Incorporating expert opinion and fine-scale vegetation mapping into statistical models of faunal distribution

1. Abiotic environmental predictors and broad-scale vegetation have been used widely to model the regional distributions of faunal species within forested regions of Australia. These models have been developed using stepwise statistical procedures but incorporate only limited expert involvement of the type sometimes advocated in distribution modelling. The objectives of this study were twofold. First, to evaluate techniques for incorporating fine-scaled vegetation and growth-stage mapping into models of species distribution. Secondly, to compare methods that incorporate expert opinion directly into statistical models derived using stepwise statistical procedures. 2. Using faunal data from north-east New South Wales, Australia, logistic regression models using fine-scale vegetation and expert opinion were compared with models employing only abiotic and broad vegetation variables. 3. Vegetation and growth-stage information was incorporated into models of species distribution in two ways, both of which used expert opinion to derive new explanatory variables. The first approach amalgamated fine-scaled vegetation classes into broader classes of ecological relevance to fauna. In the second approach, ordinal habitat indices were derived from vegetation and growth-stage mapping using rules specified by an expert panel. These indices described habitat features thought to be relevant to the faunal groups studied (e.g. tree hollow availability, fleshy fruit production). Landscape composition was calculated using these new variables within a 500-m and 2-km radius of each site. Each habitat index generated a spatially neutral variable and two spatial context variables. 4. Expert opinion was incorporated during the pre-modelling, model-fitting and post -modelling stages. At the pre-modelling stage experts developed new explanatory variables based on mapped fine-scale vegetation and growth-stage information. At the model-fitting stage an expert panel selected a subset of potential explanatory variables from the available set. At the post-modelling stage expert opinion modified or refined maps of predicted species distribution generated by statistical models. For comparative purposes expert opinion was also used to develop maps of species distribution by defining rules within a geographical information system, without the aid of statistical modelling. 5. Predictive accuracy was not improved significantly by incorporating habitat indices derived by applying expert opinion to fine-scaled vegetation and growth-stage mapping. Use of expert input at the pre-modelling stage to derive and select potential explanatory variables therefore does not provide more information than that provided by remotely mapped vegetation. 6. The incorporation of expert opinion at the model-fitting or post-modelling stages resulted in small but insignificant gains in predictive accuracy. The predictive accuracy of purely expert models was less than that achieved by approaches based on statistical modelling. 7. The study, one of few available evaluations of expert opinion in models of species distribution, suggests that expert modification of fitted statistical models should be confined to species for which models are grossly in error, or for which insufficient data exist to construct solely statistical models

The Biodiversity Forecasting Toolkit: Answering the ‘how much’, ‘what’, and ‘where’ of planning for biodiversity persistence

AbstractThis research reports on a new approach to conservation assessment that seeks to extend the target-based model traditionally underpinning systematic conservation planning. The Biodiversity Forecasting Tool (BFT) helps answer three important questions relating to regional biodiversity persistence: ‘how much’ biodiversity can persist for a given land-management scenario; ‘what’ habitats to focus conservation effort on; and ‘where’ in the landscape to undertake conservation action. The tool integrates fine-scaled variability in vegetation composition and structure with spatial context, which is critical for ensuring the viability of populations. Thus, a raster data framework is employed which deems each location or gridcell in a landscape as contributing to biodiversity benefits to various degrees. At its simplest, just two spatial inputs, vegetation community types and vegetation condition, are needed. Drawing on, as a case-study, a broad-scale biodiversity assessment for NSW, Australia, this paper reports on the successful application of the BFT tool for a variety of functions ranging from interactive scenario evaluation through to conservation benefits mapping

General Landscape Connectivity Model (GLCM): a new way to map whole of landscape biodiversity functional connectivity for operational planning and reporting

