Philippa Kaur

Pace, N. R. The small things can matter. PLoS Biol. 16(8), e3000009. https://doi.org/10.1371/journal.pbio.3000009 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hoshino, T. et al. Global diversity of microbial communities in marine sediment. PNAS 117, 27587–27597 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baker, B. J., Appler, K. E. & Gong, X. New microbial biodiversity in marine sediments. Ann. Rev. Mar. Sci. 13, 161–175. https://doi.org/10.1146/annurev-marine-032020-014552 (2021).Article 
PubMed 

Google Scholar 
Zinger, L. et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS ONE 6, e24570. https://doi.org/10.1371/journal.pone.0024570 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jiao, N. et al. Microbial production of recalcitrant dissolved organic matter: Long-term carbon storage in the global ocean. Nat. Rev. Microbiol. 8, 593–599 (2010).CAS 
PubMed 
Article 

Google Scholar 
Ward, N. D. et al. Representing the function and sensitivity of coastal interfaces in earth system models. Nat. Commun. 11, 2458 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cook, R., & Auster, P. J. Developing alternatives for optimal representation of seafloor habitats and associated communities in Stellwagen Bank National Marine Sanctuary. Marine Sanctuaries Conservation Series ONMS-06–02. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Office of National Marine Sanctuaries, Silver Spring, MD (2006).Wauchope, H. S. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 3. https://doi.org/10.1016/j.tree.2020.11.001 (2021).Article 

Google Scholar 
Stellwagen Bank National Marine Sanctuary (SBNMS) Condition Report. Office of National Marine Sanctuaries National Oceanic and Atmospheric Administration. doi:https://doi.org/10.25923/48ZK-BB07. pp. 1–263. (2020).Grieve, C., Brady, D. C. & Polet, H. Best practices for managing, measuring and mitigating the benthic impacts of fishing—Part 1. Mar. Stewardship Council Sci. Ser. 2, 18–88 (2014).
Google Scholar 
Watling, L. & Norse, E. A. Disturbance of the seafloor by mobile fishing gear: A comparison to forest clear cutting. Conserv. Biol. 12, 1180–1197 (1998).Article 

Google Scholar 
Snelgrove, P. V. R. et al. The importance of marine sediment biodiversity in ecosystem processes. Ambio 26, 578–583 (1997).
Google Scholar 
Grassle, J. F. & Maciolek, N. J. Deep-sea species richness: Regional and local diversity estimates from quantitative bottom samples. Am. Nat. 139, 313–341 (1992).Article 

Google Scholar 
Polinski, J. M., Bucci, J. P., Gasser, M. & Bodnar, A. G. Targeted metagenomic assessment of biodiversity across prokaryotic and eukaryotic taxa in sediments from the Stellwagen Bank National Marine Sanctuary. Sci. Rep. 9, 14820 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Petro, C. et al. Microbial community assembly in marine sediments. Aquat. Microb. Ecol. 79, 177–195 (2017).Article 

Google Scholar 
Cook, R. et al. The substantial first impact of bottom fishing on rare biodiversity hotspots: A dilemma for evidence-based conservation. PLoS ONE 8, e69904 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Grabowski, J. H. et al. Assessing the vulnerability of marine benthos to fishing gear impacts. Rev. Fisheries Sci. Aquacult. 22, 142–155 (2014).Article 

Google Scholar 
Silva, T. L. State of the science report: An addendum to the Stellwagen Bank National Marine Sanctuary 2020 Condition Report 1–20 (U.S. Department of Commerce, 2021).
Google Scholar 
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).CAS 
PubMed 
Article 

Google Scholar 
Bech, P. K. et al. Marine sediments hold an untapped potential for novel taxonomic and bioactive bacterial diversity. MSystems 5, e00782-e820 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Newman, D. J. & Cragg, G. M. Natural products as sources of new drugs from 1981 to 2014. J. Nat. Prod. 79, 629–661 (2016).CAS 
PubMed 
Article 

Google Scholar 
Hou, Z. Geochemical and microbial community attributes in relation to hyporheic zone geological facies. Sci. Rep. 7, 12006 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hugenholtz, P., Goebel, B. M. & Pace, N. R. Impact of culture-independent studies on the emerging phylogenetic view of bacterial diversity. J. Bacteriol. 180, 4765–4774 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durazzi, F. et al. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Sci. Rep. 11, 3030 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Asnicar, F. et al. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat. Commun. 11, 2500 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dance, A. The search for microbial dark matter. Nature 582, 301–303. https://doi.org/10.1038/d41586-020-01684-z (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Fishing Restrictions. Magnuson Fishery Conservation and Management Act (MFCMA) (16 U.S.C. Part 1801 et seq.) (1990).Begon, M., Harper, J. L. & Townsend, C. R. Ecology: Individuals, Populations, and Communities 3rd edn. (Blackwell Science Ltd., 1996).Book 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Andrews, S. Babraham bioinformatics-FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet Next Gen. Sequencing Data Anal. 17, 1 (2011).
Google Scholar 
Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lu, J., Breitwieser, F. P., Thielen, P. & Salzberg, S. L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 2, e104 (2017).Article 

Google Scholar 
Wickham, H. ggplot2. WIREs Comput. Stat. 3, 180–185 (2011).Article 

Google Scholar 
Breitwieser, F. P. & Salzberg, S. L. Pavian: Interactive analysis of metagenomics data for microbiome studies and pathogen identification. Bioinformatics 36, 1303–1304 (2020).CAS 
PubMed 
Article 

Google Scholar 
Wu, Y. W. et al. MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).CAS 
PubMed 
Article 

Google Scholar 
Alneberg, J. et al. CONCOCT: Clustering cONtigs on COverage and ComposiTion. ArXiv 1312, 4038 (2013).ADS 

Google Scholar 
Kang, D. et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Parks, D. H. et al. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a web browser. BMC Bioinform. 12, 385 (2011).Article 

Google Scholar 
Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
PubMed 
Article 

Google Scholar 
Blin, K. et al. antiSMASH 6.0: Improving cluster detection and comparison capabilities. Nucleic Acids Res. 49, W29–W35 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978–3–319–24277–4. (2016).Moon, K. W. Interactive Plot. In Learn ggplot2 Using Shiny App (ed. Moon, K.-W.) 295–347 (Springer International Publishing, 2016).Chapter 

Google Scholar 
Oksanen, J., et al. Package ‘vegan’. Community ecology package, version 2, 1-295 (2013).Wilkinson, L. SYSTAT. In Wiley Interdisciplinary Reviews: Computational Statistics, Multidimensional Scaling (eds Wegman, E. & Said, Y. H.) (John Wiley & Sons, New York, 2010).
Google Scholar 
Dexter, E., Rollwagen-Bollens, G. & Bollens, M. The trouble with stress: A flexible method for the evaluation of nonmetric multidimensional scaling. Limnol. Oceanogr. Methods 16, 434–443 (2018).Article 

Google Scholar 
Longford, N. T. Longitudinal and time-series analysis. In Studying Human Populations. Springer Texts in Statistics (Springer, 2008). https://doi.org/10.1007/978-0-387-73251-0_11.Chapter 
MATH 

Google Scholar 
NOAA Office of Law Enforcement. Speed-filtered vessel monitoring system (VMS) data from Greater. Atlantic VMS Program (2019).Palmer, M. C., & Wigley, S. E. Validating the stock apportionment of commercial fisheries landings using positional data from vessel monitoring systems (VMS). Northeast Fisheries Science Center Reference Document 07–22. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, Woods Hole, MA. (2007).Northeastern Regional Association of Coastal Ocean Observing Systems Buoy (NERACOOS) Monitoring Program. Portsmouth, NH. www.neracoos.org (2021).Stroup, W. Generalized Linear Mixed Models: Modern Concepts (Methods and Applications. Taylor & Francis Group, 2013).MATH 

Google Scholar 
Ridout, M. S., Hinde, J. P., & Demétrio, C. G. B. “Models for Count Data with Many Zeros,” in Proceedings of the 19th International Biometric Conference, 179–192, Cape Town. (1998).Barnhardt, W. A., Kelley, J. T., Dickson, S. M. & Belknap, D. F. Mapping the Gulf of maine with side-scan sonar: A new bottom-type classification for complex seafloors. J. Coast. Res. 14, 646–659 (1998).
Google Scholar 
Carrier-Belleau, C. et al. Environmental stressors, complex interactions and marine benthic communities’ responses. Sci. Rep. 11, 4194. https://doi.org/10.1038/s41598-021-83533-1 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Auster, P., Joy, K. & Valentine, P. C. Fish species and community distributions as proxies for seafloor habitat distributions: the Stellwagen Bank National Marine Sanctuary example (Northwest Atlantic, Gulf of Maine). Environ. Biol. Fishes 60, 331–346 (2001).Article 

Google Scholar 
Solan, M., Raffaelli, D. G., Paterson, D. M., White, P. C. L. & Pierce, G. J. Marine biodiversity and ecosystem function: Empirical approaches and future research needs. Mar. Ecol. Prog. Ser. 311, 175–178 (2006).ADS 
Article 

Google Scholar 
Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Dyksma, S. et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J. 10, 1939–1953 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tuttle, R. N. et al. Detection of natural products and their producers in ocean sediments. Appl. Environ. Microbiol. 85, e02830-e2918 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Heinrichs, L., Aytur, S. A. & Bucci, J. P. Whole metagenomic sequencing to characterize the sediment microbial community within the Stellwagen Bank National Marine Sanctuary and preliminary biosynthetic gene cluster screening of Streptomyces scabrisporus. Mar. Genom. 50, 100718 (2020).Article 

Google Scholar 
Belknap, K. C. et al. Genome mining of biosynthetic and chemotherapeutic gene clusters in Streptomyces bacteria. Sci. Rep. 10, 2003. https://doi.org/10.1038/s41598-020-58904-9 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sánchez-Soto Jiménez, M. F., Cerqueda-García, D., Montero-Muñoz, J. L., Aguirre-Macedo, M. L. & García-Maldonado, J. Q. Assessment of the bacterial community structure in shallow and deep sediments of the Perdido Fold Belt region in the Gulf of Mexico. PeerJ 6, e5583. https://doi.org/10.7717/peerj (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 62, 809–812 (2015).ADS 
Article 
CAS 

