Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15,542
datasets available to search
ShareScore release 0.9.0
Dataset results
15,542 results for “How long”
Data set for «Short- and long-distance avian migrants differ in exercise endurance but not aerobic capacity"
<p>Data set of body condition parameters and aerobic metabolic performances of four Old-world flycatcher species (<em>Muscicapidae</em>) during spring migration. The species differ in their migration strategies: European robins (<em>Erithacus rubecula</em>) and black redstarts (<em>Phoenicurus ochruros</em>) are short-distance migrants spending their entire annual cycle within the Palaearctic and some populations move from North African non-breeding grounds to breeding sites in continental Europe. In contrast, common redstarts (<em>P. phoenicurus</em>) and whinchats (<em>Saxicola rubetra</em>) are obligate long-distance migrants which overwinter in sub-Saharan Africa and migrate to Palaearctic breeding sites.</p> <p>We measured body condition and aerobic performances of birds at Ventotene islands - the first stopover site after crossing the Mediterranean Sea.</p> <p> </p> <p>The data set comprises the following parameters:<br> scieName: scientific name of study species: EriRub (European robin), PhoOch(black redstart), PhoPho (common redstart), SaxRub (whinchat)<br> spec: species code (1-4)<br> ring_ID: individual ring number<br> date: date of measurement<br> mig: migration strategy, 1- short-distance, 2 -long-distance<br> sex: 1-male, 2-female<br> fat: body fat scores (according EURING) (range: 1-6)<br> muscle: breast muscle score (according EURING) (range: 0-3)<br> Hb -haemoglobin concentration in peripheral blood (mg ml<sup>-1</sup>)<br> Infection - infection with haemosporidian parasites (0 - uninfected, 1 - infected)<br> body mass_RMR: body mass (g) before RMR measurement<br> body mass_MMR: body mass (g) before MMR measurement<br> RMR: minimum resting metabolic rate (min V̇O<sub>2</sub>, ml min<sup>-1</sup>)<br> mass-specRMR: mass-specific minimum resting metabolic rate (min V̇O<sub>2</sub>, ml (g min)<sup>-1</sup>)<br> MMR: maximum metabolic rate (max V̇O<sub>2</sub>, ml min<sup>-1</sup>)<br> mass-specMMR: mass-specific maximum metabolic rate (max V̇O<sub>2</sub>, ml (g min)<sup>-1</sup>)<br> TimeExhaust: time until exhaustion (min)</p>
Heterogeneity in risk-sensitive allocation of somatic reserves in a long-lived mammal
<p>Patterns of food quality and availability, when combined with energetic demands in seasonal environments, shape resource acquisition and allocation by animals and hold consequences for life-history strategies. In long-lived species with extensive maternal care, regulation of somatic reserves of energy and protein can occur in a risk-sensitive manner, wherein resources are preferentially allocated to support survival at the cost of investment in reproduction. We investigated how Rocky Mountain bighorn sheep (Ovis canadensis), an alpine mammal in a highly seasonal environment, allocate somatic reserves across seasons. In accordance with the hypothesis of risk-sensitive resource allocation, we expected accretion and catabolism of somatic reserves to be regulated relative to pre-season nutritional state, reproductive state, and vary among populations in accordance with local environmental conditions. To test that hypothesis, we monitored seasonal changes of percent ingesta-free body fat (IFBFat) and ingesta-free, fat-free body mass (IFFFBMass) in three populations of bighorn sheep in northwest Wyoming between 2015 and 2019 through repeated captures of female sheep in December and March of each year in a longitudinal study design. Regulation of somatic reserves was risk sensitive and varied relative to the amount of somatic reserves an animal had at the beginning of the season. Regulation of fat reserves was sensitive to reproductive state and differed by population, particularly over the summer. In one population with low rates of recruitment of young, sheep that recruited offspring lost fat over the summer in contrast to the other two populations where sheep that recruited gained fat. And yet, all populations exhibited similar changes in fat catabolism and risk sensitivity over winter. Magnitude of body fat and mass change across seasons may be indicative of sufficiency of seasonal ranges to meet energetic demands of survival and reproduction. Risk-sensitive allocation of resources was pervasive suggesting nutritional underpinnings are foundational to behavior, vital rates, and ultimately, population dynamics. For species living in alpine environments, risk-sensitive resource allocation may be essential to balance investment in reproduction with ensuring survival.</p>
