A Mechanistic Species Distribution Model for Monarch Butterflies:

Towards a general platform for understanding large-scale butterfly distributions

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 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

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 dataHost-plant distribution data

Our key data sources:

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

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

Talk outline

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

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

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 hostObservation records Summer “niche” map

Species modeled:7 spring27 summer

Diversity index

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

0

5

10

15

20

25

5 10 15 20 25 30 35 40 45

Daily

deg

ree

days

(Tmin+Tmax)/2

Calculating daily degree days

DZmin = 11.5°C (52.7°F)

?Total GDD required:351DD+45DD

Zalucki 198245 DD

32DD

28DD

24DD

35DD

67DD

120DD

Plus 45DD before egg-laying begins

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

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)

0

5

10

15

20

25

5 10 15 20 25 30 35 40 45

Daily

deg

ree

days

(Tmin+Tmax)/2

Calculating daily degree days

DZmin = 11.5°C

?

Sub-lethal and lethal effects

?

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

3105 weather stations

Predicted generation

s

# Generations that could be produced based on available

GDDsSpring prediction map Summer prediction map

Predicted generations

Predicted generations

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

summer

Average # accumulated LDD

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

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

MILKWEED DISTRIBUTIONS

Modeled species predicted present

# observatio

ns

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

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

# observatio

ns

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

Monarch distributions north of center of milkweed diversity

MILKWEED DIVERSITY

Modeled species predicted present

Monarchs/PH

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

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

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

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

Are monarchs avoiding excessive heat?

Average number of accumulated LDD

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

Monarchs/PH

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 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

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

Season Sp Start Thinned speciesSummer AS_AS 916 125asperula

Summer AS_CURA 488 146curassavicaSummer AS_EX 329 90exaltata

Summer AS_FA 273 82fascicularis

Summer AS_GL 181 75glaucescensSummer AS_HI 377 62hirtellaSummer AS_INC 2309 244incarnata

Summer AS_INV 279 53involucrata

Summer AS_LANU 121 62lanuginosaSummer AS_LAT 253 80latifoliaSummer AS_LINA 461 107linaria

Summer AS_OE 214 90oenotheroidesSummer AS_OV 138 54ovalifoliaSummer AS_PER 161 58perennisSummer AS_PUM 282 59pumila

Summer AS_PUR 379 91purpurascens

Summer AS_QUAD 409 104quadrifoliaSummer AS_SPEC 1138 180speciosa

Summer AS_STEN 250 74stenophylla

Summer AS_SUBV 855 108subverticillataSummer AS_SUL 314 67sullivantiiSummer AS_SYR 1457 184syriacaSummer AS_TUB 1818 255tuberosaSummer AS_VAR 183 76variegata

Summer AS_VERT 1398 195verticillata

SummerAS_VIRIDF 1088 192viridiflora

Summer AS_VIRIDI 376 86viridis

Spring AS_AS 113 94asperulaSpring AS_CURA 338 250curassavicaSpring AS_GL 102 76glaucescensSpring AS_LINA 153 121 linariaSpring AS_SUBU 86 74subulataSpring AS_VIRIDI 72 52viridis

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.