This document summarizes a presentation given by Jonathan Eisen on his research into the rice microbiome. Some key points:
- Eisen studies how the rice plant and its genotype influence the microbial communities that colonize its roots (rhizosphere, rhizoplane, endosphere).
- In greenhouse experiments, rice genotype explained a significant amount of variation in root microbial communities. Certain microbes were enriched or depleted across root compartments.
- Field experiments also found the rice cultivation site and farming practices influenced root microbiome composition.
- Dynamics studies showed microbes rapidly colonize roots within 24 hours of transplantation, with shifts in community composition over time.
- Network analysis revealed microbial modules involved in methane cycling that varied across
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...Premier Publishers
The experiment was conducted to assess genetic variability, heritability and genetic advance for yield and yield related traits in some upland rice genotypes. A total of 23 rice genotypes were evaluated in a randomized complete block design with three replications in 2017 at Pawe and Assosa. Analysis of variance revealed significant difference among the genotypes for most of the traits at individual and across locations, and error variances of the two locations were homogenous for most of the traits including grain yield. Moreover, the genotypes showed wider variability for grain yield in the range between 3707-6241kg/ha, 4853-7282kg/ha and 4280-6761kg/ha at Pawe, Assosa and over locations, respectively. A relatively high (>20%) phenotypic and genotypic coefficient of variations were estimated merely for number of unfilled grains per panicle. High heritability estimates (> 60%) were obtained for all of the traits, except plant height and Protein content. A relatively high genetic advance was obtained for traits like unfilled grains per panicle and fertile tiller per plant. Thus, this study revealed that there was higher genetic variability among the tested genotypes, which could be potentially exploited in future breeding programs.
Genetic variability, heritability, genetic advance, genetic advance as percen...Premier Publishers
Field experiment was conducted to estimate genetic variability, heritability, genetic advance, genetic advance as a percent mean and character association for forty nine genotypes of Ethiopian mustards collected from different agro ecologies. The experiment was carried out in a simple lattice design. The analysis of variance showed that there were significant differences among genotypes for all traits compared. The significant difference indicates the existence of genetic variability among the accessions which is important for improvement. High genotypic and phenotypic coefficients of variations were observed in seed yield per plot, oil yield per plot, and plant height. This shows that selection of these traits based on phenotype may be useful for yield improvement. The highest heritability in broad sense was recorded for thousand seed weight (68.80%) followed by days to flowering (65.91%), stand percent (63.14%), linolenic acid(62.58%), days to maturity (60.43%), plant height (59.63%), palmitic acid (58.19%), linoleic acid (57.46%),oil content (50.33%), oil yield (44.84%), seed yield per plot(42.99%),and primary branches(34.20%). This suggests that large proportion of the total variance was due to the high genotypic and less environmental variance. In the correlation coefficient analysis, seed yield per plot showed positive correlation with oil content, oil yield, plant height and seed yield per plant. In the path analysis, number of primary branches and oil yield showed positive direct effect on seed yield per plot. In this study, seed yield per plot, oil content, oil yield and primary branches were found to be the most important components for the improvement of seed and oil. Therefore more emphasis should be given for highest heritable traits of mustard and to those positively correlated traits to improve these characters using the tested genotypes.
Population genetics of maize domestication, adaptation, and improvementjrossibarra
The domestication of maize ~10,000 years ago resulted in dramatic differentiation from its wild ancestor teosinte. Subsequently, maize spread rapidly across the Americas, adapting to a number of new environments. Beginning in the 20th century, maize has also been subjected to intensive artificial selection by breeders. Each of these periods of adaptation have left their mark on patterns of genetic diversity. I will discuss some of our recent work using population genetics to learn about the history and process of adaptation in maize.
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...Premier Publishers
The experiment was conducted to assess genetic variability, heritability and genetic advance for yield and yield related traits in some upland rice genotypes. A total of 23 rice genotypes were evaluated in a randomized complete block design with three replications in 2017 at Pawe and Assosa. Analysis of variance revealed significant difference among the genotypes for most of the traits at individual and across locations, and error variances of the two locations were homogenous for most of the traits including grain yield. Moreover, the genotypes showed wider variability for grain yield in the range between 3707-6241kg/ha, 4853-7282kg/ha and 4280-6761kg/ha at Pawe, Assosa and over locations, respectively. A relatively high (>20%) phenotypic and genotypic coefficient of variations were estimated merely for number of unfilled grains per panicle. High heritability estimates (> 60%) were obtained for all of the traits, except plant height and Protein content. A relatively high genetic advance was obtained for traits like unfilled grains per panicle and fertile tiller per plant. Thus, this study revealed that there was higher genetic variability among the tested genotypes, which could be potentially exploited in future breeding programs.
Genetic variability, heritability, genetic advance, genetic advance as percen...Premier Publishers
Field experiment was conducted to estimate genetic variability, heritability, genetic advance, genetic advance as a percent mean and character association for forty nine genotypes of Ethiopian mustards collected from different agro ecologies. The experiment was carried out in a simple lattice design. The analysis of variance showed that there were significant differences among genotypes for all traits compared. The significant difference indicates the existence of genetic variability among the accessions which is important for improvement. High genotypic and phenotypic coefficients of variations were observed in seed yield per plot, oil yield per plot, and plant height. This shows that selection of these traits based on phenotype may be useful for yield improvement. The highest heritability in broad sense was recorded for thousand seed weight (68.80%) followed by days to flowering (65.91%), stand percent (63.14%), linolenic acid(62.58%), days to maturity (60.43%), plant height (59.63%), palmitic acid (58.19%), linoleic acid (57.46%),oil content (50.33%), oil yield (44.84%), seed yield per plot(42.99%),and primary branches(34.20%). This suggests that large proportion of the total variance was due to the high genotypic and less environmental variance. In the correlation coefficient analysis, seed yield per plot showed positive correlation with oil content, oil yield, plant height and seed yield per plant. In the path analysis, number of primary branches and oil yield showed positive direct effect on seed yield per plot. In this study, seed yield per plot, oil content, oil yield and primary branches were found to be the most important components for the improvement of seed and oil. Therefore more emphasis should be given for highest heritable traits of mustard and to those positively correlated traits to improve these characters using the tested genotypes.
