When Do We Know Enough to Re-Allocate Listed Species’ Habitat ?
Doug Bruggeman, T. Wiegand, & M. L. Jones
Dept. of Fisheries & Wildlife, Michigan State University. UFZ Dept. Ecological Modelling, Leipzig, Germany
Background
Predict population patterns in
dynamic landscapes
SEPMs often contain
uncertain parameter values
Pattern-Oriented Modeling
(Grimm et al 2005)
Individual-based,
spatially-explicit
population models
(SEPMs): mechanistic
• Patterns observed in nature provide
high information content (Grimm et al
1996)
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50
60
70
90
Step 1. Identify range of uncertain parameter values
Uncertain Dispersal Parameters
Probability of male & female floater survival
Floater dispersal steps per season
Perceptual distance of helpers and floaters
d = probability of turning 0°, 45°, 90°, 135°
m1 to m8 = permeability of 8 land cover classes to dispersal
B = strength of directional vs. forest-based dispersal
Range
considered
0.8 – 1.0
1 - 10 km
0.1 - 6 km
0.01-1.0
0.01-1.0
0 – 1.0
• Contrasting the ability of different
parameter values to reproduce observed
patterns can be used to estimate
parameter values indirectly
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50
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70
80
90
Cluster of Origin
Step 3. Test the ability of
the 100,000 different model
parameterizations to
recreate patterns observed
in nature
Step 2. Assemble
parameter values at
random generating
~100,000 SEPMs
Step 4. Remove parameterizations with
high model prediction error (filtering)
1. Pine Forest
accepted
models
0.13
0.12
0.11
0.1
0.09
0.08
0.07
0.06
0.05
0.04
0.1
0.15
0.2
0.25
0.3
For connectivity,
prediction error
estimated using
Mantel’s correlation
coefficient between
observed and
expected patterns.
0.35
Mantel's r Male Connectivity
3. Hardwood Forest
4. Forest < 10 years old
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An SEPM for the Red-cockaded Woodpecker (RCW) was
constructed to simulate the cooperative breeding system
(Letcher et al. 1998)
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6. Water
8. Off-site Areas
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Results. Our ability to reduce uncertainty varied across the dispersal parameter
values considered. We were able to remove 92% of possible parameter values for
male helper perceptual distance and 80% of possible parameter values for both female
floater perceptual distance and steps taken per season. Therefore, these parameters are
critical for reproducing patterns observed on Camp Lejeune. In other words, a narrow
range of parameter values are needed to minimize prediction error. In contrast, only about
22% of possible parameter values for permeability of land cover classes were removed
during POM. Therefore, these parameter values are not critical for reproducing patterns
observed on Camp Lejeune. However, RCW territories are so aggregated on-base and
perceptual distance is great enough that it is not surprising that permeability of land cover
is not critical. This remaining uncertainty should be reflected in decision making, so
we used all 8 remaining SEPMs to evaluate habitat trades.
5. Non-forested
7. Developed Areas
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Female
breeder
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80
2. Pine / Hardwood Forest
models capable of predicting
dynamics of non-equilibrium
populations
Observed pattern of
female
connectivity on
Camp Lejeune
(orange = 1 of bird
shared between
territories; blue = 0)
10
Mantel's r Female
Connectivity
Challenge:
We are still uncertain how males and females disperse to find breeding vacancies. Therefore, parameter values that simulate these
behaviors were varied across 100,000 SEPMs. We then tested the ability of each model parameterization to recreate patterns observed
in nature. Five patterns were derived from Dr. J. Walters long term bird banding study (i.e., number of potential breeding groups over
time, number of floaters over time, group size, connectivity, and pairwise minimum genetic distance). These observed patterns were
used to estimate a prediction error for each of the 100,000 SEPMs. Prediction errors representing the degree of agreement between
observed and expected patterns were estimated using squared deviation and correlation coefficients. Here describe the four basic steps
of POM, but simplicity we report prediction errors for only one pattern, connectivity, or the number of birds shared among territories.
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Camp Lejeune
Inbreeding Avoid.
Floater
90
Female Dispersal Speed
Challenge:
Pattern Oriented Modeling (POM) for Indirect Estimation of Helper & Floater Dispersal Behavior on Camp Lejeune
Cluster of Arrival
Installations are often required to re-allocate listed species habitat to achieve
military readiness. However, we are uncertain how changing landscape patterns
(i.e., habitat area & connectivity) affects population viability. For example, some
species require dispersal and/or variation in rates of recruitment among breeding
groups to achieve regional, or landscape, persistence. Yet, we will never be
completely certain how changing landscape patterns over time affects population
viability. We recognize three main challenges posed by habitat trading policies and
propose three solutions.
