1. science
2. weather

Tracking butterfly responses to climate
change using citizen science data
Leslie Ries
University of Maryland, Biology
National Socio-environmental Synthesis Center
Focus on the monarch as a model for
understanding climate and ecological models
N. American
Bfly Assoc.
started 1975
Ohio BMS
started ’96
Stage 3: Summer
expansion and breeding
Monarch Larvae
Monitoring
Project
started ‘99
Journey North
started ‘99
Stage 2:
Spring
migration and
breeding
Stage 1:
Overwintering
WWF-Mexico
•started ’96
•started tracking overwinter
mortality in 2003
Stage 4:
Fall migration
How does climate impact butterflies?

Changing climates can challenge the physiological
tolerances of species

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Changing climates can shift the distribution or emergence
timing of interacting species



Increased heat can allow more growth (in juveniles) or activity (in
adults), but can also be harmful at excessive temperatures
host or nectar plants
natural enemies or mutualists
These dynamics can combine to shift range distributions or
impact population numbers


Species Distribution Models help us understand range dynamics
Population models help us understand how performance at each
stage impacts population trajectories
Species Distribution Models: Mechanistic Approach
• Mechanistic models translate
environmental conditions (often thermal
constraints to growth or energetics) into
biologically relevant metrics (survivorship
or fecundity) and can be used to predict
distributions at large scales.
• BENEFITS:
• Specific mechanisms are identified a
priori
• Allows independent distribution data
to test predictions and identify specific
weaknesses and strengths of the
models
•DRAWBACKS:
• Lack of data for most organisms
• Short history of model development
• Lack of model transferability
between organisms
Growing Degree Days (GDD)
Calculating daily degree days
25
Daily degree days
• Growing degree days are used to estimate the
amount of thermal energy available for growth.
•A minimum temperature at which growth can begin
is determined (DZmin), and each degree above that is
considered a “degree day”
• In some cases, a maximum temperature is set
(DZmax) after which degree days are no longer
accumulated
?
20
DZmin =
11.5C
(52.7F)
15
10
DZmax =
33C (91.4F)
5
0
5
10
15
Zalucki 1982
20
25
30
35
40
45
(Tmin+Tmax)/2
104DD
Total GDD
required:
~323DD
31DD
27DD
58DD
25DD
34DD
Accumulated degree days
44DD
Degree days through the year
3500
Oct
3000
June
2500
2000
1500
1000
Apr
500
0
0
100
200
Julian Date
300
400
Goal: Predict current (and future)
distributions



Take laboratory-measured temperature tolerances to
develop GDD calculations
Obtain climate data to estimate number of
generations possible within a region of interest
Determine if the predicted range overlaps with the
known distribution
+
=
Building the distribution model
Take laboratory-measured temperature
tolerances and intersect with spatial patterns of
heat accumulation throughout eastern North
America to predict number of generations that
could be produced in spring and summer
Predicted number of generations on average
Spring (Mar-Apr)
Summer (May-Aug)
Testing the distribution model:
spring distributions
Data source: Journey North
All records: Mar-Apr
Testing the distribution model:
summer distributions
Data source: NABA
Average observations: July
1 year
35 year
Modeling the distribution of the sachem
butterfly using mechanistic models

Leslie Ries1, Jessica Turner1, Lisa
Crozier2, and Thomas Mueller1
of Maryland
2NOAA NW Fisheries Center




1University
The sachem butterfly (Atalopedes campestris)
recently expanded its range into Washington state.
Winter temperatures had been rising in the area
Natural history notes: a common, open-area species that uses several grasses,
including common grasses such as Bermuda and crab grass
Summer recruitment

Recruitment predictions are based on growing degree days


In this case, the lower development threshold (DZmin) was 15.5C (60F) with
no upper temperature threshold (Crozier 2001)
Number of growing degree days necessary for a single generation was
estimated at 417
Predicted
generations:
Predicted number of generations based on NOAA weather station data (1990-2009 average)
Overwinter survival

Predicted rates of overwinter survival are based on mean
January temperature
Predicted
survival:
Predicted overwinter survival based on NOAA weather station data (1990-2009 average)
Predicted growth rates

The model predicts lambda based on summer recruitment
and overwinter survival

Clearly, the scaling of the model predictions seem off…
Predicted
lambda:
Predicted lambda based on NOAA weather station data (1990-2009 average)
The model does well at capturing the
northern range limits (limited by cold), but
overestimates growth in warmer regions
Predicted
lambda:
Observed
abundances:
GDD models have historically focused on cold limitations rather
than physiological detriments of excessive heat
Another study showing the
sachem increasing in MA
*
GDD models have historically focused on cold limitations rather
than physiological detriments of excessive heat
Lethal and sub-lethal temperature
effects were tested in a laboratory
setting (Betalden et al., in prep)

