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https://doi.org/10.3897/ab.e119534 (12 Feb 2024)
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  • ContentsContents
  • Book InfoBook Info
  • CiteCite
  • MetricsMetrics
  • CommentComment
  • RelatedRelated
  • FigsFigs
  • TabsTabs
  • MapMap
  • TaxaTaxa
  • RefsRefs
  • CitedCited
  • Book title
  • Acknowledgements
  • Preface
  • Contributors
  • 1 Introduction
    • 1.1 Towards a multisensor station for automated biodiversity monitoring
    • 1.2 The test sites
      • 1.2.1 Melbgarten, Bonn
      • 1.2.2 Britz, Ecological research station of the Thünen Institute near Eberswalde
      • 1.2.3 Energieberg Georgswerder, Hamburg
    • References
  • 2 Smellscapes: automated monitoring of volatile organic compounds in ambient air
    • Abstract
    • 2.1 Introduction
      • 2.1.1 Volatile organic compounds
      • 2.1.2 Ion mobility spectrometry
      • 2.1.3 Aim & scope
    • 2.2 Material and methods
      • 2.2.1 Sampling location and plant community
      • 2.2.2 Instrumentation
      • 2.2.3 Housing and sample transfer
      • 2.2.4 Sampling and time-resolved monitoring
      • 2.2.5 In-house reference database
      • 2.2.6 Targeted measurements of characteristic plants
      • 2.2.7 Data evaluation
      • 2.2.8 Database design
      • 2.2.9 Meteorological data
      • 2.2.10 Statistical analysis
    • 2.3 Results
      • 2.3.1 Automation & telemetry
      • 2.3.2 Selective measurements of common plants
      • 2.3.3 Detection of VOCs in ambient air using GC-IMS
      • 2.3.4 Time-resolved monitoring of VOCs
      • 2.3.5 Weekly and diurnal variation of VOCs in seasonal intervals
      • 2.3.6 Correlation of eVOC/pVOC Concentration with meteorological data
    • 2.4 Discussion
      • 2.4.1 Robustness of measurements
      • 2.4.2 GC-IMS for time resolved detection of VOCs
      • 2.4.3 Differentiation of biogenic/plant related VOCs
      • 2.4.4 Outlook
    • 2.5 Conclusion
    • References
    • Appendix
      • A.1 Plant list Melbgarten
      • A.2 Reference substance database
      • A.3 Abbreviations
  • 3 Plant metabarcoding of volumetric air samplers and malaise traps
    • Abstract
    • 3.1 Introduction
    • 3.2 Material and methods
      • 3.2.1 Collection site considerations
      • 3.2.2 Pollen trap sampler setup and programming
      • 3.2.3 Pollen Metabarcoding: Quality assurance to avoid contamination
      • 3.2.4 Pollen Metabarcoding: DNA extraction from plant traces in Malaise traps
      • 3.2.5 Pollen Metabarcoding: DNA extraction from pollen material from the wind pollen trap
      • 3.2.6 PCR: Barcode choice
      • 3.2.7 PCR chemicals and conditions
      • 3.2.8 NGS-Sequencing
      • 3.2.9 Metabarcoding data pipeline
    • 3.2 Results and discussion
    • References
  • 4 Non-destructive DNA extraction and metabarcoding of arthropod bulk samples: a step-by-step protocol
    • Abstract
    • Keywords
    • 4.1 Introduction
    • 4.2 Laboratory protocol
      • 4.2.1 Materials and reagents
      • 4.2.2 Methods
    • 4.3 Notes
    • 4.4 Challenges and recommendations
    • 4.5 Application and outlook
    • References
  • 5 Development of an automated Malaise trap multisampler
    • Abstract
    • 5.1 Introduction
      • 5.1.1 Background
      • 5.1.2 The automated AMMOD multisampler
    • 5.2 The microcontroller
      • 5.2.1 Controller code
    • 5.3 The rotary mechanism
    • 5.4 The stopping mechanisms