80
Parameter error
should decrease as
we demand smaller
prediction errors
(stronger filter)
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0
719
models
Weak Filter
8
models
Strong
Filter
Female
Fledgling(s)
Breeder
Male
Fledgling(s)
Male
breeder
Helper(s)
Competition
Integration of Landscape Equivalency Analysis & Pattern Oriented Modeling for the Red-cockaded Woodpecker on Camp Lejeune
Challenge:
To achieve regional persistence, spatially subdivided populations require
migration and variation in rates of recruitment over space
How can we determine if habitat patches traded make equivalent
contributions to rates of recruitment and migration?
For the SEED project, we used LEA to evaluate hypothetical
habitat trading scenarios for Camp Lejeune. LEA estimates
tradable credits as “Landscape Service Years” (LSYs), which is a
time integrated estimate of the proportional change in ecological
service due to a change in landscape pattern. LSYs are calculated
by comparing services (i.e., abundance and genetic diversity)
expected under different landscape configurations. Due to time
constraints we completed our analysis at a small spatial extent and
defined a relative baseline landscape as that meeting Camp
Lejeune’s recovery plan. The withdrawal landscape reflected loss
of RCW habitat on-base. The mitigation landscape reflected
habitat restoration on Encroachment Partnering parcels.
Trades evaluated with all 8
SEPMs remaining after POM
Landscape Equivalency Analysis (LEA)
(Bruggeman et al 2005)
• Generally-applicable landscape-scale accounting system
• “Jeopardy standard” evaluated based on changes in Probability of
Regional Extinction due to trade
• “Take standard” evaluated based on expected changes in abundance
• To incorporate fragmentation effects, credits valued based on a trades
ability to move the balance between recruitment and migration closer to
levels observed in a “baseline” landscape
- Baseline represents the habitat allocation in which rates of
recruitment and migration are balanced to minimize genetic drift and
inbreeding while maintaining genetic variance at two spatial scales
• “Landscape-equivalent patches” make equal contributions to
abundance and genetic variance within and among breeding groups
without increasing the Probability of Regional Extinction
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“Take standard” evaluated by comparing Potential
Breeding Groups (PBGs) over time in the withdrawal and
mitigation landscape to the installation’s recovery goal
(173 active groups).
Landscape Service Years (LSYs)
Median values Debit
Credit
Credits
from 8 SEPMs LSYd
LSYc
remaining
PBGs
-3.31
3.21
6.52
Hs – genetic
diversity within
groups
0.104
-0.141
-0.236
Dst – genetic
diversity among
groups
0.251
-0.343
-0.599
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Baseline
landscape
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Take 12
clusters
starting 2007
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Trading did not violate take standard. All 8 SEPMs predicted that Camp Lejeune’s
recovery plan will exceed 173 PBGs despite the loss of habitat (i.e., debit values were
negative because PBGs in withdrawal > PBGs at recovery). Further, all 8 SEPMs
predicted that the 9 clusters off-base will be occupied generating positive credit values.
Fragmentation effects were evaluated by comparing genetic diversity within
breeding groups (Hs) and genetic divergence among breeding groups (Dst) in the
baseline, withdrawal, and mitigation landscapes.
A small debit in LSYs for Hs resulted from habitat loss, meaning that rates of
inbreeding and genetic drift increased only a little relative to the baseline landscape.
Restoration of habitat off-base resulted in a small, negative credit. This means that
average rates of inbreeding and genetic drift increased in the mitigation landscape
due to the restoration of peripheral habitat.
A small debit in LSYs for Dst resulted from habitat loss, meaning that habitat loss
increased genetic differences among clusters. Restoration of habitat off-base further
increased genetic differences among clusters creating a negative credit. Therefore,
these clusters do not contribute as much to migration as those lost on-base.
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Add 9
Clusters
Starting 2035
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Withdrawal
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Simulate recovery
starting 2005
“Jeopardy standard” evaluated based on probability of
extinction across Camp Lejeune, which was zero across all
landscapes and dispersal models.
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Mitigation
landscape
In summary, habitat loss on base is not
expected to appreciably decrease population
viability. This project provided only a
preliminary analyses because the spatial
extent was small. However, the LEA results
are in agreement with expectations from
RCW behavior. Specifically, LEA indicated
that peripheral clusters are not as productive
nor do they contribute as much to migration
as clusters found on-base.
All 8 SEPMs were in agreement regarding the
expected changes in population processes
due to the trade. Therefore, we know enough
to value trades at this spatial scale. Had
models disagreed on the value of trades, it
would indicate that empirical data on
dispersal behaviors should be collected
before the most cost-effective trade could be
determined.