Google Scholar 
Pittman, S. J. Relevance of the Northeast Integrated Ecosystem Assessment for the Stellwagen Bank National Marine Sanctuary Condition Report (2007–2018) Marine Sanctuaries Conservation Science Series ONMS-19–08. U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Office of National Marine Sanctuaries, Silver Spring, MD. (2019).Bucci, J. P., Szempruch, A. J., Caldwell, J. M., Ellis, C. & Levine, J. F. Seasonal changes in microbial community structure in freshwater stream sediment in a North Carolina River Basin. Diversity 6, 18–32 (2014).Article 

Google Scholar 
Won, N. I., Kim, K. H., Kang, J. H., Park, S. R. & Lee, H. J. Exploring the impacts of anthropogenic disturbance on seawater and sediment microbial communities in korean coastal waters using metagenomics analysis. Int. J. Environ. Res. Public Health 14, 130 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
Zinger, L. et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS ONE 6(9), e24570 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Auster, P., Lindholm, J., Cramer, A., Nenandovic, M., Prindle, C., & Tamsett, A. The seafloor habitat recovery monitoring project (SHRMP) at Stellwagen Bank National Marine Sanctuary. Final Project Report. (2013b).UN General Assembly, Transforming our world: The 2030 Agenda for Sustainable Development, 21 October, A/RES/70/1, available at: https://www.refworld.org/docid/57b6e3e44.html. (2015).Malve, H. Exploring the ocean for new drug developments: Marine pharmacology. J. Pharm. Bioall. Sci. 8, 83–91. https://doi.org/10.4103/0975-7406.171700 (2016).CAS 
Article 

Google Scholar  More

Broadcasted social calls attracted hoary bats during both the spring and fall migration. Broadcasting conspecific social calls increased hoary bat capture rates at netting sites intentionally removed from normal capture locations. We had very low capture rates during control periods, because we intentionally placed nets in locations removed from flyways to reduce incidental captures. Moreover, capture rates of hoary bats tend to be low even in many locations where they are known to occur24,25, and capture rates of approximately one bat per hour in a single mist net suggest a very strong attraction response to broadcasted calls.Hoary bat activity, as measured by acoustic monitoring was not associated with increased capture rates in response to call broadcasting. However, subsequent research has shown that hoary bats periodically use higher frequency, inconspicuous calls, or do not constantly echolocate during the fall, which may mean acoustic monitoring did not effectively measure hoary bat activity in the vicinity of our trials26,27. We recorded substantially higher acoustic activity during the spring migration, which could represent either more hoary bats and/or bat activity, or a seasonal difference in echolocation or flight behavior such as differences in flight altitude27. It remains unknown if hoary bats use inconspicuous calls or fly in silence during spring migration or other times of year other than the fall when these inconspicuous echolocation behaviors were observed, and seasonally variable behavior could affect detectability or exposure to our playback trials in ways not captured by our acoustic activity covariate. In addition, while we did audibly hear social calls of hoary bats during the fall, we did not record any during fieldwork for this study, which may be an artifact or due to differences in social behavior, context, or number of hoary bats present in the area during our trials.We only captured one female during trials in New Mexico, and were unable to locate any females during the fall migration in coastal regions of California, despite high concentrations of males in the area during what is presumably the mating season. In New Mexico, during spring migration, females migrate through the study area before males28, with very little temporal overlap. As a result, we were unable to determine sex specific responses to call playback, however we have subsequently captured several female hoary bats and Ope’ape’a (Hawaiian hoary bat, L. semotus) using call playback during capture and radio-tracking studies (GAR, pers. obs.).It is difficult to elucidate the meaning of social calls based on the behaviors observed in the field. In bats, social call complexity often reflects social behavior complexity, with a range of uses including but not limited to attracting mates, locating pups within colonies, defending roosting or foraging territory, and attracting bats to roosts10. Attraction to conspecific call broadcasting could indicate positive social interactions (e.g., maintaining group cohesion or investigation) or agonistic behavior (e.g., hoary bats approaching to chase conspecific bats), as has been observed in other bat species29 and in hoary bats during the maternity season30. We did not observe any obvious instances of aggressive hoary bat interactions, and the social calls differ from hisses and clicks that hoary bats use defensively (Fig. 2). We would also audibly hear pairs of hoary bats calling in close proximity to each other, with no indication of aggressive or territorial responses, and these calls being low frequency and audible to humans means that they attenuate at greater distances than hoary bat echolocation calls.Aggressive or territorial interactions in many taxa are often driven by seasonally variable contexts, such as mating, defending food resources, or rearing of young. It may be unlikely that migrating hoary bats would expend energy defending territory during migration when they are utilizing roosts or foraging habitat for such limited periods of time (i.e., a few hours to a day). During active migration birds are often not territorial even when foraging at stopover sites31, and there may be benefits to maintaining group cohesion during migration including navigation and identification of favorable habitat. It is unknown if hoary bats utilize stopover sites for refueling during migration. However the silver-haired bat Lasionycteris noctivagans was found to utilize a migration stopover site in Long Point, Canada, where they opportunistically foraged for short periods of time (1 to 2 days32). Tracking studies would be required to determine temporal patterns of site usage by individual bats to examine stopover behavior.As we had recorded most of our initial social calls during late summer and early fall when hoary bats mate21, we had originally hypothesized that these social calls were associated with mating behavior, which would have been consistent with observations in this study had we found both increased attraction during the fall, and less attraction to calls during the spring. However, social calls attracted hoary bats effectively during both the spring and fall migration. In addition, from acoustic recordings and capture observations in the field, hoary bats produced many social calls during the spring migration when only males were present. There is a possibility, due to our lack of understanding of the mating systems of hoary bats that some mating may continue into the spring. However the majority of taxonomic, physiological, and observational data suggests mating behavior ends by the spring migration19,33, and the majority of females are already pregnant when travelling through New Mexico28. While hoary bats may or may not use social calls as a component of mating behavior, social calls recorded during the spring likely serve purposes not associated with mating.Previous studies describe the hoary bat as solitary throughout most of the year, which would imply only brief social interactions limited to mating or association with offspring, and the many historical accounts of aggregations of hoary bats are thought to be related to mating behavior20,33,34. However the use of, and attraction to, social calls during both spring and fall migration supports that these calls are used for social interactions beyond mating behavior. Further research may determine if hoary bats use these social calls to maintain group cohesion during migration, and what, if any, relationships exist between individual hoary bats that appear to be migrating together. Baerwald and Barclay35 found that geographic and genetic relationships of hoary bats and silver-haired bat carcasses collected at wind turbines were not more closely related than expected by chance, which provides some evidence that groups of migrating hoary bats may not form based on kinship.Many studies hoping to elucidate the causes of fatalities at wind energy facilities have focused only on the fall migration period when bats are most often killed13,20,36. However hoary bats migrate during the spring as well, when they do not suffer high fatality rates. Investigating the spring migration presents a valuable baseline to compare behavioral changes and other factors that may place hoary bats or other impacted species at risk. If social behavior makes a major contribution to the risk of fatalities at wind energy developments, then social behavior should differ between spring and fall migration. We did not find a large difference in response to social calls between seasons. While this represents just an initial study into the social calling behavior of hoary bats during migration, it provides some conclusions to guide subsequent investigations: (1) detecting hoary bat social calls does not necessarily indicate mating behavior, and (2) researchers should be cautious in interpreting evidence of social interactions during the fall at wind energy sites as evidence of mating behavior as in the mating landmarks hypothesis22,37. Because it can separate out mating from other behavioral components, comparing spring and fall migration can benefit the investigation of social and other behaviors in hoary bats and other migratory species. Comparing flight behavior, diet, roost selection, hormonal and physiological changes, and further studies of social interactions including scent and, between the spring and fall migration will allow researchers to elucidate which behaviors change seasonally and which may underlie seasonal patterns of wind turbine fatalities. Additionally, exploring social attraction to audible sounds produced by turbines or other potential signals that could seasonally elicit social attraction could lead to additional insights.Hoary bats have proven challenging to capture and study in many locations across their range24, driven by their solitary tree roosting behavior and as they often fly out of the reach of mist nets or ground-based acoustic monitoring stations36,38. Using call broadcasting to increase capture rates can be a useful research tool, especially in locations where the habitat does not provide any ideal capture locations. Using this technique we have captured hoary bats on coastal sand dunes, in large open fields, and in groves of Eucalyptus trees adjacent to wind energy sites, all of which would normally yield low bat capture success without the use of lures. The ability to capture hoary bats more reliably is a great asset for research and conservation throughout the range of hoary bats.Our study tested the use of social call playback as a methodology to study the social behavior of hoary bats during migration, and the utility of using call playback as a research tool and acoustic lure for hoary bats. Increasing capture rates from conspecific social call playback during mating and non-mating season indicates social interactions during both migratory periods, despite the solitary roosting behavior of this species. Future studies to elucidate the behavioral function of these calls, and response during non-migratory seasons could refine our understanding of social behaviors of this elusive bat species. More