Population genetics of maize domestication, adaptation, and improvementjrossibarra
The domestication of maize ~10,000 years ago resulted in dramatic differentiation from its wild ancestor teosinte. Subsequently, maize spread rapidly across the Americas, adapting to a number of new environments. Beginning in the 20th century, maize has also been subjected to intensive artificial selection by breeders. Each of these periods of adaptation have left their mark on patterns of genetic diversity. I will discuss some of our recent work using population genetics to learn about the history and process of adaptation in maize.
JGI: Genome size impacts on plant adaptationjrossibarra
Genome size may impact how plant genomes adapt, offering larger mutational targets leading to more adaptation from standing variation and more adaptation in noncoding regions.
A lot of sequence data are getting accumulated with the increase in affordability to technology coupled with decreasing cost. But 'Pangenome' concept could help in efficient understanding and thereby practical utilization of sequence data
Temporal dynamics in microbial soil communities at anthrax carcass sitesThomas Haverkamp
Nutrient availability and moisture are defining parameters of soil microbes in semi-arid environments. Introduction of animal carcasses provide large inputs of nutrients, fluids and host-associated microbes into the soil. One trigger for animal death is Anthrax caused by the spore-forming bacterium Bacillus anthracis. The bacterium is present in soils as spores that are activated after ingestion by grazing mammals. After killing an animal, B. anthracis cells return to the soil where they sporulate, completing the lifecycle of the bacterium. It is unclear, how animal carcass with large numbers of B. anthracis cells influence the soil community.
We therefore studied microbial soil community dynamics over 30 days (Etosha National Park, Namibia), after decomposition of two zebra anthrax carcasses.
Time-series metagenomics data showed that during the experiment the microbial community quickly changed and became dominated by the opportunistic orders Bacillales and Pseudomonadales with genomes enriched for metabolic pathways needed for proliferation. Bacteria commonly found in semi-arid soils (e.g. Frankiales and Rhizobiales) dominated at the end of the time-series. Those orders have pathways involved in desiccation and radiation resistance. Thus metagenomic data showed that anthrax carcasses have a substantial influence on the microbial communities of semi-arid soils.
To avoid state-associated-challenges (i.e. vegetative/spore) we monitored Bacillus anthracis, throughout the period. Using shotgun metagenomics, quantitative PCR and cultivation, we observed that vegetative B. anthracis abundances peak early in the time-series and then quickly drop, at which time they are replaced by spores.
We find that DNA-based approaches underestimated total B. anthracis abundances, due to difficulty in DNA extracting from spores. Furthermore, current bioinformatic tools have difficulties differentiating between the very closely related Bacillus cereus group ‘species’. This suggests that DNA-based approaches of spore-forming bacteria in their natural habitat are insufficient for estimating their abundances. We show, however, that complementing DNA based approaches with cultivation may give a more complete picture of the ecology of spore forming pathogens.
Authors:
Karoline Valseth, Camilla L. Nesbø, W. Ryan Easterday, Wendy C. Turner, Jaran S. Olsen, Nils Chr. Stenseth and Thomas H. A. Haverkamp.
Affiliations
1) Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Blindern, Oslo, Norway
2) Norwegian Defence Research Establishment, Kjeller, Norway
3) Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
4 ) Department of biological Sciences, University of Albany, State University of New York, Albany, New York, USA
Study of Genotypic and Phenotypic Correlation among 20 Accessions of Nigerian...IOSRJAVS
Morphological techniques were used to evaluate the diversity in 20 cowpea accessions collected from some parts of Nigeria for two years (2007 and 2008) at Ibadan, South Western Nigeria. Correlation analysis was employed to show the relationships among the traits. Similarly, genotypic and phenotypic variances, genotypic coefficients of variation, heritability and expected genetic advance were estimated for the twelve traits in cowpea for each season. This study shows that for cowpea yield improvement, number of main branches, pod numbers, pods per plant, pods per peduncle and seeds per pod should be part of the selection criteria.
Genetic Variability, Heritability and Genetic Advance of Kabuli Chickpea (Cic...Premier Publishers
The present study was carried out to assess the extent of genetic variability among yield and yield related traits in selected kabuli chickpea genotypes. Forty-nine kabuli chickpea genotypes were studied for thirteen traits at Debre Zeit and Akaki using 7x7 simple lattice design in 2018 cropping season. Combined analysis of variance revealed that there was a significant difference among genotypes for all traits studied, indicating the presence of considerable amount of variability among genotypes. High Phenotypic coefficients of variation and moderate genotypic coefficients of variation value were shown for number of pods per plant and number of seeds per plant, respectively, indicating the possibility of genetic improvement in selection of these traits. High broad sense heritability coupled with high genetic advance were obtained for hundred-seed weight (91.88 and 23.81), number of pods per plant (68.07 and 28.13), number of secondary branches (80.92 and 27.80), number of seeds per plant (67.86 and 31.840), grain yield (62.33 and 24.42) and harvest index (75.70 and 28.17), respectively. This indicates that these characters could be improved easily through selection.
