Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

A systems biology approach to identify essential epigenetic regulators for specific biological processes in plants

Supplemental Files for McCoy et al "A systems biology approach to identify essential epigenetic regulators for specific biological processes in plants"

opencc-by-4.0Mar 2024View details →

Uncoupling of Behavioral and Metabolic Twenty-Four-Hour Rhythms in Reindeer (Current Biology, Meier et al. 2024): Pre-Processed metabolomics data

Pre-processed metabolomics data (peak picking, peak alignment, integration and annotation using XCMS) obtained from untargeted UPLC-MS measurements of reindeer blood plasma.

Fig. 8 Fitness landscape for models 7 and 8 in Modelling sympatric speciation by means of biologically plausible mechanistic processes as exemplified by threespine stickleback species pairs

Fig. 8 Fitness landscape for models 7 and 8. Relative fitness is a function of trait T1 and trait T2. Epistasis is modelled as follows:

Fig. 5 The probability that a female accepts a in Modelling sympatric speciation by means of biologically plausible mechanistic processes as exemplified by threespine stickleback species pairs

Fig. 5 The probability that a female accepts a male as a mate is a function of the morphological difference between them, and her stringency of choosiness S (here T ¼ S þ 0: 25), as in model 4 (variants applied in models 6 and 8). In the figure, 3 values of S are shown; S can have all values that are averages of two allelic values (from 64 or 256 equidistant values from 0 to 1)

Fig. 3 Model 1. a in Modelling sympatric speciation by means of biologically plausible mechanistic processes as exemplified by threespine stickleback species pairs

Fig. 3 Model 1. a Typical initial distribution of the allelic values at generation 0. b Typical distribution of the allelic values at generation 100. c Typical distribution of T, the phenotypic values, at generation 100. Nm = Nf =100; σ =0.25; μ = 1%; n = 256 alleles. Similar results were obtained in 20 out of 20 replicate simulations with σ =0.25, in 13 out of 20 replicate simulations with σ =0.5, and in 0 out of 10 replicate simulations with σ =1

Fig. 6 Model 4 in Modelling sympatric speciation by means of biologically plausible mechanistic processes as exemplified by threespine stickleback species pairs

Fig. 6 Model 4: Reinforcement of stringency of assortative mating. Columns: 1 Typical distribution of morphology alleles; 2 typical distribution of morphology phenotypes T; 3 typical distribution of stringency of choosiness alleles. Rows: 1 Generation 0, 2 generation

opencc-zeroOct 2019View details →

dryad28/100

Data from: Process-based species delimitation leads to identification of more biologically relevant species

Most approaches to species delimitation to-date have considered divergence-only models. While these models are appropriate for allopatric speciation, their failure to incorporate many of the population-level processes that drive speciation, such as gene flow (e.g. in sympatric speciation), places an unnecessary limit on our collective understanding of the processes that produce biodiversity. To consider these processes while inferring species boundaries, we introduce the R-package delimitR and apply it to identify species boundaries in the reticulate taildropper slug (Prophysaon andersoni). Results suggest that secondary contact is an important mechanism driving speciation in this system. By considering process, we both avoid erroneous inferences that can be made when population-level processes such as secondary contact drive speciation but only divergence is considered, and gain insight into the process of speciation in terrestrial slugs. Further, we apply delimitR to three published empirical datasets and find results corroborating previous findings. Finally, we evaluate the performance of delimitR using simulation studies, and find that error rates are near zero when comparing models that include lineage divergence and gene flow for three populations with a modest number of Single Nucleotide Polymorphisms (SNPs; 1,500) and moderate divergence times (< 100000 generations). When we apply delimitR to a complex model set (i.e. including divergence, gene flow, and population size changes), error rates are moderate (~0.15; 10000 SNPs), and, when present, misclassifications occur between highly similar models.

Fig. 1 in Modelling sympatric speciation by means of biologically plausible mechanistic processes as exemplified by threespine stickleback species pairs

Fig. 1 Flow diagram of the individual based genetic algorithm

opencc-by-4.0Apr 2013View details →

Figure 2 from: Silva M, Rezende R, Lopes Ferreira R (2013) Detritus processing in lentic cave habitats in the neotropics. Subterranean Biology 11: 3-14. https://doi.org/10.3897/subtbiol.11.5107

Figure 2 - Remaining weight of plant disks exposed to processing in the 9 mm2 litterbag mesh size in the lentic habitats of Brega and Santuário caves. Mean, Box: Mean±SE, Whisker: Mean±SD.

opencc-by-4.0Apr 2013View details →

Figure 1 from: Silva M, Rezende R, Lopes Ferreira R (2013) Detritus processing in lentic cave habitats in the neotropics. Subterranean Biology 11: 3-14. https://doi.org/10.3897/subtbiol.11.5107

Figure 1 - Remaining weight of plant disks exposed to processing in the 0.04 mm2 litterbag mesh size in the lentic habitats of Brega and Santuário caves. Mean, Box: Mean±SE, Whisker: Mean±SD

opencc-by-4.0Apr 2013View details →

Figure 3 from: Silva M, Rezende R, Lopes Ferreira R (2013) Detritus processing in lentic cave habitats in the neotropics. Subterranean Biology 11: 3-14. https://doi.org/10.3897/subtbiol.11.5107

Figure 3 - Temporal values of nitrogen and phosphorous (μg/kg) measured in plant discs exposed to decomposition in lentic habitats of the Santuário and Brega caves.

Figure 2 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390

Figure 2 - Virtual configurations used for software validation. Virtual configurations used to compile the data presented in the Table 1. Part 2.8 is one of the 10 replicates obtained with a random distribution. All other configurations have been designed in order to reach the desired level of aggregation and affinity between groups. The filled and empty shapes represented two virtual groups in the population.

Figure 1 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390

Figure 1 - Flow chart of the creation of a new NEIGHBOUR-IN file. This figure presents the different steps in the creation of a new file, from the importation of the snapshot to the calculation of the statistics of dispersion.

Figure 4 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390

Figure 4 - Spatial distribution in woodlice. Graphic outputs of spatial distribution patterns obtained in three configurations with monospecific or bispecific populations including two groups of eight individuals: a PD-PD: The two groups are Porcellio dilatatus (red and green) b PD-PS: Porcellio dilatatus (red) and Porcellio scaber (green) c PD-AV: Porcellio dilatatus (red) and Armadillidium vulgare (green). The outputs show 64 cells. Each cell is represented with a colour corresponding to the individual(s) in that cell. The colour is mixed using green and red proportional to the number of green and red individuals. If the cell is empty, the colour is black. The intensity of the colour reflects the number of individuals. The position of the individual is determined by its point G (centre-point).

Figure 3 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390

Figure 3 - Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcellio dilatatus, PS: Porcellio scaber, CC: Cylisticus convexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.