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Fig. 7. Myosotis antarctica subsp. antarctica. Illustration reproduced from Bot. Antarct. Voy. I in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data

Fig. 7. Myosotis antarctica subsp. antarctica. Illustration reproduced from Bot. Antarct. Voy. I. (Fl. Antarct.) Part I, plate 38 (Hooker 1844). Illustration by W. H. Fitch. This image is in the public domain, downloaded from the Biodiversity Heritage Library (https:// www.biodiversitylibrary.org/page/13448452#page/81/ mode/1up, accessed 8 June 2021). Draft pencil drawings for this figure are attached to the type specimen of M. antarctica (K0007878799; visible online at http:// apps.kew.org/herbcat/getImage.do?imageBarcode= K000787899, accessed 8 June 2021), which was collected by J. D. Hooker from Campbell Island.

Fig. 6 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data

Fig. 6. Myosotis antarctica subsp. antarctica photographs and distribution maps. (a, b) Habit. (c) Rosette leaves abaxial and adaxial sides. (d) Flower. (e) Nutlets. (f) Map of mainland New Zealand distribution based on georeferenced herbarium specimens observed by J. M. Prebble (163). (g) Map of Campbell Island distribution based on georeferenced herbarium specimens observed by J. M. Prebble (14). (h) Map of Chilean distribution based on georeferenced herbarium specimens observed by J. M. Prebble (2). White scale bars: 2 mm; black scale bars: 1 mm. Photo credits: a, c, e by J. M. Prebble (a: WELT SP102777, Mt Azimuth, Campbell Island; c: WELT SP093293, Port Hills, Canterbury, South Island E: WELT SP100466, cultivated ex Mt Peel, Western Nelson. South Island). b, d © Te Papa by H. M. Meudt (b: WELT SP106592, Matiri Range, Western Nelson, South Island; d: WELT SP107322, Mt Starveall, Western Nelson, South Island).

Fig. 3 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data

Fig. 3. Plots displaying (a, c) omission and commission values and (b, d) area under the receiving operating characteristic curve (AUC) for two pygmy forget-me-not taxa: (a, b) M. "Volcanic Plateau" and (c, d) M. drucei, modelled using MaxEnt and all nine environmental layers for the New Zealand extent.

opencc-by-4.0Apr 2024View details →

Data for: Interplay of abiotic conditions, density, and body size in shaping demography in a high-elevation toad population

This dataset is used to estimate vital rates of a common toad (Bufo bufo) alpine population, and how they are associated with either abiotic (environmental conditions), biotic (in our case population size), and individual factors (body size). We have individual capture histories for the period 1993-2020 for 1615 males and 933 females, as well as body size measurements taken during capture events. For more info about the study system see: https://peercommunityjournal.org/articles/10.24072/pcjournal.240/ We run a capture-mark-recapture model coupled with a growth model, the latter to obtain information on body size for the years when the individuals were not captured. Aside from sex-specific survival, we estimate female breeding probability, since they show intermittent breeding. We include as covariates for these vital rates the length of the active season, the temperature at emergence from hibernation, population size, and body size.  We obtained climatic data for the period 1980–2020 from the DaymetCH dataset (data obtained from Bioclimatic maps of Switzerland © WSL, based on station data from the Federal Office of Meteorology and Climatology MeteoSwiss, and elaborated by the Land Change Science group, WSL). The README file further describes each uploaded file

opencc-by-4.0Feb 2024View details →

Data for study " Future Climate Change Impacts on Rice in Uttar Pradesh, India's Most Populous Agrarian State "

Crop management practicrs used to calibrate the DSSAT for Uttar Pradesh. The rice genetic coefficients for various rice varieties/ cultivars are also available here.

