The evolution of the cosmic molecular gas density

One of the last missing pieces in the puzzle of galaxy formation and evolution through cosmic history is a detailed picture of the role of the cold gas supply in the star-formation process. Cold gas is the fuel for star formation, and thus regulates the buildup of stellar mass, both through the amount of material present through a galaxy's gas mass fraction, and through the efficiency at which it is converted to stars. Over the last decade, important progress has been made in understanding the relative importance of these two factors along with the role of feedback, and the first measurements of the volume density of cold gas out to redshift 4, (the "cold gas history of the Universe") has been obtained. To match the precision of measurements of the star formation and black-hole accretion histories over the coming decades, a two orders of magnitude improvement in molecular line survey speeds is required compared to what is possible with current facilities. Possible pathways towards such large gains include significant upgrades to current facilities like ALMA by 2030 (and beyond), and eventually the construction of a new generation of radio-to-millimeter wavelength facilities, such as the next generation Very Large Array (ngVLA) concept.Comment: 7 pages, 2 figures, Science White paper submitted to Astro2020 Decadal Surve

The ALMA Spectroscopic Survey in the Hubble Ultra Deep Field: Evolution of the Molecular Gas in CO-selected Galaxies

We analyze the interstellar medium properties of a sample of 16 bright CO line emitting galaxies identified in the ALMA Spectroscopic Survey in the Hubble Ultra Deep Field (ASPECS) Large Program. This CO−selected galaxy sample is complemented by two additional CO line emitters in the UDF that are identified based on their MultiUnit Spectroscopic Explorer (MUSE) optical spectroscopic redshifts. The ASPECS CO−selected galaxies cover a larger range of star formation rates (SFRs) and stellar masses compared to literature CO emitting galaxies at z > 1 for which scaling relations have been established previously. Most of ASPECS CO-selected galaxies follow these established relations in terms of gas depletion timescales and gas fractions as a function of redshift, as well as the SFR–stellar mass relation (“galaxy main sequence”). However, we find that ∼30% of the galaxies (5 out of 16) are offset from the galaxy main sequence at their respective redshift, with ∼12% (2 out of 16) falling below this relationship. Some CO-rich galaxies exhibit low SFRs, and yet show substantial molecular gas reservoirs, yielding long gas depletion timescales. Capitalizing on the well-defined cosmic volume probed by our observations, we measure the contribution of galaxies above, below, and on the galaxy main sequence to the total cosmic molecular gas density at different lookback times. We conclude that main-sequence galaxies are the largest contributors to the molecular gas density at any redshift probed by our observations (z ∼ 1−3). The respective contribution by starburst galaxies above the main sequence decreases from z ∼ 2.5 to z ∼ 1, whereas we find tentative evidence for an increased contribution to the cosmic molecular gas density from the passive galaxies below the main sequenc

The ALMA Spectroscopic Survey in the HUDF: CO Luminosity Functions and the Molecular Gas Content of Galaxies through Cosmic History

We use the results from the ALMA large program ASPECS, the spectroscopic survey in the Hubble Ultra Deep Field (HUDF), to constrain CO luminosity functions of galaxies and the resulting redshift evolution of ρ(H2). The broad frequency range covered enables us to identify CO emission lines of different rotational transitions in the HUDF at z > 1. We find strong evidence that the CO luminosity function evolves with redshift, with the knee of the CO luminosity function decreasing in luminosity by an order of magnitude from ~2 to the local universe. Based on Schechter fits, we estimate that our observations recover the majority (up to ~90%, depending on the assumptions on the faint end) of the total cosmic CO luminosity at z = 1.0–3.1. After correcting for CO excitation, and adopting a Galactic CO-to-H2 conversion factor, we constrain the evolution of the cosmic molecular gas density ρ(H2): this cosmic gas density peaks at z ~ 1.5 and drops by a factor of ${6.5}_{-1.4}^{+1.8}$ to the value measured locally. The observed evolution in ρ(H2), therefore, closely matches the evolution of the cosmic star formation rate density ρ SFR. We verify the robustness of our result with respect to assumptions on source inclusion and/or CO excitation. As the cosmic star formation history can be expressed as the product of the star formation efficiency and the cosmic density of molecular gas, the similar evolution of ρ(H2) and ρ SFR leaves only little room for a significant evolution of the average star formation efficiency in galaxies since z ~ 3 (85% of cosmic history)

