YARD2012_Upstate NY Mashbird Monitoring.pdf - New York-New ...

Katherine Yard, Wildlife Technician I, NYS Department of Environmental Conservation
Greater NJ/NY Harbor Herons and Waterbirds Working Group Annual Meeting
290 Broadway, Manhattan, NY
14 January 2013

Photo: Al AboutBirds.org
Photo: Heidi Kennedy, NYSDEC
Photo: USFWS
Photo: The Nature Conservancy
Photo: Matthew Walter / NYSDEC
endangered
threatened
threatened
threatened
special concern

Photo: Matt King, NYSDEC
Photo: USFWS
Photo: audubon.org
Photo: D. Menke, USFWS
Photo: Gerrit Vyn, from
Birds of North America
closed season
Photo: Rob Curtis,
from audubon.org

Photo: Marie Read, from
Birds of North America
Photo: Isidor Jeklin/CLO, from
Birds of North America
SGCN
Photo: G. Armistead, from
Birds of North America
threatened
Photo: Marie Read, from
Birds of North America
endangered

Estimate temporal trends in marsh bird occupancy and
abundance to inform management decisions regarding
population health (T&E species).
Determine change in annual occupancy and abundance to
inform regulatory decisions for migratory game bird species.
Document species-specific habitat associations at multiple
scales.

Call-broadcast surveys
• 2-3 survey replicates per point (May-Jun)
• 5 minutes passive listening
• 6 minutes call-broadcast (30-sec calls + 30-sec silence for each of 6 focal spp)
• Record all primary and secondary focal species detected
Rapid habitat assessment
• Water depth, edge type, percent cover for habitat classes, vegetation height,
vegetation density, invasives, etc.

Primary Sampling Unit (PSU) = 40km 2 hexagon
Random two-stage
cluster sample:
• Primary Sampling Units
• Secondary Sampling Units
• Johnson et al. (2009) design
Also NYS long-term
monitoring points
• Not random
• Wetlands of interest to
Regional offices
Secondary Sampling Units (SSU)

General survey design in emergent marsh habitat is valuable
for estimating abundance of common species (e.g., VIRA,
SORA, AMBI).
General survey design is not useful for estimating abundance
of rare species (e.g., KIRA) and is unlikely to inform
management decisions.
Stratification within a general design would allow for tailoring
monitoring to test ecological or management hypotheses.

700
Random SSUs NYS Long-term # Surveys
600
500
400
300
200
100
0
2009 2010 2011 2012 Total
Pilot study years
More surveys in state-owned wetlands

¯
6
5
9
8
7
4
Years Surveyed
2009 PSU
Long-term Monitoring Area
2010-2012 PSU*
PSU Grid
3
* PSU surveyed at least one year during 2010-2012
Kilometers
0 25 50 100
2
1

Relative abundance and distribution maps
• Objectives #1 and #2

Relative Abundance
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
2009 2010 2011 2012
*Different random sample
NYSLT
SSU
¯
2009-2012 Detections
*Average # birds/point
Virginia rail
Kilometers
0 25 50 100

Relative Abundance
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
NYSLT
SSU
2009 2010 2011 2012
¯
sora
Kilometers
0 25 50 100

Relative Abundance
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
NYSLT
SSU
2009 2010 2011 2012
¯
pied-billed grebe
Kilometers
0 25 50 100

Relative Abundance
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
NYSLT
SSU
2009 2010 2011 2012
¯
American bittern
Kilometers
0 25 50 100

Relative Abundance
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
NYSLT
SSU
2009 2010 2011 2012
¯
least bittern
Kilometers
0 25 50 100

Relative Abundance
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
NYSLT
SSU
2009 2010 2011 2012
¯
king rail
Kilometers
0 25 50 100

Site occupancy analysis:
• Objectives #1 and #2
• With long-term monitoring, can use occupancy as a “surrogate for population
size or species abundance” to track population trends over time
• More informative than Relative Abundance because accounts for detection
probability

Site Occupancy (ψ)
1.00
0.80
0.60
Virginia rail [ψ(.)p(.)]
ψ
p
*Basic singleseason
model
0.40
0.20
*Random SSUs
only
0.00
2010 2011 2012

ψ
ψ
1.0
0.8
0.6
0.4
0.2
0.0
1.0
0.8
0.6
0.4
0.2
0.0
2011
0 20 40 60 80 100
Upland habitat (%)
2011
0 50 100 150 200
Water depth (cm)
In 2011, top-supported VIRA models
included covariates UPLAND,
DEPTH and SHRUB.
Summed model weights indicated
that UPLAND (w i = 0.758) and
DEPTH (w i = 0.636) habitat
covariates had greater importance to
VIRA site occupancy than other
covariates.
VIRA site occupancy was negatively
related to UPLAND (β = -1.131; SE
= 0.464) and was positively related to
DEPTH (β = 0.726; SE = 0.251).

Marsh Bird Monitoring Program included in DEC’s new 5-year
(2012-2017) Federal Aid grant:
• Continue monitoring at both random & long-term survey routes
• Initiate king rail surveys at historic locations
• Pilot managed wetlands study at WMAs with water control structures
• Good possibility of hiring seasonal marsh bird technicians in 2013
• Build long-term dataset for occupancy analysis to estimate population trends