Force of habit: shrubs, trees and contingent evolution of wood anatomical diversity using Croton (Euphorbiaceae) as a model system

Untangling the taxonomic knot of Croton anomalus (Euphorbiaceae), a Neotropical dry forest shrub

Croton anomalus was described by Henri Pittier in 1930 from a collection made in Estado Lara, Venezuela, and the use of the name has so far been restricted to several states in this country. A reevaluation of the species has led to its recircumscription and recognition in several other countries, including Bolivia, Brazil, Colombia, French Guiana, Guyana, Mexico, and Suriname. Previously, it was confused with species referred to here as the "Croton anomalus group", namely C. acapulcensis, C. blanchetianus, C. chiapensis, C. jacobinensis (=C. sonderianus), and C. stahelianus. We integrated morphological, phylogenetic, and ecological evidence to understand species limits and relationships within the Croton anomalus group. We first studied ca. 650 herbarium specimens covering the geographic range of the group, and we inferred species phylogenetic relationships using DNA sequences from the nuclear and plastid regions (ITS and trnL-F). We also used ecological niche modeling to infer potential suitable areas for the occurrence of the studied species and to determine the variables that most contribute to their distribution model. Both morphological and phylogenetic data provide evidence for the synonymization of C. acapulcensis, C. chiapensis, and C. stahelianus under C. anomalus. On the other hand, our results support the recognition of C. blanchetianus and C. jacobinensis as two independent lineages, both distinct from C. anomalus. An emended description of C. anomalus is provided, as well as the designation of lectotypes, illustrations, updates of distribution data, and morphological comparisons with closely related species. Regarding niche modeling, annual precipitation and the precipitation of the warmest quarter were the most important variables explaining species distributions. Croton anomalus showed suitable areas in most seasonally dry tropical forests in the Neotropics, while C. blanchetianus and C. jacobinensis had their most suitable areas restricted to the Caatinga Dry Forest (Brazil), and Caatinga + northern South America, respectively. Our study shows the importance of taxonomic revisions using integrative approaches to disentangling species boundaries and to elucidate their biogeography and conservation status.

Parallel evolution of arborescent carrots (Daucus) in Macaronesia

PREMISE

Despite intensive research, the pathways and driving forces behind the evolution of derived woodiness on oceanic islands remain obscure. The genus Daucus comprises mostly herbs (therophytes, hemicryptophytes) with few rosette treelets (chamaephytes) endemic to various Macaronesian archipelagos, suggesting their independent evolution. To elucidate the evolutionary pathways to derived woodiness, we examined phylogenetic relationships and the habit and secondary xylem evolution in Daucus and related taxa.

METHODS

Sixty taxa were surveyed for molecular markers, life history, and habit traits. Twenty-one species were considered for wood anatomical characters. A dated phylogeny was estimated using Bayesian methods. The evolution of selected traits was reconstructed using parsimony and maximum likelihood.

RESULTS

Daucus dispersed independently to the Canary Islands (and subsequently to Madeira), Cape Verde, and the Azores in the late Miocene and Pleistocene. Life span, reproductive strategy, and life form were highly homoplastic; the ancestor of Daucus was probably a monocarpic, biennial hemicryptophyte. Rosette treelets evolved independently in the Canarian-Madeiran lineage and in Cape Verde, the latter within the last 0.13 Myr. Treelets and hemicryptophytes did not differ in wood anatomy. Pervasive axial parenchyma in wood occurred more often in polycarpic rather than monocarpic species.

CONCLUSIONS

Life span and life form in Daucus are evolutionarily labile and may change independently of wood anatomy, which is related to plant reproductive strategy rather than to life form. Insular woodiness may evolve rapidly (as demonstrated in D. bischoffii), and in Daucus, it does not seem to be an adaptation to lower the risk of xylem embolism.

Machine Learning Models with Quantitative Wood Anatomy Data Can Discriminate between Swietenia macrophylla and Swietenia mahagoni

Illegal logging and associated trade aggravate the over-exploitation of Swietenia species, of which S. macrophylla King, S. mahagoni (L.) Jacq, and S. humilis Zucc. have been listed in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix Ⅱ. Implementation of CITES necessitates the development of efficient forensic tools to identify wood species accurately, and ideally ones readily deployable in wood anatomy laboratories across the world. Herein, a method using quantitative wood anatomy data in combination with machine learning models to discriminate between three Swietenia species is presented, in addition to a second model focusing only on the two historically more important species S. mahagoni and S. macrophylla. The intra- and inter-specific variations in nine quantitative wood anatomical characters were measured and calculated based on 278 wood specimens, and four machine learning classifiers—Decision Tree C5.0, Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were used to discriminate between the species. Among these species, S. macrophylla exhibited the largest intraspecific variation, and all three species showed at least partly overlapping values for all nine characters. SVM performed the best of all the classifiers, with an overall accuracy of 91.4% and a per-species correct identification rate of 66.7%, 95.0%, and 80.0% for S. humilis, S. macrophylla, and S. mahagoni, respectively. The two-species model discriminated between S. macrophylla and S. mahagoni with accuracies of over 90.0% using SVM. These accuracies are lower than perfect forensic certainty but nonetheless demonstrate that quantitative wood anatomy data in combination with machine learning models can be applied as an efficient tool to discriminate anatomically between similar species in the wood anatomy laboratory. It is probable that a range of previously anatomically inseparable species may become identifiable by incorporating in-depth analysis of quantitative characters and appropriate statistical classifiers.