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Characterization of Natural and Affected Environments
Land Use Intensity-specific Global Characterization Factors to Assess Product Biodiversity Footprints Abhishek Chaudhary, and Thomas M. Brooks Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05570 • Publication Date (Web): 12 Apr 2018 Downloaded from http://pubs.acs.org on April 13, 2018
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Land Use Intensity-specific Global Characterization
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Factors to Assess Product Biodiversity Footprints
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Abhishek Chaudhary1*, Thomas M. Brooks2
4 5 6
1
Institute of Food, Nutrition and Health, ETH Zurich, 8092 Zurich, Switzerland, (*Corresponding author phone: +41 76 757 7400; fax: +41 44 632 1155; e-mail:
[email protected])
2
International Union for Conservation of Nature, 28 Rue Mauverney, 1196 Gland, Switzerland
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ABSTRACT
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The UNEP-SETAC life cycle initiative recently recommended use of the countryside species-
10
area relationship (SAR) model to calculate the characterization factors (CFs; potential species
11
loss per m2) for projecting the biodiversity impact of land use associated with a products’ life
12
cycle. However, CFs based on this approach are to date available for only six broad land use
13
types without differentiating between their management intensities, and have large
14
uncertainties that limit their practical applicability. Here we derive updated CFs for
15
projecting potential species losses of five taxa resulting from five broad land use types
16
(managed forests, plantations, pasture, cropland, urban) under three intensity levels (minimal,
17
light and intense use) in each of the 804 terrestrial ecoregions. We utilize recent global land
18
use intensity maps and International Union for Conservation of Nature (IUCN) habitat
19
classification scheme to parametrize the SAR model. As a case study, we compare the
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biodiversity impacts of 1m3 of wood produced under four different forest management
21
regimes in India and demonstrate that the new land use intensity-specific CFs have smaller
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uncertainty intervals and are able to discern the impacts of intensively managed land uses
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from the low intensity regimes, which has not been possible through previous CFs.
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Table of content (TOC) art:
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INTRODUCTION
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Environmentally conscious purchasing behavior by individual consumers, businesses and
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nations can contribute substantially towards mitigating the ongoing biodiversity crisis.1
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Informing consumers about the biodiversity impacts ‘hidden’ in the life cycle of products
34
they consume is an important step towards transforming current consumption patterns.2-8 Life
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cycle assessment (LCA) is increasingly used for estimating and comparing the ‘cradle to
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grave’ environmental impacts of products and commodities.9 In LCA, biodiversity impacts of
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land use are calculated by multiplying inventory (i.e. land occupation in m2∙year and land
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transformation in m2; ) with characterization factors (CFs, i.e. the factors indicating
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biodiversity damage caused by unit area of a particular land use).4
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A suite of CFs have been proposed to assess the species loss within a product’s life cycle,
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using different ecological models, and varying taxonomic coverage and spatial resolution.5, 9,
42
10
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employed. A lack of consensus on which CFs best reflect the damage inflicted by a given
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product system has, thus, hampered the application of these methods to inform decision
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makers and stakeholders.11
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To address this challenge, the land use task force of the United Nations Environment
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Programme/Society of Environmental Toxicology and Chemistry (UNEP-SETAC) Life
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Cycle Initiative was established to provide guidance and build scientific consensus on CFs
Different results are obtained when comparing the products depending upon which CFs are
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for assessing land use impacts on biodiversity.12 Over 2015-2017, the initiative organized
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discussions between experts and stakeholders13 as well as conducted a critical review of the
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existing framework and biodiversity models.5 It concluded that an acceptable model should
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incorporate both the intensity of human land use as well as the vulnerability of the species
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group in the region where the land use is taking place (e.g. if the region in question holds
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many endemic or threatened species). The initiative culminated in a Pellston WorkshopTM,
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which provisionally recommended the CFs calculated by Chaudhary et al.4 to assess land use
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driven biodiversity impacts within LCA (see UNEP report).14
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The CFs calculated by Chaudhary et al.4 represent biodiversity damage resulting from global
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land use (in potential species lost per m2) for four taxa: mammals, birds, amphibians, and
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reptiles. The CFs assess the potential damage caused by a unit area of six different land use
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types (annual crops, permanent crops, pasture, intensive forestry, extensive forestry and
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urban land) in each of the 804 terrestrial ecoregions.15 The CFs were calculated by combining
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countryside species-area relationship (SAR) model16 with a vulnerability score of the species
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group.4
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However, the UNEP-SETAC also recognized that a number of aspects of these CFs need
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improvement.14 As such, it recommended that until these improvements are made, the
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method should only be used for identification of potential impact hotspots but not for product
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comparison and labelling purposes. The recommended improvements were:
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1. First and most importantly, expand land use classes and include different management
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regimes. In other words, the CFs should be able to differentiate between biodiversity
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damage caused by different land use classes (e.g. pasture vs. cropland) and also between
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products coming from same land use class but varying in intensity of use (e.g.
