Biology subjects

Punyasena, S. W.

Publications and source records attributed to Punyasena, S. W..

6 recordsLinked to original sources

bioRxiv2025

Pollen morphology, deep learning, phylogenetics, and the evolution of environmental adaptations in Podocarpus

O_LIPodocarpus pollen morphology is shaped by both phylogenetic history and the environment. We analyzed the relationship between pollen traits quantified using deep learning and environmental factors within a comparative phylogenetic framework. C_LIO_LIWe investigated the influence of mean annual temperature, annual precipitation, altitude, and solar radiation in driving morphological change. We used trait-environment regression models to infer the temperature tolerances of 31 Neotropical Podocarpidites fossils. Ancestral state reconstructions were applied to the Podocarpus phylogeny with and without the inclusion of fossils. C_LIO_LIOur results show that temperature and solar radiation influence pollen morphology, with thermal stress driving an increase in pollen size and higher UV-B radiation selecting for thicker corpus walls. Fossil temperature tolerances inferred from trait-environment models aligned with paleotemperature estimates from global paleoclimate models. Incorporating fossils into ancestral state reconstructions revealed that early ancestral Podocarpus lineages were likely adapted to warm climates, with cool-temperature tolerance evolving independently in high-latitude and high-altitude species. C_LIO_LIOur results highlight the importance of deep learning-derived features in advancing our understanding of plant environmental adaptations over evolutionary timescales. Deep learning allows us to quantify subtle interspecific differences in pollen morphology and link these traits to environmental preferences through statistical and phylogenetic analyses. C_LI

evolutionary biology↗

bioRxiv2025

Addressing the open world: detecting and segmenting pollen on palynological slides with deep learning

In the "open world", categorical classes are imbalanced, test classes are not known a priori, and test data are captured across different domains. Paleontological data can be described as open-world, as specimens may include new, unknown taxa, and the data collected, such as measurements or images, may not be standardized across different studies. Fossil pollen analysis is one example of an open-world problem in paleontology. Pollen samples capture large numbers of specimens, including not only common types but also rare and even novel taxa. Pollen is diverse morphologically and features can be altered during fossilization. Additionally, there is little standardization in the methods used to capture and catalog pollen images and most collections are mounted on microscope slides. Therefore, generalized workflows for automated pollen analysis require techniques that are robust to these differences and can work with microscope images. We focus on a critical first step, the detection of pollen specimens on a palynological slide and review how existing methods can be employed to build robust and generalizable analysis pipelines. First, we demonstrate how a mixture-of-experts approach - the fusion of a general pollen detector with an expert model trained on minority classes - can be used to address taxonomic biases in detections, particularly the missed detections of rarer pollen types. Second, we demonstrate the efficiency of domain fine-tuning in addressing domain gaps - differences in image magnification and resolution across microscopes, and of taxa across different sample sources. Third, we demonstrate the importance of continual learning workflows, which integrate expert feedback, in training detection models from incomplete data. Finally, we demonstrate how cutting-edge segmentation models can be used to refine and clean detections for downstream deep learning classification models. Non-technical AbstractFossil pollen analysis presents challenges that result from the complexity of the "open world". It is not possible to anticipate all the taxa that will be encountered in geologic samples. Each sample has the potential to produce new species and introduce specimens with unique shapes and sizes. Pollen embodies diverse morphologies, its appearance can be altered depending on the conditions under which it was fossilized, and there is little standardization in the microscopy equipment used to image and catalog pollen images. AI applications in palynology need to address this complexity. We applied four machine learning methods (mixture-of-expert models, domain fine-tuning, continual learning, and foundation models for segmentation) to fossil pollen data to demonstrate their effectiveness in the detection and isolation of pollen specimens on a pollen sample slide.

paleontology↗

bioRxiv2024

Reconstructing the diversity dynamics of paleo-grasslands using deep learning on superresolution images of fossil Poaceae pollen

