Uncovering Cryptic Coevolution.

Area of Science:

• Evolutionary Biology
• Ecology
• Genetics

Background:

• Coevolution research has historically focused on specialized species with exaggerated traits, creating an impression that coevolution is rare.
• This focus on "obvious" coevolutionary examples is partly due to a lack of statistical methods to study more generalized interactions.
• Textbook examples include newt-garter snake, fly-flower, and weevil-plant interactions, which are striking but not representative of all coevolution.

Purpose of the Study:

• To argue that the study of coevolution has been biased towards conspicuous examples, neglecting "cryptic" coevolution in more common species.
• To propose that model-based approaches can overcome limitations in studying generalized coevolutionary systems.
• To highlight the potential of new methods to estimate coevolutionary selection indirectly using routine data.

Main Methods:

• Leveraging established coevolutionary theory to develop and illustrate model-based analytical approaches.
• Utilizing routinely collected data to indirectly estimate the strength of coevolutionary selection.
• Applying these methods to analyze coevolutionary selection across genomes, phylogenies, and communities over deep time.

Main Results:

• Demonstrates how model-based approaches can be used to uncover cryptic coevolution in typical communities.
• Enables the estimation of coevolutionary selection in systems previously considered too complex or mundane.
• Shows the potential for these novel approaches to revolutionize the study of coevolution by analyzing its distribution broadly.

Conclusions:

• The focus on exaggerated phenotypes has limited our understanding of coevolution; cryptic coevolution is likely widespread.
• Next-generation, model-based statistical approaches are crucial for revealing hidden coevolutionary processes.
• These methods offer a powerful new framework for studying coevolution across diverse biological scales and timescales.