying the characterization factor of agriculture land with the area occupied

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by the crop within each pixel p: ,, = ,,, × ,

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Equation 2


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Land use biodiversity impacts per country

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The total biodiversity damage caused by each land use type in a country is calculated as the sum

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of biodiversity impacts per pixel ,, calculated in equation 1 above:

,, = ,, = ,,, ∙ , 141

Equation 3

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Here n is the total number of pixels within the country k. Analogously, the total biodiversity

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damage caused by each of the 160 crops in each country (,, ) is obtained by summing the

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impacts per pixel (,, ) calculated in equation 2 above.

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Country-specific characterization factors

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As life cycle inventory and global trade databases often report the country but not the ecoregion

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of production, we also calculated the CFs per country which will be more convenient than those

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at ecoregion level previously provided by Chaudhary et al.21We calculated the country-specific

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CFs for the land use types – extensive forestry, intensive forestry and pasture for all countries k

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and taxa g by dividing the total impact due to these land use types by the total area occupied by

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the them in country k. ,,,

∑ ,,, ∙ , = ∑ ,

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Equation 4

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Here n denotes the number of pixels within the country k, ,,, are CFs on pixel level

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derived by Chaudhary et al.21 Similarly, we also calculated country-specific CFs for each of 160

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global crops by dividing their total impact (from equation-2 above) by the total area occupied by

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them in country k.

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Biodiversity impacts per m3 of roundwood production per country



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We calculate the biodiversity impacts per m3 of roundwood by dividing the country-specific

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extensive and intensive forestry CFs (species eq. lost per m2, from equation 4) with the

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corresponding harvesting intensity (m3/ha/year) in the units species eq. lost·years per m3: ,, =

,

!,,

× 10000 × (1 − %, ) ,,, × 10000 × %, + &' !, &',

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Equation 5

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Where index ext denotes extensive forestry, pla for planted (intensively managed) forests. The

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forest plantations often consisting of fast growing timber species were assumed to fall in

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intensive forestry category as they simplify the forest structure leading to higher biodiversity

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damage.6 All other types of natural managed forests were considered in extensive forestry

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category. Wood harvesting intensity per country (&' ) in m3/ha/year was derived for both

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managed natural forests and planted forests by dividing the total annual roundwood production

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by their respective area per country using values from FAO forest resource assessment report.31

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The fraction of a country’s roundwood coming from planted forests (%, ) was obtained from

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Jürgensen et al.32

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Biodiversity impacts per kg of crop

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The biodiversity damage per kg of crop c of country k (species eq. lost·years per kg) is calculated

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as the total impact due to crop land use in that country (equation 3) divided by the total crop

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production (in kg): ,,, =

,, ∑ ), ∙ ,

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Equation 6


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Here n is the total pixels within the country k, , is the area of crop c in pixel p (ha) and ), is

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the yield (kg/ha) for crop c in pixel p, also obtained from Monfreda et al.26

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Case-study: Swiss food consumption impacts

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The weight of each food item imported by Switzerland in 2011 was obtained from FAOSTAT

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trade database.33 For processed food products, we used conversion factors provided by FAO34 to

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estimate the weight of crop required to produce the item (e.g. 4 kg of oranges required per kg of

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orange juice).

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However, the FAO data only reports the last country from which the food item is traded but not

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the actual country where the item was produced. An example is Belgium as an intermediate

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trader, which exports tea to a lot of EU countries but does not produce it. We assumed that if a

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country produces the exported crop then the land use occurred there. However if it does not

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produce the product (e.g. tea from Belgium), the imported quantity was allocated to the biggest

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producers of this product worldwide in the same proportion as their global export share (data

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from FAOSTAT).33

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More sophisticated approaches exist to trace the actual country of origin such as MRIO databases

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that utilize monetary transaction and economic data (e.g. Eora9, EXIOBASE35, or Kastner et

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al.36). However, one of the shortcomings of existing MRIO tables is the grouping of multiple

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crops (e.g. staple crops) or regions (e.g. South-east Asia) into a common category. As the crops

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of one category might differ significantly in terms of their environmental impacts or the

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countries within one big region might differ in terms of impacts per unit land use, such grouping

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might lead to under/over estimation of their impacts. We used an alternative approach and utilize

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the physical supply chain data available from FAOSTAT33 to allocate the imported food item to

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correct for intermediate trading countries. We only considered the food items directly derived



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from crops and did not consider the imported livestock products as this was beyond the scope of

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this study.