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extensive/intensive pasture).
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2. CFs for plants should be calculated and included in the impact assessment.
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3. The uncertainty in the CFs, which is currently very high, should be reduced.
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4. The model predicted species loss should be compared against the global rate of extinction
76 77 78
and/or the observed species loss within the studied systems. 5. Case studies should be conducted to examine the robustness and ability of the model to rank alternative product systems in terms of their biodiversity impacts.
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In this study, we present first attempts towards addressing the above five issues, and calculate
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updated CFs for five broad land use types and their three intensity levels.
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MATERIALS AND METHODS
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Countryside SAR model
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We calculate the projected species loss for each species group (g = 1:5; mammals, birds,
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amphibians, reptiles and plants) in each of the 804 terrestrial ecoregions (j = 1:804) due to
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current land use through countryside SAR model:16
,,
, + ∑ ℎ,, ∙ , = ,, ∙ 1 − ,
86 87 88 89 90 91 92 93
Equation 1
Here ,, is the total number of species occurring in each ecoregion’s area ( , ) before any human intervention, , is the natural habitat area in the ecoregion currently (in m2),
, is the current area of land use type ( =1:16; see supplementary Table S1) in m2, is the SAR exponent for the ecoregion and ℎ,, is the affinity of the taxon g to the land use type in ecoregion j. As in Chaudhary et al.4 we obtained the species richness of four
vertebrate taxa per ecoregion ( ,, ) from WWF Wildfinder database17; species richness of
plants from Kier et al.18 and the z-values per ecoregion ( ) from Drakare et al.19 The
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procedure to calculate the land use areas per ecoregion and the taxon affinity to human land
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uses differing in terms of intensity of use is described in detail below.
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Land use areas per ecoregion
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We follow a two-step approach to calculate the areas of different land use types and their
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intensity levels in each ecoregion.
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Areas of broad land use types per ecoregion
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We first derive the total area ( , ), area of remaining natural habitat ( , ) and the areas
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of four broad human land use types (secondary vegetation, pasture, cropland and urban) per
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ecoregion (, !" ) by overlaying the ecoregion boundaries with recently available global
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‘land use’ map of Hoskins et al.20 for the year 2005. We chose the Hoskins et al.20 map for
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several reasons. First, unlike LADA21 and ANTHROME22 maps used by Chaudhary et al.4, it
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is of approximately 10 times higher resolution (30’’). Next, it has been validated using
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independent global datasets23,24 and is also compatible with global land use harmonization
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dataset.25 Due to above reasons, it is expected to be more accurate than the former. Hoskins et
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al.20 has also been the first choice in several recent global biodiversity impact assessment
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studies.26-29Although more recent and high resolution maps of individual land uses such as
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cropland30 or forests31 are available in isolation, to date, Hoskins et al.20 remains the only
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global high resolution, harmonized and validated map providing areas of primary habitat and
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four broad human land use types as a one consistent dataset.
