Gorzerino, C. | bioRxiv Science

bioRxiv2026

Biological invasion drives ecosystem state and metabolism across tipping points

Abrupt regime shifts of complex ecosystems between alternative stable states are widespread in nature. Yet, our mechanistic understanding of disturbance-shift-ecosystem functioning relationships remains poor, and it is further unclear whether biotic disturbances can drive such shifts. Using a 5-year pond experiment, we demonstrate that invasion by the red swamp crayfish (Procambarus clarkii) drove a regime shift from a clear-water, macrophyte-dominated, to a turbid, phytoplankton-dominated state. The regime shift was associated with increased water temperature due to increased water turbidity enhanced light absorption, and with a seasonal switch of ecosystem metabolism from hetero-to autotrophy due to decreased respiration in summer, despite constant gross primary production. Reducing crayfish population densities by 44 % failed to move ecosystems back towards their initial state and functioning. Our results stress that biotic disturbances may have hardly-reversible consequences on the biophysical and biogeochemical processes that support ecosystem functioning.

ecology↗

bioRxiv2025

PlanktonFlow : hands-on deep-learning classification of plankton images for biologists

High throughput image-acquisition devices tremendously increase our capacity to observe biodiversity. However, for many biologists, the high-performance deep learning models that are needed to make biological sense out of very-large image sets remain difficult to implement. To fill this gap in biologists toolkit, we developed PlanktonFlow, a Python pipeline that streamlines the automation of plankton-image taxonomic assignment. PlanktonFlow makes it easy for inexperienced users to run a whole sequence of (i) automated image pre-processing and augmentation of rare classes, (ii) training up to four different high-performance convolution neural networks (CNNs: ResNet, DenseNet, EfficientNet, and YOLO), (iii) computing model classification-performance metrics so as to choose the best-performing model, and (iv) running inference on novel image sets. PlanktonFlow further includes routines to easily fine tune model hyper-parameters and optimize models performances. Using a tutorial style, we demonstrate the usage of PlanktonFlow to analyse freshwater-plankton images produced with the FlowCAM, comparing the relative classification performances of the four optimized CNN architectures. For a baseline comparison with a reference tool used by plankton biologists, we further assessed the classification performances of the EcoTaxa web-service when used without any eye validation in a pure-prediction mode. In line with a previous study on a benchmark plankton dataset, we found that EfficientNet-B5 achieved the highest macro-averaged F1 Score, outperforming other CNN models, which all surpassed EcoTaxa. Hyper-parameter optimization was key to improving model performances. To ease an appropriation and further developments by the community, PlanktonFlow is open source, comes with a detailed documentation, and has a modular structure. We foresee that future work could integrate new deep-learning architectures (e.g., vision transformers, semi-supervised learning), and test the pipeline on images produced by other devices or from other taxonomic groups.

ecology↗