Anticipating species distributions: Handling sampling effort bias under a Bayesian framework.

Area of Science:

• Ecology
• Conservation Biology
• Spatial Modeling

Background:

• Accurate species distribution modeling is crucial for biodiversity conservation and invasive species management.
• Sampling effort bias can lead to inaccurate estimations of species occurrence and density.
• Uncertainty in model outputs necessitates careful interpretation to avoid flawed decision-making.

Purpose of the Study:

• To develop an innovative method for mapping sampling effort bias.
• To explicitly incorporate sampling bias uncertainty into species distribution models.
• To improve the accuracy of anticipating species distributions in space and time.

Main Methods:

• Utilizing cartogram models to visualize sampling effort bias.
• Employing a Bayesian framework to integrate multilevel data and prior information.
• Developing a new modeling procedure that accounts for sampling uncertainty.

Main Results:

• The proposed method effectively maps sampling effort bias.
• Incorporating bias correction improves the reliability of species distribution predictions.
• The Bayesian approach enhances the integration of diverse data sources.

Conclusions:

• The developed method offers a robust approach to address sampling bias in species distribution modeling.
• This enhances the accuracy of predictions for conservation planning and invasive species management.
• Explicitly considering uncertainty improves the robustness of ecological modeling outputs.