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Spatial Capture-Recapture Scenario Detectors Animal locations - - PowerPoint PPT Presentation
Spatial Capture-Recapture Scenario Detectors Animal locations - - PowerPoint PPT Presentation
Spatial Capture-Recapture Scenario Detectors Animal locations Effective area? No Problem: bread & butter to Distance Samplers! This is just MRDS Point Transect Survey Detector ID 1 2 3 4 1 0 1 0 0 2 0 0 1
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Effective area? – No Problem: bread & butter to Distance Samplers!
- This is just MRDS Point Transect Survey
- … but with lots of points, (and sometimes
counts instead of 0/1 data)
Detector ID 1 2 3 4 … 1 0 1 0 0 2 0 0 1 1 3 1 1 0 1 4 0 1 1 1 5 1 1 0 0 etc.
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Detection function
Distance Detector
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Combining across detectors and occasions
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Combining across detectors and occasions
ò
- =
X X d p a ) (
is the area effectively sampled by detectors
(if animal density constant in space).
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Problem: Don’t observe distance, only
- bserve capture location
- But locations of captures/non-captures
contains information about detection probability and location/distance.
- Like a multiple-point point transect survey
with errors in distance estimation.
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Where is the information on location?
−15 −10 −5 5 −26 −24 −22 −20 −18 −16 x y 200 400 600 800 1000 1200 1400
- 1
2 3 4 5
Color is elevation
Unobserved activity centre
Camera traps
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Where is the information on location?
−1 1 2 3
- 8
5 5 4
Observed capture history
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Distance
- ●
- 1 2
3 4 5
ni1=8 ni2=5 ni3=5 ni4=4 ni5=0
Where is the information on location?
−1 1 2 3
- 8
5 5 4
Observed capture history in 1D
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−10 −5 5 10 −2 2 4 6 8 Distance λ(d)T
- 1
2 3 4 5 ni1=8 ni2=5 ni3=5 ni4=4 ni5=0
Where is the information on location?
Observed capture history in 1D
−1 1 2 3
- 8
5 5 4
Detection function
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2 4 6 8
1 1 2 2 3 4 5 6 7 8
- 1
2 3 4 5
- Activity centre
Where is the information on location?
Detection function in 2D
−1 1 2 3
- 8
5 5 4
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(Can also predict locations after the event)
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Can fit Density Surface Models
300000 340000 380000 7200000 7250000 7300000 7350000 7400000 7450000 7500000 x y 0.0000 0.0005 0.0010 0.0015 0.0020 0.0025 0.0030 0.0035 300000 340000 380000 7200000 7250000 7300000 7350000 7400000 7450000 7500000 x y 0.20 0.25 0.30 0.35 0.40 0.45 0.50
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Can Model Habitat Use
−15 −10 −5 5 −26 −24 −22 −20 −18 −16 x y 2 4 6 8
1 1 2 2 2 3 3 4 4 5 5 6 6 7 8
- 1
2 3 4 5
−1 1 2 3
- 8
5 5 4
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Mist-netting red-eyed vireo
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Genetically capturing stoats
Photographs: Bruce Warburton Picture: Murray Efford
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Acoustically Surveying Gibbons
- 665
670 675 680 685 690 695 700 1550 1555 1560 1565 1570 UTM.Y (km)
Human acoustic detectors
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Acoustically Surveying Frogs
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Summary
- Mark-recapture with distance detection function
(and unknown distance)
- R library secr on CRAN (Murray Efford)
- Also Bayesian software
- R library “ascr” (Ben Stevenson) for acoustic