uid=HFR,o=lter,dc=ecoinformatics,dc=org all public read doi:10.6073/pasta/8b839deba298e910c5dc551f3e0f27a3 Aaron Ellison https://orcid.org/0000-0003-4151-6081 Amy Arnett Sara Helms Cahan https://orcid.org/0000-0001-9531-400X Nicholas Gotelli https://orcid.org/0000-0002-5409-7456 Megan Brown Researcher Andrew Nguyen https://orcid.org/0000-0002-1378-1606 Researcher Jordan Zitnay Researcher 2023 English

Aphaenogaster picea is a common forest ant species that ranges from the high elevations of Virginia into northern Minnesota and Maine. Its northern range boundary---estimated from geo-referenced museum specimens---occurs in Maine near 45 °N. To further characterize the northern range boundary of A. picea, we combined presence-absence data from previous field surveys at 27 sites (see HF147) with new data collected for this study in July and August 2015 from 75 additional sites. These 75 sites were sampled randomly along a 65-km East-West belt transect centered on 45 °N latitude and running across all of central Maine. For the July 2015 survey of 32 sites, two researchers searched each site haphazardly for A. picea colonies for 20 minutes in deciduous and mixed hardwood forests. For the August 2015 survey of 43 additional sites, two researchers established 50 × 50 m plots and searched them for colonies for 20 minutes each (40 person-minutes per plot). We used classification and regression tree (CART) analysis, implemented in the rpart package (version 4.1-10) in R version 3.4.2, to determine which of the 19 bioclimatic variables (at 2.5-min resolution) downloaded from WorldClim (http://www.worldclim.org/bioclim) best predicted the occurrence of A. picea. To obtain the optimal regression tree and to avoid over-fitting the data, we pruned the tree so that it had the lowest complexity parameter and smallest cross-validated error. For each cross-validation, CART models were fitted to a training set and then used to predict presence or absence in the testing set. In total, we analyzed 10 independent cross-validations.

To test for local adaptation in cold performance, we collected 16 colonies from 16 unique sites along the range boundary in July, 2015. At each site, we collected whole colonies, including workers, larvae, pupae, and queens (when possible). Collected colonies were housed in 22 × 16 cm plastic containers and maintained in a 12:12 light:dark cycle at ≈50% humidity and 25 °C (i.e., within the range of optimal development). To minimize the contribution of the source environment, colonies were lab-acclimated in these conditions for at least one month before any physiological measurements were taken. Ant workers typically live between a few weeks to months. Colonies readily nested within glass test-tubes that were plugged with water-saturated cotton to maintain humidity. Each colony was fed 100 μL of 20% honey in water and one bisected meal worm three times a week.

We exposed ants to a series of cold pre-treatments, recovery treatments, and subsequent temperature treatments to construct cold-performance curves for adult workers from each lab-acclimated colony. Cold-resistance was measured as the Chill Coma Recovery (CCRT): the time in seconds needed for an individual ant worker to orient itself in an upright position and take one step after a 1-hour exposure to −5 °C. To assess basal cold-resistance and cold-hardening ability, we measured the CCRT of ants that were pre-treated for 1 hour at one of four temperature treatments: 25, 5, 0, or −5 °C (fig. 1). The 25 °C pre-treatment is the control that represents basal cold-resistance, and the −5, 0, and 5 °C pre-treatments represent cold-hardening at different temperature levels. All ants were temperature-treated in a circulating water bath (Polyscience, USA) that contained a 50% ethylene glycol solution to prevent freezing. For each colony, four ants per pre-treatment were placed in a sealed 15 × 160 mm glass test tube for one hour at the pre-treatment temperature. After one hour, the ants were removed and placed into a test tube and allowed to recover at 25 °C for one hour, then placed in the −5 °C treatment for another hour. After this second hour, ants were placed in glass tubes with water-soaked cotton plugs. CCRT was measured by an observer without prior knowledge of pre-treatment groups. We excluded 24 out of 272 ants tested that were lost or did not survive handling.

