Integrating life history traits into predictive phylogeography

Abstract

Predictive phylogeography seeks to aggregate genetic, environmental and taxonomic data from multiple species in order to make predictions about unsampled taxa using machine‐learning techniques such as Random Forests. To date, organismal trait data have infrequently been incorporated into predictive frameworks due to difficulties inherent to the scoring of trait data across a taxonomically broad set of taxa. We refine predictive frameworks from two North American systems, the inland temperate rainforests of the Pacific Northwest and the Southwestern Arid Lands (SWAL), by incorporating a number of organismal trait variables. Our results indicate that incorporating life history traits as predictor variables improves the performance of the supervised machine‐learning approach to predictive phylogeography, especially for the SWAL system, in which predictions made from only taxonomic and climate variables meets only moderate success. In particular, traits related to reproduction (e.g., reproductive mode; clutch size) and trophic level appear to be particularly informative to the predictive framework. Predictive frameworks offer an important mechanism for integration of organismal trait, environmental data, and genetic data in phylogeographic studies.

Phylogeography of widespread species in Eurasia: Current progress and future prospects

“…Avise等人 [34] 认为, 需要开发基于特征(traits)的系统发 育研究方法, 并将其整合到谱系地理学的研究中, 以 促进谱系地理学的发展. Espíndola等人 [82] [83,84] . Sullivan等人 [84] The geographical distribution patterns of the widespread species in Eurasia are classified into four major categories: global distribution, holarctic distribution, mid-high latitude Eurasian distribution, and mid-low latitude Eurasian distribution patterns.…”

“…Espíndola等人 [82] [83,84] . Sullivan等人 [84] The geographical distribution patterns of the widespread species in Eurasia are classified into four major categories: global distribution, holarctic distribution, mid-high latitude Eurasian distribution, and mid-low latitude Eurasian distribution patterns. By comparing the results of recent phylogeographic studies on the widespread species in Eurasia, we have outlined three phylogeographic patterns: (i) "single-component," (ii) "east-west component," and (iii) "peripatric component" patterns.…”

Genomic evidence of an ancient Inland Temperate Rainforest

“…In this study, we test predictions about the phylogeographic history of these two species, specifically with respect to whether or not they harbor cryptic diversity across the disjunction; that is, show evidence of pre-Pleistocene divergence and no subsequent migration. These predictions serve as a test of the predictive framework that was originally developed by Espíndola et al (2016) and recently updated with life history traits by Sullivan et al (2019). We then validate these predictions, and ultimately test whether the ITR persisted throughout the Pleistocene (Brunsfeld et al .…”

“…We used these locality coordinates to extract values from 19 bioclimatic variables from WOLRDCLIM v2 on 5 Feb 2019 (Fick & Hijmans, 2017) at a resolution of 30 arc-secs (~1 km 2 ). We also assembled trait data to coincide with the trait data collected for PNW taxa for predictive phylogeography as in (Sullivan et al 2019). Using these data, we use the four classifiers and followed the procedure of Sullivan et al (2019) to predict the probability of each species being cryptic.…”

The role of cryptic diversity and its environmental correlates in global conservation status assessments: Insights from the threatened bird's‐eye primrose (Primula farinosa L.)

“…However, the number of studies that use genome‐wide data to uncover cryptic diversity remains small (see Struck et al, for a review). Additionally, there is an ever growing need to broaden the definition of cryptic diversity and incorporate environmental data when assessing intraspecific cryptic variation (Espíndola et al, ; Freudenstein, Broe, Folk, & Sinn, ; Sullivan et al, ).…”