Identifying cryptic diversity with predictive phylogeography

Area of Science:

• Organismal biology
• Biodiversity assessment
• Conservation science

Background:

• Identifying units of biological diversity is crucial.
• Cryptic diversity, deeply diverged lineages within a species, is increasingly recognized.
• Rapid biodiversity assessments are needed for conservation policy.

Purpose of the Study:

• To introduce a predictive framework for phylogeography to rapidly identify cryptic diversity.
• To develop a method for predicting cryptic diversity in taxa endemic to specific biomes.
• To transition phylogeography from a descriptive to a predictive discipline.

Main Methods:

• Collected environmental, taxonomic, and genetic data from codistributed taxa with known phylogeographic histories as a reference set.
• Categorized reference taxa as harboring or lacking cryptic diversity.
• Built a random forest classifier to predict cryptic diversity in other taxa within the same biome.

Main Results:

• The predictive framework achieved high accuracy, ranging from 65% to 98.79% depending on the ecosystem.
• The method successfully predicted the cryptic/non-cryptic nature of unknown species, aligning with recent discoveries.
• The approach is applicable to ecosystem-level questions about cryptic diversity.

Conclusions:

• The developed predictive framework is effective for rapidly identifying cryptic diversity.
• This method enables ecosystem-level assessments of cryptic diversity, informing conservation decisions.
• Phylogeography can be effectively transitioned into a predictive science.