A spatially explicit life cycle inventory of the global textile chain

Background, aim, and scope  Life cycle analyses (LCA) approaches require adaptation to reflect the increasing delocalization of production to emerging countries. This work addresses this challenge by establishing a country-level, spatially explicit life cycle inventory (LCI). This study comprises three separate dimensions. The first dimension is spatial: processes and emissions are allocated to the country in which they take place and modeled to take into account local factors. Emerging economies China and India are the location of production, the consumption occurs in Germany, an Organisation for Economic Cooperation and Development country. The second dimension is the product level: we consider two distinct textile garments, a cotton T-shirt and a polyester jacket, in order to highlight potential differences in the production and use phases. The third dimension is the inventory composition: we track CO₂, SO₂, NO _x , and particulates, four major atmospheric pollutants, as well as energy use. This third dimension enriches the analysis of the spatial differentiation (first dimension) and distinct products (second dimension). Materials and methods  We describe the textile production and use processes and define a functional unit for a garment. We then model important processes using a hierarchy of preferential data sources. We place special emphasis on the modeling of the principal local energy processes: electricity and transport in emerging countries. Results  The spatially explicit inventory is disaggregated by country of location of the emissions and analyzed according to the dimensions of the study: location, product, and pollutant. The inventory shows striking differences between the two products considered as well as between the different pollutants considered. For the T-shirt, over 70% of the energy use and CO₂ emissions occur in the consuming country, whereas for the jacket, more than 70% occur in the producing country. This reversal of proportions is due to differences in the use phase of the garments. For SO₂, in contrast, over two thirds of the emissions occur in the country of production for both T-shirt and jacket. The difference in emission patterns between CO₂ and SO₂ is due to local electricity processes, justifying our emphasis on local energy infrastructure. Discussion  The complexity of considering differences in location, product, and pollutant is rewarded by a much richer understanding of a global production–consumption chain. The inclusion of two different products in the LCI highlights the importance of the definition of a product's functional unit in the analysis and implications of results. Several use-phase scenarios demonstrate the importance of consumer behavior over equipment efficiency. The spatial emission patterns of the different pollutants allow us to understand the role of various energy infrastructure elements. The emission patterns furthermore inform the debate on the Environmental Kuznets Curve, which applies only to pollutants which can be easily filtered and does not take into account the effects of production displacement. We also discuss the appropriateness and limitations of applying the LCA methodology in a global context, especially in developing countries. Conclusions  Our spatial LCI method yields important insights in the quantity and pattern of emissions due to different product life cycle stages, dependent on the local technology, emphasizing the importance of consumer behavior. From a life cycle perspective, consumer education promoting air-drying and cool washing is more important than efficient appliances. Recommendations and perspectives  Spatial LCI with country-specific data is a promising method, necessary for the challenges of globalized production–consumption chains. We recommend inventory reporting of final energy forms, such as electricity, and modular LCA databases, which would allow the easy modification of underlying energy infrastructure. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

A spatially explicit model for tropical tree diversity patterns

A simple two-parameter model resembling the classical voter model is introduced to describe macroecological properties of tropical tree communities. The parameters of the model characterize the speciation- and global-dispersion rates. Monte Carlo type computer simulations are performed on the model, investigating species abundances and the spatial distribution of individuals and species. Simulation results are critically compared with the experimental data obtained from a tree census on a 50 hectare area of the Barro Colorado Island (BCI), Panama. Fitting to only two observable quantities from the BCI data (total species number and the slope of the log-log species-area curve at the maximal area), it is possible to reproduce the full species-area curve, the relative species abundance distribution, and a more realistic spatial distribution of species.     

