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opencc-by-4.0Jan 2021View details →

Data from: Chrysolaena obovata, A SPECIES NATIVE OF BRAZILIAN CERRADO: GENETIC DIVERSITY AND STRUCTURE OF NATURAL POPULATIONS AND POTENTIAL FOR INULIN PRODUCTION

Chrysolaena obovata (Less.) M. Dematteis, an herbaceous Asteraceae species widely distributed across different Brazilian Cerrado physiognomies, has underground organs, named rhizophores, that accumulate high concentrations of inulin-type fructans. These carbohydrates are recognized as beneficial soluble fibers for human health and are currently used in the food and pharmaceutical industries. Considering that fructans, in addition to their economic potential, provide plants with greater tolerance to drought, heat and cold, it is important to understand whether their metabolism is conserved in natural populations. In this work, we aimed to investigate if the levels of genetic diversity in the populations studied allow the selection of localities with a high genetic base and higher fructan content for future programs of in situ conservation and genetic improvement for inulin production. Therefore, we characterized the diversity, structure, and gene flow of seven natural populations from Brazilian Cerrado, using nine microsatellite loci (SSR). In addition, we compared whether the fructan composition varied between populations of different Cerrado phytophysiognomies. Overall, we found that C. obovata populations exhibited moderate levels of genetic diversity, low genetic differentiation, and high gene flow. This study identified two populations with less genetic diversity and therefore, greater attention should be given to conservation programs including these populations. Fructan metabolism is conserved in all populations, indicating that C. obovata is an important genetic resource with high potential for inulin production. File descriptions Population_code.txt - Contains a matrix that indicates the population_code, Population_name, Brazilian-state, Phytophysiognomy, Collection coordinates and Altitudes (m). Date_ Diaz et al.xlsx – Contains Genotypes crude of the individuals analyzed. Primer used for nine microsatellite loci (Camacho et al 2017).  Carbohydrates_Diaz et al  - Contains data for carbohydrates in C. obovata plant rhizophores in each population (BRA, UB, SD, SP). Location: Brazilian Cerrado

opencc-by-4.0Feb 2022View details →

Data and code for "Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago"

This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022). The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review). The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc). The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x). The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data. The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.

opencc-zeroFeb 2022View details →

Data from: The evolution of thermal performance in native and invasive populations of Mimulus guttatus

The rise of globalization has spread organisms beyond their natural range, allowing further opportunity for species to adapt to novel environments and potentially become invaders. Yet, the role of thermal niche evolution in promoting the success of invasive species remains poorly understood. Here, we use thermal performance curves (TPCs) to test hypotheses about thermal adaptation during the invasion process. First, we tested the hypothesis that if species largely conserve their thermal niche in the introduced range, invasive populations may not evolve distinct TPCs relative to native populations, against the alternative hypothesis that thermal niche and therefore TPC evolution has occurred in the invasive range. Second, we tested the hypothesis that clines of TPC parameters are shallower or absent in the invasive range, against the alternative hypothesis that with sufficient time, standing genetic variation, and temperature-mediated selection, invasive populations would re-establish clines found in the native range in response to temperature gradients. To test these hypotheses, we built TPCs for 18 native (United States) and 13 invasive (United Kingdom) populations of the yellow monkeyflower, Mimulus guttatus. We grew clones of multiple genotypes per population at six temperature regimes in growth chambers. We found that invasive populations have not evolved different thermal optima or performance breadths, providing evidence for evolutionary stasis of thermal performance between the native and invasive ranges after over 200 years post-introduction. Thermal optimum increased with mean annual temperature in the native range, indicating some adaptive differentiation among native populations which was absent in the invasive range. Further, native and invasive populations did not exhibit adaptive clines in thermal performance breadth with latitude or temperature seasonality. These findings suggest that thermal performance curves remained unaltered post-invasion, and that invasion may proceed via broad thermal tolerance and establishment in already climatically suitable areas rather than rapid evolution upon introduction.

opencc-by-4.0Feb 2022View details →

Obesity reshapes the microbial population structure along the gut-liver-lung axis in mice