Fisheries overexploitation is a problem in all oceans and seas globally. Authorities and administrations in charge of assigning quotas have very little fine-grained information on the fish captures, and instead use large-scale, coarse data to assess the health level of fisheries. Thus, being able to cross-match fish species and sizes, to the sea regions they were captured from, can be helpful in this regard, providing finer-grained information.Previous attempts at assembling datasets for fish detection and classification exist, ranging from fish detection or counting in underwater images and video streams1,2,3, to counting on belts on trawler ships4, to classification in laboratory conditions5,6, or in underwater preprocessed images of single fish7,8,9, or single fish in free-form pictures10, as well as simultaneous detection and classification of several fish11,12. However, none of the works found in the literature addresses the topic of simultaneous instance segmentation and species classification, along with fish size estimation, in a fish market environment, as is the aim of this paper. Instance segmentation refers to the extraction of pixel-level masks for each individual object (in this case fish specimens), rather than bounding boxes (object detection), or class label masks (e.g. a single mask for all fish specimens of the same species, also referred to as semantic segmentation). Moreover, works in the literature use pictures taken in laboratory conditions (with a single fish per image, shown from the side), or in underwater conditions. Only French et al.4 uses pictures of fish catches on a belt, for counting purposes. Table 1 shows a summary of the datasets identified in the literature, along with their characteristics, including how the proposed dataset compares.Table 1 Summary of previous datasets found in the literature, and comparison to proposed dataset.Full size tableThe DeepFish project (website: http://deepfish.dtic.ua.es/) is aimed at providing fish species classification and size estimation for fish specimens arriving at fish markets, both for the automation of fish sales, and the retrieval of fine-grained information about the health of fisheries. For a period of six months (April to September 2021), images have been captured at the fish market in El Campello (Alicante, Spain). Images of market trays show a variety of fish species, including targeted as well as accidental captures from the ‘Cabo de la Huerta’, an important site for protection and preservation of marine habitats and biodiversity as defined by the European Comission Habitats Directive (92/43/EEC). From the pictures, a total of 59 different species are identified with 12 species having more than 100 specimens and 25 with more than 10 specimens, as shown in Table 2. There is a high imbalance of species captured due to the natural variation in fish species populations according to seasonality and other ecological factors (rarity of the species, i.e. total population count, etc). Due to some species showing sexual dimorphism (i.e. Symphodus tinca), this species is split into two separate class labels, leading to a different number of species, and class labels (59 species, but 60 class labels). The dataset presents a high temporal imbalance too. As shown in Fig. 1, the capture of new fish tray images was not evenly distributed during the six month study period. Several factors contributed to this: wholesale fish market operating days (e.g. no weekend data, holidays and stop periods, etc.), fish species variability (one of the aims was to be able to capture at least 100 specimens from several species, and seasonality meant some could not be available for capture in later months), as well as the time availability of research group members to attend the fish arrival, tray preparation and auctioning in the evenings.Table 2 Distribution of fish species in the dataset.Full size tableFig. 1Temporal distribution of fish tray images captured. It can be observed that April (04) and May (05) were much more active than the rest of months. This is due to several contributing factors.Full size imageThe resulting DeepFish dataset introduced here contains annotated images from 1,291 fish market trays, with a total of 7,339 specimens (individual fish instances) which were labelled (species and mask) using a specially-adapted version of the Django labeller instance segmentation labelling tool13. Subsequently, another JSON file is generated, following the Microsoft Common Objects in Context (MS COCO) dataset format14, which can be directly fed to a neural network. This is done via a script that is also provided15. Figure 2 shows the distribution of individuals for the selected species within the dataset. Furthermore, Fig. 3 shows examples of the trays, with instance segmentation (ground truth silhouette, i.e. as an interpolation from human-provided points) along with species labelling (different colour shading).Fig. 2Graphical view of the distribution of fish species in the DeepFish dataset for species above 10 specimens. Note, Symphodus tinca is considered separately due to sexual dimorphism (211 male; 335 female samples).Full size imageFig. 3Examples of ground truth fish instance masks with class labelling, showing the 12 species (13 labels) with more than 100 specimens (in bold in Table 2).Full size imageFrom the point of view of research, this data is important for the classification of fish species, instance segmentation, as well as specimen size estimation (e.g. as a regression problem, or otherwise). From an end-results perspective, data automatically labelled with fish instance segmentation accompanied by species name and estimated size is useful to different stakeholders, namely: fishing authorities (to understand how much of each species is being caught per zone), maritime conservation (to calculate depletion of fisheries), but also managers of the markets themselves, as well as clients (digitized sales, e-commerce), etc.The usage of the provided data can be manifold, as it can be used for several problems, namely: object detection and classification, which involves finding objects (in this case fish specimens) providing a bounding box, and a class for each of these boxes; additionally, the data can also be used for semantic segmentation, which can provide a pixel-wise segmentation of the image providing labels (in this case species labels) to different pixel regions of the image; furthermore, also instance segmentation is possible, in which not just a single label for all instances of the same species is provided, but each specimen is provided with a mask (specimen segmentation), as well as a label (species). Furthermore, several measurements of each fish are provided, which can also be used to estimate their size, since they have been shown to be correlated with each other16. These are estimated from the calculated homography (given the tray size is known), given the burden of measuring each fish due to the large amount of specimens in the dataset. More

Rasmussen, S. O. et al. A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: refining and extending the INTIMATE event stratigraphy. Quat. Sci. Rev. 106, 14–28 (2014).Article 

Google Scholar 
Heiri, O. et al. Validation of climate model-inferred regional temperature change for late-glacial Europe. Nat. Commun. 5, 1–7 (2014).Article 
CAS 

Google Scholar 
Muschitiello, F. et al. Fennoscandian freshwater control on Greenland hydroclimate shifts at the onset of the Younger Dryas. Nat. Commun. 6, 1–8 (2015).Article 
CAS 

Google Scholar 
Renssen, H. et al. Multiple causes of the Younger Dryas cold period. Nat. Geosci. 8, 946–949 (2015).CAS 
Article 

Google Scholar 
Mangerud, J. The discovery of the Younger Dryas, and comments on the current meaning and usage of the term. Boreas 50, 1–5 (2021).Article 

Google Scholar 
Cheng, H. et al. Timing and structure of the Younger Dryas event and its underlying climate dynamics. Proc. Natl. Acad. Sci. USA 117, 23408–23417 (2020).CAS 
Article 

Google Scholar 
van Hoesel, A., Hoek, W. Z., Pennock, G. M. & Drury, M. R. The Younger Dryas impact hypothesis: a critical review. Quat. Sci. Rev. 83, 95–114 (2014).Article 

Google Scholar 
Partin, J. W. et al. Gradual onset and recovery of the Younger Dryas abrupt climate event in the tropics. Nat. Commun. 6, 1–9 (2015).Article 

Google Scholar 
Reinig, F. et al. Precise date for the Laacher See eruption synchronizes the Younger Dryas. Nature 595, 66–69 (2021).CAS 
Article 

Google Scholar 
Ammann, B. et al. Quantification of biotic responses to rapid climatic changes around the Younger Dryas — a synthesis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 159, 313–347 (2000).Article 

Google Scholar 
Hoek, W. Z. Vegetation response to the ∼ 14.7 and ∼ 11.5 ka cal. BP climate transitions: is vegetation lagging climate? Glob. Planet. Change 30, 103–115 (2001).Article 

Google Scholar 
Litt, T. et al. Correlation and synchronisation of Lateglacial continental sequences in northern central Europe based on annually laminated lacustrine sediments. Quat. Sci. Rev. 20, 1233–1249 (2001).Article 

Google Scholar 
Muschitiello, F. & Wohlfarth, B. Time-transgressive environmental shifts across Northern Europe at the onset of the Younger Dryas. Quat. Sci. Rev. 109, 49–56 (2015).Article 

Google Scholar 
Nakagawa, T. et al. The spatio-temporal structure of the Lateglacial to early Holocene transition reconstructed from the pollen record of Lake Suigetsu and its precise correlation with other key global archives: implications for palaeoclimatology and archaeology. Glob. Planet. Change 202, 103493 (2021).Article 

Google Scholar 
Ammann, B. et al. Vegetation responses to rapid warming and to minor climatic fluctuations during the Late-Glacial Interstadial (GI-1) at Gerzensee (Switzerland). Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 40–59 (2013).Article 

Google Scholar 
Engels, S. et al. Subdecadal‐scale vegetation responses to a previously unknown late‐Allerød climate fluctuation and Younger Dryas cooling at Lake Meerfelder Maar (Germany). J. Quat. Sci 31, 741–752 (2016).Article 

Google Scholar 
Van Raden, U. J. et al. High-resolution late-glacial chronology for the Gerzensee lake record (Switzerland): δ18O correlation between a Gerzensee-stack and NGRIP. Palaeogeogr. Palaeoclimatol. Palaeoecol. 391, 13–24 (2013).Article 

Google Scholar 
Blaga, C. I., Reichart, G.-J., Lotter, A. F., Anselmetti, F. S. & Sinninghe Damsté, J. S. A TEX86 lake record suggests simultaneous shifts in temperature in Central Europe and Greenland during the last deglaciation. Geophys. Res. Lett. 40, 948–953 (2013).Article 

Google Scholar 
Rach, O., Brauer, A., Wilkes, H. & Sachse, D. Delayed hydrological response to Greenland cooling at the onset of the Younger Dryas in western Europe. Nat. Geosci. 7, 109 (2014).CAS 
Article 

Google Scholar 
Strogatz, S. H. Exploring complex networks. Nature 410, 268–276 (2001).CAS 
Article 

Google Scholar 
Doncaster, C. P. et al. Early warning of critical transitions in biodiversity from compositional disorder. Ecology 97, 3079–3090 (2016).Article 

Google Scholar 
Jones, G. et al. The Lateglacial to early Holocene tephrochronological record from Lake Hämelsee, Germany: a key site within the European tephra framework. Boreas 47, 28–40 (2018).Article 

Google Scholar 
Blaga, C. I., Reichart, G.-J., Heiri, O. & Damsté, J. S. S. Tetraether membrane lipid distributions in water-column particulate matter and sediments: a study of 47 European lakes along a north–south transect. J. Paleolimnol. 41, 523–540 (2009).Article 

Google Scholar 
Bechtel, A., Smittenberg, R. H., Bernasconi, S. M. & Schubert, C. J. Distribution of branched and isoprenoid tetraether lipids in an oligotrophic and a eutrophic Swiss lake: insights into sources and GDGT-based proxies. Org. Geochem. 41, 822–832 (2010).CAS 
Article 

Google Scholar 
Lowe, J. et al. On the timing of retreat of the Loch Lomond (‘Younger Dryas’) Readvance icefield in the SW Scottish Highlands and its wider significance. Quat. Sci. Rev. 219, 171–186 (2019).Article 

