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5,145 results for “CO₂”
Data from: Modelling the co-evolution of indirect genetic effects and inherited variability
When individuals interact, their phenotypes may be affected by genes in their social partners, a phenomenon known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, competition not only affects trait levels of individuals, but also inflates variation of trait values among individuals. Variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, models of IGE and inherited variability do not allow for such relationship. Models of trait levels show IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to inherited variability. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of genetic and environmental effects of the focal individual and of the difference in trait values between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait which is measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. Our simulations show that the model results in increased variability of body weight with increase of competition. When competition decreases, variability becomes significantly smaller. Our findings suggest we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.
Data from: A multi-state dynamic occupancy model to estimate local colonization-extinction rates and patterns of co-occurrence between two or more interacting species
1. Although ecology is rife with theory that explores how multiple species co-occur through space and time, the field lacks robust statistical models to parameterize this theory with empirical data, particularly when species are detected imperfectly and data are collected as a time-series. 2. We address this need by developing an occupancy model that estimates local colonization and extinction rates for two or more interacting species when data are collected across multiple sampling occasions. This model estimates how community composition at a site may change across sampling occasions by assuming the latent occupancy state is a categorical random variable. We used a multinomial-logit model to parameterize species-specific parameters and pairwise interactions between species, both of which can be made a function of covariates. These transition probabilities between community states can then be converted to occupancy or co-occurrence probabilities to determine how community composition varies along an environmental gradient or through time. 3. As an example, we estimate patterns of co-occurrence between coyote (Canis latrans), Virginia opossum (Didelphis virginiana), and raccoon (Procyon lotor) in Chicago, Illinois, USA with data from a multi-year camera trapping study. Models with pairwise interactions between species greatly out performed models that assumed independence between species. Opossum and raccoon, for example, were far less likely to go extinct in habitat patches where coyotes were present. 4. Community composition at a site depends on species interactions and the local environment. Our model can separate such effects by estimating the underlying processes that define species occurrence patterns. As a result, our model can more explicitly quantify a wide range of ecological dynamics and therefore be used to empirically test ecological theory, such as estimating priority effects at a site or turnover rates between species, both of which can be made to vary as a function of covariates.
Data from: Environmental variation causes different (co) evolutionary routes to the same adaptive destination across parasite populations
Epidemics are engines for host-parasite coevolution, where parasite adaptation to hosts drives reciprocal adaptation in host populations. A key challenge is to understand whether parasite adaptation and any underlying evolution and coevolution is repeatable across ecologically realistic populations that experience different environmental conditions, or if each population follows a completely unique evolutionary path. We established twenty replicate pond populations comprising an identical suite of genotypes of crustacean host, Daphnia magna, and inoculum of their parasite, Pasteuria ramosa. Using a time-shift experiment, we compared parasite infection traits before and after epidemics and linked patterns of parasite evolution with shifts in host genotype frequencies. Parasite adaptation to the sympatric suite of host genotypes came at a cost of poorer performance on foreign genotypes across populations and environments. However, this consistent pattern of parasite adaptation was driven by different types of frequency-dependent selection that was contingent on an ecologically relevant environmental treatment (whether or not there was physical mixing of water within ponds). In unmixed ponds, large epidemics drove rapid and strong host-parasite coevolution. In mixed ponds, epidemics were smaller and host evolution was driven mainly by the mixing treatment itself; here, host evolution and parasite evolution were clear, but coevolution was absent. Population mixing breaks an otherwise robust coevolutionary cycle. These findings advance our understanding of the repeatability of (co)evolution across noisy, ecologically realistic populations.
Reactivity of a model SCILL: Influence of co-adsorbed [C2C1Im][OTf] on the dehydrogenation of dimethylamine on Pt(111)
<p>Raw and evaluated data of the IRAS measurements and DFT calculations</p>
CO-2 capture using alkali earth ions: an effect of the non-coordinating anion.
<p>100ps MD trajectories for 8 systems</p> <p>Mg </p> <p>Ca</p> <p>Sr</p> <p>Ba</p> <p>Mg + TFPB</p> <p>Ca + TFPB</p> <p>Sr + TFPB</p> <p>Ba + TFPB</p>
A Context-Driven Approach for Co-Auditing Smart Contracts with The Support of GPT-4
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Dataset for AI Co-pilot Bronchoscope Robot
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Detection of SARS-CoV-2 E and N mRNA at 12 h after co-transfection with SARS-CoV-2 E and N plasmids.