Data repository for the paper: Galaris A., Fanidis D. et al. Obesity reshapes the microbial population structure along the gut-liver-lung axis in mice. 2021 For further data requests and questions please contact the corresponding author of the respective publication. All fastq files have been processed to remove human and mouse sequences.

opencc-by-4.0Feb 2022View details →

Synthetic populations and trajectories for sdB stars ejected from the single degenerate helium donor channel for thermonuclear supernovae

This repository is a supplement to a journal paper (Neunteufel+ 2022) and contains the synthetic populations of sdB stars and sdB remnants ejected from the single degenerate helium donor channel for thermonuclear supernovae. See Neunteufel+ 2021 and Neunteufel+ 2022 for simulation parameters.  Synthetic populations, including initial and final positions, are contained in the MXX-out.tr (XX=10..15) files where XX is the mass of the WD companion divided by 0.1 solar masses. Each population is a snapshot of stars ejected at the end of a 300 Myr period. (Note that stellar lifetimes are not taken into account here. See Neunteufel+ 2022 on how stellar lifetimes should be truncated in order to produce realistic populations.) Columns: Zeroth column (ID) is the ID of the trajectory. These are assigned consecutively. First column (unnamed) indicates initial (0) and final (1) positions. Third column (time) is the time since ejection in Myrs. Note that stars further down in the list were ejected earlier. Fourth to Ninth columns (vx, y, vy etc..) are velocity (km/s) and position (kpc) in Gal. Carthesian coordinates (velocity first, position second) Tenth to Twelfth column are accelerations in Gal. carthesian coordinates  Thirteenth column (v_space) is the total galactocentric space velocity (km/s)  Fourteenth column (Phi) is the local Gal. potential according to Model 1 presented by Irrgang+2013 Fifteenth column (E_kin/E_pot) is the local kinetic energy of the object divided by its potential energy with respect to the Gal. potential. Sixteenth column (rho) is the local Gal. baryon density according to Model 1 presented by Irrgang+2013 Seventeenth column (label_c) is the mass of the ejected sdB star or sdB remnant in solar masses.

Human Population-Averaged dMRI Templates (FIB Files, NIFTI Files)

The templates were constructed by DSI Studio using q-space diffeomorphic reconstruction. Methods and Data Source: https://brain.labsolver.org/hcp_template.html

Data from: Environmental drivers of population-level variation in the migratory and diving ontogeny of an Arctic top predator

The development of migratory strategies that enable juveniles to survive to recruitment is critical for species that exploit seasonal niches. For animals that forage via breath-hold diving this requires a combination of both physiological and foraging skill development. Here, we assess how migratory and dive behaviour develop over the first months of life for a migratory Arctic top predator, the harp seal, tracked using animal-borne satellite relay data loggers. We reveal similarities in migratory movements and differences in diving behaviour between juveniles from breeding populations in the Northwest Atlantic and Greenland Sea. In both regions, periods of resident and transient behaviour during migration were associated with proxies for food availability; sea ice concentration and water depth. However, while ontogenetic development of dive behaviour was similar for both groups of juveniles over the first 25 days, after this time Greenland Sea animals performed shorter and shallower dives and were more closely associated with sea ice than Northwest Atlantic animals. Together, these results highlight the role of both intrinsic and extrinsic factors in shaping early-life behaviour. Differences in the environmental conditions experienced during early-life may shape how populations respond to the rapid changes occurring in the Arctic ocean ecosystem.

Stellar Population Spectra from Bruzual & Charlot (2003)

Models from <a href="http://adsabs.harvard.edu/abs/2003MNRAS.344.1000B">Bruzual & Charlot (2003; MNRAS; 344; 1000)</a>. Data was downloaded from <a href="http://www2.iap.fr/users/charlot/bc2003/">http://www2.iap.fr/users/charlot/bc2003/</a> <ul> <li> SSP_Spectra_BC2003_highResolution_imfChabrier.hdf5 <ul> <li> <a href="http://adsabs.harvard.edu/abs/2001ApJ...554.1274C">Chabrier IMF</a> </li> <li> High resolution spectra </li> </ul> </li> <li> SSP_Spectra_BC2003_highResolution_imfSalpeter.hdf5 <ul> <li> <a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a> </li> <li> High resolution spectra </li> </ul> </li> <li> SSP_Spectra_BC2003_lowResolution_imfChabrier.hdf5 <ul> <li> <a href="http://adsabs.harvard.edu/abs/2001ApJ...554.1274C">Chabrier IMF</a> </li> <li> Low resolution spectra </li> </ul> </li> <li> SSP_Spectra_BC2003_lowResolution_imfSalpeter.hdf5 <ul> <li> <a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a> </li> <li> Low resolution spectra </li> </ul> </li> </ul>

