1

The importance of estimating detection probabilities in animal sampling

Describe our research on bird, salamander, and frog populations focused on questions of interest to land management agencies.

Get you thinking about why we need to account for detection probability (the fraction of the true population recorded) when we sample animal populations.

Question…… what is the most common type of data collected in studies of animal diversity and abundance?

Answer….. Counts of animals!

Objectives

Bias and precision of count estimates

• Improve precision by– Increasing sample size– Standardizing sampling

methods to control for environmental factors, season or year effects, variations in observer skill

• Reduce bias by– Minimizing violations of method

assumptions– Accounting for spatial and

temporal variations in detection probability

Good PrecisionBiased

Average Off Center

Poor PrecisionUn-biased

Average On Center

Good PrecisionUn-biased

Accurate

Truth

Percent of Migratory Bird Species Showing Population Declines 1978 -1987

Robbins, C.S, J.R. Sauer, R.S. Greenberg and S. Droege. 1989. Population declines in North American birds that migrate to the Neotropics. Proc. Natl. Acad. Sci. USA. 86:7658-7662.

Forest Songbirds

2

The Breeding Bird Survey is a point count based abundance index

• Began by Chandler Robbins USFWS in 1966

• 3000 Roadside Routes in the US and Canada

• 25 miles, 50 points/route

• 3-minute unlimited radius point counts

Wood Thrush

• Widely used in research and environmental monitoring

• Single observers count all birds seen or heard during a fixed time interval (3 – 10 min) on a limited or unlimited radius plot

• In forested habitats most detections are by ear

• Counts provide an abundance index – no estimate of detection probability

Monitoring avian population trends: traditional point count surveys

Conceptual Model of Abundance Estimates

C = count of animals detected (seen, heard, or captured)

= detection probability, an estimate of the fraction of animals detected

N = population estimatep̂CN̂ =

Note: there are many ways to estimate , for example, double-observer, distance sampling, capture-recapture, etc.

is usually

3

Bird survey methods - Great Smoky Mountains National Park

• Point counts– Variable circular plot– 10 minute interval– Single observer– Distance sampling

• Point locations– Low use hiking trails– Stratified by vegetation type

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50 0 50 KilometersN

VegetationSpruce-FirNorthern HardwoodCove HardwoodMesic OakMixed Mesic HardwoodTulip PoplarXeric OakPine-OakPineHeath BaldGrassy BaldGrape ThicketTreelessWater

# Bird Census Point

HABITAT MODEL VARIABLES

• Elevation• Slope• Aspect• Geology (24)• Disturbance History (5)• Vegetation Type (14)• Landform Index • Relative Slope Position • Topographic Convergence Index• Topographic Relative Moisture Index• Shannon-Wiener Index of Topographic

Complexity

Models: wildlife habitat relationships and population dynamics

Black-throated Green Warbler probability of occurrence

LowMedium - LowMediumMedium - HighHigh

10 0 10 20 KilometersN

Simons et al. 2000. Evaluating Great Smoky Mountains National Park as a population source for the Wood Thrush. Conservation Biology 14:1133-1144.

# Census PointIndustrial Logging (376 km2)Undisturbed (469 km2)

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Forest Type

Northern Hardwood (195 km2)Cove Hardwood Mixed Mesic (1025 km2)

Are Primary and Secondary Forest Bird Communities Different?

Simons et al. 2006. Comparison of breeding bird and vegetation communities in primary and secondary forests of Great Smoky Mountains National Park. Biological Conservation 129: 302-311.

4

Primary Forest• Undisturbed for centuries• Big Trees• Forest Gaps• Well developed understory• Uneven Canopy• Woody Debris

Secondary Forest• 77% of Park • Logged 1900 - 1930• Highly Mechanized• Clear Cuts• Substantial Erosion

Secondary Forest Today

• ~100 Years Old• Smaller Trees• Even-aged Stands• Few Canopy Gaps• Less developed understory• Lack of Woody Debris

What factors might influence detection probabilities on primary and secondary forests?

