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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.

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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


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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?


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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?


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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:


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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


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RARE ECOLOGICAL EVENTS RATES OF RARE SPECIESIN 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


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase Studies

Model Families applied to common species:

  • Linear logistic regression (GLM)

  • Additive logistic regression (GAM)

  • Classification trees (CART)


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase Studies

Internal Validation:

  • 10 fold cross-validation.

  • (delete-one jackknife for logistic regression)

    External Validation:

  • Pilot and other random grid surveys

Training

Validation


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase 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


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase 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


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase Studies

Classification tree for Lobaria oregana

Measures of model fit

  • PCC = 94.5%.

  • PCCAbsent = 94.8%.

  • PCCPresent = 82.7%.


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Predicted RATES OF RARE SPECIES

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%.


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Predicted RATES OF RARE SPECIES

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%.


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase 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


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase 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


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase Studies

Detection likelihoods for rare species LobaScro


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase Studies

Detection likelihoods for rare species PseuRain


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Modeling Survey & Manage Data RATES OF RARE SPECIESCase 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


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Modeling Survey & Manage Data RATES OF RARE SPECIESConclusions

  • 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


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Modeling Survey & Manage Data RATES OF RARE SPECIESConclusions

  • 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


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Modeling Survey & Manage Data RATES OF RARE SPECIESConclusions

  • 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?


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