%0 Journal Article
%T Probabilistic Inference Offers a Stronger Basis for Integrative Insect Species Boundaries Than Binary Taxonomic Decisions
%A Pieter Botha
%A Anel Van Wyk
%A Johan Marais
%J Entomological Research Letters
%@ 3108-639X
%D 2026
%V 6
%N 1
%R 10.51847/gTTu0RTyZn
%P 71-81
%X Integrative insect systematics increasingly combines morphology, molecular and genomic data, ecology, geography, and behaviour, yet the transition from heterogeneous evidence to a categorical species decision remains conceptually uneven. Different evidence classes measure different biological properties, vary in diagnostic resolution, and can be statistically or biologically dependent. We develop a proposed Bayesian conceptual architecture in which species boundaries are treated as competing hypotheses supported to differing degrees rather than as immediate binary outcomes. Prior information is separated from likelihood evidence; morphological, molecular-genomic, and ecological-behavioural contributions remain analytically distinct; evidence dependence and model adequacy are assessed before synthesis; and posterior boundary support is separated from the downstream decision to recognize or reject a species hypothesis. The framework also distinguishes lack of detected differentiation from affirmative counterevidence and maintains sampling, model, geographic, and taxonomic uncertainty after evidence integration. Insect examples demonstrate why mitochondrial divergence, genomic structure, morphology, ecological association, and behavioural differentiation cannot be treated as interchangeable votes. The framework is not an empirically validated delimitation algorithm and does not prescribe universal probability thresholds. Instead, it provides a testable systems-level structure for making evidence provenance, conditionality, dependence, and uncertainty explicit in integrative species-boundary inference.
%U https://esvpub.com/article/probabilistic-inference-offers-a-stronger-basis-for-integrative-insect-species-boundaries-than-binar-k0tkmplgmujalvc