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4,753 results for “SHAPE”
Plasticity and evolution shape the scaling of metabolism and excretion along a geothermal temperature gradient
<p>Physiological rates are heavily dependent on temperature and body size. Most current predictions of organisms' response to environmental warming are based on the assumption that key physiological rates like metabolism and excretion scale independently with body size and temperature and will not evolve. However, temperature is a significant driver for phenotypic variability in the allometric scaling and thermal sensitivity of physiological rates within ectotherm species, suggesting that evolution may play a role in shaping these parameters.</p> <p>We common-reared six populations of western mosquitofish that have recently established (~100 years ago) in geothermal springs along a broad thermal gradient (19-33°C) to determine whether these scaling parameters are affected by evolutionary and/or plastic responses to warming over ecological timescales. Each population was reared at four different temperatures (23, 26, 30 and 32°C). We measured routine metabolic and nitrogen excretion rates on mosquitofish across a wide body size range.</p> <p>We found evidence for plasticity, but not evolution, increasing the allometric scaling of metabolic rate with temperature. Plasticity in metabolism allometry reflected a decrease in thermal sensitivity at smaller body sizes.</p> <p>We found evidence for evolution of phenotypic plasticity on the allometry of excretion rate, reflecting evolutionary differences in how thermal sensitivity varies with body size across different populations.</p> <p>Evolutionary differences in excretion rate scaling did not influence the relationship between excretion and metabolism across rearing temperatures, suggesting that warming does not affect the balance between mosquitofish energy demands and nutrient recycling rates.</p>
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>7_experiments.zip contains modified model code and output data of each experiment in this study.</li> <li>off-line test.zip contains off-line test code and output data.</li> <li>plot_scripts.zip are the NCL scripts used for figures in the paper.</li> </ul>
Data: Central European forest-steppe: an ecosystem shaped by climate, topography and disturbances
<p>Supplementary material of Chytrý et al. (2022): Central European forest-steppe: an ecosystem shaped by climate, topography and disturbances. Journal of Biogeography.</p> <p><strong>Appendix S1. Location of the study sites.</strong></p> <p><strong>Appendix S2. Characteristics of the study sites.</strong></p> <p>To explore the interactive effect of macroclimate and topography on the forest-steppe distribution, we selected 108 Central European forest-steppe sites formed on various substrates and under various macroclimates. The key criterium for selection was the presence of relatively well-preserved (near-)natural steppe patches with relict steppe species. Most of the sites were selected based on the empirical knowledge of authors, and some were selected based on geobotanical literature (sources indicated in Appendix S2). Sites obtained from the literature and with no coordinates in the original source were georeferenced with the help of aerial photographs (maps.google.cz) and outdoor maps (mapy.cz; openstreetmap.org). We mostly set the coordinates to the best-developed steppe patch at each site. Some lowland sites with particularly low topographic heterogeneity (especially flatland loess steppes in south-eastern Hungary) were georeferenced with lower precision because the steppe patches were not easily identifiable from aerial photographs.</p> <p> </p>
Data from: Ancestral ecological regime shapes reaction to food limitation in the Least Killifish, Heterandria formosa
<p>Populations with different densities often show genetically-based differences in life histories. The divergent life histories could be driven by several agents of selection, one of which is variation in per-capita food levels. Its relationship with population density is complex, as it depends on overall food availability, individual metabolic demand, and food-independent factors potentially affecting density, such as predation intensity. Here we present a case study of two populations of a small live-bearing freshwater fish, one characterised by high density, low predation risk, low overall food availability, and presumably low per-capita food levels, and the other by low density, high predation risk, high overall food availability, and presumably high per-capita food levels. Using a laboratory experiment we examined whether fish from these populations respond differently to food limitation, and whether size at birth, a key trait with respect to