JGI: Genome size impacts on plant adaptationjrossibarra
Genome size may impact how plant genomes adapt, offering larger mutational targets leading to more adaptation from standing variation and more adaptation in noncoding regions.
A lot of sequence data are getting accumulated with the increase in affordability to technology coupled with decreasing cost. But 'Pangenome' concept could help in efficient understanding and thereby practical utilization of sequence data
Temporal dynamics in microbial soil communities at anthrax carcass sitesThomas Haverkamp
Nutrient availability and moisture are defining parameters of soil microbes in semi-arid environments. Introduction of animal carcasses provide large inputs of nutrients, fluids and host-associated microbes into the soil. One trigger for animal death is Anthrax caused by the spore-forming bacterium Bacillus anthracis. The bacterium is present in soils as spores that are activated after ingestion by grazing mammals. After killing an animal, B. anthracis cells return to the soil where they sporulate, completing the lifecycle of the bacterium. It is unclear, how animal carcass with large numbers of B. anthracis cells influence the soil community.
We therefore studied microbial soil community dynamics over 30 days (Etosha National Park, Namibia), after decomposition of two zebra anthrax carcasses.
Time-series metagenomics data showed that during the experiment the microbial community quickly changed and became dominated by the opportunistic orders Bacillales and Pseudomonadales with genomes enriched for metabolic pathways needed for proliferation. Bacteria commonly found in semi-arid soils (e.g. Frankiales and Rhizobiales) dominated at the end of the time-series. Those orders have pathways involved in desiccation and radiation resistance. Thus metagenomic data showed that anthrax carcasses have a substantial influence on the microbial communities of semi-arid soils.
To avoid state-associated-challenges (i.e. vegetative/spore) we monitored Bacillus anthracis, throughout the period. Using shotgun metagenomics, quantitative PCR and cultivation, we observed that vegetative B. anthracis abundances peak early in the time-series and then quickly drop, at which time they are replaced by spores.
We find that DNA-based approaches underestimated total B. anthracis abundances, due to difficulty in DNA extracting from spores. Furthermore, current bioinformatic tools have difficulties differentiating between the very closely related Bacillus cereus group ‘species’. This suggests that DNA-based approaches of spore-forming bacteria in their natural habitat are insufficient for estimating their abundances. We show, however, that complementing DNA based approaches with cultivation may give a more complete picture of the ecology of spore forming pathogens.
Authors:
Karoline Valseth, Camilla L. Nesbø, W. Ryan Easterday, Wendy C. Turner, Jaran S. Olsen, Nils Chr. Stenseth and Thomas H. A. Haverkamp.
Affiliations
1) Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Blindern, Oslo, Norway
2) Norwegian Defence Research Establishment, Kjeller, Norway
3) Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
4 ) Department of biological Sciences, University of Albany, State University of New York, Albany, New York, USA
Study of Genotypic and Phenotypic Correlation among 20 Accessions of Nigerian...IOSRJAVS
Morphological techniques were used to evaluate the diversity in 20 cowpea accessions collected from some parts of Nigeria for two years (2007 and 2008) at Ibadan, South Western Nigeria. Correlation analysis was employed to show the relationships among the traits. Similarly, genotypic and phenotypic variances, genotypic coefficients of variation, heritability and expected genetic advance were estimated for the twelve traits in cowpea for each season. This study shows that for cowpea yield improvement, number of main branches, pod numbers, pods per plant, pods per peduncle and seeds per pod should be part of the selection criteria.
Genetic Variability, Heritability and Genetic Advance of Kabuli Chickpea (Cic...Premier Publishers
The present study was carried out to assess the extent of genetic variability among yield and yield related traits in selected kabuli chickpea genotypes. Forty-nine kabuli chickpea genotypes were studied for thirteen traits at Debre Zeit and Akaki using 7x7 simple lattice design in 2018 cropping season. Combined analysis of variance revealed that there was a significant difference among genotypes for all traits studied, indicating the presence of considerable amount of variability among genotypes. High Phenotypic coefficients of variation and moderate genotypic coefficients of variation value were shown for number of pods per plant and number of seeds per plant, respectively, indicating the possibility of genetic improvement in selection of these traits. High broad sense heritability coupled with high genetic advance were obtained for hundred-seed weight (91.88 and 23.81), number of pods per plant (68.07 and 28.13), number of secondary branches (80.92 and 27.80), number of seeds per plant (67.86 and 31.840), grain yield (62.33 and 24.42) and harvest index (75.70 and 28.17), respectively. This indicates that these characters could be improved easily through selection.
Genetic Variability and Morphological Diversity among Open-Pollinated Maize (...Premier Publishers
A study to characterize and determine the magnitude of genetic variation among 60 open-pollinated maize varieties was conducted at two contrasting locations in Sierra Leone during the 2015 wet cropping season. Results revealed that traits such as grain moisture content, anthesis-silking interval, plant and ear heights, number of ears harvested, field weight and grain yield showed moderate to high values of the components of genetic variation while days to 50% anthesis and silking revealed low values of the components of genetic variation. The first two PCA axes explained 54% of the total variation, of which the first principal component (PC1) accounted for 35% and PC2 contributed 19% of the total variation. The cluster diagram grouped the genotypes into seven main clusters and results suggest that crosses involving clusters I and V with any other clusters would produce segregants with low grain yields while the crosses between clusters IV, VI and VII would be expected to manifest higher heterosis and could result in segregants with higher grain yields. There was significant genetic variability observed among the genotypes evaluated thereby suggest the scope to bring about traits improvement of genotypes through direct selection and hybridization.