The Evolution of the Baryons Associated with Galaxies Averaged over Cosmic Time and Space

We combine the recent determination of the evolution of the cosmic density of molecular gas (H2) using deep, volumetric surveys, with previous estimates of the cosmic density of stellar mass, star formation rate and atomic gas (H i), to constrain the evolution of baryons associated with galaxies averaged over cosmic time and space. The cosmic H i and H2 densities are roughly equal at z ~ 1.5. The H2 density then decreases by a factor ${6}_{-2}^{+3}$ to today's value, whereas the H i density stays approximately constant. The stellar mass density is increasing continuously with time and surpasses that of the total gas density (H i and H2) at redshift z ~ 1.5. The growth in stellar mass cannot be accounted for by the decrease in cosmic H2 density, necessitating significant accretion of additional gas onto galaxies. With the new H2 constraints, we postulate and put observational constraints on a two-step gas accretion process: (i) a net infall of ionized gas from the intergalactic/circumgalactic medium to refuel the extended H i reservoirs, and (ii) a net inflow of H i and subsequent conversion to H2 in the galaxy centers. Both the infall and inflow rate densities have decreased by almost an order of magnitude since z ~ 2. Assuming that the current trends continue, the cosmic molecular gas density will further decrease by about a factor of two over the next 5 Gyr, the stellar mass will increase by approximately 10%, and cosmic star formation activity will decline steadily toward zero, as the gas infall and accretion shut down

The evolution of the cosmic molecular gas density

One of the last missing pieces in the puzzle of galaxy formation and evolution through cosmic history is a detailed picture of the role of the cold gas supply in the star-formation process. Cold gas is the fuel for star formation, and thus regulates the buildup of stellar mass, both through the amount of material present through a galaxy's gas mass fraction, and through the efficiency at which it is converted to stars. Over the last decade, important progress has been made in understanding the relative importance of these two factors along with the role of feedback, and the first measurements of the volume density of cold gas out to redshift 4, (the "cold gas history of the Universe") has been obtained. To match the precision of measurements of the star formation and black-hole accretion histories over the coming decades, a two orders of magnitude improvement in molecular line survey speeds is required compared to what is possible with current facilities. Possible pathways towards such large gains include significant upgrades to current facilities like ALMA by 2030 (and beyond), and eventually the construction of a new generation of radio-to-millimeter wavelength facilities, such as the next generation Very Large Array (ngVLA) concept

Inferring competitive outcomes, ranks and intransitivity from empirical data: A comparison of different methods

The inference of pairwise competitive outcomes (PCO) and multispecies competitive ranks and intransitivity from empirical data is essential to evaluate how competition shapes plant communities. Three categories of methods, differing in theoretical background and data requirements, have been used: (a) theoretically sound coexistence theory‐based methods, (b) index‐based methods, and (c) ‘process‐from‐pattern’ methods. However, how they are related is largely unknown. In this study, we explored the relations between the three categories by explicitly comparing three representatives of them: (a) relative fitness difference (RFD), (b) relative yield (RY), and (c) a reverse‐engineering approach (RE). Specifically, we first conducted theoretical analyses with Lotka–Volterra competition models to explore their theoretical linkages. Second, we used data from a long‐term field experiment and a short‐term greenhouse experiment with eight herbaceous perennials to validate the theoretical findings. The theoretical analyses showed that RY or RE applied with equilibrium data indicated equivalent, or very similar, PCO respectively to RFD, but these relations became weaker or absent with data further from equilibrium. In line with this, both RY and RE converged with RFD in indicating PCO over time in the field experiment as the communities became closer to equilibrium. Moreover, the greenhouse PCO (far from equilibrium) were only similar to the field PCO of earlier rather than later years. Intransitivity was more challenging to infer because it could be reshuffled by even a small competitive shift among similar competitors. For example, the field intransitivity inferred by three methods differed greatly: no intransitivity was detected with RFD; intransitivity detected with RY and RE was poorly correlated, changed substantially over time (even after equilibrium) and failed to explain coexistence. Our findings greatly help the comparison and generalization of studies using different methods. For future studies, if equilibrium data are available, one can infer PCO and multispecies competitive ranks with RY or RE. If not, one should apply RFD with density gradient or time‐series data. Equilibria could be evaluated with T tests or standard deviations. To reliably infer intransitivity, one needs high quality data for a given method to first accurately infer PCO, especially among similar competitors