Google Scholar 
Muggeo, V. M. R. Segmented: an R package to fit regression models with broken-line relationships. R news 8, 20–25 (2008).
Google Scholar 
Merkt, J. & Müller, H. Varve chronology and palynology of the Lateglacial in Northwest Germany from lacustrine sediments of Hämelsee in Lower Saxony. Quat. Int. 61, 41–59 (1999).Article 

Google Scholar 
Litt, T. & Stebich, M. Bio-and chronostratigraphy of the lateglacial in the Eifel region, Germany. Quat. Int. 61, 5–16 (1999).Article 

Google Scholar 
Reimer, P. J. et al. The IntCal20 Northern hemisphere radiocarbon age calibration curve (0–55 cal kBP). Radiocarbon 62, 725–757 (2020).CAS 
Article 

Google Scholar 
Giesecke, T. Holocene dynamics of the southern boreal forest in Sweden. The Holocene 15, 858–872 (2005).Article 

Google Scholar 
Müller, D. et al. New insights into lake responses to rapid climate change: the Younger Dryas in Lake Gościąż, central Poland. Boreas 50, 535–555 (2021).Article 

Google Scholar 
Davis, B. A. S. et al. The Eurasian Modern Pollen Database (EMPD), version 2. Earth Syst. Sci. data 12, 2423–2445 (2020).Article 

Google Scholar 
Neugebauer, I. et al. A Younger Dryas varve chronology from the Rehwiese palaeolake record in NE-Germany. Quat. Sci. Rev. 36, 91–102 (2012).Article 

Google Scholar 
Ralska-Jasiewiczowa, M. et al. Very fast environmental changes at the Pleistocene/Holocene boundary, recorded in laminated sediments of Lake Gościaż, Poland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 193, 225–247 (2003).Article 

Google Scholar 
Bonk, A. et al. Varve microfacies and chronology from a new sediment record of Lake Gościąż (Poland). Quat. Sci. Rev. 251, 106715 (2021).Article 

Google Scholar 
Brauer, A., Haug, G. H., Dulski, P., Sigman, D. M. & Negendank, J. F. W. An abrupt wind shift in western Europe at the onset of the Younger Dryas cold period. Nat. Geosci. 1, 520–523 (2008).CAS 
Article 

Google Scholar 
Mekhaldi, F. et al. Radionuclide wiggle matching reveals a nonsynchronous early Holocene climate oscillation in Greenland and western Europe around a grand solar minimum. Clim. Past 16, 1145–1157 (2020).Article 

Google Scholar 
Mayfield, R. J. et al. Metrics of structural change as indicators of chironomid community stability in high latitude lakes. Quat. Sci. Rev. 249, 106594 (2020).Article 

Google Scholar 
van der Knaap, W. O. & Van Leeuwen, J. F. N. Climate-pollen relationships AD 1901–1996 in two small mires near the forest limit in the northern and central Swiss Alps. The Holocene 13, 809–828 (2003).Article 

Google Scholar 
Bazelmans, J. et al. Environmental changes in the late Allerød and early Younger Dryas in the Netherlands: a multiproxy high-resolution record from a site with two Pinus sylvestris populations. Quat. Sci. Rev. 272, 107199 (2021).Article 

Google Scholar 
Birks, H. H., Battarbee, R. W. & Birks, H. J. B. The development of the aquatic ecosystem at Kråkenes Lake, western Norway, during the late glacial and early Holocene-a synthesis. J. Paleolimnol 23, 91–114 (2000).Article 

Google Scholar 
Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360 (2009).Article 

Google Scholar 
Lohne, Ø. S., Mangerud, J. A. N. & Birks, H. H. IntCal13 calibrated ages of the Vedde and Saksunarvatn ashes and the Younger Dryas boundaries from Kråkenes, western Norway. J. Quat. Sci 29, 506–507 (2014).Article 

Google Scholar 
Lohne, Ø. S., Mangerud, J. A. N. & Birks, H. H. Precise 14 C ages of the Vedde and Saksunarvatn ashes and the Younger Dryas boundaries from western Norway and their comparison with the Greenland Ice Core (GICC 05) chronology. J. Quat. Sci 28, 490–500 (2013).Article 

Google Scholar 
Wohlfarth, B. et al. Hässeldala–a key site for last termination climate events in northern Europe. Boreas 46, 143–161 (2017).Article 

Google Scholar 
Brauer, A. et al. High resolution sediment and vegetation responses to Younger Dryas climate change in varved lake sediments from Meerfelder Maar, Germany. Quat. Sci. Rev. 18, 321–329 (1999).Article 

Google Scholar 
Lane, C. S., Brauer, A., Blockley, S. P. E. & Dulski, P. Volcanic ash reveals time-transgressive abrupt climate change during the Younger Dryas. Geology 41, 1251–1254 (2013).Bronk Ramsey, C. et al. Improved age estimates for key Late Quaternary European tephra horizons in the RESET lattice. Quat. Sci. Rev. 118, 18–32 (2015).Rasmussen, S. O. et al. A new Greenland ice core chronology for the last glacial termination. J. Geophys. Res. Atmos. 111, https://doi.org/10.1029/2005JD006079 (2006).Brauer, A., Endres, C., Zolitschka, B. & Negendank, J. F. W. AMS radiocarbon and varve chronology from the annually laminated sediment record of Lake Meerfelder Maar, Germany. Radiocarbon 42, 355–368 (2000).CAS 
Article 

Google Scholar 
Wulf, S. et al. Tracing the Laacher See Tephra in the varved sediment record of the Trzechowskie palaeolake in central Northern Poland. Quat. Sci. Rev. 76, 129–139 (2013).Article 

Google Scholar 
Brauer, A. et al. The importance of independent chronology in integrating records of past climate change for the 60–8 ka INTIMATE time interval. Quat. Sci. Rev. 106, 47–66 (2014).Article 

Google Scholar 
Lane, C. S. et al. The Late Quaternary tephrostratigraphy of annually laminated sediments from Meerfelder Maar, Germany. Quat. Sci. Rev. 122 192–206 (2015).Article 

Google Scholar 
Adolphi, F. & Muscheler, R. Synchronizing the Greenland ice core and radiocarbon timescales over the Holocene–Bayesian wiggle-matching of cosmogenic radionuclide records. Clim. Past 12, 15–30 (2016).Article 

Google Scholar 
Muschitiello, F. et al. Deep-water circulation changes lead North Atlantic climate during deglaciation. Nat. Commun. 10, 1–10 (2019).CAS 
Article 

Google Scholar 
Adolphi, F. et al. Persistent link between solar activity and Greenland climate during the Last Glacial Maximum. Nat. Geosci. 7, 662–666 (2014).CAS 
Article 

Google Scholar 
Siegenthaler, U., Heimann, M. & Oeschger, H. 14C variations caused by changes in the global carbon cycle. Radiocarbon 22, 177–191 (1980).CAS 
Article 

Google Scholar 
Muscheler, R., Adolphi, F. & Svensson, A. Challenges in 14C dating towards the limit of the method inferred from anchoring a floating tree ring radiocarbon chronology to ice core records around the Laschamp geomagnetic field minimum. Earth Planet. Sci. Lett. 394, 209–215 (2014).CAS 
Article 

Google Scholar 
Muschitiello, F. An improved and continuous synchronization of the Greenland ice-core and Hulu Cave U-Th timescales using probabilistic inversion. Clim. Past Discuss. 1–39 https://doi.org/10.5194/cp-2021-116 (2021).Moore, P. D., Webb, J. A. & Collison, M. E. Pollen analysis. (Blackwell scientific publications, 1991).Engels, S. et al. Haemelsee: late-glacial pollen counts. PANGAEA, https://doi.org/10.1594/PANGAEA.939693 (2021).Weltje, G. J. & Tjallingii, R. Calibration of XRF core scanners for quantitative geochemical logging of sediment cores: Theory and application. Earth Planet. Sci. Lett. 274, 423–438 (2008).CAS 
Article 

Google Scholar 
Heiri, O., Lotter, A. F. & Lemcke, G. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J. Paleolimnol 25, 101–110 (2001).Article 

Google Scholar 
Brooks, S. J., Langdon, P. G. & Heiri, O. The identification and use of Palaearctic Chironomidae larvae in palaeoecology. Quat. Res. Assoc. Tech. Guid. i–vi. Vol. 10, 1–276 (2007).Heiri, O., Brooks, S. J., Birks, H. J. B. & Lotter, A. F. A 274-lake calibration data-set and inference model for chironomid-based summer air temperature reconstruction in Europe. Quat. Sci. Rev. 30, 3445–3456 (2011).Article 

Google Scholar 
Heiri, O. & Lotter, A. F. Effect of low count sums on quantitative environmental reconstructions: an example using subfossil chironomids. J. Paleolimnol 26, 343–350 (2001).Article 

Google Scholar 
Rach, O., Hadeen, X. & Sachse, D. An automated solid phase extraction procedure for lipid biomarker purification and stable isotope analysis. Org. Geochem. 142, 103995 (2020).CAS 
Article 

Google Scholar 
Huguet, C. et al. An improved method to determine the absolute abundance of glycerol dibiphytanyl glycerol tetraether lipids. Org. Geochem. 37, 1036–1041 (2006).CAS 
Article 

Google Scholar 
Hopmans, E. C., Schouten, S. & Damsté, J. S. S. The effect of improved chromatography on GDGT-based palaeoproxies. Org. Geochem. 93, 1–6 (2016).CAS 
Article 

Google Scholar 
Birks, H. J. B. & Birks, H. H. Biological responses to rapid climate change at the Younger Dryas—Holocene transition at Kråkenes, western Norway. The Holocene 18, 19–30 (2008).Article 

Google Scholar 
R CORE TEAM, A. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2012. URL http://www.R-project.org (2020).Engels, S., van Geel, B., Buddelmeijer, N. & Brauer, A. High-resolution palynological evidence for vegetation response to the Laacher See eruption from the varved record of Meerfelder Maar (Germany) and other central European records. Rev. Palaeobot. Palynol. 221, 160–170 (2015).Article 

Google Scholar 
Hughes, A. L. C., Gyllencreutz, R., Lohne, Ø. S., Mangerud, J. & Svendsen, J. I. The last Eurasian ice sheets–a chronological database and time‐slice reconstruction, DATED‐1. Boreas 45, 1–45 (2016).Article 