Individual variation explains aging patterns in a cooperatively breeding bird, the long-tailed tit (Aegithalos caudatus)
<p><span><strong>1</strong>.</span><span> Alloparental care in cooperatively-breeding species may alter breeder age-specific survival and reproduction, and subsequently senescence. The helping behaviour itself might also undergo age-related change, and decisions to help in facultative cooperative breeders are likely to be affected by the individual condition.</span></p> <p><span><strong>2</strong>.</span><span> Helpers in long-tailed</span><span> tits (<em>Aegithalos caudatus</em>) assist relatives after failing to raise their own brood, with offspring from helped nests being more likely to recruit into the breeding population.</span></p> <p><span><strong>3</strong>.</span><span> Using data collected over 25 years, we examined the age-trajectories of survival and reproduction in adult long-tailed tits to determine how these were affected by the presence or absence of helpers, and how helper behaviour changed with age.</span></p> <p><span><strong>4</strong>.</span><span> There was evidence for increased reproductive performance with breeder age, but no effect of age on the probability of survival. We found no evidence of significant senescent decline in survival or reproductive performance, although individuals accrued less inclusive fitness in their last year of life. Lifetime reproductive success was positively related to both reproductive lifespan and body mass. Within a season, breeders that were assisted by helpers enjoyed greater reproductive success through enhanced offspring recruitment in the following year. We found no evidence that age affected an individual's propensity to help, or the amount of indirect fitness accrued through helping.</span></p> <p><span><strong>5</strong>.</span><span> We found a positive correlation between lifespan and multiple components of reproductive success, suggesting that individual variation in quality underpins age-related variation in fitness in this species. Helping decisions are driven by condition, and the lifetime inclusive fitness of immigrants was predicted by body mass. These findings further support individual heterogeneity in quality being a major driver for fitness gains across the life course of long-tailed tits.</span></p>
Generation-dependent functional and numerical responses of Neoseiulus californicus (Phytoseiidae) long-term reared on thorn apple pollen
<p>Raw data of Functional and numerical response of N. califrnicus on T. urticae in different generaaations</p>
Interactions between local and global drivers determine long-term trends in boreal forest understory vegetation
Aim: Global change effects on forest ecosystems are increasingly claimed to be context dependent, indicated by interactions between global and local environmental drivers. Most examples of such context dependencies originate from temperate systems, while limited research comes from the boreal biome. Here we set out to test if interactions between climate warming, nitrogen deposition, land-use change resulting in increasing forest density and soil pH drive long-term changes of understory vegetation in boreal forests. Location: Sweden Time period: 1953-2012 Major taxa studied: Vascular plants Methods: We used long-term (50 years) National Forest Inventory data on forest understory vegetation in Sweden to model the combined effects of climate warming, nitrogen deposition, increase in forest density (tree basal area) and soil pH. Results: Our results identify increasing temperature, nitrogen deposition and denser, shadier forest conditions as main drivers of understory vegetation changes during this time period. More importantly, we found that these effects varied with local conditions, i.e. that the change towards a more