DOI: 10.5061/dryad.w0vt4b92t
title: Data from: Life stage hypothesis modeling determines insect vulnerability during developmental life stages to climate extremes

description: Butterflies are important bioindicators that can be used to monitor the
 effects of climate change, particularly in montane environments. Changes
 in butterfly population size over time, reflective of indicator life
 stages, can signal changes that have occurred or are occurring in their
 environment indicating ecosystem health. From the perspective of
 understanding butterflies as bioindicators in these systems, it is
 essential to identify influential environmental variables at each life
 stage that have the greatest effect on population dynamics. Life stage
 hypothesis modeling was used to assess the effects of multiple temperature
 and precipitation metrics on the population growth rate of a Parnassius
 clodius butterfly population from 2009 to 2018. Extreme maximum
 temperatures during the larval-pupal life stages were identified to have a
 significant negative effect on population growth rate. We speculate that
 higher temperatures during the spring ephemeral host plant’s flowering,
 and P. clodius' larval stage, may lead to earlier plant senescence
 and lower P. clodius growth. Because Parnassius butterflies are well
 studied from a global perspective, results may aid in understanding the
 potential indicator life stages of other insect species in montane
 environments to climatic changes. Findings from this study demonstrate the
 value in assessing a butterfly species’ response to short-term weather
 variation or long-term climatic changes at each life stage in order to
 protect and conserve insects and their interactions with other organisms.
description: <em>Butterfly Mark-Recapture</em>
 Mark-recapture methods were used to study a population of
 <em>P. clodius </em>at Pilgrim Creek in Grand Teton National
 Park, Wyoming, USA across annual flight seasons between 2009 and 2018
 during June and July. Surveys were not carried out in 2012 and 2013. Six
 50m x 50m plots a minimum of 100m apart, were located using GPS units,
 flagged prior to the flight season of <em>P. clodius</em>, and
 surveyed each year. Survey plots were initially established in 2000 in an
 effort to balance increasing the area sampled, decreasing the number of
 recaptures, and maintaining independent sampling within a single meadow
 (Auckland et al. 2004). Mark-recapture surveys began a few days after the
 beginning of the flight season and continued until only one or two
 butterflies per plot were caught during a survey period. Plots were
 monitored daily if weather permitted throughout each flight season.
 Surveys were conducted when temperatures were above 21°C, wind was
 &lt;16kmh<sup>-1</sup>, and clouds were not obscuring the
 sun. If weather prevented all six plots from being sampled in one day, the
 rotation was completed the following day. The order in which plots were
 surveyed rotated throughout the flight season to vary the time of day
 surveys were conducted for each plot. Two people
 surveyed each plot for 20 minutes between 10:00 and 17:00 hours. All
 <em>P. clodius</em> butterflies in a plot were netted by hand
 and held in glassine envelopes until the survey ended. The time of capture
 and activity of the butterfly when captured (i.e., nectaring, chasing,
 stationary) was noted on each envelope. Following each survey, captured
 butterflies were marked with a permanent marker on each hindwing with an
 identification number indicating the plot it was caught in and the
 butterfly’s individual number (Figure 1). The number corresponded to the
 consecutive butterfly count throughout a season. The following metrics
 were recorded for each captured butterfly: date, plot number,
 identification number, time of capture, sex, and whether the butterfly was
 a recapture. After data were collected for each butterfly and butterfly
 markings were added, all butterflies were released in the center of each
 plot by placing butterflies on surrounding plants, rather than being
 placed on the ground where they could be stung by ants.
 <em>Weather Variable Selection </em>
 A list of meaningful weather variables expected to affect
 populations of <em>P. clodius</em> butterflies was formulated
 based upon previous research that identified influential weather variables
 affecting the population dynamics of other <em>Parnassius</em>
 species (Matter et al. 2011, Roland and Matter 2013, 2016, Matter and
 Roland 2017) as well as our observations at Pilgrim Creek over time. The
 list of explanatory variables included: date of snowmelt, maximum snow
 water equivalent (cm), mean snow water equivalent (cm), mean temperature
 (C), mean maximum temperature (C), extreme maximum temperature (C), mean
 minimum temperature (C), extreme minimum temperature (C), accumulated
 rainfall (cm) from 2008 to 2018 (Table 1). All temperature data were
 sourced from the USDA Base Camp SNOTEL site located at 43.93333°N,
 -110.45000°W at 2151 m in elevation and 10.7 km from Pilgrim Creek meadow.
