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Fig. 1 in New Middle Triassic Bell-Shaped Nassellarian Radiolaria From Alpine And Carpathian Areas
Fig. 1. Geological map of the locality Cristian area, Braşov Mountains, Romania, modified from the geological maps Zărneşti and Braşov sheets, scale 1:50000, Săndulescu et al. (1972).
Fig. 10. a – Humerocyrtis infinita n in New Middle Triassic Bell-Shaped Nassellarian Radiolaria From Alpine And Carpathian Areas
Fig. 10. a – Humerocyrtis infinita n. sp, Rc4. b, d – Humerocyrtis brevithorax n. sp.: b – R78/1b, d – poorly preserved specimen, Rc4. c – Humerocyrtis asymmetrica n. sp., Rc4. f – Humerocyrtis marmoladensis n. sp., BV 85-70. e, g – Humerocyrtis mediotriassica n. sp.: e – CR25, g – Rc4. Scale bar of 50 µm is for all figures.
Fig. 6. a, b in New Middle Triassic Bell-Shaped Nassellarian Radiolaria From Alpine And Carpathian Areas
Fig. 6. a, b. – Gorispela (?) triangulocephalis n. sp., FÖ 87: a. – specimen showing internal structure, b. – holotype, D – dorsal ray. c. – Humerocyrtis superba n. sp., holotype, BV 85-70; 6d, 6c. – Humerocyrtis jekeli n. sp., Rc4, 6c. – holotype, d. - paratype; e. – Humerocyrtis deweveri n. sp., holotype, BW 85-70; f. – Humerocyrtis problematica n. sp., holotype, Rc4. Scale bar of 50 µm is for all figures.
Collapse and recovery of livestock systems shape fire regimes on the Eurasian steppe: a review of ecosystem and biodiversity implications
<p>This file contains bibliographic data from the literature research; livestock and fire data as well as Google Earth Engine and R-scripts to reproduce all analyses and figures.</p>
Рис. 1. Seychelliceps cooperi comb. nov., ЗММУ № 23252, 155 мм SL (1, 3, 5, 7, 9) и Suggrundus macracanthus, ИОРАН, Вьетнам, заΛ. Нячанг, 124 мм SL (2, 4, 6, 8, 10, 11), ΑетаΛи строения: 1, 2 — верхний преоперкуΛярный шип; 3, 4 — гребешки и/иΛи бугорки костей крыши черепа в загΛазничной части; 5, 6 — заΑний конец премаксиΛΛярных зубных Λент; 7, 8 — форма небных зубных Λент; 9, 10 — чешуи преΑорсаΛьной обΛасти; 11 — восьмая чешуйка боковой Λинии (окрашена аΛизарином красным S). Масштаб: 1, 2, 4, 7, 8 — 2,5 мм (7, 8 — Λинейка общая); 3 — 5 мм; 5, 9, 10 — 3 мм; 6 — 2 мм; 11 — 0,3 мм Fig. 1. Seychelliceps cooperi comb. nov., ZMMU no. 23252, 155 mm SL (1, 3, 5, 7, 9) and Suggrundus macracanthus, IORAS, Vietnam, Nha Trang Bay, 124 mm SL (2, 4, 6, 8, 10, 11), details: 1, 2 — uppermost preopercular spine; 3, 4 — crests and/or tubercles of cranial roofing bone in postorbital portion of head; 5, 6 — posterior ending of premaxillary tooth band; 7, 8 — shape of tooth band on palatine; 9, 10 — predorsal scales; 11 — eighth scale of lateral line (stained by alizarine red S). Scale bars: 1, 2, 4, 7, 8 — 2.5 mm (7, 8 — common bar); 3 — 5 mm; 5, 9, 10 — 3 mm; 6 — 2 mm; 11 — 0.3 mm in Of The Genus (Teleostei: Scorpaeniformes)
Рис. 1. Seychelliceps cooperi comb. nov., ЗММУ № 23252, 155 мм SL (1, 3, 5, 7, 9) и Suggrundus macracanthus, ИОРАН, Вьетнам, заΛ. Нячанг, 124 мм SL (2, 4, 6, 8, 10, 11), ΑетаΛи строения: 1, 2 — верхний преоперкуΛярный шип; 3, 4 — гребешки и/иΛи бугорки костей крыши черепа в загΛазничной части; 5, 6 — заΑний конец премаксиΛΛярных зубных Λент; 7, 8 — форма небных зубных Λент; 9, 10 — чешуи преΑорсаΛьной обΛасти; 11 — восьмая чешуйка боковой Λинии (окрашена аΛизарином красным S). Масштаб: 1, 2, 4, 7, 8 — 2,5 мм (7, 8 — Λинейка общая); 3 — 5 мм; 5, 9, 10 — 3 мм; 6 — 2 мм; 11 — 0,3 мм Fig. 1. Seychelliceps cooperi comb. nov., ZMMU no. 23252, 155 mm SL (1, 3, 5, 7, 9) and Suggrundus macracanthus, IORAS, Vietnam, Nha Trang Bay, 124 mm SL (2, 4, 6, 8, 10, 11), details: 1, 2 — uppermost preopercular spine; 3, 4 — crests and/or tubercles of cranial roofing bone in postorbital portion of head; 5, 6 — posterior ending of premaxillary tooth band; 7, 8 — shape of tooth band on palatine; 9, 10 — predorsal scales; 11 — eighth scale of lateral line (stained by alizarine red S). Scale bars: 1, 2, 4, 7, 8 — 2.5 mm (7, 8 — common bar); 3 — 5 mm; 5, 9, 10 — 3 mm; 6 — 2 mm; 11 — 0.3 mm
