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Leslie Ries (SESYNC, University of MD) Cameron Scott ( NatureServe ) PowerPoint PPT Presentation


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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).

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Leslie Ries (SESYNC, University of MD) Cameron Scott ( NatureServe )

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Leslie ries sesync university of md cameron scott natureserve

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)

A Mechanistic Species Distribution Model for Monarch Butterflies:Towards a general platform for understanding large-scale butterfly distributions


Correlative vs mechanistic species distribution models sdms

Correlative vs. Mechanistic Species Distribution Models (SDMs)

  • 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


A simple mechanistic model for butterflies

A simple mechanistic model for butterflies

  • 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

Our key data sources:

Lab data on physiological tolerances

Climate data

Host-plant distribution data

+

+


G oal build a mechanistic sdm for the monarch butterfly

Goal: build a mechanistic SDM for the monarch butterfly

  • 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


The monarch butterfly annual cycle

The monarch butterfly annual cycle

Summer expansion and breeding (May – Aug)

Fall migration (Sept – Oct)

Spring migration and breeding (Mar – Apr)

Today, focus on the eastern migratory population in North America during spring and summer

Overwintering (Nov – Feb)


Talk outline

Talk outline

  • Development of predictor layers (host plant and temperature models)

  • Citizen-science data sources used to test the model

  • Relationships between predictor layers and monarch distributions


Modeling host plant resources

Modeling host-plant resources

  • 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


Building milkweed prediction maps with niche models

Building Milkweed Prediction Maps with Niche Models

  • 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.


Example for asclepias syriaca most common milkweed and primary host

Example for Asclepiassyriaca, most common milkweed and primary host

Observation records

Diversity index

Summer “niche” map

Species modeled:

7 spring

27 summer


Modeling physiological responses to temperature using degree days dd

Modeling physiological responses to temperature using Degree Days (DD)

  • 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)

Zalucki 1982

45 DD

32DD

Total GDD required:

351DD

+45DD

120DD

28DD

67DD

24DD

35DD

Plus 45DD before egg-laying begins


Most dd formulas do not account for lethal and sub lethal effects of high temperature

Most DD formulas do not account for lethal and sub-lethal effects of high temperature

  • Laboratory results (Bataldenet 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)

Sub-lethal and lethal effects

?

?

DZmin = 11.5°C


Mapping gdd and ldd

Mapping GDD and LDD

  • 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

Predicted generations

3105 weather stations


Generations that could be produced based on available gdds

# Generations that could be produced based on available GDDs

Spring prediction map

Summer prediction map

Predicted generations

Predicted generations


Number of ldd degrees over 38 c accumulated during summer

Number of LDD (degrees over 38°C) accumulated during summer

Average # accumulated LDD


Butterfly distribution data from 2 citizen science projects

Butterfly distribution data from 2 Citizen Science Projects

Spring data:

Journey North

Summer data: North American Butterfly Association

No. Years


Spring host plant and climate resources both associated with monarch distributions

Spring: host-plant and climate resources both associated with monarch distributions

MILKWEED DISTRIBUTIONS

# observations

Modeled species predicted present

The center of milkweed diversity in TX is associated with the greatest number of spring monarch sightings


Spring host plant and climate resources both associated with monarch distributions1

Spring: host-plant and climate resources both associated with monarch distributions

MILKWEED DISTRIBUTIONS

GROWING DEGREE DAYS

# observations

Modeled species predicted present

Predicted generations

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.


Are host plant and climate resources strongly associated with summer monarch distributions

Are host-plant and climate resources strongly associated with summer monarch distributions?

MILKWEED DIVERSITY

Monarchs/PH

Modeled species predicted present

Monarch distributions north of center of milkweed diversity


Are host plant and climate resources associated with summer monarch distributions

Are host-plant and climate resources associated with summer monarch distributions?

MILKWEED DISTRIBUTIONS

Monarchs/PH

Modeled species predicted present

Monarch distributions north of center of milkweed diversity – but recall that their primary host (A. syriaca) is distributed throughout.


Are host plant and climate resources strongly associated with summer monarch distributions1

Are host-plant and climate resources strongly associated with summer monarch distributions?

MILKWEED DISTRIBUTIONS

GROWING DEGREE DAYS

Predicted generations

Monarchs/PH

Modeled species predicted present

Monarch distributions north of where the maximum number of generations are predicted, but south of where multiple generations aren’t possible.

Monarch distributions north of center of milkweed diversity – but recall that their primary host (A. syriaca) is distributed throughout.


Are monarchs avoiding excessive heat

Are monarchs avoiding excessive heat?

Monarchs seem to be found where they are least likely to encounter temperatures above 38°C.

Average number of accumulated LDD

Monarchs/PH


Conclusions

Conclusions

  • 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


Acknowledgements

Acknowledgements

  • 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 Voelkerfor 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


Towards a modeling platform for monarchs and other butterflies

Towards a modeling platform for monarchs and other butterflies

  • 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


Milkweed species modeled

Milkweed species modeled

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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