A spatially explicit neutral model of beta-diversity in tropical forests

To represent species turnover in tropical rain forest, we use a neutral model where a tree's fate is not affected by what species it belongs to, seeds disperse a limited distance from their parents, and speciation is in equilibrium with random extinction. We calculate the similarity function, the probability F(r) that two trees separated by a distance r belong to the same species, assuming that the dispersal kernel P(r), the distribution of seeds about their parents and the prospects of mortality and reproduction, are the same for all trees regardless of their species. If P(r) is radially symmetric Gaussian with mean-square dispersal distance sigma, F(r) can be expressed in closed form. If P(r) is a radially symmetric Cauchy distribution, then, in two-dimensional space, F(r) is proportional to 1/r for large r. Analytical results are compared with individual-based simulations, and the relevance to field observations is discussed.     

Coevolution across landscapes: a spatially explicit model of parasitoid-host coevolution

The geographic mosaic theory of coevolution suggests that population spatial structure may have a strong impact on coevolutionary dynamics. Therefore, coevolution must be studied across geographic scales, not just in single populations. To examine the impact of movement rate on coevolutionary dynamics, we developed a spatially explicit model of host–parasitoid coevolution. We described space as a coupled-map lattice and assumed that resistance (defined as the ability of a host to encapsulate a parasitoid egg) and virulence (defined as the successful parasitization of a host) traits were graded and costly. The model explicitly detailed population and evolutionary dynamics. When holding all parameters constant and varying only the movement rate of the host and parasitoid, profoundly different dynamics were observed. We found that fluctuations in the mean levels of resistance and virulence in the global population were greatest when the movement rate of the host and parasitoid was high. In addition, we found that the variation in resistance and virulence levels among neighboring patches was greatest when the movement rates of the host and parasitoid was low. However, as the distance among patches increased, so did the variation in resistance and virulence levels regardless of movement rate. These generalizations did not hold when spatial patterns in the distribution of resistance and virulence traits, such as spirals, were observed. Finally, we found that the evolution of resistance and virulence caused the abundance of hosts to increase and the abundance of parasitoids to decrease. As a result, the spatial distribution of hosts and parasitoids was influenced.     

Modelling the third dimension: Incorporating topography into the movement rules of an individual-based spatially explicit population model

A wide variety of topographical and environmental elements have been shown or proposed to influence the movement decisions of dispersing animals. Most real landscapes have topographical elements such as hills, valleys and urban developments, which can all act to modify a species’ perceptual range and directly influence movement behaviour. If a visual-based perceptual ability enables a dispersing individual to locate suitable habitat patches at a distance, then it is to be expected that topographical features would act to modify the overall success of this strategy. However, the majority of individual-based Spatially Explicit Population Models (SEPM) employ only two-dimensional landscapes.To investigate the effects of topographical elevation on dispersal patterns, a three-dimensional visual-based perceptual range algorithm was added to the dispersal rules of an individual-based SEPM. To explore the possible influences of a behavioural-based response to topography, an algorithm modelling valley-seeking behaviour was also developed. The performance of both algorithms was compared with that of a two-dimensional visual-based perceptual range algorithm. The overall consequences of dispersal under each algorithm were measured by recording population sizes in a target wood in the centre of a modelled, real landscape.The size of the population in the target wood, modelled using both of the three-dimensional algorithms, exhibited sensitivity to the direction of dispersal in interaction with perceptual range, which differed from that predicted by the two-dimensional approach. Population size was dependant on the spatial configuration of habitat patches and on the topography of the landscape, both of which could guide dispersers either towards or away from the target patch depending on the particular combinations of dispersal directions and perceptual ranges selected. Topography was found to have a greater effect on dispersal at shorter perceptual ranges, and thresholds in the results for all three algorithms suggested the existence of species and landscape dependant optimal perceptual ranges. It is recommended that both topography and topographical-based dispersal-altering algorithms, commensurate with the studied species’ behaviour, be incorporated into the movement rule-base of dispersal simulation models. The modelling of topography and its effects on movement in patchy landscapes are seen as essential ingredients in future landscape planning.     

