DOI: 10.5061/dryad.j9kd51cm3
title: Data from: Defining the pyro-thermal niche: do seed traits, ecosystem type and phylogeny influence thermal thresholds in seeds with physical dormancy

description: Seeds are a key pathway for plant population recovery following
 disturbance. To prevent germination during unsuitable conditions, most
 species produce dormant seeds. In fire-prone regions, physical dormancy
 (PY) enables seeds to germinate after fire. The thermal niche,
 incorporating seed dormancy and mortality temperature responses, has not
 been characterised for PY seeds from fire prone environments. We aimed to
 assess variation in thermal thresholds between species with PY seeds and
 if the pyro-thermal niche is aligned with seed mass, ecosystem type or
 phylogenetic relatedness. We collected post heat-shock germination data
 for 58 Australian species that produce PY seeds. We applied
 species-specific thermal performance curves to define three critical
 thresholds (DRT50, dormancy release temperature; Topt, optimum dormancy
 release temperature and LT50, lethal temperature), defining the
 pyro-thermal niche. Each species was assigned a mean seed weight and
 ecosystem type. We constructed a phylogeny to account for species
 relatedness and calculated phylogenetic signal (h2) for LT50, Topt, and
 DRT50. Seeds of Pomaderris (Rhamnaceae) had the highest Topt and
 LT50, and Pomaderris bodalla having the highest DRT50 of 101.3°C. Seeds
 from species within this family exhibited higher temperature thresholds
 than those from Fabaceae. Seed mass was only influential in explaining
 LT50 variation.
description: <em>Species selection and data acquisition.
 </em> We set out to acquire seed germination data
 following heat-shock for as many species as possible across temperate
 Australia. However, to provide accurate estimates of threshold conditions,
 we applied three rules that allowed data from multiple sources to be
 brought together; 1) seeds needed to be heated from ~40°C through to at
 least 120 °C (or higher if there was no evidence of seed mortality at
 120°C),  2) seeds had to be heated for either 5 mins and/or 10mins and 3)
 there was &gt; two non-zero germination data points within the
 treatments (e.g., seeds treated at 40 °C, 60 °C, 80 °C, 100 °C and 120 °C,
 but only recorded ~ 0%  germination at 80 and 100 would be discarded).
 Where there was insufficient data points to model the response, we removed
 these datasets were removed, as fitting response curves to two data points
 induces significant error into hypothetical responses. However, this was a
 rare occurrence, and only appeared once across all sampled datasets. Using
 these rules, we were able to acquire seed germination post heat-shock data
 for 58 species, gathered from either previously published articles (namely
 Auld and O'Connell (1991), Palmer et al. (2018) and Chen et al.
 (2022), Figure 2), or from newly acquired data. Protocols for heat-shock
 and subsequent germination trials for each data set are outlined in Auld
 and O'Connell (1991), Palmer et al. (2018) and Chen et al. (2022),
 heat-shock and germination methods within these publications are like
 those presented below. Raw data from Auld and O’Connell (1991) and Chen
 (2022) was provided by authors, while data from Palmer et al. 2018 was
 extracted directly from published figures using the package
 <em>metadigitise</em> ver 1.0.1<em> </em>(Pick et
 al. 2019), within the statistical platform R version 4.2.2 (R Core Team,
 2023). Due to the nature of the data presented in Palmer et al. (2018),
 only the species-specific means and standard errors for each
 species/treatment combination was able to be integrated. All germination
 data is provided in Table S1. Newly acquired data was
 generated using similar methodologies to Auld and O'Connell (1991)
 and Palmer et al. (2018) but differed slightly depending on whether the
 species and seeds were from Western Australia (WA) or New South Wales
 (NSW; Table S2). Fresh seeds from an additional 19 WA
 species were selected as a supplement the dataset. Seed fill was indicated
 by a full, intact embryo and for WA seeds, viability of all seeds was
 checked with X-ray (MX-20 Cabinet X-ray Unit, Faxitron, Wheeling, USA).
 Unfilled seeds were discarded. Viable seeds were exposed to heat pulses
 between 40°C - 120 °C at 10°C increments for 5 and/or 10 min (Table S2).
