Climate Change Refugia as a Tool
for Climate Adaptation
Toni Lyn Morelli
NE CSC / UMass
Sean Maher
Steve Beissinger
UC Berkeley
Craig Moritz
Competition with sheep had a far greater effect
than the predicted effects of future climate change
Outline
• What are Climate Change Refugia?
• Climate Change Refugia as an Adaptation Option
• Test Case - Research Objectives
• Test Case - Mapping Refugia and Connectivity
• Test Case - Predictions for a meadow specialist
• Implications for Management
The Role of Climate Change Refugia
What are Climate Refugia?
Paleo Definition
Classically defined as regions where populations
of warm-adapted species were buffered from
the intense cold of glacial climates.
What are Climate Refugia?
Physical Definition
• Defined by climate dynamics, topography;
no reference to effect on species or habitats
needed (Ashcroft 2010)
• Sites decoupled from regional patterns, such as
complex terrain that result in changes in elevation,
cold air drainage, and differences in slope and
aspect (Dobrowski 2011)
Lundquist et al.
2008
What are Climate Refugia?
Ecological Definition
• Based on species persistence/habitat stability
• An area with “favorable environmental features,
in which … populations can survive outside their
main distribution…protected from unfavorable
regional environmental conditions” (Rull 2008)
What are Climate Refugia?
“Refugia are habitats that components of biodiversity retreat to,
persist in and can potentially expand from under changing
environmental conditions…Although the study of refugia has been
largely restricted to the Quaternary, we argue that the concept is
applicable to biodiversity under potential future climates arising
from the enhanced greenhouse effect.”
Climate Change Refugia
Areas that are buffered from climate change effects
relative to other areas so as to favor greater persistence
of valued physical, ecological, and social resources
Morelli, T.L., C.I. Millar, S. Maher, C. Daly, S. Dobrowski,
D. Dulen, J. Ebersole, A. Flint, S. Jackson, J. Lundquist, W.
Monahan, K. Nydick, K. Redmond, S. Sawyer, S. Stock, &
S.R. Beissinger.
• To be submitted to Frontiers in Ecology and the
Environment.
Climate Change Refugia
Areas that are buffered from climate change effects
relative to other areas so as to favor greater persistence
of valued physical, ecological, and social resources
Climate Change Refugia as a
Climate Adaptation Option
“Refugia are likely to offer the best chance for a species negatively
impacted by climate change to survive under future climates.”
Vose, Peterson, & Patel-Weynand
Example of a climate change
adaptation strategy focused on
Resistance
Chris Swanston, Maria Janowiak, et al.
Maintain or create refugia: This
strategy seeks to identify and
maintain ecosystems that: (1) are on
sites that may be better buffered
against climate change and shortterm disturbances, and (2) contain
communities and species that are at
risk across the greater landscape
Identify unique sites that are
expected to be more resistant to
change…and emphasize
maintenance of site quality and
existing communities. A more active
adaptation tactic is to identify a suite
of potential sites for refugia and
commit additional resources to
ensuring that the characteristic
conditions are not degraded by
invasive species, herbivory, fire, or
other disturbances.
Strategy 1.4:
Conserve, restore, and as
appropriate and
practicable, establish new
ecological connections
among conservation areas
to facilitate fish, wildlife,
and plant migration, range
shifts, and other transitions
caused by climate change.
– Action 1.4.2: Assess
and prioritize critical
connectivity gaps and
needs across current
conservation areas,
including areas likely to
serve as refugia in a
changing climate.
What do managers
require to use this concept?
