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Zooplankton

Zooplankton are tiny aquatic animals that drift in bodies of water.
They play a crucial role in aquatic food webs, serving as a vital food source for larger marine creatures.
Zooplankton include a diverse array of organisms, such as microscopic crustaceans, rotifers, and the larval stages of many larger animals.
These organisms exhibit a wide range of sizes, shapes, and behaviors, and their study is essential for understanding the dynamics of aquatic ecosystems.
Zooplankton research helps scientists explore topics like nutrient cycling, food web interactions, and the impacts of environmental changes on aquatic life.
Effeicient tools like PubCompare.ai can help researchers optimize their zooplankton studies by locating the best protocols from scientific literature, ensuring reproducibiltiy and accuracy in their work.

Most cited protocols related to «Zooplankton»

Trace Metal Analysis of Marine Invertebrates

Cited 9 times

Four standard reference materials certified for MeHg and the six total metal concentrations (TORT-2 Lobster Hepatopancreas (NRCC, Ottawa, Canada); DORM-2 Dogfish muscle (NRCC, Ottawa, Canada), NIST 1566b oyster tissue (NIST, Gathersburg, USA), NIST 2976 mussel tissue), as well as a fifth certified only for total metals (Plankton BCR 414 (Geel, Belgium) were used for method validation. Sample weight was varied from 20 to 50 mg for certified reference materials, as well as five replicates of DORM-2 from 5 to 10 mg.
Aquatic invertebrates (39 blue and ribbed mussels, 48 fiddler and green crabs, 29 oysters and clams, 23 sand shrimp, 26 periwinkles, 17 amphipods, 7 polychaete, 19 zooplankton) and small fish (35 killifish, 28 silversides, 5 alewife, 9 stickleback) were collected from six estuaries in New England. Samples were freeze-dried as whole organisms, transferred to 25 mL Teflon screw top vials (machined in-house), with a 15 mm Teflon grinding ball (McMaster Carr, Aurora, OH, USA) added to each sample, and homogenized using a Retsch MM301 ball mill (Haan, Germany). Of the 285 samples included in this study 14 had dry weights of less than 10 mg, and 45 weighed less than 20 mg. Ground samples were stored in glass test tubes (I-Chem, Rockwood, TN, USA) double wrapped in plastic.
A flowchart of the sample preparation protocol is given in Fig. 1. Up to 50 mg of sample was weighed into 7mL PFA Teflon micro-vials (CEM, Matthews, NC, USA). Samples were spiked with an 10 to 50 μL of 50 μg L⁻¹ ²⁰¹Hg and 20 μg L⁻¹ ¹⁹⁹Hg, depending on sample mass. To each sample was added 2mL 4M HNO₃, and the weight recorded. For each batch of 32 samples, three digestion blanks with isotopic Hg spikes, and three standard reference materials were prepared. A duplicate sample was also prepared for every 20 samples, where sufficient sample mass was available. Micro-vials were sealed and heated overnight at 55 °C. A 50 μL aliquot (100 μL for samples less than 10 mg) of leachate was removed for MeHg determination, then vials were re-weighed, and 1.5 mL concentrated HNO₃ was added to each vessel before re-capping. Two microvessels were placed inside an HP 500 Plus high-pressure microwave vessel containing a spacer and 10 mL of deionized water. Microwave vessels were loaded into a MARS high pressure microwave (CEM), and the temperature was ramped to 150 °C over 20 min., then held at that temperature for another 10 min. Vessels were allowed to cool to room temperature prior to opening. Digested samples were then transferred to 7 mL PTFE tubes (Savillex, Minnetonka, MN, USA) and weighed. Samples were diluted another 5 times for total method determination.
Between batches, vials for milling were cleaned by scrubbing in Citranox (SPI Supplies, West Chester, PA, USA), followed by soaking overnight in 5% HCl:5% HNO₃, then overnight in 10% HNO₃, and rinsed with ultrapure water. Microvials for digestion were cleaned by scrubbing with acetone, followed by soaking overnight in 5% HCl:5% HNO₃. Vials were then rinsed, and filled with 3 mL concentrated HNO₃ and heated to 150°C in the microwave.

