PhD position: Integrating trait diversity into ecosystem models

KNAW (Nederlandse Akademie van Wetenschappen); Wageningen; On-site; EUR 30,000 - 42,000; Full time

Job summary

The Netherlands Institute of Ecology (NIOO) at Wageningen hosts a PhD candidate to study how phytoplankton trait diversity shapes lake ecosystem processes. You will integrate trait diversity into PCLake, run sensitivity analyses, and collaborate with project team members to calibrate models and validate findings.

You will develop a PhD thesis plan, conduct analyses, present results at conferences, and contribute to publications over a four-year appointment.

Qualifications

• MSc in environmental sciences or a closely related field.
• Experience with ecosystem modelling and ecological forecasting.
• Proficiency in programming with R, Python or Matlab.
• Professional working proficiency in English, including scientific writing.

Responsibilities

• Investigate how phytoplankton trait diversity affects lake ecosystem functioning.
• Run sensitivity analyses in PCLake+ and perform meta-analyses.
• Collaborate with PhD candidates and postdocs on parameterization and validation of the model.
• Present results at meetings and publish in international scientific journals.

Skills

Phytoplankton trait-based ecology

Ecosystem modelling

Ecological forecasting

Phytoplankton ecophysiology

Trait-based ecology

Education

MSc in environmental sciences / computational ecology / quantitative biology

Tools

R

Python

Matlab

Job description

We are recruiting a talented, enthusiastic, and collaborative PhD candidate to investigate how phytoplankton trait diversity affects ecosystem processes. Specifically, the PhD candidate will integrate diversity of phytoplankton resource acquisition traits into the mechanistic ecosystem model PCLake and assess consequences for ecosystem structure and functioning.

General project description

This PhD position is part of the SNF-Lead Agency funded BILMo (Biodiversity Inspired Lake Modelling) project, which aims to upgrade different mechanistic lake models to predict diversity and community composition responses to combined gradients of warming and changes in resource availability (light, nitrogen and phosphorus), and improve predictions of ecosystem processes by including the impacts of phytoplankton diversity and composition.

The project focuses on five interlinked objectives related to these aims. The research spans field observations, laboratory experiments and mechanistic models. The project team consists of two PhD candidates and three Postdoc positions hired on the BILMo project and involved researchers including Dr. Anita Narwani (EAWAG; coordinator), Dr. Keisuke Inomura (University of Rhode Island), Prof. Damien Bouffard (EAWAG), Prof. Dedmer van de Waal (NIOO-KNAW) and Dr. Mandy Velthuis (Wageningen University).

What you will be contributing

As a PhD candidate you will contribute to improving our understanding of the interplay between phytoplankton traits and lake ecosystem functioning. Specifically, you will determine to what extent (phyto)plankton inter- and intraspecific trait diversity affects the biomass, productivity and functional diversity of plankton communities in lake ecosystems, and what the implications of environmental change are on those processes.

You will run sensitivity analysis in PCLake+, using both traditional and emergent community modelling approaches, carry out meta-analyses to determine resource acquisition trait parameter space, and perform scenario analyses tailored to a set of Swiss lakes. There will be close collaboration with the other PhD candidate and Postdocs of the projects on parameterization and validation of the model.

You are expected to actively exchange ideas and results within the larger BILMo team, join the project’s annual meetings, supervise MSc students who wish to do an internship on modelling phytoplankton trait diversity, and contribute to some of the project’s outreach activities.

Your work will be performed both at the NIOO-KNAW and at Wageningen University under the guidance of your daily supervisors Dedmer van de Waal and Mandy Velthuis. You will develop a PhD thesis plan, prepare and perform the research, analyze the data, present your results at meetings and scientific conferences, and publish your results in international scientific journals. You aim to complete your PhD thesis within the official appointment duration of four years.

Further training and courses during the PhD trajectory will be offered by NIOO-KNAW, WUR, and via the Graduate School for Production Ecology & Resource Conservation (PE&RC).

Job requirements

We are looking for an enthusiastic and motivated candidate, with a keen interest in plankton trait-based ecology and ecosystem modelling. You enjoy process-based ecosystem modelling, are well-organized, have a collaborative personality but are also able to work independently.

You are furthermore in possession of:

• An MSc degree in environmental sciences, computational ecology, quantitative biology, or a closely related discipline.
• Affinity and preferably experience with ecosystem modelling, ecological forecasting, phytoplankton ecophysiology and/or trait-based ecology. Please note that candidates are not expected to master all techniques from the start, but previous experience with one or more of the listed techniques is considered an advantage.
• You are comfortable working with large datasets and modelling pipelines and are eager to develop advanced skills with programming software such as R, Python or Matlab.
• Professional working proficiency in English, including scientific writing.

About NIOO

The Netherlands Institute of Ecology (NIOO) is a national research institute of the Royal Netherlands Academy of Arts and Sciences (KNAW). At NIOO, ecological research is conducted on biodiversity, climate change and sustainable use of land and water. The institute stimulates ecological research in the Netherlands and shares ecological knowledge with society.

Terms of employment

The KNAW offers its staff an excellent package of secondary benefits that meets the different needs of employees depending on their stage of life, lifestyle or career ambitions. For example, by working an extra two hours a week, it is possible to increase the number of days off from 29 to 41 days a year (with full-time employment).

Applying for a Certificate of Good Conduct can be part of the employment procedure.