Potential ecological responses to
climate change: linking birds and
forest inventory data
Stephen Matthews1, 2, Louis Iverson,
Anantha Prasad, Matthew Peters
1-School of Environment and Natural Resources
2-Northern Research Station, U.S. Forest Service
• We appreciate the opportunity to share our work and approach toward integrating wildlife and forest data
• Lessons learned through the lens of evaluating potential climate change impact across the Eastern United States
Matt Steve
Prasad Louis
Talk premise
• Climate is changing and species are responding
• Species distribution models can provide insight into
current patterns and how these habitats may shift
– They need links to the land use (e.g., forests)
– Although maps are nice (model prediction), it
shouldn’t overshadow their contribution to
understanding wildlife habitat relationships
• Building broad-scale models aligns with coarse
distributional pressures
• Must keep in mind that landscape realities require finer
scale understanding, thus multi-model inference is key
Not reviewing here as you all are better suited to attest to changes
What are the ecological impacts
Distributional change
Phenological change
Ecosystem changes
Climate Change is Part of Complicated Network of Interconnected Issues = Global Change
Challenges of modeling species
impacts of climate change
 Future climate uncertainty
 GCM variation
 Human-produced levels of CO2 uncertain
 Species likely to respond individually
 Biology not that well known for many species
 Therefore, we need to incorporate different
approaches to develop ecologically informed
projections — modeling is a key tool to do this.
• What can broad-scale models provide to our
understanding of species patterns?
• Does a macroecology perspective provide more direct
comparison of climate change pressure?
C
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to
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C
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1.2
1
Regional signal across Influence varies by Eastern United States species and location
Broadest extent
Landscape
composition
Local processes
Habitat Suitability
0.8
0.6
0.4
0.2
0
1.3
1.8
2.3
2.8
Low ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ High Environmental gradient Biogeography
Why do species occur where they do?
Black‐Capped Chickadee Range
Cornell Birds of North America
Biotic
Interactions
Biogeography
Dispersal
Mechanisms
Why do species occur where they do?
Climate
Landforms
Patterns of abundance
Habitat
(Brown et al. 1996, Gaston 1990, Ricklefs 2004)
BBS, Sauer et al. 2011
Biotic
Interactions
Biogeography
Dispersal
Mechanisms
Why do species occur where they do?
Climate
Landforms
Patterns of abundance
Habitat
Widespread Change
(Brown et al. 1996, Gaston 1990, Ricklefs 2004)
BBS, Sauer et al. 2011
Climate play a very important role in
shaping species distributions
Limits resource availability: seasonal pulses of food
Energetic constraints: limited by metabolic processes But there is also a strong habitat component for most species How can we represent the habitat component at a coarse scale • Can utilize remote‐sensing data
– Capture land use and land use change
– Broader application of LiDAR to address structure
Can utilize plot level data of individual trees to capture wildlife habitat
• Enter the FIA program with broad enough coverage across the eastern United States
• Spatial and temporal extent align with BBS (Breeding Bird Survey) • On their own they provide important broad scale monitoring data
BBS trends 1966–2011 Worm eating warbler
Advantage of using inventory data
• Can capture vegetation characteristics • A natural ecological link to the importance of floristic composition
– Classic papers by Robertson and Holmes and others make direct link at fine scales over long time intervals about the importance of floristic composition
– Not only captures important plant animal interactions but bird communities changed over time as tree composition changed in a maturing forest
Further evidence of leveraging FIA data even at landscape scales
• Associations with bird occurrence along BBS routes in the Appalachians (Fearer et al. 2007)
• Detection of contemporary changes in southeastern birds as related to changes in forest composition (Twedt et al. 2010) Species distributions are always changing and never static
Show propensity for species to move independent of forest types
Contemporary changes are very unique—rapid and complicated by other pressures!
