An integrated data model to estimate spatiotemporal occupancy, abundance, and colonization dynamics

Area of Science:

• Ecology
• Conservation Biology
• Statistical Modeling

Background:

• Ecological invasions are dynamic processes requiring accurate monitoring.
• Estimating species distribution and abundance is crucial for effective management and conservation.
• Existing models face challenges with complex spatiotemporal data, detection probabilities, and computational demands.

Purpose of the Study:

• To develop a statistical framework for simultaneously estimating spatiotemporal occupancy and abundance dynamics of colonizing species.
• To address common issues in ecological data modeling, including multiple detection probabilities and data sources.
• To overcome computational limitations for fine-scale inference over large areas.

Main Methods:

• Developed a novel statistical framework for spatiotemporal modeling.
• Incorporated methods to handle multiple levels of detection probability.
• Integrated multiple data sources for robust estimation.
• Designed the framework to manage computational challenges in large-scale ecological studies.

Main Results:

• The framework successfully estimates colonization dynamics.
• The model accounts for complexities in ecological data, improving accuracy.
• Demonstrated application in estimating sea otter colonization in Glacier Bay, Alaska.

Conclusions:

• The proposed statistical framework offers a robust approach for modeling species colonization dynamics.
• This method enhances the ability to manage and conserve colonizing species by providing reliable spatiotemporal estimates.
• Accurate estimation of ecological invasions is vital for biodiversity conservation and ecosystem management.