Presentation on theme: "Leslie Ries (SESYNC, University of MD) Cameron Scott (NatureServe) Timothy Howard (New York Natural Heritage Program) Tanja Schuster (Norton-Brown Herbarium,"— Presentation transcript:
Leslie Ries (SESYNC, University of MD) Cameron Scott (NatureServe) Timothy Howard (New York Natural Heritage Program) Tanja Schuster (Norton-Brown Herbarium, University of MD) Rick Reeves (Foxgrove Solutions) Karen Oberhauser (University of MN)
Correlative (“Niche”) SDMs use occurrence data to infer ranges BENEFITS: Long history, broad applicability DRAWBACKS: Weak basis for causation, lack of test data Mechanistic (“process”) models use knowledge of species’ responses to abiotic or biotic conditions to predict ranges BENEFITS: A priori predictions of causal mechanisms can be tested with independent data DRAWBACKS: Species-specific Banks et al. 2008
Limited by host plant distribution Limited by physiological constraints General process-based model would combine host-plant distributions, temperature tolerances, and climate data to predict distributions + + Lab data on physiological tolerances Climate data Host-plant distribution data Our key data sources:
Well-understood biology Data to test model predictions at large scales, thanks to 1000’s of citizen scientist volunteers A model that works for species with complex annual cycle could be broadly applicable across species, thus meeting a principle challenge of building mechanistic SDMs
Overwintering (Nov – Feb) Spring migration and breeding (Mar – Apr) Summer expansion and breeding (May – Aug) Fall migration (Sept – Oct) Today, focus on the eastern migratory population in North America during spring and summer
1.Development of predictor layers (host plant and temperature models) 2.Citizen-science data sources used to test the model 3.Relationships between predictor layers and monarch distributions
Multiple niche models to predict distributions of monarch host plants (most in genus Asclepias, Apocynacaea) ~100 species in North America, ~50 with records of monarch use
Collected observation records (GBIF, on-line herbaria, iNaturalist, and Journey North) with location and date Thinned to eliminate observations <12km apart and <50 records after thinning 19,101 observations downloaded, 8,053 were left after grouping into seasonal bins and thinning on minimum separation distance 36 environmental layers used to inform niche model Random Forests in R to provide a consensus map based on 1000’s of individual regression trees Output maps for individual species compiled into single seasonal maps showing number of modeled species.
Observation records Summer “niche” map Species modeled: 7 spring 27 summer Diversity index
Determine temperature at which growth can begin (DZmin), each degree above that over 24 hrs is considered a “degree day” Often, maximum temperature is set (DZmax) after which degree days are no longer accumulated DZmin = 11.5°C (52.7°F) ? Total GDD required: 351DD +45DD Zalucki 1982 45 DD 32DD 28DD 24DD 35DD 67DD 120DD Plus 45DD before egg-laying begins
Laboratory results (Batalden et al. in press) show that for monarchs: No growth at 38°C (100.4 ° F) Some lethal effects at 40°C (104°F) Only 20% survivorship at 42°C (107.6°F) 100% mortality at 44°C (111.2°F) Model distinguishes Growing Degree Days (GDD: energy is accumulated) and Lethal Degree Days (LDD: slow growth or cause death) DZmin = 11.5°C ? Sub-lethal and lethal effects ?