Graph-theoretic approaches are commonly used to map landscape connectivity networks to inform environmental management priorities. We developed the new General Landscape Connectivity Model (GLCM), as a operationally practical way of evaluating and mapping habitat networks to inform conservation priorities and plans. GLCM is built on two complementary metapopulation ecology-based measures: Neighbourhood habitat area (Ni) and habitat link value (Li). Ni is a measure of the amount of connected habitat to each location considering its cross-scale connectivity to neighbouring habitat. The remaining Ni across a region can be reported as an indicator of Ecological Carrying Capacity for wildlife (plants and animals). Li at any location is its contribution to the landscape connectivity of the study region (i.e. which is reported as summed Ni across a region) by virtue of providing the ‘least-cost’ linkages between concentrations of habitat. Mapped Li provides valuable insights into the pattern of a region’s habitat network, highlighting functioning habitat corridors and stepping-stones, and candidate areas for conservation and restoration. Due to its foundations in ecological theory and its parsimonious design, GLCM addresses a number of criteria we list as important, while addressing criticisms often levelled at graph-theoretical approaches. We present results for three south-east Australian case studies using continuous-value ecological condition surfaces as input. However, a simple habitat/non-habitat binary surface approximating a threshold ecological condition can also be used. GLCM has been designed to specifically address the need for generic landscape connectivity assessment at regional scales, and broader. It incorporates connectivity analyses across a range of spatial scales and granularities relevant to broad ranges of taxa and movement processes (foraging, dispersal and migration). Successively finer spatial scales are more intensively sampled based on a simple scaling-law. This approach allows analysis resoluti ons to be determined by data-driven ecological relevance rather than by processing limitations. The operational advantages of GLCM means that landscape connectivity assessments can be readily updated with refined or changed inputs including time-series remote sensing of land cover, or applied to alternative scenarios of land use, ecological restoration, climate projections or combinations of these

Abiotic resource use in life cycle impact assessment : part I : towards a common perspective

At the beginning of the SUPRIM project, there was no global consensus on the assessment of impacts from the use of abiotic resources (minerals and metals), in life cycle impact assessment (LCIA). Unlike with other impact categories such as global warming, there is not just one single, explicitly agreed-upon problem arising from the use of abiotic resources. The topic is complex and new methods are still being developed, all with different perspectives and views on resource use. For this reason, the SUPRIM project initiated a consensus process together with members from the research and mining communities, with the aim to obtain an understanding of different stakeholders’ views and concerns regarding potential issues resulting from the use of resources. This paper reports on this consensus process and its outcomes. Insights from this process are twofold: First, the outcome of the process is a clear definition of the perspectives on abiotic resources which form the starting point to further refine or develop LCIA methods on abiotic resource use. Second, the process itself has been a challenging but valuable exercise, which can inspire the evolution of other complex issues in life cycle impact assessment, where research communities face similar issues as experienced with abiotic resources (e.g. water and land use, social LCA, etc.)

The Biodiversity Forecasting Toolkit: Answering the ‘how much’, ‘what’, and ‘where’ of planning for biodiversity persistence

AbstractThis research reports on a new approach to conservation assessment that seeks to extend the target-based model traditionally underpinning systematic conservation planning. The Biodiversity Forecasting Tool (BFT) helps answer three important questions relating to regional biodiversity persistence: ‘how much’ biodiversity can persist for a given land-management scenario; ‘what’ habitats to focus conservation effort on; and ‘where’ in the landscape to undertake conservation action. The tool integrates fine-scaled variability in vegetation composition and structure with spatial context, which is critical for ensuring the viability of populations. Thus, a raster data framework is employed which deems each location or gridcell in a landscape as contributing to biodiversity benefits to various degrees. At its simplest, just two spatial inputs, vegetation community types and vegetation condition, are needed. Drawing on, as a case-study, a broad-scale biodiversity assessment for NSW, Australia, this paper reports on the successful application of the BFT tool for a variety of functions ranging from interactive scenario evaluation through to conservation benefits mapping

Abiotic resource use in life cycle impact assessment : part II : Linking perspectives and modelling concepts

Starting from a lack of consensus on how to consistently assess abiotic resource use in life cycle assessment, a structured approach was developed to enable a classification of perspectives on resource use, based on the socalled role of resources. Using this classification, this paper focusses on analysing links between perspectives and modelling concepts, i.e. the conceptual implementation. To analyse the modelling concepts for a selection of existing LCIA methods and other modelling approaches, the concept of the system model is introduced. It defines the relevant inventory flows to be assessed by the LCIA method, and, at the same time, to be considered in the characterization model, and how the flows and stocks of resources used to calculate the characterization factors are positioned in relation to environment (nature) and economy (technosphere). For consistency, they should be aligned with the position of inventory flows and, at the same time, reflect the perspective on resources taken by the method. Using this concept, we critically review a selection of methods and other modelling approaches for consistency with the perspectives on resource use, as well as for their internal consistency. As a result of the analysis, we highlight inconsistencies and discuss ways to improve links between perspectives and modelling concepts. To achieve this, the new framework can be used for the development or improvement of LCIA methods on resource use