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opencc-by-4.0Dec 2020View details →

Figure 3. – Mulloidichthys flavolineatus flavolineatus from Wake Island. A in Junior synonymy of Mulloides armatus and intraspecific comparisons of the yellowstripe goatfish Mulloidichthys flavolineatus (Mullidae) using a comprehensive alpha-taxonomy approach

Figure 3. – Mulloidichthys flavolineatus flavolineatus from Wake Island. A: BPBM 4089, 286 mm SL (Loreen O'Hara); B: Adult (Phillip & Lisa Lobel); C: Several adults or subadults; one of the four fish at top right has a yellow caudal fin (Phillip & Lisa Lobel). Scale bar = 4 cm.

opencc-by-4.0Dec 2020View details →

Figure 2 in Junior synonymy of Mulloides armatus and intraspecific comparisons of the yellowstripe goatfish Mulloidichthys flavolineatus (Mullidae) using a comprehensive alpha-taxonomy approach

Figure 2. – Standard length against total length in 12 adult/subadult specimens of Mulloidichthys flavolineatus flavolineatus from the SW Pacific, with projected placement of the HT of M. armatus, allowing approximation of standard length.

opencc-by-4.0Oct 2023View details →

Insights into intraspecific diversity of central carbon metabolites in Saccharomyces cerevisiae during wine fermentation

Supplementary data including the data set used for the " Insights into intraspecific diversity of central carbon metabolites in Saccharomyces cerevisiae during wine fermentation" publication. Abstract: Saccharomyces cerevisiae, as the workhorse of alcoholic fermentation, is a major actor in winemaking. In this context, this yeast species performs alcoholic fermentation to convert sugars from the grape must into ethanol and CO2 with outstanding efficiency as it reaches on average 92% of the maximum theoretical yield of conversion. Primary metabolites produced during fermentation have a great importance in wine where they significantly impact wine characteristics. While ethanol content contributes to the overall profile, others metabolites also have significant impacts, even when present in lower concentrations: glycerol, succinate, acetate, ⍺-ketoglutarate, lactate… S. cerevisiae is known for its great genetic diversity and plasticity that is directly related to its living environment, natural or technological and therefore to domestication. This leads to a wide phenotypic diversity of metabolites production. However, the range of metabolic diversity is variable and depends on the pathway considered. With the aim to improve wine quality, the selection, development and use of strains with dedicated metabolites production without genetic modifications can rely on the already existing natural diversity. Here we detail a screening experiment that aims to assess the diversity of primary metabolites production in a set of 51 S. cerevisiae strains from various genetic backgrounds (wine, flor, rum, West African, sake…). To approximate winemaking conditions, we used a synthetic grape must as fermentation medium and measured seven metabolites by HPLC. Results pointed out great yield differences between strains depending on the metabolite considered. Ethanol appeared as the one with the smallest variation among our set of strains, although it was by far the most produced. A clear negative correlation between ethanol and glycerol was observed, confirming glycerol synthesis as a suitable  lever to reduce ethanol yield. Genetic groups were linked to specific metabolic yields such as high α-ketoglutarate and low acetate yields for wine strains. This study thus helps to characterise the phenotypic diversity of S. cerevisiae in a wine-like context and comforts the use of natural diversity in the development of new strains. Finally, it provides a detailed data set usable to study diversity of well known (ethanol, glycerol, acetate) or little-known (lactate) primary metabolites production, including in common commercial wine strains.

opencc-by-4.0Jan 2024View details →

Elevation differently shapes functional diversity patterns in understory forest communities when considering intraspecific and interspecific trait variability

Datasets used for the analysis done for the paper "Elevation differently shapes functional diversity patterns in understory forest communities when considering intraspecific and interspecific trait variability". Files present are: -Species x Plot (vegetation releveés). -Plot x Environment. -Plot x CWM_inter for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) using the traits fixed for species, i.e., holding traits constant as the species mean, thus incorporating only turnover. -Plot x CWM_intra for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) based on an individual by trait matrix, therefore incorporating both turnover and intraspecific trait variation. -Plot x SES-FD_inter (Standard Effect Size Functional Diversity) for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) using the traits fixed for species, i.e., holding traits constant as the species mean, thus incorporating only turnover. -Plot x SES-FD_intra (Standard Effect Size Functional Diversity) for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) based on an individual by trait matrix, therefore incorporating both turnover and intraspecific trait variation. All analysis were carried out using the software R version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria, <a href="http://www.R-project.org">http://www.R-project.org</a>) and can be consulted on GitHub https://github.com/AriannaFerrara/Elevation-and-Intraspecific-trait-variability.git

