Assessing biodiversity loss due to land use with Life Cycle Assessment: are we there yet?

Introduction

A large body of science compiled over the past decades shows that biodiversity is directly affected by human activities, very often negatively. This worrisome fact led the recent Rio+20 Summit to shift the focus of the sustainability agenda to biodiversity, which is now the cornerstone of sustainable development (UN, 2012).

According to the Millennium Ecosystem Assessment (MEA, 2005), habitat change brought about by land use and land-use change (LULUC) is one of the five main drivers of terrestrial biodiversity loss, together with climate change, invasive alien species, overexploitation of resources, and pollution. For most ecosystems, habitat change due to LULUC has had the highest impact of all drivers of biodiversity loss over the past century, with most plausible future scenarios predicting that such losses are likely to increase (MEA, 2005).

The MEA laid the groundwork for identifying the key drivers – which are mainly anthropogenic – that damage biodiversity and decrease the services it provides. While ecosystem services such as food production are currently well monitored, most other ecosystem services are not (Pereira & Cooper, 2006). The identification of key drivers and monitoring of such services are challenging tasks, but are of paramount importance. The MEA recognized that biodiversity is a multidimensional concept that ‘poses formidable challenges to its measurement’, and that the most common measuring techniques (direct measurement of species richness or genetic diversity) do not capture the whole picture (MEA, 2005). Issues such as ‘variability, function, quantity, and distribution’ of species are usually not taken into consideration. The concept of biodiversity is, in itself, elusive. Better metrics for biodiversity loss assessment must be interdisciplinary, inclusive (respecting the specificity of biodiversity while maintaining methodological accuracy), comprehensive (including direct and indirect effects), and replicable.

Life Cycle Assessment (LCA) meets these four criteria for good biodiversity metrics. LCA methodology can be used to quantitatively model the potential environmental impacts of a product, process or service over all stages of its life cycle, from raw material acquisition, production and use, to its final disposal (Curran et al., 2011). Data on a product's life cycle, such as its raw material consumption and emissions, are compiled and used to assess its potential environmental impacts, referred to as its ‘impact categories’. The latter may range from global (e.g. stratospheric ozone depletion, global warming) to regional (e.g. acidification) and local (e.g. aquatic ecotoxicity). In particular, impacts on biodiversity can be assessed within the LCA framework using different impact pathways, for instance habitat conversion and change in species composition. Figure 1 shows an example of various impact pathways that may lead to loss of species diversity and ecosystem services, thereby impacting the overall quality of the ecosystem.

The manufacturing of any product or group of products may have global effects, as increasingly globalized markets allow companies to obtain materials and services from complex networks of suppliers distributed over many regions of the world. LCA covers whole supply chains, comprehensively accounting for all impacts that occur at different steps and in different locations along the life cycle, regardless of the physical location of the particular process. Using LCA, pressures on biodiversity caused by local land use can be traced back to global causes (e.g. shifts in consumption patterns, technological improvements during production). LCA assigns ultimate responsibility for downstream and upstream impacts within production chains to a single unit (usually the final product – a consumer good). Given the importance of LCA today and its integration in policy instruments and in private company's environmental information systems, an impact category included in the framework will necessarily become a priority issue for the main actors in the supply chain. Decisions taken by final producers can then trickle down the supply chain and be implemented in the field. It is therefore of the utmost importance that accurate measurements of the state of biodiversity be defined in LCA.

Models that aim to assess land-use impacts using LCA have been continuously improved during the past two decades. While many methodologies have been proposed (Müller-Wenk, 1998; Koellner, 2003; Schmidt, 2008a; de Baan et al., 2013b; Souza et al., 2013), the assessment of the impacts of production systems using biodiversity indicator approaches remains a challenge (Geyer et al., 2010a; de Baan et al., 2013a; Koellner et al., 2013b).

There is a clear need to devise consensual approaches. This is the objective of a flagship project being carried out by the UNEP/SETAC Life Cycle Initiative, entitled ‘Environmental Life Cycle Impact Assessment Indicators’, which aims to provide global guidance and build consensus on life cycle impact indicators, such as biodiversity loss. Among the challenges to be addressed are the spatial variability and availability of LULUC and related biological diversity data, such as species richness, abundance, distribution, and functions (Geyer et al., 2010a). A better understanding of the dynamics of ecosystem complexity is also needed to quantify the spatial and temporal variability in changes in biodiversity (Reap et al., 2008; Geyer et al., 2010a).

