DOI: 10.5061/dryad.j0zpc86s4
title: Body size predicts ant worker longevity: A hierarchical analysis of field and laboratory survival across phylogenetic lineages

description: Lifespan is a key life history trait that shapes ecological strategies and
 colony fitness in social insects, yet its drivers remain unclear. We
 evaluated whether intrinsic traits—especially worker body size, a
 principal pace-of-life axis, and phylogeny—predict survival across 18 ant
 species from five subfamilies monitored in the field and in captivity.
 Mark–recapture data analysed with hierarchical models accounting for
 imperfect detection showed that larger workers lived longer in both
 environments, highlighting intrinsic physiological properties as the
 strongest determinant of longevity. Worker lifespans ranged from 23–394
 days, and field and lab survival were positively correlated. Body mass
 scaled negatively with colony size: small colonies invested in large,
 long-lived workers, whereas large colonies relied on smaller, short-lived
 workers. In captivity, survival was modestly higher for some clades and
 comparable for others, indicating mixed lab–field differences consistent
 with hazard reduction in certain lineages; Dolichoderinae and Myrmicinae
 showed little to no improvement. Phylogenetic signal was weak, indicating
 that lineage imposes little constraint beyond body size. These results
 confirm body size as a strong—and phylogeny as a weak—predictor of worker
 longevity. Laboratory assays capture relative field lifespans despite
 environmental complexity, based on the first comparative survival dataset
 for a local ant assemblage spanning multiple lineages.
description: Data collection We conducted our study at the
 La Trobe Wildlife Sanctuary, a 28-hectare reserve on La Trobe University’s
 Melbourne campus in southeastern Australia (Fig. S1a). The sanctuary has a
 mesic, temperate climate, with mean annual minimum and maximum
 temperatures of 9.7 °C and 20.2 °C, respectively (Bureau of Meteorology,
 1973–2017), and average rainfall of ~656 mm. During sampling, near-ground
 temperatures ranged from 5 °C to 40.5 °C. The site includes revegetated
 and remnant river red gum (<em>Eucalyptus
 camaldulensis</em>⁶²) woodland. Daily temperature and precipitation
 data were obtained from the nearby Bundoora weather station (BOM #086351,
 2024). Ants typically inhabited low-productivity, sparsely vegetated areas
 with open ground, under large rocks or dead red gums. We sampled ants
 across microhabitats and nests, recording activity from 8 am to 7 pm
 throughout the study. Field study design
 <em>Nest sampling</em> We sampled
 18 species from 5 subfamilies and 10 genera (Table S1), covering 39
 colonies. For each colony we selected 2-5 cohorts for survival estimation.
 Each cohort was selected at least one month after the previous selection
 (average 30 days). Multiple cohorts were used to minimize the effects of
 colony maturation. Each cohort consisted of 30–100 worker ants, ensuring
 uniformity in worker size and foraging tempo to minimize the influence of
 polymorphism. In genera with discrete major and minor worker castes genera
 such as <em>Camponotus</em>, only minor workers (typically ≤
 9 mg dry mass) —the primary foragers—were chosen to maintain
 consistency<sup>63, 64</sup>. While this caste-foraging link
 is well-documented in some taxa, we acknowledge that foraging roles may
 vary in other polymorphic species, which could introduce bias.  Although
 this approach excludes other castes (e.g., majors involved in nest
 defence), we focused on foragers to enable cross-species comparisons.
 Cohort sizes reflected typical foraging activity, ranging from ~30–50
 workers in some species to 100–500 in others (Supplementary File 1, Table
 S1). Workers were collected at the nest, ensuring they were active
 foragers. As in fire ants, foragers are typically older; majors begin
 foraging only in the final 25 % of adult life⁶⁶. Our estimates therefore
 reflect forager longevity, not full lifespan from eclosion.