• ~100 Years Old• Smaller Trees• Even-aged Stands• Few Canopy Gaps• Less developed understory• Lack of Woody Debris

• Undisturbed for centuries• Big Trees• Forest Gaps• Well developed understory• Uneven Canopy• Woody Debris

5

Distance sampling provides estimates of detection probability

• The probability of detection is modeled with a sighting function g(r)– Declines with

distance – We assume g(0)

= 1.0

distance (r)

g(r)1

0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

DJ BB BT WW SV VE OB RV ST GK RN CN BL BC PA HW BR RT

Mea

n R

elat

ive

Abun

danc

e

247 Paired PointsN = 30 detections/species

Primary Forest Secondary Forest

**

**

**

Scarlet Tanager Golden-crowned Kinglet

?Scarlet Tanager Golden-crowned

Kinglet

Effective detection radius247 paired points

N ≥ 30 detections/species, ± SE

0

20

40

60

80

100

120

140

DJ BB BT WW SV OB VE RV ST GK RN CN BL BC PA HW BR RT

Species

Rad

ius

(m)

Primary Forest Secondary Forest

*

***

*

Density

0

0.2

0.4

0.6

0.8

1

1.2

1.4

DJ BB BT WW SV OB VE RV ST GK RN CN BL BC PA HW BR RTSpecies

Den

sity

(pai

rs/h

a)

***

*

*

*

6

Figure 2 Amphibian population trends from 1950 to 1997 using all 936 populations. Arrows indicate the 'switchpoints‘.

Quantitative evidence for global amphibian population declinesJEFF. E. HOULAHAN*, C. SCOTT FINDLAY*†, BENEDIKT R. SCHMIDT‡, ANDREA H. MEYER§ & SERGIUS L. KUZMIN* Ottawa-Carleton Institute of Biology, University of Ottawa, 30 Marie Curie, Ottawa, Ontario K1N 6N5, Canada† Institute of Environment, University of Ottawa, 555 King Edward Street, Ottawa, Ontario K1N 6N5, Canada‡ Zoologisches Institut, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland§ Swiss Federal Statistical Office, Sektion Hochschulen und Wissenschaft, Espace de l'Europe 10 , CH-2010, Neuchâtel, SwitzerlandInstitute of Ecology and Evolution, Russian Academy of Sciences , Moscow 117071, Russia

Correspondence and requests for materials should be addressed to J.E.H (e-mail: jeffhoul@science.uottawa.ca).

Nature 404, 752 - 755 (2000)

Salamanders

• Evidence of global amphibian declines• Sensitive environmental indicators• 20% of world’s species in SE U.S. • Biology and patterns of diversity and

abundance poorly understood

Counts of the surface population (N ) represent the population available for detection

N

Problem #1: The conditional detection probability (probability of detection given availability on the surface) is

7

Problem #3: The surface population varies over time as animals move in and out of the study area

(temporary emigration)

Analyzing the problem: capture histories of marked and recaptured individuals

10”

10”

10”

5”

1m

1m

Leaf LitterCover Boards

X X X X X

Cover Boards

Leaf Litter

Capture-recapture site design

8

Elastomer marking technique

(Marked over 6,000 salamanders in 3 years)

Detection (capture) history 011001011101011=observed, 0= not observed Sampling occasions

Results: average conditional detection probability

p (..) = 0.29 ± 0.01

Results: average temporary emigration

(.) = 0.87 ± 0.01

9

Results: ‘effective detection probability’

po(.) = 0.03 ± 0.002 (0.13x0.29~0.03)

Yikes!

• Low detection probabilities suggest that terrestrial salamanders may not be good targets of environmental monitoring programs…… especially when conflicts are anticipated.

Bailey, L. L., T. R. Simons, and K. H. Pollock. 2004. Estimating site occupancy and species detection probability parameters for terrestrial salamanders. Ecological Applications 14: 692-702.

Hypothetical BBS trends

Time

+

+

SingingRate or Hearing Ability

Ambient Noise or Vegetation Density

Counts

Canadian BBS observer age

0

5

10

15

20

25

30

35

65

Age

Perc

ent

10

Methods for estimating detection probabilities from point counts

• Distance sampling• Multiple observers • Time of detection• Double sampling• Occupancy methods• Repeated counts• Combined methods

The Challenge - Observers

Validation experiments, a.k.a. “All Bird Radio”

• Simulate census conditions when most birds are identified by sound

• Quantify biases and precision of current sampling methods

• Vary conditions that will influence detection probability

• Evaluate the costs and benefits of incorporating different types of detection probability estimates

NotebookPC (A)

Transmitter (B)