Taylor V.F., Jackson B.P, & Chen C.Y. (2008). Mercury speciation and total trace element determination of low biomass biological samples. Analytical and bioanalytical chemistry, 392(7-8), 1283-1290.

Publication 2008

Acetone Amphipoda ARID1A protein, human Blood Vessel Carcinus maenas Clams Digestion Estuaries Fishes Freezing Fundulus heteroclitus Hepatopancreas Invertebrates Isotopes Metals Microvessels Microwaves Muscle Tissue Mussels Oysters Plankton Polytetrafluoroethylene Pressure Squalidae Sticklebacks Teflon Tissues Vinca Zooplankton

Global Marine Biomass Projections

Cited 7 times

Annual outputs of total animal biomass density (grams carbon per square meter) and animal biomass of >10 cm and >30 cm were derived on a 1 × 1 degree grid. We calculated time series of % biomass change from 1970 to 2100 relative to 1990–1999 (reference period), and % biomass change in 2090–2099 vs. 1990–1999 for each simulation, as absolute biomass densities were not strictly comparable across MEMs. Relative changes were combined into ensemble means and SD. The climate change effect [(RCP8.5 − RCP2.6)/RCP2.6] was calculated in a fished and unfished ocean within and across MEMs. Empirical validation was achieved by comparing historical projections with biomass trends of assessed fish stocks in a fished ocean (B/B_MSY; ref. 29 (link)) and temperature-dependent biomass hindcasts (MSY) of assessed stocks without fishing (30 (link)), in addition to published individual MEM validations with empirical data (SI Appendix, Fig. S3). Trophic amplification was evaluated by comparing mean (±SD) changes (2090s vs. 1990s) in NPP and total phytoplankton and zooplankton biomass from ESMs with higher trophic level biomass from MEMs across RCPs. Mean biomass changes were also compared with global air temperature changes since preindustrial times (1861–1870) from ESMs. Spatial patterns were mapped as mean % biomass changes in 2090–2099 vs. 1990–1999, the SD of the mean to assess intermodel variability in the magnitude of change, and the % model agreement on the direction of change (14 ). We also mapped the climate change effect with and without fishing and the variability of results across ESMs and MEMs. For further details, see SI Appendix, SI Methods.

Lotze H.K., Tittensor D.P., Bryndum-Buchholz A., Eddy T.D., Cheung W.W., Galbraith E.D., Barange M., Barrier N., Bianchi D., Blanchard J.L., Bopp L., Büchner M., Bulman C.M., Carozza D.A., Christensen V., Coll M., Dunne J.P., Fulton E.A., Jennings S., Jones M.C., Mackinson S., Maury O., Niiranen S., Oliveros-Ramos R., Roy T., Fernandes J.A., Schewe J., Shin Y.J., Silva T.A., Steenbeek J., Stock C.A., Verley P., Volkholz J., Walker N.D, & Worm B. (2019). Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change. Proceedings of the National Academy of Sciences of the United States of America, 116(26), 12907-12912.

Publication 2019

Animals Carbon Climate Change Phytoplankton Richieri Costa Pereira syndrome Zooplankton