Steps for Managing Climate Change Refugia
Morelli et al. In prep
Climate Change Refugia
A Test of the Concept
Climate Change Patterns
California Climate Tracker
Climate Change Patterns
California Climate Tracker
Exceptions to the Pattern
Modern (1970-1999) vs. Historic (1910-1939)
Annual Temperature (Actual)
Annual Precipitation (Relative)
Montane Meadows
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Botanically diverse
Important to animal communities
Critical to hydrological function
Important to recreation and economy
Objective: To develop a validated map of climate
change refugia and associated connectivity
Steps:
1. Hypothesize Connectivity
2. Hypothesize Climate Change Refugia
3. Use Survey Data to Test Hypotheses
4. Use Genetics Data to Test Hypotheses
Meadows
ICE – UC Davis
Connectivity Hypotheses
Isolation by
a) Distance (Null)
- Even surface
b) Topography
Sean Maher
- Resistance based on topographically weighted distance
c) Watercourses
- Resistance (treat as barrier) and Conductance (treat as
vector) based on presence and distance
d) Roads (Human Use/Development)
- Resistance based on distance from roads
Connectivity based on presence or
absence of watercourses
Overall patterns of connectivity
depends on surface
Null
Watercourses as
Vector (Binary)
Watercourses
Topography
Roads
Watercourses as
Vector (Cont)
Watercourses as
Barrier (Cont)
Differences in proportion of refugia
within network of meadows
Variable
WC
0.289
rWC
0.467
Rest
0.640
Binomial test
Direction
CWD
Measure (Threshold)
Central Tendency (10%)
P < 0.001
Lower
SWE
Central Tendency (10%)
0.472
0.458
0.287
P < 0.001
Higher
Annual Temp.
Central Tendency (1°C)
0.791
0.817
0.934
P < 0.001
Lower
Annual Precip.
Central Tendency (10%)
0.538
0.453
0.302
P < 0.001
Higher
Max. Temp.
Central Tendency (1°C)
0.636
0.662
0.705
P = 0.019
Lower
Min. Temp.
Central Tendency (1°C)
0.330
0.237
0.316
P = 0.028
Higher
Mean. Temp. of
Coldest Quarter
Central Tendency (1°C)
0.696
0.658
0.805
P = 0.020
Lower
Monthly
Min. Temp.
Extreme Warming
(30 Months)
0.332
0.226
0.212
P < 0.001
Higher
Monthly
Min. Temp.
Extreme Warming
(60 Months)
0.570
0.482
0.507
P = 0.001
Higher
Monthly Precip.
Extreme Wet
(30 Months)
0.021
0.008
0.003
P < 0.001
Higher
Monthly Precip.
Extreme Wet
(60 Months)
0.968
0.961
0.904
P < 0.001
Higher
Monthly Precip.
Extreme Dry (30 Months)
0.174
0.221
0.290
P < 0.001
Lower
Annual Temp. &
Annual Precip.
Central Tendencies
0.419
0.363
0.291
P < 0.001
Higher
Central Tendency &
Extreme (30 Months)
0.094
0.067
0.033
P < 0.001
Higher
SWE &
Monthly Min. Temp
Hypothesized Climate Change Refugia
1910-1939 in 2010-2039
Maher, Morelli et al. In prep
GFDLB1 Annual Temp
Testing the
Refugia and Connectivity Maps
Belding’s Ground Squirrel
(Urocitellus beldingi)
Site Extirpations (N=31)
Site Persistence (N=43)
42% Rate of
Site Extirpations
Original Surveys: 1902-1966
Resurveys: 2003-2011
Morelli et al. 2012 Proc. B
Site Extinction at Hotter Sites
Modern Winter Temperature (°C)
-6
-4
-2
0
p < 0.005
Extirpated Sites
Persistent Sites
Classification error rate (OOB estimate)
= 18.9%
Modern Winter
Temperature
p = 0.007
>-4°C
Human
modification
p = 0.036
≤-4°C
Artificial
n = 18
n = 40
-
1
- 0.8
- 0.6
- 0.4
- 0.2
Persistence = Blue
Extirpation = White
- 0
Proportion of Sites
Where U. beldingi Persist
n = 16
Natural
Anthropogenic Refugia?
Climate Adaptation Option?
Morelli et al. 2012 Proc. B
Did Mapped Connectivity and Refugia
Predict Occupancy?
2011 Surveys for Belding’s Ground Squirrel
• 38 sites, distributed
throughout YNP
• Occupancy analysis
• Present in 20,
Absent in 18 meadows
Belding’s Occupied
CC Refugia
Connectivity Measure
Belding’s GS Occupied More Connected Meadows
Did Connectivity
Predict Genetic Diversity?