 Seeds were exposed to 140 °C if insufficient seed mortality was recorded
 at 120 °C (i.e., &gt;50% survival) and seeds were available. To ensure
 a uniform and consistent heating environment between replications of
 heating, all seeds were heated inside stainless-steel mesh bags (2‐μm
 diameter mesh holes) and the bags were inserted into a bed of dry,
 bleached white silica sand that had been preheated to the required
 temperature in a laboratory oven (WA oven, Contherm, Korokoro, New
 Zealand; NSW Oven, D170fs, Steridium, Queensland, Australia). Each of the
 replicates were heat‐treated separately and sand temperature was monitored
 independently of the oven settings using a K‐type thermocouple attached to
 a digital thermometer (Model TX-1004X-SP, ThermoWorks, Utah, USA). After
 heat treatment, the seeds were quickly removed from the bags to accelerate
 cooling at room temperature and humidity. Following heat treatment, seed
 viability status was assessed via germination testing. For WA seeds, seeds
 were surface sterilised using 3% calcium hypochlorite (CaOCl2) for 20 min
 under vacuum, and each replicate was plated on a Petri dish containing
 0.7% (w/v) water agar. Dishes were incubated at 15°C under a 12/12 hr
 light/dark regime (30 μM m−2 s−1, 400–700 nM, Model 620RHS Growth Chamber,
 Contherm Scientific, Wellington, New Zealand). This temperature regime was
 chosen to match seasonal germination requirements for seeds from
 southwestern Australia (Merritt et al. 2007). Only four
 additional NSW species were used to acquire new data. For these, seed fill
 was assessed via a cut-test, whereby a firm, fully intact embryo was
 considered ‘filled’. Following heat-shock, each replicate was plated on
 moistened filter paper in a petri dish, and wetted regularly using reverse
 osmosis water to ensure seeds did not dry out during germination (except
 for <em>Acacia melanoxylon </em>which was plated on 0.7 % w/v
 water agar). Dishes were incubated at alternating 11-25°C temperature
 regime under a 12/12 hr light/dark regime (30 μM m−2 s−1, 400–700 nM).
 This temperature regime was chosen to match seasonal germination
 requirements for species from the Sydney basin region (Mackenzie et al.
 2016). For both experiments, seed germination was recorded for a minimum
 of six weeks, or until no further germination was recorded for at least
 two weeks. At the end of the germination period, total germination for
 each replicate was recorded. For NSW seeds, post-germination viability was
 checked using a cut test (Ooi et al. 2004).
 <em>Phylogenetic relationships and tree
 development</em>. As we assessed species with
 differing degrees of relatedness, we wanted to test if this influenced the
 arrangement and patterning in the pyro-thermal niche amongst species. To
 achieve this, we assembled a hypothetical phylogenetic tree using data
 from published papers (Lavin et al. 2005, Boatwright et al. 2008, Bruneau
 et al. 2008, Murphy et al. 2010, Miller et al. 2011, Särkinen et al. 2012,
 Davies et al. 2013, Nge et al. 2021, Zhao et al. 2021) and built the tree
 using TreeGraph2.15.0-887 (Stöver and Müller 2010). Where divergence times
 were uncertain or not readily available for a target species, we used
 published tree topology and species arrangements, along with estimated
 median divergence times. This was based on comparisons between the target
 species and known outgroups using TimeTree version 5 (TToL5, (Kumar et al.
 2022). For species without sequence data or phylogenetic information,
 divergence times were assigned to be equal with the divergence length of
 the node, indicating separation from sister taxa. If we lacked resolution
 to distinguish divergence times within clades, resulting in a polytomy, we
 separated taxa by allocating a divergence time of 1 million years
 (approximately equal to the minimum known divergence time) to each
 subsequent closest related species. Divergence times were trimmed to a
 common time ~ 131 MYA from root to tip, before divergence times were
 rounded using “force.ultrametric” in  <em>phytools</em> ver
 1.9-10  (Revell 2012), implementing the “nnls” method from the package
 <em>phangorn</em> ver 2.11.1 (Schliep 2010) which computes the
 edge lengths that result in a minimized sum-of-squares distance between
 the patristic distance of the output and input trees. The resulting tree
 is ultrametric, which was subsequently scaled to unit length (Hadfield and
 Nakagawa 2010). Phylogenetic trees and the covariates were visualised
 using ggtree ver 3.10.1 (Yu et al. 2017).