A spatially explicit model of sex ratio evolution in response to sex-biased dispersal

Sex-biased dispersal occurs in all seed plants and many animal species. Theoretical models have shown that sex-biased dispersal can lead to evolutionarily stable biased sex ratios. Here, we use a spatially explicit chessboard model to simulate the evolution of sex ratio in response to sex-biased dispersal range and sex-biased dispersal rate. Two life cycles are represented in the model: one in which both sexes disperse before mating (DDM), the other in which males disperse before mating and mated females or zygotes disperse after mating (DMD). Model parameters include factors like dispersal rate, dispersal range, number of individuals per patch, and habitat heterogeneity.When dispersal range is sex biased, we find that, in a homogeneous environment, the sex ratio is generally biased towards the sex that disperses more widely (sex ratio range: 0.47–0.52). In a heterogeneous environment, the sex ratio is generally biased towards the more dispersive sex in good habitats, and towards the less dispersive sex in poor habitats (sex ratio range: 0–1). This is opposite to the effect of sex-biased dispersal rate, which favours the production of the more dispersive sex in poor habitats and the less dispersive sex in good habitats (sex ratio range: 0–1). To allow for a comparison with theoretical predictions, data concerning sex-biased dispersal and habitat-dependent sex ratios should thus incorporate information about the spatial scale of both dispersal and environmental heterogeneity.     

A spatially explicit model simulating western corn rootworm (Coleoptera: Chrysomelidae) adaptation to insect-resistant maize

A stochastic spatially explicit computer model is described that simulates the adaptation by western corn rootworm, Diabrotica virgifera virgifera LeConte, to rootworm-resistance traits in maize. The model reflects the ecology of the rootworm in much of the corn belt of the United States. It includes functions for crop development, egg and larval mortality, adult emergence, mating, egg laying, mortality and dispersal, and alternative methods of rootworm control, to simulate the population dynamics of the rootworm. Adaptation to the resistance trait is assumed to be controlled by a monogenic diallelic locus, whereby the allele for adaptation varies from incompletely recessive to incompletely dominant, depending on the efficacy of the resistance trait. The model was used to compare the rate at which the adaptation allele spread through the population under different nonresistant maize refuge deployment scenarios, and under different levels of crop resistance. For a given refuge size, the model indicated that placing the nonresistant refuge in a block within a rootworm-resistant field would be likely to delay rootworm adaptation rather longer than planting the refuge in separate fields in varying locations. If a portion of the refuge were to be planted in the same fields or in-field blocks each year, rootworm adaptation would be delayed substantially. Rootworm adaptation rates are also predicted to be greatly affected by the level of crop resistance, because of the expectation of dependence of functional dominance on dose. If the dose of the insecticidal protein in the maize is sufficiently high to kill >90% of heterozygotes and approximately 100% of susceptible homozygotes, the trait is predicted to be much more durable than if the dose is lower. A partial sensitivity analysis showed that parameters relating to adult dispersal affected the rate of pest adaptation. Partial validation of the model was achieved by comparing output of the model with field data on population dynamics, and with field data documenting rootworm adaptation to cyclodienes and organophosphates.     

Overgrowth competition, fragmentation and sex-ratio dynamics: a spatially explicit, sub-individual-based model

Sessile organisms that compete for access to resources by overgrowing each other may risk the local elimination of one sex or the other, as frequently happens within clumps of the dioecious liverwort Marchantia inflexa. A multi-stage, spatially implicit differential-equation model of M. inflexa growing in an isolated patch, analysed in a previous study, indicated that long-term coexistence of the sexes within such patches may be only temporary. Here we derive a spatially explicit, sub-individual-based model to reconsider this interpretation when much more ecological realism is taken into account, including the process of fragmentation. The model tracks temporally discrete growth increments in continuous space, representing growth architecture and the overgrowth process in significant geometric detail. Results remain generally consistent with the absence of long-term coexistence of the sexes in individual patches of Marchantia. Dynamics of sex-specific growth qualitatively resemble those generated by differential-equation models, suggesting that this much simpler framework may be adequate for multi-patch metapopulation models. Direct competition between fragmenting and non-fragmenting clones demonstrates the importance of fragmentation in overgrowth competition. The results emphasize the need for empirical work on mechanisms of overgrowth and for modeling and empirical studies of life history tradeoffs and sex-ratio dynamics in multi-patch systems.     