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Areas of broad land use types under three intensity levels
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Next, we utilize the land use intensity information from Global Land Systems dataset (year
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2000)32 and Arets et al.33 in order to derive the proportion (#,
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(secondary habitat, pasture, cropland and urban land use) under three different intensity levels
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(minimal, light and intense use) per ecoregion (Table S1). Here we assume that land use
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intensity did not change significantly between the year 2000 and 2005 (year of Hoskins et
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al.20 map). The area of a particular broad land use type under a particular intensity level in an
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ecoregion j ( , ) is then simply calculated as: $
%$ , = , !" × #, 121
Equation 2
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As an illustration, consider the ecoregion Biak-Numfoor Rain Forests [WWF ecoregion code:
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AA0103] on the island of New Guinea.17 According to Hoskins et al.20 map, the total area of
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this ecoregion ( , ) is 2793 km2, the remaining natural habitat area (, ) is 2047 km2
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!" and the area of cropland use (' (!", ) is 168 km2. Now according to Global Land
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Systems map32, the proportion of cropland (#' (!", ) under minimal, light and intense use
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in this ecoregion is 0.14, 0.58 and 0.28 respectively. Therefore, by Eq. 2 above, the area of
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cropland under the minimal use is 168 × 0.14 = 23 km2. Similarly, area of light and intense
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use cropland is equal to 168 × 0.58 = 98 km2 and 168 × 0.28 = 47 km2 respectively.
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We consider ‘minimal use secondary vegetation’ to have been abandoned and therefore to be
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currently regenerating and not used for production (following definition of Newbold et al.26).
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The ‘light’ and ‘intense’ use levels of secondary vegetation in the forest biomes are
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considered as ‘managed forests’ class, which is further divided into four intensity classes:
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clear-cut, selective logging, plantations and reduced impact logging based on their fraction in
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different world regions as reported by Arets et al.33 that in turn are based on Integrated Model
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to Assess the Global Environment (IMAGE) output.34 Similar to Newbold et al.26, we
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assumed that the plantation area per ecoregion was composed of equal proportions under
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minimal, light and intense use. This is because none of the Global Land Systems32 classes
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could inform about the intensity of plantation forestry. We divided the secondary forest
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vegetation further into four different well-known classes (plantations, clear-cut, selective
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logging and reduced impact logging) because these are the major wood production systems
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across the globe.35 Separate characterization factors for these systems are therefore needed to
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differentiate the impacts caused by them.
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Finally, this yielded the areas of 16 land classes, i.e. ‘regenerating secondary vegetation’ plus
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areas of five broad land use types (managed forests, plantations, pasture, cropland and urban)
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under three intensity levels each (minimal, light and intense use) (see Table S1 in
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Supplementary Information for more details on land use classification).
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Taxon affinity calculation
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As a proxy for taxon affinity (0 < ℎ,, < 1) to different human land uses, past studies
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(including Chaudhary et al.4) have relied on published literature documenting the ratio of
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observed species richness in a particular land use type to that in a natural undisturbed area,
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obtained from few plot-scale field monitoring within the region.4,16,36 However, there are
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large gaps in our knowledge of how damaging a particular land use type is to different taxa in
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different parts of the world. The uncertainty in ℎ estimates is therefore substantial and
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although global field monitoring efforts are underway, these gaps are unlikely to be filled in
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near future.4 For example, Chaudhary et al.4 derived the affinity of five taxa to six land use
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types in 14 biomes15 through a meta-analysis of 861 field monitoring data points comparing
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species richness in human land use and nearby natural (primary) vegetation. For most taxa,
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land use and ecoregion combinations, there were no field study available and therefore a
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biome-average value was taken as a proxy. Furthermore, the data represented only a fraction
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of the total number of species of a taxa present globally (e.g. the affinity values for mammals,
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birds and amphibians were just based on 66, 211 and 11 data points globally).4 This rendered
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the affinity values with large uncertainty intervals.
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Here, we therefore apply a novel hybrid two-step approach to calculate the affinity of taxa
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(for which the data is available, i.e. mammals, birds and amphibians) to different land use
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types and their intensity levels.
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Taxon affinity to broad land use types
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We first calculate the affinity of taxa to five broad land use types (managed forests,
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plantations, pasture, cropland and urban) in each ecoregion through the recently proposed
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approach by Chaudhary & Brooks28, i.e. using the species-specific information on habitat
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utilization documented through the IUCN Red List Habitat Classification Scheme.37 For each
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of the 22,386 species of these three taxa, the IUCN Habitat Classification Scheme provides
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information on the human land use types which a particular species utilises. This information
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is collected on each species through field observation and literature synthesis by the Red List
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assessors of the species in question (see Section 2.4.1, page 24, of IUCN’s documentation
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standards and consistency checks for species accounts).38
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For example, the House Sparrow (Passer domesticus) utilises all broad human land use types.