Nature GeNetics VOLUME 46 NUMBER 10 OCTOBER 2014 1 0 8 9.docxgemaherd
Nature GeNetics VOLUME 46 | NUMBER 10 | OCTOBER 2014 1 0 8 9
A suite of forces and factors, including mutation, recombination,
selection, population history and gene duplication influence patterns
of intraspecific genetic variation. Distinguishing which factors have
shaped sequence variation across a genome requires extensive whole-
genome sequencing of multiple individuals, which has only recently
become tractable1. Most large-scale whole-genome resequencing
studies have focused on model and domesticated species1–5. However,
extensive sequencing of natural populations holds great promise for
advancing understanding of evolutionary biology, including identify-
ing functional variation and the molecular bases of adaptation. Recent
work in a number of species has identified genomic regions that show
signatures of positive selection, suggesting that such regions contain
loci that control adaptive traits4,6–8. Relatively few studies, however,
have combined genome-wide scans with phenotypic data to determine
whether computationally identified selected regions influence adap-
tive phenotypic variation5,9–13. Genome-wide studies of large natural
populations combined with phenotypic measurements are necessary
to determine which factors shape patterns of genetic variation within
species and, therefore, enhance understanding of adaptation.
With large geographic ranges spanning wide environmental gradi-
ents and a long history of research showing local adaptation14, forest
trees are ideal for examining the processes shaping genetic variation
in natural populations. Forest trees cover approximately 30% of ter-
restrial land area15, provide direct feedback to global climate15 and
are often foundation species that organize entire biotic communities
and biogeochemical systems16,17. Clearly, biotic and abiotic interac-
tions have influenced population sizes and distributions of forest
trees, leaving diagnostic signatures in the genomes of present-day
populations14,18,19. A deeper understanding of the evolutionary and
ecological forces that shaped these patterns will offer insights and
options for ecosystem management, applied tree improvement and
accelerated domestication efforts20.
Black cottonwood, Populus trichocarpa Torr. & Gray, is a dominant
riparian tree that has become a model for the advancement of genome-
level insights in forest trees21. The sequencing of 16 P. trichocarpa
genomes revealed widespread patterns of linkage disequilibrium (LD)
and population structure22 and extensive genecological studies have
revealed a high degree of adaptive phenotypic variation in growth,
vegetative phenology and physiological traits such as water-use effi-
ciency and photosynthesis23–25, suggesting that local adaptation is
prevalent. To date, candidate gene–association analyses have revealed
loci with significant effects on phenotypic traits26,27. However, thus
far there have been no publications describing whole-genome asso-
.
Nature GeNetics VOLUME 46 NUMBER 10 OCTOBER 2014 1 0 8 9.docxvannagoforth
Nature GeNetics VOLUME 46 | NUMBER 10 | OCTOBER 2014 1 0 8 9
A suite of forces and factors, including mutation, recombination,
selection, population history and gene duplication influence patterns
of intraspecific genetic variation. Distinguishing which factors have
shaped sequence variation across a genome requires extensive whole-
genome sequencing of multiple individuals, which has only recently
become tractable1. Most large-scale whole-genome resequencing
studies have focused on model and domesticated species1–5. However,
extensive sequencing of natural populations holds great promise for
advancing understanding of evolutionary biology, including identify-
ing functional variation and the molecular bases of adaptation. Recent
work in a number of species has identified genomic regions that show
signatures of positive selection, suggesting that such regions contain
loci that control adaptive traits4,6–8. Relatively few studies, however,
have combined genome-wide scans with phenotypic data to determine
whether computationally identified selected regions influence adap-
tive phenotypic variation5,9–13. Genome-wide studies of large natural
populations combined with phenotypic measurements are necessary
to determine which factors shape patterns of genetic variation within
species and, therefore, enhance understanding of adaptation.
With large geographic ranges spanning wide environmental gradi-
ents and a long history of research showing local adaptation14, forest
trees are ideal for examining the processes shaping genetic variation
in natural populations. Forest trees cover approximately 30% of ter-
restrial land area15, provide direct feedback to global climate15 and
are often foundation species that organize entire biotic communities
and biogeochemical systems16,17. Clearly, biotic and abiotic interac-
tions have influenced population sizes and distributions of forest
trees, leaving diagnostic signatures in the genomes of present-day
populations14,18,19. A deeper understanding of the evolutionary and
ecological forces that shaped these patterns will offer insights and
options for ecosystem management, applied tree improvement and
accelerated domestication efforts20.
Black cottonwood, Populus trichocarpa Torr. & Gray, is a dominant
riparian tree that has become a model for the advancement of genome-
level insights in forest trees21. The sequencing of 16 P. trichocarpa
genomes revealed widespread patterns of linkage disequilibrium (LD)
and population structure22 and extensive genecological studies have
revealed a high degree of adaptive phenotypic variation in growth,
vegetative phenology and physiological traits such as water-use effi-
ciency and photosynthesis23–25, suggesting that local adaptation is
prevalent. To date, candidate gene–association analyses have revealed
loci with significant effects on phenotypic traits26,27. However, thus
far there have been no publications describing whole-genome asso-
...
Genetic diversity enhances the resistance of aseagrass ecosyMatthewTennant613
Genetic diversity enhances the resistance of a
seagrass ecosystem to disturbance
A. Randall Hughes* and John J. Stachowicz
Section of Evolution and Ecology, One Shields Avenue, University of California, Davis, CA 95616
Edited by G. David Tilman, University of Minnesota, St. Paul, MN, and approved May 4, 2004 (received for review April 14, 2004)
Motivated by recent global reductions in biodiversity, empirical
and theoretical research suggests that more species-rich systems
exhibit enhanced productivity, nutrient cycling, or resistance to
disturbance or invasion relative to systems with fewer species. In
contrast, few data are available to assess the potential ecosystem-
level importance of genetic diversity within species known to play
a major functional role. Using a manipulative field experiment, we
show that increasing genotypic diversity in a habitat-forming
species (the seagrass Zostera marina) enhances community resis-
tance to disturbance by grazing geese. The time required for
recovery to near predisturbance densities also decreases with
increasing eelgrass genotypic diversity. However, there is no effect
of diversity on resilience, measured as the rate of shoot recovery
after the disturbance, suggesting that more rapid recovery in
diverse plots is due solely to differences in disturbance resistance.