A simple landscape-scale test of a spatially explicit population model: patch occupancy in fragmented south-eastern Australian forests

The results of a landscape‐scale test of ALEX, a widely used metapopulation model for Population Viability Analysis (PVA), are described. ALEX was used to predict patch occupancy by the laughing kookkaburra and the sacred kingfisher in patches of eucalypt forest in south‐eastern Australia. These predictions were compared to field surveys to determine the accuracy of the model. Predictions also were compared to a “naïve” null model assuming no fragmentation effects.
The naïve null model significantly over‐predicted the number of eucalypt patches occupied by the sacred kingfisher, but the observed patch occupancy was not significantly different from that predicted using ALEX. ALEX produced a better fit to the field data than the naïve null model for the number of patches occupied by the laughing kookaburra. Nevertheless, ALEX still significantly over‐predicted the number of occupied patches, particularly remnants dominated by certain forest types – ribbon gum and narrow‐leaved peppermint. The predictions remained significantly different from observations, even when the habitat quality of these patches was reduced to zero. Changing the rate of dispersal improved overall predicted patch occupancy, but occupancy rates for the different forest types remained significantly different from the field observations. The lack of congruence between field data and model predictions could have arisen because the laughing kookaburra may move between an array of patches to access spatially separated food and nesting resources in response to fragmentation. Alternatively, inter‐specific competition may be heightened in a fragmented habitat. These types of responses to fragmentation are not incorporated as part of traditionally applied metapopulation models. Assessments of predictions from PVA models are rare but important because they can reveal the types of species for which forecasts are accurate and those for which they are not. This can assist the collection of additional empirical data to identify important factors affecting population dynamics.     

The assessment of mangrove biomass and carbon in West Africa: a spatially explicit analytical framework

Mangrove forests are highly productive and have large carbon sinks while also providing numerous goods and ecosystem services. However, effective management and conservation of the mangrove forests are often dependent on spatially explicit assessments of the resource. Given the remote and highly dispersed nature of mangroves, estimation of biomass and carbon in mangroves through routine field-based inventories represents a challenging task which is impractical for large-scale planning and assessment. Alternative approaches based on geospatial technologies are needed to support this estimation in large areas. However, spatial data processing and analysis approaches used in this estimation of mangrove biomass and carbon have not been adequately investigated. In this study, we present a spatially explicit analytical framework that integrate remotely sensed data and spatial analyses approaches to support the estimation of mangrove biomass and carbon stock and their spatial patterns in West Africa. Forest canopy height derived from SRTM and ICESat/GLAS data was used to estimate mangrove biomass and carbon in nine West African countries. We developed a geospatial software toolkit that implemented the proposed framework. The spatial analysis framework and software toolkit provide solid support for the estimation and relative comparisons of mangrove-related metrics. While the mean canopy height of mangroves in our study area is 10.2 m, the total biomass and carbon were estimated as 272.56 and 136.28 Tg. Nigeria has the highest total mangrove biomass and carbon in the nine countries, but Cameroon is the country with the largest mean biomass and carbon density. The resulting spatially explicit distributions of mangrove biomass and carbon hold great potential in guiding the strategic planning of large-scale field-based assessment of mangrove forests. This study demonstrates the utility of online geospatial data and spatial analysis as a feasible solution for estimating the distribution of mangrove biomass and carbon at larger or smaller scales.     

A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas

This study formulates a model to maximize the profit of a lignocellulosic biofuel supply chain ranging from feedstock suppliers to biofuel customers. The model deals with a time-staged, multi-commodity, production/distribution system, prescribing facility locations and capacities, technologies, and material flows. A case study based on a region in Central Texas demonstrates application of the proposed model to design the most profitable biofuel supply chain under each of several scenarios. A sensitivity analysis identifies that ethanol (ETOH) price is the most significant factor in the economic viability of a lignocellulosic biofuel supply chain.     