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Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions

Understanding geographic ranges and species distributions is crucial for effective conservation, especially in the light of climate and land use change. However, the spatial, temporal and intraspecific resolution of digital accessible information on species distributions is often limited. Here, we suggest to make better use of high-resolution tracking data to address existing limitations of occurrence records such as spatial biases (e.g. lack of observations in parts of the geographic range), temporal biases (e.g. lack of observations during a certain period of the year), and insufficient information on intraspecific variability (e.g. lack of population- or individual-level variation). Addressing these gaps can improve our knowledge on geographic ranges, intra-annual changes in species distributions, and population-level differences in habitat and space use. We demonstrate this with tracking data and species distribution models (SDMs) of the Barnacle Goose, a migratory bird species wintering in western Europe and breeding in the Arctic. Our analyses show that tracking data can (1) supplement occurrence records from the Global Biodiversity Information Facility (GBIF) in remote areas such as the European and Russian Arctic, (2) improve information on the temporal use of wintering, staging and breeding areas of migratory species, and (3) provide insights into the differences of population-level responses to environmental variables. We recommend a broader use of tracking data to address the Wallacean shortfall (i.e. the incomplete knowledge on the geographic distribution of species) and to improve forecasts of biodiversity responses to climate and land use change (e.g. species vulnerability assessments). To avoid common pitfalls, we provide six recommendations for consideration during the research cycle when using tracking data in species distribution modelling, including steps to assess biases and integrate information on intraspecific variability in modelling approaches.

opencc-zeroFeb 2024View details →

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How to render species comparable taxonomic units through deep time: A case study on intraspecific osteological variability in extant and extinct lacertid lizards

Generally, the species is considered to be the only naturally occurring taxon. However, species recognized and defined using different species delimitation criteria cannot readily be compared, impacting studies of biodiversity through Deep Time. This comparability issue is particularly marked when comparing extant with extinct species because the only available data for species delimitation in fossils are derived from their preserved morphology, which is generally restricted to osteology in vertebrates. Here, we quantify intraspecific, intrageneric, and intergeneric osteological variability in extant species of lacertid lizards using pairwise dissimilarity scores based on a data set of 253 discrete osteological characters for 99 specimens referred to 24 species. Variability is always significantly lower intraspecifically than between individuals belonging to distinct species of a single genus, which is in turn significantly lower than intergeneric variability. Average values of intraspecific variability and associated standard deviations are consistent (with few exceptions), with an overall average within a species of 0.208 changes per character scored. Application of the same methods to six extinct lacertid species (represented by 40 fossil specimens) revealed that intraspecific osteological variability is inconsistent, which can at least in part be attributed to different researchers having unequal expectations of the skeletal dissimilarity within species units. Such a divergent interpretation of intraspecific and interspecific variability among extant and extinct species reinforces the incomparability of the species unit. Lacertidae is an example where extant species recognized and defined based on a number of delimitation criteria show comparable and consistent intraspecific osteological variability. Here, as well as in equivalent cases, application of those skeletal dissimilarity values to paleontological species delimitation potentially provides a way to ameliorate inconsistencies created by the use of morphology to define species.

opencc-zeroOct 2021View details →

Fig. 9 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 9. Morphometric parameters of free-living females of S. papillosus: а — body length; b — body width; c — length of esophagus; d — length of intestine; e — length of tail; f — distance to vulva; g — number of eggs; on the ordinate axis — length in µm, on the abscissa axis — species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).

Fig. 8 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 8. Morphometric parameters of S. papillosus males: a — body length; b – body width; c — length of esophagus; d — length of intestine; e — length of the tail; f — length of spicules; on the ordinate axis — length in µm, on the abscissa axis — species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).

Fig. 7 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 7. Free-living female of S. papillosus: a — general view; b — anterior part (the arrowheads indicate the bulbs); c — uterus with eggs (the pointer indicates the genital opening); scale bars 100 µm.