nitrophilic understory vegetation was more pronounced at low than high soil pH. Forest density was an important modulator of nitrogen deposition and temperature increase, with effects generally decreasing with density. Decreased cover of ericaceous dwarf shrubs was driven by both forest density and nitrogen deposition, with a stronger effect at low than at high pH. Main conclusions: Our results highlight that to understand forest ecosystems´ response to global change, and to make adequate management decisions to mitigate the effects of global change, we need to understand how changes in local environmental factors (forest density and soil pH) interact with global-scale drivers (nitrogen deposition and climate warming). Neglecting such interactions will lead to incorrect estimations of effects. In our case, we would e.g. have underestimated the eutrophication effects on acid soils, which constitute a considerable part of the boreal biome. --
A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China
<p>** VIC_forcings_4vars: 1/4 Degree Gridded Daily Meteorological VIC Forcing Data Set over China domain </p> <p>1. Data sources</p> <p>This dataset is from 1/1/1952 to 12/31/2012, which were derived by interpolating gauged daily precipitation, maximum temperature, minimum temperature and wind speed of 756 ground mornitoring stations from Chinese Meteorological Administration (CMA). </p> <p>** This section provides only a very brief description of the data source. For a full explanation, the user need refer to the published papers in the references ** </p> <p>2. References to Cite</p> <p>We request that users of this data set cite Zhang et al.(2014) in any reports or publications using it. </p> <p>Zhang, X., Tang, Q., Pan, M., Tang, Y., 2014. A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. Journal of Hydrometeorology. (Accepted)</p> <p>3. Dataset format</p> <p>This data set is available in netCDF (which cannot be read directly by VIC). </p> <p>Please click the year number in the table below to download the corresponding daily forcings (i.e., precipitation, maximum temperature, minimum temperature and wind speed). </p> <p><br> ** VICoutput_fluxes: VIC Retrospective Land Surface Dadaset over China: 1952-2012</p> <p>1. Background</p> <p>The Variable Infiltration Capacity (VIC) model was driven using the gridded daily observed forcings (including precipitation, maximum temperature, minimum temperature, and wind speed; if necessary, please download the VIC forcings from http://hydro.igsnrr.ac.cn/public/vic_forcings_4vars.html) to simulate the land surface hydrological cycle from 1952-2012 over China. The modeling study was done at a 3-hourly time step and at a spatial resolution of 0.25 degree. Details can be found in the journal article:</p> <p>Zhang, X., Q. Tang, M. Pan, and Y. Tang, 2014: A Long-Term Land Surface Hydrologic Fluxes and States Dataset for China. Journal of Hydrometeorology. doi: 10.1175/JHM-D-13-0170.1</p> <p>2. Archived data information and format</p> <p>This website provides access to parts of the model derived variables(including the water balance variables and states and energy balance varibales) at daily scale. This dataset is available in netCDF format. Please click the varible names below to download the corresponding variable.</p> <p>3. Download the dataset</p> <p>Model Derived Variables, 1952-2012 (Water Balance Variables and States)</p> <p>Evaporation<br> Runoff<br> Baseflow<br> Soil Moisture Layer 1<br> Soil Moisture Layer 2<br> Soil Moisture Layer 3<br> Snow Water Equivalent</p> <p>Contact: tangqh@igsnrr.ac.cn</p>
Simulation data for the article ,,Filling the gap with long n-alkanes: incorporation of C20 and C30 into phospholipid membranes" - Set3
<p>First set of simulation trajectories and input files for the manuscript ,,Filling the gap with long n-alkanes: incorporation of C20 and C30 into phospholipid membranes".</p> <p>System: DPPC + n-triacontane (CHARMM36); 288 DPPC, 19 water molecules per lipid, 343K</p> <p>Numbers of n-triacontane molecules are given in the file names.</p> <p>Simulations consisted of three 100ns runs, an .xtc trajectory is only included for the final run. Trajectories were created with -dt 10(ps) and -pbc nojump, as used for analysis in the manuscript. Solvent molecules were removed from the trajectories for file size reduction.</p>