This review synthesizes how current impact assessment methodologies in LCA take into account the impacts of LULUC on biodiversity, and makes some suggestions to help improve modeling activities and reduce some of the uncertainties involved. For the benefit of readers that are less familiar with LCA, we start with a brief description of the basic methods and key-related concepts, including a review of the current methods and the framework used to assess land-use impacts. We then describe relevant aspects on which we consider further research should be carried out, and propose some insights on issues to be considered. We conclude with indications collected from recent reviews of the topic (Curran et al., 2011; Koellner et al., 2013a), to which we add further insights, and make suggestions as to how to address some of the main areas that require further research.

Impact assessment in LCA

LCA can be used to assess the environmental impacts of systems through part of or all the stages of its life cycle. It is an important methodological tool that can help to ensure environmentally sound choices in the context of decision-making based on scientific evidence (EEA, 1997). Its application may range from single products, with a short life cycle, to large-scale policy decision issues.

LCA comprises four main phases: the definition of goal and scope of the study, including the description of the product system, process or service; the collection of input and output data; impact assessment; and the interpretation of the results in terms of damages to the environment. The first phase – goal and scope definition – consists of the description of the aim of the study and all related activities and processes in the life cycle of the product that impact the environment. This is followed by the life cycle inventory (LCI) phase, which involves data collection of all input (raw-materials, energy) and output (waste, emissions) flows during the product's lifecycle. LCI results are then translated, i.e. classified and characterized, into specific environmental impact categories (e.g. global warming, human toxicity, land use) at the life cycle impact assessment (LCIA) phase (ISO, 2006b). The last phase consists of the interpretation of results, whereby the information obtained is checked and evaluated, and the results are reported clearly (ISO, 2006a).

The potential environmental impact of the product's inventory flows are assessed during the LCIA phase. These potential impacts may be expressed at the midpoint or endpoint level, within a cause–effect chain or environmental mechanism (Fig. 2). Midpoints (the impact categories) lie between the life cycle inventory phase and the endpoints (the damage categories). Due to the use of more accurate environmental data and relevant indicators, and less subjective evaluations of the relative importance of each indicator (Finnveden et al., 2009), midpoint results are well-characterized in terms of physical units, as shown in Fig 2. Endpoints are located beyond midpoints and represent quality changes aggregated in three main areas of protection: human health, ecosystem quality, and natural resources. Endpoints are more concise and easy to understand for decision-making purposes. However, the uncertainty of endpoint models is higher, due to their complexity and the need for reliable data to carry out robust modeling (Bare et al., 2000). Characterization factors, at mid- or endpoint levels (e.g. loss of species richness in a defined area), are the values that translate life cycle inventory data into their damage impacts. They allow the magnitude of potential impacts of each of the substances related to each category to be evaluated (Curran, 1996).

LCA does not assess the changing state of biodiversity or ecosystems, but rather the relative environmental impact of anthropogenic production systems. It links units produced to their marginal/average impacts on the environment. These impacts are assumed to grow linearly with the quantity produced, assuming that the total system is not otherwise affected by the change in production. The total system, or state of biodiversity, is mainly assessed within a static framework when evaluating the marginal/average unit impacts. Ecological interventions and resources were introduced into this static framework on an ad hoc basis. There is no normative or scientific definition of the scale of impacts to the health or resilience of the ecosystem (unlike concepts such as the ecological footprint).

Conceptual framework for land-use impact assessment in LCA

Although research on the development of biodiversity indicators to assess land-use impacts in LCA has been ongoing for more than 15 years, such indicators are not yet widely applied (Koellner et al., 2013b). Table 1 lists ecological models applied in the literature, specifying their geographic reach, and the biodiversity indicators they use. Most models use ‘species richness’ as the indicator of the state of biodiversity, and use the Species–Area Relationship (SAR) (Sarkar & Margules, 2002) ecological model to calculate species loss due to land use. Other studies use different models and meta-analysis (Michelsen, 2008; de Souza et al., 2013; Coelho & Michelsen, 2014), or apply Species Distribution Models (SDM) (de Baan et al., 2013a) to assess local environmental impacts based on empirical patterns observed between land-use classes. Geyer et al. (2010a,b) calculated regional biodiversity impact indicators based on wildlife species and their habitats. Indicators based on ecosystem characteristics (Brentrup et al., 2002) have also been used in LCA, as have those based on the concept of exergy (Wagendorp et al., 2006). In the following paragraphs, we explain the current general framework for land-use impact assessment in LCA, before moving on to the limitations of the choices made in modeling.