Williams et al. (2000)
Background and elaboration on our research program • Tree abundance
Data
• Bird abundance
• Climate
• Environment
• Forest density
• Species traits
DISTRIB model
Species habitat prediction
Tree &
Bird Atlases
Current and future species management
• Management guidelines
• Implications & tools
Stress model reliability
• Not all the same
• How are variables influencing
• Stressing habitat
ModFacs
• Biological factors
• Disturbance factors
• Model uncertainty
SHIFT model
Species colonization
likelhood
Atlas ingredients: terminologies used in the atlas
Modelled responses
Trees: FIA ‐> Importance Value (IV) ‐> measure of relative abundance
Birds: BBS ‐> Incidence ‐> coarse‐index of relative abundance
Forest Inventory and Analysis
(FIA)
• Eastern US extent (37 states)
• 134 tree taxa
• > 100,000 plots
• ~ 3 million tree records
Breeding Bird Survey
(BBS)
• Eastern US extent (37 states)
• 147 bird species
• ~ 1000 BBS routes



Importance value (IV)
for 134 tree species
(Range: 0‐100)
(IV=0 => species absent
IV=100 => only species present)
Incidences
for 147 bird species
(Range of incidences: 0‐1)
Predictor variables
Method: Random Forest regression based
Models 20‐km resolution
Predictor variables
Climate
AVGT
JANT
JULT
TMAYSEPT
PMAYSEPT
PPT
JANJULDif
Mean annual temperature (deg. C)
Mean January temperature (deg. C)
Mean July temperature (deg. C)
Mean May-September temperature
or precipitation
Annual precipitation (mm)
Difference temp Jan/Jul
GCMs: (Hadley-Hi & Lo; PCM-Hi & Lo;
GFDL Hi & Lo;)
Elevation
ELV_CV
ELV_MAX
ELV_MEAN
ELV_MIN
ELV_RANGE
Soil Class
ALFISOL
ARIDISOL
ENTISOL
HISTOSOL
INCEPTSOL
MOLLISOL
SPODOSOL
ULTISOL
VERTISOL
Elevation coefficient of variation
Maximum elevation (m)
Average elevation (m)
Minimum elevation (m)
Range of elevation (m)
Alfisol (%)
Aridisol (%)
Entisol (%)
Histosol (%)
Inceptisol (%)
Mollisol (%)
Spodosol (%)
Ultisol (%)
Vertisol (%)
Soil Property
BD
CLAY
KFFACT
NO10
NO200
OM
ORD
PERM
PH
ROCKDEP
ROCKFRAG
SLOPE
TAWC
Soil bulk density (g/cm3)
Percent clay (< 0.002 mm size)
Soil erodibility factor, rock fragments
free
Percent soil passing sieve No. 10 (coarse)
Percent soil passing sieve No. 200 (fine)
Organic matter content (% by weight)
Potential soil productivity, (m3 of timber/ha)
Soil permeability rate (cm/hour)
Soil pH
Depth to bedrock (cm)
Percent weight of rock fragments 8-25 cm
Soil slope (percent) of a soil component
Total available water capacity
(cm, to 152 cm)
Land Use and Fragmentation
AGRICULT
Cropland (%)
FOREST
Forest land (%)
FRAG
Fragmentation Index (Riitters et al. 2002)
NONFOREST
Non-forest land (%)
For birds, in addition to Climate and
Elevation, we use tree species–
iv12, iv318, iv827 etc...