 <em>Data analysis </em> All
 analysis was conducted within the statistical platform R version 4.2.2 (R
 Core Team, 2023). Raw data from Auld and O’Connell (1991) and Chen et al
 2022 were combined with mean and standard errors of data reported in
 Palmer et al. (2018). These were combined with the newly acquired data to
 form a dataset of 58 species (Figure 2A), of which 28 species had been
 subject to both 5 and 10-minute heating durations, 23 subject to only
 10-minute heating duration and only 7 species were subjected to 5-minute
 exposures only. Figure 2: Visual depiction of analytic
 pipeline. A) Data sources of germination data B) Generating thermal
 performance curves for each species using rTPC. C) Extracting seed traits
 from AusTraits and combining them with ecosystem types D) Implementing
 MCMCGLMM models that integrate phylogenies as random effects.
 Total germination (final germination proportion at the end of the
 germination trial) at each  heat treatment was used to build
 species-specific thermal performance curves (TPCs) using the
 <em>rTPC</em> package ver 1.0-2 (Padfield et al., 2021). TPCs
 were used to define the species-specific T<sub>opt</sub>,
 DRT<sub>50</sub> and LT<sub>50</sub> (Figure
 2B).  To determine the most suitable model for each
 species-specific response, we applied a limited suite of non-linear (nls)
 models. The nls models used are described in Johnson and Lewin 1946,
 O’Neill et al. 1972, Lynch and Gabriel 1987, and Angilletta Jr 2006. We
 applied all the nls models as a set to the germination data for each
 species and fitted curves to each species independently. Curve fitting
 suitability was assessed using Akaike information criterion (AIC;
 Wagenmakers and Farrell, 2004; Bedrick and Tsai 1994), with the lowest
 ranking AIC model chosen for each species to provide the best fit. Model
 fit was further assessed visually, and obvious visual errors were used to
 remove spurious overfitted models (See data availability statement). The
 most suitable model was reconstructed using the
 <em>minpack.lm</em> ver 1.2-3 package (Elzhov et al. 2016),
 which provides functionality for bootstrapping within the
 <em>car</em> ver 3.1-2 package (Fox et al. 2012). Residual
 bootstrapping was undertaken for 100 iterations for each species-specific
 model to create 95% confidence intervals around modelled parameters and
 the mean DRT<sub>50</sub>,<sub>
 </sub>T<sub>opt</sub>, and LT<sub>50
 </sub>(Supplementary Data S2). We implemented residual bootstrapping
 to ensure predictor variables remain fixed during resampling in this
 method (Davison and Hinkley 1997), which is consistent with design of the
 germination data. To assess how the
 T<sub>opt</sub>, DRT<sub>50</sub> and
 LT<sub>50</sub> varied between species and ecosystems we
 compiled a simple set of co-variables, specifically seed mass and
 ecosystem type (Figure 2C). Seed mass was extracted from the AusTraits
 database (Falster et al. 2021). In cases where there were multiple records
 for seed mass for a single species, we calculated the mean seed mass and
 standard error for each species. Where there was no data in AusTraits and
 we had seed available, we calculated seed mass by weighing 10 replicates
 of 100 seeds. All seed masses are presented as single seed mass (mg).
 Ecosystem type was assigned based on the seed collections outlined in Auld
 and O'Connell (1991), Palmer et al. (2018) and Chen et al (2022).
 Newly acquired seed germination data was assigned an ecosystem type based
 on seed collection locations for that seed lot (Supplementary Data S2).
