47 research outputs found
Replication Data for: Interactive population effects of sublethal copper exposure and predation risk in a naturally stressful environment
No full textThe dataset contains data used in the study “Interactive population effects of sublethal copper exposure and predation risk in a naturally stressful environment
”. It contains experimental data from a mesocsom with the copepod Tigriopus brevicornis. The project was part of the MULTICOP project financed by the Norwegian Research Council (project number 301153). (2024-01-21)
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Methods
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We investigated the cumulative effects of copper and predation risk exposure on T. brevicornis over three generations. T. brevicornis is commonly found along European Atlantic shorelines and in high densities in tidal and splash water pools. The experiment was conducted at the University of Oslo Biological Research Station “Biologen” in Drøbak, Norway,. We established 36 populations by transferring 11 ovigerous T. brevicornis females from nearby splash pools into glass trays placed on the quay of the station’s harbor at th shoreline.
Two days before adding copepods, we filled each tray with 2.5 liters of seawater and their respective treatments: predation risk, copper (10 µg Cu L-1), combined, and control. Predation risk was simulated using fish kairomones. We incubated three-spined stickleback in seawater, similar to Lode et al. (2020). The 12-week experiment involved water and treatment renewals every 2 weeks. Before each renewal, we screened each microcosm for copepods (fewer than 10 or many) and other visible animals and debris. We sampled populations every 4th week by taking down 1/3 of the replicates, resulting in three independent replicates per timepoint and treatment. If possible, we randomly picked 100 individual late-stage copepodites and adults for pigment and stable isotope analysis. They were depurated in filtered seawater for two hours at ambient salinity. We then sampled adults for pigment analysis and stable isotope analysis. The remaining microcosm was filtered and preserved in 96% ethanol to determine T. brevicornis population density, structure, and abundances of other organisms. To analyze copepods’ Astaxanthin pigment content of individidual copepods, we used Thrane et al.’s (2015) spectrophotometric analysis. To calculate the specific Astaxanthin content, we used the ashfree dryweight derived from the length-weight regression for harpactcoids (Hopcroft et al., 1998). The δ15N and δ13C ratios of individual copepod samples were measured on a Thermo Fisher DeltaV Stable Isotope Mass Spectrometer configured with a Flash Elemental Analyzer Isolink system at the CLIPTlab of the Univeristy of Oslo.
References
Hopcroft, R.R., Lombard, D., Roff, J.C., 1998. Production of tropical copepods in Kingston Harbour, Jamaica: the importance of small species. Marine Biology 130, 593–604. https://doi.org/10.1007/s002270050281
Thrane, J.-E., Kyle, M., Striebel, M., Haande, S., Grung, M., Rohrlack, T., Andersen, T., 2015. Spectrophotometric Analysis of Pigments: A Critical Assessment of a High-Throughput Method for Analysis of Algal Pigment Mixtures by Spectral Deconvolution. PLOS ONE 10, e0137645. https://doi.org/10.1371/journal.pone.0137645
Lode, Torben, Jan Heuschele, Tom Andersen, Josefin Titelman, Ketil Hylland, and Katrine Borgå. 2020. Contrasting effects of predation risk and copper on copepod respiration rates. Environmental Toxicology and Chemistry, 39(9), pp.1765-1773. https://doi.org/10.1002/etc.4804.https://doi.org/10.1002/etc.4804.
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Datafiles
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MesocosmDataHeuscheleetal.csv
ID: unique mesocosm id
Counting.order: The order in which the samples were counted.
Copper: “Copper” indicates that the treatment had added copper at a concentration of 10 ug L-1, while “No copper” was not treated with copper.
Kairomone: “Kairomone” indicates that the smell of fish was added to the treatment, while “No kairomone” indicates the lack thereof.
Date: Sampling date as d/m/y
Month: Month
Day: Day in the year
MeanT: Average daily temperature of sampling day
maxT: maximum daily temperature of sampling day
minT: minimum daily temperature of sampling day
rangeT: daily temperature range of sampling day
ESDD needed to calculate accumulated extreme stress degree days.
ESDDaccum accumulated extreme stress degree days, i.e. days mesocosm inhabitants mesocosm likely experienced above 32 degrees.
filamentous_green_algae: amount of filamentous algae present in the mesocosms. None,Few,Many,Lots, “NA” indicates that it was not assessed
water_colour: Assessment of the color of the water in the mesocosm, “NA” indicates that it was not assessed.
salinity_psu: Salinity of the mesososm measured with a refractometer.
