Beyond description: the active and effective way to infer processes from spatial patterns.

Area of Science:

• Ecology
• Spatial Ecology
• Ecological Modeling

Background:

• Ecological processes driving spatial patterns are traditionally studied via direct measurement or experimental manipulation.
• Direct measurement and experimentation can be challenging, costly, or impossible in many ecological contexts.
• Understanding the link between ecological processes and spatial patterns is a long-standing challenge.

Purpose of the Study:

• To formalize and define the
• space as a surrogate
• approach for inferring ecological processes from spatial patterns alone.

Main Methods:

• Utilizing a priori hypotheses, ecological theory, and precise spatial analysis.
• Analyzing existing spatial patterns or spatial residuals to infer underlying ecological processes.
• Reviewing six case studies demonstrating the application of this approach.

Main Results:

• The
• space as a surrogate
• method successfully infers ecological processes in situations where direct measurement is not feasible.
• Demonstrated applications include competition, dispersal, species movement, invasion, disturbance dynamics, and biodiversity patterns.
• This approach provides valuable insights even when experiments are possible, by measuring in situ process importance.

Conclusions:

• The
• space as a surrogate
• approach offers a powerful alternative for ecological inference.
• It maximizes understanding of ecological processes by leveraging spatial data when direct methods are limited.
• This methodology advances the study of process-pattern relationships in ecology.