DOI: 10.5061/dryad.hhmgqnkdd
title: Data from: Behavioral constraints on local adaptation and counter-gradient variation: implications for climate change

description: Resource allocation to growth, reproduction, and body maintenance varies
 within species along latitudinal gradients. Two hypotheses explaining this
 variation are local adaptation and counter-gradient variation. The local
 adaptation hypothesis proposes that populations are adapted to local
 environmental conditions and are therefore less adapted to environmental
 conditions at other locations. The counter-gradient variation hypothesis
 proposes that one population out performs others across an environmental
 gradient because its source location has greater selective pressure than
 other locations. Our study had two goals. First, we tested the local
 adaptation and counter-gradient variation hypotheses by measuring effects
 of environmental temperature on phenotypic expression of reproductive
 traits in the burying beetle, Nicrophorus orbicollis Say, from three
 populations along a latitudinal gradient in a common garden experimental
 design. Second, we compared patterns of variation to evaluate whether
 traits co-vary or whether local adaptation of traits preclude adaptive
 responses by others. Across a latitudinal range, N. orbicollis exhibits
 variation in initiating reproduction and brood sizes. Consistent with
 local adaptation, (1) beetles were less likely to initiate breeding at
 extreme temperatures, especially when that temperature represents their
 source range; (2) once beetles initiate reproduction, source populations
 produce relatively larger broods at temperatures consistent with their
 local environment. Consistent with counter-gradient variation, lower
 latitude populations were more successful at producing offspring at lower
 temperatures. We found no evidence for adaptive variation in other adult
 or offspring performance traits. This suite of traits does not appear to
 coevolve along the latitudinal gradient. Rather, response to selection to
 breed within a narrow temperature range may preclude selection on other
 traits. Our study highlights that N. orbicollis uses temperature as an
 environmental cue to determine whether to initiate reproduction, providing
 insight into how behavior is modified to avoid costly reproductive
 attempts. Furthermore, our results suggest a temperature constraint that
 shapes reproductive behavior.
description: <i>Nicrophorus orbicollis</i>
 populations We derived the laboratory beetle
 populations used for this experiment from wild-caught beetles captured
 with baited pitfall traps near Big Falls, Wisconsin (high latitude; HL;
 44.6165°N, -89.0161°W), Waveland, Indiana (mid latitude; ML; 39.9417°N,
 -87.0917°W), and Spavinaw, Oklahoma (low latitude; LL; 35.3704°N,
 -95.0486°W) (Fig. 2a) in May-June of 2014 and 2015. We housed all
 <i>N. orbicollis </i>in individually marked plastic containers
 (15 x 11 x 7 cm) in an environmental chamber at
 21<sup>o</sup>C with a 14:10 h light:dark (L:D) cycle and fed
 chicken liver <i>ad libitum</i>. These conditions simulated
 the natural light/dark pattern and temperature consistent with the
 beetles’ summer breeding season in their natural environment (Cook et al.
 2019). We used these wild-caught beetles to establish the first generation
 (F1) laboratory populations used for experiments. We bred wild-caught
 beetles by placing a male and female with a fresh mouse carcass in plastic
 containers (18 x 15 x 10 cm) filled two-thirds full with top soil. We
 removed the wild-caught males when larvae first appeared on the carcass,
 and we removed wild-caught females when larvae dispersed from the carcass.
 We left F1 larvae undisturbed until eclosion (approximately 28-30 days),
 and then we maintained them in individual plastic containers as described
 above until used for the experiments at sexual maturity (21-28 days old
 post eclosion). Common garden experiment
 To assess genetically based latitudinal patterns, we tested
 representatives from each of the three source locations at each of five
 constant air temperatures (12-13°C, 15°C, 20°C, 25°C, or 27-28°C) in a
 common garden experimental design. We began each trial by randomly
 selecting a laboratory-reared, sexually mature (21-28 days old) male and
 female from the same population, and from different parental lines. At the
 beginning of each reproductive bout, we weighed females and males and
 measured their pronotum width. We placed each pair of beetles in a plastic
 container (18 x 15 x 10 cm) filled two-thirds full with commercially
 purchased topsoil and given a freshly thawed 30 g (± 1 g) mouse carcass
 (Fig. 3). Because either parent can successfully raise a brood if their
 partner is handicapped or removed (Smiseth, et al. 2005, Creighton et al.
