ST506 L6-08

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Lecture 6

DISTANCE SAMPLING (Ch 13)

NEW MATERIAL: POINT COUNTS

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New Homework Sets

Homework Set 3-Very short homework onan example of using the multiple observer

methods in aerial surveys of waterbirds

Homework Set 4- Work on distance

sampling for line transects and point counts.

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Review: Key Concepts in Distance

Analysis An observer travels along a line and counts animals

plus measure theirperpendicular distance. Detection probability is related to perpendicular

distance.

There is a detection function g (x) which starts at 1 on

the line and decreases monotonically with distance.

We estimate g (x) and use it to compute theprobability of detecting an animal in the strip.

detectionperfectifcurve/areaunderarea0

=

=

w

a g(x)dx/wP

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Key Concepts in Distance Analysis

To fit g(x) we need to fit a series of models based on akey function plus a series expansion. This is to give us

more and more flexible functions We distinguish between competing models using the

AIC we

minimize AIC = -2 log L + 2(# pars)

For the chosen model with min AIC ML Estimation isused to estimate the parameters and hence the density

parameter with its SE and Confidence Interval based onapprox normality.

We also assess goodness of fit of the chosen model.

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Any Questions on this Example?

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Program DISTANCE

Windows Driven Program

-Down Loading from Web Site at St Andrews

University

-Link on ST 506 Site

-Line transect and point count analyses

-User Friendly data entry and analysis interfaces,good help procedures

-Recommend you try using it yourself.

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Program DISTANCE

Windows Driven Program

Data Entry-Data Entry Wizard-Data Import Wizard

AnalysesEasy to build multiple models

- AIC used to chose between models

- Detailed information for each model

including density estimates and SEs, goodness of fittests and plots

More on the Program Later

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Point Transects or Point Counts for Estimating BirdNumbers

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Point Count Surveys Widely used in research

and environmental

monitoring Single observer counts allbirds seen or heard duringa fixed time interval (3 10 min) on a limited orunlimited radius plot

In forested habitats mostdetections are by ear(90%)

In open meadow habitatsmost detections may be bysight

This distinction may bevery important as to howuseful distance samplinglikely to be.

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The Breeding Bird Survey Is A

Large Point Count Survey 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

P i t C t T t

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Point Counts or Transects

Brief Summary Notes

1. The Method

Sampling points are set up in study area. They could beplaced systematically along transects or scattered randomlythroughout area. (Systematic random placement is likely to

be better than random for practical reasons)

The distance between stations should be such that the samebird is very unlikely to be counted at more than one station.This will vary depending on the habitat.

Count all the birds of all the species seen or heard withtheir distances in a fixed time interval say 3 minutes.

In some cases distance bins may be used instead ofmeasuring the exact distance to each bird. This could makeit easier for the observer to record the data quickly if therewere a lot of birds to be counted and recorded.

Sid C S Old

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Side Comment: Some Older

Point-Count Surveys Count all birds seen or heard during a fixed time

interval

No estimate of detection probability. Analysesgenerally assume detection probability is invariant

Counts considered as an index . This is true in the

BBS survey and is a major limitation of the survey Ted Simons found that using Counts as indiciesmay be very misleading.

Recall Scarlet Tanagers vs Golden CrownedKinglets example discussed in lecture 3 onIndicies.

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0.0

0.2

0.4

0.6

0.8

1.0

1.2

MeanRelative

Abundance

247 Paired Points

N = 30 detections/species

Primary Forest Secondary Forest

**

**

**

Scarlet Tanager Golden-crownedKinglet

?

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

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Density: Kinglet jumps to thirdwhen detection probability

accounted for using distancesampling!!

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 RT

Species

Density(pairs/ha)

***

*

*

*

Point Counts or Transects using Distance

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Point Counts or Transects using Distance

Brief Summary Notes

1. The Method

Each bird seen or heard during a fixed period around astation is counted and the horizontal distance to its locationis measured.

Often the bird is heard rather than seen and thus thedistance has to be estimated rather imprecisely as some

birds may never be seen. (Training programs to try tominimize the errors).

DISTANCE used for analyses

Multiple species are counted at the same time.

Aside: Fisheries examples. Individuals of a variety of speciescould be counted from underwater points in clear watere.g., coral reefs.