    • 5.5 Additional sensors and modules
      • 5.5.1 Additional sensors
      • 5.5.2 External modules
    • 5.6 User Interface
      • 5.6.1 Manual use
      • 5.6.2 Serial communication
    • 5.7 Energy supply
    • 5.8 Outlook
    • References
  • 6 Bioacoustic data acquisition and species recognition
    • Abstract
    • 6.1 Introduction
    • 6.2 Material and methods
      • 6.2.1 Sensor
      • 6.2.2 Hardware
      • 6.2.3 Software
      • 6.2.4 Recording and analysis pipeline
    • 6.3 Results
      • 6.3.1 Preliminary inventory of bird species
      • 6.3.2 Four-channel audio recordings
      • 6.3.3 Ultrasonic recordings
      • 6.3.4 Performance assessment
      • 6.3.5 Raw classification results
    • 6.4 Discussion
      • 6.4.1 Importance of interdisciplinarity
      • 6.4.2 Selection of the recording equipment
      • 6.4.3 Use of four-channel recordings
      • 6.4.4 Training and validation datasets
      • 6.4.5 Interpretation of raw classification results
    • Acknowledgments
    • References
  • 7 Directional Spectrogram (D-SPEC) and Signal Source Separation: software description and operational guide
    • 7.1 Overview
    • 7.2 D-SPEC Algorithm, Software Description
      • 7.2.1 Parameter determination
      • 7.2.2 Beam-forming setup
      • 7.2.3 Data Processing
      • 7.2.4 Beamforming and Calculating the D-Spec
    • 7.3 Clustering Algorithm, Software Description
      • 7.3.1 Parameters
      • 7.3.2 Pre-processing
      • 7.3.3 Clustering algorithm
    • 7.4 Using the D-Spec clustering software
      • 7.4.1 File preparation
      • 7.4.2 Launching the program
      • 7.4.3 Loading and saving parameters
      • 7.4.4 Loading data
      • 7.4.5 Setting time range
      • 7.4.6 Computing and Plotting D-SPEC
      • 7.4.7 D-SPEC parameters
      • 7.4.8 Clustering and plotting clusters
      • 7.4.9 Clustering parameters
      • 7.4.10 Generating and Playing Time-Series
    • 7.5 Neural Network Implementation of D-SPEC
      • 7.5.1 Overview
      • 7.5.2 Software implementation
    • References
  • 8 Depth-aware Visual Monitoring
    • 8.1 State of the art
    • 8.2 Hardware
      • 8.2.1 Design and specification
      • 8.2.2 Challenges and limitations
    • 8.3 Software
      • 8.3.1 Control
      • 8.3.2 Stereo processing
      • 8.3.3 Animal localization
    • 8.4 Workflows
    • 8.5 Data quality
    • 8.6 Outlook
    • References
  • 9 The Base Station
    • 9.1 Introduction
    • 9.2 Power supply
    • 9.3 Software
      • 9.3.1 Software architecture
      • 9.3.2 Communication with sensors
      • 9.3.3 Configuration file
      • 9.3.4 Maintenance flow
      • 9.3.5 Design Space Exploration
    • 9.4 Communication links
      • 9.4.1 Internet connectivity
      • 9.4.2 Sensor links
      • 9.4.3 Weather and time data
    • 9.5 Conclusions
    • References
  • 10 Data management: connecting the AMMOD base station to the AMMOD data portal
    • Abstract
    • 10.1 Introduction
    • 10.2 Material and methods
      • 10.2.1 Requirements
    • 10.3 Results
    • 10.4 Discussion
      • 10.4.1 Possible storage space limitations and the NFDI4Biodiversity core storage solution
      • 10.4.2 Maintenance of the system and bug/feedback report after the end of the project
      • 10.4.3 Scalability of the service and critical aspects
      • 10.4.4 Data archival and publication
    • Acknowledgments
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