Fig. 5 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 5. Morphometric parameters of L3: а — body length; b — body width; c — length of esophagus; d — length of intestine; e — length of the tail end; on the ordinate axis — length in µm, on the abscissa axis — species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).

Fig. 6 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 6. Mature male of S. papillosus: a — general view; b — anterior part (arrowheads indicate bulbs); c — caudal part with spicules in lateral view; d — caudal part with spicules in dorsal view; scale bars 100 µm.

Fig. 3 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 3. Morphometric parameters of L2 of S. papillosus: а — body length; b — body width; c — length of esophagus; d — length of intestine; e — length of the tail; on the ordinate axis — length in µm, on the abscissa axis – species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).

Fig. 2 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 2. Morphometric parameters of L1 of S. papillosus: а — body length; b — body width; c — length of esophagus; d — length of intestine; e — length of tail; on the ordinate axis — length in µm, on the abscissa axis — species of host (Ch — Capra hircus, Oc — Oryctolagus cuniculus, Cp — Cavia porcellus).

Fig. 4 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 4. Third-stage larva of S. papillosus: a — general view; b — anterior part (the pointer indicates the place where the esophagus joins the intestine); c — posterior part; scale bars 100 µm.

Fig. 1 in Intraspecific Morphological Variation In Free- Living Stages Of Strongyloides Papillosus (Nematoda, Strongyloididae) Parasitizing Various Mammal Species

Fig. 1. Rhabditiform larvae of S. papillosus: a — L1 from C. hircus (arrowheads indicate the bulb-like dilatations on the esophagus and the tail); b — L1 from O. hircus (arrowheads indicate bulb-like dilatations on the esophagus and the tail); c — L2 from C. hircus (arrowheads indicate two bulb-like dilatations); scale bars 100 µm.

Fig. 10 in Scandinavian Oncophorus (Bryopsida, Oncophoraceae): species, cryptic species, and intraspecific variation

Fig. 10. Oncophorus integerrimus Hedenäs sp. nov (holotype). A. Stem leaves. B. Upper leaf acumen. C. Portion of leaf at widest point, showing the broadly recurved margin. D. Lower part of sheathing leaf portion, showing differentiated alar region (margin to the right).

Fig. 9 in Scandinavian Oncophorus (Bryopsida, Oncophoraceae): species, cryptic species, and intraspecific variation

Fig. 9. Oncophorus virens (specimen P286; cf., Table 1). A. Stem leaves. B. Upper leaf acumen. C. Portion of leaf at widest point, showing the broadly recurved margin. D. Lower part of sheathing leaf portion, showing differentiated alar region (margin to the left).

Fig. 8. Oncophorus wahlenbergii B in Scandinavian Oncophorus (Bryopsida, Oncophoraceae): species, cryptic species, and intraspecific variation

Fig. 8. Oncophorus wahlenbergii B (specimen P234; cf., Table 1). A. Stem leaves. B. Upper leaf acumen. C. Uppermost leaf acumen at higher magnification. D. Sheathing leaf portion, showing short marginal leaf cells and alar region (margin to the left).

Fig. 7. Oncophorus wahlenbergii A in Scandinavian Oncophorus (Bryopsida, Oncophoraceae): species, cryptic species, and intraspecific variation

Fig. 7. Oncophorus wahlenbergii A (specimen P225; cf., Table 1). A. Stem leaves. B. Upper leaf acumen. C. Portion of leaf at widest point, showing the flat margin. D, E. Sheathing leaf portions, showing short marginal leaf cells (margin to the right) and, in D, alar region.

Fig. 5. A in Scandinavian Oncophorus (Bryopsida, Oncophoraceae): species, cryptic species, and intraspecific variation

Fig. 5. A. Latitudinal and longitudinal distribution of Oncophorus elongatus samples belonging to different haplotypes or groups of haplotypes (cf., Fig. 1) based on ITS and plastid (trnG, rps4) data. Sample P218 is related to O. elongatus B according to ITS data and O. elongatus A according to plastid data (Fig. 1). The positions of the Swedish towns Kiruna (K), Stockholm (St), Sundsvall (Su), and Östersund (Ö) are indicated as references. B. Elevational distribution of the molecularly sampled Oncophorus virens A and B (P278-P280, P282, P289, P299, P300) in Scandinavia.