Identifying cryptic diversity with predictive phylogeography

Abstract

Identifying units of biological diversity is a major goal of organismal biology. An increasing literature has focused on the importance of cryptic diversity, defined as the presence of deeply diverged lineages within a single species. While most discoveries of cryptic lineages proceed on a taxon-by-taxon basis, rapid assessments of biodiversity are needed to inform conservation policy and decision-making. Here, we introduce a predictive framework for phylogeography that allows rapidly identifying cryptic diversity. Our approach proceeds by collecting environmental, taxonomic and genetic data from codistributed taxa with known phylogeographic histories. We define these taxa as a reference set, and categorize them as either harbouring or lacking cryptic diversity. We then build a random forest classifier that allows us to predict which other taxa endemic to the same biome are likely to contain cryptic diversity. We apply this framework to data from two sets of disjunct ecosystems known to harbour taxa with cryptic diversity: the mesic temperate forests of the Pacific Northwest of North America and the arid lands of Southwestern North America. The predictive approach presented here is accurate, with prediction accuracies placed between 65% and 98.79% depending of the ecosystem. This seems to indicate that our method can be successfully used to address ecosystemlevel questions about cryptic diversity. Further, our application for the prediction of the cryptic/non-cryptic nature of unknown species is easily applicable and provides results that agree with recent discoveries from those systems. Our results demonstrate that the transition of phylogeography from a descriptive to a predictive discipline is possible and effective.

Integrating life history traits into predictive phylogeography

“…Because GBIF also includes observations that correspond to living collections (e.g., botanical gardens, private gardens, parks), GBIF occurrences were excluded if they fell outside the focal range of the focal taxa (35° to 65° latitude, −160° to −100° longitude). This filter applied mostly to plant localities and was similar to that applied by Espíndola et al (). We downloaded the bioclimatic variables from worldclim version 2 on 8 June 2018 (Fick & Hijmans, ) and we collected trait data from different sources, such as public US Forest Service reports, NatureWeb, and the literature (Supporting Information Table ).…”

“…Espíndola et al () also examined a set of taxa from comparative phylogeography of the Southwest Arid Lands (SWAL) for the presence or absence of a deep phylogeographic break across the Colorado River, which separates Baja Californian from the Sonoran Desert (see figure 1b in Espíndola et al, ). This system has played a central role in the maturation of comparative phylogoegraphy (e.g., Riddle, Hafner, Alexander, & Jaeger, ; Zink, ), and we follow Espíndola et al () in using 14 amphibian, bird, and mammal species/complexes as a reference set of taxa for predicting the presence or absence of this deep phylogeographic break. Their initial classifier achieved a moderate prediction accuracy (~69%) with environmental variables and taxonomy used as predictor variables.…”

“…As above, occurrence data were from Espíndola et al (), and we downloaded the bioclimatic variables from worldclim version 2 on 8 June 2018 (Flick & Hijmans, ). We did not include the species Melozone fuscus (formerly Pipilo fuscus ) because, as currently defined, it does not include populations in Baja California.…”

“…Therefore, the third and fourth RF classifiers excluded two species/species complexes: Chonaphe armata and the Prophysaon vanattae/humile species complex (Curated Taxa & All Traits). These taxa have been difficult to classify as having or lacking cryptic diversity because of either incongruence between morphological and genetic data (Smith et al, ), or incongruence between validation approaches using the same genetic data (Espíndola et al, ), and therefore may mislead the RF classifier (Espíndola et al, ; Smith et al, ). The third and fourth RF classifiers were constructed with and without taxonomy, respectively (Curated Taxa, All Traits & Taxonomy; Curated Taxa, All Traits & No Taxonomy).…”

“…Concurrent with this colloquium, Espíndola et al () introduced a supervised machine‐learning approach using random forests (RF) (Breiman, ) that integrates comparative phylogeography with distributional and climate data housed in publicly available databases such as GBIF and WorldClim (Fick & Hijmans, ). The method of Espíndola et al () uses these geospatial data as predictor variables to develop a classifier that permits predictions to be made about the nature of genetic variation (e.g., the presence or absence of cryptic diversity, as indicated by deep genetic divergence or phylogeographic breaks) in taxa that have little to no genetic data yet available. Specifically, an existing comparative phylogeographic data set is used as the training data set to build the classifier, and, of the various supervised machine‐learning options available, RF permit an assessment of the importance of predictor variables in the predictive function.…”