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Abstract In an ocean that is rapidly warming and losing oxygen, accurate forecasting of species’ responses must consider how this environmental change affects fundamental aspects of their physiology. Here, we develop an absolute metabolic index (Φ A ) that quantifies how ocean temperature, dissolved oxygen and organismal mass interact to constrain the total oxygen budget an organism can use to fuel sustainable levels of aerobic metabolism. We calibrate species-specific parameters of Φ A with physiological measurements for red abalone ( Haliotis rufescens ) and purple urchin ( Strongylocentrotus purpuratus ). Φ A models highlight that the temperature where oxygen supply is greatest shifts cooler when water loses oxygen or organisms grow larger, providing a mechanistic explanation for observed thermal preference patterns. Viable habitat forecasts are disproportionally deleterious for red abalone, revealing how species-specific physiologies modulate the intensity of a common climate signal, captured in the newly developed Φ A framework. 

Oxygen levels in the atmosphere and ocean have changed dramatically over Earth history, with major impacts on marine life. Because the early part of Earth’s history lacked both atmospheric oxygen and animals, a persistent co-evolutionary narrative has developed linking oxygen change with changes in animal diversity. Although it was long believed that oxygen rose to essentially modern levels around the Cambrian period, a more muted increase is now believed likely. Thus, if oxygen increase facilitated the Cambrian explosion, it did so by crossing critical ecological thresholds at low O2. Atmospheric oxygen likely remained at low or moderate levels through the early Paleozoic era, and this likely contributed to high metazoan extinction rates until oxygen finally rose to modern levels in the later Paleozoic. After this point, ocean deoxygenation (and marine mass extinctions) is increasingly linked to large igneous province eruptions—massive volcanic carbon inputs to the Earth system that caused global warming, ocean acidification, and oxygen loss. Although the timescales of these ancient events limit their utility as exact analogs for modern anthropogenic global change, the clear message from the geologic record is that large and rapid CO2 injections into the Earth system consistently cause the same deadly trio of stressors that are observed today. The next frontier in understanding the impact of oxygen changes (or, more broadly, temperature-dependent hypoxia) in deep time requires approaches from ecophysiology that will help conservation biologists better calibrate the response of the biosphere at large taxonomic, spatial, and temporal scales. 

A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015–2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resulted in overlapping signals from adjacent crossings. The energy corresponding to a particular bunch crossing of interest is estimated using the known pulse shapes of energy depositions in the calorimeter, which are measured as functions of both energy and time. A variety of algorithms were developed to mitigate the effects of adjacent bunch crossings on local energy reconstruction in the hadron calorimeter in Run 2, and their performance is compared.

 

A search for decays to invisible particles of Higgs bosons produced in association with a top-antitop quark pair or a vector boson, which both decay to a fully hadronic final state, has been performed using proton-proton collision data collected at$${\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V}}$$$\sqrt{s}=13\text{Te}\text{V}$by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138$$\,\text {fb}^{-1}$$${\text{fb}}^{-1}$. The 95% confidence level upper limit set on the branching fraction of the 125$$\,\text {Ge}\hspace{-.08em}\text {V}$$$\text{Ge}\text{V}$Higgs boson to invisible particles,$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$$B(\text{H}\to \text{inv})$, is 0.54 (0.39 expected), assuming standard model production cross sections. The results of this analysis are combined with previous$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$$B(\text{H}\to \text{inv})$searches carried out at$${\sqrt{s}=7}$$$\sqrt{s}=7$, 8, and 13$$\,\text {Te}\hspace{-.08em}\text {V}$$$\text{Te}\text{V}$in complementary production modes. The combined upper limit at 95% confidence level on$${\mathcal {B}({\textrm{H}} \rightarrow \text {inv})}$$$B(\text{H}\to \text{inv})$is 0.15 (0.08 expected).