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Once the food item was allocated to countries of origin, the biodiversity damage associated with

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its import was calculated by multiplying its mass (kg) with newly calculated per kg impacts for

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that combination of crop and country (equation 6). The imported impacts were also compared

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with impacts occurring due to net agriculture land use within Switzerland for Swiss consumption

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(i.e. total agricultural land use minus land use for exports).

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RESULTS

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Biodiversity damage due to crop, pasture and forest land use

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Figure 1(a) shows the total global mammal species loss per pixel due to agriculture, pasture and

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forestry land use combined. Geographic hotspots of land-use driven mammal species loss are

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located in Indonesia, Madagascar, Philippines, Brazil, Papua New Guinea, China, India, DR

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Congo and Mexico. Figures 1(b) and 1(c) show the zoomed in maps for Brazil and Indonesia

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respectively to help identify hotspots of mammal species loss within these countries at a 5 arc

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minute resolution. Managed forest is the main driver of mammal species loss in Indonesia,

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whereas pasture is most damaging land use type in Brazil. Agriculture land use was identified as

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main driver in Philippines India, and Sri Lanka. See supporting information-1 for all maps

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showing impacts due to each land use type per taxa in each pixel.

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Figure 1. a). Total mammal impacts (global species eq. lost·years) at 5 arc minute resolution due

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to current anthropogenic land occupation. Impacts per pixel due to agriculture, pasture and forest

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land use were calculated separately using equation 1 and then summed, b). Zoomed in map of

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Brazil showing high damage areas lie mostly along the east coast and in the Andes, c). Zoomed

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in map of Indonesia showing global hotspots of mammal species loss (dark brown pixels). See

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supporting information-1 for all maps showing impacts due to each land use type per taxa.

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The impact due to total agriculture land use per country for all taxa was calculated using

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equation 3. Table S1 of supporting information-2 (SI-2) shows the detailed results of biodiversity

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loss due to each land use type per country and taxa. Here again, although countries with large



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agriculture area were expected to incur high impacts, smaller countries such as Sri Lanka,

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Malaysia, Philippines, all featured in top 10 countries suffering high biodiversity loss due to high

231

species richness and vulnerability scores for ecoregions within these countries (Table S1, SI-2).

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Table S1 further shows that for pasture land use, highest mammal species loss was found to

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occur in Madagascar, China, Brazil, Australia and Colombia causing ~45% of the total mammal

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species loss due to global pasture land use. Together these five countries account for 28% of

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global pasture area. For birds, in addition to the above countries, New Zealand also showed high

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species loss. For amphibians, Colombia, Brazil, Ecuador, Peru and Venezuela were identified as

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hotspots of species loss due to pasture land use.

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Regarding the managed forests, Russia, Brazil, Canada, USA and China are the top five

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countries in terms of area. However they do not feature in the top 5 countries with highest

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species loss. For instance, despite the fact that Russia has highest amount of forest land use, it

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ranks 21, 87, 101 and 73rd on the mammal, birds, amphibians and reptile species lost

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respectively. This is due to the fact that these countries have low species richness to start with

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and the low vulnerability score of taxa hosted by them, thereby having low characterization

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factors (equation 1).21Indonesia, Papua New Guinea, Madagascar, DR Congo, Brazil and

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Malaysia suffer the highest species loss due to anthropogenic forest land use (Table S1, SI-2).

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Table S2 in SI-2 shows the total biodiversity loss associated with each of the 160 crops. Rice,

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maize and wheat were expected to contribute most to biodiversity loss because together these

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three crops occupy ~40% of global agricultural land. However, crops such as coffee, rubber, tea,

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palm oil and soybean have a disproportionally high biodiversity footprint considering the fact

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that they only occupy less than 10% of global agricultural land. This is because these crops


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occupy biodiversity-rich regions hosting high number of endemic and threatened species (i.e.

252

with high CFs, equation 1).

253

Table S3 in SI-2 throws further insight into the biodiversity burden of global agricultural land

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use by listing the impacts for each of the 7,666 crop × country combinations. While wheat from

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the USA, Canada, and Russia occupy large agricultural areas on earth, their contribution to

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global land-use related biodiversity loss is meagre. Land use for rice, coconut, rubber and palm

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oil production in South-east Asian countries Indonesia, Malaysia and Philippines were found to

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contribute the most to biodiversity loss among all combinations of crop and country. For

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amphibians and reptiles, maize production in Mexico and tea in Sri Lanka also contributed

260

significantly to species loss. For each taxa, just the top 30 combinations of crop × country are

261

responsible for ~50% of global agriculture impact.