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The frog Adenomera martinezi utilises agriculture and pasture but not in degraded forests,
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urban areas and plantations. The primate Arctocebus calabarensis utilises degraded forests
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only and cannot survive in any other human land use.37 The species utilisation of a particular
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land use type depends upon its biophysical and life history traits such as diets, body size,
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temperature range etc.
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We obtained the list of all species of mammals, birds and amphibians present in each of the
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804 ecoregions from WWF Wildfinder database.17 Next, for each terrestrial ecoregion j, we
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counted the number of species of each taxon g for a particular land use type i (e.g. cropland)
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was coded as suitable in the Habitat Classification Schemes of the IUCN Red List.37 The
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number of species for which a given habitat was suitable were divided by total number of
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species of that taxon occurring in the ecoregion, to obtain the fractional species richness 8 ACS Paragon Plus Environment
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(+,, ). The affinity of taxonomic group g to the broad land use type i is then calculated as the
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proportion of all species that can survive in it (fractional richness), raised to the power 1⁄
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(see Pereira et al):16 !" ℎ,,
,,, = ,,
= .+,, /
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Equation 3
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Taxon affinity to land use intensity types
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The IUCN Habitat Classification Scheme only provides data on species utilisation of five
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broad human land uses - plantations, agriculture, pasture, urban areas, and degraded forests.
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In order to derive taxon affinity to land use intensity classes (ℎ0, ,1 ), we therefore combined
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the calculated affinity to broad land use types (Eq. 3) with information from existing meta-
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analysis studies26,35 documenting species richness in different land use intensity levels relative
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to each broad land use class (fractional relative richness, 233 ). The taxon affinity to a land use
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intensity type (ℎ0, ,1 ) is calculated from the affinity to broad land use type as: !" ℎ,, = ℎ,, ∙ .233,, /
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Equation 4
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Here the fractional relative richness .233,, / is the local species richness in a land use
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intensity type (e.g. intense use pasture) divided by the average local species richness in a
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broad land use type (e.g. pasture). The mean and 95% confidence interval for the 233,, value
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for minimal, light and intense use pasture, plantations, cropland and urban land uses was
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obtained from Newbold et al.26 The data for fractional relative richness of the three managed
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forest intensity classes (reduced-impact logging, selective logging and clear-cut) was
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obtained from the meta-analysis of Chaudhary et al.35
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For example, the forestry meta-analysis of Chaudhary et al.35 showed that the average
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while their fractional species richness in forests managed under clear-cut (intense use) regime
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is 0.544. Therefore the fractional relative richness (233 ) value is equal to: 0.544⁄0.89 = 0.6. In
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other words, the local species richness in ‘intense use’ (clear-cut) forests is just 60% of the
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species richness value in broad land use ‘managed forests’.
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For reptiles and plants, the IUCN habitat classification scheme37 does not yet provide data for
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all species, and therefore we derive the affinity values solely using the fractional species
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richness from Newbold et al.26 The affinity of taxa to ‘regenerating secondary vegetation’
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(under no or minimal use) was also taken directly from Newbold et al.26
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As an illustration, again consider the ecoregion Biak-Numfoor Rain Forests [WWF ecoregion
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code: AA0103] that hosts three amphibian species: Papurana papua, Cornufer papuensis and
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Nyctimystes infrafrenatus. According to IUCN Habitat Classification Scheme37, out of these
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three species, only one (Nyctimystes infrafrenatus) utilises pastureland. Being an island, the
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z-value for this ecoregion is 0.44. Therefore, the affinity of amphibians to broad land use
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!" ‘pasture’ in this ecoregion according to Eq. 3 is: ℎ!9(:
!,(!$; ,90%). In the biodiversity context, selective logging>reduced impact logging. The
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differences between the CFs for agriculture and pasture intensity classes were less
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pronounced. These trends reflect the relative order of taxon affinities to different land use
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intensity types (Table 2).