Genotypic diversity did not affect ecosystem processes in the
absence of disturbance. Thus, our results suggest that genetic
diversity, like species diversity, may be most important for enhanc-
ing the consistency and reliability of ecosystems by providing
biological insurance against environmental change.
There is growing recognition that humans are highly depen-dent on natural ecosystems for a variety of goods and
services (1). Maintaining the provision of these goods and
services in the face of natural and anthropogenic disturbances is
critical to achieving both conservation and economic goals.
Motivated by accelerating rates of worldwide decline in biodi-
versity (2), considerable research has focused on the conse
quences of local species loss for goods and services provided by
ecosystems (2– 8). Much of this work focuses on the effects of
declining species richness on short-term processes such as pro-
duction, community respiration, and nutrient cycling (2). Al-
though the results are far from unequivocal and subject to
varying interpretation (e.g., ref. 9), it does appear that, in some
systems, reductions in local species diversity contribute to a
decline in ecosystem properties such as productivity and resis-
tance to disturbance (see review in ref. 2).
Nevertheless, many important ecosystems, such as kelp forests,
cattail marshes, and fir forests, are dominated by, and dependent
on, one or a few key plant species (10). Furthermore, individual
predator and herbivore species often play a disproportionate role in
determining ecosystem processes, overwhelming any effect of spe-
cies diversity (11). Dominant, numerically abundant s ...
Innovations in Sequencing & Bioinformatics
Talk for
Healthy Central Valley Together Research Workshop
Jonathan A. Eisen University of California, Davis
January 31, 2024 linktr.ee/jonathaneisen
Thoughts on UC Davis' COVID Current ActionsJonathan Eisen
Slides I used for a presentation to Chancellor May's leadership council about the current state of UC Davis' response to COVID and how it could be improved
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
12. Host
Microbiome
Model Host
Selection /
Stress
A model organism is a species
that has been widely studied,
usually because it is easy to
maintain and breed in a laboratory
setting and has particular
experimental advantages.
13. Host
Microbiome
Model Host
Selection /
Stress
A model organism is a species
that has been widely studied,
usually because it is easy to
maintain and breed in a laboratory
setting and has particular
experimental advantages.
14. Drosophila microbiome
Both natural surveys and
laboratory experiments
indicate that host diet plays a
major role in shaping the
Drosophila bacterial
microbiome. Laboratory strains
provide only a limited model of
natural host–microbe
interactions
Jenna
Lang
Angus
Chandler
15. Model Systems - Rice
Edwards et al. 2015. Structure, variation,
and assembly of the root-associated
microbiomes of rice. PNAS
9
Supplementary Figures31
32
Fig. S1 Map depicting soil collection locations for greenhouse experiment.33
10
234
Fig. S2. Sampling and collection of the rhizocompartments. Roots are collected from rice235
plants and soil is shaken off the roots to leave ~1mm of soil around the roots. The ~1 mm of soil236
three separate rhizocompartments: the rhizosphere, rhizoplane,
and endosphere (Fig. 1A). Because the root microbiome has
been shown to correlate with the developmental stage of the
plant (10), the root-associated microbial communities were
sampled at 42 d (6 wk), when rice plants from all genotypes were
well-established in the soil but still in their vegetative phase of
growth. For our study, the rhizosphere compartment was com-
w
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z
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Fig. 1. Root-associated microbial communities are separable by rhizo-
compartment and soil type. (A) A representation of a rice root cross-section
depicting the locations of the microbial communities sampled. (B) Within-
sample diversity (α-diversity) measurements between rhizospheric compart-
ments indicate a decreasing gradient in microbial diversity from the rhizo-
sphere to the endosphere independent of soil type. Estimated species
richness was calculated as eShannon_entropy
. The horizontal bars within boxes
represent median. The tops and bottoms of boxes represent 75th and 25th
quartiles, respectively. The upper and lower whiskers extend 1.5× the
interquartile range from the upper edge and lower edge of the box, re-
spectively. All outliers are plotted as individual points. (C) PCoA using the
WUF metric indicates that the largest separation between microbial com-
munities is spatial proximity to the root (PCo 1) and the second largest
source of variation is soil type (PCo 2). (D) Histograms of phyla abundances in
each compartment and soil. B, bulk soil; E, endosphere; P, rhizoplane; S,
rhizosphere; Sac, Sacramento.
2 of 10 | www.pnas.org/cgi/doi/10.1073/pnas.1414592112
igate the relationship between rice ge-
icrobiome, domesticated rice varieties
rated growing regions were tested. Six
spanning two species within the Oryza
2 d in the greenhouse before sampling.
a) cultivars M104, Nipponbare (both
ties), IR50, and 93-11 (both indica va-
gside two cultivars of African cultivated
g7102 (Glab B) and TOg7267 (Glab E).
ed that rice genotype accounted for
ariation between microbial communities
% of the variance, P < 0.001; Dataset
f the variance, P < 0.066; Dataset S5H);
ntations for clustering patterns of the
nt on the first two axes of unconstrained
ppendix, Fig. S10). We then used CAP
effect of rice genotype on the microbial
ng on rice cultivar and controlling for
and technical factors, we found that ge-
ice have a significant effect on root-
mmunities (5.1%, P = 0.005, WUF, Fig.