 Ecosystem types were restricted to high level categorisation, with three
 levels: Forest, Woodland, or Heath. To assess whether
 the metrics of the pyro-thermal niche correlate with seed mass and
 ecosystem type, we developed phylogenetic mixed models using Markov Chain
 Monte-Carlo generalized linear mixed models (MCMCglmm) within a Bayesian
 framework, inside the <em>MCMCglmm</em> package (Hadfield
 2010). We tested each pyro-thermal niche metrics (i.e.,
 DRT<sub>50</sub>,<sub>
 </sub>T<sub>opt</sub>, and LT<sub>50</sub>)
 against seed mass and ecosystem type while controlling for phylogeny
 (Figure 2D). Once we had defined models for
 DRT<sub>50</sub>,<sub>
 </sub>T<sub>opt</sub>, and LT<sub>50
 </sub>for each species, we compiled co-variables, seed mass and
 ecosystem type, with phylogeny included as a random factor. To reduce the
 chance of autocorrelation, we ran 1,000,000 iterations with a burn-in of
 1000 and thinning of 10. Effective sample size was ~99,900. Model
 diagnostics were assessed using the ‘plot.MCMCglmm’ function, with
 autocorrelation between iterations and overall model convergence visually
 assessed using trace plots, and the posterior density of each parameter
 assessed using density plots. To assess whether
 pyro-thermal niche metrics were influenced by seed mass, we used the
 MCMCglmm models with a residual covariance structure included to associate
 each data point with a unique residual (Hadfield and Nakagawa 2010). To
 assess whether ecosystem type in isolation or through an interaction with
 seed mass shaped the pyro-thermal niche, we used the same approach but
 included ecosystem type into the model as a secondary covariable,
 specifically interacting with seed mass. Ecosystem Type was restricted to
 a 3-level factor. Seed mass was log transformed in all MCMC models. Again,
 we included a residual covariance structure to associate each data point
 with a unique residual. In both cases we included an inverse phylogenetic
 relatedness matrix as a random continuous variable. Posterior means
 (pmeans) are reported along with lower and upper 95 % credible intervals
 (LCI and UCI) in every instance and a partial MCMC value (pMCMC) was
 reported only where pMCMC ≤ 0.05. For each model we also report residual
 variance (ςe).  To assess how the metrics of the
 pyro-thermal niche are aligned with phylogenetic relatedness, we report
 phylogenetic signal (via Lynch’s phylogenetic heritability
 h<sup>2</sup>: Housworth et al. (2004)). Heritability
 (h<sup>2</sup>) of a trait can be calculated by examining the
 proportion of the variance in the trait that is explained by the
 relationship among taxa given the phylogeny, and is defined by Equation 1
 (Housworth et al. 2004), which compares the phylogenetic variance
  with the variance explained by the residual variance or
 environmental variance  (Housworth et al. 2004, de Villemereuil
 and Nakagawa 2014). As h<sup>2</sup> is a ratio of
 phylogenetic variance and environmental variance, values of
 h<sup>2</sup> occur exclusively between zero and one, with
 values close to zero indicating trait expression in the absence of
 phylogenetic structure, while values close to one indicate complete
 phylogenetic structuring in line with Brownian motion evolution (Pearse et
 al. 2023). For each pyro-thermal metric we report the estimated posterior
 mean for h<sup>2</sup> along with lower and upper 95 %
 credible intervals (LCI and UCI). 
                                                                                       Equation 1 h^2= σ_p^2/(σ_p^2+σ_e^2)
description: # Data from: Defining the pyro-thermal niche: do seed traits, ecosystem
 type and phylogeny influence thermal thresholds in seeds with physical
 dormancy Change log: 25/02/2025 Data files and phylogeny files were
 updated to align with recently accepted article. Changes updated values
 based on peer reviewed comments but not metadata was changed. Phylogeny
 was updated to capture recently published topology within certain genus
 arrangements effecting 6 genus in total. the full script and original data
 generated for this paper is available with the original article. As such,
 we have removed the redundant upload of the .html file here as well. We
 have also removed all .png outputs as these are now embedded within the
 output file linked directly with the original article as supplementary
 notes. Data_Long.csv, New_dataD50.csv, New_dataT50.csv, New_dataTopt.csv,
 have been removed due to not being used in the supplied script. This
 dataset provides the full data requirements to implement the
 "pyrothermal niche" markdown file in R. It also includes model
 fitting figures for each of the species used in the analysis.
 **Description of the data and file structure** The script provided calls
 upon each of the included .csv files and the phylogenetic tree. The bulk
 of the csv files are subsets and cuts of the two main datasets,
 germination data and master data.csv. The figures included here are
 directly associated with the species-specific model selection undertaken
 in the manuscript. Each figure indicates the model fit of the 7 models
 used and included suitability metrics for each model. For further
 information please see the R.markdown file included.