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We found that for a given land use type, the new CFs are in general higher than the CFs
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calculated by Chaudhary et al.4 mainly because their taxon affinity estimates were higher than
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those calculated in this study. Importantly, the 95% confidence intervals of our new CF
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values are significantly narrower than the CFs of Chaudhary et al.4 which ranged from
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negative to positive. Finally, unlike the CFs of Chaudhary et al.4, the new CFs enable
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comparison of products coming from three different management intensity levels (minimal,
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light and intense).
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In order the newly calculated CFs can be operationalized into the LCA softwares, we also
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provide a look up table that matches the land use intensity categories used in this study with
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the land use intensity classes recommended by the UNEP-SETAC for use in LCA (Koellner
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et al.52) and also used in LCA inventory database such as ecoinvent v3 (supplementary Table
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S7).
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Application Example
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Figure 2 shows that the new characterization factors (CFs) are better able to discern the
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impact of different forest management types as compared to the old CFs provided by
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Chaudhary et al.4 It can be seen that using the Chaudhary et al.4 CFs for each taxa, the 21 ACS Paragon Plus Environment
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calculated 95% confidence interval of the potential species loss values under all four regimes
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overlap with each other as well as cross the CF=0 horizontal line. This means that one cannot
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conclude if the calculated damage is significantly different from zero (statistically) or if the
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wood sourced from any one regime is less damaging than the other.
Potential species loss/m 3 (*10 -10)
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464 465
Figure 2. Biodiversity damage caused by 1m3 of wood sourced from four different forest
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management regimes in India calculated by using a). New characterization factors derived in this
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study, b). Characterization factors derived by Chaudhary et al.4 Except timber plantations, the other
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three management regimes fall into ‘extensive forestry’ category of Chaudhary et al.4 See
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supplementary Table S8 for detailed calculations (including plants) and data sources.
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In contrast, the potential species loss for any given taxa, calculated using the new CFs clearly
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show that wood sourced from plantation forest causes the most damage followed by that
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sourced from clear-cut, selective logging and reduced impact logging (RIL) regimes because
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the 95% confidence interval under a particular regime neither overlaps with the other three
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nor intersects the CF = 0 line (except RIL).
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For example, using the new CFs, the calculated impact due to land occupation associated
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with producing a 1 m3 of wood in a selectively logged forest and a forest managed using RIL
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is equal to 2.04 × 10-10 and 7.09 × 10-12 potential species loss respectively (Figure 2;
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Supplementary Table S8). In other words, the land occupation impact is 30 times lower if the
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wood is sourced from RIL forest compared to selectively logged forest. However, the
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calculated impact using the old CFs of Chaudhary et al. is equal to 5.19 × 10-11 for both
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selectively logged and RIL forest. This is because both these forestry regime fall under the
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‘extensive forestry’ definition of Chaudhary et al.4
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Therefore, the simple example above shows that new CFs are able to differentiate the impact
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caused by different land use intensity classes, something that was not possible with old CFs.4
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Note that here we only compared the land occupation impacts of the four forestry regimes but
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the ranking of total impact might change if other factors such as land use change, fuel use etc.
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associated with each regime are taken into account.
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DISCUSSION
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The study presents first attempts towards addressing the gaps identified by UNEP-SETAC
490
life cycle initiative regarding calculation of land use biodiversity impacts within life cycle
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assessment (LCA).14 First and most importantly, by using the global land use and land use
492
intensity maps20,32,33, we derived the new characterization factors (CFs) that enables one to
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discern the damage caused by three levels of intensity classes within a particular broad land
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use type, something not possible through the CFs of Chaudhary et al.4
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Note that the UNEP-SETAC recommended to both expand the number of land use classes as
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well as include different management regimes in the biodiversity model (see page 139 of
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UNEP report).14 While we do not improve upon the number of land use classes, we do
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include three management regimes (minimal, light and intense use) for each broad land use
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class that were absent from Chaudhary et al.4 Second, we derive CFs for plants. 23 ACS Paragon Plus Environment
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Third, for calculating taxon affinity to different land use intensity types, we applied a novel
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hybrid approach of leveraging Habitat Classification Scheme from the IUCN Red List37 and
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combining it with empirical data on species richness comparisons from literature.26,35 This
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enabled us to bring down the uncertainty range of the CFs within a factor of two (Fig. 2;
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Table S3-S6). Previous CFs from Chaudhary et al.4 had huge uncertainty range varying over
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several orders of magnitude (from positive to negative) and thereby limiting their practical
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utility.