FIGURE 13. Superimposed landmark diagrams visualizing shape changes between M1 in Identifying tooth position of isolated teeth of sparassodonts (Mammalia: Metatheria) using geometric morphometrics
FIGURE 13. Superimposed landmark diagrams visualizing shape changes between M1 (large gray circles) and M3 (small black circles) of selected non-borhyaenid sparassodonts: (A) Allqokirus australis (MNHC 8267), (B) Patene coluapiensis (AMNH 28448), (C) Procladosictis anomala (MACN-A 10327), (D) Hondadelphys fieldsi (UCMP 37960), (E) Sipalocyon gracilis (AMNH 9254), (F) Lycopsis longirostrus (UCMP 38061), (G) Prothylacynus patagonicus (MACN-A 707), (H) Thylacosmilus atrox (MMP 1443).
FIGURE 13. A-I in The intriguing shapes of the ammonoid whorl
FIGURE 13. A-I) Variation of the predicted whorl area, the whorl width index (WWI=ww/wh), and the imprint zone rate (IZR=iz/wh) considering the first three PCs measured from the virtual models presented in Figure 10. The predicted area has no spatial units. A) Predicted area for PC1 at different PC2 values. Note PC1 has a local maximum depending on the score values of PC2 and PC3. B) Predicted area for PC2 at different PC1 values. C) Predicted area for PC3 at different PC1 values. D) Predicted WWI for PC1 at different PC2 values. E) Predicted WWI for PC2 at different PC1 values. F) Predicted WWI for PC3 at different PC1 values. Note that at low PC1 values changes for the WWI are not noticeable, and only at high PC1 values the changes become significant for PC3. G) Predicted IZR for PC1 at different PC2 values. H) Predicted IZR for PC2 at different PC1 values. I) Predicted IZR for PC3 at different PC1 values.
FIGURE 12 in The intriguing shapes of the ammonoid whorl
FIGURE 12. Variation of the measured area against different response variables. A) Relationship between the area and the diameter. B) Relationship between the area and the whorl height in mm. C). Relationship between the area and centroid size (CS). D) variation of the measured area for each of the selected subtaxa.
FIGURE 11 in The intriguing shapes of the ammonoid whorl
FIGURE 11. Predicted score PC1 values for the simple additive model 1 presented in Table 2 including the centroid size (CS), and the subtaxa, as predictors. Estimates with 95% of confidence. A) Predicted values for the centroid size (CS). B) Predicted values of PC1 for the selected subtaxa.
FIGURE 5 in The intriguing shapes of the ammonoid whorl
FIGURE 5. Major subclades (order level) within Ammonoida based on sutural/septal classification through time. Occurrences of major extinction events are signalled as dashed lines, minor extinction events relevant to ammonoid diversity are illustrated as dotted lines based on Bambach et al. (2004).
FIGURE 1 in The intriguing shapes of the ammonoid whorl
FIGURE 1. Summary of the morphometric and geometric morphometric (GM) models used in this work. A) Cross-section of an ammonoid showing the standard morphometric model to study the ammonoid conch morphology presented by Korn (2010) and Klug et al. (2015). Abbreviations: ah, aperture height; dm, diameter; iz, imprint zone; wh, whorl height, ww, whorl width. B-E) Geometric morphometric model to study the whorl shape presented in Morón-Alfonso et al. (2021). B) Basic virtual model (grey area) showing the corresponding configuration based on 18 semi-landmarks. C) GM model adapted to the specimen shown in A. D) GM model applied to the whorl of an evolute specimen. E) GM model applied to the whorl profile of an involute specimen (modified from Raup 1967).
FIGURE 10 in The intriguing shapes of the ammonoid whorl
FIGURE 10. Bivariate plots illustrating the shape variation from the obtained morphospace for the first three principal components. Shadowed cells indicate the space that is occupied by the ammonoid genera.