RF Tx Module

RFAmp

Antenna

232

RF Rx moduleMicroprocessor

Off-the-shelf MP-3 player Rear

Speaker

Interface

Receiver/players (up to 64) (C)

Coded RF message - device number and track number

Front Speaker

Antenna

System diagram

AB

C

11

Factors affecting auditory detection probabilities(availability x detection given availability)

• Measurement error factors (observer skill and ability)– Localization, species identification, # individuals– Ability to apply meaningful movement and counter-singing rules to avoid double counting

• Signal to noise ratio factors– Call spectral qualities – Call volume– Singing rate of individual birds– Orientation of calling birds (toward or away from observer)– # birds and # species calling– Habitat (vegetation structure)– Topography– Ambient noise

Field experiments• November 2003 –

March 2007• > 50 Volunteers• > 5000 individual point

counts

Ambient noise experiments

• 6 skilled observers• 25 players at 5 m intervals (40 – 160m) • 6 species (BTBW, BTNW, CSWA, HOWA, YEWA, NOPA)• Ambient noise

− None (40 dB)− 10 – 20 km/hr breeze− +10 db white noise− 1 – 3 background birds (WIWR, YTWA, OVEN)

Ambient noise experiment

12

Ambient noise on 20 NC BBS routes in 2006

20

30

40

50

60

70

80

90

5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00

Time

Mea

n so

und

leve

l (d

B)

Scarlet Tanager

Golden-crowned Kinglet

Hypothetical BBS trends

Time

+

+

Declining Singing Rate Declining Hearing Ability

Increasing Ambient NoiseIncreasing Veg Density

Counts

Detection Probability, Distance, and Singing Rate

Logistic regression models for a single observer illustrating the relationship between detection probability and distance for counts of five species singing at high (solid line) and low (dashed line) singing rates. Note the consistent affect of singing rate on detection probability.

Detection probabilities averaged across seven observers ranged from 0.60 (Black-and-white Warbler) to 0.83 (Hooded Warbler) at the high singing rate and 0.41 (Black-and-white Warbler) to 0.67 (Hooded Warbler) at the low singing rate. Logistic regression analyses indicated that species, singing rate, distance, and observer were all significant factors affecting detection probabilities.

Observer, Species, Singing Rate, Distance

Worst Observer Best Observer

Distance Low Rate High Rate Low Rate High Rate

BAWW

30 (40) 0.87 0.99 0.94 1.00

60 (120) 0.61 0.92 0.80 0.97

90 (200) 0.26 0.55 0.48 0.75

120 (280) 0.08 0.11 0.17 0.23

150 (360) 0.02 0.01 0.05 0.03

Expected Count 190 294 295 382

HOWA

30 0.97 1.00 0.99 1.00

60 0.88 0.99 0.95 1.00

90 0.64 0.93 0.82 0.97

120 0.29 0.55 0.51 0.76

150 0.08 0.11 0.19 0.24

Expected Count 382 538 529 653

Detection probabilities at distances from 30 m to 150 m, and expected counts for a simulated population of 1,000 birds, basedon the logistic models for BAWW (least detectable species) and HOWA (most detectable species) using the best and worst observers and both high and low singing rates.

13

Distance Measurement Errors

Differences in distance estimation errors for songs oriented toward observers (diamonds) compared to those oriented away from observers (open squares). Errors for six observers averaged across three distance categories.

Simons, T. R., K. H. Pollock, J. M. Wettroth, M. W. Alldredge, K. Pacifici, and J. Brewster. 2009. Sources of measurement error, misclassification error, and bias in auditory avian point count datain D.L. Thomson et al. (eds.), Modeling Demographic Processes in Marked Populations.

Ribbit RadioEvaluating Detection Bias on Auditory Frog Call Surveys

Objectives• Identify potential factors that influence our

ability to detect and correctly classify calling anurans in “occupancy” (repeated presence/absence species count) surveys– Understand the frequency of “false positive”

errors and “expectation bias” – Understand covariates affecting detection and

false positives; wind, distance, # species– Effects of observer skill– Can training reduce false positives?

“Expectation” Bias

Riddle, J.D., R.S. Mordecai, K.H. Pollock, and T.R. Simons 2010. Effects of prior detections on estimates of detection probability, abundance, and occupancy. The Auk 127(1):94-99.