 All snow water equivalent and precipitation data were sourced from the
 USDA Snake River Station SNOTEL site located at 44.13333°N, -110.66667°W
 at 2109 m in elevation and 25.1 km from Pilgrim Creek meadow. 
 <em>Analysis</em> The goal of
 these analyses was to identify which weather variables, known to affect
 other butterfly species, explain variation in <em>P. clodius
 </em>population growth through each life stage. Estimates of
 population change were calculated for the Pilgrim Creek, WY population of
 <em>P. clodius</em> for the duration of the study (i.e.,
 2009-2011, 2014-2018).  Calculations of population change
 (R<sub>t</sub>) were estimated as the ratio of the number of
 caught butterflies (N<sub>t</sub>) for one flight season after
 accounting for effort, relative to the number of caught butterflies of the
 previous flight season after accounting for effort.  Effort was measured
 as the number of two-person 20-minute plot surveys conducted during each
 flight season. R<sub>t</sub> was calculated as
 log<sub>10</sub>((N<sub>t+1</sub>/effort<sub>t+1</sub>)/(N<sub>t</sub>/effort<sub>t</sub>)) (Roland and Matter 2016). This simplified approach to estimating population size used in previous research (Roland and Matter 2016) uses information regarding known observed butterflies in combination with standardized sampling effort allowing researchers to produce tangible values.  Our estimate of population size was included in each model to evaluate density dependence as an explanatory variable on population growth. Population size was estimated by taking the log of the number of caught butterflies divided by the number of surveys conducted for each flight season (i.e., log<sub>10</sub>(N<sub>t</sub>/effort<sub>t</sub>)). A life stage hypothesis modeling approach was used to evaluate the effect of each chosen weather variable during each life stage on the population growth (i.e., R<sub>t</sub>) of <em>P. clodius </em>butterflies. The observed values for each weather variable were partitioned by life stage to evaluate each variable’s effect during each stage of the <em>P. clodius</em> lifecycle. Dates of each life stage were estimated from researcher observations (L. Crees, personal communication, October 2022) of <em>P. clodius </em>over time to capture the estimated beginning and end dates of each life stage.  We categorized the egg stage as July 21 - April 30, the larva-pupa stage as May 1 - May 31, and the adult stage June 1 - July 20. Each weather variable was tabulated for the egg stage, larva-pupa stage, and the adult butterfly stage (Table 1). The larva and pupa life stages were combined due to the uncertainty of the date of when one stage ended and the other began. Correlation tests were conducted between all explanatory weather variables to prevent the inclusion of highly correlated variables in models, which could lead to multicollinearity and unreliable estimates. Uncorrelated precipitation and temperature explanatory variables and logN<sub>t</sub> (i.e., density dependence) were included in each generalized linear model. Then, generalized linear models using the <em>glm</em> function in R were fit to evaluate which explanatory weather variables were the most descriptive in explaining population change (R<sub>t</sub>). Additionally, generalized additive models using the <em>gam </em>function in R were fit to evaluate quadratic relationships between population change (R<sub>t</sub>) and explanatory weather variables. AIC comparison between models was conducted to determine the most informative explanatory variables. Following generalized linear models that included logN<sub>t</sub> and interactions between the most informative temperature and precipitation explanatory variables were used to evaluate potential interactive effects between weather variables on population change. Final AIC comparison was conducted to determine the best model to describe population change for each <em>P. clodius</em> life stage.
