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Autonomous Monitoring of Vulnerable Habitats

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Title: Autonomous Monitoring of Vulnerable Habitats


1
Autonomous Monitoring of Vulnerable Habitats
  • And other tales..

Robin Freeman, CEES, Microsoft Research 13 July
2007
2
Overview
  • Introduction
  • Previous Work
  • Analysing Avian Navigation
  • Habitat Monitoring
  • Brief Results
  • Future Work

3
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.

4
(No Transcript)
5
9hrs
15min
6
Introduction
  • Analysing Avian Navigation
  • GPS Tracking of Pigeons, Oxford
  • GPS Tracking of Manx Shearwaters, Skomer
  • Habitat Monitoring
  • Manx Shearwater
  • Skomer Island, Wales

7
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

8
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.

9
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, theres that house!

10
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
11
  • Nearest Neighbour Analysis
  • Shows offset and variance between controls and
    familiar clock-shift.

12
Delayed Clock shift response (landmark related)
Tracks ranked by Mahalonobis distance from
recapping distribution
13
  • 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)
14
Behavioural Segmentation
- Hidden-Markov Models - Positional Entropy
15
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
16
Paired Homing Pigeon Flight
  • 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

Actual pair
Bird paired with self
Bird random bird from different site
17
Birds which flew together show significantly (p lt
0.05) higher coupling than other possible
pairings. Implies some form of information
transfer.
18
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.

19
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.

20
Motivation
UK Seabird decline over recent years
Source JNCC, UK Seabirds 2005
21
(No Transcript)
22
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

23
Skomer Island
24
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.

25
  • 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
26
  • Each 2-hourly fix gives a small burst of 1Hz
    data.
  • Bursts can be segmented into different
    behaviours.
  • Speed Vs Directionality

27
Sitting Erratic Movement
Directional Movement
28
  • Speed has no obvious effect on depth
  • Time of day appears to (right)

29
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

30
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

31
Methods
  • Approx. 10 Burrow monitored
  • Ringed and RFID tagged pair of birds in each
    burrow
  • Sensors wireless sensor node to each burrow

32
Methods
  • Network
  • ScatterWeb platform from Freie Universitat
    Berlin
  • Nodes
  • 2 x Passive Infrared
  • 2 x Temp/Humidity
  • RFID Detector

33
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

34
Initial Results
  • Obvious nocturnal distribution of activity
  • Bimodal?
  • Resolution and density of data already
    significantly higher that achievable using
    traditional methods.

All recorded events
35
2007/05/14 1200
2007/05/15 0000
2007/05/15 1200
36
Initial Results
37
0000
  • Temperature Variation over 4 days (20-23 June)
  • Red Temp Outside
  • Green Temp Inside

0600
1800
1200
38
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?

39
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.

40
9hrs
15min
41
An Aside (1)
42
An Aside (2)
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