 Field workers were marked for identification as illustrated in
 Figure S1(b), with colours (green, purple, blue) assigned sequentially to
 new cohorts every three months. Laboratory workers were left unmarked
 because survival was recorded by directly counting all live individuals in
 closed containers, and preliminary trials indicated that applying paint
 caused unnecessary stress. To evaluate potential mark loss, we conducted a
 paint‑retention trial on 30 workers (10 each of <em>Papyrius A,
 Camponotus consobrinus</em>, and
 <em>Meranoplus fenestratus</em>) kept under laboratory
 conditions; no paint loss or colour fading was observed over their entire
 laboratory lifespans (≤ 4 months), indicating that mark loss is unlikely
 to bias field estimates. Same-species colonies were intentionally spaced
 (min. 58.5 m; all other minima ≥ 120 m; Fig. S1a; Table S6) to reduce
 between-colony movement during the field window.
 Colonies were categorized by foraging rate: low (&lt;0.5
 workers/min; 30 ants), intermediate (0.5–5; 50 ants), and high (&gt;5;
 100 ants). Within cohorts, individuals were indistinguishable by markings.
 Biweekly 1-hour observations recorded forager counts and marked workers.
 Observation length varied by cohort and ended after 30 consecutive days
 without sightings or at day 400 (right-censored). Colonies that relocated
 were excluded for consistency. <em>Field
 assumptions</em> Ant cohorts not
 observed for a month were considered deceased.
 Workers within marked cohorts were of similar age and were
 mature enough for foraging; cohorts were marked simultaneously to ensure
 age similarity. Individuals within a colony were
 indistinguishable, as each cohort was marked with the same
 color. Each colony was treated as a 'closed
 system' during the observation period, with no emigration or
 immigration, allowing changes in cohort size to be attributed solely to
 mortality or non-detection. *Colony
 closure and spacing<br> *To minimise potential movement among
 same-species colonies during the recapture window, we deliberately spaced
 same-species colonies during site selection. GPS coordinates (Fig. S1a)
 show a minimum within-species inter-colony distance of 58.5 m
 (<em>Camponotus claripes gp. A</em>), with all other minima ≥
 120 m (Table S6). We did not observe cross-marked individuals at
 non-source colonies during field checks. This design reduces the
 likelihood that immigration/emigration among same-species colonies biased
 daily survival (φ) estimates. <em>Ant trait
 measurements</em> We examined how body mass and
 colony size affected survival. For each colony, we measured 10 ants and
 used mean dry mass for analysis. Foraging activity—used to stratify
 cohorts and model survival—was quantified by counting workers joining a
 trail over 60s (Table S1). To estimate colony size for each species, we
 employed Chapman's estimator<sup>67</sup>, a refinement
 of the Lincoln-Petersen estimator, using mark-recapture data collected
 over multiple days of field observations. We selected Chapman’s estimator
 because it corrects for small-sample bias inherent in the classic
 Lincoln–Petersen method under closed-population,
 sampling-without-replacement assumptions<sup>68, 69</sup>.
 Initially, ants were marked on Day 0, and the total number of ants marked
 was recorded. Recapture data were subsequently collected on the following
 days (Days &gt; 0), documenting both the total number of ants observed
 and the number of marked ants recaptured.
 Chapman's estimator was calculated for each observation day
 using the formula: <em>Chapman= (M + 1)(C + 1)/
 (R + 1) - 1</em> where M is the total number of
 initially marked ants, C is the total number of ants observed on a
 recapture day, and R is the number of marked ants recaptured on a
 recapture day. Given our design of single-marking on Day 0 with
 independent daily recaptures, applying Chapman separately to each valid
 day and averaging results mitigates bias from variable and sometimes low
 daily recapture numbers. Recent simulations confirm Chapman’s superior
 bias and variance performance among two-visit estimators across a range of
 sample sizes<sup>70</sup>. We included all observation days
 with ant activity (C &gt; 0), excluding days with no activity (C = 0),
 as valid capture-recapture estimates require at least one observed
 individual. Colonies contained multiple cohorts. We calculated the Chapman
 estimator separately for each cohort using recapture data from active
 days, then averaged cohort estimates to produce a single colony size. This
 approach accounted for daily variation and improved robustness across
 species. Laboratory study design We
 conducted laboratory observations using the same colonies as the field
 study (Figure S1(c)). On the field marking day, one laboratory fragment of
 30 workers was created from each source colony, yielding 38 fragments in
 total (1–3 per species, median = 2; Table S5). All fragments were
 maintained at 25 °C under a reduced light regime simulating nest
 conditions. Lights remained off except during feeding and hydration to
 minimize stress. Each species had two replicates of 30 workers, housed in
 3.75-liter plastic containers (13 × 13 × 9.5 cm) coated with fluon and
 covered with mesh lids. Containers included an egg carton nest on a
 Plaster of Paris™ base⁷¹. Colony fragments were randomly positioned.
