Species distribution modelling for conservation planning in Victoria of Australia

Biodiversity and natural ecosystems are under pressure from a large range of drivers. In south eastern Australia these include periods of drought and occasional floods, increasing urbanisation pressures, changing land use patterns, altered fire frequency and regime, habitat loss and degradation, impacts of invasive species, etc. Detailed and reliable information about the spatial distribution of species provides critical information for effective conservation planning. In Victoria, Australia, this responsibility is held by the Department of Sustainability and Environment, which maintains and curates a database of site records for species: the Victorian Biodiversity Atlas (VBA). Although this database is comprehensive and contains many records for numerous species, the information is provided in point form. This point data can be useful for some planning and management tasks, but does not provide an adequate view of distribution for all management purposes. By integrating known occurrences of species with environmental GIS data layers using statistical or machine learning algorithms, it is possible to build models and to predict the likelihood that the species may occur at particular locations across large landscapes. We have used the VBA to build species distribution models (SDMs) for 523 vertebrate fauna species across the whole state, providing predictive ‘maps’ that are available for use in various conservation planning activities. In this paper, we introduce and discuss the methods we developed and implemented for producing these models. Modelling requires a number of steps: gathering species occurrence data, vetting or mining this data for useful records and winnowing out potentially misleading records, collating existing and / or developing new relevant environmental variables, selecting an appropriate modelling algorithm, training and evaluating models, mapping predictions to the whole state scale, and selecting a threshold to transform the continuous model result into binary (i.e. presence/absence) mapped outputs. All the occurrence data for the 523 fauna species were derived from the VBA. This database contains presence data for fauna species, but for the majority of survey techniques information on absences cannot be known or inferred. Twenty four explanatory variables were selected for use in developing models including 12 bioclimatic, terrain and soil radiometric (BTSR) variables, two coordinates (latitude and longitude), and 10 remotely sensed variables primarily derived from a time series of Landsat imagery. For each species, three versions of models (V1, V2 and V3 models) reflecting different constraint were built. V1 models used 12 BTSR variables; V2 models used 12 BTSR variables and two coordinates; and V3 models used all the 24 variables. Since true absence data was not available, pseudo-absences were created by filtering random points by a profile model based on Mahalanobis distance method. Using both pseudo-absences and the true presences data SDMs were developed with a machine learning algorithm, random forest, which was implemented in the statistical computing environment R. The models were evaluated using six accuracy measures, which have been demonstrated suitable for evaluating presence-only models. These metrics include AUL (area under the lift curve), MVDl (the maximum vertical distance from lift curve to the diagonal line), MPA (minimal predicted area with pre-specified true positive rate), VDl (vertical distance from lift curve to the diagonal line), Lift and Se (sensitivity). These measures can be used for both within-species and among-species model comparisons. A threshold was set for each model with a method based on maximising the sum of sensitivity and specificity to provide binary outputs or maps of species’ distributions.