Google Scholar  More

Caycedo-Marulanda, A. & Mathur, S. Suggested strategies to reduce the carbon footprint of anesthetic gases in the operating room. Can. J. Anaesth. J. Can. Anesth. 69, 269–270 (2022).CAS 
Article 

Google Scholar 
World Health Organization. COP24 Special Report Health & Climate Change. https://apps.who.int/iris/bitstream/handle/10665/276405/9786057496713-tur.pdf (2018).Gadani, H. & Vyas, A. Anesthetic gases and global warming: potentials, prevention and future of anesthesia. Anesth. Essays Res. 5, 5 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Vollmer, M. K. et al. Modern inhalation anesthetics: potent greenhouse gases in the global atmosphere. Geophys. Res. Lett. 42, 1606–1611 (2015).CAS 
Article 
ADS 

Google Scholar 
Sulbaek Andersen, M. P., Nielsen, O. J., Karpichev, B., Wallington, T. J. & Sander, S. P. Atmospheric chemistry of isoflurane, desflurane, and sevoflurane: kinetics and mechanisms of reactions with chlorine atoms and OH radicals and global warming potentials. J. Phys. Chem. A 116, 5806–5820 (2012).CAS 
PubMed 
Article 

Google Scholar 
Ravishankara, A. R., Daniel, J. S. & Portmann, R. W. Nitrous oxide (N2O): the dominant ozone-depleting substance emitted in the 21st century. Science 326, 123–125 (2009).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Ryan, S. M. & Nielsen, C. J. Global warming potential of inhaled anesthetics: application to clinical use. Anesth. Analg. 111, 92–98 (2010).PubMed 
Article 

Google Scholar 
American Society of Anesthesiologists. Task Force on Environmental Sustainability Committee on Equipment and Facilities. Greening the Operating Room and Perioperative Arena: Environmental Sustainability for Anesthesia Practice. https://www.asahq.org/about-asa/governance-and-committees/asa-committees/committee-on-equipment-and-facilities/environmental-sustainability/greening-the-operating-room#intro (2014).McGain, F., Story, D., Kayak, E., Kashima, Y. & McAlister, S. Workplace sustainability: the “cradle to grave” view of what we do. Anesth. Analg. 114, 1134–1139 (2012).PubMed 
Article 

Google Scholar 
Yasny, J. S. & White, J. Environmental implications of anesthetic gases. Anesth. Prog. 59, 154–158 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Byhahn, C., Wilke, H. J. & Westpphal, K. Occupational exposure to volatile anaesthetics: epidemiology and approaches to reducing the problem. CNS Drugs 15, 197–215 (2001).CAS 
PubMed 
Article 

Google Scholar 
Sherman, J., Le, C., Lamers, V. & Eckelman, M. Life cycle greenhouse gas emissions of anesthetic drugs. Anesth. Analg. 114, 1086–1090 (2012).CAS 
PubMed 
Article 

Google Scholar 
Mankes, R. F. Propofol wastage in anesthesia. Anesth. Analg. 114, 1091–1092 (2012).CAS 
PubMed 
Article 

Google Scholar 
Weller, M. A general review of the environmental impact of health care, hospitals, operating rooms, and anesthetic care. Int. Anesthesiol. Clin. 58, 64–69 (2020).PubMed 
Article 

Google Scholar 
Dawidowicz, A. L. et al. Investigation of propofol renal elimination by HPLC using supported liquid membrane procedure for sample preparation. Biomed. Chromatogr. BMC 16, 455–458 (2002).CAS 
PubMed 
Article 

Google Scholar 
Costa, G. L. et al. Influence of ambient temperature and confinement on the chemical immobilization of fallow deer (Dama dama). J Wildl Dis 53, 364–367 (2017).CAS 
PubMed 
Article 

Google Scholar 
Costa, G. et al. Comparison of tiletamine-zolazepam combined with dexmedetomidine or xylazine for chemical immobilization of wild fallow deer (Dama dama). J. Zoo Wildl. Med. 52, 1009–1012 (2021).PubMed 
Article 

Google Scholar 
Lin, H. C., Thurmon, J. C., Benson, G. J. & Tranquilli, W. J. Telazol: a review of its pharmacology and use in veterinary medicine. J. Vet. Pharmacol. Ther. 16, 383–418 (1993).CAS 
PubMed 
Article 

Google Scholar 
Dixon, W. J. Staircase bioassay: the up-and-down method. Neurosci. Biobehav. Rev. 15, 47–50 (1991).CAS 
PubMed 
Article 

Google Scholar 
Lin, C.-M. et al. Sitting position does not alter minimum alveolar concentration for desflurane. Can. J. Anesth. Can. Anesth. 54, 523–530 (2007).Article 

Google Scholar 
Wadhwa, A. & Sessler, D. I. Women have the same desflurane minimum alveolar concentration as men. J. Am. Soc. Anesthesiol. 99, 4 (2003).
Google Scholar 
Monteiro, E. R., Coelho, K., Bressan, T. F., Simões, C. R. & Monteiro, B. S. Effects of acepromazine-morphine and acepromazine-methadone premedication on the minimum alveolar concentration of isoflurane in dogs. Vet. Anaesth. Analg. 43, 27–34 (2016).CAS 
PubMed 
Article 

Google Scholar 
Campagnol, D., Neto, F. J. T., Giordano, T., Ferreira, T. H. & Monteiro, E. R. Effects of epidural administration of dexmedetomidine on the minimum alveolar concentration of isoflurane in dogs. Am. J. Vet. Res. 68, 1308–1318 (2007).CAS 
PubMed 
Article 

Google Scholar 
Valverde, A., Morey, T. E., Hernandez, J. & Davies, W. Validation of several types of noxious stimuli for use in determining the minimum alveolar concentration for inhalation anesthetics in dogs and rabbits. Am. J. Vet. Res. 64, 957–962 (2003).CAS 
PubMed 
Article 

Google Scholar 
Aguado, D., Benito, J. & Gómez de Segura, I. A. Reduction of the minimum alveolar concentration of isoflurane in dogs using a constant rate of infusion of lidocaine–ketamine in combination with either morphine or fentanyl. Vet. J. 189, 63–66 (2011).CAS 
PubMed 
Article 

Google Scholar 
Muir, W. W. III., Wiese, A. J. & March, P. A. Effects of morphine, lidocaine, ketamine, and morphine-lidocaine-ketamine drug combination on minimum alveolar concentration in dogs anesthetized with isoflurane. Am. J. Vet. Res. 64, 1155–1160 (2003).CAS 
PubMed 
Article 

Google Scholar 
Dixon, W. J. The up-and-down method for small samples. J. Am. Stat. Assoc. 60, 967–978 (1965).MathSciNet 
Article 

Google Scholar 
Paul, M. & Fisher, D. M. Are estimates of MAC reliable?. Anesthesiology 95, 1362–1370 (2001).CAS 
PubMed 
Article 

Google Scholar 
Sonner, J. M. Issues in the design and interpretation of minimum alveolar anesthetic concentration (MAC) studies. Anesth. Analg. 95, 609–614 (2002).CAS 
PubMed 
Article 

Google Scholar 
Flecknell, P. et al. Preanesthesia, anesthesia, analgesia, and euthanasia. in Laboratory Animal Medicine 1135–1200 (Elsevier, 2015). https://doi.org/10.1016/B978-0-12-409527-4.00024-9.Grimm, K. A., Lamont, L. A., Tranquilli, W. J., Greene, S. A. & Robertson, S. A. Veterinary Anesthesia and Analgesia (Wiley, 2015).Book 

Google Scholar 
Grimm, K. A., Tranquilli, W. J. & Lamont, L. A. Essentials of Small Animal Anesthesia and Analgesia (Wiley, 2011).
Google Scholar 
Hanna, M. & Bryson, G. L. A long way to go: minimizing the carbon footprint from anesthetic gases. Can. J. Anesth. Can. Anesth. 66, 838–839 (2019).Article 

Google Scholar 
Andersen, M. P. S., Nielsen, O. J., Wallington, T. J., Karpichev, B. & Sander, S. P. Assessing the impact on global climate from general anesthetic gases. Anesth. Analg. 114, 1081–1085 (2012).CAS 
Article 

Google Scholar 
Ishizawa, Y. General anesthetic gases and the global environment. Anesth. Analg. 112, 213–217 (2011).PubMed 
Article 

Google Scholar 
Brown, A. C., Canosa-Mas, C. E., Parr, A. D., Pierce, J. M. T. & Wayne, R. P. Tropospheric lifetimes of halogenated anaesthetics. Nature 341, 635–637 (1989).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Lucio, L. M. C., Braz, M. G., don Nascimento Junior, P., Braz, J. R. C. & Braz, L. G. Occupational hazards, DNA damage, and oxidative stress on exposure to waste anesthetic gases. Braz. J. Anesthesiol. Engl. Ed. 68, 33–41 (2018).
Google Scholar 
Waste anesthetic gases-occupational hazards in hospitals. https://www.cdc.gov/niosh/docs/2007-151/ (2007). https://doi.org/10.26616/NIOSHPUB2007151.MacNeill, A. J., Lillywhite, R. & Brown, C. J. The impact of surgery on global climate: a carbon footprinting study of operating theatres in three health systems. Lancet Planet. Health 1, e381–e388 (2017).PubMed 
Article 

Google Scholar 
Rauchenwald, V. et al. New method of destroying waste anesthetic gases using gas-phase photochemistry. Anesth. Analg. 131, 288–297 (2020).CAS 
PubMed 
Article 

Google Scholar 
Özelsel, T.J.-P., Sondekoppam, R. V., Ip, V. H. Y. & Tsui, B. C. H. Re-defining the 3R’s (reduce, refine, and replace) of sustainability to minimize the environmental impact of inhalational anesthetic agents. Can. J. Anesth. Can. Anesth. 66, 249–254 (2019).Article 