 Workers received ad libitum honey–water (50:50), dried insects, and
 saturated cotton for hydration. Laboratory room humidity tracked ambient
 outdoor conditions (Melbourne average daily RH 56–76%; Bureau of
 Meteorology, 1973–2017). Each 3.75 L container provided a humidity
 gradient: ants could occupy drier open areas away from the cotton-wool
 water source or seek more humid microclimates beneath the egg-carton nest.
 Water was replenished every 48 h. While this standardized setup was
 designed to mitigate extreme humidity stress and allow some behavioural
 regulation of moisture exposure, we acknowledge that it may not equally
 match the natural humidity or thermal preferences of all species, even
 though they were all collected from the same local site. Species-level
 differences in microhabitat use may still introduce survival bias (see
 Supplementary Table S1 for summary of nest types and temporal activity
 niches across species). Mortality was recorded daily, with deceased ants
 removed; to account for collection stress, death counts began two days
 after transfer, providing exact estimates of days to mortality for all 30
 workers in each colony fragment. We therefore regard laboratory
 survivorship as an index or bioassay of intrinsic longevity under this
 standardized abiotic regime, not an absolute measure of each species’
 maximal lifespan. <em>Survival
 analyses</em> <em>Natural
 colonies</em>: We employed a hierarchical modeling approach to
 estimate survival of individuals within natural ant colonies while
 controlling for imperfect detection. Survival probabilities, as unitless
 measures, represent the proportion of individuals expected to survive over
 a given interval, enabling approximate comparison across field and
 laboratory conditions while acknowledging differences in colony structure
 (e.g., queen presence) and detection methods. Our approach is a
 modification of the standard N-mixture model<sup>72</sup>,
 following Lyons et al. (2019), which contains two binomial models
 representing the apparent survival of an ant (state process) and the
 ability to detect an individual (observation process).
 State Process:  S<sub>ij</sub> ~
 Binomial(H<em><sub>i</sub></em>,
 Φ<em><sub>i</sub></em><sup>days</sup>) Observation Process: C<sub>ij ~~ Binomial(S</sub>ij<sub>, d</sub>ij~) The number of ants surviving for the <em>i</em>th colony at time j (S<em><sub>ij</sub></em>) was considered as having a binomial probability where the number of trials equates to the initial number of marked individuals (H<em><sub>i</sub></em>) and the probability of success equates to the probability of survival to a given number of days (Φ<em><sub>i</sub></em>)<sup>days,</sup>. The number of marked individuals observed (C<em><sub>ij</sub></em>) at each cohort (<em>i</em>) and at each observation period (<em>j</em>) was also considered a binomial process with the number of trials equating to the true, but unobservable number of individuals surviving for the <em>i</em>th cohort (S<em><sub>ij</sub></em>) and the detection probability (d<sub>ij</sub>) of an ant within the <em>i</em>th cohort during the <em>j</em>th visit. Because we followed multiple cohorts per colony and multiple colonies per species, we modeled species and cohort-within-species as random effects on daily survival. This lets species differ in their average survival while also capturing extra among-cohort variation within species. For each species we summarized the cohort effect size as a standard deviation (reported as cohortsig[i]) and also as an intra-class correlation (ICC), which expresses the share of (logit-scale) variance attributable to cohorts. We used weakly informative uniform (0–2) priors for the random-effect standard deviations. Detection was modeled as described above using the same environmental covariates. Survival probabilities were assumed constant over time and modeled on a daily scale. We investigated differences in survival and detection probability among subfamilies, environmental, and behavioural variables (foraging activity, rain, minimum temperature) using a logit link, where log transformed colony size and mass were included as fixed effects on survival, and foraging activity, rain, and minimum temperature were included as fixed effects describing the detection process. Our approach assumes that worker ants are exchangeable (survival and detection are the same) and not double counted during an observation period, and that survival and detection probability are homogenous among colonies or are accounted for via colony (species, families, etc.) or observation period (temperature, precipitation, etc.) specific covariates.  <em>Laboratory Colonies</em>: We estimated survival (Φ<em><sub>i</sub></em>) of marked individuals using a geometric model, which is a special case of the negative binomial distribution. In our analysis, the number of failures for the <em>i</em>th individual in colony j (C<sub>ij</sub>) refers to the number of days an individual survived.  There is only 1 success event (mortality), and the probability of success (a mortality event) equaled the probability of mortality (1 - Φ<em><sub>i</sub></em>). C~ij ~~ Negative Binomial(1- Φ<em><sub>i</sub></em>, 1) We investigated differences in survival among species using the logit link. Specifically, we included each species as a fixed effect, along with the log transformed estimate of size of the colony they were collected from along with the log transformed estimate of mass. We controlled for multiple colonies within species using a normally distributed random effect. Lab assumptions It is assumed that resources such as food and water are consistently available and provided equally across all laboratory colonies, thus not influencing survival differentially. The absence of a queen or brood has a limited effect on survival, although queenlessness can extend worker lifespan in some species<sup>74, 75</sup>. This represents an important limitation when comparing laboratory and field estimates. <em>Model implementation and statistical framework</em> We produced separate subfamily‐level and species‐level models within a Bayesian hierarchical framework to estimate field and laboratory survival via JAGS (version 4.3.0, Plummer) interfaced with R (version 4.0) through the jagsUI package<sup>77</sup>. Survival and detection probabilities were modeled using a logit link function. Covariates for survival and detection were fitted with vague, normally distributed priors (mean = 0, precision = 0.25) on the logit scale, following Northrup and Gerber (2018). The species random effect was modeled as normally distributed (mean = 0; precision equal to the inverse square of an uninformative uniform distribution on [0, 2]). Cohorts, nested within species, had random effects modeled similarly, with cohort variances drawn from uniform priors. In the lab survival model, colony was treated as a random effect, modeled as normal (mean = 0) with precision from a uniform prior on [0, 2]. Derived estimates of 10‐day survival were calculated for both field and lab settings, as this period adequately captured the uniform decline in survival rates, simplifying analysis and presentation. Derived quantities. In addition to daily and 10-day survival, we reported two cohort-level summaries per species: (i) the cohort random-effect SD (Table S8), and (ii) the cohort intra-class correlation (ICC) on the logit scale,     where is the logistic residual variance (Table S7). We visualized with a forest plot (Fig. S6).Posterior survival estimates and 95% credible intervals were used to assess survival and model coefficients. We ran three parallel MCMC chains of 1,000,000 iterations each, discarding the first 100,000 samples as burn‐in after a 5,000‐iteration adaptation phase. Every 10th sample was retained, yielding 270,000 posterior estimates across chains. Convergence and mixing were confirmed by visual inspection of trace and density plots and via the Gelman–Rubin diagnostic (all R̂ &lt; 1.1). The species‐level model differed from the subfamily‐level model by using identity parameterization for 18 individual species rather than a species random effect, allowing species‐specific survival and detection effects. Derived survival estimates (daily) were calculated separately for each species, providing finer resolution than broader subfamily groupings. The species‐level model used identity parameterization for all 18 species, allowing species-specific survival and detection estimates, while the subfamily model pooled colonies into five taxonomic groups. To quantify information loss from pooling, we computed a two-way, absolute-agreement intraclass correlation coefficient (ICC[A,1]) on posterior mean daily survival values, treating each species as having two ratings: its own estimate and its subfamily mean. Concordance was low (ICC = 0.154, 95% CI = –0.341 to 0.574; F₁₇,₁₇.