national marsh bird monitoring: methods, pilot study, and where we go from here 16 january 2013

National Marsh Bird Monitoring:Methods, Pilot Study, and Where We Go From Here

16 January 2013

Mark SeamansU.S. Fish and Wildlife Service

Lakewood, CO

Webinar Outline

• Background– History of Marsh Bird Monitoring– Survey Protocol and Sampling Design

• Pilot Study– Methods– Results

• Transition from Pilot to Operational Program

Target Species

• Rallidae: clapper rail, black rail, king rail, sora, Virginia rail, and yellow rail, common moorhen, purple gallinule, American Coot, purple swamphen

• Ardeidae: American bittern, least bittern• Aramidae: limpkin• Podicipedidae: pied-billed grebe• Scolopacidae: Wilson’s snipe

Background

• Workshops– 1998, 2006, 2011

• King Rail Conservation Plan 2006• Waterbird Conservation for the Americas

(Waterbirds Initiative) 2006 Assessment• AFWA-Webless Funding Priorities Report 2008• Independent research

Background Continued

• Survey Protocol– Courtney Conway– http://www.cals.arizona.edu/research/azfwru/NationalMarshBird/

– Details of Protocol• Study Design

– Johnson, D. H., J. P. Gibbs, M. Herzog, S. Lor, N. D. Niemuth, C. A. Ribic, M. Seamans, T. L. Shaffer, W. G. Shriver, S. V. Stehman, and W. L. Thompson. 2009. A sampling design framework for monitoring secretive marshbirds. Waterbirds 32:230-215.

Example of Hexagon Selection

Example of Point Selection in Hexes

Pilot Study

• Wisconsin 2008• Idaho 2009 – 2010• Kentucky 2009• New York 2009• Florida 2010• Michigan 2010• Ohio 2011

HQ

Objectives of Pilot

• Do protocol and design work together• Sampling effort to achieve certain levels of

precision for abundance or trend estimates. This included thoughts on how to stratify

• As pilot progressed shifted focus to work under a new paradigm– How to use monitoring to address management issues– Can monitoring meet information needs for species of

greatest concern

Methods• The Data

– repeat visits within & among years, strata– Individuals identified (counted) each survey– Distance to individual estimated– Two-stage sample (variance estimator)– Covariates related to detection and abundance

• Analysis– Binomial Mixture Model with Horvitz-Thompson Estimator

• Detection related to distance done first– Zero-inflated Poisson model with Bayesian Framework– Abundance (& Occupancy) estimated by strata & year

RESULTS

Pilot Results: Abundance

ID KY MI NY OH WI0

10,000

20,000

30,000

40,000

50,000

American Bittern

200920102011

Abun

danc

e

ID KY MI NY OH WI0

50,000

100,000

150,000

200,000

Sora

200920102011

Abun

danc

e

Pilot Results: Abundance

ID KY MI NY OH WI0

50,000

100,000

Virginia Rail

200920102011

Abun

danc

e

FL0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

Clapper Rail

20102011

Abun

danc

e

Pilot Results: OccupancyFlorida Clapper Rail

2010: φ = 0.81 (0.70-0.92) 95% CI 2011: φ = 0.90 (0.80-0.97)

Idaho Sora High Quality Stratum2009: φ = 0.76 (0.63-0.83) 2010: φ = 0.86 (0.76-0.95)

General Stratum2009: φ = 0.38 (0.18-0.76) 2010: φ = 0.21 (0.10-0.39)

Wisconsin Sora2009: φ = 0.59 (0.50-0.70) 2010: φ = 0.49 (0.38-0.64) 2011: φ = 0.35 (0.25-0.54)

Clapper Rail Detection Probability

Survey Period

15-31 Mar 1-14 Apr 15-30 Apr 1-14 May 15-31 May

Det

ectio

n P

roba

bilit

y

0.0

0.2

0.4

0.6

0.8

1.0

20102011

Detection Probability of American Bittern in Idaho (A) and the

Upper Midwest (B)Survey Period

15-30 April

1-14 May

15-31 May

1-14 June

15-30 June

Det

ectio

n P

roba

bilit

y

0.2

0.4

0.6

0.8

1.0

200920102011

Survey Period

15-30 April 1-14 May 15-31 May 1-15 June 16-30 June

Det

ectio

n P

roba

bilit

y

0.0

0.2

0.4

0.6

0.8

1.0

20092010

B

A

Precision of N as Function of % PSUs Surveyed

0 10 20 30 40 50 60 700

20

40

60

80

100

120

American bittern sora

Virginia rail

Percentage of Primary Sampling Units Surveyed

Coeffi

cien

t of V

aria

tion

B

Precision of N as Function of # PSUs Surveyed

5 10 15 20 25 30 35 40 45 500

20

40

60

80

100

120

American bittern sora

Virginia rail

Number of Primary Sampling Units Surveyed

Coeffi

cien

t of V

aria

tion

Partitioning Variance

𝑣𝑎𝑟 (�̂� )=𝑁 (𝑁−𝑛 )𝑛 (𝑛−1 ) ∑

𝑖=1

𝑛

(𝑦 𝑖−𝑟 𝑀 𝑖)2+𝑁𝑛 ∑

𝑖=1

𝑛

𝑀𝑖 (𝑀𝑖−𝑚𝑖)𝑠𝑖2

𝑚𝑖

𝑠𝑖2= 1

𝑚𝑖−1∑𝑗=1

𝑚 𝑖

(𝑦 𝑖𝑗− 𝑦 𝑖 )2�̂�=

∑𝑖=1

𝑛

�̂� 𝑖

∑𝑖=1

𝑛

𝑀𝑖

.

�̂� 𝑖=𝑀 𝑖

𝑚𝑖∑𝑗=1

𝑚𝑖

𝑦 𝑖𝑗

Inferences from Pilot

• There are a lot of some species on the landscape

• Rare species are an issue• Omnibus approach to monitoring and what

we did during the Pilot

Inferences Cont.

• What can omnibus approach give us?– Inform harvest management, except for KIRA– Inform state conservation plans? Depends.– May reveal general habitat affinities

• What omnibus approach cannot give us.– An assessment of KIRA or BLRA populations– Why they are declining and what to do about it– How any species responds to habitat management

• Water levels, burning, invasive management, etc.

Proposed Way Forward

• Mix of omnibus and “management monitoring”

• Mix of two would give us:– Experimental comparisons– Efficient way to meet needs of multi-species

survey

King Rail Management

Data can be used to:

• Nwrp = abundance from treatment areas

• Ngen = abundance from general whole area

• H0: Dwrp = Dgen

• Ntotal = Nwrp + Ngen (a status assessment)

Marsh Bird Conservation Program

Steps to Conserving & Managing Marsh Birds1. Define Conservation Issues2. Develop Hypotheses or Management

Objectives3. Develop & Implement Management Actions4. Monitor5. Learn and repeat as necessary