We adapted multivariate methods from quantitative genetics to estimate the correlation structure between basal cold-resistance and cold hardening in A. picea. In Kingsolver's original analyses, the data consist of performance traits that were measured for replicated genotypes For a set of n traits, the resulting n × n G matrix measures the additive genetic variance in the traits along the diagonal, and the additive covariances between pairs of traits in the off-diagonal elements. In our analyses, the data consist of chill coma recovery time (CCRT) that was measured at four different pre-treatment temperatures for 16 different colonies. We equate CCRT measured at the 4 treatments as 4 "traits" of the populations. We estimated the colony level, 4 × 4 variance-covariance matrix by first fitting a mixed-effect model and then extracting variance and covariance components using the lme4 package in R. Y_ijk = μ + C_jk + ϵ_ijk where Y_ijk represents the value of CCRT for ant worker i of colony j measured for each pre-treatment temperature k, μ is the fixed effect of the intercept, C_jk is the random effect of colony j at pre-treatment temperature k, and ϵ_ijk represents the residual error. Colony and residual within colony-level effects were treated as random effects with an unconstrained covariance structure. We extracted variance and covariance components that make up the 4 × 4 matrix with the VarCorr() function from the mixed effects model. Estimates of the variance-covariance matrix were calculated from untransformed data because each trait (CCRT under each pre-treatment temperature) have the same units. We then decomposed G using principal components analysis (PCA) to produce orthogonal eigenvectors, which represent independent axes of genetic correlations between traits. The first principal component, gmax, is the eigenvector that explains most of the variation in the G matrix. The pattern of loadings for gmax can be biologically interpreted as falling within one of three broad scenarios. In the first scenario, if colonies exhibited a constant difference in performance across all pre-treatment temperatures (additive variation), then the gmax loadings would be all negative or all positive. In the second scenario, if colonies varied in optimal cold performance between low and high pre-treatment temperatures (cooler-warmer variation), then the gmax loadings would be positive and negative or vice versa from low to high pre-treatment temperatures. In the third scenario, if colonies exhibited generalist-specialist variation, then the gmax loadings would be opposite between intermediate and extreme pre-treatment temperatures.

To explore how variation in cold resistance traits is related to the local thermal environment at the range margin, we tested for an effect of local temperature MAT on baseline cold resistance and cold hardening capacity. To calculate baseline cold resistance, we transformed CCRT so that higher values indicate greater cold resistance. Specifically, the maximum CCRT value in the entire dataset CCRTmax was treated as a reference level, and each CCRT measurement (CCRTobs) was subtracted from CCRTmax (CCRTmax – CCRTobs) to measure relative cold resistance. To calculate cold hardening capacity, we used the same data transformation and for each colony subtracted the average CCRT at each pre-treatment temperature (−5,0, 5 °C) from the average CCRT at the 25 °C pre-treatment temperature (−5, 0, and 5 °C; CCRT25 °C − CCRTpre-treatment temperature). To detect simple linear and non-linear relationships between local temperature and cold resistance or cold-hardening, we fit a regression model with cold resistance or cold-hardening as the response variable, and a linear and a quadratic term for temperature as the predictor variable. We used AIC model selection to determine whether the quadratic term should be retained or dropped from the final model.