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Use of Distance

Measure distances to birds at least to the level of distancebins rather than using fixed radius points and assuming

perfect detection

Allows more options for analyses

(i) Full distance analysis

(ii) Fixed radius for individual species i.e., different radii)

Allows detectability to be modeled and hence should be less biased

If concerned about disruption, have a waiting period before

counting begins

Simons Study in GSMNP: A Good

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Simons Study in GSMNP: A GoodExample of a Large Regional Point Count

Survey Point counts

Variable circular plot

10 minute interval Single observer Distance sampling

Point locations

Low use hiking trails Stratified by

vegetation type Distance Sampling

Can be used to estimatedetection probabilities

Allows comparisons withmost fixed-width plots

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Distance Sampling: Similar

Detection Function Idea

The Sightingfunction g(r)

Declines withdistance

We assumeg(0) = 1.0 distance (r)

g(r)

1

0

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Estimation of Density: SlightlyDifferent Geometry but same Idea

aPAnD =

=kw2withA k is no of pointssurveyed

=w

wdrrgr0

2/)(2aPand

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3. Point Count Assumptions

(same as for line transects)

(i) Birds that are very close to the station

will always be detected.

(ii) There is no movement of birds in response

to the observer (attraction or repulsion)and none are counted twice.

(iii) All distances are measured without error.

(iv) Sightings of different birdsare independent events.

(v) Points placed randomly or

systematic random

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House Wrens Point Count Study Design

- From Buckland et al. (2001) P 361-366.

- Study Area was riparian habitat along the South PlatteRiver in Colorado by Knopf (1986).

- 155 points total in 10 different 16 ha plots.

- House wren was the most common species.

- The data analysis is presented first untruncated and then

truncated to 42.5 meters.

- I did not check the original paper but I suspect most birds

were only heard as riparian habitats usually have dense

vegetation.

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House Wrens Point Count Study

Analysis from DISTANCE (Table 13.3)

-Use of AIC to choose models like for the line transect

example.-The best model had an estimate of 8.14 birds per square km

(CI was 6.44-10.30). The next two models had similar

estimates and were close in terms of AIC.

-Note that we also need to look at the goodness of fit tests.

What do we see here compared to the stakes example?

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Note that none of the models fitted very well! This was not

the case with the stakes line transect data discussed in the lastlecture.

The use of truncation is a complex one I wont consider in

great detail here. However, note that the data fitted a littlebetter when truncated but still not very good when one looks

at the goodness of fit tests.

Poor fit often happens with point count data. Why mightthat be the case?

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-Think about the fact that most birds likely heard but not

seen. Does that explain why the fit might not be good?- Lets revisit the assumptions.

4. Comparison of Distance Methods

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p

Line Transects Variable Circular Plots

-cover larger areas more quickly - More practical in rough terrain

- have to assign line segments to - Variable habitats it is easier tohabitats assign point to a habitat type

-speed may vary - fixed time at each station

- more time to see or hear birds

in high canopy

M th ds f sti ti d t ti

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Methods for estimating detection

probabilities from point counts Distance Sampling

Multiple Observers Time of Detection (later) Repeated Counts (not

covered)

Other Analyses for Estimating Detection Probability with

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Other Analyses for Estimating Detection Probability with

Bird Point Counts.

1. Consider doing the same points with multiple observers.

Nichols et al. (2000). Dependent observer methods.

Alldredge et al. (2006). Independent observer methods.

2. Also consider using times of detection of an individual

Farnsworth et al. (2000)

Alldredge et al. (2007)

We will come back to this later as we need some more knowledge ofclosed capture-recapture models to implement it.

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Multiple observer approaches Models the

probability ofdetection given

availability Primary-secondaryobserver approach

Independentobserver approach

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Primarysecondary observer method

Primary observer counts all birds he or she

sees or hears Secondary observer knows what birds the

primary observer detects and records

birds that the primary missed. Nichols et al. (2000). Key reference. Generalization of the removal model, with 2

groups. Data are analyzed in a closedpopulation capture-recapture frameworkusing Program MARK. We will not cover.

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Independent observer method Two or more independent observers

conduct a point count simultaneously Following each count observers compare

detections to determine the completedetection history

Data are analyzed in a closed population

capture-recapture framework usingProgram MARK. With two observers it isbasically the Lincoln Petersen model.