Stellar Population Spectra from BPASS v2.0

The BPASS models of <a href="http://adsabs.harvard.edu/abs/2012MNRAS.419..479E">Eldridge & Stanway (2012, MNRAS, 419, 479)</a>; <a href="http://adsabs.harvard.edu/abs/2011MNRAS.414.3501E">Eldridge, Langer & Tout (2011, MNRAS, 414, 3501)</a>; <a href="http://adsabs.harvard.edu/abs/2009MNRAS.400.1019E">Eldridge & Stanway (2009, MNRAS, 400, 1019)</a>; <a href="http://adsabs.harvard.edu/abs/2008MNRAS.384.1109E">Eldridge, Izzard & Tout (2008, MNRAS, 384, 1109)</a>. Data was downloaded from <a href="http://flexiblelearning.auckland.ac.nz/bpass/2/files/">http://flexiblelearning.auckland.ac.nz/bpass/2/files/</a>. <ul> <li> SSP_Spectra_BPASS_binary.hdf5 <ul> <li> Canonical BPASS IMF </li> <li> Includes binary stars </li> <li> Uses v2.0 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASS_single.hdf5 <ul> <li> Canonical BPASS IMF </li> <li> Does not include binary stars </li> <li> Use v2.0 of BPASS </li> </ul> </li> </ul>

Stellar Population Spectra from BPASS v2.2.1

The BPASS models of <a href="https://ui.adsabs.harvard.edu/abs/2017PASA...34...58E">Eldridge, Stanway et al. (2017)</a> and <a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.479...75S">Stanway & Eldridge et al. (2018)</a>. Data was downloaded from <a href="https://drive.google.com/drive/folders/1BS2w9hpdaJeul6-YtZum--F4gxWIPYXl?usp=sharing">https://drive.google.com/drive/folders/1BS2w9hpdaJeul6-YtZum--F4gxWIPYXl?usp=sharing</a>. <ul> <li> SSP_Spectra_BPASSv2.2.1_bin-imf100_100.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.00 from 0.5M☉ to 100M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_bin-imf100_300.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.00 from 0.5M☉ to 300M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_bin-imf135_100.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 100M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_bin-imf135_300.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_bin-imf135all_100.hdf5 <ul> <li> Power law IMF (α=-2.35 from 0.1M☉ to 100M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_bin-imf170_100.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.70 from 0.5M☉ to 100M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_bin-imf170_300.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.70 from 0.5M☉ to 300M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_bin-imf_chab100.hdf5 <ul> <li> Chabrier IMF (α=-2.00; maximum mass 100M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_bin-imf_chab300.hdf5 <ul> <li> Chabrier IMF (α=-2.00; maximum mass 300M☉) </li> <li> Includes binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf100_100.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.00 from 0.5M☉ to 100M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf100_300.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.00 from 0.5M☉ to 300M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf135_100.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 100M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf135_300.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf135all_100.hdf5 <ul> <li> Power law IMF (α=-2.35 from 0.1M☉ to 100M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf170_100.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.70 from 0.5M☉ to 100M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf170_300.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.70 from 0.5M☉ to 300M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf_chab100.hdf5 <ul> <li> Chabrier IMF (α=-2.00; maximum mass 100M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.2.1_sin-imf_chab300.hdf5 <ul> <li> Chabrier IMF (α=-2.00; maximum mass 300M☉) </li> <li> Does not include binary stars </li> <li> Uses v2.2.1 of BPASS </li> </ul> </li> </ul>

Stellar Population Spectra from Conroy et al. (2010)

