Bark Beetles and Climate Change:
Implications for Mountainous Regions
of the Western US
Barbara J. Bentz
Rocky Mountain Research Station
USDA Forest Service, Logan UT
www.usu.edu/beetle
Temperatures of mountain pine beetle (MPB) habitats are increasing
Photo Jeff Foott
From: Weed Bentz Ayres Holmes, In Review
Forested Area Affected
by Bark Beetles
2000 – 2012
46.1 million acres
Photo Ken Gibson
http://foresthealth.fs.usda.gov/portal
Take-home Points
• Climate is a major driver of MPB performance. Precipitation
influences host tree resistance. Temperature influences generation
time and winter survival. Both affect population irruptions.
 Recent climatic changes positively affected recent population
outbreaks.
 More warming may not = faster growth.
• Influence of temperature on MPB performance can be modeled
mechanistically with relatively good accuracy.
• Downscaled climate projections and mechanistic models suggest
variability among geographic locations in future forest vulnerability
to outbreaks.
 Without adaptation, continued warming may not result in
continued outbreak suitability in all geographic locations.
 Populations in the coldest and warmest habitats have the
greatest capacity to take advantage of future warming.
• There is one high elevation pine species that appears to be not
attractive to MPB.
Thresholds and positive feedback processes at multiple scales
contribute to the irruptive, outbreak nature of ‘aggressive’ bark
beetle populations.
Raffa et al. 2008, Bioscience
Precipitation
precipitation can result in thicker phloem.
precipitation can result in host trees with weakened defenses that
are easier to overcome by fewer attacking beetles.
Shaw et al. 2005, Berg et al. 2006,
Chapman et al. 2012, DeRose and Long 2012,
Hart et al. 2014
How does temperature influence mountain pine beetle (MPB)?
1. Cold tolerance – the ability to withstand freezing temperatures
through supercooling.
30
20
10
Max
Min
0
-10
-20
SCP, Max and Min Phloem Temperatures ( oC)
-30
Upper SCP
Mean SCP
Lower SCP
---
---
---
-40
---
---
---
South Utah
30
20
10
0
-10
-20
---
-----
---
-30
---
---
-40
Central Idaho-1
30
20
10
0
-10
-20
---
-30
---
---
---
---
---
-40
Central Idaho-2
Aug 12 Oct 1 Nov 20 Jan 9 Feb 28 April 19 June 8
Date
From: Bentz and Mullins 1999
Régnière and Bentz 2007,
Sambaraju et al. 2012,
Preisler et al. 2012
Did climatic release from
winter cold cause increased
MPB-caused pine mortality
across all areas in the
western US?
No, not in all areas.
From: Weed Bentz Ayres Holmes, In Review
How does temperature influence mountain pine beetle (MPB)?
1. Cold tolerance – the ability to withstand freezing temperatures
through supercooling.
2. Diapause – a physiologically arrested state, often cued by
temperature, photoperiod or thermoperiod.
3. Development time – the time required to complete a life
stage or life cycle.
Development time-1 (rate) across
temperatures has a similar shape
in all insects.
From Duetsch et al. 2008
MPB is polyphagous and has a large distribution influenced by
postglacial expansion and climate.
Genetic analyses of MPB range expansion
(Mock et al. 2007, Samarasekera et al. 2012)
Whitebark pine
Lodgepole pine
Richardson et al. 2002
?
Godbout et al. 2008
MPB Phenology
Egg
Instar 1
Instar 2
Instar 3
Instar 4
Pupae
Teneral Adult
Oviposition
From Regniere et al. 2012, Bentz et al. 1991
Development time influences Voltinism
Voltinism: number of generations per year
Generation: number of days for a brood of individuals to develop
and emerge from a tree.
