The track-based monitoring technique and the estimation of occupancy and detection rates

1. The track-based monitoring technique and the estimation of occupancy and detection rates Rick Southgate1 and Rachel Paltridge2 1 Envisage Environmental Services 2 Desert Wildlife Services

2. Outline • Track-based monitoring • Types of data • Occupancy and detection modelling • PRESENCE • Asserting absence • Bayesian approach • The way forward

3. Track-based monitoring: motivation ~2000 Track plots + experienced trackers = meaningful data

4. Track-based monitoring: motivation ~2000 Track plots + indigenous communities = meaningful work

5. Track-based monitoring: motivation ~2000 Potential applicationenormous 2.1 M km2 of sand dunes

6. Track-based monitoring: motivation ~2006 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

7. Track-based monitoring: 2013 • Over 1500 plot locations • Proponents: • KJ • CDNTS • CLC • NRAW SA • NRAL SA • Consultants • Envisage Env. Ser. • Desert Wildlife Ser. • Ecological Horizons Bilby occurrence IBRA7 regions

8. 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

9. Track-based monitoring - 2 ha plots • 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

10. 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

11. Site (occupancy) covariates - 2 ha plots • Potential management factors • Fire age pattern, dist. to community & water • Threats • Invasive predators, herbivores etc • Habitat • Substrate, rainfall, veg composition, cover etc

12. 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

13. Species detection in relation to tracking conditions

14. 2 ha tbm data by latitude

15. 2 ha tbm data by bioregion

16. 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.

17. Monitoring Observed state Detected Not detected Actual state Genuine presenceTrue presenceFalse absence Genuine absenceFalse presenceTrue absence

18. Monitoring Observed state Detected Not detected Actual state Genuine presenceTrue presenceFalse absence Genuine absenceFalse presenceTrue absence

19. Monitoring Repeat surveys Observed state Detected Not detected Actual state Genuine presenceTrue presenceFalse absence Genuine absenceFalse presenceTrue absence Incorrect ids not tolerated: Validate! If in doubt, leave out

20. 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.

21. Occupancy and detection modelling PRESENCE • Developed by Darryl MacKenzie and colleagues • use standard maximum likelihood based methods to obtain estimates • logistic models to incorporate covariates • strength covariables associating with detection eg. observer • strength 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

22. 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?

23. 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

24. Thank you Acknowledgements • KJ • CDNTS • Maralinga Tjarutja Council • DENR • AWNRMB • ALNRMB