Models from <a href="http://adsabs.harvard.edu/abs/2010ascl.soft10043C">Conroy & Gunn (2010; ascl:1010.043)</a>. <ul> <li> SSP_Spectra_Conroy-et-al_v2.5_imfChabrier.hdf5 <ul> <li> Computed with v2.5 of FSPS </li> <li> <a href="http://adsabs.harvard.edu/abs/2001ApJ...554.1274C">Chabrier IMF</a> </li> </ul> </li> <li> SSP_Spectra_Conroy-et-al_v2.5_imfKroupa.hdf5 <ul> <li> Computed with v2.5 of FSPS </li> <li> <a href="http://adsabs.harvard.edu/abs/2001MNRAS.322..231K">Kroupa IMF</a> </li> </ul> </li> <li> SSP_Spectra_Conroy-et-al_v2.5_imfSalpeter.hdf5 <ul> <li> Computed with v2.5 of FSPS </li> <li> <a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a> </li> </ul> </li> </ul>

Stellar Population Spectra from Maraston et al. (2005)

Models from <a href="http://adsabs.harvard.edu/abs/2005MNRAS.362..799M">Maraston (2005, MNRAS, 362, 799)</a>; <a href="http://adsabs.harvard.edu/abs/1998MNRAS.300..872M">Maraston (1998, MNRAS, 300, 872)</a>. Data downloaded from <a href="http://web.archive.org/web/20220121155147/http://www.icg.port.ac.uk/~maraston/Claudia's_Stellar_Population_Model.html">http://web.archive.org/web/20220121155147/http://www.icg.port.ac.uk/~maraston/Claudia's_Stellar_Population_Model.html</a>. <ul> <li> SSP_Spectra_Maraston_hbMorphologyRed_imfKroupa.hdf5 <ul> <li> <a href="http://adsabs.harvard.edu/abs/2001MNRAS.322..231K">Kroupa IMF</a> </li> <li> “Red” horizontal branch morphology </li> </ul> </li> <li> SSP_Spectra_Maraston_hbMorphologyRed_imfSalpeter.hdf5 <ul> <li> <a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a> </li> <li> “Red” horizontal branch morphology </li> </ul> </li> <li> SSP_Spectra_Maraston_hbMorphologyBlue_imfKroupa.hdf5 <ul> <li> <a href="http://adsabs.harvard.edu/abs/2001MNRAS.322..231K">Kroupa IMF</a> </li> <li> “Blue” horizontal branch morphology </li> </ul> </li> <li> SSP_Spectra_Maraston_hbMorphologyBlue_imfSalpeter.hdf5 <ul> <li> <a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a> </li> <li> “Blue” horizontal branch morphology </li> </ul> </li> </ul>

Stellar Population Spectra from Venkatesan et al. (2003)

Simple stellar population spectra from  <a href="http://adsabs.harvard.edu/abs/2003ApJ...584..621V">Venkatesan et al. (2003; ApJ; 584; 621)</a> in the Galacticus format. Two models are provided. Both assume a <a href="http://adsabs.harvard.edu/abs/1955ApJ...121..161S">Salpeter IMF</a>, one from 10-140M☉ and the other from 1-100M☉.

Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles

Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioral ecology is the expectation that the distribution of resources shapes patterns of social interactions. We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions. We experimentally manipulated the distribution of an essential food and reproductive resource in semi-natural populations of forked fungus beetles (Bolitotherus cornutus). We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half-way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioral differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments. We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length, and global clustering coefficient) did not differ between environments with dramatically different distributions of resources. Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggests that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.

Foot anomalies in four post-medieval, Dutch populations (17th-19th century)