The current land-use framework consists of an area–time model, which combines spatial and temporal dimensions. Biodiversity levels are measured according to a chosen indicator – species richness being the most commonly used unit – and assigned to different land-use classes, assuming a defined land area is used for a specific time period.

To consider the temporal dimension of land use/cover in LCA (Fig. 3), land-use impacts are associated with two interventions: land occupation (land use, LU), and land transformation (land-use change, LUC), also referred to as land conversion (Lindeijer, 2000; Koellner, 2003; Liu et al., 2003; Schmidt, 2008a). For simplification, in LCA, land occupation and transformation impacts are not separated when representing land quality changes (Milà i Canals et al., 2007). It is assumed that land transformation causes a sudden change in the land quality indicator (e.g. a drop in quality from Q_o (land quality just before LUC) to Q_i (land quality right after LUC) at the point in time t_o in Fig. 3), while land occupation is simply defined (in the context of LCA) as the postponement of the recovery of land to its potential natural vegetation state (Schmidt, 2008a). Land quality is considered to be constant during its use, as seen in Fig. 3, in which Q_i = Q_f., where Q_f represents the land quality right after LU. The impacts of land occupation and transformation are calculated by integrating the overall impacts on land quality over time (Milà i Canals et al., 2007), on a per unit of area basis.

According to the calculation procedure set by Milà i Canals et al. (2007), impacts resulting from land occupation (I_occ), represented by the area B in Fig. 3, are determined as the product of the area occupied (A_occ), the period of use, or land occupation (t_occ), and the quality difference between the actual and potential quality (Q_PNV) after land recovery. PNV refers to the Potential Natural Vegetation assumed to be reached after ecosystem recovery. The impact of land occupation (with land quality level starting in Q_i and ending in Q_f, considering Q_i = Q_f) is determined as:

(1)

The change in land quality over a defined area (ΔQ) is considered to be reversible. In Fig. 3, the transformation impacts (I_trans) correspond to the area of a trapezium (base t_rec and height ∆Q) and can be derived from the following equation:

(2)

The recovery time of land is represented by t_rec.

Permanent impacts (I_perm), for example, the irreversible damage caused to land use in terms of species loss (∆Q_perm), are calculated as

(3)

We turn next to a discussion of the substantial gaps in the impact assessment methods and framework.

Gaps in modeling

The biodiversity metrics currently integrated into LCA are not yet sufficiently inclusive and comprehensive, and leave ample room for improvement. Albeit a useful abstraction, the land quality model is of doubtful realism as its framework is a linearization of transient and dynamic ecosystem processes. Furthermore, it is based on assumptions that are at odds with observations and can compromise the accuracy of results.

We identified and summarized some of the existing gaps in modeling in Table 2 and divided them into four classes: conceptual issues, inventory, definition of indicators, and refinement of impact assessment methods. In the following sections, we highlight the most important issues, providing a thorough explanation of the major limitations identified and suggestions of how to address the identified shortcomings.

Our discussion of the limitations posed by the methodological framework of LCA (Curran et al., 2011) and related ecological models begins by addressing some of the limitations of using the SAR to predict species extinctions resulting from land use. We further assess the potential of other ecological methods such as species distribution models (de Baan et al., 2013b), including habitat suitability models (Geyer et al., 2010a,b) that are also applied in LCA to model damages to biodiversity. We then explore the use of surrogate species and their limitations as predictors of changes in ecosystems, and introduce the concepts of immigration credit and extinction debt to discuss the frailness of considering land interventions as being static. This brings the discussion back to the overall conceptual framework and we draw considerations on the choice of baseline scenario and assumptions to calculate occupation and transformation impacts (e.g. instantaneous drop in quality during land conversion). The issue of data demand at the global scale is also touched upon in these sections.

The limitations of using species–area relationships (SARs) and species distribution models (SDMs)

The starting point of our discussion is the Species–Area Relationship (SAR) model, which is much used in LCA for land-use impact assessments. According to SAR, the larger an area of land is, the greater variety of species it will contain. This model was first described by Arrhenius (1921) as

(4)

where S (A) is the number of species in a changed patch of area A, S_o is the number of species found in an entire biome of area A_o prior to the change, and z – the gradient of the increasing number of species with increasing area (Rybicki & Hanski, 2013) – is a constant typically in the range of 0.10–0.35, depending on the taxon, and the land-use class and scale.

