Biology subjects

Campbell, D. A.

Publications and source records attributed to Campbell, D. A..

4 recordsLinked to original sources

bioRxiv2025

Inter-strain diversity among coastal picocyanobacteria across salinity gradients

Strains of the picocyanobacteria genera Synechococcus and Cyanobium occur in cold-temperate zones from coastal waters to the open ocean, spanning a range of salinities and water depths. To thrive across different salinities, picocyanobacteria strains acclimate internal turgor by expending metabolic energy to accumulate compatible solutes and extrude inorganic ions. We grew five picocyanobacterial strains, derived from a range of source habitat salinities, under a matrix of light and salinity levels. Although Chl is a general proxy for phytoplankton biomass, Chl/cell is itself a key component of acclimation. Indeed, for all strains, except the brackish water CZS48M, the conditions supporting the highest Biovolume-specific growth rates shifted upwards towards higher salinities and PAR, compared to conditions for maximum Chl-specific growth. Therefore, the apparent optimal niche for a strain varies depending upon the metric used to track growth. Strains CZS48M and CZS25K derived from brackish lagoons of the Baltic Sea may be true brackobionts, with maximal growth at brackish salinities and high PAR, coinciding with their maximal metabolic capacities. CZS48M and CZS25K both also show high metabolism under higher salinity and low PAR, suggesting a situation of rapid metabolism, but lower achieved growth, under stress at the edges of their environmental tolerance ranges. Conversely, NIES981 (full marine, East China Sea) shows rapid metabolism under high, but also under low, salinities. The preferred salinity of each strain is consistent with its genome-encoded capabilities to synthesize different compatible solutes. In general, across strains, maximal metabolic rate is often offset from growth rate optima, showing conversion of metabolic electrons to biomass varies widely depending upon strain and condition, likely partly reflecting the costs of turgor regulation at the limits of salinity tolerance.

microbiology↗

bioRxiv2025

Fast or slow - light climate modulates intra-population sinking velocities in small phytoplankton

The global carbon cycle depends heavily on the carbon sequestration rates of aquatic ecosystems. Sinking of phytoplankton is a rapid mediator of carbon sequestration, because phytoplankton are globally abundant photoautotrophs that grow rapidly. Pico- and nano-phytoplankton sinking velocities vary depending on their growth state, viability, clumping, and distribution in the water column. We introduced high throughput fluorescence microscopy of well-plates, to measure sinking velocities of three diatom strains, and three cyanobacteria strains, with cell radii spanning an order of magnitude, all grown under three different light levels. Cultures were measured for sinking velocities repeatedly across their growth trajectories. Tracking multiple fluorescence wavebands allowed us to simultaneously determine sinking velocities for living vs. dead cells. Sinking velocities varied strongly across growth light levels, and across growth stages. These monoclonal cultures furthermore show distinct sub-populations of slow- and fast-sinking cells. Our results departed widely from simple Stokes Law estimates of sinking based upon radii and mass density of cells. Complex, heterogeneous phytoplankton communities likely show more complicated sinking patterns than are currently expressed in biogeochemical ocean models. Our well-plate microscopy approach using parallel imaging of many samples generates high-throughput measures of cell sinking at population- or community-scales, to in turn improve modelling of carbon export to deeper layers.

ecology↗

bioRxiv2023

Genome of the glacier alga Ancylonema and its insights into the evolution of streptophyte life on ice and land

Contemporary glaciers and ice sheets are home to communities of streptophyte glacier algae that must balance their requirements for photosynthesis and growth with tolerance of extremes in temperature, desiccation and UV radiation. These same environmental challenges have been hypothesized as the driving force behind the evolution of land plants from streptophyte algal ancestors in the Cryogenian (720-635 million years ago). Here, we sequence, assemble and analyze the metagenome-assembled genome (MAG) of the glacier alga Ancylonema nordenskioldii to investigate its adaptations to life in ice, and whether this represents a vestige of Cryogenian anydrophyte exaptations. Phylogenetic analysis confirms the placement of glacier algae within the sister lineage to land plants, Zygnematophyceae. The MAG is characterized by an expansion of genes involved in high irradiance and UV light tolerance, whilst lineage-specific diversification led to the novel screening pigmentation of glacier algae. We found no support for the hypothesis of a common genomic basis for adaptations to ice and to land in streptophytes. Comparative genomic analysis revealed that reductive genome evolution in the ancestor of Zygnematophyceae correlates with their reductive morphological evolution. This first genome-scale data for glacier algae supports an Ancylonema-specific adaptation to the cryosphere, as well as shedding light on the genome evolution of land plants and Zygnematophyceae.

evolutionary biology↗

bioRxiv2018

Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning

The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.\n\nAuthor SummaryFood security is a growing global concern. Farmers, plant breeders, and geneticists are hastening to address the challenges presented to agriculture by climate change, dwindling arable land, and population growth. Scientists in the field of plant phenomics are using satellite and drone images to understand how crops respond to a changing environment and to combine genetics and environmental measures to maximize crop growth efficiency. However, the terabytes of image data require new computational methods to extract useful information. Machine learning algorithms are effective in recognizing select parts of images, but they require high quality data curated by people to train them, a process that can be laborious and costly. We examined how well crowdsourcing works in providing training data for plant phenomics, specifically, segmenting a corn tassel - the male flower of the corn plant - from the often-cluttered images of a cornfield. We provided images to students, and to Amazon MTurkers, the latter being an on-demand workforce brokered by Amazon.com and paid on a task-by-task basis. We report on best practices in crowdsourcing image labeling for phenomics, and compare the different groups on measures such as fatigue and accuracy over time. We find that crowdsourcing is a good way of generating quality labeled data, rivaling that of experts.

plant biology↗