₂ = 1.35, p = 0.269; package <em>irr</em><sup>79</sup>. Despite this, subfamily pooling was retained to increase sample size tenfold and enable stable estimation of the continuous body mass effect. Including both species-level intercepts and body mass in the same model led to overdispersed posteriors and unstable trace plots. Full species-level survival estimates and 95% credible intervals are provided in Supplementary Table S3. The subfamily model included five subfamily indicators and a continuous body mass effect under the fast–slow life history hypothesis. The species model used identity parameterisation (18 indicator variables) without mass to yield unadjusted species-level estimates for rank testing, phylogenetic analyses, and visualization. Due to strong collinearity (Spearman’s ρ = −0.73) between body mass and colony size, we retained mass alone in final survival models. In field and lab, mass was a continuous logit‑scale predictor entered additively with subfamily indicators (β₁–β₅) and random effects for species and cohorts. The β coefficients received vague normal priors (mean 0, precision 0.25), and posterior samples quantified effect size. Full model details are in Supplementary File 1. <em>Rank order</em> We used a Bayesian framework to evaluate the relationship between lab and field survival at the species level. Specifically, we obtained posterior samples of each species’ survival estimates from a Markov chain Monte Carlo (MCMC) analysis. At each iteration, we fit a linear model of field survival (y) as a function of lab survival (x):<br> <em>Field survivalᵢ ∼ α + β × lab survivalᵢ</em><br> We extracted the slope (β), intercept (α), and R² at each iteration to generate posterior distributions. To test subfamily-level deviations from this relationship, we grouped species-level residuals by subfamily and performed a one-way ANOVA at each iteration, yielding posterior F-statistics and p-values. Analyses were conducted in R (v4.4.2), primarily using base functions and ggplot2. <em>Phylogenetic signal</em> We tested for phylogenetic signal in survival using Blomberg's K⁸⁰, defined relative to a Brownian motion model, which assumes trait variation accumulates with branch length. Although ecological traits may be shaped by adaptation, Brownian motion serves as a null for detecting deviations due to convergence or non-neutral dynamics⁸⁰–⁸². We used a time‐calibrated phylogeny from Economo et al. (2019), retaining one representative per genus. Study species were added as soft polytomies, and trimmed taxa were removed. <em>Chelaner</em>, absent from the original tree, was added as a tip, sister to <em>Monomorium</em>⁸⁴.<br> We extracted MCMC samples of species-level survival from the Bayesian model and calculated Blomberg's K at each iteration to account for uncertainty and obtain posterior distributions. We summarized these distributions by their mean and credible intervals, and used a likelihood ratio test at each iteration to assess departure from zero (no phylogenetic signal), with p-values summarized across iterations.<br> Given the limited power of our 18-species sample—below the recommended minimum of 21 tips (Blomberg et al. 2003)—we used a significance threshold of p &lt; 0.10. The ‘phylosig’ function in the phytools package (v2.3)⁸⁵ was used for all K calculations. We also applied Blomberg’s K to species-mean body mass and to residuals from the field–lab survival relationship to test for phylogenetic structure in each. <em>Exploratory trait–mismatch check</em><br> To evaluate whether species’ traits could explain laboratory–field survival differences, we computed Spearman rank correlations between lab–field contrasts (daily and 10-day derived from daily) and colony size, foraging rate, worker mass, and thermal niche, applying Benjamini–Hochberg correction across tests (n = 18 species). Full outputs are reported in Table S5; these analyses were interpretive only and did not affect the primary model estimates.
description: # Body size predicts ant worker longevity: A hierarchical analysis of
 field and laboratory survival across phylogenetic lineages ## Directory
 structure (root = `DATA/`) ``` DATA.zip/ exports/ cohort_effects/
 TableA_cohortsig_by_species.csv TableA_sigma.csv TableB_ICC_by_species.csv
 figures/ source/ # figure-source CSVs (.csv), listed below models/
 antsSBB.txt antsSBB_fixed.txt antsSPECIES18.txt .RData dic_comparison.csv
 Field_ants_final_3.csv Final_Markdown_LS_Riskas.html
 JUSTMASS_cohort_nodes.rds Lab_ants_final_3.csv mr_phylo.tre
 nest_locations.xlsx README.txt # this file sessionInfo.txt
 species_averages.csv species_posterior_draws.csv ``` ## File inventory
 (exact names) `Field_ants_final_3.csv` — Daily field encounter/survival
 data with covariates (see dictionary). `Lab_ants_final_3.csv` —
 Individual-level laboratory survival (time-to-event) with covariates (see
 dictionary). `mr_phylo.tre` — Newick phylogeny for the 18 focal species.
 `species_posterior_draws.csv` — Posterior draws for species-level
 parameters. `dic_comparison.csv` — Model-selection diagnostic (DIC)
 comparison. `nest_locations.xlsx` — Site/colony location metadata.
 `species_averages.csv` — Species-level trait/summary metadata.
