mathematical model

Shared preferences along stress gradients: how a growth-tolerance trade-off drives unimodal diversity and trait lumping

Environmental gradients are pervasive across ecosystems and play a fundamental role in structuring species distributions and community dynamics. While ecological theory mainly focuses on species with distinct preferences for specific niches along the …

Network Flow Methods for NMR-Based Compound Identification

In this work, we introduce a novel method for compound identification in mixtures based on nuclear magnetic resonance spectra. Contrary to many other methods, our approach can be used without peak-picking the mixture spectrum and simultaneously …

AGELESS

Modelling Spatial Paeleo Foodwebs

DynaCom

Trait-based metacommunities

Mechanisms underpinning the net removal rates of dissolved organic carbon in the global ocean

With almost 700 Pg of carbon, marine dissolved organic carbon (DOC) stores more carbon than all living biomass on Earth combined. However, the controls behind the persistence and the spatial patterns of DOC concentrations on the basin scale remain …

Driving forces of Antarctic krill abundance

Krill population model reveals recruitment is driven by intercohort competition and age-specific seasonal environmental forcings.

Gauge-and-compass migration: inherited magnetic headings and signposts can adapt to changing geomagnetic landscapes

For many migratory species, inexperienced (naïve) individuals reach remote non-breeding areas independently using one or more inherited compass headings and, potentially, magnetic signposts to gauge where to switch between compass headings. Inherited …

3rd Stanislaw Lem Workshop on Biodiversity and Evolution

The next iteration of our notorious series of Stanislaw Lem Workshops, March 6-9, 2023 at the WasserCluster Lunz, Austria

Project on Movement Ecology extended for a second phase

Our Collaborative Research Centre on 'Magnetoreception and Navigation in Vertebrates' has been extended for a seond phase

Estimation of functional diversity and species traits from ecological monitoring data

The rampant loss of biodiversity is starting to be recognized as a global crisis rivaling the climate emergency. To address this crisis, scientists need robust methods to measure the diversity in a system. Importantly, these methods should not only count species but capture the variety of different functions that the species in a system can perform. In this paper, we propose a machine learning method by which existing data from ecosystem monitoring can be reanalyzed to reveal changes of functional biodiversity over time.