Generally more efficient (smaller variance)than the primary-secondary method

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Multiple Observers Bird Point Counts

Two independent observers at each pointRevisit the estimation BrieflyApply the Lincoln-Petersen model

- seen by first observer- seen by second observer- seen by both observers

1n

2n

2

m

2

21m

nnN=

and1

22

2

21

n

mp

n

mp ==

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Multiple Observers Bird Point Counts

Conversion to Density to compare to Distance

2

2

21

wk

N

A

ND

m

nnN

==

=

kis the number of points surveyed and w is

the radius of the conceptual circle around thepoint. Ignore birds detected beyond this

radius.

M lti l Ob

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Multiple Observers

Two independent observersKey Assumptions1. No matching errors that is n1,n2 and m2 are accurate*. (Our research has

suggested that this can be a problem when birds are detected by soundalone. )2. Closed population3. The observers really are independent*. (We thought this would be aproblem but the observers are quite busy and do not seem to pay anyattention to each other. If there were very few birds at a point this could beserious and the primary secondary observer method would be better)

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Recall: Modeling Availability &

Perception in Detection Processes

Two Processes

Availability- animals have to be available to be

detected.In many animal populations not all animals are

available.(eg birds may not sing)

Perception- even if animals are available then they still

have to be detected. This is also uncertain.( a bird that

does sing may not be detected)

RECALL MODELING OVERALL DETECTION

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RECALL: MODELING OVERALL DETECTION

PROBABILITYProbability of detection is made up of a probability

of availability and a detection process if the animal is

available

P[overall] = P[available] x P [ detected l animal available]

assumptionby1pOften

a

det

=

= daection ppp

Modeling Availability & Perception in

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Modeling Availability & Perception in

Detection Processes

In bird point count surveys it turns out that

we are assuming all the birds sing (are

available) when we use either the distance

method or the multiple observer method.(That is pa=1)

We will return to this later in the semesterwhen we talk about the time of detection

method.

Some References:

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Point Count Alternative Approaches

*Alldredge, M. W. et al. (2006a). Estimating detection probabilities from multipleobserver point counts. The Auk 123, 1172-1182.

*Alldredge, M. W. et al. (2007). Time of detection method for estimating

abundance from point count surveys. The Auk 124(2): 653-664.

*Buckland, S.T. et al. (2001). Distance Sampling. Chapman and Hall. Chapter 5.

*Farnsworth, G., Pollock, K.H., Nichols, J.D., Simons, T.R.,Hines, J.E., and Sauer,

J.R. (2002). A removal model for estimating the detection probability during point

counts divided into time intervals. The Auk 119, 414-425.

*Nichols, J.D., Hines, J.E., Sauer, J.R., Fallon, F.W., Fallon, J.E., and Heglund.,

P.J. (2000). A double-observer approach for estimating for estimating detecting

probability and abundance from avian point counts. The Auk 117, 393-408.

Simons, T.R., M.W. Alldredge, K. H. Pollock, and J. M. Wettroth. (2007).

Experimental analysis of the auditory detection process on avian point counts.

The Auk 124(3): 986-999.

All Bird Radio Papers at: http://www4.ncsu.edu/~simons/Bird%20Radio.htm

Validation experiments a k a

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Validation experiments, a.k.a.

All Bird Radio Simulate census conditions

when most birds are

identified by sound Quantify biases and

precision of currentsampling methods

Vary conditions that willinfluence detectionprobability (See SimpleExamples Next)

Evaluate the costs andbenefits of incorporatingdifferent types of

detection probabilityestimates

BTBW

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no background noise

30 70 110 150

DISTANCE (m)

HEARD CORRECT WRONG

02

4

6

8

#Observers

BTBW

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BTBW

10-20 km/hr breeze

30 70 110 150

DISTANCE (m)

HEARD CORRECT WRONG

0

2

4

68

#Observers

BTBW

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+10 dB white noise

30 70 110 150

DISTANCE (m)

HEARD CORRECT WRONG

0

2

4

6

8

#Observers

BTBW

background birds

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background birds

30 70 110 150

DISTANCE (m)

HEARD CORRECT WRONG

0

2

4

6

8

#Observers

Methods for estimating detection

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g

probabilities from point counts Distance Sampling

Multiple Observers Time of Detection (later) Repeated Counts (not

covered)

Documents

ST506 L6-08