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Autonomous Monitoring of Vulnerable Habitats. And other tales. Robin Freeman, CEES, Microsoft Research 13 July 2007. Overview. Introduction Previous Work Analysing Avian Navigation Habitat Monitoring Brief Results Future Work. Introduction. About Me

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autonomous monitoring of vulnerable habitats

Autonomous Monitoring of Vulnerable Habitats

And other tales..

Robin Freeman, CEES, Microsoft Research

13 July 2007

overview
Overview
  • Introduction
  • Previous Work
    • Analysing Avian Navigation
  • Habitat Monitoring
  • Brief Results
  • Future Work
introduction
Introduction
  • About Me
    • BSc CS-AI, MSc Evolutionary and Adaptive Systems,
    • D.Phil (Engineering and Zoology)
      • Part of the Life Sciences Interface Doctoral Training Centre, Oxford
      • Trains physical and computation sciences graduates in biology before starting PhD in life sciences.
    • Now a Post-Doc at Microsoft Research
      • Computational Ecology and Biodiversity Science Group
      • European Science Initiative, External Research Office.
slide5

~9hrs

~15min

introduction6
Introduction
  • Analysing Avian Navigation
      • GPS Tracking of Pigeons, Oxford
      • GPS Tracking of Manx Shearwaters, Skomer
  • Habitat Monitoring
      • Manx Shearwater
        • Skomer Island, Wales
introduction7
Introduction
  • Zoological Interest
    • Specific questions (Sensory basis of navigation),
    • Conservation (home range, behavioural anomalies),
    • Other general questions.
  • Technical Interest
    • Novel algorithms/methods
      • Analysis of positional information
      • Feedback to bio-robotics, Complex Systems, Artificial Life, etc
pigeons why pigeons
Pigeons? - Why Pigeons?
  • Model Navigational Species
    • Much easier to study than wild birds,
      • Birds return to a maintained loft (Wytham).
        • Allows attachment of GPS device
    • Large body of research to draw on.
      • Pigeon navigation has been studied for over 100 years.
how do they navigate
How Do They Navigate?
  • Two hypotheses for the sensory basis of navigation in the familiar area
    • ‘Map and Compass’
      • Compass controlled navigation (as it is at unfamiliar locations).
        • Series of decision points using compass.
    • ‘Pilotage’
      • Independent of a compass, relying directly on visual cues
        • Oh look, there’s that house!
clock shift
Clock Shift
  • Experiment
    • Train the birds to ‘recapitulate’ routes to home,
    • Then ‘clock-shift’ the birds by 90°
      • Sets up a direct competition between visual landmarks (the recapitulated route) and erroneous compass instructions

With D Biro, J Meade, T Guilford & S J Roberts

slide11

Nearest Neighbour Analysis

  • Shows offset and variance between controls and familiar clock-shift.
slide12

Delayed Clock shift response (landmark related)

Tracks ranked by Mahalonobis distance from recapping distribution

slide13
Demonstrates that both mechanisms must be involved.
    • The birds must be able to home using visual information alone (they recapitulate)
    • Consistent deviation from recapitulated path
      • Offset? Zigzag?

Biro D, Freeman R, Meade J, S. Roberts, Guilford T. (2007) PNAS. 104(18)

behavioural segmentation
Behavioural Segmentation

- Hidden-Markov Models

- Positional Entropy

slide15

Landscape Analysis

  • More likely to fly over edge ‘rich’ areas
  • Flight pattern becomes less predictable over edge rich areas.

Lau KK, Roberts S, Biro D, Freeman R, Meade J, Guilford T. (2006) J. Theo. Bio. 239(1) pp71-78

slide16

Paired Homing Pigeon Flight

Actual pair

  • GPS data for 48 Pigeons from 4 diff. sites
  • All possible pairs considered
  • Any real interaction between the birds should be seen as higher coupling between real pairs
  • Other pairs may show
    • High coupling due to same landscape/other unknown variables

Bird paired with self

Bird & random bird from different site

slide17

Birds which flew together show significantly (p < 0.05) higher coupling than other possible pairings. Implies some form of information transfer.