 `JUSTMASS_cohort_nodes.rds` — Archived fit object (subfamily/body-mass
 model with cohort nodes). `.RData` — Convenience workspace image
 (optional). `models/antsSBB.txt`, `models/antsSBB_fixed.txt`,
 `models/antsSPECIES18.txt` — JAGS model code.
 `exports/cohort_effects/TableA_sigma.csv` — Cohort-level σ estimates.
 `exports/cohort_effects/TableA_cohortsig_by_species.csv` — Cohort SD (σ)
 by species. `exports/cohort_effects/TableB_ICC_by_species.csv` —
 Intraclass correlation (ICC) by species. `figures/source/` — figure-source
 CSV files; exact files: `fig1a_surv_daily_field_lab.csv`
 `fig1a_surv10_field_lab.csv` `fig1b_survival_curves.csv`
 `fig2_surv_daily_difference.csv` `fig2_surv10_difference.csv`
 `figS3_points.csv` `figS3_ribbon.csv` `figS3_slope_R2_summary.csv`
 `figS4_size_mass.csv` `figS5_residuals.csv` `figureS2_anova_summary.csv`
 `figureS2_posterior_anova.csv` `figureS2_residuals.csv`
 `table_residual_size_anova.csv` `table_size_mass_lm.csv`
 `table_size_mass_spearman.csv` `tableS_bodymass_effect.csv`
 `Final_Markdown_LS_Riskas.html` — Human-readable knit of the end-to-end
 analysis (see models + Supplement for code details). `sessionInfo.txt` — R
 session details. ## Variable dictionaries ### `Lab_ants_final_3.csv` (18
 columns; individual level) | Column | Type | Units / Allowed values |
 Meaning | | ----------------- | ---- | ---------------------- |
 -------------------------------------------------- | | specimen | int | —
 | Individual lab specimen ID | | colony | chr | e.g., An.E.A5 | Source
 colony | | SF | chr | subfamily | Taxonomic subfamily | | GENUS | chr |
 genus | Taxonomic genus | | SPEC | chr | species | Taxonomic species | |
 dd | int | — | Deprecated | | sp.c | chr | species code | Species code
 used in modeling | | temp.niche | chr | matinal, diurnal, … | Thermal
 niche category | | peak.niche | num | hour | Hour of peak activity | |
 C.colonysize | num | individuals | Estimated colony size (continuous) | |
 nacat | chr | small/medium/large | Deprecated | | logw\.mass | num |
 log10(mg) | Log10 worker mass (base-10; mg) | | time | int | days |
 Time-to-event in days (header is literally `time`) | | event | int | 0/1 |
 Event indicator (1 = death; 0 = censored) | | NULL, NULL2–NULL4 | NA | — |
 Placeholder empty columns | ### `Field_ants_final_3.csv` (40 columns;
 daily colony/cohort level) | Column | Type | Units / Allowed values |
 Meaning | | --------------- | ---- | ---------------------- |
 ------------------------------------------------------------------------------------ | | Date | date | d/m/Y | Calendar date of observation | | colony | chr | e.g., An.E.A5 | Colony identifier | | Day | int | days | Day index since first observation (0-based) | | obs | int | count | Sequential observation number | | NULL1 | NA | — | Placeholder/empty export artifact | | NULL2 | NA | — | Placeholder/empty export artifact | | NULL3 | NA | — | Placeholder/empty export artifact | | NULL4 | NA | — | Placeholder/empty export artifact | | SF | chr | subfamily | Taxonomic subfamily | | GENUS | chr | genus | Taxonomic genus | | SPEC | chr | species | Taxonomic species | | dd | int | — | Deprecated | | ch | chr | A, B, … | Cohort label within species | | sp.c | chr | species code | Modeling key (species) | | sp.c.ch | chr | species+cohort code | Hierarchical key (species × cohort) | | peak.niche | num | hour | Hour of peak activity | | C.colonysize | num | individuals | Estimated colony size (continuous) | | temp.niche | chr | matinal, diurnal, … | Thermal niche category | | ncat/nacat | chr | small/medium/large | Colony size category (binned) | | total | int | individuals | Number marked | | forage | num | count | Total foraging (sum count; see Methods) | | forage.rate | num | 0–1 | Scaled foraging rate | | prob | num | — | Deprecated (not used) | | event\_wd | int | 0/1 | Event indicator used for Kaplan–Meier survival summaries (see KM section) | | event\_fu | int | 0/1 | Follow-up/censor