 2015), we removed males after 48 hours to allow sufficient time for mating
 to occur, but to minimize male impact on female life history
 characteristics thereafter (Rauter and Moore 2004, Smiseth et al.<i>
 </i>2005, Creighton et al. 2015, Smith et al. 2015). We randomly
 assigned pairs from each of the three populations to a treatment of one of
 five temperatures: 12-13 ºC (T<sub>min</sub>), 15°C (average
 three month daily low temperature for Wisconsin), 20 ºC (average three
 month daily temperature for Indiana), 25ºC (average three month daily high
 temperature for Oklahoma), or 27-28 ºC (T<sub>max</sub>);
 (L:D;14:10; Fig. 3). Temperatures represent the average daily high and low
 temperatures experienced within the range of the collection sites for the
 known breeding months of May-August. We determined experimental breeding
 temperatures by calculating a three-month daily mean air temperature for
 each location using ten-year temperature data sets obtained from the
 National Climatic Data Center (NCDC) Annual Climatological Summaries of
 U.S. station data (ncdc.noaa.gov accessed on 9/10/2013). We checked,
 photographed, and monitored broods daily to measure response variables. We
 monitored all broods until beetles completed the reproductive cycle or
 until we determined brood failure (i.e., carcass preparation stopped).
 Each temperature treatment by location combination had at least 10
 replicates, but more broods were required at the intermediate temperatures
 to allow for analysis of offspring traits. We summarized sample sizes for
 each temperature treatment by location combination in Table 1.
 Parental reproductive performance We
 measured both parental and offspring response variables from each brood to
 characterize variation in reproductive performance across the temperature
 gradient. To measure parental performance, we evaluated the following
 variables: reproductive success, hatching asynchrony, offspring number,
 and developmental timelines. Each of these response variables yielded one
 measure per brood, so sample sizes are equivalent to those in Table 1 for
 the three intermediate temperatures. <i>Carcass
 preparation and reproductive success</i> We
 measured reproductive success by two response variables – degree of
 carcass preparation and probability of producing offspring that survived
 to adulthood. We scored carcasses for degree of preparation and assigned a
 score of zero to four to represent the stage of carcass preparation (Table
 2). We evaluated the final stage of carcass preparation for each brood as
 a measure of reproductive success at different temperatures. To
 characterize the probability of producing offspring at a given
 temperature, we scored each brood as a success (score = 1) if any adult
 offspring were produced, or a failure (score = 0) if no adult offspring
 were produced. <i>Hatching
 asynchrony</i> We calculated brood hatching
 asynchrony as the spread of hatching dates within a brood (i.e., the
 number of days between the first and last arriving first instar larvae on
 the prepared carcass; Aparicio 1999). While there may be an adaptive value
 to increased asynchrony, for this analysis we assumed that less asynchrony
 in larval arrival to a carcass is advantageous.
 <i>Parental developmental timelines and reproductive
 output</i> To determine parental developmental
 timelines we used two variables: time in days, 1) to fully prepare the
 carcass, and 2) for larvae to fully develop and consume the carcass (Scott
 1998). To characterize reproductive output, or number of offspring
 produced, we determined the number of final (3<sup>rd</sup>)
 instar larvae that dispersed from the carcass.
 Offspring performance To measure offspring
 performance, we evaluated the following variables: offspring growth rate,
 adult offspring body size, developmental stability (as measured by degree
 of fluctuating asymmetry of newly eclosed adults), and percentage
 offspring body fat. <i>Offspring growth rate and
 brood size</i> To evaluate offspring growth
 rate, we subtracted the average mass of an individual larvae from the
 first day they were present on the carcass from the average mass of an
 individual larvae on the final day they were present on the carcass. We
 then divided this value by the total number of days that larvae were on
 the carcass to account for asynchronous arrival and dispersal to the
 carcass by larvae. The response variable was mean offspring growth rate
 per brood. To measure offspring body size, we measured the adult offspring
 body mass from all offspring in the brood at the time of eclosion. We used
 mean offspring body size as the response variable for analysis.
