Biology subjects

Hardin, R.

Publications and source records attributed to Hardin, R..

3 recordsLinked to original sources

bioRxiv2026

Laser scanning identifies large trees as a major source of uncertainty in mangrove carbon accounting

BackgroundMangrove forests are crucial ecosystems which support biodiversity, protect coastlines and store vast amounts of carbon. Mangrove conservation and protection rely on accurate carbon accounting to unlock investment. However, the allometric equations underpinning these carbon estimates remain poorly constrained, particularly for the large trees. MethodsWe used terrestrial laser scanning (TLS) to estimate the biomass of 187 mangrove stems across Suriname, Panama, Colombia and Jamaica, including 84 stems >20 cm DBH. TLS-derived biomass estimates were used to evaluate local, regional and pantropical allometric equations. ResultsMost diameter-based allometric equations underestimated biomass by 8-65%. Equations additionally incorporating tree height performed better, but still underestimated biomass by 12-16% on average. Applying alternative allometries to a representative mangrove inventory from Panama produced biomass estimates ranging from 80 to 200 Mg ha-{superscript 1}, demonstrating that allometric uncertainty alone can generate more than a two-fold difference in estimated carbon stocks. ConclusionsCurrent allometric equations systematically underestimate the biomass of large mangrove trees and are therefore likely to underestimate mangrove carbon stocks. TLS provides a practical, non-destructive approach for expanding biomass datasets and improving allometric equations. Reducing allometric uncertainty should be a priority for strengthening blue carbon accounting and mangrove conservation.

ecology↗

bioRxiv2026

Beyond student outcomes: How creating Open Educational Resources benefits authors in a research coordination network

Open educational resources (OERs) contain authentic materials that benefit students, but few studies have focused on the benefits to authors of OERs. This gap needs attention considering the challenges that OER authors face, given their commitments to multiple professional activities while also being motivated to take part in OER development. It is critical to understand what benefits authors receive, to help in the continued development of these valuable educational tools. To this end, we investigated what benefits a specific group of researcher-educators perceived from investing their limited time and energy to design, create, and share authentic OERs in the OCELOTS (Online Content for Experiential Learning of Tropical Systems) Research Coordination Network in Undergraduate Biology Education. Our study was based on conceptual frameworks for teaching and learning, communities of practice, and self-determination theory. We used qualitative data from a survey specifically designed to address the question of benefits perceived by OER authors, complemented with quantitative and qualitative data from existing internal evaluations of this network. In a content-analysis framework, we analyzed the open-ended responses to identify broader themes emerging about author benefits. OER authors reported improved pedagogical practice, increased visibility of research and outreach efforts, professional rewards, and increased collaborations. Authors reported gains in pedagogical knowledge and personal fulfillment as benefits that they received, along with satisfaction from contributing to their discipline and society in general. While benefits around improving pedagogical practice was the richest theme, creation of modules also generated new collaborations and helped strengthen and broaden authors professional networks. In particular, the sense of belonging to and building the community was a significant benefit, providing implications for how to support future OER development and the critical role of peer networks. We discuss connections across these themes and compare our results with related previous studies. These results indicate that sustained investment in intentionally designed, interdisciplinary networks can generate substantial and diverse benefits for the educators and researchers who create these resources. Open Research StatementThe de-identified data associated with this manuscript will be permanently archived in Zenodo, upon the acceptance of the manuscript.

scientific communication and education↗

bioRxiv2026

A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments

Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language SummaryTemporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.

plant biology↗