, UUF, SI Appendix, Fig. S11A). Ordi-
AP analysis revealed clustering patterns
only partially consistent with genetic
UF and UUF metrics. The two japonica
her and the two O. glaberrima cultivars
ver, the indica cultivars were split, with
O. glaberrima cultivars and IR50 clus-
cultivars.
enotypic effect manifests in individual
eparated the whole dataset to focus on
vidually and conducted CAP analysis
and technical factors. The rhizosphere
eight sites were operated under two cultivation practices: organic
cultivation and a more conventional cultivation practice termed
“ecofarming” (see below). Because genotype explained the least
variance in the greenhouse data, we limited the analysis to one
cultivar, S102, a California temperate japonica variety that is
widely cultivated by commercial growers and is closely related to
M104 (26). Field samples were collected from vegetatively
growing rice plants in flooded fields and the previously defined
rhizocompartments were analyzed as before. Unfortunately,
collection of bulk soil controls for the field experiment was not
Fig. 3. Host plant genotype significantly affects microbial communities in
the rhizospheric compartments. (A) Ordination of CAP analysis using the
WUF metric constrained to rice genotype. (B) Within-sample diversity
measurements of rhizosphere samples of each cultivar grown in each soil.
Estimated species richness was calculated as eShannon_entropy
. The horizontal
bars within boxes represent median. The tops and bottoms of boxes repre-
sent 75th and 25th quartiles, respectively. The upper and lower whiskers
extend 1.5× the interquartile range from the upper edge and lower edge of
the box, respectively. All outliers are plotted as individual points.
oi/10.1073/pnas.1414592112 Edwards et al.
fields are too high to find representative soil that is unlikely to
be affected by nearby plants. Amplification and sequencing of
the field microbiome samples yielded 13,349,538 high-quality
sequences (median: 54,069 reads per sample; range: 12,535–
148,233 reads per sample; Dataset S13). The sequences were
clustered into OTUs using the same criteria as the greenhouse
experiment, yielding 222,691 microbial OTUs and 47,983 OTUs
with counts >5 across the field dataset.
We found that the microbial diversity of field rice plants is
significantly influenced by the field site. α-Diversity measure-
ments of the field rhizospheres indicated that the cultivation site
significantly impacts microbial diversity (SI Appendix, Fig. S14A,
P = 2.00E-16, ANOVA and Dataset S14). Unconstrained PCoA
using both the WUF and UUF metrics showed that microbial
communities separated by field site across the first axis (Fig. 4B,
WUF and SI Appendix, Fig. S14B, UUF). PERMANOVA agreed
with the unconstrained PCoA in that field site explained the
largest proportion of variance between the microbial communi-
ties for field plants (30.4% of variance, P < 0.001, WUF, Dataset
S5O and 26.6% of variance, P < 0.001, UUF, Dataset S5P). CAP
analysis constrained to field site and controlled for rhizocom-
partment, cultivation practice, and technical factors (sequencing
batch and biological replicate) agreed with the PERMANOVA
results in that the field site explains the largest proportion of
variance between the root-associated microbial communities in
field plants (27.3%, P = 0.005, WUF, SI Appendix, Fig. S15A
and 28.9%, P = 0.005, UUF, SI Appendix, Fig. S15E), sug-
gesting that geographical factors may shape root-associated
microbial communities.
Rhizospheric Compartmentalization Is Retained in Field Plants. Sim-
ilar to the greenhouse plants, the rhizospheric microbiomes of
field plants are distinguishable by compartment. α-Diversity of
the field plants again showed that the rhizosphere had the
highest microbial diversity, whereas the endosphere had the least
S15). PCoA
the WUF a
compartmen
Appendix, F
separation i
ond largest
(20.76%, P
UUF, Data
biomes cons
trolled for f
agreed with
variance bet
compartmen
and 10.9%,
Taxonomi
overall sim
Chloroflexi,
microbiota.
endosphere
Proteobacter
and Plancto
distribution
trend from t
Appendix, F
We again
OTUs in the
S16). We fo
endosphere c
representing
Fig. S17). Th
the genus A
and Alphap
terestingly,
found to b
greenhouse
OTUs were
sisted of tax
and Myxoco
bidopsis roo
Cultivation Pr
The rice fiel
practices, org
tion called
farming in th
are all perm
harvest fumi
itself does si
partments ov
a significant
the rhizocom
indicating th
affected diffe
the rhizosph
practice, with
zospheres th
Dataset S14)
crobial comm
tests; Datase
practices are
the WUF m
S14D). PERFig. 4. Root-associated microbiomes from field-grown plants are separable
by cultivation site, rhizospheric compartment, and cultivation practice. (A)
Variation w/in Plant
Cultivation Site Effects
Rice Genotype Effects
and mitochondrial) reads to analyze microbial abundance in
the endosphere over time (Fig. 6A). Using this technique, we
confirmed the sterility of seedling roots before transplantation.
We found that microbial penetrance into the endosphere oc-
curred at or before 24 h after transplantation and that the pro-
portion of microbial reads to organellar reads increased over the
first 2 wk after transplantation (Fig. 6A). To further support the
evidence for microbiome acquisition within the first 24 h, we
sampled root endospheric microbiomes from sterilely germi-
nated seedlings before transplanting into Davis field soil as well
as immediately after transplantation and 24 h after transplan-
tation (SI Appendix, Fig. S24). The root endospheres of sterilely
germinated seedlings, as well as seedlings transplanted into
Davis field soil for 1 min, both had a very low percentage of
microbial reads compared with organellar reads (0.22% and
0.71%), with the differences not statistically significant (P = 0.1,
Wilcoxon test). As before, endospheric microbial abundance
increased significantly, by >10-fold after 24 h in field soil (3.95%,
P = 0.05, Wilcoxon test). We conclude that brief soil contact
does not strongly increase the proportion of microbial reads, and
therefore the increase in microbial reads at 24 h is indicative of
endophyte acquisition within 1 d after transplantation.