Fungal, Bacterial, and Mammalian Cell Culture Sources

Cited 6 times

Fungi: Z. tritici wild type strain IPO323 (CBS 115943) was obtained from the Fungal Biodiversity Center, Utrecht, Netherlands (http://www.cbs.knaw.nl). Z. tritici strains IPO323_eGFP-Sso1 and IP0323_Acd1-ZtGFP were described previously (for references see Supplementary Table 2) and can be obtained from the laboratory of the first author. The U. maydis wild type strain FB1 (CBS 132774) was provided by R. Kahmann, MPI Marburg, Marburg, Germany, and can also be obtained from the Fungal Biodiversity Center, Utrecht, Netherlands (http://www.cbs.knaw.nl). The M. oryzae wild type strain Guy11 (FGSC 9462) was provided by N. Talbot, Sainsbury Laboratory, Norwich, UK; it can also be requested from The Fungal Genetics Stock Center, Manhattan, KS, USA (http://www.fgsc.net/). Strain IPO323_mCh-ZtSsoI was generated by transforming plasmid pHmCherrySso1^{33 (link)} into WT IPO323 using A. tumefaciens-mediated transformation^{66 (link)}. Ustilago maydis strain FB1GSso1 was generated by ectopic integration of plasmid poGSso1^{67 (link)} into strain FB1. Both strains can be obtained from the laboratory of the first author. Genotypes of all strains and plasmids are in Supplementary Table 2; experimental strain usage is in Supplementary Table 3.
Zooplankton: The water flea D. magna was obtained from the Northampton Reptile Center, Northampton, UK (https://www.reptilecentre.com/).
Bacteria: Salmonella typhimurium LT2 strains TA1535, TA1537, TA98, TA100, and Escherichia coli WP2 strain uvrA/pKM101 were provided by Gentronix Ltd., Cheshire, UK.
Mammalian cell culture cells: Human skin fibroblasts (C109) were provided by H. Waterham, University of Amsterdam, NL, and human hepatoblastoma cells (HepG2, HB-8065) were obtained from ATCC, Virginia, USA (http://www.atcc.org).

Steinberg G., Schuster M., Gurr S.J., Schrader T.A., Schrader M., Wood M., Early A, & Kilaru S. (2020). A lipophilic cation protects crops against fungal pathogens by multiple modes of action. Nature Communications, 11, 1608.

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Publication 2020

Bacteria Cell Culture Techniques Cells Escherichia coli Fibroblasts Fungi Genes, Fungal Genotype Hepatoblastoma Homo sapiens Mammals Plasmids Reptiles Salmonella typhimurium LT2 Skin Strains Ustilago maydis Water Flea Zooplankton

Microplastic Quantification in Copepods

Cited 5 times

The optimized enzymatic digestion protocol was further considered as a technique for detecting microplastics ingested by marine organisms. Specimens of the marine copepod Temora longicornis were isolated from a zooplankton trawl, and then three individuals (n = 5) placed in a Petri-dish containing 20 mL of filtered seawater containing fluorescent polystyrene beads (100 microplastics mL⁻¹) overnight at ambient sea surface temperature. Post-exposure, specimens were retained on a mesh-filter, preserved using 4% formalin and rinsed thoroughly with Milli-Q. Copepods were visualized under a microscope (fitted with fluorescence) to quantify the number of specimens that had ingested the polystyrene beads and it was confirmed that no external microplastics were present. T. longicornis were enzymatically digested per the standardized protocol, using smaller volumes of homogenizing solution, Proteinase-K and sodium perchlorate owing to the smaller mass of biological material being digested. Digested extract was filtered onto a 0.2 μm glass fibre filter (GF/F) and residue visualized under a microscope to enumerate and photograph the microplastics that had been previously internalized by the copepods.

Cole M., Webb H., Lindeque P.K., Fileman E.S., Halsband C, & Galloway T.S. (2014). Isolation of microplastics in biota-rich seawater samples and marine organisms. Scientific Reports, 4, 4528.

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Publication 2014

Biopharmaceuticals Copepoda Digestion Endopeptidase K Enzymes Fluorescence Formalin Hyperostosis, Diffuse Idiopathic Skeletal Marine Organisms Marines Microplastics Microscopy Polystyrenes sodium perchlorate Zooplankton

Dietary δ15N-Dependent Trophic Level Modeling

Cited 5 times

As a negative linear Δ¹⁵N vs. dietary δ¹⁵N relationship implies a limit in δ¹⁵N values, we developed a dietary δ¹⁵N value-dependent enrichment model based on an alternative form of the von Bertalanffy growth equation:

with δ¹⁵N_TP being the consumer isotope value at a given TP, δ¹⁵N_lim the saturating isotope limit as TP increases, δ¹⁵N_base the isotope value for a known baseline consumer in the food web, and k the rate at which δ¹⁵N_TP approaches δ¹⁵N_lim per TP step. This model is value-dependent in that δ¹⁵N_lim is reached when the rates of ¹⁵N and ¹⁴N uptake balance those of ¹⁵N and ¹⁴N elimination (i.e. Δ¹⁵N = 0 at δ¹⁵N_lim), the rates of which are assumed constant among consumers and diets (see Supplementary Material S4).
Solved for TP this equation becomes