FIGURE 3 in The intriguing shapes of the ammonoid whorl
FIGURE 3. Density plot of the ammonoid genera through time. Occurrences of major extinction events are signalled as dashed lines, minor extinction events relevant to ammonoid diversity are illustrated as dotted lines based on Bambach et al. (2004).
FIGURE 2 in The intriguing shapes of the ammonoid whorl
FIGURE 2. Workflow employed in this work. The squares identify the produced data, and the hexagons indicate the analyses and procedures.
FIGURE 8. A in The intriguing shapes of the ammonoid whorl
FIGURE 8. A) Backtransform morphospace for PC1 and PC2 showing the variations on the semilandmark configurations. B) Bivariate plots for the predicted configurations obtained from A for PC3score = 0.
FIGURE 6 in The intriguing shapes of the ammonoid whorl
FIGURE 6. Boxplots for the first three principal components for the subtaxa used in this work. A) Subtaxa against PC1. B) Subtaxa against PC2. C) Subtaxa against PC3.
3DKMI: A MATLAB package to generate shape signatures from Krawtchouk moments and an application to species delimitation in planktonic foraminifera
<p>The rapid and repeatable characterization of individual morphology has advanced automated taxonomic classification. The most direct study of evolutionary processes is, however, not from taxonomic description, but rather of the evolution of the traits that comprise individuals and define species. Repeatable signatures of individual morphology are crucial for analyzing the response to selection at scale, and thus tracking evolutionary trajectories through time and across species boundaries. Here, we introduce our 3DKMI – an open-source MATLAB package designed for the study of morphology using three-dimensional (3D) Krawtchouk moment invariants. The volumetric features derived from the 3D images remain stable under translation, scaling, and rotation and, for an image of size 128x128x128 can be computed in less than 0.1 seconds. We applied our package as a case study on a collection of 300 X-ray computed tomography scans of planktonic foraminifera specimens across five species to (1) assess the invariance of the features under different transformations and (2) analyze morphological differences among species based on the extracted characteristics. We show that 3DKMI has the capacity to efficiently and repeatedly characterize the signatures of individual morphology. In the future, we hope that the 3D feature extraction technique 3DKMI will be widely applied to digital collections to advance research in ecology and evolution.</p>
Data from: Tree growth-forms reveal dominant browsers shaping the vegetation
<p>This data repository belongs to the publication "Tree growth-forms reveal dominant browsers shaping the vegetation" by Churski et al. in Functional Ecology</p> <p>Authors Marcin Churski<sup>1</sup>, Dries P.J. Kuijper<sup>1</sup>, Katharina Semmelmayer<sup>1</sup>, William J. Bond<sup>2</sup>, Joris P.G.M. Cromsigt<sup>3</sup><sup>,</sup><sup>4</sup>, Yan Wang<sup>5</sup> & Tristan Charles-Dominique<sup>6</sup><sup>,</sup><sup>7</sup></p> <p>Author for correspondence: Marcin Churski email: <a href="mailto:mchurski@ibs.bialowieza.pl">mchurski@ibs.bialowieza.pl</a></p> <p><sup>1</sup>Mammal Research Institute Polish Academy of Sciences, ul. Stoczek 1, 17-230 Białowieża; <sup>2</sup>University of Cape Town, HW Pearson Building, University Ave N, Rondebosch, Cape Town, 7701; <sup>3</sup>SLU, Department of Wildlife, Fish and Environmental Studies, 901 83 Umeå, Sweden; <sup>4</sup>Centre for African Conservation Ecology, Department of Zoology, Nelson Mandela University, PO Box 77000, Gqeberha, 6031, South Africa; <sup>5</sup>Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland; <sup>6</sup>AMAP, University of Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France; <sup>7</sup>CNRS UMR7618; Sorbonne University; Institute of Ecology and Environmental Sciences Paris; 4, place Jussieu 75005 PARIS</p> <div> <h4>Summary</h4> <a href="https://github.com/mripasteam/transformers#summary"></a></div> <ul> <li>Plants adopt particular growth-forms when they are exposed to extreme environmental conditions. In this study, we describe a unique woody plant growth-form induced by large mammalian herbivores and discuss that this growth-form could have evolved as a strategy for escaping the browser zone in herbivore driven ecosystems.</li> <li>We analysed responses of key architectural and morphological attributes (branching and thorn density, tree dimensions, presence of flowers and fruits) of three Eurasian spiny tree species (Malus sylvestris, Prunus cerasifera, Pyrus pyraster) to different levels of browsing by large herbivores in the temperate Białowieża Forest, Poland.</li> <li>Under high browsing pressure, studied trees displayed two distinct forms of the crown: a bottom sterile part developing into a densely branched structure with high density of thorns (‘cage-form’), and an upper reproductive part that escaped from herbivore control (‘escaped-form’). The size of cage-form influenced the feeding behaviour of red deer (Cervus elaphus) by increasing the time deer spend foraging and increasing the bite rate. The height at which cages started to escape and their diameter matched with foraging reach of red deer.