Chapple, D. G. | Search bioRxiv

bioRxiv2024

Evolutionary bursts drive morphological novelty in the world's largest skinks

Animal phenotypes evolve and diverge as a result of differing selective pressures and drift. These processes leave unique signatures in patterns of trait evolution, impacting the tempo and mode of morphological macroevolution. While there is a broad understanding of the history of some organismal traits (e.g. body size), there is little consensus about the evolutionary mode of most others. This includes the relative contribution of prolonged (Darwinian gradualist) and episodic (Simpsonian jump) changes towards the evolution of novel morphologies. Here we use new exon-capture and linear morphological datasets to investigate the tempo and mode of morphological evolution in Australo-Melanesian Tiliquini skinks. We generate a well-supported time-calibrated phylogenomic tree from [~]400 nuclear markers for more than 100 specimens including undescribed diversity, and provide unprecedented resolution of the rapid Miocene diversification of these lizards. By collecting a morphological dataset that encompasses the lizard body plan (19 traits across the head, body, limb, and tail) we are able to identify that most traits evolve conservatively but infrequent evolutionary bursts result in morphological novelty. These phenotypic discontinuities occur via rapid rate increases along individual branches, inconsistent with both gradualistic and punctuated equilibrial evolutionary modes. Instead, this punctuated gradualism has resulted in the rapid evolution of blue-tongued giants and armored dwarves in the [~]20 million years since colonizing Australia. These results outline the evolutionary pathway towards new morphologies and highlight the heterogeneity of evolutionary tempo and mode, even within individual traits.

evolutionary biology↗

bioRxiv2022

Automated assessment reveals extinction risk of reptiles is widely underestimated across space and phylogeny

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision making. However, despite substantial effort, numerous species remain unassessed, or have insufficient data available to be assigned a Red List threat category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here we aimed to: 1) present a machine learning based automated threat assessment method that can be used on less known species; 2) offer provisional assessments for all reptiles - the only major tetrapod group without a comprehensive Red List assessment; and 3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. Our models range in accuracy from 88% to 93% for classifying species as threatened/non-threatened, and from 82% to 87% for predicting specific threat categories. Unassessed and Data Deficient reptiles were more likely to be threatened than assessed species, adding to mounting evidence that they should be considered threatened by default. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap on other less known taxa.

ecology↗