This dataset was compiled for the master's thesis "Familiar Feet. Kinship analysis using foot anomalies in the cemetery of Middenbeemster (Netherlands, 17th to 19th century)" at Leiden University (Netherlands) by the author. The thesis was submitted on June 15th, 2021 (<a href="https://hdl.handle.net/1887/3204807">https://hdl.handle.net/1887/3204807</a>). The data was collected in the period from January to May 2021 at the Laboratory of Human Osteoarchaeology at Leiden University. It includes the data of 380 adult individuals from four different post-medieval, Dutch populations: the rural population of Middenbeemster (MB) (Hakvoort 2013) and the urban populations of Eindhoven (Catharinakerk) (EH) (Arts 2013), Zwolle (Broerenkerk) (ZW) (Clevis and Constandse-Westermann 1992) and Arnhem (Eusebiuskerk) (AR) (Baetsen et al. 2018). For every individual, an estimate of sex and age-at-death was collected. If a DNA analysis was performed, then this result was used instead of an estimation. The sex estimations of the individuals were based on pelvic and cranial morphology (Bainbridge & Genoves Tarazaga, 1956; Bass, 1987; Buikstra & Ubelaker, 1997; Maat & Mastwijk, 2009; McCormick et al., 1991; Phenice, 1969; Stewart, 1979; Steyn & Işcan, 1999; Workshop for European Anthropologists (WEA), 1980) and were supported by archival data. For the Eindhoven collection, the DNA-determined sex estimations took precedence over the estimations by traditional bioarchaeological techniques (Baetsen & Weterings-Korthorst, 2013). The used abbreviations are M = male, PM = probable male, I = indeterminate, PF = probable female, F = female.  Age-at-death estimations were made according to traditional bioarchaeological techniques (Brooks & Suchey, 1990; Buckberry & Chamberlain, 2002; İşcan et al., 1984, 1985; Lovejoy et al., 1985; Maat, 2001; Meindle & Lovejoy, 1985; Todd, 1920). The used abbreviations are EYA = early young adult, LYA = late young adult, MA = middle adult, OA = old adult. The foot anomalies included are accessory navicular (AccessNav), brachydactyly D (BrachyD), brachydactyly of the first metatarsal (BrachyMT1), brachydactyly of the fourth metatarsal (BrachyMT4), brachydactyly of the first proximal phalanx (BrachyPP1), calcaneocuboid coalition (CalcCubCoal), calcaneonavicular coalition (CalcNavCoal), coalition between the intermediate and lateral cuneiform (CF2CF3Coal), coalition between the lateral cuneiform and the third metatarsal (CF3MT3Coal), talocalcaneal coalition (TaloCalcCoal), talonavicular coalition (TaloNavCoal) and os intermetatarseum with the medial cuneiform (CF1Intermet), the first metatarsal (MT1Intermet) and/or the second metatarsal (MT2Intermet) involved. The scoring itself was performed without prior knowledge of skeletal data like sex and age to avoid observer bias. The trait could be marked absent (0), present (1), indiscernible if the bone was damaged (6), or missing if the bone was not present (9) for each separate foot (left (L) or right (R) side). Tarsal coalitions were scored if at least one of the two bones could be marked “absent/present”. In the remarks, some of the found lesions are described in more detail. For some of the individuals, there is archival data available. This is indicated with 'Yes' or 'No' in the 'Archive data?' column. For the Middenbeemster population, the location of the individual graves was determined from the grave polygons in the excavation data in QGIS by calculating the centroids of these polygons and resulted in a single pair of (x, y) coordinates. This dataset was used to identify probable genetic relatives within the Middenbeemster skeeltal collection through developmental foot anomalies and to analyse the spatial structure of the Middenbeemster cemetery in the context of intracemetery kinship relations. The Middenbeemster trait frequencies for these anomalies were compared to those of a reference sample of the post-medieval Dutch population (consisting of individuals from the Dutch post-medieval collections of Arnhem, Eindhoven, Zwolle). A hypothetical kinship group could be identified when the trait frequencies of the Middenbeemster sample were considerably higher than those in the reference sample. Other sources had only limited validation value in relation to the hypothesis. Visual examination and spatial statistics of the distribution of the hypothetical kinship group revealed a possible patrilineally structured cemetery, although this is based on a small sample. By putting the observed trait frequencies in a broader context, the data suggested a rather high inter-relatedness of the Middenbeemster community. It also exposed the need for a better understanding of the used traits and perhaps a different approach to kinship analysis (due to necessarily large time investment in contrast to limited results). In conclusion, this study gave an insight into the social structure of post-medieval Dutch communities. Future improvements to kinship analysis may not only be beneficial for bioarchaeology, but also for other fields such as forensic anthropology. For more information on this research, see the thesis (<a href="https://hdl.handle.net/1887/3204807">https://hdl.handle.net/1887/3204807</a>) or the article published in International Journal of Osteoarchaeology (DOI: 10.1002/oa.3100).   