</li> <li>Synthesis. We argue that the frequency and cage dimensions of this woody growth-form in the landscape could inform on the type and intensity of recent herbivory. Moreover, its distinctive inducibility suggests that this growth-form did not emerge recently under anthropogenic pressure but could be the legacy of ancient herbivory effects. Observational evidence suggests that this growth-form emerged in several herbivore-driven systems around the globe and may be used to identify the dominant herbivores that control vegetation structure in these ecosystems.</li> </ul> <p>Data</p> <p>The table 'cage_traits.csv' contains data on morphological traits measured on individual trees and was used to describe the key architectural attributes defining the cage and escape forms (trapped branches vs. escaped branches), test if the cage form is induced by mammalian herbivores or not and if the dimensions of the cage form could inform on which animal induced them.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Status: if the tree individual grow taller than animal reach (escaped or trapped) Species: tree species Branch_N: observed branch ID Length: branch length (cm) N_Thorns: number of thorns N_Twigs: number of twigs Longest_thorn: the length of longest thorn on the branch (mm) N_browsed_Twigs: Number of browsed twigs (1) vs non browsed (0) twigs on a branch Branch_esc: branch position, "escape" indicates the branch growing on the escaped part of a tree FlowerOrFruit: flower or fruit number found on the branch BDI: branch density index. It is calculated by twigs number divided by branch length Thorn_density:Thorn number divided by branch length BrowRate: observed number of browsed_Twigs divided by branch length Bite: 1 indicates the branch was browsed, 0 indicated the branch was not browsed. browsing_environment: if the tree is exposed to high browsing environment or not. </code></pre> <div> </div> </div> <p>The table 'foraging_time_barplot.csv' contains camera trap data on total foraging time of all the animal on all the tree species and was used to answer the question how the presence of cage form affect herbivore foraging behaviour. This data set was specifically used to produce the bar plot in Figure 5C.</p> <div> <pre><code>Column headers description: N_Protocol : Tree ID animal_species : observed animal species Species: tree species Foraging_time: observed total foraging time. </code></pre> <div> </div> </div> <p>The table 'foraging_time.csv' contains camera trap data on total foraging time of all the animal on all the tree species and was used to answer the question how the presence of cage form affect herbivore foraging behaviour.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Total_foraging_time: total record foraging time per tree individual Total_bite_rate : bite rate per tree individual Bite_rate : bite rate per tree branch BDI: branch density index. Foraging_time_av: record foraging time per tree branch Surface: the surface of the crown </code></pre> <div> </div> </div> <p>The table 'escape_height.csv' contains data on individual tree heights in relation to their status (escaped vs trapped). This data set was used to test if the dimensions of the cage form could inform on which animal induced them.</p> <div> <pre><code>Column headers description: N_Protocol: Tree ID Status: if the tree individual grow taller than animal reach Species: Tree species Height: Tree height</code></pre> </div>
Supplementary data from: Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic
<p>The Arctic ecosystems and their species are exposed to amplified climate warming and, in some regions, to rapidly developing economic activities. We used macroecological modeling to estimate the community-level species richness across the Western Siberian tundra, with climate variables and anthropogenic influence identified as main explanatory factors. Our results reveal complex spatial patterns of community-level species richness in the Western Siberian Arctic. We show that climatic factors such as temperature (including paleotemperature) and precipitation are the main drivers of plant species richness in this area, and the role of relief is clearly secondary.</p> <p>Here we present a supplementing dataset to the analysis of our paper "Current and past climate co-shape community-level plant species richness in the Western Siberian Arctic"<strong> </strong>(<a href="https://doi.org/10.1002/ece3.11140">https://doi.org/10.1002/ece3.11140</a>). Our research is based on the Western Siberian part of the Russian Arctic Vegetation Archive (AVA-RUS, <a href="http://avarus.space">http://avarus.space</a>), with 1483 Braun-Blanquet plots observed from 2005-2018.</p> <p>The dataset contains geolocated species richness data along with sampled raster data on environmental and anthropogenic predictors used for modeling. The scripts are used for paleoclimatic data sampling; testing univariate predictive performance and limited collinearity for all predictors; fitting four different modes: random forest, gradient boosting machine, generalized linear model, and generalized additive model; their validation and projection. Detailed information regarding the data structure and the applied methods could be found in the paper.</p>
Fig. 5 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 5. Relative mean GSI vs. relative mean HSI of color pattern variants of Cichla temensis. Points for GSI represent the mean value for each CPV grade as compared to the range encountered. Points for HSI represent the mean value for each CPV grade compared to the range encountered.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.