Thomas C. Edwards, Jr. USGS Utah Cooperative Research Unit

1. MODEL-BASED STRATIFICATIONS FOR ENHANCING SURVEY DETECTION RATES OF RARE SPECIES Thomas C. Edwards, Jr. USGS Utah Cooperative Research Unit Richard Cutler, Mathematics & Statistics, Utah State University Niklaus Zimmermann Swiss Federal Research Institute WSL

2. RARE ECOLOGICAL EVENTSIN TIME AND SPACE • Overview A (Biased) Historical Perspective of the PNW Forest Plan The Case of Survey and Manage Species as Rare Events • Design and Sampling Issues Detection of rare events • Example Analyses Sampling issues related to rare ecological events: lichens as an example • Some Final Thoughts

3. RARE ECOLOGICAL EVENTSIN TIME AND SPACE Historical Overview • The Context: • Northern spotted owls like old forest … • Timber companies like old forest … • A Socio-Economic, Political, Ecological collision led to … • Listing under the ESA … • And the Northwest Forest Plan

4. RARE ECOLOGICAL EVENTSIN TIME AND SPACE Historical Overview • More Context: • Northwest Forest Plan Record of Decision identified >350 rare species to be surveyed for management, including lichens, bryophytes, fungi, and a few token vertebrates • These species are identified as Survey and Manage • They represent species for which little to no information is known

5. RARE ECOLOGICAL EVENTSIN TIME AND SPACE • Objectives of survey and manage effort were to obtain estimates of, and/or determine, for EACH of the >350 S&M species: • Abundance: Is the species abundant at local and regional scales? • Spatial distribution: Is the species well-distributed across the area of the Northwest Forest Plan? • Persistence: Do management activities ensure long-term persistence?

6. RARE ECOLOGICAL EVENTSIN TIME AND SPACE Objectives of Survey and Manage • Information to meet objectives comes from: • Existing data • New data • Expert opinion • All must be merged so that simple policy decisions can be made for each species • Decision framework must be multi-faceted

7. RARE ECOLOGICAL EVENTSIN TIME AND SPACE Objectives of Survey and Manage • Meeting these objectives required significant exploration into issues of: • Sampling, • Estimation, • Non-Spatial Modelling, and • Spatial Modelling • Can we detect, model, and eventually estimate, attributes of rare species at landscape scales?

8. RARE ECOLOGICAL EVENTSIN TIME AND SPACE • Rare species are, well, rare! • Limited life history information available • Some populations exhibit irruptive behaviors, necessitating multiple site visits through time • Efficient sample designs a must • Some constraints affecting ability to meet objectives:

9. RARE ECOLOGICAL EVENTSIN TIME AND SPACE • Analytical approach • Develop models for common lichens based on topographic and weather (DAYMET) variables • Translate these models into spatially explicit maps • Use maps as basis of stratification for sampling associated rare species • Evaluate with independent data and determine if the models increase detection rates of rare species

10. RARE ECOLOGICAL EVENTSIN TIME AND SPACE Example Analysis: Lichens • Characteristics of data • Forest Service CVS/FIA plots were basis of sample design • All plots visited; number of visits variable • Only first visit considered in subsequent analyses • All lichen species searched for at each plot

11. Modeling Survey & Manage DataCase Studies Model Families applied to common species: • Linear logistic regression (GLM) • Additive logistic regression (GAM) • Classification trees (CART)

12. Modeling Survey & Manage DataCase Studies Internal Validation: • 10 fold cross-validation. • (delete-one jackknife for logistic regression) External Validation: • Pilot and other random grid surveys Training Validation

13. Modeling Survey & Manage DataCase Studies Rare & Common overlap (%) Common LobaOreg LobaPulm PseuAnom PseuAnth - 78.7 83.0 - - 96.0 76.0 76.0 - 76.9 100.0 - - 77.4 87.1 - 88.9 88.9 77.8 - Rare LobaScro NephLaev NephOccu NephPari PseuRain

14. Modeling Survey & Manage DataCase Studies • Differences in mean values for presences and absences for: • Topographic: Elevation, Easting, and Northing • Weather: Minimum temperature, Relative humidity, Rainfall Summary statistics for Lobaria oregana

15. Modeling Survey & Manage DataCase Studies Classification tree for Lobaria oregana Measures of model fit • PCC = 94.5%. • PCCAbsent = 94.8%. • PCCPresent = 82.7%.

16. Predicted Absent Present Actual Absent 608 26 Present 52 134 Modeling Survey & Manage DataCase Studies 10-fold internal cross-validation of Lobaria oregana model Measures of model fit • PCC = 90.5%. • PCCAbsent = 95.9%. • PCCPresent = 72.0%.

17. Predicted Absent Present Actual Absent 571 63 Present 91 95 Modeling Survey & Manage DataCase Studies External validation of Lobaria oregana model Measures of model fit • PCC = 81.2%. • PCCAbsent = 90.0%. • PCCPresent = 51.1%.

18. Modeling Survey & Manage DataCase Studies Measures of error (%) for classification tree models for three other common lichen species used to model rarer species Cross-validation Model Prediction • LobaPulm 15.2 18.3 19.3 • PseuAnom 12.6 15.4 15.0 • PseuAnth 10.2 13.2 15.3

19. Modeling Survey & Manage DataCase Studies • Models of common species applied to spatial data for PNW and probability of lichen occurrence estimated for each location • Estimated number of detections for each rare species using stratifications based on common species

20. Modeling Survey & Manage DataCase Studies Detection likelihoods for rare species LobaScro

21. Modeling Survey & Manage DataCase Studies Detection likelihoods for rare species PseuRain

22. Modeling Survey & Manage DataCase Studies Observed / Expected (Efficiency) Common LobaOreg LobaPulm PseuAnom PseuAnth - 13/26 (2.0) 13/36 (2.8) - - 19/23 (1.2) 19/48 (2.5) 19/60 (3.2) - 1/5 (5.0) 1/5 (5.0) - - 7/14 (2.0) 7/16 (2.3) - 2/1 (0.5) 2/5 (2.5) 2/5 (2.5) - Rare LobaScro NephLaev NephOccu NephPari PseuRain

23. Modeling Survey & Manage Data Conclusions • Stratification applied to independent region for field validation • Expected detections for rare species should be apportioned across likelihood bins • Ideal concordance would be 45° line

24. Modeling Survey & Manage Data Conclusions • Common problem when designing surveys for rare species is sufficient detections for analysis • Design-based approaches provide least biased estimates, but can lead to low detections • Model-based stratification using more common species can improve probability of detecting more rare species • 2 to 5-fold gains in detection realized when process applied to rare epiphytic lichens

25. Modeling Survey & Manage Data Conclusions • Edwards et al. Enhancing survey detection rates of rare species using model-based stratifications. In press, Ecology. • Download at: ella/gis.usu.edu/~utcoop/tce Questions?