Inferring competitive outcomes, ranks and intransitivity from empirical data: A comparison of different methods

1. The inference of pairwise competitive outcomes (PCO) and multispecies competitive ranks and intransitivity from empirical data is essential to evaluate how competition shapes plant communities. Three categories of methods, differing in theoretical background and data requirements, have been used: (a) theoretically sound coexistence theory‐based methods, (b) index‐based methods, and (c) ‘process‐from‐pattern’ methods. However, how they are related is largely unknown. 2. In this study, we explored the relations between the three categories by explicitly comparing three representatives of them: (a) relative fitness difference (RFD), (b) relative yield (RY), and (c) a reverse‐engineering approach (RE). Specifically, we first conducted theoretical analyses with Lotka–Volterra competition models to explore their theoretical linkages. Second, we used data from a long‐term field experiment and a short‐term greenhouse experiment with eight herbaceous perennials to validate the theoretical findings. 3. The theoretical analyses showed that RY or RE applied with equilibrium data indicated equivalent, or very similar, PCO respectively to RFD, but these relations became weaker or absent with data further from equilibrium. In line with this, both RY and RE converged with RFD in indicating PCO over time in the field experiment as the communities became closer to equilibrium. Moreover, the greenhouse PCO (far from equilibrium) were only similar to the field PCO of earlier rather than later years. Intransitivity was more challenging to infer because it could be reshuffled by even a small competitive shift among similar competitors. For example, the field intransitivity inferred by three methods differed greatly: no intransitivity was detected with RFD; intransitivity detected with RY and RE was poorly correlated, changed substantially over time (even after equilibrium) and failed to explain coexistence. 4. Our findings greatly help the comparison and generalization of studies using different methods. For future studies, if equilibrium data are available, one can infer PCO and multispecies competitive ranks with RY or RE. If not, one should apply RFD with density gradient or time‐series data. Equilibria could be evaluated with T tests or standard deviations. To reliably infer intransitivity, one needs high quality data for a given method to first accurately infer PCO, especially among similar competitors.This study has been supported by the German Science Foundation (RO2397/8) in the framework of the Jena Experiment (FOR 456/1451). Y.H.F. was also supported by the Fundamental Research Funds for the Central Universities (lzujbky-2019-32). S.S. was supported by the Spanish Government under a Ramón y Cajal contract (RYC-2016-20604)

Predicting species abundances in a grassland biodiversity experiment: Trade‐offs between model complexity and generality

Models of natural processes necessarily sacrifice some realism for the sake of tractability. Detailed, parameter‐rich models often provide accurate estimates of system behaviour but can be data‐hungry and difficult to operationalize. Moreover, complexity increases the danger of ‘over‐fitting’, which leads to poor performance when models are applied to novel conditions. This challenge is typically described in terms of a trade‐off between bias and variance (i.e. low accuracy vs. low precision). In studies of ecological communities, this trade‐off often leads to an argument about the level of detail needed to describe interactions among species. Here, we used data from a grassland biodiversity experiment containing nine locally abundant plant species (the Jena ‘dominance experiment’) to parameterize models representing six increasingly complex hypotheses about interactions. For each model, we calculated goodness‐of‐fit across different subsets of the data based on sown species richness levels, and tested how performance changed depending on whether or not the same data were used to parameterize and test the model (i.e. within vs. out‐of‐sample), and whether the range of diversity treatments being predicted fell inside or outside of the range used for parameterization. As expected, goodness‐of‐fit improved as a function of model complexity for all within‐sample tests. In contrast, the best out‐of‐sample performance generally resulted from models of intermediate complexity (i.e. with only two interaction coefficients per species—an intraspecific effect and a single pooled interspecific effect), especially for predictions that fell outside the range of diversity treatments used for parameterization. In accordance with other studies, our results also demonstrate that commonly used selection methods based on AIC of models fitted to the full dataset correspond more closely to within‐sample than out‐of‐sample performance. Synthesis. Our results demonstrate that models which include only general intra and interspecific interaction coefficients can be sufficient for estimating species‐level abundances across a wide range of contexts and may provide better out‐of‐sample performance than do more complex models. These findings serve as a reminder that simpler models may often provide a better trade‐off between bias and variance in ecological systems, particularly when applying models beyond the conditions used to parameterize them