The track-based monitoring technique and the estimation of occupancy and detection rates
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Transcript of The track-based monitoring technique and the estimation of occupancy and detection rates
The track-based monitoring technique and the estimation of occupancy and detection rates
Rick Southgate1 and Rachel Paltridge2
1 Envisage Environmental Services2 Desert Wildlife Services
Outline
• Track-based monitoring
• Types of data
• Occupancy and detection modelling PRESENCE
• Asserting absenceBayesian approach
• The way forward
Track plots + experienced trackers = meaningful data
Track-based monitoring: motivation ~2000
Track plots + indigenous communities = meaningful work
Track-based monitoring: motivation ~2000
Potential applicationenormous
2.1 M km2 of sand dunes
Track-based monitoring: motivation ~2000
structured + national = positive broad-scale program coordination monitoring &
community benefits
Methodology
Verification
Training
Accreditation
Data collation
Analysis
Feedback
Federal agencies
• DEEWR
• DAFF
• DEWHA
- NRM
- IPA
State agencies
Indigenous comm.
NGOs
Consultants Camel occurrence
Track-based monitoring: motivation ~2006
Track-based monitoring: 2013
Bilby occurrence IBRA7 regions
Over 1500 plot locations
Proponents:
• KJ
• CDNTS
• CLC
• NRAW SA
• NRAL SA
• Consultants
•Envisage Env. Ser.
•Desert Wildlife Ser.
•Ecological Horizons
Track-based monitoring: 2 ha plots
Similar to BirdsAustralia 2 ha sample method
Provide a snap-shot of spp. present/absent at a site (spp. >~100 g)
Standarise effort & approach, repeatable
• 200 x 100 m plot searched
• 25-30 minute
• Experienced observers
Three components to site selection:
• Spacing between sites to achieve independence (generally > 5 km)
• Repeat visits to sites to address imperfect detection
• Stratify sites on substrate & sub-bioregion
Track-based monitoring - 2 ha plots
Response variable - 2 ha plots
• Id species based on track characteristics
• Age of sign (1-2 day, 3-7, >7 days) - comparison of small: large animal sign
• On-plot: on-road
- comparison of transit v non-transit spp
− Juvenile sign
− Abundance of sign
− Diggings, burrows, scats
Site (occupancy) covariates - 2 ha plots
• Potential management factorsFire age pattern, dist. to community & water
• ThreatsInvasive predators, herbivores etc
• HabitatSubstrate, rainfall, veg composition, cover etc
Detection covariates - 2 ha plots
• Time of day (tracks crisp, sun angle, observer fatigue)
• Light intensity (shadow strength: track visibility)
• Track surface continuity (gait visibility)
• Track surface quality (small v. large animals)
Additive:
=> Ordinal detection score
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
4 5 6 7 8 9 10 11 12
ODS
dect
ecta
bilit
y
hopm
greyk
redk
cat
fox
dingo
camel
rabbit
Species detection in relation to tracking conditions
2 ha tbm data by latitude
latitude bilby dingo fox cat camel rabbit n
16 0.14 0.26 0.00 0.37 0.00 0.00 37
18 0.14 0.21 0.03 0.59 0.26 0.00 220
20 0.20 0.25 0.20 0.65 0.32 0.02 189
22 0.07 0.29 0.13 0.34 0.44 0.02 603
24 0.06 0.16 0.04 0.24 0.18 0.02 71
26 0.08 0.34 0.11 0.34 0.32 0.38 80
28 0.00 0.34 0.62 0.26 0.21 0.62 385
30 0.00 0.19 0.41 0.32 0.03 0.61 133
2 ha tbm data by bioregion
bioregion bilby dingo fox cat camel rabbit n
gsd 0.12 0.27 0.17 0.43 0.51 0.03 306
pilbara 0.00 0.50 0.07 0.25 0.30 0.00 44
lsd 0.03 0.24 0.12 0.26 0.37 0.01 271
gas 0.02 0.28 0.06 0.34 0.28 0.02 47
gid 0.10 0.23 0.00 0.35 0.35 0.00 69
Types of data
Abundance of species at a site -> ordinal or continuous data
Presence/absence of species at a site -> binary data: 0 or 1
Binary data from multiple sites -> propn of area occupied (f)
provides a surrogate for sp. abundance
- true for broad-scale surveys - true for cryptic, low density
species. - occurrence less expensive than abund.
Problems arise if a species is not detected perfectly
• Non-detection may mean the sp. is not genuinely absent
• Propn area occupied underestimated etc.
Monitoring
Observed state
Detected Not detected
Actual state
Genuine presence True presence False absence
Genuine absence False presence True absence
Monitoring
Observed state
Detected Not detected
Actual state
Genuine presence True presence False absence
Genuine absence False presence True absence
Monitoring
Observed state
Detected Not detected
Actual state
Genuine presence True presence False absence
Genuine absence False presence True absence
Incorrect ids not tolerated: Validate! If in doubt, leave out
Repeat surveys
Data types and probability estimates
Revisits to multiple sites -> detection history for each site eg.00101
-> naïve est. (which is of more value than f )
-> prob. of detection (p)
-> prob. of occupancy (psi)
an unbiased estimate of propn area occupied.
Occupancy and detection modelling PRESENCE
Developed by Darryl MacKenzie and colleagues
use standard maximum likelihood based methods to obtain estimates
logistic models to incorporate covariatesstrength covariables associating with detection eg. observerstrength covariables associating with occupancy eg. bioregion
Important parameters: Prob of occupancy (psi): prob. that a species is present at a site
(constant across all sites)Prob of detection (p): prob. a species will be detected in a single
survey at a particular site given a site is occupied
-> used to determine sampling effort, assert absence, species status etc
Detection: survey effort
Survey effort (n*) to determine the status of a species at a site depend on:
• the suitability of a habitat (psi’) • the reliability of a survey to detect a species (p) • the probability of the occupancy required when
the survey fails to detect the species (psi). Do we
need 95% or 99% confidence?
n*= (log(psi/(1-psi))-log(psi'/(1-psi')))/log(1-p')
where
psi =posterior prob of presence at a site (confidence you need)
p'= prior belief about detectability of species
psi' = prior prob that the species is present
• Need to apply standarised techniques
• Revisiting, resampling sites – funding agencies need to recognise importance
• Data sharing – sort out data ownership, management and access agreements
Summary
Thank you
Acknowledgements• KJ• CDNTS• Maralinga Tjarutja Council• DENR• AWNRMB• ALNRMB