Current
Sweetgum
Water oak
Hadley‐Hi
Assessing model drivers
• Unlike RTA with direct interpretation of
predictor variables, RF is more
challenging
(Bootstrap sampling + randomized subset
of predictors for each split)
• What kind of information would be
useful
– Recapture some of the hierarchical nature
of RTA
– Identify if a species shows associations
that would lend to be more climate or
vegetation limited
• Variable importance plots only
take us so far
Assessing model drivers
• Retrieve the tree structure for each of the iterations
– Split location and split value for each predictor
• Summarize the variable occurrence across all “trees”
– Identify the mode and weighted node location of the highest split of each variable Rose‐Breasted Grosbeak Black‐Throated Green Warbler
X12
X261
X375
ptmaysep
tmaysep
Elv_min
tjul
X97
Elv_mean
ppt
X316
Elv_max
X972
X241
X371
tjan
juljandiff
X746
X762
tavg
X315
X372
X95
X129
X541
X802
X833
X318
X531
X543
Elv_rang
X621
X832
X71
X132
X693
X462
X126
X835
X131
Variable Importance
Recapturing some of the spatial interpretation: Southern extent for Winter Wren
25
20
15
10
5
Blue balsam fir mean split IV ~ 3.5
Eastern hemlock ~ 6 iv
0
1
0.8
0.6
0.4
0.2
Relative importance
1.2
0
veg_regional
climate_withindist
veg_withindist
Black‐throated Blue Warbler
Ovenbird
Winter Wren
White‐breasted Nuthatch
Red‐eyed Vireo
Least Flycatcher
Wood Thrush
Gray Catbird
Rose‐breasted Grosbeak
Black‐throated Green Warbler
Purple Finch
Black‐capped Chickadee
Baltimore Oriole
climate_regional
Species and groups tend to vary in an expected fashion but it is only a first‐level assessment.
Further, we can show that tree species variables play a larger role and are more important in forest bird models
Climate Change
Bird & Tree Atlas
http://www.nrs.fs.fed.us/atlas
Examples of species with projected habitat increases Prothonotary Warbler
Low
Emissions scenarios
High
?
Brown‐Headed Nuthatch
http://www.nrs.fs.fed.us/atlas
Examples of species with projected habitat decreases Black‐Throated Blue Warbler Low
Emissions scenarios
High
?
Black‐Capped Chickadee
http://www.nrs.fs.fed.us/atlas
Species
General trends of all 147 species
across the Eastern United States
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Incidence change (Ratio)
Greater change in habitat (+ and ‐) as scenarios become harsher
>2
1.2 - 2
Hadhi
AVGhi
AVGlo
PCMlo
0.9 - 1.1
0.5 - 0.9
< 0.5
Mean Center Potential
Movement
Km (sd)
PCMlo 109 (64.3)
Avglo 142 (88.9)
Avghi 210 (139.5)
Matthews et al. 2011 Ecography
Hadhi 212 (149.9)
Do the models really
Comparison of models when tree Black-throated
blue warbler
benefit when trees are
species are not used as predictors
(Abundance index)
used as a predictors?
90
(Matthews et al. 2011)
80
Number of species
70
60
50
- 55%
- 93%
40
Climate,
elevation and trees
30
Climate/elevation only
20
10
0
More change
No change
Less change
Divergent
Climate/elevation only – greater loss and gains
Ecologically less of a link to key habitat features with only clim
Are these data
being used?
The Goal: Identify strategies and approaches to climate change adaptation and mitigation
Bridge the gap between
Scales of prediction
 Management activities
on national forests
 Interactions with the
greater community

Sugar Maple
Low
?
High
Sugar Maple – change through time
HadHi
Loblolly Pine
Low
?
High
Forest Types
Low
?
High
But many other factors come into play to
determine more likely outcomes
Minor
?
Time
Future stand
Current stand Major Resulting forest Time
Climate change pressure increases thus altering habitat suitability of species
But many other factors come into play to
determine more likely outcomes
Minor
Time
Current stand Major Resulting forest stand
Time
Climate change pressure increases thus altering habitat suitability of species
Modification Factors
12 Disturbance Factors and 9 Biological Factors considered
High Adaptability
Red Maple: • Projected habitat declines (Eastern U.S., not MN)
• Characteristics suggest high adaptability Black Oak:
• Projected habitat increases
• Positive ModFac profile suggests it may be able to persist in harsh areas
Low Adaptability
White Ash: • Projected habitat declines
• Negative ModFac
• Metrics suggest it will likely face severe limits in Eastern U.S.