description: # Life stage hypothesis modeling determines insect vulnerability during
 developmental life stages to climate extremes
 [https://doi.org/10.5061/dryad.w0vt4b92t](https://doi.org/10.5061/dryad.w0vt4b92t) ## Description of the data and file structure ### Files and variables #### File: p.clodius\_environmental.variables.binned.by.lifestage\_2009-2021\_SR.BC\_Jan2023\_ALL\_metric.csv **Description:** Mark-Recapture-Release data for Parnassius clodius butterflies in Pilgrim Creek, Wyoming, U.S.A. from 2009-2011 and 2014-2018 ##### Variables * Year: calendar year * effort.num.surveys: number of surveys conducted per year * caught: total number of butterflies caught per year * caught.effort: total number of butterflies caught per year divided by the total number of surveys conducted per year * caught.recap: total number of butterflies caught, including recaptures, per year * est.popsize: estimated population size using Rmark * logNt.caught: log transformation of the total number of butterflies caught per year * logNt.caught.recap: log transformation of the total number of butterflies caught, including recaptures, per year * logNt.estpopsize: log transformation of the estimated poulation size using Rmark * logNt.caught.effort: log10(Nt/effortt); density dependence variable; used in analyses in the associated R markdown file * Rt.caught.effort: log10((Nt+1/effortt+1)/(Nt/effortt)); used in analyses in the associated R markdown file * JulianSnowmelt: Day of Year * egg.maxSWE.cm: maximum snow water equivalent in centimeters during the *P. clodius* egg life stage (July 21 - April 30) * egg.meanSWE.cm: mean snow water equivalent in centimeters during the *P. clodius* egg life stage (July 21 - April 30) * egg.meantemp: mean temperature in Celsius during the *P. clodius* egg life stage (July 21 - April 30) * egg.meanmax: mean maximum temperature in Celsius during the *P. clodius* egg life stage (July 21 - April 30) * egg.extmax: extreme maximum temperature in Celsius during the *P. clodius* egg life stage (July 21 - April 30) * egg.meanmin: mean minimum temperature in Celsius during the *P. clodius* egg life stage (July 21 - April 30) * egg.extmin: extreme minimum temperature in Celsius during the *P. clodius* egg life stage (July 21 - April 30) * egg.rainfall: accumulated rainfall in centimeters during the *P. clodius* egg life stage (July 21 - April 30) * larvapupa.maxSWE.cm: maximum snow water equivalent in centimeters during the *P. clodius* larva-pupa life stage (May 1 - May 31) * larvapupa.meanSWE.cm: mean snow water equivalent in centimeters during the *P. clodius* larva-pupa life stage (May 1 - May 31) * larvapupa.meantemp: mean temperature in Celsius during the *P. clodius* larva-pupa life stage (May 1 - May 31) * larvapupa.meanmax: mean maximum temperature in Celsius during the *P. clodius* larva-pupa life stage (May 1 - May 31) * larvapupa.extmax: extreme maximum temperature in Celsius during the *P. clodius* larva-pupa life stage (May 1 - May 31) * larvapupa.meanmin: mean minimum temperature in Celsius during the *P. clodius* larva-pupa life stage (May 1 - May 31) * larvapupa.extmin: extreme minimum temperature in Celsius during the *P. clodius* larva-pupa life stage (May 1 - May 31) * larvapupa.rainfall: accumulated rainfall in centimeters during the *P. clodius* larva-pupa life stage (May 1 - May 31) * adult.maxSWE.cm: maximum snow water equivalent in centimeters during the *P. clodius* adult life stage (June 1 - July 20) * adult.meanSWE.cm: mean snow water equivalent in centimeters during the *P. clodius* adult life stage (June 1 - July 20) * adult.meantemp: mean temperature in Celsius during the *P. clodius* adult life stage (June 1 - July 20) * adult.meanmax: mean maximum temperature in Celsius during the *P. clodius* adult life stage (June 1 - July 20) * adult.extmax: extreme maximum temperature in Celsius during the *P. clodius* adult life stage (June 1 - July 20) * adult.meanmin: mean minimum temperature in Celsius during the *P. clodius* adult life stage (June 1 - July 20) * adult.extmin: extreme minimum temperature in Celsius during the *P. clodius* adult life stage (June 1 - July 20) * adult.rainfall: accumulated rainfall in centimeters during the *P. clodius* adult life stage (June 1 - July 20) #### File: Pclodius\_PopDynAnalysis\_LifeStageHypothesis.Rmd **Description:** Life-Stage Hypothesis Modeling using* Parnassius clodius* as a model species ## Code/software The data is stored in a .csv Microsoft Excel file. R - Rstudio is required to complete the analyses. The following R packages were used to conduct the analyses: * readr * car * mgcv * ggplot2 * tibble * gridExtra The R code for life stage hypothesis analyses can be found in the *Pclodius_PopDynAnalysis_LifeStageHypothesis.rmd* file. The file is annotated for users to conduct life stage hypothesis models themselves. ## Access information Other publicly accessible locations of the data: * Data is only available through Dryad Data was derived from the following sources: * Data was collected by members of the Debinski Lab over time. Calculations, data wrangling, and statistical analysis was conducted by J. Simone Durney under the advisement of Diane M. Debinski and Stephen F. Matter.