Endresen, D. | Search bioRxiv

bioRxiv2025

Investigating the dynamics of the aquatic community in Oslofjord through time series analysis of eDNA

Understanding temporal dynamics of marine communities is critical for assessing ecosystem health and guiding conservation efforts. Here, we conducted a survey using environmental DNA (eDNA) metabarcoding with two primer sets, MiFish and Elas02, to investigate seasonal and interannual changes in the fish community of Oslofjord (Norway) over two consecutive years. Using the MitoFish reference database through the GBIF querying tool, we identified 61 fish species and found significant changes in the dominant taxa between seasons and years. Clupea harengus (Atlantic herring) consistently peaked in early spring, while Scomber scombrus (Atlantic mackerel) dominated winter months in the second year. The MiFish primer set showed increased species richness in the second year, whereas the Elas02 primer set showed stable richness despite compositional turnover. Non-metric multidimensional scaling (NMDS) revealed distinct community separation between years in presence/absence data, driven by species turnover rather than abundance read changes. Our findings support the use of eDNA metabarcoding to capture fine-scale temporal dynamics and emphasise the importance of multi-year datasets for distinguishing ecological trends from stochastic changes. This strategy improves monitoring practices for marine ecosystems under anthropogenic stressors.

ecology↗

bioRxiv2024

TwinEco: A Unified Framework for Dynamic Data-Driven Digital Twins in Ecology

1.A Digital Twin (DT) is a virtual replica of a physical object or process that is continuously updated at a certain frequency and can steer change on the physical system, enabling a seamless integration of observation, understanding, and action. Although initially applied primarily in industry, DT is emerging as a powerful tool in ecology, offering new possibilities for dynamic simulations of change in the biosphere. However, since DTs are relatively new in this field, there is currently no standard framework to guide their conceptualisation and development. Thus, DTs in ecological applications are already experiencing fragmentation in software concepts and design philosophies, leading to incompatibilities across DT implementations. This fragmentation risks undermining the progress and potential of the DT concept in ecology. A unifying framework, such as TwinEco, can address these discrepancies and establish a cohesive foundation for the effective adoption and integration of DTs across ecological domains. TwinEco is a modular framework designed to aid and harmonise ecologists efforts to build DTs. In doing so, TwinEco focuses on three major design goals: O_LIModularity, flexibility, and interoperability of DTs facilitated through distinct "components" nested within DT "layers". C_LIO_LIDynamic modeling of ecological processes and states that evolve over time. C_LIO_LILinking ecological modelling to downstream actions or decisions made on the ecological object or process of study. C_LI TwinEcos architecture builds upon the feedback loops and state management strategies introduced in the Dynamic Data-Driven Application Systems (DDDAS) paradigm, which has already inspired many DTs across scientific domains. We also discuss the usefulness and ease-of-use of TwinEco by demonstrating its applicability to computational case studies and suggesting future recommendations to the community of data infrastructure builders and modellers in regards to open considerations. By introducing a shared terminology and emphasising model-data fusion, TwinEco highlights the importance of a unified framework to avoid fragmentation in the burgeoning field of ecological digital twinning.

ecology↗