Biology subjects

Hsiang, A. Y.

Publications and source records attributed to Hsiang, A. Y..

4 recordsLinked to original sources

bioRxiv2023

Relative Homoplasy Index: A New Cross-comparable Metric for Quantifying Homoplasy in Discrete Character Datasets

Homoplasy is among the main hinderances to phylogenetic inference. However, investigating patterns of homoplasy can also improve our understanding of macroevolution, for instance by revealing evolutionary constraints on morphology, or highlighting convergent form-function relationships. Several methods have been proposed to quantify the extent of homoplasy in discrete character matrices, but the consistency index (CI) and retention index (RI) have remained the most widely used for decades, with little recent scrutiny of their function. Here, we test the performance of CI and RI using simulated and empirical datasets and investigate patterns of homoplasy with different matrix scenarios. In addition, we describe and test a new scaled metric, the relative homoplasy index (RHI), implemented in the R statistical environment. The results suggest that, unlike the RI, the CI does not constitute a direct measure of homoplasy. However, the RI consistently underestimates the extent of homoplasy in phylogenetic character-taxon matrices, particularly in datasets characterised by high levels of homoplasy. By contrast, RHI--the newly proposed metric--outperforms both methods in sensitivity to homoplasy levels, and is scaled between zero and one for comparison of values between different datasets. Using both simulated and empirical phylogenetic datasets, we show that relative levels of homoplasy remain constant with the addition of novel characters, and, in contrast to earlier work, decrease with the addition of taxa. Our results help illuminate the inherent properties of homoplasy in cladistic matrices, opening new potential avenues of research for investigating patterns of homoplasy in macroevolutionary studies.

evolutionary biology↗

bioRxiv2023

AI in Paleontology

Accumulating data have led to the emergence of data-driven paleontological studies, which reveal an unprecedented picture of evolutionary history. However, the fast-growing quantity and complication of data modalities make data processing laborious and inconsistent, while also lacking clear benchmarks to evaluate data collection and generation, and the performances of different methods on similar tasks. Recently, Artificial Intelligence (AI) is widely practiced across scientific disciplines, but has not become mainstream in paleontology where manual workflows are still typical. In this study, we review more than 70 paleontological AI studies since the 1980s, covering major tasks including micro-and macrofossil classification, image segmentation, and prediction. These studies feature a wide range of techniques such as Knowledge Based Systems (KBS), neural networks, transfer learning, and many other machine learning methods to automate a variety of paleontological research workflows. Here, we discuss their methods, datasets, and performance and compare them with more conventional AI studies. We attribute the recent increase in paleontological AI studies to the lowering bar in training and deployment of AI models rather than real progress. We also present recently developed AI implementations such as diffusion model content generation and Large Language Models (LLMs) to speculate how these approaches may interface with paleontological research. Even though AI has not yet flourished in paleontological research, successful implementation of AI is growing and show promise for transformative effect on the workflow in paleontological research in the years to come. HighlightsO_LIFirst systematic review of AI applications in paleontology. C_LIO_LIThere is a 10 to 20-year gap between AI in paleontology and mainstream studies. C_LIO_LIRecent progress in paleontological AI studies is likely a result of lowering bar in training and deployment. C_LIO_LIFuture direction discussed for interactions between paleontology and AI. C_LI

paleontology↗

bioRxiv2022

Automated community ecology using deep learning: a case study of planktonic foraminifera

The development of deep learning methods using convolutional neural networks (CNNs) has revolutionised the field of computer vision in recent years. The automation of taxonomic identification using CNNs leads naturally to the use of such technology for rapidly generating large organismal datasets in order to study the evolutionary and ecological dynamics of biological communities across time and space. While CNNs have been used to train machine learning classifiers that can identify organisms to the species level for several groups, this vision of automated community ecology has yet to be thoroughly tested or fulfilled. Here, we present a case study of automated community ecology using a large dataset of Atlantic planktonic foraminifera for which the generation of species labels and morphometric measurements was completely automated. We compare standard community diversity metrics between the fully automated dataset and a "traditional" dataset with human-identified specimens. We show that there is high congruence between the results, and that machine classifications help avoid biases that can result in the inference of misleading biodiversity patterns. Our study demonstrates the viability and potential of fully automated community ecology and sets the stage for a new era of ecological and evolutionary inquiry driven by artificial intelligence.

ecology↗

bioRxiv2020

Stepwise Bayesian Phylogenetic Inference

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost that would be incurred. Instead, phylogenetic pipelines generally consist of chained analyses, whereby a single point estimate from a given analysis is used as input for the next analysis in the chain (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step in the chain, which can lead to inaccurate or spuriously certain results. Here, we formally develop and test the stepwise approach to Bayesian inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior produced in the previous step. We show that this approach is identical to the joint approach given sufficient information in the data and in the importance sample. This is demonstrated using both a toy example and an analysis pipeline for inferring divergence times using a relaxed clock model. The stepwise approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in program choice (and hence model availability) and can be more computationally efficient than the traditional joint approach when multiple models are being tested.

evolutionary biology↗