density variation in this species, is associated with any such differential responses. While at the lower food level growth was slower, body size smaller, maturation delayed and survival reduced in both populations, these fitness costs were smaller in fish from the high-density population. At low food, only 15% of high-density fish died, compared to 75% of low-density fish. This difference was much smaller at high food (0% vs. 15% mortality). The increased survival of high-density fish may, at least partly, be due to their larger size at birth. Moreover, being larger at birth enabled fish to mature relatively early even at the lower food level. We demonstrate that sensitivities to food limitation differ between study populations, consistent with selection for a greater ability to tolerate low per-capita food availability in the high-density population. While we cannot preclude other agents of selection from operating in these populations simultaneously, our results suggest that variation in per-capita food levels is one of those agents.</p>
Dataset from: Changes in cell size and shape during 50,000 generations of experimental evolution with Escherichia coli
<p>Bacteria adopt a wide variety of sizes and shapes, with many species exhibiting stereotypical morphologies. How morphology changes, and over what timescales, is less clear. Previous work examining cell morphology in an experiment with Escherichia coli showed that populations evolved larger cells and, in some cases, cells that were less rod-like. That experiment has now run for over two more decades. Meanwhile, genome sequence data are available for these populations, and new computational methods enable high-throughput microscopic analyses. In this study, we measured stationary-phase cell volumes for the ancestor and 12 populations at 2,000, 10,000, and 50,000 generations, including measurements during exponential growth at the last time point. We measured the distribution of cell volumes for each sample using a Coulter counter and microscopy, the latter of which also provided data on cell shape. Our data confirm the trend toward larger cells while also revealing substantial variation in size and shape across replicate populations. Most populations first evolved wider cells but later reverted to the ancestral length-to-width ratio. All but one population evolved mutations in rod shape maintenance genes. We also observed many ghost-like cells in the only population that evolved the novel ability to grow on citrate, supporting the hypothesis that this lineage struggles with maintaining balanced growth. Lastly, we show that cell size and fitness remain correlated across 50,000 generations. Our results suggest that larger cells are beneficial in the experimental environment, while the reversion toward ancestral length-to-width ratios suggests partial compensation for the less favorable surface area-to-volume ratios of the evolved cells.</p>
The timing of spring warming shapes reproductive effort in a warm-water fish: the role of mismatches between hepatic and gonadal processes
Spring-spawning fishes native to northern environments rely on both increasing temperature and lengthening photoperiod to cue reproduction and may thus be particularly sensitive to rapid warming earlier in the year while day lengths remain short. We investigated the reproductive response of pumpkinseed sunfish Lepomis gibbosus to spring warming commencing at a range of day lengths (9 – 15 hours), corresponding to various calendar days (January 10 – May 22). In both the laboratory and field, both male and female fish that experienced early warming while day lengths were <11 hours: 1) failed to initiate reproductive preparation in the liver before gonad development began, and 2) had reduced reproductive allocation. Analysis of published data on temperate fishes suggested that liver development prior to gonad development is widespread across warm-, cool-, and cold-water thermal guilds, though the precise phenology of liver relative to gonad development appears to vary widely among species. Together, our results point toward dampened reproductive preparation as a novel mechanism mediating reduced reproductive output in both warm- and cool-water fish following earlier spring warming.
Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".
<p>The model codes, data, and plot scripts used in the paper, "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".</p> <ul> <li>Figs&Table are the NCL scripts used for figures and table in the paper.</li> <li>Model_Results contains output data of each experiment in this study.</li> <li>Mods_Scripts contains modified model code.</li> <li>Offline_Code contains off-line test code.</li> </ul>
MITgcm model setup and output for "What determines the shape of the Pine-Island-like ice shelf?"
