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