Effects of spoilage on nitrogen and carbon stable isotopes signatures of the clam Ruditapes decussatus

Fish and seafood products are highly susceptible to post-mortem spoilage due to autolytic reactions at start, then microbiological activity and eventually oxidative reactions. Chemical and microbiological parameters are usually used to assess quality and make decisions for protecting public health, but they lack precision in defining which spoilage pathway is occurring at each moment. The objective of this work was to assess the effects of spoilage reactions on nitrogen and carbon stable isotopes in the grooved carpet shell clam, Ruditapes decussatus, and compare them to biochemical indicators of seafood deterioration, in order to better understand the relations between the different spoilage pathways during commercial storage conditions. Clams were kept in a refrigerator at 5 ºC, to simulate normal commercial storage conditions, and sampled in the beginning of the experiment, and after eight, ten and twelve days. Moisture, condition index, percentage edibility, total volatile basic nitrogen (TVB-N), pH, nitrogen and carbon percentages and stable isotopes were determined for each sampling moment. Stable isotope analyses were performed using a Costech Elemental Analyzer (ECS 4010) coupled to a ThermoFinnigan Delta V Advantage. Stable isotopes analysis, especially for nitrogen, proved to be a good tool for the study of clam deterioration. Nitrogen stable isotopes results showed a relation with other spoilage indicators, such as pH and TVB-N, and allowed identifying spoilage specific pathways, such as amino acids decarboxylation and production of volatile nitrogen compounds.info:eu-repo/semantics/publishedVersio

A functional definition to distinguish ponds from lakes and wetlands

Ponds are often identified by their small size and shallow depths, but the lack of a universal evidence-based definition hampers science and weakens legal protection. Here, we compile existing pond definitions, compare ecosystem metrics (e.g., metabolism, nutrient concentrations, and gas fluxes) among ponds, wetlands, and lakes, and propose an evidence-based pond definition. Compiled definitions often mentioned surface area and depth, but were largely qualitative and variable. Government legislation rarely defined ponds, despite commonly using the term. Ponds, as defined in published studies, varied in origin and hydroperiod and were often distinct from lakes and wetlands in water chemistry. We also compared how ecosystem metrics related to three variables often seen in waterbody definitions: waterbody size, maximum depth, and emergent vegetation cover. Most ecosystem metrics (e.g., water chemistry, gas fluxes, and metabolism) exhibited nonlinear relationships with these variables, with average threshold changes at 3.7 ± 1.8 ha (median: 1.5 ha) in surface area, 5.8 ± 2.5 m (median: 5.2 m) in depth, and 13.4 ± 6.3% (median: 8.2%) emergent vegetation cover. We use this evidence and prior definitions to define ponds as waterbodies that are small (< 5 ha), shallow (< 5 m), with < 30% emergent vegetation and we highlight areas for further study near these boundaries. This definition will inform the science, policy, and management of globally abundant and ecologically significant pond ecosystems.Fil: Richardson, David C.. State University of New York at New Paltz; Estados UnidosFil: Holgerson, Meredith A.. Cornell University; Estados UnidosFil: Farragher, Matthew J.. University of Maine; Estados UnidosFil: Hoffman, Kathryn K.. No especifíca;Fil: King, Katelyn B. S.. Michigan State University; Estados UnidosFil: Alfonso, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Andersen, Mikkel R.. No especifíca;Fil: Cheruveil, Kendra Spence. Michigan State University; Estados UnidosFil: Coleman, Kristen A.. University of York; Reino UnidoFil: Farruggia, Mary Jade. University of California at Davis; Estados UnidosFil: Fernandez, Rocio Luz. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Hondula, Kelly L.. No especifíca;Fil: López Moreira Mazacotte, Gregorio A.. Leibniz - Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Paul, Katherine. No especifíca;Fil: Peierls, Benjamin L.. No especifíca;Fil: Rabaey, Joseph S.. University of Minnesota; Estados UnidosFil: Sadro, Steven. University of California at Davis; Estados UnidosFil: Sánchez, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; ArgentinaFil: Smyth, Robyn L.. No especifíca;Fil: Sweetman, Jon N.. State University of Pennsylvania; Estados Unido

