TY - JOUR
T1 - Towards a multisensor station for automated biodiversity monitoring
AU - Wägele, J. Wolfgang
AU - Bodesheim, Paul
AU - Bourlat, Sarah J.
AU - Denzler, Joachim
AU - Diepenbroek, Michael
AU - Fonseca, Vera
AU - Frommolt, Karl Heinz
AU - Geiger, Matthias F.
AU - Gemeinholzer, Birgit
AU - Glöckner, Frank Oliver
AU - Haucke, Timm
AU - Kirse, Ameli
AU - Kölpin, Alexander
AU - Kostadinov, Ivaylo
AU - Kühl, Hjalmar S.
AU - Kurth, Frank
AU - Lasseck, Mario
AU - Liedke, Sascha
AU - Losch, Florian
AU - Müller, Sandra
AU - Petrovskaya, Natalia
AU - Piotrowski, Krzysztof
AU - Radig, Bernd
AU - Scherber, Christoph
AU - Schoppmann, Lukas
AU - Schulz, Jan
AU - Steinhage, Volker
AU - Tschan, Georg F.
AU - Vautz, Wolfgang
AU - Velotto, Domenico
AU - Weigend, Maximilian
AU - Wildermann, Stefan
N1 - Publisher Copyright:
© 2022
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
KW - AMMOD
KW - Artificial intelligence
KW - Bioacoustic monitoring
KW - Biodiversity monitoring
KW - Computer science
KW - Computer vision
KW - Metabarcoding
KW - Pattern recognition
KW - Visual monitoring
KW - Volatile organic compounds
UR - http://www.scopus.com/inward/record.url?scp=85123872761&partnerID=8YFLogxK
U2 - 10.1016/j.baae.2022.01.003
DO - 10.1016/j.baae.2022.01.003
M3 - Article
AN - SCOPUS:85123872761
SN - 1439-1791
VL - 59
SP - 105
EP - 138
JO - Basic and Applied Ecology
JF - Basic and Applied Ecology
ER -