α-Diversity significantly varied by rhizocompartment (P < 2E-
16; Dataset S23) and there was a significant interaction between
rhizocompartment and collection time (P = 0.042; Dataset S23);
however, when each rhizocompartment was analyzed individ-
(13 d) approach the endosphere and rhizoplane microbiome
compositions for plants that have been grown in the green-
house for 42 d.
There are slight shifts in the distribution of phyla over time;
however, there are significant distinctions between the com-
partments starting as early as 24 h after transplantation into soil
(Fig. 6D, SI Appendix, Figs. S24B and S26, and Dataset S24).
Because each phylum consists of diverse OTUs that could ex-
hibit very different behaviors during acquisition, we next ex-
amined the dynamics and colonization patterns of specific
OTUs within the time-course experiment. The core set of 92
endosphere-enriched OTUs obtained from the previous green-
house experiment (SI Appendix, Fig. S9C) was analyzed for
relative abundances at different time points (Fig. 6E). Of the 92
core endosphere-enriched microbes present in the greenhouse
experiment, 53 OTUs were detectable in the endosphere in the
time-course experiment. The average abundance profile over
time revealed a colonization pattern for the core endospheric
microbiome. Relative abundance of the core endosphere-
enriched microbiome peaks early (3 d) in the rhizosphere and
then decreases back to a steady, low level for the remainder of
the time points. Similarly, the rhizoplane profile shows an in-
crease after 3 d with a peak at 8 d with a decline at 13 d. The
endosphere generally follows the rhizoplane profile, except that
relative abundance is still increasing at 13 d. These results sug-
gest that the core endospheric microbes are first attracted to the
Fig. 5. OTU coabundance network reveals modules of OTUs associated with methane cycling. (A) Subset of the entire network corresponding to 11
modules with methane cycling potential. Each node represents one OTU and an edge is drawn between OTUs if they share a Pearson correlation of
greater than or equal to 0.6. (B) Depiction of module 119 showing the relationship between methanogens, syntrophs, methanotrophs, and other
methane cycling taxonomies. Each node represents one OTU and is labeled by the presumed function of that OTU’s taxonomy in methane cycling. An
edge is drawn between two OTUs if they have a Pearson correlation of greater than or equal to 0.6. (C) Mean abundance profile for OTUs in module 119
across all rhizocompartments and field sites. The position along the x axis corresponds to a different field site. Error bars represent SE. The x and y axes
represent no particular scale.
PLANTBIOLOGYPNASPLUS
Function x Genotype
of magnitude greater than in any single plant species to date.
Under controlled greenhouse conditions, the rhizocompartments
described the largest source of variation in the microbial com-
munities sampled (Dataset S5A). The pattern of separation be-
tween the microbial communities in each compartment is
consistent with a spatial gradient from the bulk soil across the
rhizosphere and rhizoplane into the endosphere (Fig. 1C).
Similarly, microbial diversity patterns within samples hold the
same pattern where there is a gradient in α-diversity from the
rhizosphere to the endosphere (Fig. 1B). Enrichment and de-
pletion of certain microbes across the rhizocompartments indi-
cates that microbial colonization of rice roots is not a passive
process and that plants have the ability to select for certain mi-
crobial consortia or that some microbes are better at filling the
root colonizing niche. Similar to studies in Arabidopsis, we found
that the relative abundance of Proteobacteria is increased in the
endosphere compared with soil, and that the relative abundances
of Acidobacteria and Gemmatimonadetes decrease from the soil
to the endosphere (9–11), suggesting that the distribution of
different bacterial phyla inside the roots might be similar for all
land plants (Fig. 1D and Dataset S6). Under controlled green-
house conditions, soil type described the second largest source
of variation within the microbial communities of each sample.
However, the soil source did not affect the pattern of separation
between the rhizospheric compartments, suggesting that the
rhizocompartments exert a recruitment effect on microbial con-
sortia independent of the microbiome source.
