Biology subjects

Rademaker, M.

Publications and source records attributed to Rademaker, M..

4 recordsLinked to original sources

bioRxiv2025

Nanoclustering of a plant transcription factor enables strong yet specific DNA binding

Transcription factors (TFs) are traditionally depicted as monomeric, or oligomeric, units that recognize and bind specific DNA sequences to regulate transcription. Emerging evidence suggests that many TFs can undergo liquid phase separation, resulting in condensates that bind DNA through a different mechanism. For the Auxin Response Factors (ARFs), canonical plant TFs, evidence for both scenarios exists. But which of these scenarios is operational in the plant nucleus is unclear. Here, we demonstrate using MpARF2 of Marchantia polymorpha that a third scenario is operational: MpARF2 forms nanoscopic clusters under physiological conditions. Nanoclusters combine DNA-binding features that cannot be accessed by the other two scenarios: high-affinity, switch-like, and sequence-specific. Our results suggest nanoclustering as a mechanism that equips TFs with the DNA-binding properties necessary for transcriptional regulation. TeaserTranscription factor nanoclusters balance the DNA-binding trade-off between weakly binding oligomers and nonspecific condensates.

plant biology↗

bioRxiv2024

Population regulation in semelparous deep-sea squid is driven by ecological conditions in surface waters and whale predation at depth

Predator-prey interactions present a powerful framework for understanding population regulation in natural systems, including the vast and understudied pelagic deep sea. Oceanic squid are among the most abundant and important species groups in this habitat and function as primary prey for the largest marine top predators, deep-diving cetaceans. At the same time, these cryptic animals are highly data deficient. Insight into the dynamics of deep-sea squid populations and the impacts of predation by top-predators is crucial for designing conservation strategies and developing a general theory of deep-sea ecology. Here, we offer fundamental new insights into how individual life history and predation interact to shape population regulation in deep-sea squid. Using empirical data, we develop a size-structured population model for a highly abundant histioteuthid deep-sea squid. We show that population regulation is driven primarily by conditions experienced as paralarvae in the upper water column, where they face intense resource competition. Relaxation of this competition following ontogenetic migration as juveniles drives exponential-like growth curves. Population dynamics exhibit single-cohort cycles, producing regular seasonal patterns in reproduction, even in the absence of any environmental seasonality. Furthermore, we demonstrate that predation by deep-diving cetaceans at different depths can lead to emergent facilitation between top predators. This reveals the complex interdependencies in trophic networks connecting the deep sea with surface waters. Our findings provide critical insights into the ecological functioning of the pelagic deep-sea and their link with surface waters. These insights are urgently needed to better understand and conserve this vast, data-deficient habitat, which endangered deep-diving predators depend on but is under major stress from anthropogenic activities.

ecology↗

bioRxiv2023

Local reflects global: Life-stage dependent changes in the phenology of coastal habitat use by North Sea herring

Climate warming is affecting the suitability and utilisation of coastal habitats by marine fishes around the world. Phenological changes are an important indicator of population responses to climate-induced changes but remain difficult to detect in marine fish populations. The design of large-scale monitoring surveys does not allow fine-grained temporal inference of population responses, while the responses of ecologically and economically important species groups such as small pelagic fish are particularly sensitive to temporal resolution. Here, we use the longest, highest-resolution time series of species composition and abundance of marine fishes in northern Europe to detect possible phenological shifts in the small pelagic North Sea herring. We detect a clear forward temporal shift in the phenology of nearshore habitat use by small juvenile North Sea herring. This forward shift can best be explained by changes in water temperatures in the North Sea. We find that reducing the temporal resolution of our data to reflect the resolution typical of larger surveys makes it difficult to detect phenological shifts and drastically reduces the effect sizes of environmental covariates such as seawater temperature. Our study therefore shows how local, long-term, high-resolution time series of fish catches are essential to understand the general phenological responses of marine fishes to climate warming and to define ecological indicators of system-level changes.

ecology↗

bioRxiv2019

Modelling the niches of wild and domesticated Ungulatespecies using deep learning

Knowledge of global biodiversity remains limited by geographic and taxonomic sampling biases. The scarcity of species data restricts our understanding of the underlying environmental factors shaping distributions, and the ability to draw comparisons among species. Species distribution models (SDMs) were developed in the early 2000s to address this issue. Although SDMs based on single layered Neural Networks have been experimented with in the past, these performed poorly. However, the past two decades have seen a strong increase in the use of Deep Learning (DL) approaches, such as Deep Neural Networks (DNNs). Despite the large improvement in predictive capacity DNNs provide over shallow networks, to our knowledge these have not yet been applied to SDM. The aim of this research was to provide a proof of concept of a DL-SDM1. We used a pre-existing dataset of the worlds ungulates and abiotic environmental predictors that had recently been used in MaxEnt SDM, to allow for a direct comparison of performance between both methods. Our DL-SDM consisted of a binary classification DNN containing 4 hidden layers and drop-out regularization between each layer. Performance of the DL-SDM was similar to MaxEnt for species with relatively large sample sizes and worse for species with relatively low sample sizes. Increasing the number of occurrences further improved DL-SDM performance for species that already had relatively high sample sizes. We then tried to further improve performance by altering the sampling procedure of negative instances and increasing the number of environmental predictors, including species interactions. This led to a large increase in model performance across the range of sample sizes in the species datasets. We conclude that DL-SDMs provide a suitable alternative to traditional SDMs such as MaxEnt and have the advantage of being both able to directly include species interactions, as well as being able to handle correlated input features. Further improvements to the model would include increasing its scalability by turning it into a multi-classification model, as well as developing a more user friendly DL-SDM Python package.

evolutionary biology↗