References: Arts, Nico. 2013. Een knekelveld maakt geschiedenis. Het archeologisch onderzoek van het koor en het grafveld van de middeleeuwse Catharinakerk in Eindhoven, circa 1200-1850. Rapport 22. Utrecht: Uitgeverij Matrijs. Baetsen, Steffen, Willem Baetsen, Martijn Defilet, and Gerben Zielman. 2018. ‘Sint-Jansbeek brengt Oude Kerkhof boven water. Graven bij de Arnhemse Eusebiuskerk’. Archeologie in Nederland 3: 34–43. Baetsen, Steffen, and L. Weterings-Korthorst. 2013. ‘De menselijke overblijfselen’. In Een knekelveld maakt geschiedenis. Het archeologisch onderzoek van het koor en het grafveld van de middeleeuwse Catharinakerk in Eindhoven, circa 1200-1850, edited by Nico Arts and E. Altena, 288. Utrecht: Uitgeverij Matrijs. Bainbridge, D., and S. Genoves Tarazaga. 1956. ‘A Study of Sex Differences in the Scapula’. Journal of the Royal Anthropological Institute of Great Britain and Northern Ireland 86 (2): 109–34. https://doi.org/10.2307/2843994. Bass, W. M. 1987. Human Osteology: A laboratory and Field Manual. Missouri Archaeological Society. Special Publication 2. Columbia (Mo): Missouri Archaeological Society. Brooks, S., and J. M. Suchey. 1990. ‘Skeletal Age Determination Based on the Os Pubis: A Comparison of the Acsadi-Nemeskeri and Suchey-Brooks Methods’. Human Evolution 5 (3): 227–38. Buckberry, J.L., and A.T. Chamberlain. 2002. ‘Age Estimation from the Auricular Surface of the Ilium: A Revised Method’. American Journal of Physical Anthropology 119 (3): 231–39. https://doi.org/10.1002/ajpa.10130. Buikstra, Jane E., and Douglas H. Ubelaker. 1997. Standards for Data Collection from Human Skeletal Remains. 3rd ed. Arkansas Archaeological Survey Research Series 44. Fayetteville, Arkansas: Arkansas Archaeological Survey. Clevis, Hemmy, and T. S. Constandse-Westermann. 1992. De doden vertellen: opgraving in de Broerekerk te Zwolle 1987-1988. Kampen: Stichting Archeologie IJssel/Vechtstreek III. Hakvoort, A. 2013. ‘De begravingen bij de Keyserkerk te Middenbeemster’. Hollandia Reeks 464. Hollandia Reeks. Zaandijk: Hollandia Archeologen. İşcan, M. Y., S. R. Loth, and R. K. Wright. 1985. ‘Age Estimation from the Rib by Phase Analysis: White Females’. Journal of Forensic Sciences 30 (3): 853–63. İşcan, M. Yaşar, Susan R. Loth, and Ronald K. Wright. 1984. ‘Metamorphosis at the Sternal Rib End: A New Method to Estimate Age at Death in White Males’. American Journal of Physical Anthropology 65 (2): 147–56. https://doi.org/10.1002/ajpa.1330650206. Lovejoy, C. Owen, Richard S. Meindl, Thomas R. Pryzbeck, and Robert P. Mensforth. 1985. ‘Chronological Metamorphosis of the Auricular Surface of the Ilium: A New Method for the Determination of Adult Skeletal Age at Death’. American Journal of Physical Anthropology 68 (1): 15–28. https://doi.org/10.1002/ajpa.1330680103. Maat, G. J. R., and R. W. Mastwijk. 2009. Manual for the Physical Anthropological Report. 6th ed. Barge’s Anthropologica 6. Leiden: Barge’s Anthropologica. Maat, George J. R. 2001. ‘Diet and Age-At-Death. Determination from Molar Attrition: A Review Related to the Low Countries’. The Journal of Forensic Odonto-Stomatology 19: 18–21. McCormick, W. F., J. H. Stewart, and H. Greene. 1991. ‘Sexing of Human Clavicles Using Length and Circumference Measurements’. The American Journal of Forensic Medicine and Pathology 12 (2): 175–81. https://doi.org/10.1097/00000433-199106000-00017. Meindl, Richard S., and C. Owen Lovejoy. 1985. ‘Ectocranial Suture Closure: A Revised Method for the Determination of Skeletal Age at Death Based on the Lateral-Anterior Sutures’. American Journal of Physical Anthropology 68 (1): 57–66. https://doi.org/10.1002/ajpa.1330680106. Phenice, T. W. 1969. ‘A Newly Developed Visual Method of Sexing the Os Pubis’. American Journal of Physical Anthropology 30 (2): 297–301. https://doi.org/10.1002/ajpa.1330300214. Stewart, T. D. 1979. Essentials of Forensic Anthropology. Springfield (Ill.): C. C. Thomas. Steyn, M., and M. Y. Işcan. 1999. ‘Osteometric Variation in the Humerus: Sexual Dimorphism in South Africans’. Forensic Science International 106 (2): 77–85. https://doi.org/10.1016/s0379-0738(99)00141-3. Todd, T. 1920. ‘Age Changes in the Pubic Bones, I: The White Male Pubis’. American Journal of Physical Anthropology 3: 285–334. Workshop for European Anthropologists (WEA). 1980. ‘Recommendations for Age and Sex Diagnoses of the Skeleton’. Journal of Human Evolution 9: 517–49. https://doi.org/10.1016/j.jchb.2005.07.002.