Calculating TP from this model requires estimates of both δ¹⁵N_lim and k which are given from the meta-analysis as

The estimated scaled TP (TP_scaled) for each consumer is then estimable from the posterior distribution of the meta-analysis, given its δ¹⁵N value (δ¹⁵N_TP) and a δ¹⁵N_base value for a given food web. Baseline TL2 consumers used to estimate δ¹⁵N_base were zooplankton, including copepod, Euphausia frigida and mysid, Undinula vulgaris for the South African food web and copepod, Calanus hyperboreus for the Canadian Arctic food web. The full meta-analytical model was implemented in a Bayesian framework, using the PyMC package (Patil et al. 2010 ) for the Python programming language. The model was run for 100,000 iterations, with an 80,000 iteration burn-in and thinned by a factor of 10; convergence was assessed through visual inspection of chains, plots of the model fit with the data, and Bayesian P-values (Gelman et al. 2006 ). Model code is included in Supplementary Materials S5.

Hussey N.E., MacNeil M.A., McMeans B.C., Olin J.A., Dudley S.F., Cliff G., Wintner S.P., Fennessy S.T, & Fisk A.T. (2013). Rescaling the trophic structure of marine food webs. Ecology Letters, 17(2), 239-250.

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Publication 2013

Copepoda Diet Euphausia factor A Food Web Isotopes Python Southern African People Zooplankton

Most recents protocols related to «Zooplankton»

Foraging Humpback Whale Kinematic and Acoustic Prey Monitoring

We used a multi-sensor suction-cup tag (Customized Animal Tracking Solutions, CATS, www.cats.is) to collect high sample rate kinematic and behavioural data from a foraging humpback whale. The whale was actively engaged in feeding underwater and was easily approached while recovering from a dive. The tag was attached near the dorsal fin using a slow vessel approach from behind and to the side of the whale, and a 7-m handheld carbon fiber pole. The whale returned to its pre-approach feeding behaviour within seconds of tag attachment. The tag contained a 3-axis magnetometer, gyroscope, and accelerometer sampling at 20 Hz, and a pressure sensor sampling at 10 Hz. The tag also contained a VHF transmitter that enabled close tracking of the whale while the tag was attached. After a pre-set release time of 4 hours, the tag detached from the whale and was recovered for downloading the data.
The vertical distribution of mesozooplankton and fish was continuously recorded near the tagged whale using an Acoustic Zooplankton and Fish Profiler (AZFP) from ASL Environmental Sciences, Victoria, British Columbia. The AZFP is an autonomous scientific echosounder, designed for long-term monitoring of the water column from a mooring on the seafloor. We tested the portability of the AZFP in a vessel-mounted, downward-looking orientation from the sea surface. The transducers were mounted on a metal strut and lowered over the side of the boat to 1-m water depth, while the instrument in its pressure case remained on the boat. We used individually calibrated 125 and 200 kHz channels (7° and 10° conical beams) that transmitted sequentially, providing an acoustic sample every two seconds at a pulse duration of 300 μs (Table 1). Power levels of the AZFP were well below the levels emitted by a hull-mounted system typically used in mobile acoustic surveys (e.g., [45 (link)]), while the 125 and 200 kHz frequencies are well above the estimated hearing range of humpback whales (0.02–24 kHz [46 (link)]). Volume backscatter data (S_v, dB) were recorded and stored by the instrument in Compact FLASH memory. Acoustic data were corroborated using regional information from Fisheries and Oceans Canada multi-year, integrated trawl and acoustic survey data on Pacific hake (Merluccius productus) and Strait of Georgia pelagic ecosystem surveys [47 ,48 ].
Upon tag attachment to the whale, acoustic prey sampling was initiated to record whole water column data from the surface to the seafloor within 10 to 200 m of the tagged whale during the period of tag data logging. The whale was followed at 1.0–2.6 m s^-1 (2–5 knots) based on surface observations with the acoustic survey track assumed to follow the general swimming track of the whale. Continuous GPS positions were recorded at 0.5 s intervals by a handheld Garmin GPS, while periodic GPS surfacing locations were noted, based on either the boat’s position when close to the surfacing whale, or on the whale’s fluke print location (a calm patch of water created by the diving whale). The AZFP and handheld GPS clocks were matched at the start and end of the deployment, while surface observations were manually logged and time-synchronized with the GPS clock, and continued until the tag was released from the whale.

