This new data product, developed by SMHI, offers a spatial-temporal interpolation of zooplankton biodiversity patterns across the greater Baltic Sea area, including the Skagerrak and Kattegat. Based on observed Shannon diversity indices from Swedish and Finnish environmental monitoring data, the product utilizes the DIVAnd (Data-Interpolating Variational Analysis in n-Dimensions) method to generate a three-dimensional interpolation across longitude, latitude, and seasonal time dimensions.
Biodiversity loss due to human activities is an increasing threat for marine ecosystems and the services we obtain from them. As biodiversity is directly related to the resilience of ecosystems to temporary disturbance, biodiversity monitoring is a vital task for areas subjected to conservation goals. Environmental factors often control the community composition and biodiversity of marine plankton, such as the pronounced salinity gradient in the Baltic Sea. Time series data of biodiversity can therefore provide an indication of changes in community composition due to environmental stressors, such as climate change or eutrophication.
This new data product provides a spatial-temporal interpolation of zooplankton alpha diversity in the greater Baltic Sea area (including Skagerrak and Kattegat) between 2006–2022. Using observed Shannon diversity indices from Swedish and Finnish environmental monitoring data, the product employs the DIVAnd (Data-Interpolating Variational Analysis in n-Dimensions) to create a three-dimensional interpolation across longitude, latitude, and seasonal time dimensions.
The output includes interpolated diversity values, an associated error map, and metadata on the spatial-temporal grid resolution. It is tailored for ecological and oceanographic studies, aiding in the visualization and analysis of diversity trends over time and space.
Since biodiversity estimates are often biased by sampling effort, caution is necessary when interpreting alpha diversity from microscopy counts. Although rarefaction and evenness estimation, as applied in this data product, help to reduce these biases, they cannot fully eliminate them.