Life on the edge:
Monitoring forest community ecotones
in a changing climate
Todd Lookingbill
Department of Geography and the Environment
University of Richmond
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Considerations of scale
• Environmental Change
Lookingbill & Rocca 2010. in Predictive Modeling
in Landscape Ecology.
• Ecological Process
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Climate change and scale
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Models of ecological response provide
inferences at specific spatial scales
Mountain monkeyflower,
Mimulus tilingii
Sebasky, Wu, & Lookingbill. 2010. Evolution.
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Models of ecological response provide
inferences at specific spatial scales
MaxEnt (Baldwin. 2009. Entropy)
Sebasky, Wu, & Lookingbill. 2010. Evolution.
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Implications
• Two challenges to predicting changes to species
distributions with changes in climate:
– Choice of sampling design
– Choice of inferential model
Landscape
scale
Ecotone scale
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H.J.
Andrews
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H.J. Andrews
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Forest sampling – hybrid designs
Stevens & Olsen. 2004. J. Amer. Statistic. Assoc.
Philippi. 2005. Ecology.
100s-1000s m
N = 175 plots
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Two Communities
Western hemlock
Douglas fir
True firs
Silver fir
Noble fir
Environmental covariates
•Elevation (m)
•Slope (o)
•Taspect (radiation)
•Terrain shape index
•TCI (wetness)
•Mean Litter Depth (cm)
•Mean Soil Depth (cm)
•St. Dev. Soil Depth
•Clay (%)
•Silt (%)
•Sand (%)
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•pH
•C (%)
•N (%)
•C:N
•P (ug/g)
•Ca (cmol(+)/kg)
•Mg (cmol(+)/kg)
•K (cmol(+)/kg)
•Ac (cmol(+)/kg)
•ECEC
•BS (%)
Elevation model
Elevation<1203
Hemlock
Elevation<1326
Fir
TCI<60
Fir
Hemlock
Elevation < 1443
Fir
CART analysis
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Mt. Hemlock
Elevation-based model
of current vegetation
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Temperature
sampling design
Fridley, 2009. J. Applied Meteor. & Climate
45 Portable Microloggers
Stratified approach within
watersheds
Vanwalleghem & Meentemeyer, 2010. Ecosystems
Temperature
Tmax
Tmean
T = β0 - β1 Elevation + β2 ln(dstrm) + β3 Radiation + ε
Lookingbill & Urban. 2003. Agric. Forest Meteor.
Tmin
Comparison of temperature models
• Regression-based model is highly correlated with other
HJA temperature models for mea and max
• Poorly correlated for minimum temperatures
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Cross-validation Relative Error
Temperature-based model
1
0.9
0.8
0.7
0.6
0.5
Proxy xerror
Plant-relevant xerror
0.4
0
1
2
3
Number of Splits
Lookingbill & Urban 2005. Can J. Forest Res.
4
Moderate
change scenario
25 years
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Current
50 years
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Rapid
change scenario
25 years
(moderate)
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Current
25 years
(rapid)
Temperature inversion
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Evidence of temperature inversion
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Inversion projection
Current
50 years
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Climate change and mountain ecotones
review
Drought 8
Grazing 8
Edaphic 7
Human disturb 6
Fire 5
Eolian 5
Other factors:
Frost
Glacial retreat
Invasive species
Pests and pathogens
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Ecotone monitoring – multistage sampling
(Stohlgren and others)
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Ecotone monitoring
20m
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Step 1: Identifying spatial pattern
Edge detection
Camarero, Gutierrez, Fortin 2006. Global Ecol Biogeog.
Step 1: Identifying spatial pattern
Point pattern analysis
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Step 2: Relate species distributions
to environmental patterns
July.Soil.Moisture<30
Regression tree model
of western hemlock
seedling counts
July.Soil.Moisture<19.5
10
21
57
Step 2: Relate species distributions
to environmental patterns
Noble fir
Low radiation
High radiation
(t=14.95, d.f. = 391, p <0001)
(Charlie Halpern)
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x Noble fir
x Silver fir
x Hemlock
Depth (cm)
Parting shot
100
50
0
0
30
60
90
120
Distance (m)
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150
180
Summary and Conclusions
• To capture the variety of ecological processes potentially
affected by changes in climate, fine-grained data need to
be collected over large spatial extents.
» GRTS, adaptive clustering, stratified clusters, multi-stage
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Nusser, Breidt & Fuller. 1998. Design and estimation for investigating the dynamics of natural resources. Ecol.
Applications
Philippi. 2005. Adaptive cluster sampling for estimation within local populations of low-abundance plants. Ecol.
Stevens & Olsen. 2004. Spatially balanced sampling of natural resources. J. Amer. Statistic. Assoc.
Stohlgren, Owen & Lee. 2000. Monitoring shifts in plant diversity in response to climate change: a method for
landscapes. Biodiversity and Conservation.
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Summary and Conclusions
• These spatially rich data can be best leveraged by
statistical techniques that (1) account for spatial
autocorrelation in the data, (2) capture patterns across
multiple spatial scales, and (3) explore relationships
between variables at each spatial scale.
» Mantel correllograms, regression kriging, PLNM, CART, wavelet analysis
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Carl & Kuhn. 2008. Analyzing spatial ecological data using linear regression and wavelet analysis. Stochastic
Environ. Research & Risk Assess
Dormann. 2007. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global
Ecol & Biogeogr
Goslee & Urban. 2007. The ecodist package for dissimilarity-based analysis of ecological data. J Statistic. Software
Peres-Neto, Legendre, Dray & Borcard. 2006. Variation partitioning of species data matrices: Estimation and
comparison of fractions. Ecology
Wagner & Fortin. 2005. Spatial analysis of landscapes: Concepts and statistics. Ecology
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Acknowledgements
Carly Vendegna-Rameriz,
Jonathan Mayfield, Megan
Sebasky Monique Rocca,
Dean Urban
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