A tool for simulating and communicating uncertainty when modelling species distributions under future climates.

Area of Science:

• Ecology and Evolutionary Biology
• Environmental Science
• Computational Biology

Background:

• Accurate spatial predictions of species distributions under future climate conditions are essential for conservation.
• Existing methods often struggle to adequately explore and communicate the impact of various uncertainty sources.
• Uncertainty in species distribution modeling (SDM) arises from data quality, model choice, and future climate scenarios.

Purpose of the Study:

• To develop and present a novel tool for simulating and visualizing uncertainty in species distribution models.
• To assess the impact of data quality uncertainty on spatial predictions for a Tasmanian endemic species.
• To illustrate differences in model projections under various global climate models and emissions scenarios.

Main Methods:

• Development of a simulation tool to quantify uncertainty stemming from data quality in SDMs.
• Application of the tool to a case study involving a Tasmanian endemic species.
• Comparison of model projections using six global climate models and two contrasting emissions scenarios.

Main Results:

• The simulations provided probabilistic, spatially explicit illustrations of uncertainty's impact on species distribution projections.
• Different sources of uncertainty were shown to have varying impacts on model outputs.
• The geographic distribution of uncertainty was demonstrated to vary significantly across different scenarios.

Conclusions:

• The study provides a conceptual framework and a practical tool for understanding and communicating uncertainty in SDMs.
• The developed tool aids in addressing uncertainty related to climate models and emissions scenarios.
• This approach represents a significant advancement for informing conservation policy and practice under future climate change.