Google Scholar 
Thiel, C. L. et al. Environmental impacts of surgical procedures: life cycle assessment of hysterectomy in the United States. Environ. Sci. Technol. 49, 1779–1786 (2015).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Mastrangelo, G., Comiati, V., dell’Aquila, M. & Zamprogno, E. Exposure to anesthetic gases and Parkinson’s disease: a case report. BMC Neurol. 13, 194 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Casale, T. et al. Anesthetic gases and occupationally exposed workers. Environ. Toxicol. Pharmacol. 37, 267–274 (2014).CAS 
PubMed 
Article 

Google Scholar 
Sharma, A. et al. Should total intravenous anesthesia be used to prevent the occupational waste anesthetic gas exposure of pregnant women in operating rooms?. Anesth. Analg. 128, 188–190 (2019).PubMed 
Article 

Google Scholar 
Hughes, J. M. L. Comparison of disposable circle and ‘to-and-fro’ breathing systems during anaesthesia in dogs. J. Small Anim. Pract. 39, 416–420 (1998).CAS 
PubMed 
Article 

Google Scholar 
Suttner, S. & Boldt, J. Low-flow anaesthesia: does it have potential pharmacoeconomic consequences?. Pharmacoeconomics 17, 585–590 (2000).CAS 
PubMed 
Article 

Google Scholar 
Jones, R. S. & West, E. Environmental sustainability in veterinary anaesthesia. Vet. Anaesth. Analg. 46, 409–420 (2019).PubMed 
Article 

Google Scholar 
Feldman, J. M. Managing fresh gas flow to reduce environmental contamination. Anesth. Analg. 114, 1093–1101 (2012).CAS 
PubMed 
Article 

Google Scholar 
Davies, T. V. S. Low flow anaesthesia: frequently asked questions (2020).Pattanapon, N., Bootcha, R. & Petchdee, S. The effects of anesthetic drug choice on heart rate variability in dogs. J. Adv. Vet. Anim. Res. 5, 485 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Hampton, C. E. et al. Effects of intravenous administration of tiletamine-zolazepam, alfaxalone, ketamine-diazepam, and propofol for induction of anesthesia on cardiorespiratory and metabolic variables in healthy dogs before and during anesthesia maintained with isoflurane. Am. J. Vet. Res. 80, 33–44 (2019).CAS 
PubMed 
Article 

Google Scholar 
Ratnu, D. A., Anjana, R. R., Parikh, P. V. & Kelawala, D. N. Effects of tiletamine-zolazepam and isoflurane for induction and maintenance in xylazine premedicated dogs. Indian J. Vet. Sci. Biotechnol. 17, 86–88 (2021).CAS 

Google Scholar 
Malavasi, L. M., Jensen-Waern, M., Augustsson, H. & Nyman, G. Changes in minimal alveolar concentration of isoflurane following treatment with medetomidine and tiletamine/zolazepam, epidural morphine or systemic buprenorphine in pigs. Lab. Anim. 42, 62–70 (2008).CAS 
PubMed 
Article 

Google Scholar 
Malavasi, L. M. et al. Effects of extradural morphine on end-tidal isoflurane concentration and physiological variables in pigs undergoing abdominal surgery: a clinical study. Vet. Anaesth. Analg. 33, 307–312 (2006).CAS 
PubMed 
Article 

Google Scholar 
Krimins, R. A., Ko, J. C., Weil, A. B., Payton, M. E. & Constable, P. D. Hemodynamic effects in dogs after intramuscular administration of a combination of dexmedetomidine-butorphanol-tiletamine-zolazepam or dexmedetomidine-butorphanol-ketamine. Am. J. Vet. Res. 73, 1363–1370 (2012).CAS 
PubMed 
Article 

Google Scholar 
Nam, S.-W., Shin, B.-J. & Jeong, S. M. Anesthetic and cardiopulmonary effects of butorphanol-tiletamine-zolazepam-medetomidine and tramadol-tiletamine-zolazepam-medetomidine in dogs. J. Vet. Clin. 30(6), 421–427 (2013).
Google Scholar 
Ko, J. C. H., Payton, M., Weil, A. B., Kitao, T. & Haydon, T. Comparison of anesthetic and cardiorespiratory effects of tiletamine–zolazepam–butorphanol and tiletamine–zolazepam–butorphanol– medetomidine in dogs. Vet. Ther. 8, 14 (2007).
Google Scholar 
Grimm, K. A., Tranquilli, W. J., Thurmon, J. C. & Benson, G. J. Duration of nonresponse to noxious stimulation after intramuscular administration of butorphanol, medetomidine, or a butorphanol-medetomidine combination during isoflurane administration in dogs. Am. J. Vet. Res. 61, 42–47 (2000).CAS 
PubMed 
Article 

Google Scholar  More

Here we provide an overview of the key elements of our framework including describing the contact function that links economic activities to contacts, the SIRD (Susceptible-Infectious-Recovered-Dead) model, the dynamic economic model governing choices, and calibration. The core of our approach is a dynamic optimization model of individual behavior coupled with an SIRD model of infectious disease spread. Additional details are found in the SI.Contact functionWe model daily contacts as a function of economic activities (labor supply, measured in hours, and consumption demand, measured in dollars) creating a detailed mapping between contacts and economic activities. For example, all else equal, if a susceptible individual reduces their labor supply from 8 to 4 h, they reduce their daily contacts at work from 7.5 to 3.75. Epidemiological data is central to calibrating this mapping between epidemiology and economic behavior. Intuitively, the calibration involves calculating the mean number of disease-transmitting contacts occurring at the start of the epidemic and linking it to the number of dollars spent on consumption and hours of labor supplied before the recession begins.We use an SIRD transmission framework to simulate SARS-CoV-2 transmission for a population of 331 million interacting agents. This is supported by several studies (e.g.,77,78) that identify infectiousness prior to symptom onset. We consider three health types m ∈ {S, I, R} for individuals, corresponding to epidemiological compartments of susceptible (S), infectious (I), and recovered (R). Individuals of health type m engage in various economic activities ({A}_{i}^{m}), with i denoting the activities modeled. One of the ({A}_{i}^{m}) is assumed to represent unavoidable other non-economic activities, such as sleeping and commuting, which occur during the hours of the day not used for economic activities (see SI 2.3.1). Disease dynamics are driven by contacts between susceptible and infectious types, where the number of susceptible-infectious contacts per person is given by the following linear equation:$${{{{{{{{mathscr{C}}}}}}}}}^{SI}({{{{{{{bf{A}}}}}}}})=mathop{sum}limits_{i}{rho }_{i}{A}_{i}^{S}{A}_{i}^{I}$$
(1)
while similar in several respects to prior epi-econ models15,16,74, a methodological contribution is that ρi converts hours worked and dollars spent into contacts. For example, ρc has units of contacts per squared dollar spent at consumption activities, while ρl has units of contacts per squared hour worked.We also consider robustness to different functional forms in Fig. 6F, G as a reduced-form way to consider multiple consumption and labor activities with heterogeneous contact rates. Formally:$${{{{{{{{mathscr{C}}}}}}}}}^{SI}({{{{{{{bf{A}}}}}}}})=mathop{sum}limits_{i}{rho }_{i}{({A}_{i}^{S}{A}_{i}^{I})}^{alpha },$$
(2)
where α  > 1 (convex) corresponds to a contact function where higher-contact activities are easiest to reduce or individuals with more contacts are easier to isolate. α  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Qiao, L. et al. Soil quality both increases crop production and improves resilience to climate change. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01376-8 (2022). More

Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. (eds.) Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).Weiskopf, S. R. et al. Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. 733, 137782. https://doi.org/10.1016/j.scitotenv.2020.137782 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Turner, J. & Marshall, G. J. Climate Change in the Polar Regions (Cambridge University Press, 2011).Book 

Google Scholar 
Meredith, M. et al. Polar Regions. Chapter 3, IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. https://www.ipcc.ch/srocc/chapter/chapter-3-2/ (2019).Rignot, E. et al. Four decades of Antarctic Ice Sheet mass balance from 1979–2017. Proc. Natl. Acad. Sci. USA 116, 1095–1103. https://doi.org/10.1073/pnas.1812883116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Siegert, M. et al. The Antarctic Peninsula under a 1.5°C global warming scenario. Front. Environ. Sci. 7, 102. https://doi.org/10.3389/fenvs.2019.00102 (2019).Article 

Google Scholar 
Iz, H. B. Is the global sea surface temperature rise accelerating?. Geod. Geodyn. 9, 432–438. https://doi.org/10.1016/j.geog.2018.04.002 (2018).Article 

Google Scholar 
Qiu, Z. et al. Future climate change is predicted to affect the microbiome and condition of habitat-forming kelp. Proc. R. Soc. B. 286, 20181887. https://doi.org/10.1098/rspb.2018.1887 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Burge, C. A., Kim, C. J., Lyles, J. M. & Harvell, C. D. Special issue Oceans and Humans Health: The ecology of marine opportunists. Microb. Ecol. 65, 869–879. https://doi.org/10.1007/s00248-013-0190-7 (2013).Article 
PubMed 

Google Scholar 
Cavicchioli, R. et al. Scientists’ warning to humanity: Microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586. https://doi.org/10.1038/s41579-019-0222-5 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Harvell, C. D. et al. Emerging marine diseases–climate links and anthropogenic factors. Science 285, 1505–1510. https://doi.org/10.1126/science.285.5433.1505 (1999).CAS 
Article 
PubMed 

Google Scholar 
Egan, S. & Gardiner, M. Microbial dysbiosis: Rethinking disease in marine ecosystems. Front. Microbiol. 7, 991. https://doi.org/10.3389/fmicb.2016.00991 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Wilkins, L. G. E. et al. Host-associated microbiomes drive structure and function of marine ecosystems. PLoS Biol. 17, e3000533. https://doi.org/10.1371/journal.pbio.3000533 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Seuront, L., Nicastro, K. R., Zardi, G. I. & Goberville, E. Decreased thermal tolerance under recurrent heat stress conditions explains summer mass mortality of the blue mussel Mytilus edulis. Sci. Rep. 9, 17498. https://doi.org/10.1038/s41598-019-53580-w (2019).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tsuchiya, M. Mass mortality in a population of the mussel Mytilus edulis L. caused by high temperature on rocky shores. J. Exp. Mar. Biol. Ecol. 66, 101–111. https://doi.org/10.1016/0022-0981(83)90032-1 (1983).Article 