Larvae at various stages were exposed to potentially
lethal or sublethal temperatures for a different
number of days





38C (100.4F), 40C (104F), 42C (107.6F), 44C (111.2F)
and a control (30C, 86F)
First, Third, Fifth instars exposed
Exposed for 1, 2, 4, or 6 days
Nighttime temperatures were kept at 25C
Larvae were reared to determine survivorship rates
and total development time (in degree days)
Results: Survivorship rates
(Betalden et al., in prep)
• Survivorship begins to
decline for the 40C treatment
only when larvae are exposed
for 6 days (and only for 3rd and
5th instars)
• Survivorship is very low
overall at 42C
• No individuals in the 44C
treatment survived
Results: Development Time
(Betalden et al., in prep)
• Development time increases as individuals are exposed to
higher temperatures for longer periods of time
• There is a treatment effect even for individuals exposed
for 1 day (suggesting sublethal effects may occur at lower
temperatures)
So how should GDD be calculated?

Calculating daily degree days
Sub-lethal and
lethal effects
Daily degree days
25
20
?
15
?
DZmin = 11.5C
(52.7F)
10
DZmax = 31.5C
(88.7F)
5
0
5
10
15
20
25
30
(Tmin+Tmax)/2
35
40
45
Mapping out lethal and sub-lethal zones:
Average from 1990-2009
ABOVE 38
ABOVE 40
ABOVE 42
AVG NUMBER OF DAYS
•Lethal and sub-lethal temperatures seem
to correspond to limits in population
densities, especially in the midwest
• Next up:
•Comparing lethal temperatures to
larval development observed in the
field
MLMP SITES
Preference and performance relative to
mean number of days >38C
Performance
Proportion Eggs
1
0.8
0.6
0-1
0.4
1-5
>5
0.2
0
June
July
Aug
Month
Mean number of days
with temps >38C
In zones where caterpillars are more
likely to experience sub-lethal
temperatures, there appears to be less
development
But climate differs from year to year – so did
temperature really drive those patterns?
Accumulated sublethal degree days:
1999
2000
2002
2003
2001
2004
• These are accumulated over the main summer growing season
(2 months)
• To truly test the impacts of sub-lethal and lethal temperatures,
we need to tie temperature events to survey dates
Relationship between development and
accumulated sub-lethal degree days
Proportion late instar larvae
0.16
n=518
0.14
n=267
0.12
n=70
0.1
0.08
n=24
0.06
0.04
0.02
0
None
0-1
1-10
>10
Number of accumulated sub-lethal degree days
• Conclusions
• Monarch distributions are limited by cold temperatures in the
spring and both cold and hot temperatures in the summer
• But what stage is the most crucial for monarch populations?
Understanding monarch population dynamics
is critical for their conservation

Notable population patterns:



Eastern monarchs may be declining,
but examining different life stages
suggests different patterns
Monarch populations show large
fluctuations from year to year
Underlying mechanisms


