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Keywords:

  • Bayesian belief network;
  • bioclimatic envelope modelling;
  • climate change adaptation;
  • expert elicitation;
  • habitat suitability;
  • land-use change;
  • lippia;
  • Queensland Murray-Darling Basin;
  • riparian zone

Abstract

Climate change is likely to affect plants in multiple ways, but predicting the consequences for habitat suitability requires a process-based understanding of the interactions. This is at odds with existing approaches that are mostly phenomenological and largely restricted to predicting the effects of changing temperature and rainfall on species distributions at a coarse spatial scale. We examine the multiple effects of climate change, including predicting the effects of altered flood regimes and land-use change, on the potential distribution of the invasive riparian species lippia (Phyla canescens) across a 26 000 km2 catchment in eastern Australia. We determined habitat suitability for lippia by combining process-understanding of experts and an eco-physiological bioclimatic model within a Bayesian belief network. The bioclimatic model predicted substantial changes in habitat suitability by 2070 under both a wetter (Echam Mark 3) and drier (Hadley Centre Mark 2) climate change scenario, but only the more likely drier scenario reduced suitability in our test region. The area suitable for lippia was predicted to increase at least threefold with increased flooding under a wet climate scenario, although this would be partially negated by land-use change to cultivation. The region would become unsuitable to lippia with reduced flooding under a drier scenario irrespective of land-use changes, although existing populations would persist if grazing persisted. Independent field validation verified model structure and parameterization, and therefore the opinion of experts, but identified site-scale deficiencies in the available environmental data layers. Model predictions suggest that adaptation options for managing lippia will be greatly reduced under a drying scenario, but identify potential restoration opportunities under either scenario. This work highlights the value of predictive models that incorporate process-understanding at sufficiently fine spatial resolution to capture the important processes underpinning habitat suitability.