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Aumont, O.

Publications and source records attributed to Aumont, O..

3 recordsLinked to original sources

Purchases dominate the carbon footprint of research laboratories

Despite increasing interest for the carbon footprint of higher education institutions, little is known about the carbon footprint associated to research activities. Air travel and attendance to conferences concentrate recent data and debates but purchases have attracted little attention. Here we develop a hybrid method to estimate the greenhouse gas emissions (GHG) associated to research purchases. To do so, we combine macroe-conomic databases, research-centered companies footprints and life-cycle assesments to construct a public database of monetary emission factors (EF) for research purchases. We apply it to estimate the purchases emissions of a hundred of research laboratories in France, belonging to the Labos 1point5 network and gathering more than 20000 staff, from all disciplines. We find that purchases dominate laboratory emissions, accounting for more than 50% of emissions, with a median of 2.7 t CO2e/pers, which is 3 to 4-fold the separate contribution from travel, commutes and heating. Median electricity emissions are 5-fold lower in our dataset of laboratories using low carbon electricity but they become preponderant for high carbon electricity mixes (3.5 t CO2e/pers). Purchases emissions are very heterogeneous among laboratories and are linearly correlated with budget, with an average carbon intensity of 0.31 {+/-} 0.07 kg CO2e/{euro} and differences between research domains. Finally, we quantify the effect of a series of demand-driven mitigation strategies obtaining up to -20 % in total emissions (-40 % in purchases emissions), suggesting that effectively reducing the carbon footprint of research activities calls for systemic changes. Significance statementResearch activities are recently interrogating their contribution to global warming, mainly through the impact of air travel but neglecting the emissions embodied in scientific purchases. However, goods and services used in a research laboratory emit greenhouse gases when they are produced. Here we construct a public and robust database of emission factors to quantify purchases emissions in a laboratory and we use it to assess emissions from a hundred of laboratories in France, from all disciplines. We find that purchases emissions represent half of the of the 6.3 t CO2e/pers per year emitted on average per laboratory. Emissions, however, vary greatly between laboratories and disciplines and an analysis of mitigation strategies shows that decreasing demand may significantly reduce purchases emissions.

scientific communication and education↗

Phytoplankton adaptive resilience to climate change collapses in case of extreme events; A modeling study

As climate change unravels, ecosystems are facing a warming of the climate and an increase in extreme heat events that are unprecedented in recent geological history. We know very little of the ability of oceanic phytoplankton communities, key players in the regulation of Earths climate by the oceans, to adapt to these changes. Quantifying the resilience of phytoplankton communities to environmental stressors by means of adaptive evolution is however crucial to accurately predict the response of marine ecosystems to climate change. In this work, we use an eco-evolutionary model to simulate the adaptive response of marine phytoplankton to temperature changes in an initially temperate oligotrophic water-column. By exploring a wide range of scenarios of phytoplankton adaptive capacity, we find that phytoplankton can adapt to temperature increases -even very large ones- as long as they occur over the time scale of a century. However, when rapid and extreme events of temperature change are considered, the phytoplankton adaptive capacity breaks down in a number of our scenarios in which primary productivity plummets as a result. This suggests that current Earth System Models implicitly assuming perfect and instantaneous phytoplankton adaptation to temperature might be overestimating the phytoplanktons resilience to climate change.

ecology↗

Contribution of genome scale metabolic modeling to niche theory

Standard niche modeling is based on probabilistic inference from organismal occurrence data but does not benefit yet from genome-scale descriptions of these organisms. This study over-comes this shortcoming by proposing a new conceptual niche that encompasses the whole metabolic capabilities of an organism. The so-called metabolic niche resumes well-known traits such as nutrient needs and their dependencies for survival. Despite the computational challenge, its implementation allows the detection of traits and the formal comparison of niches of different organisms, emphasizing that the presence-absence of functional genes is not enough to approximate the phenotype. Further statistical exploration of an organisms niche sheds light on genes essential for the metabolic niche and their role in understanding various biological experiments, such as transcriptomics, paving the way for incorporating better the genome-scale description in ecological studies.

ecology↗