Monitoring Mountain Pine Beetle Life Cycle Timing at Multiple - PowerPoint PPT Presentation

Monitoring Mountain Pine Beetle Life Cycle Timing at Multiple Elevations and Latitudes in California Barbara J. Bentz Dendroctonus ponderosae Rocky Mountain Research Station Jim Vandygriff USDA Forest Service, Logan UT www.usu.edu/beetle Rocky Mountain Research Station Logan, UT

Cooperators Sheri Smith, Forest Health Protection, Susanville, CA Patricia Maloney, UC Davis, Davis, CA Camille Jensen, UC Davis, Davis, CA Tom Coleman, Forest Health Protection, Riverside, CA Amanda Garcia, Forest Health Protection, Flagstaff, AZ

Current MPB host Dendroctonus ponderosae tree associations: Mountain pine beetle (MPB) Pinus contorta P. monticola P. ponderosa P. lambertiana P. monophylla P. albicaulis P. flexilis P. balfouriana P. aristata P. longaeva P. strobiformis P. banksiana Mountain pine beetle (MPB) distribution is limited by climate not host trees. MPB distribution is expanding. ~41m acres affected in western US (1999 – 2011) Matt Ayres photo

Temperature can directly influence MPB success Seasonality – Appropriately timed phenology that is synchronized among individuals to facilitate a mass attack on host trees. 0.6 2001 2002 0.5 2003 Proportion of Total Emergence 0.4 0.3 0.2 0.1 0.0 June 29 July 19 Aug 8 Aug 28 Beetle loses Beetle wins

MPB Phenology Egg ‘Effective’ beetles Instar 1 Instar-specific development rates and thresholds influence population Instar 2 synchrony and success. Instar 3 Instar 4 Pupae Teneral Adult Oviposition From Bentz et al. 1991, Powell and Bentz 2009, Regniere, Powell, Bentz and Nealis 2012

Temperature can directly influence MPB success Seasonality – Appropriately timed phenology that is synchronized among individuals to facilitate a mass attack on host trees. DEATH Mortality due to cold DEATH temperatures Bentz and Mullins 1999, Regniere and Bentz 2007

Common Garden Rearing Experiments 1.0 22 C 22˚C 0.8 Cumulative Emergence 0.6 ID 0.4 OR CA3 UT 0.2 CA CA1 AZ 0.0 0 25 50 75 100 125 150 175 200 225 250 Days from Infestation OR ID 1.0 CA3 SD 22˚C 0.8 Cumulative Emergence UT 0.6 0.4 ID CA2 AZ CA1 CA1 0.2 CA2 SD 0.0 40 60 80 100 120 140 160 180 200 220 Days from Infestation Bracewell et al. 2010; Bentz et al. 2001, 2011

Phylogeography of mountain pine beetle • Based on AFLP data, gene flow occurs in a horseshoe-shaped distribution around the Great Basin and Mojave deserts. • CA and AZ populations are the most divergent. • Mating studies show a reproductive incompatibility between populations on the eastern and western sides of the Great Basin. ID CA1 AZ F1 F1 No offspring due to sterile males !! From Mock et al. 2007; Bentz et al. 2011; Bracewell et al. 2010

Objectives • Develop baseline information on mountain pine beetle lifecycle timing across multiple latitudes and elevations in California. • Evaluate the potential for bivoltine (2 generations per year) populations in California. • Evaluate how well our mountain pine beetle phenology model predicts developmental timing in California.

Mountain pine beetle lifecycle Phloem temperatures monitoring in California, 2009 - 2012 1393m 2576m 1756m 2907m 2865m 2079m

Number MPB Lassen Lassen 2011 Emergence 2010 2009 Attacks 2010 Attacks Emergence Prosser Prosser Incline Incline 7000 Lassen Relay Peak Relay Peak 6000 Incline Relay Peak Temperature > 12 C San Bernardino 5000 4000 3000 Inyo 2000 Inyo 1000 0 July 19 Oct 28 Feb 4 June 15 Aug 23 Date in 2009-2010 San Bernardino Tree 2 San Bernardino Tree 7 6000 Lassen Prosser 5000 Incline Temperature > 12 C Relay Peak 4000 Inyo San Bernardino 3000 July 19 Oct 28 Feb 4 June 15 Aug 23 San Bernardino Tree 5 2000 Date in 2010 - 2011 1000 0 July 19 Oct 28 Feb 4 June 15 Aug 23 Date in 2010-2011 July 19 Oct 28 Feb 4 June 15 Aug 23 Date in 2009 - 2010

Relay Peak, Tahoe Basin MU Pinus albicaulis 2907 m 180 2010 Emergence 160 140 Univoltine – Semivoltine Mix Number MPB 2011 120 Emergence 100 2009 Attacks 2000 80 Relay Peak 2009 attacks 60 Relay Peak 2010 attacks 1500 Temperature > 12 C 40 20 1000 0 July 19 Feb 4 Aug 23 March 11 Sept 27 500 0 July 19 Oct 28 Feb 4 June 15 Aug 23 Date 180 160 2010 Attacks 140 Number MPB 120 2012 100 Emergence? 80 60 2011 Emergence 40 20 0 July 19 Feb 4 Aug 23 March 11 Sept 27