262

Country-specific characterization factors (CFs)

263

The calculated CFs for pasture, extensive and intensively managed forest land use for each

264

country (from equation 4) are listed in SI-2, Table S4. Pasture land use had in general higher

265

CFs, reflecting the relatively low affinity of species to them as compared to natural managed

266

forests but were close to intensively managed forest. All three CFs were within one order of

267

magnitude for most countries. The CFs were in general higher for tropical countries and small,

268

island countries (such as in Caribbean); lower for countries in temperate and boreal regions and

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varied over 4 orders of magnitude across 250 countries. For agricultural land use, CFs were

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calculated for 7,666 crop and country combinations and are shown in SI-2, Table S5.

271

Impacts per m3 of roundwood production

272

Figure 2 and Table S6 in SI-2 show that for an equal amount of roundwood production, the

273

biodiversity impacts range over 4 orders of magnitude across different countries. Island and



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tropical countries such as Madagascar, Comoros, Sao Tome and Dominican Republic suffer

275

highest species lost per m3 of roundwood produced. However most of these countries have little

276

forest area and thus low wood production (Table S6, SI-2). Countries producing majority of

277

global roundwood such as the US, Canada, Russia, Brazil -rank very low in terms of impact per

278

m3. The impacts differ because of interplay of factors such as harvesting intensity, CFs and forest

279

management regime prevalent in different countries. For example, the forestry CFs for mammal

280

species loss (equation 4) for Indonesia is around 500 times higher than that for Germany (both

281

for plantations and natural managed forests, Table S6). Further, the average wood yield from

282

forests in Germany is more than four times higher than in Indonesia. Consequently, mammal

283

species lost per m3 of roundwood is ~2000 times higher in Indonesia (4.7 ×10-7) than in Germany

284

(2.5 ×10-10). In general, countries with low yields per hectare along with high biodiversity and

285

presence of endemic and threatened species (high global CFs) performed the worst in terms of

286

impacts per m3.

287 288

Figure 2. Total mammal impacts per m3 of roundwood produced in 139 countries (unit- global

289

species eq. lost·years/m3) calculated using equation 5 above. See Table S6 in supporting


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290

information-2 for impacts on other taxa per country. NA denotes the countries with negligible

291

roundwood production.

292

Biodiversity impacts per kg of crop

293

Table S5 in SI-2 shows the biodiversity impacts per kg of crop in each country. For all four taxa,

294

the impacts per kg for a particular crop ranged over five orders of magnitude (~10-11 to 10-16

295

species eq. lost·years/kg) depending on the country. For example, 1 kg of wheat from Brazil

296

results in 1.05 x 10-11 mammal species eq. lost·years as compared to 5.94 x 10-13 in Canada

297

(Table S5). The impacts per kg were generally higher for amphibians and reptiles followed by

298

mammals and least for birds but were mostly within an order of magnitude.

299

Case-study: Swiss food import impacts

300

Table 1 shows the top 10 crop × country combinations causing highest mammal impacts along

301

with their net mass and area imported (i.e. after subtracting the exports) into Switzerland for the

302

year 2011. Cocoa, coffee and soybean imports resulted in highest mammal species loss in

303

countries of origin.

304

Table 1. Top 10 crop items imported into Switzerland for the year 2011 with highest embodied

305

mammal impacts (in units – species eq. lost·years) along with their country of origin. Mass of

306

crop items (in tons) and corresponding area embodied (in ha) is also shown. Taxa-aggregated

307

impacts are in the units - potentially disappeared fraction·years (PDF·years). See Table S7 in

308

supporting information-2 for full list of imported crop items and their embodied impacts. Crop

cocoa

From

Ecuador

Mass

Land

Mammal

Birds

Amphibians

Reptiles

Aggregated

imported

imported

(Species eq.

(Species eq.

(Species eq.

(Species eq.