Univoltine:
Semivoltine:
Bivoltine:
1 generation per year
1 generation in 2 years
2 complete generations within 1 year
MPB lifecycle timing has historically been
reported as:
1) univoltine at low elevations
2) a mix of univoltine and semivoltine
at high elevations
(Reid 1962, Amman 1973)
P. ponderosae
Helena NF, MT
P. albicaulis
Yellowstone
NP, WY
Proposed that MPB was
bivoltine (2 generations in 1 yr)
at Niwot Ridge, CO 3040 m.
Photo Anna Schoettle
Development
Rate
MPB bivoltinism is constrained by evolved developmental traits
Instar 4
pre-pupae
From Regniere et al. 2012
From Bentz and Powell In Press
MPB Phenology in the Field
MPB Host tree species
studied:
Pinus albicaulis
P. contorta
P. lambertiana
P. monticola
P. flexilis
P. monophylla
Elevation: 1400 – 2920 m
From Bentz et al. 2014
Pinus contorta, 1780 m
Prosser creek
CA39.1780 LE = 53.2
Univoltine
200
Number mountain pine beetle
2009
Parent adult attacks
Brood emergence
150
100
50
0
250
200
2010
May 22 2010
150
100
50
0
July 19
Oct 27
Feb 4
May 15
2009
Aug 23
Dec 1
2010
March 11
June 19
Sept 27
2011
64%
14%
June 17 2011
2009
2010
2011
2012
From Bentz et al. 2014
Generation time (days)
700
600
500
400
Average generation time = 374 d
300
200
100
0
50
52
54
56
58
60
62
LE
Warmest
Coolest
WA48.1730-10
WA48.1400-10
UT41.2190-11
UT41.2190-10
CA40.1700-09
CA40.1700-10
CA39.2920-09
CA39.2920-10
CA39.2590-09
CA39.2590-10
CA39.1780-10
CA39.1780-09
CA34.2100-09 T2
CA34.2100-10 T5
From Bentz et al. 2014
Generation time (days)
700
600
500
400
Average generation time = 374 d
300
WA48.1730-10
WA48.1400-10
UT41.2190-11
UT41.2190-10
CA40.1700-09
CA40.1700-10
CA39.2920-09
CA39.2920-10
CA39.2590-09
CA39.2590-10
CA39.1780-10
CA39.1780-09
CA34.2100-09 T2
CA34.2100-10 T5
200
100
DD > 15C for generation time
0
Accumulated
thermal energy at warm univoltine site was
600
> 4 times that at the coolest, mostly semivoltine site
500
R2 = 0.7409
400
MPB populations at
coolest sites required
fewer thermal units to
complete a generation
than populations
at warmest sites.
300
200
100
0
50
52
54
56
58
60
62
LE
Warmest
Coolest
From Bentz et al. 2014
Take-home Points
• Climate is a major driver of MPB performance. Precipitation
influences host tree resistance. Temperature influences generation
time and winter survival. Both affect population irruptions.
 Recent climatic changes positively affected recent population
outbreaks.
 More warming may not = faster growth.
• Influence of temperature on MPB performance can be modeled
mechanistically with relatively good accuracy.
• Downscaled climate projections and mechanistic models suggest
variability among geographic locations in future forest vulnerability
to outbreaks.
 Without adaptation, continued warming may not result in
continued outbreak suitability in all geographic locations.
 Populations in the coldest and warmest habitats have the
greatest capacity to take advantage of future warming.
• There is one high elevation pine species that appears to be not
attractive to MPB.
MPB Phenology Model
Egg
Development rates are summed
(integrated) over short time steps.