 **GermBook-working1.csv** – This sheet provides the full germination data
 for all species in the analysis. Species – full species name. Temp –
 temperature at which seeds were exposed (°C) Speccode – shorthand code for
 each species. Rate – germination proportion at each treatment temperature
 (%). Dur – duration of exposure (mins). **SeedData_Master.csv** – full
 data table for all species and duration used in this analysis- point of
 truth for all predicted thresholds. Species – full species name Dur –
 duration of exposure. tOPT – predicted optimum breaking temperature (°C).
 tOPTlower - lower 95% CI of predicted optimum breaking temperature (°C).  
 tOPTupper - upper 95% CI of predicted optimum breaking temperature (°C).
 D50 -  predicted dormancy release temperature (°C). D50lower -  lower 95%
 CI of predicted dormancy release temperature (°C).   D50upper -  upper 95%
 CI of predicted dormancy release temperature (°C).   T50 -  predicted
 lethal temperature (°C). T50lower -   lower 95% CI of predicted lethal
 temperature (°C). T50upper -   upper 95% CI of predicted lethal
 temperature (°C). SeedMass - mean seed mass (mg) Phylo - label to link
 with phylogeny. FR – fire response, either fire killed or resprouter
 BothExposure - indicates whether this species has been exposed to only 5
 min duration, only 10 min duration or Both duration. Family - plant family
 VegType – vegetation type where the seeds are collected from.
 **SeedData_Master_comb.csv** – a subset of SeedData_Master.csv, where only
 thermal metrics aligning with 10min exposures are included. Or where
 species did not have a record of 10min exposure, the 5 min exposure
 metrics are included. ** **All column headers are defined the same as
 SeedData_Master.csv. **SeedMasSet_NumComb.csv** – simple subset of
 SeedData_Master_comb.csv to only include species phylo and metric
 predictions (tOPT, D50 and T50) used for linking metrics to phylogenies.
 All column headers are defined the same as SeedData_Master.csv. **FF.csv,
 FF3.csv, FFSM.csv and DurLabs.csv** – simple subsets of
 SeedData_Master_comb.csv along with predicted rate data for the creation
 of and labels for figure 7. Param – pyro-thermal niche metric. Temp –
 temperature (°C). Rate – predicted germination proportion (%). Species –
 species name. Dur – duration. **AA_params.csv, AP_params.csv,
 BE_params.csv. GT_params.csv, GT_params5mins.csv and SM_params.csv** are
 all used in the creation of figure 7. Param – pyro-thermal niche metric.
 estimate – estimated temperature (°C) for this pyro-thermal niche metric
 2.5% - estimated lower bound of confidence interval in temperature (°C)
 for this pyro-thermal niche metric 97.5% - estimated upper bound of
 confidence interval in temperature (°C) for this pyro-thermal niche metric
 method - method used for bootstrapping **D50dataPreds.csv,
 T50dataPreds.csv and ToptdataPreds.csv** are all data frames containing
 predictions from MCMC_glmm outputs.   Dur – see above .         SeedMass -
 see above.  VegType – vegetation type where the seeds are collected from.
 D50 – see above. T50 – see above. Topt- see above. Phylo – see above.
 PredictedValue.fit   - predicted value (°C). PredictedValue.lwr  - lower
 95% CI of predicted value (°C). PredictedValue.upr - upper 95% CI of
 predicted value (°C). **VegType.csv** is a combined data-frame containing
 all predicted values from D50dataPreds.csv, T50dataPreds.csv and
 ToptdataPreds.csv dur    - see above. Value   - value originally modeled
 from nls models (°C). SeedMass   - see above. phylo   - see above.
 VegType  - see above. fit  - modeled value from vegtype MCMC glmm (°C)
 lwr  - lower 95% CI of modeled value (°C) upr  - upper 95% CI of modeled
 value (°C) Metric – specific pyro thermal niche metric of interest
 **Code/Software** The main script in included on the main article
 supplementary files, provided as a PDF. It is fully compiled to re-conduct
 the analysis as is included in the manuscript, aside from the
 species-specific model selection which requires slight alterations to the
 code for each species. Phylogenetic tree included here. **Missing data**
  Missing data is indicated by "null" in cells.
 **WorkingTreeV18.tre** WorkingTreeV15 is the hypothesized phyolgenetic
 tree created for this analysis. It is comprised of composite trees
 combined using TreeGraph2 and visualized in R using ggtree and phytools.
 *.tre* file types can be read using many standalone software packages
 including MEGA and TreeGraph2 but also can be read and edited in R using
 phytools and ggtree.