indicator used for Kaplan–Meier survival summaries (see KM section) | | nest | chr | e.g., site/colony code | Nest identifier (site/colony nest code) | | result\_day | int | — | Deprecated | | total\_days | int | days | Days observed for colony/cohort | | prop.days | num | — | Deprecated | | mortality\_rate | num | — | Deprecated (diagnostic only) | | av.wmass | num | mg | Average worker mass | | logw\.mass | num | log10(mg) | Log10 worker mass (base-10; mg) | | mint | num | °C | Daily minimum temperature | | maxt | num | °C | Daily maximum temperature | | precip.mm. | num | mm | Daily precipitation | | avgt | num | °C | Daily mean temperature | | old\_f | num | — | Deprecated | | avg\_prob | num | — | Deprecated | | X | int | — | Export artifact | | X.1 | int | — | Export artifact | | | | | | Glossary — non-obvious variables in figure & table sources Uncertainty bands / intervals Q2.5, Q97.5 — 2.5th and 97.5th percentiles (i.e., 95% uncertainty interval). Method basis (credible vs bootstrap CI) per Methods. CI_lo, CI_hi — Lower/upper bounds of a 95% uncertainty interval (same interpretive note as above). y_lower, y_upper, y_median — Ribbon bounds and central line for fitted relationships (95% interval around the fit). Survival summaries SurvDaily — Estimated one-day survival probability (per-day survival). Surv10 — Estimated 10-day survival probability. Diff, Diff10 — Difference between environments defined as Field − Lab for daily and 10-day survival, respectively (positive ⇒ field higher). Field vs lab species points & regression FieldMean, LabMean — Species-level mean survival in Field/Lab used in cross-environment comparisons (same scale as SurvDaily/Surv10). Slope — Regression slope of LabMean on FieldMean (species-level relationship). R2 — Coefficient of determination for that regression. ANOVA / model summaries F, F_stat, statistic — F-statistic from ANOVA or model fit (naming varies by file). p, p.value, p_value — p-value (naming varies by file). Mean, Median — Point summaries of the target statistic (e.g., F, slope). term, df, sumsq, meansq — ANOVA table fields: factor name, degrees of freedom, sums of squares, mean square. Num_df, Den_df — Numerator and denominator degrees of freedom for F-tests. Correlations Spearman_rho (Statistic), Estimate — Spearman rank correlation coefficient (ρ) and its value; n = sample size. Residual diagnostics Residual — Model residual used for diagnostics (either from size–mass regression or posterior residuals for species; units follow the modeled response). Iteration — Posterior/bootstrapped sample index associated with a residual or statistic (used in posterior residual plots). Size–mass scaling (naming harmonization) logw.mass and WorkerMass_log10 — Both denote log10 worker mass (mg), retained under two names for plotting compatibility. ColonySize_log10 — log10 colony size. avg_prob — Legacy field retained for provenance; deprecated (not used). Taxonomy: `SF`, `GENUS`, `SPEC` follow the manuscript nomenclature. Composite keys: `sp.c` (species) and `sp.c.ch` (species+cohort). Thermal niche: `temp.niche` categorical; `peak.niche` hour of day. Colony size: `C.colonysize` continuous; `nacat` binned category. Missing-data policy Not available: intended measurement but unavailable → `NA`. Not applicable / deprecated: columns flagged “Deprecated” are not used analytically; may be `NA`. Censoring (lab): encoded via `event = 0` with finite `time` (not `NA`). --- ## Reproducibility Software: R 4.3+, JAGS 4.3.2. JAGS model code is in `models/`. Figure-source CSVs are in `figures/source/`. The human-readable pipeline summary is `Final_Markdown_LS_Riskas.html`; wrangling/model steps are also documented in the manuscript Supplement. Suggested R packages r install.packages(c( "tidyverse","data.table","reshape2","doBy","jagsUI", "RColorBrewer","ggrepel","ape","phytools","geiger", "coda","MCMCglmm","caper","phylolm","irr","ggplot2" )) Typical compute: 4 cores; ≥ 8 GB RAM.