To rise to temperature: Variation in temperature effects within and among populations

Temperature drives physiological changes on three timescales: acute, acclimatory and evolutionary. Acutely, passive temperature effects often dictate an expected two-fold increase in metabolic processes for every 10°C increase (Q10). Yet for acclimatory or evolutionary time scales, selection often mitigates these acute effects. This selection also should concomitantly reduce interindividual variation. However, the individual variation in physiological trait thermal sensitivity is not well characterized. Here we quantified physiological responses to a 16°C temperature difference across nine thermally distinct Fundulus heteroclitus populations. Traits included whole animal metabolism (WAM), critical thermal maximum (CTmax), and substrate-specific cardiac metabolism measured in approximately 350 individuals. These traits exhibit high variation among both individuals and populations that depends on acclimation temperature. Thermal sensitivity or Q10 variation is unexpected and ranges from 0.6 to 5.4 for WAM. Thus, with a 16°C temperature increase, some individuals have the same or lower metabolic rates while others have metabolic rates almost seven-fold higher (Q10 = 5.4). Furthermore, a significant portion of this variation is related to habitat temperature, such that warmer populations have a significantly lower Q10 for WAM and CTmax than colder populations. These data support a novel hypothesis: individual variation in thermal sensitivity reflects different physiological strategies to respond to environmental temperature variation and provides the potential for many different adaptive responses to temperature.

Stellar Population Spectra from BPASS v2.3

The BPASS models of <a href="https://ui.adsabs.harvard.edu/abs/2017PASA...34...58E">Eldridge, Stanway et al. (2017)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.479...75S">Stanway & Eldridge et al. (2018)</a> and <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.512.5329B">Byrne et al. (2022, MNRAS, 512, 5329)</a>. Data was downloaded from <a href="https://livewarwickac.sharepoint.com/sites/Physics-BinaryPopulationandSpectralSynthesisBPASS/Shared%20Documents/Forms/AllItems.aspx?id=%2Fsites%2FPhysics%2DBinaryPopulationandSpectralSynthesisBPASS%2FShared%20Documents%2FPaper%20Data%20Packages%2FByrne23&p=true&ga=1">here</a>. <ul> <li> SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha-02.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Does not include binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=-0.2 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha+00.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Does not include binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=+0.0 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha+02.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Does not include binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=+0.2 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha+04.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Does not include binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=+0.4 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_sin-imf135_300-alpha+06.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Does not include binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=+0.6 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha-02.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Includes binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=-0.2 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha+00.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Includes binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=+0.0 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha+02.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Includes binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=+0.2 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha+04.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Includes binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=+0.4 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> <li> SSP_Spectra_BPASSv2.3_bin-imf135_300-alpha+06.hdf5 <ul> <li> Broken power law IMF (α=-1.30 from 0.1M☉-0.5M☉, α=-2.35 from 0.5M☉ to 300M☉) </li> <li> Includes binary stars </li> <li> α-alement enhancement of Δlog₁₀(α/Fe)=+0.6 </li> <li> Uses v2.3 of BPASS </li> </ul> </li> </ul>