(PDF·years)

(Tons)

(ha)

lost·years)

lost·years)

lost·years)

lost·years)

4

1.1×10

5.1×10

4

9.3×10

-3

5.7×10

-3


-3

74×10

4.6×10

-3

7.2×10

-6


4

1.2×10

5

6.8E×10

3

6.9×10

3

2.7×10

4

4.3×10

4

2.50×10

3

6.0×10

3

2.50×10

4

2.4×10

4

2.30×10

3

2.4×10

3

1.90×10

3

9.2×10

3

1.70×10

4

2.2×10

4

1.40×10

5

3.2×10

4

1.30×10

cocoa

Ghana

3.5×10

cocoa

Cameroon

2.4×10

sunflower

Tanzania

2.6×10

coffee

India

5.3×10

coffee

Brazil

1.9×10

coconut

Philippines

9.6×10

coffee

Colombia

8.4×10

cocoa

Ivory coast

1.5×10

soybean

Brazil

2.5×10

-3

-3

1.1×10

-3

Page 16 of 27

5.6×10

-3

-3

2×10

-4

4.3×10

-4

1.40×10

-4

7.30×10

-4

1.50×10

-4

1.50×10

-4

1.30×10

-4

2.70×10

0.33×10

-3

7.90×10

-3

4.10×10

-3

7.60×10

-3

5.90×10

-3

8.40×10

-3

2.20×10

-3

4.80×10

-3

-3

3.2×10

-3

1.7×10

-6

0.2×10

-3

4.8×10

-7

-4

7.1×10

-7

-2

3.0×10

-6

-4

8.4×10

-7

-3

7.3×10

-7

-4

1.4×10

-6

-4

3.5×10

-7

-4

3.8×10

-7

5.30×10

-2

1.10×10

-3

2.40×10

-3

2.40×10

-2

8.90×10

-3

6.10×10

-3

1.80×10

309 310

Total Swiss crop imports resulted in loss of 0.0556 mammals, 0.019 birds, 0.189 amphibians and

311

0.046 reptile species eq. lost·years (Table S7, SI-2). The total imported impacts are 20-300 times

312

higher than those due to domestic crop land use for Swiss consumption in Switzerland for

313

different taxa considered (Table S7). This is much higher in contrast to the ratio of total land

314

embodied in imported products and net domestic agricultural land used for consumption (= 3.5).

315

This suggests embodied land is not a good proxy for embodied biodiversity impacts. Overall

316

>95% of biodiversity impacts of Swiss food consumption occur outside its border.

317

DISCUSSION

318

The study is first to present the spatially-explicit biodiversity impacts due to global agriculture,

319

pasture and forest land use at 5 arc minute grid cell level (Figure 1, Figure S1 to S3 in supporting

320

information-1) and aggregated to country level (Table S1, SI-2). As land use decisions are

321

mostly made at national and sub-national level, country-specific impacts and their distribution


Page 17 of 27


322

within the nation for pasture, forestry land and specific crops can be used by producer nations to

323

identify geographical hotspots and most damaging land use types. This in turn can induce the

324

changes in production methods and other measures to control further damage (e.g. by shifting

325

from high to low intensity agriculture or forestry or by protecting ecologically valuable habitats).

326

On the consumption side, the study is first to provide ready to use factors quantifying

327

biodiversity impacts per m3 of roundwood (Table S5, SI-2) and per kg of crop (Table S6, SI-2)

328

from each country. The Swiss case study demonstrated how these factors can be linked with

329

trade data to identify commodities causing high biodiversity loss (e.g. imports of cocoa from

330

Ecuador, coffee from India or soybean from Brazil). Reducing the volume of imported trade

331

commodities that cause high species loss and raising consumers’ awareness about the

332

biodiversity damage caused by the products they buy can go a long way in reducing the existing

333

rate of biodiversity loss.

334

Overall the study marks a significant improvement over previous land use impact assessment

335

methods within LCA that provide characterization factors (CFs) at an ecoregion level for broad

336

land use categories.19,21 Combining the high-resolution yield and area maps of global crops26

337

with the ecoregion specific CFs for annual and permanent crops from Chaudhary et al.21, enabled

338

us to calculate the impacts and CFs for each of the 160 individual crops from 250 different

339

countries (Table S2, S3 in SI-2). Within LCA inventory databases, the land use flow information

340

is often available at country level rather than ecoregion, therefore the country-specific CFs are

341

more useful and can be directly used for impact assessment (Table S4, SI-2).