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Data from: Interactive effects of soil moisture, air temperature and litter nutrient diversity on soil microbial communities and Folsomia candida population

Soil organisms play a key role in carbon and nutrient cycling in forest ecosystems. While soil organisms are strongly influenced by litter chemistry and are highly sensitive to abiotic conditions, little is known about how the interactive effects of these two factors. To address this gap in knowledge, we conducted a 10-week microcosm experiment in which we simulated the effects of climate change on soil ecology. More specifically, we studied relationships among litter nutrient concentration, microbial biomass, Collembola demographic parameters, and litter decomposition, exploring the potential impacts of increasing air temperature and decreasing soil moisture. To develop a gradient of nutrient concentrations, we created six tree litter mixtures with materials gathered from Quercus pubescens and its companion species. In contrast to microbes, we observed that Collembola abundance and litter decomposition were interactively affected by soil moisture and air temperature: the negative effect of increasing air temperature on Collembola abundance was amplified by reduced soil moisture, whereas the positive effect of increasing air temperature on litter decomposition disappeared under reduced soil moisture conditions. In contrast to fungi, the response of bacterial biomass and Collembola abundance to litter nutrient concentration was dependent on abiotic conditions. More specifically, the relationships between nutrients, especially calcium and magnesium, and bacterial biomass and Collembola abundance were less robust or disappeared under drier or warmer conditions. In conclusion, our findings underscore that ongoing climate change could affect soil organisms directly as well as indirectly, by altering their responses to litter nutrient concentrations. In addition, we found that nutrient-rich habitats might be more affected than nutrient-poor habitats by altered climatic conditions.

opencc-zeroApr 2024View details →

Fig. 1. Maps displaying all 290 in Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data

Fig. 1. Maps displaying all 290 occurrence points used for Myosotis pygmy species group niche modelling (Supplementary Table S1). Maps, clockwise from top: World, New Zealand, Campbell Island, and southern South America. Colour represents a priori species: M. antarctica (pink circles); M. drucei (dark blue circles); M. pygmaea (green circles); M. brevis (yellow circles); M. glauca (light blue circles); M. "Volcanic Plateau" (grey triangles).

opencc-zeroDec 2016View details →

dryad32/100

Data from: Demographic inference from whole-genome and RAD sequencing data suggests alternating human impacts on goose populations since the last ice age

We investigated how population changes and fluctuations in the pink-footed goose might have been affected by climatic and anthropogenic factors. First, genomic data confirmed the existence of two separate populations: western (Iceland) and eastern (Svalbard/Denmark). Second, emographic inference suggests that the species survived the last glacial period as a single ancestral population with a low population size (100-1,000 individuals) that split into the current populations at the end of the Last Glacial Maximum with Iceland being the most plausible glacial refuge. While population changes during the last glaciation were clearly environmental, we hypothesize that more recent demographic changes are human-related: (1) the inferred population increase in the Neolithic is due to deforestation to establish new lands for agriculture, increasing available habitat for pink-footed geese (2) the decline inferred during the Middle Ages is due to human persecution and (3) improved protection explains the increasing demographic trends during the 20th century. Our results suggest both environmental (during glacial cycles) and anthropogenic effects (more recent) can be a threat to species survival.

Fig. 10 in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 10. Yearly number of stag beetles found dead by predation at the Jagersveld CMR site during the period 2001- 2023. Areas in grey refer to predation by Corvids, areas in yellow to predation by hedgehogs. The annual sex-ratio measured on these beetle remains is indicated, as is the yearly total number of beetles killed by predators and the total number of natural deaths.

Fig. 9 in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 9. Variation, as a function of year, of the mean and standard error of head width (KB) of males found dead at the Jagersveld CMR site. Numbers indicate the size of the samples, and letters and colors indicate samples not significantly differing in size from each other at the P = 0.05 threshold, by using the non-parametric Kruskal-Wallis test for multiple comparisons. Samples of years 1924 and 2021 were excluded from the K-W analysis.

Fig. 8 in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 8. Box plots of total length (TL) of stag beetles measured in the order of their first appearance at the Jagersveld site during the capture-mark-recapture exercise of 2007. Median, quartiles and extremes of male sizes over successive weeks and of female sizes over successive ten-day periods.