<p><strong>(Contents)</strong><br> Here it contains<br> <br> jim_run3 = CTRL (Similar to Jordan et al., 2018 but with smaller ocean)</p> <p>melt/run/ = IOCTRL, M(all)V(dyn)U(dyn)</p> <p>melt2/ = M( changing, see below ) V( dyn ) U( 0 )<br> melt2/run/ = M(all)V(dyn)U(0)<br> melt2/run2/ = M(20)V(dyn)U(0)<br> melt2/run3/ = M(GL10)V(dyn)U(0)<br> melt2/run4/ = M(GL20)V(dyn)U(0)</p> <p><br> melt3/ = M ( changing, see below ) V( changing ) U( 0 )<br> melt3/run5/ = M(all)V(2000)U(0)<br> melt3/run10/ = M(20)V(2000)U(0)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> Input melt data for running ice only experiments<br> #data.streamice#<br> meltCTRL.bin = M(all) ... melt rate data of CTRL for every time steps <br> meltCTRL2.bin = M(GL10)<br> meltCTRL3.bin = M(20 m/a)<br> meltCTRL4.bin = M(GL20)</p> <p>Input velocity data for running ice only experiments <br> melt3/<br> uvel_ext4.bin = U(0 m/a)<br> vvel_ext5.bin = V(2000 m/a)</p> <p><strong>(How to compile and run)</strong><br> mkdir build<br> cd build/<br> module load intel/2021.2.0<br> module load impi/2021.2.0<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> ../../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas_tokyo3 -mpi -mods ../code/<br> make depend<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> make -j 16</p> <p> mkdir ../run<br> cd ../run/<br> cp ../build/mitgcmuv .<br> cp ../input/* .<br> pjsub job*.pbs<br> <br> <strong>Other important links</strong><br> https://github.com/hgu784/MITgcm_67s<br> <br> For more info please send an email to Yoshihiro.Nakayama@lowtem.hokudai.ac.jp</p>
History and environment shape spatial genetic variation and predict climate maladaptation in a narrowly distributed serotinous pine, Pinus muricata
<p><span></span></p> <p>Understanding the distribution of genetic diversity and differentiation in species with disjunct and isolated populations is critical for assessing how environment shapes genetic variation and the potential response to climate change. In contrast to the large distributions and population sizes of most pine species, <em>Pinus muricata</em> (Bishop pine) occurs in a small number of isolated and disjunct populations occupying a narrow band of environmental conditions along the coast of western North America. We used genotyping by sequencing to generate population genomic data for trees sampled from nearly all existing populations of <em>P. muricata</em> (12 populations, 213 individuals, 7,828 loci) to describe the spatial arrangement of genetic differentiation and diversity. We used genetic-environment association (GEA) analyses to quantify the contribution of environmental variables to local adaptation and spatial genetic structure. Based on these results, we quantified relative levels of potential maladaptation given future climate projections at 2041 – 2060 and 2081 – 2100. Our analyses reveal pronounced spatial genetic structure across the distribution, with most populations forming genetically identifiable groups across a latitudinal gradient, and remarkable evidence for differentiation among three proximally distributed stands on Santa Cruz Island. Despite occurring in small, isolated populations, <em>P. muricata</em> do not exhibit strongly reduced diversity. GEA analyses suggested that specific soil and climate variables have contributed to local adaptation. Genomic offset analyses suggest geographic variation in potential maladaptation, with northern populations experiencing higher levels under projected climate change. Overall, our results suggest that isolation and local adaptation have shaped genetic variation among disjunct populations, and illustrate the consequences of this variation for <em>P. muricata</em> under projected climate change.</p>
Propulsion of bullet- and cup-shaped nano- and microparticles by traveling ultrasound waves
<p>Supplementary data for the following manuscript: Johannes Voß, Raphael Wittkowski, "Propulsion of bullet- and cup-shaped nano- and microparticles by traveling ultrasound waves".</p>
Photoperiod influences the shape and scaling of freshwater phytoplankton responses to light and temperature
Light fluctuations are ubiquitous, exist across multiple spatial and temporal scales, and directly affect the physiology and ecology of photoautotrophs. However, the indirect effects of light fluctuations on the sensitivity of organisms to other key environmental factors are unclear. Here, we evaluate how photoperiod regime (period of time each day where organisms receive light), a dynamic element of aquatic ecosystems, can influence the interactive effects of temperature and irradiance (intensity of light) on the growth rate of phytoplankton populations. We first completed a literature review and meta-analysis that suggests photoperiod alters the individual effects of temperature – but not irradiance – on algal growth rates and that highlights how few studies experimentally manipulate photoperiod, temperature, and irradiance. To address this empirical gap, we conducted a set of laboratory experiments on three freshwater phytoplankton species (Chlamydomonas reinhardtii, Chlorella vulgaris, and Cryptomonas ovata). We measured performance surfaces relating growth rate to irradiance and temperature gradients for each species in constant (24:0 hours of light:dark) environments. We then evaluated whether analogous surfaces measured under different photoperiods and scaled by the duration of light availability could be inferred from results under constant light. For a majority of the combinations of species and photoperiods examined, photoperiod meaningfully altered the intercept and shape of performance surfaces. These differences were most pronounced under the shortest photoperiod (6:18 light:dark), where populations underperformed expectations. Alterations to performance surfaces were non-linear and mostly structured by temperature with higher temperatures yielding higher than anticipated growth rates. Collectively, these experiments and synthesis reveal the potential for photoperiod regime to influence the effects of temperature, irradiance, and their interaction on phytoplankton growth. Beyond the environmental variables and organisms presently considered, this research highlights the capacity for dynamic, abiotic variables to exert direct effects while also influencing relationships among other environmental factors.
