Towards a multisensor station for automated biodiversity monitoring

• Published in: Basic and applied ecology Vol. 59; pp. 105 - 138
• Main Authors: Wägele, J.Wolfgang, Bodesheim, Paul, Bourlat, Sarah J., Denzler, Joachim, Diepenbroek, Michael, Fonseca, Vera, Frommolt, Karl-Heinz, Geiger, Matthias F., Gemeinholzer, Birgit, Glöckner, Frank Oliver, Haucke, Timm, Kirse, Ameli, Kölpin, Alexander, Kostadinov, Ivaylo, Kühl, Hjalmar S., Kurth, Frank, Lasseck, Mario, Liedke, Sascha, Losch, Florian, Müller, Sandra, Petrovskaya, Natalia, Piotrowski, Krzysztof, Radig, Bernd, Scherber, Christoph, Schoppmann, Lukas, Schulz, Jan, Steinhage, Volker, Tschan, Georg F., Vautz, Wolfgang, Velotto, Domenico, Weigend, Maximilian, Wildermann, Stefan
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.03.2022; Elsevier
• Subjects: AMMOD; Artificial intelligence; Bioacoustic monitoring; Biodiversity monitoring; Computer science; Computer vision; Metabarcoding; Pattern recognition; Visual monitoring; Volatile organic compounds; Volatile organic compounds; Computer science; Computer vision; Metabarcoding; Bioacoustic monitoring; Biodiversity monitoring; Pattern recognition; Visual monitoring; Artificial intelligence; AMMOD
• ISSN: 1439-1791
• DOI: 10.1016/j.baae.2022.01.003

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution.