Droevendaalsesteeg 10
6708 PB Wageningen
The Netherlands
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About
Aiming to understand the heartbeat of the forest - how is beechnut production affecting populations of mice, marten, wild boar and other species in the forest?
Biography
Background
During my studies, I was first focussing on behavioural biology working with primates, switching to nature conservation and ecology in my masters to finally write my master's thesis about phenology and selection on reproductive timing of great tits and blue tits. For my PhD, I shifted my focus once more to now study seed production of trees and interaction networks in Dutch forests.
My PhD project
The annual production of beechnuts, the seeds of European beech (Fagus sylvatica), is highly variable across years and provides a valuable food resource for a variety of species. In my PhD project, I am looking at the beech seeding patterns in the Veluwe, Netherlands and aim to understand how fluctuations in seed availability influence the population dynamics of the species that are relying on the beechnuts. For this, I am looking at the broader interaction network of forest species that are directly or indirectly affected by the seeding patterns.
Since beech seeding is sensitive to climate change, I further want to understand how future climate change will affect the beech seeding dynamics and forecast how these effects will cascade through the species network.
Other work
In my previous role at NIOO as a FAIR data analyst, I was involved in making different ecological datasets FAIR (Findable, Accessible, Interoperable, Reusable) and use this experince to write a hands-on guide for ecologists to make their own data more FAIR.
Research groups
CV
Employment
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2024–PresentPhD Candidate at NIOO-KNAW
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2023–2024FAIR data analyst at NIOO-KNAW
Education
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2020–2023MSc Biodiversity, Ecology and Evolution - Georg-August University Göttingen
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2017–2020BSc Biology - Georg-August University Göttingen
Publications
Peer-reviewed publications
Putting FAIR into practice for ecologists
https://doi.org/10.1016/j.ecoinf.2026.103712Ecological data is highly diverse due to the complex nature of the systems they describe. Proper documentation and management are often lacking or not designed for data reuse by others, making the data difficult to find, understand, and at risk to be lost. Adopting the FAIR (Findable, Accessible, Interoperable, Reusable) principles into data practices is a way to mitigate these problems. However, the FAIR principles are abstract and not easily understood by domain scientists. Despite a growing body of assessment tools and resources about FAIR, applying it in practice remains challenging as clear implementation guidelines are missing. We aim to fill this gap by translating the FAIR principles into four data components (metadata, storage, standard and structure) that can be successively worked on to enhance the FAIRness and structure of data and provide a general workflow together with a hands-on guide to give practical suggestions on how to improve the reusability of ecological data. For every workflow step, we introduce the rationale behind it and point towards implementation solutions tailored to ecology. Additionally, we introduce an evaluation tool that facilitates the entry to this workflow by guiding to only those steps that are necessary for the evaluated dataset. With the workflow, guide and tool introduced here, we lower the threshold for ecologists to start making ecological data FAIR, which will ensure long-term reusability of valuable data sources.
Climate change does not equally affect temporal patterns of natural selection on reproductive timing across populations in two songbird species
Climate change has led to changes in the strength of directional selection on seasonal timing. Understanding the causes and consequences of these changes is crucial to predict the impact of climate change. But are observed patterns in one population generalizable to others, and can spatial variation in selection be explained by environmental variation among populations? We used long-term data (1955–2022) on blue and great tits co-occurring in four locations across the Netherlands to assess inter-population variation in temporal patterns of selection on laying date. To analyse selection, we combine reproduction and adult survival into a joined fitness measure. We found distinct spatial variation in temporal patterns of selection which overall acted towards earlier laying, and which was due to selection through reproduction rather than through survival. The underlying relationships between temperature, bird and caterpillar phenology were however the same across populations, and the spatial variation in selection patterns is thus caused by spatial variation in the temperatures and other habitat characteristics to which birds and caterpillars respond. This underlines that climate change is not necessarily equally affecting populations, but that we can understand this spatial variation, which enables us to predict climate change effects on selection for other populations.https://doi.org/10.1098/rspb.2023.1474