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Fig. 4 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 4 View of the tupe localitu of Neusticomys vossi sp. nov
Participatory Design Materials: Co-designing Child-Robot Relational Norm Intervention to Regulate Children's Handwriting Posture
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Sustentabilidad y co-creación de valor como herramienta para el desarrollo del turismo
<p>Ponencia - II Congreso Latinoamericano de Marketing Social</p>
Spin-Crossover Grafted Monolayer of a Co(II) Terpyridine Derivative Functionalized with Carboxylic Acid Groups
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Supplementary material 1 from: Foster R, Peeler E, Bojko J, Clark PF, Morritt D, Roy HE, Stebbing P, Tidbury HJ, Wood LE, Bass D (2021) Pathogens co-transported with invasive non-native aquatic species: implications for risk analysis and legislation. NeoBiota 69: 79-102. https://doi.org/10.3897/neobiota.69.71358
Table S1
Supplementary material 2 from: Foster R, Peeler E, Bojko J, Clark PF, Morritt D, Roy HE, Stebbing P, Tidbury HJ, Wood LE, Bass D (2021) Pathogens co-transported with invasive non-native aquatic species: implications for risk analysis and legislation. NeoBiota 69: 79-102. https://doi.org/10.3897/neobiota.69.71358
Table S2
Supplementary material 3 from: Foster R, Peeler E, Bojko J, Clark PF, Morritt D, Roy HE, Stebbing P, Tidbury HJ, Wood LE, Bass D (2021) Pathogens co-transported with invasive non-native aquatic species: implications for risk analysis and legislation. NeoBiota 69: 79-102. https://doi.org/10.3897/neobiota.69.71358
Table S3
Data from: Spatial variation in bidirectional pollinator-mediated interactions between two co-flowering species in serpentine plant communities
<p>Pollinator-mediated competition and facilitation are two important mechanisms mediating co-flowering community assembly. Experimental studies, however, have mostly focused on evaluating outcomes for a single interacting partner at a single location. Studies that evaluate spatial variation in the bidirectional effects between co-flowering species are necessary if we aim to advance our understanding of the processes that mediate species coexistence in diverse co-flowering communities. Here, we examine geographic variation (i.e., at landscape level) in bidirectional pollinator-mediated effects between co-flowering <em>Mimulus guttatus</em> and <em>Delphinium uliginosum</em>. We evaluated effects on pollen transfer dynamics (conspecific and heterospecific pollen deposition) and plant reproductive success. We found evidence of asymmetrical effects (one species is disrupted and the other one is facilitated) but the effects were highly dependent on geographical location. Furthermore, effects on pollen transfer dynamics did not always translate to effects on overall plant reproductive success (i.e., pollen tube growth) highlighting the importance of evaluating effects at multiple stages of the pollination process. Overall, our results provide evidence of a spatial mosaic of pollinator-mediated interactions between co-flowering species and suggest that community assembly processes could result from competition and facilitation acting simultaneously. Our study highlights the importance of experimental studies that evaluate the prevalence of competitive and facilitative interactions in the field, and that expand across a wide geographical context, in order to more fully understand the mechanisms that shape plant communities in nature.</p>
Can prescribed fires restore C4 grasslands invaded by a C3 woody species and a co-dominant C3 grass species?
<p>Prescribed fire is used to reduce woody plant invasion and restore herbaceous production and diversity in grasslands and savannas worldwide. Here we determined if a concentrated series of repeated-winter, repeated-summer, or alternate-season (winter and summer) fires in a short timeframe ("transition fires") could catalyze the restoration of C<sub>4</sub> perennial grasses in Southern Great Plains, USA grasslands that had become dominated by a fire-tolerant C<sub>3</sub> woody N<sub>2</sub>-fixer (honey mesquite, <i>Prosopis glandulosa</i>) and a C<sub>3</sub> perennial bunchgrass (Texas wintergrass, <i>Nassella leucotricha</i>). We applied transition fires over a 5-year span, and maintenance fires on a portion of each plot 7 or 8 years later. We measured herbaceous standing biomass and cover and soil variables (soil organic C, N, δ<sup>13</sup>C and δ<sup>15</sup>N) in unburned, transition-burned and maintenance-burned treatments. Greater δ<sup>13</sup>C at 10-20 (-17 ‰) than 0-10 (-20 ‰) cm depth increment confirmed that vegetation was historically mostly C<sub>4</sub> grassland that shifted towards C<sub>3</sub> dominance. Transition treatments with summer fire were most effective at top-killing mesquite, but no treatments root-killed >3%. Regrowth of top-killed mesquite was similar in all treatments and reached pre-fire height by 9 to 10 years post-fire. Herbaceous production and cover responses showed that: (1) alternate-season transition fires increased C<sub>4</sub> mid-grass, but did not change Texas wintergrass, (2) repeated-summer fires reduced Texas wintergrass, but did not change C<sub>4</sub> mid-grass, and (3) repeated-winter fires did not change C<sub>4</sub> mid-grass or Texas wintergrass compared to the unburned control. All maintenance fires stimulated Texas wintergrass biomass and cover, thus eliminating the reduction of Texas wintergrass caused by repeated-summer transition fires. There were no long-term effects of transition fires on soil C, N, δ<sup>13</sup>C or δ<sup>15</sup>N. Results advance our understanding of the expectations and limitations of prescribed fire in shifting a woodland alternate state toward what was historically a fire supported C<sub>4</sub> grassland/savanna.</p>
Supplementary material 2 from: Sildever S, Laas P, Kolesova N, Lips I, Lips U, Nagai S (2021) Plankton biodiversity and species co-occurrence based on environmental DNA – a multiple marker study. Metabarcoding and Metagenomics 5: e72371. https://doi.org/10.3897/mbmg.5.72371
Supplementary tables
Supplementary material 1 from: Sildever S, Laas P, Kolesova N, Lips I, Lips U, Nagai S (2021) Plankton biodiversity and species co-occurrence based on environmental DNA – a multiple marker study. Metabarcoding and Metagenomics 5: e72371. https://doi.org/10.3897/mbmg.5.72371
Supplementary figures
Fig. 3 in Karyotype characterization of Mugil incilis Hancock, 1830 (Mugiliformes: Mugilidae), including a description of an unusual co-localization of major and minor ribosomal genes in the family
Fig. 3. Metaphase plates of Mugil incilis after FISH with 45SrDNA (a) and 5SrDNA (c), respectively (b and d) DAPI counterstained. Arrows indicate chromosome pair number 1.
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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.