Data from: T cell morphodynamics reveal periodic shape oscillations in 3D migration
<p>Surface segmentation data of cytotoxic T cells migrating in 3D collagen matrices, imaged by lattice light-sheet microscopy and used for quantitative morphodynamic analysis in the manuscript: T Cell Morphodynamics Reveal Periodic Shape Oscillations in 3D Migration.</p>
FIGURE. Cauline Leaves of Sanicula orthacantha var. brevispina from China, Sichuan, Emei Shan, showing the size and shape of leaves within and between populations (each line represents a plant individual). A. C.H. Li 1122 (NAS). B. H.M. Li & Y.S. Zhang 1151 (NAS). in Taxonomic studies on the genus Sanicula (Apiaceae) from China ( ): The clarification of some morphological distinction between S. orthacantha var. orthacantha and S. orthacantha var. brevispina, with the reduction of S. petagnioides to the synonymy of the former, and S. orthacantha var. stolonifera to
FIGURE. Cauline Leaves of Sanicula orthacantha var. brevispina from China, Sichuan, Emei Shan, showing the size and shape of leaves within and between populations (each line represents a plant individual). A. C.H. Li 1122 (NAS). B. H.M. Li & Y.S. Zhang 1151 (NAS).
Infant mode of delivery shapes the skin mycobiome of prepubescent children
<p>STORMS checklist for <strong>Infant mode of delivery shapes the skin mycobiome of prepubescent children</strong>.</p>
FIGURE. Jensenia decipiens (Mitt.) Grolle II. (A) Fan-shaped erect shoots (B) Marginal tooth of thallus. A, B Ruklani & Rubasinghe 66-14SR (PDA) in Thalloid Liverworts (Marchantiopsida) of Sri Lanka
FIGURE. Jensenia decipiens (Mitt.) Grolle II. (A) Fan-shaped erect shoots (B) Marginal tooth of thallus. A, B Ruklani & Rubasinghe 66-14SR (PDA)
Subspecies and Distribution. S. l. leucotis Bailey, 1902 — Y-shaped distributional pattern in Mexico from approximately 27° N latitude in the Sierra Madre Occidental and 25° N in the Sierra Madre Oriental SE into the C part of the Transvolcanic Beltas far S as the state of Mexico. S. I alticola Bailey, 1902 — montane areas from N Puebla S to NW Oaxaca. in Cricetidae
Subspecies and Distribution. S. l. leucotis Bailey, 1902 — Y-shaped distributional pattern in Mexico from approximately 27° N latitude in the Sierra Madre Occidental and 25° N in the Sierra Madre Oriental SE into the C part of the Transvolcanic Beltas far S as the state of Mexico. S. I alticola Bailey, 1902 — montane areas from N Puebla S to NW Oaxaca.
Data from: Malaysian weedy rice shows its true stripes: wild Oryza and elite rice cultivars shape agricultural weed evolution in Southeast Asia
Weedy rice is a close relative of domesticated rice (Oryza sativa) that competes aggressively with the crop and limits rice productivity worldwide. Most genetic studies of weedy rice have focused on populations in regions where no reproductively compatible wild Oryza species occur (North America, Europe, northern Asia). Here we examined the population genetics of weedy rice in Malaysia, where wild rice (O. rufipogon) can be found growing in close proximity to cultivated and weedy rice. Using 375 accessions and a combined analysis of 24 neutral SSR loci and two rice domestication genes (sh4, controlling seed shattering, and Bh4, controlling hull color), we addressed the following questions: 1) What is the relationship of Malaysian weedy rice to domesticated and wild rice, and to weedy rice strains in the US? 2) To what extent does the presence of O. rufipogon influence the genetic and phenotypic diversity of Malaysian weeds? 3) What do the distributions of sh4 and Bh4 alleles and associated phenotypes reveal about the origin and contemporary evolution of Malaysian weedy rice? Our results reveal: independent evolutionary origins for Malaysian weeds and US strains, despite their very close phenotypic resemblance; wild-to-weed gene flow in Malaysian weed populations, including apparent adaptive introgression of seed-shattering alleles; and a prominent role for modern Malaysian cultivars in the origin and recent proliferation of Malaysian weeds. These findings suggest that the genetic complexity and adaptability of weedy crop relatives can be profoundly influenced by proximity to reproductively compatible wild and domesticated populations.