Instar 1
Physiological age , a, proportion of
the stage completed from 0 at the
onset to 1 at completion -
Instar 2
t
at =
t
∫ r (T , A)dt ≅ ∑ r (T , A)∆t
t
0
Instar 3
t
0
Instar 4
Pupae
Teneral Adult
Oviposition
From Bentz et al. 1991, Logan and Bentz 1999,
Gilbert et al. 2004, Regniere et al. 2012
Number MPB
2010
Oct 27
40
Feb 4
May 15
Observed emergence and
phenology modelpredicted emergence
Aug 23
Date
30
20
10
0
-10
1.0
-20
-30
July 19
Oct 27
Feb 4
May 15
Date
Aug 23
Cumulative Emergence
Temperature C
Observed
Emergence
Observed
Attacks
July 19
Prosser Creek
Tahoe National Forest, 1757 m
Lodgepole pine
Univoltine lifecycle
2011
0.8
Predicted North aspect
Predicted South aspect
Observed North aspect
Observed South aspect
0.6
median emergence
0.4
0.2
0.0
June 24
July 14
Aug 3
Aug 23
Date in 2011
Sept 12
Oct 2
2010
Observed
Emergence
Relay Peak
Tahoe Basin Mgmt Unit, 2920 m
Whitebark pine
Univoltine - Semivoltine lifecycle
2011
Observed
Emergence
Number MPB
2009
Observed
Attacks
Oct 27
Feb 4
May 15
Aug 23
Dec 1 March 11 June 19 Sept 27
40
30
20
10
0
-10
-20
-30
-40
0.8
Predicted emergence
Observed emergence
Univoltine
0.6
0.4
0.2
0.0
June 4 June 24 July 14 Aug 3 Aug 23 Sept 12 Oct 2 Oct 22
Date in 2010
July 19
Oct 27
Feb 4
May 15
Aug 23
Date
1.0
Dec 1 March 11 June 19 Sept 27
Cumulative Emergence
Temperature C
July 19
Cumulative Emergence
1.0
0.8
Predicted emergence
Observed emergence
Semivoltine
0.6
0.4
0.2
0.0
June 19
July 9
July 29
Aug 18
Date in 2011
Sept 7
Sept 27
Oct 17
How does phenology influence population success?
Mass attacks
Photo Jeff Foott
Photo Jeff Foott
Converting MPB Phenology to a Population Growth Rate:
Mass Attacks & the Influence of Host Resistance
If the sum of emerging adults E(t) between
June 1 and September 1 is above a threshold
‘A’ then attacks are successful.
E(t) > A
E(t)
I(t)
Infesting
adults
A
June 1 to Sept 1
If E(t) < A, then attacks
are not successful.
E(t) < A
A
E(t)
June 1 to Sept 1
From: Powell and Bentz 2009
Converting MPB Phenology to a Population Growth Rate:
Mass Attacks & the Influence of Host Resistance
Total emerging MPB from phenology model
based on hourly temperatures
t1
I n = ∫ max[N E (t ) − A, 0] dt
t0
Infesting MPB/RT
(above threshold)
Growth Rate
of Population
Attack Threshold


Rn = α I n exp − β ∑ H j 
 ≤n −1 
From: Powell and Bentz 2009
R-Model Prediction of MPB Population Growth Rates in the
Sawtooth National Recreation Area, ID
From Powell and Bentz 2009
Take-home Points
• Climate is a major driver of MPB performance. Precipitation
influences host tree resistance. Temperature influences generation
time and winter survival. Both affect population irruptions.
 Recent climatic changes positively affected recent population
outbreaks.
 More warming may not = faster growth.
• Influence of temperature on MPB performance can be modeled
mechanistically with relatively good accuracy.
• Downscaled climate projections and mechanistic models suggest
variability among geographic locations in future forest vulnerability
to outbreaks.
 Without adaptation, continued warming may not result in
continued outbreak suitability in all geographic locations.
 Populations in the coldest and warmest habitats have the
greatest capacity to take advantage of future warming.
• There is one high elevation pine species that appears to be not
attractive to MPB.