Google Scholar 
Malham, S. K. et al. Summer mortality of the Pacific oyster, Crassostrea gigas, in the Irish Sea: The influence of temperature and nutrients on health and survival. Aquaculture 287, 128–138. https://doi.org/10.1016/j.aquaculture.2008.10.006 (2009).CAS 
Article 

Google Scholar 
Beyer, J. et al. Blue mussels (Mytilus edulis spp.) as sentinel organisms in coastal pollution monitoring: A review. Mar. Environ. Res. 130, 338–365. https://doi.org/10.1016/j.marenvres.2017.07.024 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ladeiro, M. P. et al. Mussel as a tool to define continental watershed quality. In Organismal and Molecular Malacology (ed Ray, S.), IntechOpen. https://doi.org/10.5772/67995 (2017).Bonacci, S. et al. Esterase activities in the bivalve mollusc Adamussium colbecki as a biomarker for pollution monitoring in the Antarctic marine environment. Mar. Pollut. Bull. 49, 445–455. https://doi.org/10.1016/j.marpolbul.2004.02.033 (2004).CAS 
Article 
PubMed 

Google Scholar 
Storhaug, E. et al. Seasonal and spatial variations in biomarker baseline levels within Arctic populations of mussels (Mytilus spp.). Sci. Total Environ. 656, 921–936. https://doi.org/10.1016/j.scitotenv.2018.11.397 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Caza, F. et al. Liquid biopsies for omics-based analysis in sentinel mussels. PLoS ONE 14, e0223525. https://doi.org/10.1371/journal.pone.0225359 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ignatiadis, M., Sledge, G. W. & Jeffrey, S. S. Liquid biopsy enters the clinic – implementation issues and future challenges. Nat. Rev. Clin. Oncol. 18, 297–312. https://doi.org/10.1038/s41571-020-00457-x (2021).Article 
PubMed 

Google Scholar 
Kowarsky, M. et al. Numerous uncharacterized and highly divergent microbes which colonize humans are revealed by circulating cell-free DNA. Proc. Natl. Acad. Sci. USA 114, 9623–9628. https://doi.org/10.1073/pnas.1707009114 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, H. et al. Circulating microbiome DNA: An emerging paradigm for cancer liquid biopsy. Cancer Lett. 521, 82–87. https://doi.org/10.1016/j.canlet.2021.08.036 (2021).CAS 
Article 
PubMed 

Google Scholar 
Lokmer, A. et al. Spatial and temporal dynamics of Pacific oyster hemolymph microbiota across multiple scales. Front. Microbiol. 7, 1367. https://doi.org/10.3389/fmicb.2016.01367 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Lokmer, A. & Wegner, M. K. Hemolymph microbiome of Pacific oysters in response to temperature, temperature stress and infection. ISME J. 9, 670–682. https://doi.org/10.1038/ismej.2014.160 (2015).CAS 
Article 
PubMed 

Google Scholar 
Auguste, M. et al. Exposure to TiO2 nanoparticles induces shifts in the microbiota composition of Mytilus galloprovincialis hemolymph. Sci. Total Environ. 670, 129–137. https://doi.org/10.1016/j.scitotenv.2019.03.133 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Vezzulli, L. et al. Climate influence on Vibrio and associated human diseases during the past half-century in the coastal North Atlantic. Proc. Natl. Acad. Sci. USA 113, E5062–E5071. https://doi.org/10.1073/pnas.1609157113 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Musella, M. et al. Tissue-scale microbiota of the Mediterranean mussel (Mytilus galloprovincialis) and its relationship with the environment. Sci. Total Environ. 717, 137209. https://doi.org/10.1016/j.scitotenv.2020.137209 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Féral, J.-P. et al. PROTEKER: Implementation of a submarine observatory at the Kerguelen islands (Southern Ocean). Underw. Technol. 34, 3–10. https://doi.org/10.3723/ut.34.003 (2016).Article 

Google Scholar 
Spain, E. A. et al. Shallow seafloor gas emissions near Heard and McDonald Islands on the Kerguelen Plateau, southern Indian Ocean. Earth Space Sci. 7, e2019EA000695. https://doi.org/10.1029/2019EA000695 (2020).ADS 
Article 

Google Scholar 
Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome. 8, 47. https://doi.org/10.1186/s40168-020-00826-9 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L.-Y. et al. Comparison of bacterial community in aqueous and oil phases of water-flooded petroleum reservoirs using pyrosequencing and clone library approaches. Appl. Microbiol. Biotechnol. 98, 4209–4221. https://doi.org/10.1007/s00253-013-5472-y (2014).CAS 
Article 
PubMed 

Google Scholar 
Gutierrez, T., Berry, D., Teske, A. & Aitken, M. D. Enrichment of Fusobacteria in sea surface oil slicks from the Deepwater Horizon oil spill. Microorganisms. 4, 24. https://doi.org/10.3390/microorganisms4030024 (2016).CAS 
Article 
PubMed Central 

Google Scholar 
Michelou, V. K., Caporaso, J. G., Knight, R. & Palumbi, S. R. The ecology of microbial communities associated with Macrocystis pyrifera. PLoS ONE 8, e67480. https://doi.org/10.1371/annotation/48e29578-a073-42e7-bca4-2f96a5998374 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Florez, J. Z. et al. Structure of the epiphytic bacterial communities of Macrocystis pyrifera in localities with contrasting nitrogen concentrations and temperature. Algal Res. 44, 101706. https://doi.org/10.1016/j.algal.2019.101706 (2019).Article 

Google Scholar 
Minich, J. J. et al. Elevated temperature drives kelp microbiome dysbiosis, while elevated carbon dioxide induces water microbiome disruption. PLoS ONE 13, e0192772. https://doi.org/10.1371/journal.pone.0192772 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lin, J. D., Lemay, M. A. & Parfrey, L. W. Diverse bacteria utilize alginate within the microbiome of the giant kelp Macrocystis pyrifera. Front. Microbiol. 9, 1914. https://doi.org/10.3389/fmicb.2018.01914 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Pierce, M. L. & Ward, J. E. Microbial ecology of the Bivalvia, with an emphasis on the family Ostreidae. J. Shellfish Res. 37, 793–806. https://doi.org/10.2983/035.037.0410 (2018).Article 

Google Scholar 
Pierce, M. L. & Ward, J. E. Gut Microbiomes of the Eastern Oyster (Crassostrea virginica) and the Blue Mussel (Mytilus edulis): Temporal variation and the influence of marine aggregate-associated microbial communities. mSphere. 4, e00730-19. https://doi.org/10.1128/mSphere.00730-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Delille, D. & Gleizon, F. Distribution of enteric bacteria in Antarctic seawater surrounding the Port-aux-Francais permanent station (Kerguelen Island). Mar. Pollut. Bull. 46, 1179–1183. https://doi.org/10.1016/S0025-326X(03)00164-4 (2003).CAS 
Article 
PubMed 

Google Scholar 
Nguyen, T. V. & Alfaro, A. C. Metabolomics investigation of summer mortality in New Zealand Greenshell mussels (Perna canaliculus). Fish Shellfish Immunol. 106, 783–791. https://doi.org/10.1016/j.fsi.2020.08.022 (2020).CAS 
Article 
PubMed 

Google Scholar 
Vezzulli, L. et al. Comparative 16SrDNA gene-based microbiota profiles of the Pacific oyster (Crassostrea gigas) and the Mediterranean Mussel (Mytilus galloprovincialis) from a shellfish farm (Ligurian Sea, Italy). Microb. Ecol. 75, 495–504. https://doi.org/10.1007/s00248-017-1051-6 (2018).CAS 
Article 
PubMed 

Google Scholar 
Romalde, J. L., Diéguez, A. L., Lasa, A. & Balboa, S. New Vibrio species associated to molluscan microbiota: A review. Front. Microbiol. 4, 413. https://doi.org/10.3389/fmicb.2013.00413 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Narayan, N. R. et al. Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences. BMC Genom. 21, 56. https://doi.org/10.1186/s12864-019-6427-1 (2020).CAS 
Article 

Google Scholar 
Peng, W. et al. Integrated 16S rRNA sequencing, metagenomics, and metabolomics to characterize gut microbial composition, function, and fecal metabolic phenotype in non-obese type 2 diabetic Goto-Kakizaki rats. Front. Microbiol. 10, 3141. https://doi.org/10.3389/fmicb.2019.03141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Koner, S. et al. Assessment of carbon substrate catabolism pattern and functional metabolic pathway for microbiota of limestone caves. Microorganisms 9, 1789. https://doi.org/10.21203/rs.3.rs-549787/v1 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Y. F. et al. Temperature elevation and Vibrio cyclitrophicus infection reduce the diversity of haemolymph microbiome of the mussel Mytilus coruscus. Sci. Rep. 9, 16391. https://doi.org/10.1038/s41598-019-52752-y (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Scanes, E. et al. Climate change alters the haemolymph microbiome of oysters. Mar. Pollut. Bull. 164, 111991. https://doi.org/10.1016/j.marpolbul.2021.111991 (2021).CAS 
Article 
PubMed 

Google Scholar 
Hylander, B. L. & Repasky, E. A. Temperature as a modulator of the gut microbiome: What are the implications and opportunities for thermal medicine?. Int. J. Hyperth. 36, 83–89. https://doi.org/10.1080/02656736.2019.1647356 (2019).CAS 
Article 

Google Scholar 
Lo Giudice, A. et al. Marine bacterioplankton diversity and community composition in an antarctic coastal environment. Microb. Ecol. 63, 210–223. https://doi.org/10.1007/s00248-011-9904-x (2012).Article 
PubMed 

Google Scholar 
Yumoto, I. et al. Temperature and nutrient availability control growth rate and fatty acid composition of facultatively psychrophilic Cobetia marina strain L-2. Arch. Microbiol. 181, 345–351. https://doi.org/10.1007/s00203-004-0662-8 (2004).CAS 
Article 
PubMed 

