Summary1. 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 postmodelling 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 evaluati...

Aim  To describe a general modelling framework for integrating multiple pattern‐ and process‐related factors into biodiversity conservation assessments across whole landscapes. Location  New South Wales (Australia), and world‐wide. Methods  The framework allows for a rich array of alternatives to the target‐based model traditionally underpinning systematic conservation planning and consists of three broad modelling components. The first component models the future state (condition) of habitat across a landscape as a function of present state, current and projected pressures acting on this state, and any proposed, or implemented, management interventions. The second component uses this spatially explicit prediction of future habitat state to model the level of persistence expected for each of a set of surrogate biodiversity entities. The third component then integrates these individual expectations to estimate the overall level of persistence expected for biodiversity as a whole. Results  Options are explored for tailoring implementation of the framework to suit planning processes varying markedly in purpose, and in availability of data, time, funding and expertise. The framework allows considerable flexibility in the nature of employed biodiversity surrogates (species‐level, discrete or continuous community‐level) and spatial data structures (polygonal planning units, or fine‐scaled raster), the level of sophistication with which each of the three modelling components is implemented (from simple target‐based assessment to complex process‐based modelling approaches), and the forms of higher‐level analysis supported (e.g. optimal plan development, priority mapping, interactive scenario evaluation). Main conclusions  The described framework provides a logical, and highly flexible, foundation for integrating disparate pattern‐ and process‐related factors into conservation assessments in dynamic, multiple‐use landscapes.

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1. Planning for the conservation of river biodiversity must involve a wide range of management options and account for the complication that the effects of many actions are spatially removed from these actions. Reserve design algorithms widely used in conservation planning today are not well equipped to address such complexities. 2. We used process-based models to estimate the expected persistence of river biodiversity under alternative catchment-wide management scenarios and applied it in the Hunter Region (37 000 km 2 ) in southeastern Australia. 3. The biological condition of 12 197 subcatchments was estimated using a multiple linear regression model that related assessments of the integrity of macroinvertebrate assemblages to human-induced disturbances at river sites. The best-fit model (R 2 = 0.76) used measures of both local and catchment-wide disturbances as well as elevation and distance from source as predictor variables. Based on the outputs of this model, we estimated that substantial loss of river biodiversity had occurred in some parts of the coastal fringes and the lower parts of the larger river systems. The most affected river type was small, lowgradient streams. 4. The predicted biodiversity condition together with river types based on macroinvertebrate assemblages and abiotic attributes was used to estimate a biodiversity persistence index (BDI). 5. A priority value for each subcatchment was calculated for different actions by changing the disturbance values for that subcatchment and calculating the resulting marginal change in regional BDI. Maps were thereby created for three different types of priority: catchment protection priority, catchment restoration priority and river section conservation priority. 6. The subcatchments of high catchment protection priority for river biodiversity were mostly in the uplands and within protected areas. The river sections of high conservation priority included many coastal lowland rivers in and around protected areas as well as many upland headwater streams. Subcatchments of high priority for catchment restoration were mostly in coastal areas or lowland floodplains. 39 7. This approach may be particularly well suited to guide the integrated implementation of three place-based protection strategies proposed for freshwaters: focal areas, critical management zones and catchment management zones.

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