MPB Case Study using Downscaled Daily Temperatures
Multivariate Adaptive Constructed Analogs(MACA)
Statistical Downscaling Method
maca.northwestknowledge.net
4 km resolution
Daily max min
Univoltine Popn Growth
Bivoltine Potential
< 1000 m
1000-2000 m
CanESM2
2000-3000 m
> 3000 m
< 1000 m
1000-2000 m
2000-3000 m
> 3000 m
CCSM4
Univoltine Popn Growth
Bivoltine Potential
Grasslands
West
Central
CanESM2
East
GYA
Grasslands
West
Central
East
GYA
CCSM4
Projected Univoltine Population Growth Rates
CanESM2 RCP-45 Projected
2000-09
2090-99
CanESM2 Historical
1950-59
CanESM2 RCP-85 Projected
2000-09
2090-99
1990-99
Projected Univoltine Population Growth Rates
CCSM4 RCP-45 Projected
2000-09
2090-99
CCSM4 Historical
1950-59
CCSM4 RCP-85 Projected
2000-09
2090-99
1990-99
Projected Bivoltine Population Growth Rates
CanESM2 RCP-45 Projected
2000-09
2090-99
CanESM2 Historical
1950-59
CanESM2 RCP-85 Projected
2000-09
2090-99
1990-99
Projected Bivoltine Population Growth Rates
CCSM4 RCP-45 Projected
2000-09
2090-99
CCSM4 Historical
1950-59
CCSM4 RCP-85 Projected
2000-09
2090-99
1990-99
Univoltine Popn Growth
Bivoltine Potential
< 1000 m
1000-2000 m
CanESM2
2000-3000 m
> 3000 m
< 1000 m
1000-2000 m
2000-3000 m
> 3000 m
CCSM4
Take-home Points
• Climate is a major driver of MPB performance. Precipitation
influences host tree resistance. Temperature influences generation
time and winter survival. Both affect population irruptions.
 Recent climatic changes positively affected recent population
outbreaks.
 More warming may not = faster growth.
• Influence of temperature on MPB performance can be modeled
mechanistically with relatively good accuracy.
• Downscaled climate projections and mechanistic models suggest
variability among geographic locations in future forest vulnerability
to outbreaks.
 Without adaptation, continued warming may not result in
continued outbreak suitability in all geographic locations.
 Populations in the coldest and warmest habitats have the
greatest capacity to take advantage of future warming.
• There is one high elevation pine species that appears to be not
attractive to MPB.
Mountain pine beetle is polyphagous on Pinus
In stands with multiple species, there is often not
a preference for a single species.
80
Mass attacks
Pitch-out / strip attacks
S. Wood 1982
Pinus contorta
P. monticola
P. ponderosa
P. albicaulis
P. flexilis
P. balfouriana
P. aristata
P. strobiformis
P. monophylla
P. edulis
P. lambertiana
% of Trees in Species Attacked
70
60
50
40
30
20
10
0
WBP LPP
WBP LPP
WBP LPP
WBP
MIXED
LPP
Tree Species and Stand Type
WBP = P. albicaulis, LPP = P. contorta
From Bentz, Boone, Raffa In prep
Mountain pine beetle is polyphagous on Pinus
S. Wood 1982
Pinus contorta
P. monticola
P. ponderosa
P. albicaulis
P. flexilis
P. balfouriana
P. aristata
P. strobiformis
P. monophylla
P. edulis
P. lambertiana
Pinus longaeva
Great Basin Bristlecone Pine
Does MPB attack Pinus longaeva?
Extensive MPB-caused mortality to Pinus flexilis
P. balfouriana
southern population
Pinus longaeva is dying, but MPB does not appear to be a
contributing factor at this time.
Wood boring insects
Mistletoe
James Powell
Acknowledgements
Department of Mathematics and Statistics
Utah State University, Logan UT
Matt Hansen, Jim Vandygriff, Jacob Duncan,
Ben Crabb, Aaron Weed, Matt Ayres, Logan
Christian, Tom Coleman, Andreana Cipollone,
David Fournier, Amanda Grady, Stacy
Hishinuma, Leverett Hubbard, Camillle
Jensen, Michael Jones, Joey Keeley, Brian
Knox, Joshua Lambdin, Patricia Maloney,
Connie Mehmel, Greta Schen, Sheri Smith
Photo Jeff Foott