Simulation data for the article ,,Filling the gap with long n-alkanes: incorporation of C20 and C30 into phospholipid membranes" - Set1
<p>First set of simulation trajectories and input files for the manuscript ,,Filling the gap with long n-alkanes: incorporation of C20 and C30 into phospholipid membranes".</p> <p>Systems: DPPC + n-decane (CHARMM36); 72 DPPC, 7 water molecules per lipid, 333K unless otherwise noted in the file names.</p> <p>Numbers of n-decane molecules are given in the file names.</p> <p>Simulations consisted of three 100ns runs, an .xtc trajectory is only included for the final run. Trajectories were created with -dt 10(ps) and -pbc nojump, as used for analysis in the manuscript. Solvent molecules were removed from the trajectories for file size reduction.</p>
Simulation data for the article ,,Filling the gap with long n-alkanes: incorporation of C20 and C30 into phospholipid membranes" - Set2
<p>First set of simulation trajectories and input files for the manuscript ,,Filling the gap with long n-alkanes: incorporation of C20 and C30 into phospholipid membranes" (submitted to Langmuir, under peer-review).</p> <p>System: DPPC + n-eicosane (CHARMM36); 72 DPPC, 18 water molecules per lipid, 333K, unless otherwise noted in the file name.</p> <p>Numbers of n-eicosane molecules are given in the file names.</p> <p>Simulations consisted of three 100ns runs, an .xtc trajectory is only included for the final run. Trajectories were created with -dt 10(ps) and -pbc nojump, as used for analysis in the manuscript. Solvent molecules were removed from the trajectories for file size reduction.</p>
Long distance migration is a major factor driving local adaptation at continental scale in a Pacific Salmon
<p><span>Inferring the genomic basis of local adaptation is a long-standing goal of evolutionary biology. Beyond its fundamental evolutionary implications, such knowledge can guide conservation decisions for populations of conservation and management concern. Here, we investigated the genomic basis of local adaptation in the Coho salmon (<i>Oncorhynchus kisutch</i>) across its entirety North American range. We hypothesized that extensive spatial variation in environmental conditions and the species' homing behavior may promote the establishment of local adaptation. We genotyped 7,829 individuals representing 217 sampling locations at more than 100,000 high-quality RADseq loci to investigate how recombination might affect the detection of loci putatively under selection and took advantage of the precise description of the demographic history of the species from our previous work to draw accurate population genomic inferences about local adaptation. Results indicated that genetic differentiation scans and genetic-environment association analyses were both significantly affected by variation in recombination rate as low recombination regions displayed increased rate of outliers. By taking these confounding factors into consideration, we revealed that migration distance was the primary selective factor driving local adaptation and partial parallel divergence among distant populations. Moreover, we identified several candidates SNP associated with long distance migration and altitude including gene known to be involved in adaptation to altitude in other species. The evolutionary implications of our findings are discussed along with conservation applications.</span></p>
On following pages: 3. Pel's Anomalure (Anomalurus pelii); 4. Lesser Anomalure (Anomalurus pusillus); 5. Long-eared Pygmy Anomalure (Idiurus macrotis); 6. Zenker's Pygmy Anomalure (/diurus zenkeri); 7. Cameroon in Anomaluridae
On following pages: 3. Pel's Anomalure (Anomalurus pelii); 4. Lesser Anomalure (Anomalurus pusillus); 5. Long-eared Pygmy Anomalure (Idiurus macrotis); 6. Zenker's Pygmy Anomalure (/diurus zenkeri); 7. Cameroon
On following pages: 5. Sangay Shrew-opossum (Caenolestes sangay); 6. Incan Shrew-opossum (Lestoros inca); 7. Long-nosed Shrew-opossum (Rhyncholestes raphanurus). in Caenolestidae
On following pages: 5. Sangay Shrew-opossum (Caenolestes sangay); 6. Incan Shrew-opossum (Lestoros inca); 7. Long-nosed Shrew-opossum (Rhyncholestes raphanurus).