The SAR model is frequently used in conservation biology to estimate species extinctions due to changes in the landscape. However, the SAR model is often used without the consideration of key factors that influence its application, and it has been criticized for its simplicity and its tendency to make extrapolation mistakes (Smith, 2010). For example, the model is not time- and scale-invariant (Šizling et al., 2009), and its variance for small-scale observations is very high, probably due to sampling effects (Dengler, 2008). The number of parameters is low [only one parameter, z, controls relative extinction dynamics, as shown in Eqn 4] and while it has been shown to work particularly well in islands, other functional models for SARs are better suited in continental situations (Dengler, 2009; Rybicki & Hanski, 2013).

Some shortcomings of the SAR model have been discussed in recent studies, such as its dependence on scale, which seems poorly understood (Dolnik & Breuer, 2008), and which may determine the degree of differences among the species–area curves (Scheiner, 2003). Moreover, other factors should be taken into account (Turner & Tjørve, 2005), such as habitat heterogeneity (Triantis et al., 2003). Specific spatial scales associated with relevant environmental factors should be defined. Fattorini & Borges (2012) discuss the inappropriateness of using the SAR model when the species loss does not take place as a consequence of habitat reduction, or when the shape, fragmentation level or contiguous character of the area is not identified (Dengler, 2008). Despite these considerations, the SAR model as described in Eqn 4 has proved to be robust. Over time, many changes have been tested and proposed to the generic SAR framework (Triantis et al., 2003, 2012; Adler et al., 2005; Dengler, 2008; Koh & Ghazoul, 2010), but so far no model has consistently replaced the Arrhenius (1921) SAR.

As an alternative, Species Distribution Models (SDMs) can describe patterns and predict the natural geographical distribution of species, based on observations of species occurrence and/or abundance (Austin, 2007; Elith & Leathwick, 2009). Abundance and species distribution data reduce the problems associated with the spatial accuracy of data and changes in scale (He & Legendre, 2002) but, as with the SAR model, the application of SDMs may still represent a challenge for time- and scale-dependent assessments. Guisan & Thuiller (2004) summarized some of the potential problems to be considered, following aspects such as target species, scale, selection of predictors, data model, and model uncertainties. A spatial scale, for example, has several components, such as the grain, i.e. the smallest spatial unit used in the analysis, and the extent of the geographic area (domain), which are relevant to species distributions and their environment (Scheiner, 2003; Elith & Leathwick, 2009). The spatial accuracy and characteristics of the data applied will have implications on the model's performance (Ferrier & Watson, 1997), and the use of mixed models may be required (Dormann et al., 2007). Guidance on the application of relevant methods still needs to be clearly provided (Elith & Graham, 2009).

The discussion as to which macro-ecological model should be used to estimate species loss transcends LCA and invites scientists with different backgrounds to explore a more broad cooperation and exchange of knowledge. The topic is still heavily debated. For LCA impact assessments, the question is whether the chosen model assumptions fit the goals of the studies.

Issues identified and contribution toward their resolution

Future developments in making informed choices of indicators should be linked to the valuation of biodiversity by its existence or by its value to society (or the value society attributes to the species/ecosystem services it provides). This topic is properly addressed by Duelli & Obrist (2003), who discuss the difference between indicators that define biodiversity itself and the ones that use biodiversity to define other environmental and social attributes.

Given the complexity of biodiversity assessment, all the different aspects cannot be understood by applying one single indicator. Several aspects of biodiversity will probably need to be addressed to do so – at midpoint and endpoint levels. This implies, for instance, a more in-depth assessment of the different issues in the cause–effect chain (Fig. 1), which may lead to a better clarification of ecological mechanisms and biodiversity dynamics. It also requires a better definition or clear statement of ‘what is to be sustained’, to appropriately orient the decision-making process. For example, the choice of indicators will depend on whether the goal is to preserve specific ecosystem services or a group of targeted species.

The LCA community is already moving beyond the use of species richness as an indicator of biodiversity, toward the inclusion of other types of diversity, such as functional diversity (Souza et al., 2013). Other levels of analysis, such as populations and landscapes, should also be included. However, if multiple indicators are to be used to determine impacts at each level of the cause–effect chain, further research should be carried out to determine how each indicator should be evaluated, and how results should be interpreted and communicated to avoid double counting. If indicators are used, for example, in eco-labeling, it must be decided as to which and how much information regarding biodiversity loss should be passed along to consumers and the public to help them make informed decisions.

The conventional approach of using species richness as a biodiversity indicator can be enhanced by applying different macro-ecological models, while taking their limitations into account.

