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Acmispon Glaber Var Finding Balance: Considering Species' Traits, Species Distribution Models, and Climate Forecasting in Seed Sourcing Decisions Arlee M. Montalvo Nov. 15, 2016 Do No Harm Workshop, Davis, CA Acknowledgements: My collaborators! Erin C. Riordan- the modeling and maps Jan L. Beyers- editing, profile assistance Thanks to USFS Pacific Southwest Research Station and RCRCD for funding and UCR and UCLA for logistical support! California shrublands occupy a diverse landscape: Ecological Sections and Subsections Goudey and Smith (1994) updated with ECOMAP (2007) Parent geology continental influence topography ocean influence Precipitation and temperature Ecological Sections can be diverse and contain many contrasting Subsections Elevation: 300 to 11,500 ft Precipitation: 6 to 40 inches Temperature: 40° to 70°F Goudey & Smith 1994 Growing Season: 150 to 300 days Environmental diversity supports amazing biological diversity California shrublands and their foundation plant taxa are diverse protecting, supporting, managing native biodiversity chaparral alluvial scrub coastal sage scrub Many foundation species of shrubs are distributed across multiple Ecological Sections and plant communities Plant Community Plant Community Scientific Name Life Form‡ ALSC CHAP CSS MIX Scientific Name Life Form‡ ALSC CHAP CSS MIX Acmispon glaber var. brevialatus† suffr subshr X X Eriodictyon trichocalyx var. lanatum suffr subshr X X Acmispon glaber var. glaber † Forsuffr subshreachX ofX 36X shrubsX E. t. var &. trichocalyx subshrubs: suffr subshr X X X Adenostoma fasciculatum shrub X X X Eriogonum fasciculatum var. Arctostaphylos glandulosa (3 subsp) shrub X fasciculatum subshrub X X Artemisia californica subshrub X X X E. f. var. foliolosum subshrub X X X X E. f. var. polifolium subshrub X X Ceanothus crassifolius var. c. shrub X X • Running models that estimate currentEriophyllum confertiflorumand future var. c.† suitablesuffru per. X X X Ceanothus cuneatus var. cuneatus shrub X Hesperoyucca whipplei suffr rosette X X X X Ceanothushabitat leucodermis shrub X Heteromeles arbutifolia shrub X X Ceanothus megacarpus var. m. shrub X Lepidospartum squamatum shrub X Ceanothus oliganthus shrub X Malacothamnus fasciculatus var. f.† subshrub X X X Ceanothus• Assembling perplexans profilesshrub withX information about physiology, ecology, Malosma laurina shrub X X X X Ceanothus tomentosus shrub X demographics, life-history traits, Prunusand ilicifolia population subsp. ilicifolia geneticsshrub to X X Ceanothus vestitus shrub X Quercus berberidifolia shrub X X Cercocarpusinform betuloides interpretation var. b. shrub andX practicalX Rhamnus applications crocea of modelshrub resultsX X Corethrogyneand filaginifolia† seed sourcingsuffru per. decisions X X X X Rhamnus ilicifolia shrub X Encelia californica subshrub X Rhus ovata shrub X X Encelia farinosa subshrub X Salvia apiana suffr subshr X X X Eriodictyon crassifolium var. c. suffr subshr X X Salvia mellifera subsh/shrub X X X X E. c. var. nigrescens suffr subshr X X X Most taxa have been studied for fire response, many for drought tolerance, but few for genetic patterns Distribution of California sagebrush Genetically based latitudinal variation in (Artemisia californica) Artemisia californica secondary chemistry Common garden studies have shown correlated patterns in use of plants by arthropods and in traits related to water economy Pratt et al. 2014. Oikos. 123(8) pp 953-963. Figure 3. DOI: 10.1111/oik.01156 Acmispon glaber (aka Lotus scoparius), California broom, deerweed •self-compatible subshrub with infra-specific variation •two named varieties - distributions differ but overlap •differ in morphology and habitat affinities •common garden work local adaptation, outbreeding issues A. g. var. glaber A. g. var. brevialatus (Montalvo & Ellstrand. 2000 Conservation Biology, 2001 AJB) Careful choice of species, subspecific taxa and seed sources for projects can: • Minimize risk of maladaptation • Maintain variation and adaptive potential • Reduce risk of inbreeding and outbreeding depression • Preserve important interactions • Increase long-term success of projects How do we factor in climate change? Observed shifts in climate variables for 1981-2010 relative to 1951-1980 Δ MIN DJF Temp (C) ΔMAX JJA Temp (C) using CA-BCM downscaled Δ PPT % Δ CWD (mm) climate data (270 m resolution) Dealing with a complex world: Outline • Starting point for seed sourcing • Consider climate change and Species Distribution Modeling • Plant traits important in decision frameworks • Decision frameworks and provenancing models to help keep us on right track For the many species with no seed transfer research, Provisional seed zones for native plants reflect the complex landscape and associated climate a starting tool to be combined with expert knowledge of plants A. D. Bower, J. B. St. Clair, V. Erikson. 