On following pages: 5. Goodfellows's Tuco-tuco (Ctenomys goodfellowi); 6. Natterer's Tuco-tuco (Ctenomys nattereri tuco (Ctenomys yates); 10. Chacoan Tuco-tuco (Ctenomys conover); 11. Forest Tuco-tuco (Ctenomys fraten); 12. Lessa's fulvus): 15. Andean Tuco-tuco (Ctenomys opimus); 16. Salta Tuco-tuco (Ctenomys saltarius); 17. Scaglia's Tuco-tuco Furtive Tuco-tuco (Ctenomys occultus); 21. Tucuman Tuco-tuco (Ctenomys tucumanus); 22. Brazilian Tuco-tuco (Ctenomys); 7. Rondon's Tuco-tuco (Ctenomys rondoni); 8. Steinbach's Tuco-tuco (Ctenomys steinbachi); 9. Yates's Tuco-Tuco-tuco (Ctenomys lessai); 13. Lewis's Tuco-tuco (Ctenomys lewisi); 14. Long-tailed Tuco-tuco (Ctenomys (Ctenomys scagliai); 18. Argentine Tuco-tuco (Ctenomys argentinus); 19. Mottled Tuco-tuco (Ctenomys latro); 20. brasiliensis). in Ctenomyidae
On following pages: 5. Goodfellows's Tuco-tuco (Ctenomys goodfellowi); 6. Natterer's Tuco-tuco (Ctenomys nattereri tuco (Ctenomys yates); 10. Chacoan Tuco-tuco (Ctenomys conover); 11. Forest Tuco-tuco (Ctenomys fraten); 12. Lessa's fulvus): 15. Andean Tuco-tuco (Ctenomys opimus); 16. Salta Tuco-tuco (Ctenomys saltarius); 17. Scaglia's Tuco-tuco Furtive Tuco-tuco (Ctenomys occultus); 21. Tucuman Tuco-tuco (Ctenomys tucumanus); 22. Brazilian Tuco-tuco (Ctenomys); 7. Rondon's Tuco-tuco (Ctenomys rondoni); 8. Steinbach's Tuco-tuco (Ctenomys steinbachi); 9. Yates's Tuco-Tuco-tuco (Ctenomys lessai); 13. Lewis's Tuco-tuco (Ctenomys lewisi); 14. Long-tailed Tuco-tuco (Ctenomys (Ctenomys scagliai); 18. Argentine Tuco-tuco (Ctenomys argentinus); 19. Mottled Tuco-tuco (Ctenomys latro); 20. brasiliensis).
On following pages: 4. Heptner's Pygmy Jerboa (Salpingotus heptneri); 5. Pallid Pygmy Jerboa (Salpingotus pallidus); 6. Thomas's Pygmy Jerboa (Salpingotus thomasi): 7. Balochistan Pygmy Jerboa (Salpingotus michaelis) ' 8. Long-eared Jerboa (Euchoreutes naso); 9. Great Jerboa (Allactaga majon; 10. Severtzov's Jerboa (Allactaga severtzovi); 11. Bobrinski's Jerboa (Allactodipus bobrinskii: 12. Siberian Jerboa (Orientallactaga sibirica); 13. Gobi Jerboa (Orientallactaga bullata); 14. Balikun Jerboa (Orientallactaga balikunica). in Dipodidae
On following pages: 4. Heptner's Pygmy Jerboa (Salpingotus heptneri); 5. Pallid Pygmy Jerboa (Salpingotus pallidus); 6. Thomas's Pygmy Jerboa (Salpingotus thomasi): 7. Balochistan Pygmy Jerboa (Salpingotus michaelis) ' 8. Long-eared Jerboa (Euchoreutes naso); 9. Great Jerboa (Allactaga majon; 10. Severtzov's Jerboa (Allactaga severtzovi); 11. Bobrinski's Jerboa (Allactodipus bobrinskii: 12. Siberian Jerboa (Orientallactaga sibirica); 13. Gobi Jerboa (Orientallactaga bullata); 14. Balikun Jerboa (Orientallactaga balikunica).
Subspecies and Distribution. C. p. paca Linnaeus, 1766 — E & S Colombia, Venezuela, the Guianas, and E Brazil to Paraguay, N Argentina (Misiones and Corrientes provinces), and Uruguay. C. p. guanta Lonnberg, 1921 — W Brazil, Ecuador, Peru, and N Bolivia. C. p. mexianae Hagmann, 1908 — E Brazil, mouth of the Amazon in Para State. C. p. nelsoni Goldman, 1913 — E & S Mexico (Atlantic slope lowlands from San Luis Potosi State to Yucatan Peninsula), then through Central America to N Costa Rica. C. p. virgatus Bangs, 1902 — W Costa Rica, through Panama to N & W Colombia. It is also naturally present in Trinidad and Tobago Is (subspecies unknown), but it was extinct in Tobago I long ago due to overexploitation by Amerindians. They also introduced the Lowland Paca (nominate subspecies) from Venezuela into Curacao I, the largest of the Netherlands Antilles. In the 20" century it was successfully introduced into Cuba from Mexico, so it should belong to the Mexican subspecies nelson in Cuniculidae