REGIONAL CONNECTIONS: How
do dynamics in one phase of the
migratory cycle influence dynamics
in subsequent phases?
ENVIRONMENTAL
INFLUENCE: How much do
environmental factors influence the
connection between these phases?
SUMMER MONITORING DATA
WINTER MONITORING DATA
Focus on climate impacts during
migration and breeding
N. American
Bfly Assoc.
started 1975
Ohio BMS
started ’96
Stage 3: Summer
expansion and breeding
Monarch Larvae
Monitoring
Project
started ‘99
Journey North
started ‘99
Stage 2:
Spring
migration and
breeding
Stage 1:
Overwintering
WWF-Mexico
•started ’96
•started tracking overwinter
mortality in 2003
Stage 4:
Fall migration
Data Available for Analysis
Monarch Larvae Monitoring
Project (MLMP)
North American Butterfly
Association Counts (NABA)
Ohio Butterfly Monitoring
Scheme (OH)
WWF: Mexico
Mexican sites
Data Available for Analysis
N-East
Monarch Larvae Monitoring
Project (MLMP)
North American Butterfly
Association Counts (NABA)
N-Central
Ohio Butterfly Monitoring
Scheme (OH)
WWF: Mexico
South
Mexican sites
Tracking the population through each region and stage
N-East
N-Central
South
Mexican sites
1. Do the number of adults surviving
the winter in Mexico relate to the
number of adults arriving in the
Texas area in spring?
2. Do the number of spring arriving
adults relate to the number of 1st gen
eggs that are recorded?
Tracking the population through each region and stage
N-East
N-Central
South
Mexican sites
1. Do the number of adults surviving
the winter in Mexico relate to the
number of adults arriving in the
Texas area in spring?
2. Do the number of spring arriving
adults relate to the number of 1st gen
eggs that are recorded?
3. How do the number of spring adults
or eggs relate to the number of 1st
generation arrivals in the northern
regions?
Tracking the population through each region and stage
N-East
N-Central
South
Mexican sites
1. Do the number of adults surviving
the winter in Mexico relate to the
number of adults arriving in the
Texas area in spring?
2. Do the number of spring arriving
adults relate to the number of 1st gen
eggs that are recorded?
3. How do the number of spring adults
or eggs relate to the number of 1st
generation arrivals in the northern
regions?
4. Can the number of 1st generation
adults / 2nd generation eggs predict
numbers in subsequent generations?
Q1 and Q2. How do overwintering numbers relate to the number
of arriving adults and how do arriving adults influence the
number of eggs we see in the spring?
Q3. How do the number of spring adults or eggs relate to the number of
1st generation arrivals in the northern regions?
1st gen eggs in south to 2nd gen eggs North Migrant adults in south to 1st gen adult arrivals
r=0.5 p=0.13
NABA 1st Gen adults (N-East)
2nd gen MLMP egg density (N-Central)
0.6
0.5
0.4
0.3
0.2
0.1
0
0
1
2
1st gen MLMP egg density (South)
3
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
r=0.69 p=0.12
0
0.5
1
1.5
2
NABA spring migrants (South)
A weak, or non-existent, relationship between the spring generation and summer
arrivals in the north could be due to lack of data, or swamping out by environmental
factors.
2.5
4
3
2
1
0
0.001
N-Central:
Summer MLMP egg dens (N-Central)
Summer MLMP egg density (N-East)
5
0.6
0.01
0.1
2nd gen MLMP egg density (N-East)
1
r=0.7925 p=<0.0001
0.5
0.4
0.3
0.2
0.1
0
0.01
0.1
2nd gen MLMP egg density (N-Central)
1
Summer NABA detections (N-Central)
Summer NABA detections (N-East)
Q3. Can the number of 1st generation adults / 2nd generation eggs predict numbers in
subsequent generations?
6
0.8
N-East:
r=0.899 p=<0.0001
r=0.85 p=0.004
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.001
8
0.01
0.1
2nd gen MLMP egg density (N-East)
1
r=0.73 p=0.005
7
6
5
4
3
2
1
0
0
1
2
1st Gen Spring arrivals NABA (N-Central)
3
YES: This suggests that the number of arrivals in the northern breeding grounds from the
southern spring generation has a strong influence on the ultimate size of that year’s population.
Tracking climate’s impacts on the
migratory monarch butterfly
We examined the impacts on
population growth in Ohio of:
1. Spring temperature (in Texas)
2. Spring precipitation (in Texas)
3. Summer temperature (in Ohio)
4. Summer precipitation (in Ohio)
Patterns based on simple climate
metrics aren’t informative
Meaningful patterns emerge when patterns are
evaluated in a multiple regression framework
The story so far…
N-East
N-Central
South
• No relationship between adults leaving
Mexico, arriving in the South, and laying
eggs
• Weak (or non-existent) relationship
between adults arriving in the South, next
generation arrivals in the North and egglaying
•The disconnect may be due to the
importance of spring climate on the
ultimate population size (and/or
health) of migrants to the North
• A strong relationship between the
numbers arriving in the North and laying
eggs and the size of the population at the
end of the summer.
•This suggests that the size of that
first generation produced in the
spring that arrives in the North is an
important contributor to yearly
population sizes and (again) that
spring climate is important
But are climate impacts direct or indirect?
Could climate impact the phenological
linkage between milkweeds and monarchs
Data source: Journey North
Arrival dates - 2001
Arrival mismatches are leading to at
least anecdotal incidents of egg loading
Take home messages

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Climate is impacting butterfly populations
Using models that link physiological tolerance data to
large-scale distributions is a powerful way to tease out the
complex interactions between climate and ecology
We could not possibly explore these questions in a
rigorous way without a data stream from dedicated
citizen-science networks, so public participation in
scientific research is CRUCIAL to answer the most
relevant questions in today’s world.
So THANK YOU!!!
Questions?