Google Scholar 
Weingarten, E. A., Atkinson, C. L. & Jackson, C. R. The gut microbiome of freshwater Unionidae mussels is determined by host species and is selectively retained from filtered seston. PLoS ONE 14, e0224796. https://doi.org/10.1371/journal.pone.0224796 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rosa, M., Ward, J. E. & Shumway, S. E. Selective capture and ingestion of particles by suspension-feeding bivalve molluscs: A review. J. Shellfish Res. 37, 727–746. https://doi.org/10.2983/035.037.0405 (2018).Article 

Google Scholar 
Griffiths, C. L. & King, J. A. Some relationships between size, food availability and energy balance in the ribbed mussel Aulacomya ater. Mar. Biol. 51, 141–149. https://doi.org/10.1007/BF00555193 (1979).Article 

Google Scholar 
Riisgård, H. U. Filtration rate and growth in the blue mussel, Mytilus edulis Linneaus, 1758: Dependence on algal concentration. J. Shellfish Res. 10, 29–36 (1991).
Google Scholar 
Sonier, R. et al. Picophytoplankton contribution to Mytilus edulis growth in an intensive culture environment. Mar. Biol. 163, 73. https://doi.org/10.1007/s00227-016-2845-7 (2016).Article 

Google Scholar 
Jacobs, P., Troost, K., Riegman, R. & Van der Meer, J. Length-and weight-dependent clearance rates of juvenile mussels (Mytilus edulis) on various planktonic prey items. Helgol. Mar. Res. 69, 101–112. https://doi.org/10.1007/s10152-014-0419-y (2015).ADS 
Article 

Google Scholar 
Ward, J. E. & Shumway, S. E. Separating the grain from the chaff: Particle selection in suspension- and deposit-feeding bivalves. J. Exp. Mar. 300, 83–130. https://doi.org/10.1016/j.jembe.2004.03.002 (2004).Article 

Google Scholar 
Waite, A. M., Safi, K. A., Hall, J. A. & Nodder, S. D. Mass sedimentation of picoplankton embedded in organic aggregates. Limnol. Oceanogr. 45, 87–97. https://doi.org/10.4319/lo.2000.45.1.0087 (2000).ADS 
Article 

Google Scholar 
Ward, J. E. & Kach, D. J. Marine aggregates facilitate ingestion of nanoparticles by suspension-feeding bivalves. Mar. Environ. Res. 68, 137–142. https://doi.org/10.1016/j.marenvres.2009.05.002 (2009).CAS 
Article 
PubMed 

Google Scholar 
Ward, J. E. Biodynamics of suspension-feeding in adult bivalve molluscs: Particle capture, processing, and fate. Invertebr. Biol. 115, 218–231. https://doi.org/10.2307/3226932 (1996).Article 

Google Scholar 
Rosa, M. et al. Physicochemical surface properties of microalgae and their combined effects on particle selection by suspension-feeding bivalve molluscs. J. Exp. Mar. 486, 59–68. https://doi.org/10.1016/j.jembe.2016.09.007 (2017).CAS 
Article 

Google Scholar 
Allam, B. & Espinosa, E. P. Bivalve immunity and response to infections: Are we looking at the right place?. Fish Shellfish Immunol. 53, 4–12. https://doi.org/10.1016/j.fsi.2016.03.037 (2016).CAS 
Article 
PubMed 

Google Scholar 
Barr, J. J. et al. Bacteriophage adhering to mucus provide a non-host-derived immunity. Proc. Natl. Acad. Sci. USA 110, 10771–10776. https://doi.org/10.1073/pnas.1305923110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Allam, B. & Espinosa, E. P. Mucosal immunity in mollusks. In Mucosal Health in Aquaculture (eds Beck, B. H. & Peatman, E.) 325–370 (Academic Press, 2015).Chapter 

Google Scholar 
Huang, J. et al. Hemocytes in the extrapallial space of Pinctada fucata are involved in immunity and biomineralization. Sci. Rep. 8, 4657. https://doi.org/10.1038/s41598-018-22961-y (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, H. J. et al. Isolation and characterization of two bacteriophages and their preventive effects against pathogenic Vibrio coralliilyticus causing mortality of Pacific oyster (Crassostrea gigas) larvae. Microorganisms. 8, 926. https://doi.org/10.3390/microorganisms8060926 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Ihara, H. et al. Sulfur-oxidizing bacteria mediate microbial community succession and element cycling in launched marine sediment. Front. Microbiol. 8, 152. https://doi.org/10.3389/fmicb.2017.00152 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jørgensen, B. B. & Nelson, D. C. Sulfide oxidation in marine sediments: Geochemistry meets microbiology. Geol. S. Am. S. 379, 63–81. https://doi.org/10.1130/0-8137-2379-5.63 (2004).Article 

Google Scholar 
Zhou, M. et al. Surface currents and upwelling in Kerguelen Plateau regions. Biogeosci. Discuss. 11, 6845–6876. https://doi.org/10.5194/bgd-11-6845-2014 (2014).ADS 
Article 

Google Scholar 
Gille, S. T., Carranza, M. M., Cambra, R. & Morrow, R. Wind-induced upwelling in the Kerguelen Plateau region. Biogeosciences 11, 6389–6400. https://doi.org/10.5194/bg-11-6389-2014 (2014).ADS 
Article 

Google Scholar 
Park, Y. H., Roquet, F., Durand, I. & Fuda, J. L. Large-scale circulation over and around the Northern Kerguelen Plateau. Deep Sea Res. II(55), 566–581. https://doi.org/10.1016/j.dsr2.2007.12.030 (2008).ADS 
Article 

Google Scholar 
Renac, C. et al. Hydrothermal fluid interaction in basaltic lava units, Kerguelen Archipelago (SW Indian Ocean). Eur. J. 22, 215–234. https://doi.org/10.1127/0935-1221/2009/0022-1993 (2010).CAS 
Article 

Google Scholar 
Vancanneyt, M. et al. Sphingomonas alaskensis sp. nov., a dominant bacterium from a marine oligotrophic environment. Int. J. Syst. Evol. 51, 73–79. https://doi.org/10.1099/00207713-51-1-73 (2001).CAS 
Article 

Google Scholar 
Helmuth, B. S. & Hofmann, G. E. Microhabitats, thermal heterogeneity, and patterns of physiological stress in the rocky intertidal zone. Biol. Bull. 201, 374–384. https://doi.org/10.2307/1543615 (2001).CAS 
Article 
PubMed 

Google Scholar 
Testut, L., Wöppelmann, G., Simon, B. & Téchiné, P. The sea level at Port-aux-Français, Kerguelen Island, from 1949 to the present. Ocean Dyn. 56, 464–472. https://doi.org/10.1007/s10236-005-0056-8 (2006).ADS 
Article 

Google Scholar 
Pohl, B. et al. Recent climate variability around the Kerguelen Islands (Southern Ocean) seen through weather regimes. J. Appl. Meteorol. Climatol. 60, 711–731. https://doi.org/10.1175/JAMC-D-20-0255.1 (2021).ADS 
Article 

Google Scholar 
PROTEKER. Ilôt Channer (Passe Royale)—Sea water temperature at 5 and 13 m depth (T°C) daily average 2014–2019. https://www.proteker.net/swt-ilot-channer-passe-royale/ (2021).Caza, F. et al. Comparative analysis of hemocyte properties from Mytilus edulis desolationis and Aulacomya ater in the Kerguelen Islands. Mar. Environ. Res. 110, 174–182. https://doi.org/10.1016/j.marenvres.2015.09.003 (2015).CAS 
Article 
PubMed 

Google Scholar 
Caza, F., Cledon, M. & St-Pierre, Y. Biomonitoring climate change and pollution in marine ecosystems: A review on Aulacomya ater. J. Mar. Biol. 2016, 7183813. https://doi.org/10.1155/2016/7183813 (2016).Article 

Google Scholar 
Rey-Campos, M. et al. High individual variability in the transcriptomic response of Mediterranean mussels to Vibrio reveals the involvement of myticins in tissue injury. Sci. Rep. 9, 3569. https://doi.org/10.1038/s41598-019-39870-3 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Caza, F. et al. Hemocytes released in seawater act as Trojan horses for spreading of bacterial infections in mussels. Sci. Rep. 10, 19696. https://doi.org/10.1038/s41598-020-76677-z (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yao, C. L. & Somero, G. N. Thermal stress and cellular signaling processes in hemocytes of native (Mytilus californianus) and invasive (M. galloprovincialis) mussels: Cell cycle regulation and DNA repair. Comp. Biochem. Physiol. 165, 159–168. https://doi.org/10.1016/j.cbpa.2013.02.024 (2013).CAS 
Article 

Google Scholar 
Lockwood, B. L., Sanders, J. G. & Somero, G. N. Transcriptomic responses to heat stress in invasive and native blue mussels (genus Mytilus): Molecular correlates of invasive success. J. Exp. Biol. 213, 3548–3558. https://doi.org/10.1242/jeb.046094 (2010).CAS 
Article 
PubMed 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1. https://doi.org/10.1093/nar/gks808 (2013).CAS 
Article 
PubMed 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods. 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. & Blanchet, F. G. Vegan: Community Ecology Package. 2. 3-0 (2015).Ssekagiri, A., Sloan, W. & Ijaz, U. Z. microbiomeSeq: an R package for analysis of microbial communities in an environmental context, In ISCB Africa ASBCB Conference (Kumasi, Ghana, 2017).Cao, Y. Microbiome marker: Microbiome Biomarker Analysis Toolkit. R package version 0.99.0 (2020). https://github.com/yiluheihei/microbiomeMarker. Accessed March 2022.Kanehisa, M. et al. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).CAS 
Article 
PubMed 

Google Scholar 
Iwai, S. et al. Piphillin: Improved prediction of metagenomic content by direct inference from human microbiomes. PLoS ONE 11, e0166104. https://doi.org/10.1371/journal.pone.0166104 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dhariwal, A. et al. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188. https://doi.org/10.1093/nar/gkx295 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More