Development and validation of a spatially explicit individual-based mixed crop growth model

Spatial disposition of plants in intercrops, and differences in sowing time between species, can strongly affect their ecological interactions and, in consequence, the system’s viability and performance. Empirical exploration of a wide range of spatial and temporal plant arrangements is costly and time-consuming. Modelling the growth of mixed crops is a tool which, combined with empirical tests, can greatly reduce the time and investment required for this task. Spatially explicit, individual-based dynamic models seem well suited for this purpose; their exploration and experimental validation for the case of simple, two-species, artificial plant communities, can also provide further insight as to how the spatial and temporal scales of a plant’s multispecific neighbourhood affect its growth and performance. The aim of this investigation was to further develop a published spatially explicit individual-based mixed crop growth model [Vandermeer, J. H. (1989). The Ecology of Intercropping, Cambridge, U.K.: Cambridge University Press, p. 237], and to validate it experimentally. With this purpose in mind: (1) computer programs to simulate individual plant growth and to perform statistical analysis of both deterministic and stochastic versions of the model were developed; (2) the model was parametrized using a complex experimental diculture with several cohorts and spatial arrangements; (3) the predictive capacity of the model was tested using independent spatio-temporal experimental arrangements; (4) a modified version of the model was written, which abandons the assumption of linearity of the neighbourhood index at the cost of increasing the number of parameters; (5) The performance of stochastic versions of both Vandermeer’s and our modified model were compared, employing a non-parametric measure of goodness of fit. We conclude that this approach to modelling plant growth subject to intra and interspecific competition is a remarkably efficient, general, conceptually elegant, heuristic tool whose predictive power can be further improved when nonlinear terms are introduced into the neighbourhood competition index, as done in our modified version of Vandermeer’s model.     

A simplified model for spatially differentiated impact assessment of air emissions

We developed a simplified emission dispersion and exposure-assessment model designed to reflect the site-specific health impacts of air pollution in life-cycle impact assessment (LCIA). We proposed an EXposure Per Emission Coefficient (EXPEC), a dimensionless parameter representing the relative amount of pollutant inhaled per emission. EXPEC values were calculated for two typical source categories - electric power plants and road vehicles — on a concentric circle model. The EXPEC values were significantly different for different locations and source categories. We examined the application of EXPEC in a case study that compared the effects of emissions from electric and gasoline-engine vehicles. EXPEC is a useful tool for assessing spatially differentiated potential impacts.     

A spatially explicit life cycle assessment midpoint indicator for soil quality in the European Union using soil organic carbon

Purpose

Improving land use assessment in life cycle assessment (LCA) is a priority. Recently, soil organic carbon (SOC) depletion has been proposed as a transformation and occupation midpoint indicator to estimate impacts on biotic production potential (BPP). SOC depletion is recommended by the European Union in the International Reference Life Cycle Data System (ILCD) Handbook as a land use indicator. There is a consensus method to calculate SOC depletion in LCA, and ILCD proposes a set of characterization factors (CFs), but these lack geographical discrimination.

Methods

Our method of calculation for midpoint CFs follows Brandão and Milà i Canals (Int J Life Cycle Assess 18:1243–1252, 2013). We operationalize the method using SOC stocks from the LUCASOIL database of field measurements in Europe. We use potential natural vegetation (PNV) as the reference situation. CFs were calculated on a cell basis for 23 countries in Europe and grouped in three spatial scales (an administrative classification, NUTS II, and two biophysical classifications, ecoregion and climate region) according to soil type and land cover following a consensus map of cover classes. To evaluate the method’s results, CFs were applied in a case study.

Results and discussion

SOC stocks of European soils were obtained according to land use and soil type classes (excluding non-European Union countries) for the three spatial scales. A database of European transformation and occupation CFs is also presented and analyzed. The aggregation of CFs at biophysical scales (ecoregion and climate region) is similar, but NUTS II aggregation of CFs is problematic. The application of the CFs in the case study revealed significant differences compared to the outcome of using CFs collected from other land use models.