Fig. 6 A-B in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 6 A-B. Survivorship curves of marked male stag beetles during (A) the 2001 and (B) 2007 CMR exercises, in function of death as end event. Kaplan-Meyer step-curves with 95% upper and lower confidence bounds. The arrows indicate the medians of the known life span. A Weibull curve is drawn over the K-M curve. C: illustrating the lack of statistical difference between the 2001 and 2007 K-M curves.

Fig. 4 in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 4. Cumulative number of new individuals at the Jagersveld site during the CMR sessions of 2001 and 2007. (a): appearance of the first males. (b): geometric increase of new individuals during a short period. The graphs in the right insets show this increase in semi-logarithmic coordinates. (i): inflection point. (c): more regular increase of new individuals until reaching (d) a plateau (p). The increase of new females was more progressive.

Fig. 3 in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 3. Calendar of male capture and recapture histories at the Jagersveld site. The marked individuals are sorted by date of first capture and by known duration of longevity. 1: captured. 0: not encountered in the interval where the individual was known to be alive. +: found dead by predation on the sampling site. *: natural death. MNA: minimum number known alive.

Fig. 5 in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 5. Distribution of the number of males confirmed to be still alive, as a function of the number of days elapsed since they were marked during the 2001 and 2007 CMR exercises. The construction of the curves is based on the first two columns of Table 1 and is limited to 23 days for the year 2007.

Fig. 1 in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 1. Aerial view of part of the locality of Boitsfort, a suburb in the south-east of the city of Brussels, with, encircled in yellow, the stag beetle brood sites as they were known for at least the years 2000-2010. Larval sites were (and are) mainly located outside densely wooded plots, and even in the alignments of ornamental cherry trees along certain avenues. Note also the dispersion of these sites, whose circle size do not prejudge the size of their beetle population. Orthophoto plan from UrbIS datastore.brussels.

Fig. 7 in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 7. Daily estimates of the number of beetles at the Jagersveld site in 2001 and 2007 according to the POPAN formulation of the Jolly-Seber method, under the acceptance of constant survival and capture probabilities over time. Minimum daily number of known survivors (MNA). Rainy periods and, for 2001, stormy evening episodes, are figured at the bottom of the graphs.

Fig. 2. A in Demographic data on a stag beetle (Lucanus cervus) population self-established in an artificial suburban habitat

Fig. 2. A, The ramp to the playground of the Jagersveld school, with its central staircase in 2001. B, The palisade of beams of the east side of the ramp in its 2007 state, however photographed in 2013, the staircase having been removed in the meantime. C, The earthen platform at the top of the ramp in 2002, with the alignment of short beams separating it from the concrete of the ramp. D, The same platform fenced and overgrown with maple saplings, as viewed in 2022. E - F, Stag beetle larvae and a pupa found in 2005 when some of the beams were replaced. G - H, damage to the paling, west side and east side, in 2021. © E and F, courtesy Olivier Beck.

opencc-by-4.0Jun 2024View details →

PixelPop: Nonparametric analysis of correlations in the binary black hole population with LIGO–Virgo–KAGRA data

Data release accompanying the PixelPop papers, analyzing gravitational wave populations. The first dataset (in gwtc3_result_files) is the posterior samples for the runs presented in analysis of LIGO--Virgo--KAGRA data, following the third gravitational wave catalog, see https://arxiv.org/abs/2406.16844. We include a python notebook (example_plot.ipynb) showing how to create the plots presented in this paper. In v2, we also include samples from the predictive distributions. Due to the large uncertainties, marginalizing over the hyperposterior may be a poor representation of the inferred distribution, and so instead we provide samples from the median predictive distribution. That is, samples from the distribution shown in the central panels of the figures.  The second dataset (in o4inj_result_files) is the posterior samples accompanying the runs presented in the technical background paper, see https://arxiv.org/abs/2406.16813.