Tag: JGI Team

JGI Seedcorn funding 2024-25: Nicola Wiseman & Léo Gorman

Introduction

The oceans play a key role in keeping the earth cool by absorbing carbon dioxide from the atmosphere into the surface waters. This carbon dioxide is either slowly transported to the deep ocean by currents or taken up by microscopic marine algae called phytoplankton and used for photosynthesis. Like plants on land, phytoplankton need sunlight and nutrients to be able to perform photosynthesis, which gives them the energy they need to grow. As sunlight and carbon dioxide are abundantly available in the surface ocean, the rate of phytoplankton photosynthesis is limited by the nutrients that are available, particularly nitrogen and phosphorus (Moore et al., 2013). The ratio of nutrients to carbon in phytoplankton is referred to as “stoichiometry” and can be used to understand the efficiency of carbon storage in the global ocean.  

What were the aims of the seed corn project?

Previously, stoichiometry was assumed to be relatively stable throughout the ocean, known as the Redfield Ratio (Redfield, 1934). This ratio was used to simplify climate models and heavily controls the amount of carbon storage in the ocean. However, field observations and inverse modelling studies have shown that stoichiometry varies significantly with latitude and nutrient availability (Martiny et al., 2013; Galbraith & Martiny, 2015; Weber and Deutsch, 2010). Laboratory culture studies have tested the responses of individual species to changing environmental conditions, but this level of species-specific detail is not represented in climate models, which instead focus on a small number (n  = 3 or 4) of “functional groups” which aim to summarize diverse species by their traits or distributions within the ocean (Le Quéré et al., 2005). To further our understanding of ocean carbon storage and phytoplankton stoichiometric response, we ask the following questions: 

• How do different phytoplankton species vary their stoichiometry based on environmental conditions?  

• What does the response to temperature, light, or individual nutrients look like?  

• What are the primary drivers of these responses?  

• Does grouping phytoplankton by function groups underestimate their unique stoichiometry responses? 

• Which drivers are key for informing current state-of-the-art climate models? 

Methods

We used data from Tanioka & Matsumoto, 2020, which is a compilation of laboratory experiments that looked at the stoichiometric (carbon:nitrogen and carbon:phosphorus) response of individual plankton species to different perturbations of environmental conditions (temperature, light intensity, photoperiod, nitrogen limitation, phosphorus limitation, and iron limitation).  

We needed to select a model which could account for the fact that there are similarities between species and functional groups. Variance components models, a type of multi-level model, are able to quantify how much of the variation in stoichiometry can be attributed to differences between species, and differences between functional groups. These models can also be extended to assess: 

1. What is the association between stoichiometry and explanatory variables (such as light), accounting for the fact that there are similarities between samples from the same species? 

2. Does the association between stoichiometry and these explanatory variables vary across species? (i.e. can stoichiometry in different species be more or less sensitive to light). 

A range of models were compared both in terms of their interpretability and their predictive power. Data cleaning, visualisation, and modelling were carried out in R with the package “brms” used for fitting the Bayesian models. 

What was achieved? 

With the model, we first focused on the carbon:nitrogen stoichiometry response to the explanatory variables. Categorizing the phytoplankton by genus provided the most effective predictive power, followed by the combined four phytoplankton with eight subgroups model.  

The greatest variance for the fixed effects was due to nitrogen limitation, which had a positive effect (where the distribution was greatly positive, such that nitrogen limitation is associated with higher carbon:nitrogen ratios). This is a logical primary driver in variation as if there is less nitrogen available, it will be more difficult for phytoplankton to assimilate nitrogen into their cells. The next strongest effect was phosphorus limitation, followed by iron limitation. Interestingly, these are acting in opposite directions where phosphorus limitation is associated with a positive effect, while iron limitation is associated with a negative effect. Increased nitrogen and phosphorus limitation have previously been proposed to be positively associated with increased C:N ratios (Tanioka & Matsumoto, 2020), where proposed mechanisms involving the changing allocations to phosphorus and nitrogen rich cell components. Iron, however, is associated with protein cofactors as part of electron transport for photosynthesis. This could then potentially impact production of carbon-rich carbohydrates by limiting photosynthetic efficiency, thereby reducing the carbon:nitrate ratio. 

