Integrated models to support multiobjective ecological restoration decisions.
Many objectives motivate ecological restoration, including improving vegetation condition, increasing the range and abundance of threatened species, and improving species richness and diversity. Although models have been used to examine the outcomes of ecological restoration, few researchers have attempted to develop models to account for multiple, potentially competing objectives. We developed a combined state-and-transition, species-distribution model to predict the effects of restoration actions on vegetation condition and extent, bird diversity, and the distribution of several bird species in southeastern Australian woodlands. The actions reflected several management objectives. We then validated the models against an independent data set and investigated how the best management decision might change when objectives were valued differently. We also used model results to identify effective restoration options for vegetation and bird species under a constrained budget. In the examples we evaluated, no one action (improving vegetation condition and extent, increasing bird diversity, or increasing the probability of occurrence for threatened species) provided the best outcome across all objectives. In agricultural lands, the optimal management actions for promoting the occurrence of the Brown Treecreeper (Climacteris picumnus), an iconic threatened species, resulted in little improvement in the extent of the vegetation and a high probability of decreased vegetation condition. This result highlights that the best management action in any situation depends on how much the different objectives are valued. In our example scenario, no management or weed control were most likely to be the best management options to satisfy multiple restoration objectives. Our approach to exploring trade-offs in management outcomes through integrated modeling and structured decision-support approaches has wide application for situations in which trade-offs exist between competing conservation objectives.
School of BioSciences, The University of Melbourne, Parkville, Victoria, 3010, Australia.
Hannah
H
Fraser
School of BioSciences, The University of Melbourne, Parkville, Victoria, 3010, Australia.
Libby
L
Rumpff
School of BioSciences, The University of Melbourne, Parkville, Victoria, 3010, Australia.
Jian D L
JDL
Yen
Trust for Nature, 5/379 Collins Street, Melbourne Victoria, 3000, Australia.
Doug
D
Robinson
School of BioSciences, The University of Melbourne, Parkville, Victoria, 3010, Australia.
Brendan A
BA
Wintle
D063147:Animal Distribution; D000818:Animals; D044822:Biodiversity; D003247:Conservation of Natural Resources; D052918:Environmental Restoration and Remediation / Q000379:methods*; D065928:Forests; D008954:Models, Biological; D020308:Songbirds / Q000502:physiology*; D013045:Species Specificity; D014739:Victoria
Bayesian network; análisis multicriterio de decisión; compensaciones; modelo de distribución de especies; modelo de estado y transición; multicriteria decision analysis; red Bayesiana; species distribution models; state-and-transition model; trade-offs