 <i>Offspring developmental stability</i>
 To measure developmental stability and percentage body fat, we
 randomly selected one adult male and one adult female offspring from each
 brood. This selection resulted in two replicates of developmental
 stability and percent body fat per brood for analysis. These individuals
 were pinned for the purpose of taking photographs which were then used to
 measure fluctuating asymmetry. We used three variables: 1) an anterior to
 posterior transect through the beetles upper orange elytra spot, 2) a
 basal to distal transect from the elytra edge to the lower orange elytra
 spot, and 3) a basal to distal transect from the “pronotum cleft” to the
 edge of the pronotum (Fig. 2b). We used the difference between the left
 and right side for each of these variables as a measurement of
 developmental stability (Van Valen 1962). Additionally, we determined,
 percentage body fat on these same individuals following the lipid
 extraction techniques used by Marden (1989). Data
 analysis No beetles produced offspring at either the
 highest or lowest of the five temperatures in the experimental design. For
 this reason, only degree of carcass preparation was analyzed at all five
 temperatures. Because the response variable was discrete (i.e., scored as
 an integer from 0-4) we used a generalized linear model with a log link
 function and a Poisson distribution (Neter et al. 2004). The model had two
 fixed factors: temperature treatment (5 levels) and location (3 levels).
 We included the interaction between temperature treatment and location,
 and female and male body size as covariates. We used Proc GENMOD in SAS
 for the analysis (SAS 9.3 SAS Institute, Cary, North Carolina,
 USA). We modeled the probability of producing offspring
 (i.e., reproductive success) as a binomial response (0 or 1) with a probit
 link function in a generalized linear model framework with a Poisson
 distribution (Neter et al. 2004). Because no beetles produced offspring at
 the highest and lowest temperatures in the experimental design, we used
 only the three intermediate temperatures for this analysis. The model had
 two fixed factors: temperature treatment (3 levels) and location (3
 levels). We included the interaction between temperature treatment and
 location, and female and male body size as covariates. We used Proc GENMOD
 in SAS for the analysis (SAS 9.3 SAS Institute, Cary, North Carolina,
 USA). Number of offspring produced (i.e., final brood
 size), hatching asynchrony, and developmental timing (2 response
 variables) were represented as count data, so we used a generalized linear
 model with a log link function and a Poisson distribution (Neter et al.
 2004). The model had two fixed factors: temperature treatment (3 levels)
 and location (3 levels), and we included the interaction between
 temperature treatment and location. For number of offspring produced, time
 preparing the carcass, and time until dispersal of offspring, we included
 female and male body size as covariates. For the model of hatching
 asynchrony, we included female mass and brood size as covariates. We used
 Proc GENMOD in SAS for each of these analyses (SAS 9.3 SAS Institute,
 Cary, North Carolina, USA). Growth rate of offspring
 and size of adult offspring (measured as mass in g) were continuous
 response variables so we used a general linear model and a Poisson
 distribution (Neter et al. 2004) on untransformed data for analysis. Data
 consisted of means calculated for each brood so we have one replicate per
 brood. Raw data exhibited normally distributed residuals and fit the
 assumptions of the model. The model had two fixed factors: temperature
 treatment (3 levels) and location (3 levels), and we included the
 interaction between temperature treatment and location. For the growth
 rate model we included female combined parental body size and brood size
 as covariates, and for the offspring size model we included female body
 size, male body size, and brood size as covariates. We used Proc MIXED in
 SAS for the analysis (SAS 9.3 SAS Institute, Cary, North Carolina,
 USA). To evaluate percent body fat and developmental
 stability (three measures of fluctuating asymmetry; S1) of adult offspring
 we used a general linear model and a Poisson distribution (Neter et al.
 2004). The models had three fixed factors: temperature treatment (3
 levels), location (3 levels), and sex (2 levels), and we included all
 interactions among temperature treatment, location, and sex. For the model
 evaluating percent body fat, we included two covariates – offspring size
 (pronotum width) and brood size. For the models evaluating fluctuating
 asymmetry, we included brood size as a covariate. Raw data exhibited
 normally distributed residuals and fit the assumptions of the model. We
 included the ID number of the brood as a random effect in these models
 because we measured two individuals from each brood. We used Proc MIXED in
 SAS for the analysis (SAS 9.3 SAS Institute, Cary, North Carolina,
 USA).