342

High biodiversity impacts associated with certain land use types in particular regions can be

343

traced back to high CFs for these regions (see all maps in SI-1). To calculate biodiversity

344

damage, we used the CFs from Chaudhary et al.21which were calculated by combining



Page 18 of 27

345

countryside species area relationship (SAR) and taxa-specific vulnerability score (VS). The SAR

346

model includes aspects of ecosystem vulnerability (i.e. how much an ecosystem is already

347

affected by land use pressures), and the VS addresses the vulnerability of species inhabiting a

348

particular region to future land use pressures. The VS give particular weight to regions hosting

349

range-restricted and threatened species that are near extinction and whose loss can result in

350

permanent loss of unique evolutionary history associated with them.21 The CFs are higher for

351

ecoregions hosting biodiversity that is unique and endemic and is found nowhere else, and they

352

are lower for the ecoregions that contain only tiny fractions of species’ range (mostly range

353

edges). As the VS also incorporates IUCN assigned species threat level, it means that for two

354

regions hosting equal endemic richness, the CFs will be higher for regions containing more

355

threatened species than those containing non-threatened species.21 The CFs therefore help flag

356

regions with high land use impacts on species requiring immediate conservation attention.

357

Tropical and island countries were found to suffer the highest biodiversity damage due to land

358

use. For example, Ecuador ranks 61st globally in terms of total forest land use area. However, it

359

ranks 9th on the country suffering highest amphibian species loss due to forest land use (Table

360

S1, SI-2). This is because it is home to 336 amphibian endemic species that are not found

361

anywhere else in the world and thus any land use poses high extinction risk to them. Our results

362

show that for the biodiversity impacts, the region where the land use takes place is more

363

important than the total area occupied.

364

Regarding the calculated impacts per m3 of roundwood, the results showed that countries with

365

high forestry CFs and low wood harvesting yields fare the worst (Table S6, SI-2). Interestingly,

366

we found that some sub-tropical, species rich countries hosting many endemic and threatened

367

species (i.e. with high CF) performed very well in terms of biodiversity loss per m3 of


Page 19 of 27


368

roundwood production. For example, in India, the majority of roundwood is sourced from

369

planted forests (94%) which have 50 times higher yield (3.8 m3/ha/year) than corresponding

370

natural managed forests (0.07 m3/ha/year), leading to low impacts per m3. The case-study results

371

showed that food imports to Switzerland are responsible for ~20-300 times the biodiversity loss

372

occurring domestically due to its total agricultural land use (Table S7, SI-2). This ratio actually

373

might be even higher as we did not include finished livestock products imported from abroad and

374

just considered crop products in our analysis. Overall the results corroborate with unequal-

375

exchange theory37 and previous studies who also found that food consumption in industrialized

376

nations drives biodiversity loss in tropical developing countries through international trade.9

377

Limitations and data gaps

378

The input data used to calculate the characterization factors through SAR model as well as the

379

global crop, pasture and forestry land use maps used come with uncertainties and limitations that

380

should be considered when interpreting the results. For example, the species affinity estimates

381

(ℎ,,+ , equation S1 of SI-1) fed into the SAR model were derived from empirical data from

382

literature review (see supporting information-1).21 As more plot-scale local biodiversity

383

monitoring data comes along (e.g. PREDICTS database)38, these estimates can be updated and

384

accuracy of results can be improved. Further, the yield and harvested area maps used to derive

385

impacts per kg (equation 6) were based on the year 2000.26 Yields and area of some crops might

386

have increased (or decreased) over last decade in different world regions.39 Thus we might have

387

over or under estimated some of the impacts but currently these maps are the best available

388

source for high resolution inventory of global crop land use.

389

We used species richness loss as an indicator of land use driven biodiversity loss. However, this

390

indicator masks the biodiversity damage due to changes in species composition that can take



Page 20 of 27

391

place following disturbances. Future studies should explore the alternative measures of

392

biodiversity loss such as beta diversity index 40 or mean species abundance41 which might reveal

393

alternate hot-spots of land use impacts. Another limitation of the analysis is that we only

394

considered four taxa and owing to lack of data, impacts on other species groups such as

395

arthropods, bacteria, fungi that make up majority of global terrestrial species richness could not

396

be calculated. These species groups perform several important ecological functions and the data

397

gaps should be filled through future research efforts.

398

For roundwood production, we took average yield of plantation forests and managed natural

399

forest per country. However, biodiversity damage and yields differ greatly depending upon the

400

harvesting techniques used in the region. In this study all managed natural forests were grouped

401

into broad category of ‘extensive forestry.’ In reality, low yields observed in some of the

402

countries might be because of low intensity harvesting techniques rather than technological

403

limitations and therefore our calculated impacts might be overestimated for these countries.

