Keywords
ecological modelling, water suitability, preference curves, ecological knowledge uncertainty
Start Date
1-7-2012 12:00 AM
Abstract
Freshwater aquatic ecosystems are increasingly under threat due to climate change and river regulation. Water management is considered important for maintaining and restoring freshwater aquatic ecosystems in Australia. A habitat model based on the water requirements of four riverine vegetation species was developed to assess the suitability of water regimes for the maintenance and regeneration of these species in the Namoi Catchment, Australia. This model identifies characteristics of flooding events (e.g. duration, timing and inter-flood dry period) from hydrological data or models, and generates suitability indices based on the species’ water requirement preference curves. These preference curves are central to the model and were established based on expert knowledge. Two comprehensive sources are available on the water requirements of vegetation species in the Murray-Darling Basin, Australia: Rogers and Ralph [2010] and Roberts and Marston [2011]. Both of these sources are based on extensive review of studies on species’ response to flooding in the Murray-Darling Basin. The water requirements proposed in these two sources differ due to differences in literature selection and interpretation of the literature by the authors. In this paper, we examine how these interpretations affect the outcomes of the Namoi ecological model. Two sets of preference curves are produced based on Rogers and Ralph [2010] and Roberts and Marston [2011], respectively, and the model outputs are compared. The goal is to understand the robustness of the model and how the model behaves in face of imperfect knowledge and information in ecosystem’s response to water regime. Significant differences in model outputs were found for periods with large winter floods and for less commonly studied species such as water couch where there is higher uncertainty associated with water requirements. This understanding is critical to guide data collection and future model development.
The Impact of Uncertain Ecological Knowledge on a Water Suitability Model of Riverine Vegetation
Freshwater aquatic ecosystems are increasingly under threat due to climate change and river regulation. Water management is considered important for maintaining and restoring freshwater aquatic ecosystems in Australia. A habitat model based on the water requirements of four riverine vegetation species was developed to assess the suitability of water regimes for the maintenance and regeneration of these species in the Namoi Catchment, Australia. This model identifies characteristics of flooding events (e.g. duration, timing and inter-flood dry period) from hydrological data or models, and generates suitability indices based on the species’ water requirement preference curves. These preference curves are central to the model and were established based on expert knowledge. Two comprehensive sources are available on the water requirements of vegetation species in the Murray-Darling Basin, Australia: Rogers and Ralph [2010] and Roberts and Marston [2011]. Both of these sources are based on extensive review of studies on species’ response to flooding in the Murray-Darling Basin. The water requirements proposed in these two sources differ due to differences in literature selection and interpretation of the literature by the authors. In this paper, we examine how these interpretations affect the outcomes of the Namoi ecological model. Two sets of preference curves are produced based on Rogers and Ralph [2010] and Roberts and Marston [2011], respectively, and the model outputs are compared. The goal is to understand the robustness of the model and how the model behaves in face of imperfect knowledge and information in ecosystem’s response to water regime. Significant differences in model outputs were found for periods with large winter floods and for less commonly studied species such as water couch where there is higher uncertainty associated with water requirements. This understanding is critical to guide data collection and future model development.