Grass pollen is largely overlooked in investigating grassland evolution because the pollen of most species cannot be differentiated using traditional optical microscopy. However, deep learning can quantify small variations in pollen morphology visible under superresolution microscopy. We use the abstract features output by deep learning to estimate the taxonomic diversity and physiology of fossil grass pollen assemblages. Using a semi-supervised learning strategy, we trained convolutional neural networks (CNNs) on superresolution pollen images of modern grasses and unlabeled fossil Poaceae. Our models captured features that reflected both the taxonomic diversity of grass communities along an elevational gradient and morphological differences between C3 and C4 species. We applied our trained models to fossil grass pollen assemblages from a 25,000-year lake-sediment record from eastern equatorial Africa (Mt. Kenya) and correlated past shifts in grass diversity with atmospheric CO2 concentration and proxy records of local temperature, precipitation, and fire occurrence. We quantified changes in grass diversity using morphological variability of fossil pollen assemblages, approximated by the Shannon entropy of CNN features. Our data show that grassland species diversity was strongly reduced between 21,500 and 16,000 years ago, coincident with most severe regional cooling during the last ice age. C3:C4 ratios reconstructed using a gradient-boosted decision tree classifier infer a gradual decrease in C4 grasses since the late-glacial to Holocene transition, associated with decreasing fire activity and elevated temperatures. Our results demonstrate that CNN features of pollen morphology can advance palynological analysis, enabling robust estimation of grass diversity and C3:C4 ratio in ancient grassland ecosystems. SignificanceAlthough the pollen of most grass species are morphologically indistinguishable using traditional optical microscopy, we show that they can be differentiated through deep learning analyses of superresolution images. Abstracted morphological features derived from convolutional neural networks can be used to quantify the biological and physiological diversity of grass pollen assemblages, without a priori knowledge of the species present, and used to reconstruct past changes in the taxonomic diversity and relative abundance of C4 grasses in ancient grasslands. This approach unlocks ecological information previously unattainable from the fossil pollen record and demonstrates that deep learning can solve some of the most intractable identification problems in the reconstruction of past vegetation dynamics.

paleontology↗

bioRxiv2024

Niche conservatism in the Legume Amherstieae tribe: Insights from the tropical Berlinia and Brownea clades

The concept of niche conservatism describes the tendency of organisms to retain ecological traits through time and space. Reviewing this concept in different groups of angiosperms is critical to understanding what factors drove their expansion and geographic distribution, as well as assessing how, in taxonomic levels higher than species, ecological traits have remained relatively constant through time and space. Studying niche conservatism can also help us understand how the distribution of clades may be affected by climate change. Niche conservatism has been observed in many clades of legumes. Amherstieae, the largest tribe of the Detarioideae subfamily, has a geographic distribution, evolutionary history, and phylogeny that makes it a good candidate for evaluating patterns in niche conservatism. We analyzed the distribution of two Amherstieae suprageneric clades, the Berlinia and Brownea clades. The former is endemic to sub-Saharan Africa and the latter is restricted to the Neotropics. We used the geographic distributions of each clade to define their G-space (geographic space) and extracted 19 climatic variables to define the E-space (environmental space) of each clade. We used two tests to evaluate the similarity in the climatic niche of both clades, the niche overlap test (NOT) to estimate similarities between the occupied E-spaces (realized niche space) and the niche divergence test (NDT) to assess the similarity of the environmental niche relative to the accessible analogous E-space (potential niche space) of each biogeographic region. Our results suggest that the Brownea clade are descendants of a climatic subset of the Berlinia clade preferring less variable temperature and higher precipitation levels, and that the dry-adapted subset of Berlinia may represent a more recent evolutionary expansion.

ecology↗

bioRxiv2023

CuticleTrace: A toolkit for capturing cell outlines of leaf cuticle with implications for paleoecology and paleoclimatology

PremiseLeaf epidermal cell morphology is closely tied to plants evolutionary histories and growth environments, and is therefore of interest to many plant biologists. However, cell measurement can be time-consuming and restrictive with current methods. CuticleTrace is a suite of FIJI and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and ResultsWe evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand-tracings across a taxonomically diverse 50-image dataset of variable image qualities. We observed [~]93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods. ConclusionsCuticleTrace is broadly applicable, modular, and customizable, and integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape datasets.

paleontology↗

bioRxiv2023

Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes

The phylogenetic interpretation of pollen morphology is limited by our inability to recognize the evolutionary history embedded in pollen features. Deep learning offers tools for connecting morphology to phylogeny. Using neural networks, we developed an explicitly phylogenetic toolkit for analyzing the overall shape, internal structure, and texture of a pollen grain. Our analysis pipeline determines whether testing specimens are from unknown species based on uncertainty estimates. Features of novel specimens are passed to a multi-layer perceptron network trained to transform these features into predicted phylogenetic distances from known taxa. We used these predicted distances to place specimens in a phylogeny using Bayesian inference. We trained and evaluated our models using optical superresolution micrographs of 30 Podocarpus species. We then used trained models to place nine fossil Podocarpidites specimens within the phylogeny. In doing so, we demonstrate that the phylogenetic history encoded in pollen morphology can be recognized by neural networks and that deep-learned features can be used in phylogenetic placement. Our approach makes extinction and speciation events that would otherwise be masked by the limited taxonomic resolution of the fossil pollen record visible to palynological analysis. Significance StatementMachine learned features from deep neural networks can do more than categorize and classify biological images. We demonstrate that these features can also be used to quantify morphological differences among pollen taxa, discover novel morphotypes, and place fossil specimens on a phylogeny using Bayesian inference. Deep learning can be used to characterize and identify and morphological features with evolutionary significance. These features can then be used to infer phylogenetic distance. This approach fundamentally changes how fossil pollen morphology can be interpreted, allowing greater evolutionary inference of fossil pollen specimens. The analysis framework, however, is not specific to pollen and can be generalized to other taxa and other biological images.

paleontology↗