By using differential OTU abundance analysis in the com-
partments, we observed that the rhizosphere serves an enrich-
ment role for a subset of microbial OTUs relative to bulk soil
(Fig. 2). Further, the majority of the OTUs enriched in the
rhizosphere are simultaneously enriched in the rhizoplane and/or
endosphere of rice roots (Fig. 2B and SI Appendix, Fig. S16B),
consistent with a recruitment model in which factors produced by
the root attract taxa that can colonize the endosphere. We found
that the rhizoplane, although enriched for OTUs that are also
Time Series
32. What makes a model host-microbiome system?
• Host
• Function / roles interesting and/or important
• Relevance to other key hosts
• Resources / Knowledge / Tools
• Community
• Microbiome
• Functions / roles interesting and/or important
• Resources / Knowledge / Tools
• Community
• Host-Microbiome Interactions
• Tools to manipulate
• Tools to monitor / assess
• Relevance to other systems
• Interesting stress / selection questions
41. Jay Stachowicz - Seagrass Guru
• Stachowicz lab
Image from Reynolds PL. Seagrass and Seagrass Beds
http://ocean.si.edu/seagrass-and-seagrass-beds
• Seagrass Importance
• Ecosystem Structure
• Living Habitat
• Foundation of Food
Webs
43. Slide from Jay Stachowicz
Z. marina is abundant throughout northern hemisphere
44. What makes a model host-microbiome system?
• Host
• Function / roles interesting and/or important
• Relevance to other key hosts
• Resources / Knowledge / Tools
• Community
• Microbiome
• Functions / roles interesting and/or important
• Resources / Knowledge / Tools
• Community
• Host-Microbiome Interactions
• Tools to manipulate
• Tools to monitor / assess
• Relevance to other systems
• Interesting stress / selection questions
45. Zostera marina - Microbiome System ~ 2012
• Host
• Function / roles interesting and/or important
• Relevance to other key hosts
• Resources / Knowledge / Tools
• Community
• Microbiome
• Functions / roles interesting and/or important
• Resources / Knowledge / Tools
• Community
• Host-Microbiome Interactions
• Tools to manipulate
• Tools to monitor / assess
• Relevance to other systems
• Interesting stress / selection questions
46. • Host
• Function / roles interesting and/or important
• Relevance to other key hosts
• Resources / Knowledge / Tools
• Community
• Microbiome
• Functions / roles interesting and/or important
• Resources / Knowledge / Tools
• Community
• Host-Microbiome Interactions
• Tools to manipulate
• Tools to monitor / assess
• Relevance to other systems
• Interesting stress / selection questions
Zostera marina - Microbiome System ~ 2012
51. Rhizome Roots vs. Shoot Roots vs. Leaf
Variation in microbial community composition in Z. marina. PCoA plot of weighted Unifrac distances between
samples. Communities cluster by tissue type (PERMANOVA, p <0.001). Within root samples, rhizome roots
differ from shoot roots (PERMANOVA, p < 0.001).
56. ZEN Microbiome Sampling
Emmett Duffy
Pamela Reynolds Kevin Hovel
Jay Stachowicz
http://zenscience.org
• Sent kits
• Asked to sample leaves,
roots, sediment and water
58. Global Structure of Eelgrass Microbiome
Results
PcoA Environmental
Similarity
• Leaf, roots and
sediment different
• Leaves resemble
water
• Leaves more similar
to local water
Fahimipour AK, Kardish MR, Lang JM, Green JL, Eisen JA,
Stachowicz JJ. 2017. Global-scale structure of the eelgrass
microbiome. Appl Environ Microbiol 83:e03391-16. https://
doi.org/10.1128/AEM.03391-16.
Jenna
Lang
Ashkaan
Fahimipour
Melissa
Kardish
59. Don’t Forget the Fungi
Ettinger CL, Eisen JA. Characterization of the mycobiome of the seagrass, Zostera marina, reveals
putative associations with marine chytrids. Frontiers in Microbiology 10: 2476. doi: 10.3389/
fmicb.2019.02476.
Cassie Ettinger
63. Predicted Sulfur Metabolism Enriched on Roots
Results
Fahimipour AK, Kardish MR, Lang JM, Green JL, Eisen JA, Stachowicz JJ. 2017. Global-scale
structure of the eelgrass microbiome. Appl Environ Microbiol 83:e03391-16. https://doi.org/10.1128/
AEM.03391-16.
64. Edge Effects: Does in Matter Where Plants Are?
Ettinger CL, Voerman SE, Lang JM, Stachowicz JJ,
Eisen JA. (2017) Microbial communities in sediment
from Zostera marina patches, but not the Z. marina leaf
or root microbiomes, vary in relation to distance from
patch edge. PeerJ 5:e3246 https://doi.org/10.7717/
peerj.3246
Jenna
Lang
Cassie
Ettinger
Sofie
Voerman
67. David Coil
Jeanine
Olsen
Laura
Vann
Yves van
De Peer
Guillaume
Jospin
Melissa
Kardish
Alana
Firl
Laura
Reynolds
Jessica
Abbott
Susan
Williams
Katie
DuBois
Cassie
Ettinger
Sofie
Voerman
Ashkaan
Fahimipour
Russell
Neches
James
Doyle
Jenna LangJessica GreenJay Stachowicz
Hannah
Holland-Moritz
Ruth
Lee
Pamela
Reynolds
• Karley Lujuan
• Marcus Cohen
• Katie Somers
• Taylor Tucker
• Hoon San Ong
• Neil Brambhatt
• Hena Hundal
• Daniel Oberbauer
• Briana Pompa-Hogan
• Alex Alexiev
• Ruth Lee
68. Other Advances
• Small but growing culture collection of
bacteria and fungi
• Reference genomes of some isolates and
many “MAGs”
• Some tools for manipulating the microbiome
• Genome sequence of Z. marina published in
2006
• Population genomics of HMI
• High quality genomes of other species
coming
• Growing community of researchers
69. • Host
• Function / roles interesting and/or important
• Relevance to other key hosts
• Resources / Knowledge / Tools
• Community
• Microbiome
• Functions / roles interesting and/or important
• Resources / Knowledge / Tools
• Community
• Host-Microbiome Interactions
• Tools
• Relevance to other systems
• Interesting stress / selection questions
Zostera marina - Microbiome System ~ 2019
70. Z. marina as a model HMS system
Jay
Stachowicz
Maggie
Sogin
See seagrassmicrobiome.org
72. Some Pressing Needs for the Unicorn
• ZM microbiome culture resource
• Methods for manipulating the ZM
microbiome
• Functional readouts of ZM interactions
75. Istmobiome Project
~ 3 million years
ago…
Formation of the Panama
Isthmus split the Atlantic
and Pacific Oceans
This geographic barrier
facilitated the speciation of
macro- and micro-organisms
“Divergence of Marine Symbiosis After the
Rise of the Isthmus of Panama”
Collaboration Between STRI and UC Davis
See http://istmobiome.net
Bill Wcislo