Coupling GIS and LCA for biodiversity assessments of land use

Geospatial details about land use are necessary to assess its potential impacts on biodiversity. Geographic information systems (GIS) are adept at modeling land use in a spatially explicit manner, while life cycle assessment (LCA) does not conventionally utilize geospatial information. This study presents a proof-of-concept approach for coupling GIS and LCA for biodiversity assessments of land use and applies it to a case study of ethanol production from agricultural crops in California. GIS modeling was used to generate crop production scenarios for corn and sugar beets that met a range of ethanol production targets. The selected study area was a four-county region in the southern San Joaquin Valley of California, USA. The resulting land use maps were translated into maps of habitat types. From these maps, vectors were created that contained the total areas for each habitat type in the study region. These habitat compositions are treated as elementary input flows and used to calculate different biodiversity impact indicators in a second paper (Geyer et al., submitted). Ten ethanol production scenarios were developed with GIS modeling. Current land use is added as baseline scenario. The parcels selected for corn and sugar beet production were generally in different locations. Moreover, corn and sugar beets are classified as different habitat types. Consequently, the scenarios differed in both the habitat types converted and in the habitat types expanded. Importantly, land use increased nonlinearly with increasing ethanol production targets. The GIS modeling for this study used spatial data that are commonly available in most developed countries and only required functions that are provided in virtually any commercial or open-source GIS software package. This study has demonstrated that GIS-based inventory modeling of land use allows important refinements in LCA theory and practice. Using GIS, land use can be modeled as a geospatial and nonlinear function of output. For each spatially explicit process, land use can be expressed within the conventional structure of LCA methodology as a set of elementary input flows of habitat types

Viable Reserve Networks Arise From Individual Landholder Responses To Conservation Incentives

Abstract Conservation in densely-settled biodiversity hotspots areas often requires setting up reserve networks that maintain sufficient contiguous habitat to support viable species populations. Because it is difficult to secure landholder compliance with an tightly constrained reserve network design, attention has shifted to voluntary incentive mechanisms, such as purchase of conservation easements by reverse auction or through a fixed-price offer. These mechanisms carry potential advantages of transparency, simplicity, and low cost. But uncoordinated individual response to these incentives has been assumed to be incompatible with conservation goals of viability (which depends on contiguous habitat) and biodiversity representation. We model such incentives for southern Bahia in the Brazilian Atlantic Forest, one of the biologically richest and most threatened global biodiversity hotspots. Here, forest cover is spatially autocorrelated and associated with depressed land values, a situation that may be characteristic of longsettled areas with forests fragmented by agriculture. We find that in this situation, a voluntary incentive system can yield a reserve network characterized by large, viable patches of contiguous forest, and representation of subregions with distinct vegetation types and biotic assemblages -without explicit planning for those outcomes

Change in Urban Land Use and Associated Attributes in the Upper San Francisco Estuary, 1990-2006

Land use is an ultimate driver of many of the stressors on the Upper San Francisco Estuary, but the magnitude and pattern of land use change has not been analyzed. This paper attempts to fill this knowledge gap through a screening-level risk assessment. Urban land use was compared within hydrodynamic subregions in 1990, 2000, and 2006. Ancillary data were then used to quantify secondary measures such as impervious cover, housing density, road density and road crossings. Despite the rapid growth of the Bay Area, Sacramento, and Stockton metropolitan areas, the percentage of urban area and rates of change in the subregions are generally low to moderate when compared to other estuaries in the United States. The spatial data sets used in this analysis have been posted online to a public repository to be used by other researchers

Change in Urban Land Use and Associated Attributes in the Upper San Francisco Estuary, 1990-2006

Land use is an ultimate driver of many of the stressors on the Upper San Francisco Estuary, but the magnitude and pattern of land use change has not been analyzed. This paper attempts to fill this knowledge gap through a screening-level risk assessment. Urban land use was compared within hydrodynamic subregions in 1990, 2000, and 2006. Ancillary data were then used to quantify secondary measures such as impervious cover, housing density, road density and road crossings. Despite the rapid growth of the Bay Area, Sacramento, and Stockton metropolitan areas, the percentage of urban area and rates of change in the subregions are generally low to moderate when compared to other estuaries in the United States. The spatial data sets used in this analysis have been posted online to a public repository to be used by other researchers

Biofuels And Biodiversity In California: Trade-off Analysis

Biofuels from agricultural and other biomass are an important part of California’s strategy to reduce greenhouse gas emissions and dependence on foreign oil. Ongoing land conversion for agricultural and urban uses has already imperiled many animal species in the state. This study investigated the potential impacts on wildlife of shifts in agricultural activity to increase biomass production. We applied knowledge of the suitability of California’s agricultural landscapes for wildlife species to evaluate wildlife effects associated with plausible scenarios of expanded production of five potential biofuel crops (sweet sorghum, sugar beets, bermudagrass, canola, and safflower). We generated alternative, spatially-explicit scenarios that minimized loss of habitat for a given level of biofuel production. Trade-off analysis compared the marginal changes per unit of energy for transportation costs, wildlife, and water-use. The choice of crop matters. Sweet sorghum and sugar beets require three times less land area per gallon of gasoline equivalent than bermudagrass and five to six times less than canola and safflower. Canola and safflower scenarios had the largest impacts on wildlife but the greatest reduction in water use. The bermudagrass scenarios would result in a slight overall improvement for wildlife over the current situation. Relatively minor redistribution of lands converted to biofuel could produce the same energy yield with much less impact on wildlife and very small increases in transportation costs for any crop. This framework provides a means to systematically evaluate potential wildlife impacts of alternative production scenarios. The accuracy of the predicted impacts could be improved with additional field study of wildlife use of agricultural landscapes, especially habitats associated with biofuel crops

Optimization in the utility maximization framework for conservation planning: a comparison of solution procedures in a study of multifunctional agriculture

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management

Optimization in the utility maximization framework for conservation planning: a comparison of solution procedures in a study of multifunctional agriculture.

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management

Optimization in the utility maximization framework for conservation planning: a comparison of solution procedures in a study of multifunctional agriculture.

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management

Extinction rates under nonrandom patterns of habitat loss

Most models that examine the effects of habitat conversion on species extinctions assume that habitat conversion occurs at random. This assumption allows predictions about extinction rates based on the species–area relationship. We show that the spatially aggregated nature of habitat conversion introduces a significant bias that may lead species-loss rates to exceed those predicted by species–area curves. Correlations between human activity and major compositional gradients, or species richness, also alter predicted species extinction rates. We illustrate the consequences of nonrandom patterns of habitat conversion by using a data set that combines the distribution of native vascular plants with human activity patterns in California