A glossary for biometeorology

Here we present, for the first time, a glossary of biometeorological terms. The glossary aims to address the need for a reliable source of biometeorological definitions, thereby facilitating communication and mutual understanding in this rapidly expanding field. A total of 171 terms are defined, with reference to 234 citations. It is anticipated that the glossary will be revisited in coming years, updating terms and adding new terms, as appropriate. The glossary is intended to provide a useful resource to the biometeorology community, and to this end, readers are encouraged to contact the lead author to suggest additional terms for inclusion in later versions of the glossary as a result of new and emerging developments in the field

The burden of heat-related mortality attributable to recent human-induced climate change

Medical Research Council-UK (Grant ID: MR/M022625/1); Natural Environment Research Council UK (Grant ID: NE/R009384/1); European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655); N. Scovronick was supported by the NIEHS-funded HERCULES Center (P30ES019776); Y. Honda was supported by the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency, Japan (JPMEERF15S11412); J. Jaakkola was supported by Academy of Finland (Grant No. 310372); V. Huber was supported by the Spanish Ministry of Economy, Industry and Competitiveness (Grant ID: PCIN-2017-046) and the German Federal Ministry of Education and Research (Grant ID: 01LS1201A2); J Kysely and A. Urban were supported by the Czech Science Foundation (Grant ID: 20-28560S); J. Madureira was supported by the Fundação para a Ciência e a Tecnologia (FCT) (SFRH/BPD/115112/2016); S. Rao and F. di Ruscio were supported by European Union’s Horizon 2020 Project EXHAUSTION (Grant ID: 820655); M. Hashizume was supported by the Japan Science and Technology Agency (JST) as part of SICORP, Grant Number JPMJSC20E4; Y. Guo was supported by the Career Development Fellowship of the Australian National Health and Medical Research Council (#APP1163693); S. Lee was support by the Early Career Fellowship of the Australian National Health and Medical Research Council (#APP1109193)

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

The SSC: A decade of climate-health research and future directions

This year marks the tenth anniversary of the development of the revised Spatial Synoptic Classification, the "SSC", by Scott Sheridan. This daily weather-type classification scheme has become one of the key analytical tools implemented in a diverse range of climatological investigations, including analysis of air quality variability, human health, vegetation growth, precipitation and snowfall trends, and broader analyses of historical and future climatic variability and trends. The continued and expanding use of the SSC motivates a review and comparison of the system's research and geographic foci to date, with the goal of identifying promising areas for future efforts, particularly within the context of human health and climate change. This review also assesses how the SSC has complemented and compares with other current environmental epidemiological studies in weather and health. © 2013 ISB

Perspectives on synoptic climate classification and its role in interdisciplinary research

Synoptic climatology has a long history of research where weather data are aggregated and composited to gain a better understanding of atmospheric effects on non-atmospheric variables. This has resulted in an applied scientific discipline that yields methods and tools designed for applications across disciplinary boundaries. The spatial synoptic classification (SSC) is an example of such a tool that helps researcher bridge methodological gaps between disciplines, especially those studying weather effects on human health. The SSC has been applied in several multi-discipline projects, and it appears that there is ample opportunity for growth into new topical areas. Likewise, there is opportunity for the SSC network to be expanded across the globe, especially into mid-latitude locations in the southern hemisphere. There is some question of the utility of the SSC in tropical locations, but such decisions must be based on the actual weather data from individual locations. Despite all of the strengths and potential uses of the SSC, there are some research problems, some locations, and some datasets for which it is not suitable. Nevertheless, the success of the SSC as a cross-disciplinary method is noteworthy because it has become a catalyst for collaboration