Subspecies and Distribution. C. p. paca Linnaeus, 1766 — E & S Colombia, Venezuela, the Guianas, and E Brazil to Paraguay, N Argentina (Misiones and Corrientes provinces), and Uruguay. C. p. guanta Lonnberg, 1921 — W Brazil, Ecuador, Peru, and N Bolivia. C. p. mexianae Hagmann, 1908 — E Brazil, mouth of the Amazon in Para State. C. p. nelsoni Goldman, 1913 — E & S Mexico (Atlantic slope lowlands from San Luis Potosi State to Yucatan Peninsula), then through Central America to N Costa Rica. C. p. virgatus Bangs, 1902 — W Costa Rica, through Panama to N & W Colombia. It is also naturally present in Trinidad and Tobago Is (subspecies unknown), but it was extinct in Tobago I long ago due to overexploitation by Amerindians. They also introduced the Lowland Paca (nominate subspecies) from Venezuela into Curacao I, the largest of the Netherlands Antilles. In the 20" century it was successfully introduced into Cuba from Mexico, so it should belong to the Mexican subspecies nelson
On following pages: 37 Northern Giant Pouched Rat (Cricetomys gambianus); 38. Kivu Giant Pouched Rat (Cricetomys kivuensis); 39. Southern Giant Pouched Rat (Cricetomys ansorgel); 40. East African Pouched Mouse (Saccostomus mearnsi); 41. Gray-bellied Pouched Mouse (Saccostomus umbriventer); 42. Southern African Pouched Mouse (Saccostomus campestris); 43. Nikolaus's African Climbing Mouse (Megadendromus nikolaus)); 44. Lachaise's African Climbing Mouse (Dendromus lachaisei); 45. Cameroon African Climbing Mouse (Dendromus oreas); 46. Banana African Climbing Mouse (Dendromus messorius): 47. Rupp's African Climbing Mouse (Dendromus ruppl); 48. Lovat's African Climbing Mouse (Dendromuslovati); 49. Mount Kahuzi African Climbing Mouse (Dendromus kahuziensis); 50. Montane African Climbing Mouse (Dendromus insignis); 51. Vernay's African Climbing Mouse (Dendromus vernayl); 52. Monard's African Climbing Mouse (Dendromus leucostomus); 53. Kivu African Climbing Mouse (Dendromus nyasae); 54. Nyika African Climbing Mouse (Dendromus nyikae); 55. Chestnut African Climbing Mouse (Dendromus mystacalis); 56. Gray African Climbing Mouse (Dendromus melanotis); 57 Brants's African Climbing Mouse (Dendromus mesomelas): 58. Velvet African Climbing Mouse (Dendroprionomys rousselot)); 59. Bates's African Climbing Mouse (Prionomys bates); 60. North-western Fat Mouse (Steatomys caurinus); 61. Dainty Fat Mouse (Steatomys cuppedius); 62. Jackson's Fat Mouse (Steatomys jackson); 63. Pousargues's Fat Mouse (Steatomys opimus); 64. Bocage's Fat Mouse (Steatomys bocage); 65. Tiny Fat Mouse (Steatomys parvus); 66. Common Fat Mouse (Steatomys pratensis); 67. Krebs's Fat Mouse (Steatomys krebsii): 68. Long-eared Desert Mouse (Malacothrix typical). in Nesomyidae
On following pages: 37 Northern Giant Pouched Rat (Cricetomys gambianus); 38. Kivu Giant Pouched Rat (Cricetomys kivuensis); 39. Southern Giant Pouched Rat (Cricetomys ansorgel); 40. East African Pouched Mouse (Saccostomus mearnsi); 41. Gray-bellied Pouched Mouse (Saccostomus umbriventer); 42. Southern African Pouched Mouse (Saccostomus campestris); 43. Nikolaus's African Climbing Mouse (Megadendromus nikolaus)); 44. Lachaise's African Climbing Mouse (Dendromus lachaisei); 45. Cameroon African Climbing Mouse (Dendromus oreas); 46. Banana African Climbing Mouse (Dendromus messorius): 47. Rupp's African Climbing Mouse (Dendromus ruppl); 48. Lovat's African Climbing Mouse (Dendromuslovati); 49. Mount Kahuzi African Climbing Mouse (Dendromus kahuziensis); 50. Montane African Climbing Mouse (Dendromus insignis); 51. Vernay's African Climbing Mouse (Dendromus vernayl); 52. Monard's African Climbing Mouse (Dendromus leucostomus); 53. Kivu African Climbing Mouse (Dendromus nyasae); 54. Nyika African Climbing Mouse (Dendromus nyikae); 55. Chestnut African Climbing Mouse (Dendromus mystacalis); 56. Gray African Climbing Mouse (Dendromus melanotis); 57 Brants's African Climbing Mouse (Dendromus mesomelas): 58. Velvet African Climbing Mouse (Dendroprionomys rousselot)); 59. Bates's African Climbing Mouse (Prionomys bates); 60. North-western Fat Mouse (Steatomys caurinus); 61. Dainty Fat Mouse (Steatomys cuppedius); 62. Jackson's Fat Mouse (Steatomys jackson); 63. Pousargues's Fat Mouse (Steatomys opimus); 64. Bocage's Fat Mouse (Steatomys bocage); 65. Tiny Fat Mouse (Steatomys parvus); 66. Common Fat Mouse (Steatomys pratensis); 67. Krebs's Fat Mouse (Steatomys krebsii): 68. Long-eared Desert Mouse (Malacothrix typical).