manx shearwater puffinus puffinus
Manx Shearwater (Puffinus puffinus)
  • Highly pelagic, migratory seabird.
    • Burrow dwelling, central place forager.
    • UK summer breeding
    • Winters in South America
  • 250, 000 – 300, 000 breeding pairs.
    • 45% on three Pembrokeshire islands, Skomer, Skokholm and Middleholm;
    • 36% on Rum.
motivation
Motivation
  • Ecology and Behaviour very similar to other Procellariiformes
    • Albatrosses, Petrels and Shearwaters.
      • 19 of 21 Albatross Species now globally threatened;
      • Devastating impact of long-line fishing
      • Understanding their behaviour, habitat and ecology may allow us to reduce this decline.
motivation20
Motivation

UK Seabird decline over recent years

Source: JNCC, UK Seabirds 2005

skomer island
Skomer Island
  • Small Island (~2km long) off coast of Wales
    • Home to large populations of Guillemots, Razorbills, Kittiwakes, Puffins, Fulmars
  • Worlds largest population of Manx Shearwaters
    • Well established research centre and study programmes
previous work
Previous Work
  • GPS Tracking of Manx Shearwater
    • Distribution of foraging was largely unknown;
      • South to Spain;
    • Interaction
      • With fisheries?
      • Environmental variables?
    • Establishment of Marine protection zones.
slide25

Foraging largely confined to Irish Sea;

  • Birds did not fly far south..
    • Even when they had the opportunity to do so.
    • Climate effect?
  • Clustered areas;
  • Rafting.

Right: Distribution of individual over trips of 1 to 7 days. Red shows incubating birds, blue chick rearing

slide26

Each 2-hourly fix gives a small burst of 1Hz data.

  • Bursts can be segmented into different behaviours.
  • Speed Vs Directionality
slide27

Sitting & Erratic Movement

Directional Movement

slide28

Speed has no obvious effect on depth

  • Time of day appears to (right)
autonomous habitat monitoring
Autonomous Habitat Monitoring
  • Working closely with Academic Partners
    • University of Oxford
      • Prof. Tim Guilford, Animal Behaviour
      • Prof. Chris Perrins, Edward Grey Ornithology Institute
    • University of Freie Berlin
      • Tomasz Naumowicz, PHD, Free University Berlin
      • Prof Torben Weis, U Duisburg-Essen
autonomous habitat monitoring30
Autonomous Habitat Monitoring
  • Create and deploy a wireless sensor network that can:
    • Monitor the visitations of individual birds;
    • Monitor environmental conditions inside and outside the burrow;
    • Provide a pilot system for eventual integration with GPS tracking;
    • Do this all night, every night…
methods
Methods
  • Approx. 10 Burrow monitored
    • Ringed and RFID tagged pair of birds in each burrow;
    • Sensors & wireless sensor node to each burrow;
methods32
Methods
  • Network
    • ScatterWeb platform from Freie Universitat Berlin;
  • Nodes
    • 2 x Passive Infrared
    • 2 x Temp/Humidity
    • RFID Detector
initial results
Initial Results
  • No observable impact on birds’ behaviour
    • No evidence of digging, distress or abandonment.
  • Of 10 monitored burrows
    • 7 hatched (last week)
    • Remainder still on eggs
initial results34
Initial Results
  • Obvious nocturnal distribution of activity
    • Bimodal?
  • Resolution and density of data already significantly higher that achievable using traditional methods.

All recorded events

slide35

2007/05/15 12:00

2007/05/15 00:00

2007/05/14 12:00

slide37

00:00

Temperature Variation over 4 days (20-23 June)

  • Red: Temp Outside
  • Green: Temp Inside

06:00

18:00

12:00

future questions
Future Questions…
  • Do individuals return at specific times?
  • How do pairs alternate feeding strategies?
  • How does activity/environment vary across space and time?
  • How do the results vary with weather?
future directions
Future Directions
  • Deploy second network
    • Pilot has allowed us to iron out most problems;
    • Hope to set up additional network this winter.
      • Create a toolkit that any ecologist can deploy and use.
  • Integrate GPS tracking with network
    • Continual monitoring of foraging behaviour.
slide40

~9hrs

~15min

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