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
Our key data sources:
Host-plant distribution data
Lab data on physiological
tolerances
+
+
Climate data



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
Summer expansion and
breeding (May – Aug)
Fall migration
(Sept – Oct)
Spring migration
and breeding
(Mar – Apr)
Overwintering
(Nov – Feb)
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
Diversity index
Species modeled:
7 spring
27 summer
•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
Zalucki 1982
45 DD
Calculating daily degree days
120DD
Total GDD
required:
351DD
+45DD
32DD
28DD
Daily degree days
25
?
20
DZmin =
11.5°C
(52.7°F)
15
10
5
0
67DD
24DD
35DD
Plus 45DD before egg-laying begins
5
10
15
20
25
30
(Tmin+Tmax)/2
35
40
45
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)
Calculating daily degree days
Sub-lethal
and lethal
effects
25
Daily degree days

20
?
15
?
DZmin =
11.5°C
10
5
0
5
10
15
20
25
30
(Tmin+Tmax)/2
35
40
45
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 (MarApr) and summer (May-Aug) and converted to number of
generations


Predicted
generations
3105 weather stations
Spring prediction map
Summer prediction map
Predicted
generations
Predicted
generations
Average #
accumulated LDD
Spring data:
Journey North
Summer data: North American
Butterfly Association
No. Years
MILKWEED DISTRIBUTIONS
#
observations
Modeled species
predicted present
The center of milkweed diversity in TX is
associated with the greatest number of
spring monarch sightings
MILKWEED DISTRIBUTIONS
GROWING DEGREE DAYS
#
observations
Modeled species
predicted present
The center of milkweed diversity in TX is
associated with the greatest number of
spring monarch sightings
Predicted
generations
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 DIVERSITY
Monarchs/PH
Modeled species
predicted present
Monarch distributions north of center of
milkweed diversity
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.
MILKWEED DISTRIBUTIONS
GROWING DEGREE DAYS
Predicted
generations
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.
Monarch distributions north of where the
maximum number of generations are
predicted, but south of where multiple
generations aren’t possible.
Monarchs seem to be found
where they are least likely to
encounter temperatures above
38°C.
Average number of
accumulated LDD
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, ABI1147049 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
Season
Sp
Summer AS_AS
Start
916
Thinned species
125 asperula
Summer AS_CURA
Summer AS_EX
488
329
146 curassavica
90 exaltata
Summer AS_FA
273
82 fascicularis
Summer AS_GL
Summer AS_HI
Summer AS_INC
181
377
2309
75 glaucescens
62 hirtella
244 incarnata
Summer AS_INV
279
53 involucrata
Summer AS_LANU
Summer AS_LAT
Summer AS_LINA
121
253
461
62 lanuginosa
80 latifolia
107 linaria
Summer
Summer
Summer
Summer
AS_OE
AS_OV
AS_PER
AS_PUM
214
138
161
282
90 oenotheroides
54 ovalifolia
58 perennis
59 pumila
Summer AS_PUR
379
91 purpurascens
Summer AS_QUAD
Summer AS_SPEC
409
1138
Summer AS_STEN
250
74 stenophylla
855
314
1457
1818
183
108 subverticillata
67 sullivantii
184 syriaca
255 tuberosa
76 variegata
1398
1088
376
195 verticillata
192 viridiflora
86 viridis
Summer
Summer
Summer
Summer
Summer
AS_SUBV
AS_SUL
AS_SYR
AS_TUB
AS_VAR
Summer AS_VERT
Summer AS_VIRIDF
Summer AS_VIRIDI
104 quadrifolia
180 speciosa
Spring
Spring
Spring
Spring
Spring
Spring
AS_AS
AS_CURA
AS_GL
AS_LINA
AS_SUBU
AS_VIRIDI
113
338
102
153
86
72
94 asperula
250 curassavica
76 glaucescens
121 linaria
74 subulata
52 viridis
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.