Several factors need to be considered when using surrogates in species models, such as knowledge of the biota, number and geographic range (spatial scale) of the species in the area, and temporal changes. The correlation of temporal and spatial dynamics with the surrogate species and the area of assessment also need to be taken into account. Extinction debt and immigration credit can be included using a mix of multivariate spatial models. Gibson et al. (2011) use a meta-analytic approach that integrates variables that determine biodiversity dynamics to infer immigration credit and time since conversion as an estimate of extinction debt. These biodiversity dynamics variables are indirect measures of both effects, and they can also be modeled directly. Unfortunately, this topic is still poorly understood in the ecological literature and the way to address it transcends LCA (Jackson & Sax, 2010).

The issue of temporal dynamics encompasses more than these two effects. Failing & Gregory (2003) mention the importance of specifying the time scale of assessment of a given indicator, to consider cross-temporal trade-offs. One alternative to be explored would be to define indicators at different scales (local, regional, national, global) or at ecosystem-specific levels, which are able to grasp the particularities of several species groups and their dynamic interactions with their habitats. To an extent, there has been an effort in this sense (de Baan et al., 2013b), but it is likely that different levels of biodiversity (genes, species, ecosystems) will be impacted differently depending on their scale, and they should thus be modeled separately. Therefore, future proposals of characterization factors should be scale-dependent, and not simply an extrapolation from local to global or vice-versa. Characterization factors must be carefully validated against field data and local/national case studies.

The issues regarding the impact assessment model will require revamping the ideas of impacts of LULUC, as well as their dynamic representation. In some cases, no sudden loss in biodiversity occurs due to land conversion, as the response of ecosystems is often gradual. Improved SAR models should also take the spatial and temporal dimensions of biodiversity loss into consideration. Similarly, the use of PNV concept needs to be re-re-evaluated. After anthropogenic changes, natural systems go back to a near-equilibrium state (O'Neill, 1999), but the condition reached after recovery may not reflect the original natural or close-to-natural conditions – as currently assumed in LCIA. Moreover, built-up areas such as urban or industrial areas can hardly be set aside for land recovery.

The choice of the baseline is not an arbitrary topic of discussion in LCA land-use impact assessment. Weidema & Lindeijer (2001) led part of this discussion, presenting the different choices of the baseline land quality level: original state before any human intervention (Blonk et al., 1997), state immediately before land use (Baitz et al., 1998), and state immediately after the land-use activity (Koellner, 2000). The topic has also been discussed by Milà i Canals et al. (2007), de Baan et al. (2013a), and Souza et al. (2013). Rather than using PNV, the baseline may also be a reference year with globally identified land cover types (e.g. Global Land Cover 2000). However, this approach also has some drawbacks. First, future assessments would need to conform to the (often poor) level of accuracy and differentiation of land cover information as presented in the reference dataset, ignoring improvements in land cover data. This is also the case with regard to the use of PNV as a baseline reference, as land cover is measured and PNV is estimated. Second, if at the moment of the assessment, the land use/cover in a specific area was the same as the land use/cover of the reference year, no impact would be assigned to the activity taking place in that area, even though the area may have undergone changes. Alternatives exist, such as taking local or regional conservation values into account. The choice of baseline reference involves value choices that are beyond the scope of the scientific method, and must be carefully assessed before any specific assessment method is adopted. This is a matter that should be comprehensively investigated and further discussed.

Conclusions

The current modeling of land use using biodiversity indicators significantly simplifies the real transient dynamics and complexity of natural processes and interactions among and between species and their habitats. In this paper, we explained the main limitations of LCA in grasping the scale and extent of the impacts of land use on biodiversity. We compiled some of the limitations reported in the specialized literature and proposed several issues that have yet to be addressed.

Assigning responsibilities for biodiversity loss in supply chains is an important first step toward reaching conservation goals. LCA is a suitable top-down method that can contribute toward this goal. However, such an effort is multidisciplinary and LCA would greatly benefit from a stronger engagement of experts in related fields, such as biologists and ecologists, who could help organize and integrate knowledge based on their extended experience in understanding and modeling complex ecological systems (e.g. modeling population dynamics). LCA calculates potential impacts rather than making accurate depictions of field-level dynamics, but it must provide practitioners with accurate and consensual measures of biodiversity. This target, though within reach, has not yet been met.

Author contributions

D.M.S conceived and designed the research review. D.M.S and R.F.M.T. carried out the review and wrote the manuscript, contributing equally to this work. O.P.O. proofread the final version of the manuscript.