Reidy R., Gauthier S., Doniol-Valcroze T., Lemay M.A., Clemente-Carvalho R.B., Cowen L.L, & Juanes F. (2023). Integrating technologies provides insight into the subsurface foraging behaviour of a humpback whale (Megaptera novaeangliae) feeding on walleye pollock (Gadus chalcogrammus) in Juan de Fuca Strait, Canada. PLOS ONE, 18(3), e0282651.

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Publication 2023

3-acetonylidene-2-oxindole Acoustics Animals Blood Vessel Carbon Fiber Cetacea Ecosystem Feeding Behaviors Felis catus Fishes Hakes M-200 Megaptera novaeangliae Memory Metals Neoplasm Metastasis Pressure Pulse Rate Suction Drainage Transducers Trematoda Zooplankton

Acoustic Detection of Whale Prey Biomass

AZFP data were processed using Echoview (v.12; Echoview Software Pty Ltd.). Mean volume backscattering strength (S_v in dB re 1 m^-1), a relative measure of density, was analyzed from 10 m below the surface to 5 m above the sounder-detected seafloor in bins of 5 m vertically by 5 pings horizontally. Sound speed [55 (link)] and absorption coefficients [56 (link)] were estimated at each frequency using temperature and salinity values reported for the closest oceanographic sampling station in Juan de Fuca Strait in September 2017 (approximately mid-Strait off Sooke Basin), as measured by Fisheries and Ocean Canada. Background noise was removed following the approach described in de Robertis & Higginbottom [57 (link)], using a minimum signal-to-noise ratio of 10 dB and maximum noise threshold of -125 dB re 1 m^-1. Removal of acoustic noise was done through visual inspection of the echograms and applying filters following Ryan et al. [58 (link)]. Impulse and transient noise were removed with a maximum threshold of 10 dB and 12 dB, respectively. Backscatter in the processed echograms was scrutinized and classified based on echo morphology (shape and structure of the aggregations), depth distribution (including bottom association), and single target attributes. Mean volume backscattering strength (S_v) at 125 kHz was subtracted from that at 200 kHz to assess differences (MVBS_200-125). Values greater or equal to 0 dB or lower or equal to 4 dB would be indicative of swimbladder-bearing fish, while MVBS _200–125 values greater than 5 dB would indicate backscatter dominated by zooplankton [59 (link)]. The processed volume scattering data were gridded into 1-min horizontal cells by 10-m vertical cells, then echo-integrated using an integration threshold of -70 dB over the whole water column into nautical area scattering coefficients (NASC; m² nmi^-2; [60 (link)]) to obtain relative measures of water column biomass where the whale was feeding. The overall pattern of log₁₀-transformed NASC and the whale’s feeding rate during tag attachment were plotted in R, using a generalized additive model (GAM) with integrated smoothness estimation (y~s(x)). To estimate biomass per lunge count, we averaged NASC for each bottom-foraging time interval, and further averaged NASC across foraging intervals that had matching lunge counts to obtain a single estimate in each lunge-count category.