Nauplii: Number of Tigriopus brevicornis Nauplii
Copepodites: Number of Tigriopus brevicornis copepodites
Copepod.with.eggsac: Number of Tigriopus brevicornis females with eggsacs
Chironomide Number of Chironomides
Other Number of other animals (dead or alive), such as drowned bumblebees, etc.
Shorefly: Number of Ephydridae
Adults: Number of Tigriopus brevicornis adults
AllCopepodites: Sum of copepodites and adult copepods
Allcopepods: Sum of all copepod individuals
STRESS: Stress factor
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Concentration.animals.csv
Sample: MesocosmID
Date: Date of takedown
Month: Takedown month in the year
X: identifier
Sample.ID.new: name of the image the measurements were taken from
Imagenames: name of the image the measurements were taken from
AvgProsomeLength: prosome length in um
DWug:ash free dryweight calculated (ug)
uniqueID: unique ID of the individual
ng.well: nanogram astaxanthin in the well
Dataset: Indicating whether it is from actual animal measurements or not
Treatment: Treatment the animal was exposed to in the experiment
Plate: microwell plate id
ng.animal: ng astaxanthin per animal
Kairomone: “Kairomone” indicates that the smell of fish was added to the treatment, while “No kairomone” indicates the lack thereof.
Copper: “Copper” indicates that the treatment had added copper at a concentration of 10 ug L-1, while “No copper” was not treated with copper.
Censored: indicating whether the Astaxanthin value was below the assumed limit fo detection
AstaxanthinPerDW: Astaxanthin per animal dryweight / specific Astaxanthin mass (ng Astaxanthin per ug dry weight)
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StableIsotopeData.csv
ID: Mesocosm ID
Treatment: treatment coded as a four-level factor
Copper: “Copper” indicates that the treatment had added copper at a concentration of 10 ug L-1, while “No copper” was not treated with copper.
Kairomone: “Kairomone” indicates that the smell of fish was added to the treatment, while “No kairomone” indicates the lack thereof.
Date: Sampling date as d/m/y
Month: Sampling month
ID2: id specific to the stable isotope analysis
c13: delta 13C isotope value
%C: percent carbon
C Notes: annotation for the carbon values (all NA)
N15: delta 15N isotope value
%N: percent nitrogen
N Notes: annotation for the nitrogen values (all NA
Adult and offspring size in the ocean over 17 orders of magnitude follows two life history strategies
No full textExplaining variability in offspring vs. adult size among groups is a necessary step to determine the evolutionary and environmental constraints shaping variability in life history strategies. This is of particular interest for life in the ocean where a diversity of offspring development strategies is observed along with variability in physical and biological forcing factors in space and time. We compiled adult and offspring size for 407 pelagic marine species covering more than 17 orders of magnitude in body mass including Cephalopoda, Cnidaria, Crustaceans, Ctenophora, Elasmobranchii, Mammalia, Sagittoidea, and Teleost. We find marine life following one of two distinct strategies, with offspring size being either proportional to adult size (e.g., Crustaceans, Elasmobranchii, and Mammalia) or invariant with adult size (e.g., Cephalopoda, Cnidaria, Sagittoidea, Teleosts, and possibly Ctenophora). We discuss where these two strategies occur and how these patterns (along with the relative size of the offspring) may be shaped by physical and biological constraints in the organism's environment. This adaptive environment along with the evolutionary history of the different groups shape observed life history strategies and possible group-specific responses to changing environmental conditions (e.g., production and distribution)
Erratum to: Adult and offspring size in the ocean over 17 orders of magnitude follows two life history strategies
No full textIn Neuheimer et al. (2015), a conversion factor error regarding mysid and saggitoidea size resulted in errors to Fig. 2a, b; Tables 1–3 and Appendix A. The corrected figures, tables, and appendices are reproduced here. Note: forthe “Crustaceans: Other” group, the slope of the corrected adult vs. offspring size relationship is slightly but significantly less than 1 (0.90; Table 1; Fig. 2a).We apologize for these errors
Malignant catarrhal fever in sika deer (Cervus nippon) in the UK
No full text[Extract]
