Forecasting species range dynamics with process-explicit models: matching methods to applications.

Area of Science:

• Ecology
• Environmental Science
• Computational Biology

Background:

• Species distribution models (SDMs) traditionally rely on correlative approaches, implicitly capturing ecological processes.
• There is increasing interest in process-explicit models that directly incorporate factors like physiology, dispersal, demography, and biotic interactions.
• Process-explicit models are hypothesized to provide more reliable predictions, especially under novel environmental conditions.

Purpose of the Study:

• To review and assess the strengths, limitations, and current applications of various process-explicit models.
• To determine the suitability of different process-explicit models for common species distribution modeling applications.
• To identify barriers hindering the adoption of process-explicit models and propose solutions.

Main Methods:

• Literature review of process-explicit species distribution models.
• Analysis of model performance across four key applications: regulatory planning, extinction risk assessment, climate refugia identification, and invasive species management.
• Identification of challenges and opportunities for implementing process-explicit models in ecological research and management.

Main Results:

• A range of process-explicit models exist, each with distinct advantages and disadvantages.
• The suitability of specific models varies depending on the ecological application and management objectives.
• Significant barriers, including technical, data-related, and evaluation challenges, impede the widespread use of these advanced models.

Conclusions:

• Process-explicit models hold significant promise for advancing ecological understanding and informing conservation and management.
• More rigorous evaluation of model predictions is essential to guide future methodological development and ensure the effective application of these tools.
• Overcoming identified barriers is critical for realizing the full potential of process-explicit modeling in addressing pressing environmental challenges.