Temperature data from NOAA temperature stations Used ordinary kriging to interpolate temperatures between stations every day from 1990-2009. GDD and LDD were accumulated by season for spring (Mar- Apr) and summer (May-Aug) and converted to number of generations 3105 weather stations Predicted generations
Spring prediction map Summer prediction map Predicted generations
Spring data: Journey North Summer data: North American Butterfly Association No. Years
The center of milkweed diversity in TX is associated with the greatest number of spring monarch sightings MILKWEED DISTRIBUTIONS Modeled species predicted present # observations
The center of milkweed diversity in TX is associated with the greatest number of spring monarch sightings Monarch sightings in spring reaches their northern-most distribution within a zone where there is warmth for growth, but not enough for a full spring generation. MILKWEED DISTRIBUTIONS GROWING DEGREE DAYS Modeled species predicted present Predicted generations # observations
Monarch distributions north of center of milkweed diversity MILKWEED DIVERSITY Modeled species predicted present Monarchs/PH
Monarch distributions north of center of milkweed diversity – but recall that their primary host (A. syriaca) is distributed throughout. MILKWEED DISTRIBUTIONS Modeled species predicted present Monarchs/PH
Monarch distributions north of center of milkweed diversity – but recall that their primary host (A. syriaca) is distributed throughout. Monarch distributions north of where the maximum number of generations are predicted, but south of where multiple generations aren’t possible. MILKWEED DISTRIBUTIONS GROWING DEGREE DAYS Modeled species predicted present Predicted generations Monarchs/PH
Average number of accumulated LDD Monarchs seem to be found where they are least likely to encounter temperatures above 38°C. Monarchs/PH
Built models of milkweed distributions and GDD/LDD Spring: Northward migration limited by energy for growth, seems concentrated near the center of milkweed availability Summer: Southern limits driven by stressful temperatures, northern by host-plant availability and sufficient energy for multiple generations
Monarch Citizen Scientists for documenting monarch distributions Elizabeth Howard and Journey North Staff, Jeff Glassberg and NABA Staff, Xerces Society for starting and maintaining Journey North and Fourth of July Butterfly Counts Emily Voelker for helping compile the milkweed database NSF # DBI-1052875 to SESYNC, ABI- 1147049 to SESYNC and UMD for providing funding USGS’s John Wesley Powell Center for Analysis and Synthesis working group, Animal Migration and Spatial Subsidies: Establishing a Framework for Conservation Markets, for good conversations Photo by Tony Gomez
Our goal is to develop a modeling framework that can account for both climate and host-plant resources Host-plant distributions and climate expressed as GDD and LDD may prove to be a useful modeling framework for many species of butterflies (and potentially other invertebrate herbivores) – meaning this approach could provide a general mechanistic model for understanding butterfly range dynamics Species interactions may also be critical for many species, and that may require more species-specific approaches For the monarch, we want to be able to use this platform to explore many issues of conservation concern: Loss of milkweed habitat in the midwest due to Roundup-Ready crops Increase in winter breeding in the southern US Track population trends and try to pinpoint their cause or causes
SeasonSpStartThinnedspecies SummerAS_AS916125asperula SummerAS_CURA488146curassavica SummerAS_EX32990exaltata SummerAS_FA27382fascicularis SummerAS_GL18175glaucescens SummerAS_HI37762hirtella SummerAS_INC2309244incarnata SummerAS_INV27953involucrata SummerAS_LANU12162lanuginosa SummerAS_LAT25380latifolia SummerAS_LINA461107linaria SummerAS_OE21490oenotheroides SummerAS_OV13854ovalifolia SummerAS_PER16158perennis SummerAS_PUM28259pumila SummerAS_PUR37991purpurascens SummerAS_QUAD409104quadrifolia SummerAS_SPEC1138180speciosa SummerAS_STEN25074stenophylla SummerAS_SUBV855108subverticillata SummerAS_SUL31467sullivantii SummerAS_SYR1457184syriaca SummerAS_TUB1818255tuberosa SummerAS_VAR18376variegata SummerAS_VERT1398195verticillata SummerAS_VIRIDF1088192viridiflora SummerAS_VIRIDI37686viridis SpringAS_AS11394asperula SpringAS_CURA338250curassavica SpringAS_GL10276glaucescens SpringAS_LINA153121linaria SpringAS_SUBU8674subulata SpringAS_VIRIDI7252viridis predictor layers created for 36 different variables: percent forest, percent cropland, percent water, percent wetland, percent urban/barren land, population density, presence of railroads, mean annual temperature, mean annual temperature, mean monthly temperature (12 variables), mean monthly precipitation (12 variables), elevation, latitude, and longitude.
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