2014. Ecological Applications 24:913-919. Ecological Archives A024-053-A1. Based on high resolution climate data & aridity index. Seed Zones Mobile http://www.fs.fed.us/wwetac/threat_map/seed_zones/Seed_Zone_Google_Map_Links.pdf How effective are these tools for southern CA? What about climate change? Will rate of climate change exceed species’ capacity to respond? Prov. Seed Zones Tmin/AHM 35 - 40 Deg. F. / 6 - 12 Baseline 1951-1980 MIROC RCP8.5 CNRM RCP 8.5 2040-2069 2040-2069 Detailed studies of long-lived tree species Genetic maladaptation of coastal Douglas-fir seedlings to future climates Assisted migration: ”the purposeful movement of individuals or propagules of a species to facilitate or mimic natural range expansion or long distance gene flow within the current range, as a direct management response to climate change” (Havens et al. 2015. NAJ) St. Clair, B. J., and G. T. Howe. 2007. Global Change Biology 13(7): 1441-1454 Seeding sourcing for the future? • First, what is projected loss of suitable habitat? • Consider risks from moving too much or too soon: • poor adaptation to current conditions • growth phase mismatches with seed/pollen dispersers • outbreeding depression • unanticipated changes in community interactions • unanticipated aggressiveness/weakness in novel environment • Are there other, interacting risk factors? • Action should not cause more harm than no action Dealing with a complex world • Starting point for seed sourcing • Consider climate change and Species Distribution Modeling • Plant traits important in decision frameworks • Decision frameworks and provenancing models to help keep us on right track Downscaled climate data: CA-BCM California Basin Characterization Model (CA-BCM): applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270 m grid • High resolution (270 m) baseline (1951-1980), current (1981-2010) and projected (2049-2060) climate and hydrologic surfaces • Variables used in modeling: strong drivers of plant distribution – Winter Tmin, Summer Tmax, – T seasonality – Winter PPT, Summer PPT – Climatic Water Deficit (CWD), Actual Evapotranspiration (AET) Flint et al. 2013 Ecological Processes Projected change in southwestern CA climate 2040-2069 relative to 1951-1980 RCP Percent change in annual PPT (%) PPT annual in change Percent Increase in minimum winter Temp (°C) Projected change in southwestern CA climate 2040-2069 relative to 1951-1980 “Hot/Wetter” “Hot/Drier” “Warmer/Drier” RCP “Hot/Drier” Percent change in annual PPT (%) PPT annual in change Percent “Hottest/Very Dry” Increase in minimum winter Temp (°C) Species distribution models for southern California region Habitat suitability + baseline Species occurrences Baseline conditions future (Herbarium records (1951‒1980) and plot data) MAXENT Species Distribution Model (SDM) algorithm Planning Tools Tseas Future conditions (2040‒2069) Species Distribution Modeling (SDM) • Estimates spatial pattern of suitable habitat and shifts in suitable habitat by pooling data over species’ range • Results vary with global change model employed • Some caveats (concerns) – Doesn’t show population level patterns of variation, biotic interactions, or demographics – Not all populations likely to do well in all suitable habitat – Assumes no adaptive capacity to respond to change – Other, very important risks factors not in model – Climate may interact with other drivers of change Extending SDM to incorporate population level data is enormously complex SDM’s run on the southern California extent of species’ ranges and on infra-taxa Genetically based latitudinal variation in • Most species likely to have Artemisia californica secondary chemistry populations that differ genetically from north to south • SDM results on smaller extent differ from SDMs run on full species range • Resulting projections of habitat suitability are likely more realistic for the extent under consideration Pratt et al. 2014. Oikos. 123(8) pp 953-963. Figure 3. DOI: 10.1111/oik.01156 Species Distribution Models (SDM) estimate future stability, loss, and gain of suitable habitat SDM of projected change in habitat suitability for Artemisia californica Occurrence data Baseline (1951-1980) suitability Projected (2040-2060) stable suitability Projected future suitability gain Projected future suitability loss MAXENT algorithm modeling extent =urban/agriculture Example of plant with infra-specific taxa •distributions of Acmipson glaber varieties differ, but they overlap •common garden work showed local adaptation and outbreeding issues A. g.var. brevialatus A. g. var. glaber Acmispon glaber var. brevialatus Acmispon g. var.
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