Matthews et al. 2011, For. Ecol. Manag.; Iverson et al. 2011 Ecosystems
Example from northern
Wisconsin
Legend:
Class 1: extirpated (1 spp)
Class 2: large decrease (12 spp)
Class 3: small decrease (6 spp)
Class 4: no change (6spp)
Class 5: small increase (4 spp)
Class 6: large increase (17 spp)
Class 7: new entry‐high & low emissions (11 spp)
Class 8: new entry‐high emissions (16 spp)
Class 2
Class 6
Class 2,
Large Decreasers
(especially under Had High scenario of climate change)
Using concepts developed for flood risk in New York, it is possible to visualize risks through time.**
Combine data from habitat models and life history traits and place it in the context of likelihood and consequence. These merely provide a starting point for deliberation of adaption strategies. Iverson et al. 2012
Ecosystem Vulnerabilities
The potential changes in composition may lead to vulnerabilities
•
•
Lowland hardwood (dominated by black ash) and lowland conifer forests
( dominated by balsam fir)
Several ecosystems with species that have been recently declining will
likely continue to decline (hemlock, paper birch)
Ecosystems that may be better able to accommodate change
•
•
•
Species with wider ecological range of tolerances
Species adapted to disturbances and/or warmer, drier climates
Ecosystems with diverse communities and species
These vegetation changes will have significant effects on wildlife
•
•
Forest/shrub birds with potential habitat declines ( e.g. Mourning Warbler,
Red-Breasted Nuthatch, Veery)
Forest/shrub birds with potential habitat increases (e.g. Northern Cardinal,
Yellow-Billed Cuckoo, Hooded Warbler)
Swanston et al. 2011
• But as we begin to think about the management
implications as climate changes, there is a tendency to
try to change the level of inference the models provide.
• The underling complexity of the landscape begins to
emerge as well as the reality of contemporary challenges.
• From our tree species research, evidence from joint
modeling efforts in Missouri, Minnesota, Wisconsin
indicates high correspondence with finer scale modeling
of LANDIS-II and LANDIS-PRO
• Also a nice wildlife link as these models are coming more
from demographic perspective
Pushing the resolution of our modeling effort for trees and birds
• Our modeling of suitable habitat has been at a relatively coarse scale (20 × 20 km) , but new bird, FIA, and soil data are allowing us to now assessing tradeoffs at 10 × 10 and 4 × 4 km scales. • Nested grid effective way to
make cross scale inference
• But at what cost for meaningful interpretation? Modeling Migration and Assisted Migration
• A preliminary example for northern Wisconsin
• Black oak (Quercus velutina) – a species modeled to move habitat north
(Gainers)
SHIFT: Model 100‐year migration
• Based on ~50 km/century (same as Holocene rate of migration)
• Output is probability of a 1 km cell getting colonized in 100 years
• Overlay with where habitat will be suitable in 100 years under 2 scenarios of climate change
Add Locations for Assisted Migration
Final thoughts
• The value of a broad-scale approach
– Large extent allows for the capture of the probable
contributing conditions at various boundaries as well
as the species’ core distribution
– Continuous data provide greater specificity to
quantify bird and tree patterns
• Landscape context is an environmental integrator of ecological systems from individuals to populations to communities: framework to bring data together
• Within landscapes is where researchers and managers must evaluate the ecological response to human activity
Final thoughts
• To make results more transferable, need to
assess performance and determinants
– Will lend more towards hypotheses of ecological
relationships at varying scales
• Establishing ideas of how to meld different
approaches and resolutions will build more
robust perspective of how ecological systems
might respond to climate change
• Data coordinated in time and space provide
need tools to monitor and quantify relationships
and sustain ecological systems in rapidly
changing landscapes
Thank you!
• Website for most data presented today www.nrs.fs.fed.us/atlas:
– Species‐environment data for 147 birds and 134 trees, and relevant papers
– Its been updated and newly launched! Improved flow, Enhanced search, ModFacs
incorporated
Acknowledgments
•
•
Thanks to USDA Forest Service Northern Global Change Program for support
USDA Forest Service Northern Research Station