Shape of evasive prey can be an important cue that triggers learning in avian predators
<p>This repository contain the complete dataset used for all analysis, presented within the publication "Shape of evasive prey can be an important cue that triggers learning in avian predators". </p>
Nest shape influences colony organization in ants: spatial distribution and connectedness of colony members differs from that predicted by random movement and is affected by nest space
<p><strong>Overview</strong></p> <p>Data used for the manuscript: Nest shape influences colony organization in ants: spatial distribution and connectedness of colony members differs from that predicted by random movement and is affected by available space</p> <p><strong>Purpose of the study</strong></p> <p>Investigating how nest shape influences how <em>Temnothorax rugatulus</em> colonies spatially organize in their nests. This includes physical location of colony members and their distances from the entrance, mobile colony member distance to the brood center, worker distance to the physical center of the nest, and comparing worker distributions with those predicted by a random walk model.</p> <p><strong>Structure of the data</strong></p> <p>EMPIRICAL DATA</p> <p>WORKERS: FullDataCoordWorkers.csv, FullDataCoordWorkersRD2.csv</p> <p>Raw experimental data with worker x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>ColorID: The unique color marking assigned to an individual worker's head, thorax, abdomen1, abdomen2 (i.e., Yellow, White, Green, Green = Y,W,G,G)</li> <li>Density: The density treatment (High / Low)</li> </ul> <p>BROOD / QUEENS: FullDataCoordBrood.csv, FullDataCoordBroodRD2.csv; FullDataCoordQueen.csv, FullDataCoordQueenRD2.csv</p> <p>Raw experimental data with brood (OR) queen x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>Density: The density treatment (High / Low)</li> </ul> <p>ALATES: FullDataCoordAlate.csv</p> <p>Raw experimental data with alate (winged reproductive individuals) x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>SexID: The unique sex assignment and number given to an individual alate: Sex, SexNumber, TotalNumber (i.e., the first male alate observation that came after three queen alates making it the fourth total observation = M,1,4)</li> </ul> <p>NETLOGO SIMULATIONS: ArchitectureMoveModelFull.csv</p> <p>Raw netlogo simulation data with agent x and y positions in nests</p> <ul> <li>RunNumber: The simulation number - 1 to 4000 - there are 1000 simulations for each combination of nest shape and size</li> <li>NestSize: The size of the nest area that agents were allowed to move throughout (Small / Large)</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>TimeStep: The duration of each simulation (should be 50000)</li> <li>xcor: a list of every agent x coordinate position at the end of the simulation</li> <li>ycor: a list of every agent y coordinate position at the end of the simulation</li> </ul> <p>REFERENCE DATA </p> <p>NEST BINS: Empirical</p> <p>BinsNullFull.csv</p> <p>Null data sheet with eight bins for tube and circle nests in every colony</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> </ul> <p>BinCoordFull.csv</p> <p>Reference binning coordinates to group empirical coordinates into nest sections</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>CoordID: The unique coordinate identifier within each colony and nest combination</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> </ul> <p>NEST BINS: Netlogo Simulations</p> <p>BinsNullNetlogo.csv</p> <p>Null data sheet with eight bins for tube and circle nests in each simulation treatment</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>Bin: Nest section identifier (1-8)</li> </ul> <p>BinCoordNetlogo.csv</p> <p>Reference binning coordinates to group Netlogo simulation coordinates into nest sections</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>ScaledX: X-axis coordinate</li> <li>ScaledY: Y-axis coordinate</li> <li>CoordID: The unique coordinate identifier within each colony and nest combination</li> </ul> <p>CORNERS: Empirical</p> <p>CornerFull.csv</p> <p>Whether a nest section has a corner or not</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> <li>Corner: Presence of a corner (Y / N)</li> </ul> <p>CORNERS: Empirical</p> <p>CornerFullSim.csv</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> <li>Corner: Presence of a corner (Y / N)</li> </ul> <p>REFERENCE DATA </p> <p>DISTANCES IN THE NEST: Empirical</p> <p>DistBinsFull.csv</p> <p>Reference coordinates for the entrance of nest sections (Bin) front-to-back and shortest distance to the entrance from each nest section entrance</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Distance: Reference shortest distance from a nest section to the entrance</li> <li>Bin: Nest section identifier (1-8)</li> <li>BinX: X-axis reference coodinate for a nest section entrance</li> <li>BinY: Y-axis reference coodinate for a nest section entrance</li> <li>Xmax: Max X-axis coordinate possible within the nest</li> <li>Ymax: Max Y-axis coordinate possible within the nest</li> <li>MaxDist: Max possible shortest distance from the nest entrance</li> <li>TubeRatio: Ratio of shortest distance to the nest entrance in circle nest / tube nest</li> </ul> <p>DISTANCES IN THE NEST: Netlogo Simulations</p> <p>DistBinsFullNetlogo.csv</p> <p>Reference coordinates for the entrance of nest sections (Bin) front-to-back and shortest distance to the entrance from each nest section entrance</p> <ul> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Distance: Reference shortest distance from a nest section to the entrance</li> <li>Bin: Nest section identifier (1-8)</li> <li>BinX: X-axis reference coodinate for a nest section entrance</li> <li>BinY: Y-axis reference coodinate for a nest section entrance</li> <li>Xmax: Max X-axis coordinate possible within the nest</li> <li>Ymax: Max Y-axis coordinate possible within the nest</li> <li>MaxDist: Max possible shortest distance from the nest entrance</li> <li>TubeRatio: Ratio of shortest distance to the nest entrance in circle nest / tube nest</li> </ul> <p>REFERENCE DATA </p> <p>WORKER SITE FIDELITY (SPATIAL FIDELITY & OCCURRENCE ZONE SIZES), ALSO RELATING SIZES TO DISTANCES IN THE NEST</p> <p>ColorRefFull.csv</p> <p>Reference of all possible unique color identifiers paint marked workers</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Head: Head color mark</li> <li>Thorax: Thorax color mark</li> <li>Abd1: Left side abdomen mark</li> <li>Abd2: Right side abdomen mark</li> </ul> <p>NestAreaFull.csv</p> <p>Reference for colony size (number of workers in the colony) and nest area</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Number.ants: Number of workers in the colony after painting</li> <li>Diameter: The diameter of the circle nest</li> <li>Area: The area of the nest</li> </ul> <p>ScalingCircleSFZ.csv</p> <p>Reference to scale the radius of circle nests to make coordinates representing fidelity zone bins</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Scaling: The scaling factor that is applied to the radius of each circle nest</li> </ul>
FIGURE. Drosera hirtella (a–i): a, b, c, habit of the "type morphotype"; d, habit of the "western morphotype"; e, rosette of the "type morphotype"; f, emerging inflorescence, highlighting the red scape with red eglandular trichomes characteristic of the species; g, fertile individuals of D. hirtella (left plant, with inflorescence emerging to the bottom) and D. lutescens (right plant, with inflorescence emerging to the top left) growing under shaded conditions side by side, highlighting the morphological differences between the two species regarding leaf shape and scape and indumentum color; h, i, flower. a–c, f and h at Serra do Cipó, MG; d and h at Chapada dos Veadeiros, GO; e at Diamantina, MG; g at Cristalina, GO. Photo credits: all by PMG. in A synopsis of the genus Drosera (Droseraceae) in Brazil
FIGURE. Drosera hirtella (a–i): a, b, c, habit of the "type morphotype"; d, habit of the "western morphotype"; e, rosette of the "type morphotype"; f, emerging inflorescence, highlighting the red scape with red eglandular trichomes characteristic of the species; g, fertile individuals of D. hirtella (left plant, with inflorescence emerging to the bottom) and D. lutescens (right plant, with inflorescence emerging to the top left) growing under shaded conditions side by side, highlighting the morphological differences between the two species regarding leaf shape and scape and indumentum color; h, i, flower. a–c, f and h at Serra do Cipó, MG; d and h at Chapada dos Veadeiros, GO; e at Diamantina, MG; g at Cristalina, GO. Photo credits: all by PMG.
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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.