San Bernardino NF, CA 0.8 0.30 June 28 Oct. 20 Tree 2 Pinus monophylla , 2079 m baited Cages installed 0.25 Samples: teneral adults 0.6 most brood gone Proportion MPB 0.20 Attacks Emergence holes 0.4 0.15 Emergence cage 2009 0.10 0.2 0.05 0.0 0.00 July 19 Oct 27 Feb 4 May 15 Aug 22 Dec 1 Tree 5 0.5 0.20 0.4 Proportion MPB 2010 0.15 0.3 0.10 0.2 June 28 3 generations in ~2½ 0.05 0.1 years. NOT Bivoltine 0.00 0.0 July 19 Oct 27 Feb 4 May 15 Aug 22 Dec 1 0.35 Date Tree 7 0.30 Proportion MPB 0.25 2011 0.20 0.15 0.10 0.05 0.00 July 19 Oct 27 Feb 4 May 15 Aug 23 Dec 1 Date

San Bernardino NF - pinyon pine Oviposition 1.2 Predicted MPB Lifestages 2079 m Egg MPB Phenology Model T2 North phloem Instar 1 1.0 Instar 2 Validation Instar 3 0.8 Instar 4 Pupae 2009 2010 0.6 Adult emergence 0.4 0.2 0.0 60 < Univoltine Mix 50 Observed MPB 40 Observed 30 Attacks 20 Observed Emergence 10 3.0 0 Predicted Oviposition Lake Tahoe Basin MU June 21 Sept 29 Jan 7 April 17 July 26 Nov 3 2.5 Predicted Teneral Adults Whitebark pine Predicted MPB 2932 m T1S 2.0 250 16 1.5 2009 2010 2011 Observed attacks 1.0 14 Observed emergence Observed Attack and Emergence 200 Predicted emergence 0.5 12 Predicted Adults Prosser Creek 0.0 10 35 150 Lodgepole pine 30 8 Observed Observed MPB 25 attacks 1757m 100 6 20 15 Observed emergence 4 10 50 2 5 0 July 28 Nov 5 Feb 13 May 24 Sept 1 Dec 10 March 20 June 28 Sept 27 0 0 July 19 Oct 27 Feb 4 May 15 Aug 23 Dec 1 2010 Date 2011 Univoltine Univoltine – Semivoltine Mix

140 16 Can climate change and Attacks 2010 Temperatures 14 120 increasing temperature Plus 2.5 C Plud 4.0 C 12 result in a bivoltine 100 MPB observed MPB predicted 10 lifecycle at Prosser Creek? 80 8 60 6 Temperature increase up 40 4 to 4.0°C results in 20 2 NO BIVOLTINISM 0 0 April 10 July 19 Oct 27 Feb 4 May 15 Date Development Rate Instar 4 WHY? 15 – 17°C threshold for development to pupal lifestage Pupae Temperature

San Bernardino, CA – Mixed Univoltine 0.8 0.30 When temperatures at a site are June 28 Oct. 20 Tree 2 baited Cages installed 0.25 Samples: teneral adults at optimal or above for 0.6 most brood gone Proportion MPB 0.20 development (~25°C) then - Attacks Emergence holes 0.15 0.4 Emergence cage 0.10 Development rate will decrease 0.2 0.05 with increasing temperature 0.0 0.00 causing later emergence timing. July 19 Oct 27 Feb 4 May 15 Aug 22 Dec 1 2009 temperature 0.6 Plus 2.5 degrees Plus 4.0 degrees 25°C is optimal development rate Attacks Predicted MPB Development Rate 0.4 Instar 4/pre-pupae Adult emergence timing is later with increasing temperature 0.2 Pupae 0.0 June 21 Sept 29 Jan 7 April 17 Date in 2009 Temperature

Lake Tahoe NF , CA We can use these data and Prosser creek models to analyze trends in Lodgepole pine, 1757 m MPB population success 90 Predicted number of days for 30 to 90% emergence Predicted Number Days Low 80 70 MPB Population Success Development and 60 emergence timing 50 40 Predicted number of days for 30 to 90% emergence High 30 1.0 High Probability of Survival 0.8 Cold temperature 0.6 survival 0.4 0.2 Low 0.0 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Year

No Whitebark pine recording death date 20 250000 Shoshone NF, WY Kipfmueller & Swetnam - MT Whitebark pine Acres Affected Perkins & Swetnam - ID 200000 15 Togwotee Pass 150000 Whitebark pine, ~2900 m 10 100000 5 50000 0 0 1920 1930 1940 1950 1960 1970 1980 1990 2000 Year 1.0 Predicted Univoltinism High Proportion Univoltine 0.8 0.6 MPB Population Success Development and emergence timing 0.4 0.2 Low 0 1.0 High Probability of Survival 0.8 Cold temperature 0.6 survival 0.4 0.2 Low 0.0 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Year

Conclusions • Field-observed mountain pine beetle lifecycle timing confirm the role of temperature and phenotypic plasticity in population success at multiple sites across CA. • We did not observe bivoltine lifecycle timing at any site, despite warm temperatures. • Based on our knowledge of mountain pine beetle physiology, bivoltinism is not possible without adaptation that would result in new developmental thresholds. • Projections with our temperature-driven mechanistic models can provide important information on population success in a changing climate.

Collaborators and Funding Acknowledgements • USDA FS, Forest Health Protection Region 5 • USDA FS, Forest Health Monitoring, Jacques Régnière WC-EM-09-02 Matt Hansen Jim Powell Stacy Hishinuma Andreana Cipollone Brian Knox Tom Coleman’s SOCAL crew