Crosstaxa

Crosstaxa is a term used to describe analytic approaches that operate across multiple taxonomic groups, focusing on interactions, traits, and phylogenetic patterns that span more than one lineage. It encompasses methods that compare, transfer, or integrate information across diverse clades, such as plants and animals or hosts and pathogens.

The word combines "cross" and "taxa" and is employed in discussions of cross-taxon analysis where insights from one clade inform investigations in another. In this sense, crosstaxa refers not to a single technique but to a family of approaches aimed at understanding similarities and differences across broad biological divisions.

Core concepts in crosstaxa include data integration across taxa, cross-taxa comparative methods, and cross-taxa trait evolution modeling. Methods often rely on phylogenetically informed models, hierarchical or multi-task learning, and transfer learning that leverages information from one taxonomic group to improve inference in another. Analytical challenges include reconciling different data types, scales of measurement, and phylogenetic uncertainty.

Applications span comparative genomics across clades, study of convergent trait evolution, ecological network analysis across communities, and cross-taxon disease ecology. For example, researchers might compare trait correlations across multiple vertebrate and invertebrate groups to identify shared functional patterns or predictive signals that hold across distant lineages.

Challenges and limitations include uneven data availability, sampling bias, and achieving measurement invariance across taxa. Conceptual issues also arise in ensuring that cross-taxa inferences are biologically meaningful rather than artifacts of unequal data quality.

See also: Comparative method, Phylogenetics, Cross-taxon transfer, Transfer learning, Multi-task learning.