404

Many studies have shown that forests managed using low intensity harvesting techniques such as

405

reduced impact logging42 or retention harvesting43 result in negligible species loss as compared

406

to conventional selective logging or clear-cut regimes. However, spatially-explicit maps

407

depicting forest management regimes and their harvesting intensity are currently unavailable on

408

a global scale.

409

Finally, any application of the methods and results presented should be accompanied by an

410

analysis of other impact categories. In this paper, we calculate biodiversity impacts due to land

411

use which is the main global driver of biodiversity loss. However, methods compatible with our

412

approach are still missing and more effort is needed to harmonize the methods and metrics so

413

that the biodiversity impacts from other stressors can be jointly assessed. We used global


Page 21 of 27


414

(permanent) species extinctions as a metric for biodiversity loss, but other impact categories

415

within LCA typically quantify impacts as local or regional species loss.13 We assess the impacts

416

for four vertebrate taxa while the LCA methods for other stressors such as acidification44

417

consider impacts on vascular plants, while eutrophication impacts are usually assessed on fish

418

species45 due to data availability restrictions. To allow for a comparison of biodiversity impacts

419

from different impact categories, consistent approaches for harmonizing the metrics and

420

aggregating the impacts across different taxa are needed.13,46 Efforts are underway in this

421

direction as Verones et al.46 proposed approaches for harmonized assessment of biodiversity loss

422

from land and water use, but future studies should aim to include additional stressors. Similarly,

423

other impacts of land use such as soil erosion47 or appropriation of net primary productivity48

424

must also be taken into account in environmental decision making. For the Swiss case-study,

425

including these impacts will further increase the imported biodiversity impacts.

426

Outlook

427

The overall results are useful for producer nations and can be a first step for further in-depth

428

investigations aimed at sustainable land use management on a global and regional level, as they

429

reveal current geographical hotspots and the drivers of biodiversity loss. Results are also relevant

430

to consumers, global retailers and food processing companies who are increasingly interested in

431

the environmental product information. By quantifying biodiversity impacts per kg of crop from

432

different locations, the results can help improve the life-cycle based product information, which

433

currently often only address carbon emission impacts (e.g. UK carbon reduction label).9 The

434

calculated country, crop and taxa–specific impacts can also be used as a basis for compensatory

435

mechanisms or offsetting programs. For example, food products with high biodiversity impacts

436

could be made more expensive and the premium can go towards financing ecosystem service or



Page 22 of 27

437

biodiversity conservation programs. Such biodiversity compensation programs could

438

complement already existent efforts to reduce and compensate for greenhouse gas emissions.9 To

439

ensure progress toward reducing the rate of global biodiversity loss, policies aimed at all the

440

actors in the supply chain (be it exporters, traders or consumers) would have to be implemented

441

in parallel. 9

442

The calculated impacts per m3 of roundwood production demonstrated how species rich regions

443

with currently high forest land use impacts can potentially benefit by producing wood from high-

444

yield planted forests. Planted forests are less biodiversity-benign than extensively managed

445

natural forests6, but, if grown on previously degraded lands and inducing no land-use change

446

from natural ecosystems, they can serve dual purpose of timber production and alleviating the

447

pressure on the remaining natural forests which in turn can spared for biodiversity conservation.

448

While several trade databases9,33,35,49 documenting the flow of commodities between countries

449

exist, the factors giving biodiversity impacts per unit commodity have not been available. Our

450

estimates of biodiversity impacts per kg of crop or per m3 of roundwood fill these gaps. The

451

Swiss case study showed how the results can be combined with trade data to identify the location

452

and severity of environmental impacts caused by imported goods in a country. Future studies

453

should expand this analysis to quantify biodiversity impacts embodied in global trade of food

454

and forestry products.

455

Supporting Information

456

The supporting information-1 contains additional methods and maps. Supporting information-2

457

excel file contains Tables S1 to S7. This information is available free of charge via the Internet at

458

http://pubs.acs.org.

459

Corresponding Author


Page 23 of 27


460

* E-mail: [email protected]; phone: +41-44-633-02-54; fax: +41-44-633-10-61

461

ACKNOWLEDGEMENTS

462

This research was funded within the National Research Programme “Resource Wood” (NRP 66)

463

by the Swiss National Science Foundation (project no. 136612).

464

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