Harvard Forest

324 North Main Street Petersham MA 01366 USA

(978) 724-3302 (978) 724-3595 https://harvardforest.fas.harvard.edu https://ror.org/059cpzx98 pointOfContact The Harvard Forest Long-Term Ecological Research (LTER) program examines ecological dynamics in the New England region resulting from natural disturbances, environmental change, and human impacts. National Science Foundation LTER grants: DEB-8811764, DEB-9411975, DEB-0080592, DEB-0620443, DEB-1237491, DEB-1832210. hf323-01-cold-phys.csv cold physiology hf323-01-cold-phys.csv 22729 8c586f2a5dd9a35f61d8c13131aa3f53 1 \r\n column , https://harvardforest.fas.harvard.edu/data/p32/hf323/hf323-01-cold-phys.csv colony unique colony name unique colony name NA missing value date date collected YYYY-MM-DD 1 day NA missing value state Vermont or Maine, USA Vermont or Maine, USA NA missing value county county in which the colony was collected county in which the colony was collected NA missing value town town in which the colony was collected town in which the colony was collected NA missing value lat latitude degree 0.000001 real NA missing value lon longitude degree 0.000001 real NA missing value altitude.ft altitude foot 1 whole NA missing value temp ground temperature celsius 0.1 real NA missing value wind.speed recorded wind speed at time of collection metersPerSecond 0.1 real NA missing value humidity recorded humidity at time of collection dimensionless 0.1 real NA missing value wind.chill recorded wind chill at time of collection fahrenheit 0.1 real NA missing value heat.stress.index heat stress index at time of collection dimensionless 0.1 real NA missing value barometric.pressure barometric pressure at time of collection atmosphere 0.01 real NA missing value soil.temp soil temperature at time of collection celsius 0.1 real NA missing value canopy.photo reference number for the picture of the canopy at the site of collection reference number for the picture of the canopy at the site of collection NA missing value tree.species types of trees at collection site types of trees at collection site NA missing value understory type of understory at collection site type of understory at collection site NA missing value habitat.photo.number reference for habitat photo at collection point reference for habitat photo at collection point NA missing value nest.substrate type of substrate collected from type of substrate collected from NA missing value ct.max upper thermal limit, time at which ants lose motor function (mean) when ramped at 0.1 degree C per minute celsius 0.001 real NA missing value mat from bioclim database: mean annual temperature celsius 0.1 real NA missing value mdr from bioclim database: mean diurnal temperature celsius 0.1 real NA missing value iso from bioclim database: isothermality dimensionless 0.01 real NA missing value sd from bioclim database: standard deviation (standard deviation * 10) dimensionless 0.01 real NA missing value t.max from bioclim database: thermal maximum celsius 0.1 real NA missing value t.min from bioclim database: thermal minimum celsius 0.1 real NA missing value tar from bioclim database: temperature annual range celsius 0.1 real NA missing value twq from bioclim database: mean temperature at wettest quarter celsius 0.1 real NA missing value tdq from bioclim database: mean temperature at driest quarter celsius 0.1 real NA missing value t.warm.q from bioclim database: mean temperature at warmest quarter celsius 0.1 real NA missing value t.min.q from bioclim database: mean temperature at coldest quarter celsius 0.1 real NA missing value ap from bioclim database: annual precipitation, mean millimeter 1 natural NA missing value pwm from bioclim database: precipitation at wettest month millimeter 1 natural NA missing value pdm from bioclim database: precipitation at driest month millimeter 1 natural NA missing value psd from bioclim database: precipitation standard deviation (coefficient of variation) dimensionless 1 natural NA missing value pwq from bioclim database: precipitation at wettest quarter millimeter 1 natural NA missing value pdq from bioclim database: precipitation at driest quarter millimeter 1 natural NA missing value p.warm.q from bioclim database: precipitation at warmest quarter millimeter 1 natural NA missing value p.min.q from bioclim database: precipitation at coldest quarter millimeter 1 natural NA missing value pretreat.temp pre-treatment temperature celsius 1 real NA missing value pre chill coma recovery time after the pre-treatment temperature in seconds second 0.00000001 real NA missing value treatment.recovery.s chill comma recovery time in seconds second 0.0000001 real NA missing value diff the difference in chill coma recovery time between treatment.recovery.s and pre second 0.00000001 real NA missing value 84 hf323-02-maine-ants-dist.csv Aphaenogaster picea distribution hf323-02-maine-ants-dist.csv 12853 ee3fd6f89f13a64a0a58fb8deef71788 1 \r\n column , https://harvardforest.fas.harvard.edu/data/p32/hf323/hf323-02-maine-ants-dist.csv n reference number for sample reference number for sample NA missing value date date YYYY-MM-DD 1 day NA missing value state Vermont or Maine, USA Vermont or Maine, USA NA missing value county county county NA missing value locality micro site identification micro site identification NA missing value habitat type of area type of area NA missing value lat latitude degree 0.000001 real NA missing value lon longitude degree 0.000001 real NA missing value masl elevation meter 0.001 real NA missing value subfamily subfamily of ant species - Myrmicinae subfamily of ant species - Myrmicinae NA missing value ant.genus genus of ant species – Aphaenogaster genus of ant species – Aphaenogaster NA missing value ant.species species name of ant; picea species name of ant; picea NA missing value code HF code for genus species HF code for genus species NA missing value collection who collected the colony who collected the colony NA missing value collector person who collected the colony who collected the colony NA missing value found.notfound presence-absence data 0 absent 1 present NA missing value 102 hf323-03-range-limits-cold-phys.txt R markdown script hf323-03-range-limits-cold-phys.txt 43734 5a28e13eb3c74ab93fdc9765a5df8192 none R Markdown https://harvardforest.fas.harvard.edu/data/p32/hf323/hf323-03-range-limits-cold-phys.txt script hf323-04-range-limits-cold-phys.pdf R markdown script output hf323-04-range-limits-cold-phys.pdf 799189 e00a60997bc93e1ed2a5e74b4896dfaf none pdf https://harvardforest.fas.harvard.edu/data/p32/hf323/hf323-04-range-limits-cold-phys.pdf document informatics community regional short-term measurement modeling https://harvardforest.fas.harvard.edu/exist/apps/datasets/showData.html?id=hf147