Genetic structure in patchy populations of a candidate foundation plant: a case study of Leymus chinensis using genetic and clonal diversity

PREMISE: The distribution of genetic diversity on the landscape has critical ecological and evolutionary implications. This may be especially the case on a local scale for foundation plant species since they create and define ecological communities, contributing disproportionately to ecosystem function. METHODS: We examined the distribution of genetic diversity and clones, which we defined first as unique multilocus genotypes (MLG), and then by grouping similar MLGs into multilocus lineages (MLL). We used 186 markers from inter-simple sequence repeats (ISSR) across 358 ramets from 13 patches of the foundation grass Leymus chinensis. We examined the relationship between genetic and clonal diversities, their variation with patch-size, and the effect of the number of markers used to evaluate genetic diversity and structure in this species. RESULTS: Every ramet had a unique MLG. Almost all patches consisted of individuals belonging to a single MLL. We confirmed this with a clustering algorithm to group related genotypes. The predominance of a single lineage within each patch could be the result of the accumulation of somatic mutations, limited dispersal, some sexual reproduction with partners mainly restricted to the same patch, or a combination of all three. CONCLUSIONS: We found strong genetic structure among patches of L. chinensis. Consistent with previous work on the species, the clustering of similar genotypes within patches suggests that clonal reproduction combined with somatic mutation, limited dispersal, and some degree of sexual reproduction among neighbors causes individuals within a patch to be more closely related than among patches.

Data accompanying Polyphenisms and polymorphisms: genetic variation in plasticity and color variation within and among bluefin killifish populations

The presence of stable color polymorphisms within populations begs the question of how genetic variation is maintained. Consistent variation among populations in coloration, especially when correlated with environmental variation, raises questions about whether environmental conditions affect either the fulcrum of those balanced polymorphisms, the plastic expression of coloration, or both. Color patterns in male bluefin killifish provoke both types of questions. Red and yellow morphs are common in all populations. Blue males are more common in tannin-stained swamps relative to clear springs. Here we combined crosses with a manipulation of light to explore how genetic variation and phenotypic plasticity shape these patterns. We found that the variation in coloration is attributable mainly to two axes of variation: (1) a red-yellow axis with yellow being dominant to red, and (2) a blue axis that can override red-yellow and is controlled by genetics, phenotypic plasticity, and genetic variation for phenotypic plasticity. The variation among populations in plasticity suggests it is adaptive in some populations but not others. The variation among sires in plasticity within the swamp population suggests balancing selection may be acting not only on the red-yellow polymorphism but also on plasticity for blue coloration.

Monitoring demography of resurrected populations of locally extinct and extant species to investigate drivers of species loss

Extinctions are predicted to rise by an order of magnitude over the next century. Although contemporary documented extinctions are uncommon, local extirpations likely provide hints about global extinction risks. Comparing responses to global change of locally extinct versus extant species pairs in a phylogenetic framework could highlight why certain species are more vulnerable to extinction than others and which anthropogenic changes are most relevant to their decline. As anthropogenic changes likely interact to affect population declines, demographic studies partitioning the effects of multifactorial stressors are needed but remain rare. I examine demographic responses to nitrogen addition and deer herbivory, two major drivers of species losses in grasslands, in experimental reintroductions of fourteen locally extinct and extant confamilial native plants from Michigan prairies. Nitrogen consistently reduces survival, especially in locally extinct species, and growth of locally extinct species benefits less from nitrogen than growth of extant species. Nitrogen reduces population growth rates, largely via reductions in survival. Deer herbivory, meanwhile, had inconsistent effects on vital rates among species and did not affect population growth. Nitrogen and herbivory rarely interacted to affect vital rates. These results link community-level patterns of species loss under nitrogen addition to the population-level processes underlying those losses.