“trap happy” observers

Predictions:

• False negative detections will increase during an experiment for commonly played species -“tuning out”

• False positive detections will increase during an experiment for commonly played or “anticipated” species

• False positive detections will increase when “companion” species are played

14

Methods

• ~35 Observers, 8 half day sessions• Distance

– 10 - 55m• 12 Species

– Pickerel Frog (Rana palustris)– Wood Frog (Rana sylvatica)– Southeastern Chorus Frog

(Pseudacris feriarum)• Competing Species “Treatments”

– None– Southern Leopard Frog (Rana

sphenocephala)– Spring Peeper (Pseudacris crucifer)

chorus

Results• Despite the participation of expert observers in simplified field

conditions, both false positive errors and detection probability variability were extensive for most species in the experiments

• We found that even low levels of false positive errors, constituting as little as 1% of all detections, caused severe overestimation of site occupancy, colonization, and local extinction probabilities

• Further, unmodeled detection probability heterogeneity induced substantial bias - underestimation of occupancy and overestimation of colonization and local extinction probabilities

McClintock et al. 2010. Unmodeled observation error induces bias when inferring patterns and dynamics of species occurrence via aural detections. Ecology 91(8): 2446-2454.

McClintock et al. 2010. Experimental investigation of observation error in anuran call surveys. Journal of Wildlife Management 74(8): 1882-1893.

Miller et al. 2012. Experimental investigation of false positive errors in auditory species occurrence surveys. Ecological Applications 22: 1656-1674.

Miller, et al. 2015. Performance of occupancy estimators when basic assumptions are not met: a test with field data where truth is known. Methods in Ecology and Evolution 6: 557-565.

Conceptual Model of Abundance Estimates

C = count of animals detected (seen, heard, or captured)

= detection probability, an estimate of the fraction of animals detected

N = population estimatep̂CN̂ =

Note: there are many ways to estimate , for example, double-observer distance sampling, capture-recapture, etc.

is usually

15

Accounting for Presence(repeated counts over time)

ˆˆˆ

N̂daap ppp

C

pp̂

N

ap̂

dap̂

ˆWhere:

= the population estimate

= the probability an animal is present in the sample area

C = the count statistic

= the probability that an animal is available to count

= the probability of detection given availability

Hypothetical study area with 10 territories of species A

In any given 5 minute period, this species only uses 25% of its territory on average. The yellow area represents the portion of each territory that is occupied in this example.

In any given 5 minute period, species A has a 70% chance of being available (singing). Therefore 3 out the 10 birds shown here are not available to be counted.

16

Given that a bird is available, the average observer has a 71% chance of detecting it. Therefore, only 5 of the 7 available birds would be counted. The available, but undetected birds are shown in light grey.

1

5

3

4

2

Therefore, 5 sampling scenarios exist for species A with 5 minute point counts:

1) Point count is located where there is no bird.2) Point count contains bird territory, but not the bird.3) Point count contains bird, but bird is not singing and therefore not available for detection.4) Point count contains singing bird, but it is not detected.5) Point count contains singing bird which is detected.

Sampling Situation Method(s) to Use Detection Estimate

-Multiple Observers (including dependent

observers, independent observers, and unreconciled

observers)

-Distance

Presence and availability are constant over space and time

or equal to 1, but not all present and available animals

are detected

Presence is constant over space and time or equal to 1,

but not all animals are available and/or detected if available

-Time-of-Detection

-Time-of-Removal

Detection probability (PpPaPd) is not constant over space and

time or equal to 1-Repeated Counts

(Occupancy and n-mixture)

PaPd

PpPaPd*

Pd

No, or unsure

YesDetection probability (PpPaPd) is constant over space and time or equal to 1 (very unlikely)

Not necessary-Simple Count

* Pp = probability of a bird being present in sample area during the count, Pa = probability of being available for detection, Pd = probability of being detected given availability.

Yes

Yes

No, or unsure

No, or unsure

Yes

Take-home lessons• Most count-based estimates of animal diversity or

abundance are subject to similar (and often multiple) sources of bias.

• Adjusting counts by estimating detection probabilities directly can reduce bias in count-based abundance estimates.

• Use your ecological experience to identify the most important sources of bias and apply the best method available to meet your objectives.

• Recognize that unmodeled uncertainty exists in most estimates based on count data, i.e. inferences are generally weaker than we assume.

17