Linking species distribution models with structured expert elicitation for prediction of management effects

Another chapter of my PhD has been completed with a peer-reviewed publication in the pipelines. It is a collaboration with Dr Victoria Hemming, Dr Yung En Chee, Dr Anca Hanea and Professor Brendan Wintle. It is about how we can predict effects of management actions on biodiversity, in a spatially explicit way, when key habitat variables are not spatially explicit. Outputs from this approach can inform decision-makers about which actions (or action-sets) are likely to be best for individual species at a site and regional scale, and can serve as an important preliminary step to a regional spatial prioritisation for planning and strategy development.

Effective biodiversity conservation requires robust and transparent methods for cost-effective prioritisation of management actions. However, such prioritisations are often hampered by a lack of empirical data on the effect of management actions and spatially-explicit data on habitat variables. Approaches exist that integrate structured expert elicitation (SEE) with species distribution models (SDMs) to predict species responses across habitat gradients. However, difficulties remain in predicting region-wide management outcomes when key habitat covariates are not spatially explicit. Yet, SDMs built with species occurrence and environmental data (albeit, lacking key habitat variables) are often readily available. Therefore, we developed an approach to integrate SDMs with SEE to a) improve understanding of likely outcomes of management actions for frogs; and b) enable spatial prediction of management effects for multiple species when data is incomplete.

We demonstrate our approach across approximately 4000 wetlands in greater Melbourne, Victoria, Australia. As a measure of management effectiveness, we used the difference in predicted probability of occupancy of seven frog species at wetlands 10 years after conservation actions are implemented (or not implemented). Management effect was elicited from experts under do-nothing and five alternative action scenarios. Individual expert estimates were aggregated using generalised linear models that were used to spatially predict expected management effects, and a measure of uncertainty in the prediction, at all wetlands.

Analyses showed that predicted action effect was influenced by species initial occupancy. In our case, enhancing aquatic vegetation, along with surrounding vegetation, was a key action for most species. We demonstrate a straightforward approach to predicting management effects over large spatial areas when data is incomplete by augmenting information from SDMs with SEE. This approach offers insight for decision-makers on how to understand and maximise benefit across species on a wetland and regional scale while minimising risks of negative effects.

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