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Fourth, model validation and goodness of fit metrics (Table 3) showed that extinction
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projections by our parametrized model compare very well with the number of species
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documented as threatened with extinction by the IUCN Red List44, and much better than the
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model parametrized by Chaudhary et al.4
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Finally, we demonstrate the application of new CFs through a case study on comparing the
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land occupation biodiversity impacts of 1m3 of wood sourced from four different forest
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management regimes (Fig. 2).
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The innovations presented here do not resolve all issues identified by the UNEP14, but rather,
515
are steps forward in advancing the field. The main novelty of this study is the hybrid
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approach of combining the Hoskins et al.20 map with a land use intensity datasets32,33 to
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calculate the areas of five broad land use types under three intensity levels (Eq. 2, Table 2)
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and combining the IUCN Habitat Classification Scheme37 with data from meta-analyses26,35
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on fractional richness to calculate affinity of taxon to the land use types under different
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intensity levels (Eq. 3, 4). Previous studies (Chaudhary & Brooks28) used the Hoskins et al.20
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map to calculate the areas of four broad human land use types per ecoregion and the IUCN
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Habitat Classification Scheme37 to calculate the affinity of mammals, birds and amphibians to
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these four land use types only.
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Our intermediate results on the calculation of taxa affinity to different land use intensity types
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(Table 2) and projected extinctions per ecoregion (Fig. 1a, b) provide important insights for
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biodiversity conservation and policy making. For example, we found that taxon affinity
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values differ substantially across the three intensity levels for managed forest land use (Table
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2). This implies that shifting from minimal use forestry regimes (reduced impact logging) to
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light/intense use forestry (selective logging/clear-cut) would cause substantial additional
530
losses. This has implications for regions planning to intensify their production systems, or,
531
conversely, to shift them towards biodiversity-friendly management practices.
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Despite significant improvements, substantial uncertainties in accurately predicting the
533
biodiversity damage caused by a particular land use in different parts of the world remain due
534
to several data gaps, model and parameter uncertainties and the value choices involved in the
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characterization approaches. Where available, such as in case of z-values19 and fractional
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relative richness26,35 (fRR, Eq. 4), we propagated the uncertainty in model parameters (Table
537
1) into the characterization factors. However, in many cases it was not possible because the
538
underlying datasets do not report the uncertainty intervals around their estimates such as in
539
the case of the land use and land use intensity maps we used to derive areas of different land
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use types under minimal, light and intense use20,32,33.
541
We acknowledge that our approach of combining two different land use datasets might add
542
uncertainty to the results that could not be quantified by us. We could only consider five
543
broad land use classes and their three intensity levels but future studies should attempt to
544
include further land use classes (e.g. annual, permanent crops, organic farms etc.) Although
545
rough procedure do exist, such as adopted by Chaudhary et al.4, to translate country level
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proportion39 of annual and permanent crops to ecoregion level proportion, these are bound to
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add further unknown amount of uncertainty into the results and therefore not applied here.
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Environmental Science & Technology
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New global high-resolution, harmonized and validated land use maps with more land use and
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intensity classes are needed to reduce the uncertainty in characterization factors.
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Next, the Habitat Classification Scheme of the IUCN37 is not yet available for all species of
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plants and reptiles and is only available for six broad human land uses for mammals, birds
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and amphibians. We therefore still had to rely on fractional relative richness factors
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(233,, , RS. 4) from published meta-analyses26,35 of limited field studies (Eq. 4). A big
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limitation of the fractional relative richness factors that we used is that they are taxa and
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region generic. It is possible that one taxa is more sensitive to an increase in intensity of land
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use than others or that an increase in intensity affects the species differentially in different
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ecoregions. This might be the reason that the calculated CFs do not differ much across the
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three intensity classes for cropland and pasture land use (Table S3). For example, according
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to Newbold et al.26, the average fractional species richness (+,, , Eq. 3) in ‘minimal use’ and
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‘intense use’ cropland is 0.73 and 0.64 respectively, i.e. a difference of just 9%.
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Consequently, the calculated global CFs for cropland vary