Fig. 6 in The explosive radiation, intense host-shifts and long-term failure to speciate in the evolutionary history of the feather mite genus Analges (Acariformes: Analgidae) from European passerines
Fig. 6. The co-phylogenetic scenarios revealed by JANE analysis. Thin black lines are the Analges phylogenetic tree, wide grey lines are the bird tree. The legend shows the cost of each event (in parentheses) near every co-phylogenetic event and the number of reconstructed events. A, original analysis with the full set of taxa in multi-host Analges–bird associations.? – after speciation, the ancestral species still exists. B, dated best-cost scenarios of the multi-host associations reduced to a single host species and supplemented by spread events for remaining host species. Clades in multi-host species remained as in the original analysis, i.e. originated by failure to speciate. The circles near Analges species names designate the components of host nests: black circles, fine feathers and/or down in nest material; white circles, no feathers in nest material (after Gotzman & Jablonski, 1972 and Beuch, 2013).
Fig. 5 in The explosive radiation, intense host-shifts and long-term failure to speciate in the evolutionary history of the feather mite genus Analges (Acariformes: Analgidae) from European passerines
Fig. 5. Character tracing of two key morphological characteristics of Analges: finger-like process on tarsi III in males (A) and chelate hypertrophied legs III in heteromorph males (B). Changes were traced onto the final tree (Fig. 4) using likelihood asymmetrical two-parameter Markov model with differently estimated forward/backward rates of character state changes.
Fig. 4 in The explosive radiation, intense host-shifts and long-term failure to speciate in the evolutionary history of the feather mite genus Analges (Acariformes: Analgidae) from European passerines
Fig. 4. Diversification of Analges through time. A, dated maximum clade credibility tree revealed by BEAST analysis from concatenated COI, 16S and 28S sequences for Analges species and their outgroups. Intensity of node colouration designates PP of Bayesian analysis. The chronostratigraphic scale is given with absolute geological ages (MYA, million years ago). The node bars indicate credibility intervals (± 95% highest posterior densities HPD). Two columns of coloured squares on the right designate two taxonomic hypotheses of intrageneric groupings. The category 'ungrouped' describe male Analges without both chelate legs III and finger-like processes on tarsi III. B, lineages through time (LTT) plot for Analges. The upturn around 23 Mya reflects an acceleration in the rate of speciation which coincides with the origin of the crown in the Analges clade.
Fig. 3 in The explosive radiation, intense host-shifts and long-term failure to speciate in the evolutionary history of the feather mite genus Analges (Acariformes: Analgidae) from European passerines
Fig. 3. Phylogenetic conflict in the BI post-burnin trees reconstructed from concatenated sequences of COI, 16S and 28S as shown by consensus network analyses for threshold values 0.3 (A) and 0.012 (B). The hypothesized Analges corvinus–A. sturninus clade is depicted in red, the hypothesized Analges sp.n. 6–A. sturninus clade is depicted in blue. The numbers near splits are confidence values for alternate hypotheses.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.