Genetic Analysis
• 187 adults
• 12 nuclear
microsatellite loci
• Genepop
• FSTAT
• STRUCTURE
–Model-based clustering
method
• BayesAss
Genetic Structure Across CA
187 Belding’s sampled
At 15 sites
2003-2011
Allelic Richness
CC Refugia Predict Genetic Diversity
Mean Temperature of the Coldest Quarter (°C)
Allelic Richness
Positive relationship between
Genetic Diversity and Connectivity
Log (Mean Connectivity)
Is genetic distance related to connectivity?
• Permutations to
examine patterns of Fst
0.047
0.16
• Support for dispersal
limitation by
watercourses
~45 km
Conclusions and Implications
• Climate is changing and robust adaptation
options are needed
• Climate change refugia and connectivity in
montane meadows explain genetic diversity,
occupancy, and population dynamics
• Climate change refugia can be a prioritization
tool for climate adaptation in the short-term –
but they need to be tested
Thanks!
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UC Davis
Information Center for the
Environment (ICE)
Sean Maher
Steve Beissinger
Craig Moritz
Michelle Koo
Michelle Hershey
Moritz Lab
Beissinger Lab
Christina Kastely, Marisa
Lim, Ilaria Mastroserio and
other student assistants
Climate change Prediction
demonstrated by variable
Variable
Test…
Wilcoxon W P value
Number of Months Exceeding
Warmer (More months above the 97.5 Presence less often
75
0.00083
Historical Extreme Monthly Minimum
than absence
percentile)
Temp
Number of Months Exceeding
Warmer (More months above the 97.5 Presence less often
264
0.99
Historical Extreme Monthly Maximum
than absence
percentile)
Temp
Number of Months Exceeding
Wetter (More months above the 97.5 Presence less often
96
0.0067
than absence
percentile)
Historical Extreme Monthly Precip
Drier (More months above the 2.5
percentile)
Presence less often
than absence
235
0.95
Number of Years Exceeding Historical
Climatic Water Deficit
More months above the 97.5 percentile
Presence less often
than absence
203
0.75
Number of Years Exceeding Historical
Extreme Climatic Water Deficit
More months below 2.5 percentile
Presence less often
than absence
133
0.088
154
0.23
301
1.0
68
0.00036
133
0.088
73
0.00066
Number of Months Exceeding
Historical Extreme Monthly Precip
Mean Annual Temperature
Warmer
Mean Maximum Temperature
Warmer
Mean Minimum Temperature
Warmer
Mean Temperature of the Coldest
Quarter
Warmer
Mean Annual Precipitation
Wetter
Presence less than
absence
Presence less than
absence
Presence less than
absence
Presence less than
absence
Presence less than
absence
Presence less than
Connectivity based on distance from
roads (Hyp #4)
Connectivity based on conductance as
distance from roads
Connectivity based on conductance as
distance from rivers
river_cont river_bin roads pathdist rivers_good_resist rivers_good_binary null_circuit
1 0.9597 0.5912 0.5842
0.0483
0.573
0.611
river_cont
0.9597
1 0.6450 0.637
0.0645
0.640
0.667
river_bin
0.0483 0.0645 0.1302 0.0818
1
0.193
0.169
rivers_good_resist
0.5731 0.6403 0.8753 0.7894
0.1930
1
0.995
rivers_good_binary
0.5912 0.6450
1 0.7696
0.1302
0.875
0.888
roads
0.5841 0.637 0.7696
1
0.0818
0.789
0.802
pathdist
0.611 0.667 0.8877 0.802
0.1692
0.995
1
null_circuit
log10 connectivity values of survey sites for each of the surfaces.
The Wilcoxon rank tests suggest all of these are statistically
signficant (inlcuding the null). Using logistic regression, we can
compare AIC values for the models, which yield similar support for
all but the null (all are within 2 AIC units), and less support for the
null (~3 AIC units greater than the lowest model).
For Discussion
• Ecological versus Physical Definitions
• Definitions need to be applicable for future
climate change
• Obstacles to action