Conclusions

This paper is the first operationalization using field measurements of an updated version of the ILCD-recommended model for land use impacts in LCA. We obtained CFs for SOC depletion in Europe that can be nested within CFs suggested by ILCD since our results possess better spatial resolution but are only for European Union countries. The case study application highlighted the need for inventories to improve the spatial resolution of the life cycle processes to match the detail of LCIA models.

     

Assembling spatially explicit landscape models of pollen and spore dispersal by wind for risk assessment

Models of windblown pollen or spore movement are required to predict gene flow from genetically modified (GM) crops and the spread of fungal diseases. We suggest a simple form for a function describing the distance moved by a pollen grain or fungal spore, for use in generic models of dispersal. The function has power-law behaviour over sub-continental distances. We show that air-borne dispersal of rapeseed pollen in two experiments was inconsistent with an exponential model, but was fitted by power-law models, implying a large contribution from distant fields to the catches observed. After allowance for this 'background' by applying Fourier transforms to deconvolve the mixture of distant and local sources, the data were best fit by power-laws with exponents between 1.5 and 2. We also demonstrate that for a simple model of area sources, the median dispersal distance is a function of field radius and that measurement from the source edge can be misleading. Using an inverse-square dispersal distribution deduced from the experimental data and the distribution of rapeseed fields deduced by remote sensing, we successfully predict observed rapeseed pollen density in the city centres of Derby and Leicester (UK).     

A spatially explicit computer model for immature distributions of Glyptotendipes paripes (Diptera: Chironomidae) in central Florida lakes

Density and spatial distribution data of immature Glyptotendipes paripes Edwards (Diptera: Chironomidae), in relation to selected water and sediment characteristics prevailing in three central Florida lakes, were used to develop and calibrate a qualitative model of G. paripes distributions with the purpose of developing more efficient sampling plans for research or population management. For the model, each lake habitat was defined by a two-dimensional matrix, while a three-dimensional matrix was used to simulate the life stage-structured population. A Lefkovitch population matrix was used to project survival and development of the population in each spatial unit. The model incorporated lake bathymetry, sediment dry weight, water level, food availability (as a function of Secchi disk transparency) and strength of density dependence as influences on immature survival and adult fecundity. Temperature-dependent development data for G. paripes were incorporated to estimate development time. A simple redistribution function was used to simulate the dispersal of adults over the simulated habitat for oviposition. Immatures and relevant environmental data taken for eight dates, at each of two other central Florida lakes were used to validate the model. The mean correct prediction rate of the model for field density distributions within 0.5 log(n+1) immatures/m² density in spatial habitat cells was 0.64 and 0.66 for the validation lakes. Presence/absence correct prediction rates were 0.61 and 0.76, while matching to a proposed sampling stratification based on a treatment threshold was 0.85 and 0.88. These values indicate that the model is efficient for preparing stratified immature G. paripes sampling plans for central Florida lakes.     

A spatially explicit analysis of seedling recruitment in the terrestrial orchid Orchis purpurea

Seed dispersal and the subsequent recruitment of new individuals into a population are important processes affecting the population dynamics, genetic diversity and spatial genetic structure of plant populations. Spatial patterns of seedling recruitment were investigated in two populations of the terrestrial orchid Orchis purpurea using both univariate and bivariate point pattern analysis, parentage analysis and seed germination experiments. Both adults and recruits showed a clustered spatial distribution with cluster radii of c. 4-5 m. The parentage analysis resulted in offspring-dispersal distances that were slightly larger than distances obtained from the point pattern analyses. The suitability of microsites for germination differed among sites, with strong constraints in one site and almost no constraints in the other. These results provide a clear and coherent picture of recruitment patterns in a tuberous, perennial orchid. Seed dispersal is limited to a few metres from the mother plant, whereas the availability of suitable germination conditions may vary strongly from one site to the next. Because of a time lag of 3-4 yr between seed dispersal and actual recruitment, and irregular flowering and fruiting patterns of adult plants, interpretation of recruitment patterns using point patterns analyses ideally should take into account the demographic properties of orchid populations.