Next Steps

These are just the preliminary results from the first iteration of the model. From here, the effects of each explanatory variable on specific phytoplankton groups can also be examined, which could highlight potential variations in biogeography and phytoplankton adaptation. For example, are globally adapted genera less susceptible to temperature effects than genera found within the polar regions? One advantage of the model we are using is that we can incorporate categorical variables of phytoplankton traits like motility and size to investigate the impacts of trait ecology on cellular nutrient uptake. This is the next step planned to incorporate into the dataset. Additionally, we will apply this approach to carbon:phosphorus stoichiometry as well. This project is the initial step to start investigating the response of phytoplankton to a changing climate and will be used as a proof of concept for continued efforts to disentangle the variability in phytoplankton stoichiometry to ultimately be used to improve the biogeochemical models used for future climate prediction on multi-century timescales. 

Contact Details

The code for this project can be found at https://github.com/nicola-wiseman/C-GAPS. Nicola can be contacted via email a nicola.wiseman@bristol.ac.uk. 

JGI Seedcorn funding 2024-25: Sarah Koerner & Louise Millard

Body temperature changes across the menstrual cycle with a small rise indicating ovulation. New wearable technologies make it possible to track these changes continuously during the user’s sleep. This type of data can provide valuable insights into menstrual health patterns and cycle variability, offering researchers ways to study reproductive health in real-world settings. 

What are the aims of the project?  

This study investigates two distinct wearable devices: the OvuSense sensor, which is worn vaginally, and the Oura Ring a smart ring worn on the finger. Both devices are worn during sleep and measure body temperature to detect ovulation. The aims of this project are to (1) assess how similar the temperature measurements are between the two devices and (2) to explore participants experiences of wearing the devices for a research study.  

What has been achieved so far?  

We spent a considerable amount of time refining the study design to ensure it effectively addressed our research questions while being sensitive to the intimate nature of vaginal temperature measurements. One particular aspect that needed careful consideration was the duration of data collection. We had initially planned to collect data over the course of a single menstrual cycle. However, this approach would mean that participants were wearing the devices for different durations, which could make their feelings towards wearing the devices less comparable. We would have also needed to exclude those with longer cycles due to budget and time constraints of the project. Therefore, to ensure inclusivity and consistency, we instead opted for a fixed 40-night data collection period.  

We also paid close attention to the language used in our study materials. We aimed to make all participant communications inclusive, and in particular used gender-inclusive language throughout (e.g. our study requires participants to have a vagina rather than identify as a women).   

The design of our study is shown in the image below. Each participant is asked to wear both the OvuSense and Oura Ring devices during their sleep for 40 consecutive nights. They also complete questionnaires at the start and end of the data collection period. The first questionnaire collects clinical and demographic information, while the second questionnaire asks about their experiences using each device. 

After receiving ethical approval, we began recruitment through university networks and successfully enrolled our target of fifteen participants. We have finished collecting data for seven participants and are in the process of collecting data for three participants, with five due to start in the next couple months.  

To ensure good research practice, we have been developing a detailed data analysis plan before beginning any actual analysis. This pre-specified plan outlines how the data will be processed and analysed, helping to reduce bias and ensure transparency. We will compare nightly temperature readings from both devices to assess how closely they compare. Additionally, we will analyse questionnaire responses to evaluate how acceptable participants found each device, for example, how willing they would be to participate in future research studies using these devices. 

Future plans for the project 

Data collection is on track to conclude by November 2025. The next steps include analysing the collected temperature and questionnaire data and then writing up our findings for publication.  

Our findings will help inform appropriate use of these technologies in future studies on reproductive disorders, menstrual health and fertility. 

Contact Details

Blog post written by Miss Sarah Koerner sarah.koerner@bristol.ac.uk and Dr Louise AC Millard louise.millard@bristol.ac.uk. 

Study researchers: Miss Sarah Koerner, Dr Louise AC Millard (PI), Professor Tom R Gaunt, Dr Kayleigh Easey, Dr Robyn Wootton, Professor Jon Heron.