Malignant catarrhal fever (MCF) is a viral disease characterised by lymphoproliferation, vasculitis and erosive-ulcerative mucosal and cutaneous lesions (Brown and others 2007, Russell and others 2009); it is commonly fatal. The antigenic 15-A epitope and base similarity in conserved regions have been used to define the group of MCF-causing viruses (Li and others 2001) and four viruses from the genus Rhadinovirus, subfamily Gammaherpesvirinae, are currently associated with clinical MCF. The disease has been described in over 30 species of wild and domestic ruminant species (Heuschele 1988). Based on the host in which the virus was originally detected, the MCF-causing viruses include alcelaphine herpesvirus type 1 (AlHV-1) from wildebeest, ovine herpesvirus type 2 (OvHV-2) from domestic sheep, whose reservoir host remains unidentified, the MCF-causing virus in white-tailed deer (Odocoileus virginianus) (Li and others 2000), and caprine herpesvirus type 2 (CpHV-2), identified from domestic goats and a pig in Germany (Chmielewicz and others 2001), and from goats in North America (Li and others 2001). These viruses are considered to be usually transmitted directly from the reservoir host
The chemical ecology of copepods
No full textAn increasing number of studies show the importance of chemical interactions in the aquatic environment. Our understanding of the role of chemical cues and signals in larger crustaceans has advanced in the last decades. However, for copepods, the most abundant metazoan zooplankton and essential for the functioning of the marine food web, much is still unknown. We synthesize current knowledge about chemical ecology of copepods including foraging, survival and reproduction. We also compile information on the sensory apparatus and new analytical approaches that may facilitate the identification of signal molecules. The review illustrates the importance of chemical interactions in many aspects of copepod ecology and identifies gaps in our knowledge, such as the lack of identified infochemicals and electrophysiological studies to confirm the function of sensory structures. We suggest approaches that are likely to further our understanding of the role of chemical interactions in the pelagic ecosystem
Carl J. Burckhardt Humanist und Staatsmann : zum 75. Geburtstag am 10. September 1966 : eine Rede : diese Rede wurde am 13. September 1966 in der Württ. Bibliotheksgesellschaft, Stuttgart, gesprochen und am 10. September 1966 in der Zürcher Tageszeitung "Die Tat" veröffentlicht
No full textEavesdropping on plankton—can zooplankton monitoring improve forecasting of biotoxins from harmful algae blooms?
No full textHarmful algae bloom (HAB) forecasting has developed rapidly over recent decades, but predicting harmful levels of marine biotoxins in shellfish is still a challenge. New discoveries suggest that predator-prey interactions may be an important driver in the formation of HABs. Key species of harmful algae respond to copepod infochemicals with increased toxin production. In addition, copepods feed selectively on less defended prey, which may further promote harmful taxa. Here we explore if eavesdropping on predator-prey dynamics by monitoring zooplankton can improve HAB forecasting. We first examine an 8-yr time series including copepod biomass, harmful algae cells (Dinophysis spp.), and diarrhetic shellfish toxins in blue mussels (Mytilus edulis) using generalized additive models. Models including copepod biomass more accurately predicted okadaic acid in mussels than phytoplankton alone. We then apply this connection more narrowly by analyzing the specific copepod exudates known to induce toxin production, copepodamides, from the mussels sampled in biotoxin monitoring. Adding copepodamide data from shellfish extracts increased model performance compared to copepod biomass. Results suggest that including grazing effects through copepodamide measurements may provide a cost-efficient way to improve accuracy and lead time for predicting the accumulation of microalgal toxins in shellfish
Characteristic Sizes of Life in the Oceans, from Bacteria to Whales*
No full textThe size of an individual organism is a key trait to characterize its physiology and feeding ecology. Size-based scaling laws may have a limited size range of validity or undergo a transition from one scaling exponent to another at some characteristic size. We collate and review data on size-based scaling laws for resource acquisition, mobility, sensory range, and progeny size for all pelagic
marine life, from bacteria to whales. Further, we review and develop simple theoretical arguments for observed scaling laws and the characteristic sizes of a change or breakdown of power laws. We divide life in the ocean into
seven major realms based on trophic strategy, physiology, and life history strategy. Such a categorization represents a move away from a taxonomically oriented description toward a trait-based description of life in the oceans.
Finally, we discuss life forms that transgress the simple size-based rules and identify unanswered questions
Using Artificial Intelligence to Improve and Accelerate the Breeding Process for Root Structure Architecture in Alfalfa
No full textYields for alfalfa, the world’s most popular forage crop, have declined over the last ~50 years, and breeders, farmers, and other stakeholders are interested in breaking the yield bottleneck in innovative ways, such as with root system improvements and state-of-the-art methods including artificial intelligence (AI)