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Publication 2023

Acoustics Air Sacs Cells Cetacea ECHO protocol Fishes M Cells Multivesicular Body Salinity Sound Transients Zooplankton

Isotopic Mixing Model for Aquatic Consumers

To quantify the contribution of the different food sources to the isotopic signature of each consumer or functional group of consumers a separate Bayesian mixing model^{27 (link)} with a specific number of putative sources was run over years in MixSiar-package^{28 (link)} in R²⁶. In European perch (n = 21), a four-source model was run (omnivorous fish, crayfish, zoobenthos and crustacean zooplankton; Table S2). In roach (n = 20) and noble crayfish (n = 21), a five-source model was run (zoobenthos, crustacean zooplankton, macrophytes, algae and detritus; Table S3). For predatory zoobenthos (n = 20), a two-source model including crustacean zooplankton and zoobenthos was run (Table S4.). In detritivorous zoobenthos (n = 20), a three-source model was run (macrophytes, algae and detritus, Table S5).

Veselý L., Ercoli F., Ruokonen T.J., Bláha M., Duras J., Haubrock P.J., Kainz M., Hämäläinen H., Buřič M, & Kouba A. (2023). Strong temporal variation of consumer δ13C value in an oligotrophic reservoir is related to water level fluctuation. Scientific Reports, 13, 3642.

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Publication 2023

Astacoidea Crustacea Europeans Fishes Food Isotopes Perch Zooplankton

CMCC-ESM2 Earth System Model Biogeochemistry

Based on the Community Earth System Model, CMCC-ESM2 uses the Nucleus for European Modeling of the Ocean (NEMO v3.6; Madec & NEMO Team, 2016 (link)) for the ocean general circulation model and the global version of the Biogeochemical Flux Model (BFM 5.2; Vichi et al., 2020 ) for the biogeochemical component. The ocean model grid consists of 362 × 292 longitude/latitude and 50 vertical depth levels (i.e., 1–5,904 m, depth intervals varying from 1 m to 17 m in the upper 100 m and from 20 m to 400 m below) based on the ORCA tripolar grid at a nominal 1° latitude × longitude with a meridional refinement of 1/3° near the Equator. CMCC-ESM2 is run by CMCC (Fondazione Centro euro-Mediterraneo sui Cambiamenti Climatici, Italy) in native nominal resolutions of 100 km for both the ocean general circulation and biogeochemical models. The BFM equations solve the fluxes of C, N, P, Si, and Fe among living functional groups including bacteria, unicellular planktonic autotrophs (i.e., nano-phytoplankton, diatoms), zooplankton (i.e., micro- and mesozooplankton), and non-living functional groups for dissolved and particulate organic matter. In particular, phytoplankton groups and organic matter pools have a variable stoichiometry, while bacterial and zooplanktonic groups are modeled in terms of the sole carbon constituent with fixed stoichiometric ratios. The bacterial scheme in BFM simulates time-evolving dynamics of free-living and particle-attached bacteria explicitly. The BFM configuration used in CMCC-ESM2 is detailed in Lovato et al. (2022) (link).

Kim H.H., Laufkötter C., Lovato T., Doney S.C, & Ducklow H.W. (2023). Projected 21st-century changes in marine heterotrophic bacteria under climate change. Frontiers in Microbiology, 14, 1049579.

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Publication 2023

Bacteria Biological Models Carbon Cell Nucleus Diatoms Europeans Fixation, Carbon MCC protocol Orcinus orca Phytoplankton Plankton Simulate composite resin Zooplankton

Raman Analysis of Copepod Fecal Pellets

Zooplankton was collected in the Bay of Villefranche sur Mer, France (43°40′N, 7°19′E) with a 10-min haul from 10 m to the surface, using a 20-μm-mesh-size plankton net. Captured copepods were transferred into 0.5 liters of freshly filtered natural seawater and starved for 12 h. Centropages typicus, Temora longicornis, and Acartia sp. were then picked under a dissection microscope. All experiments were carried out at cultivation temperature of the prey species of diplonemids (13°C for Lacrimia sp. YPF1808, room temperature for N. karyoxenos). Ten copepods were kept in 20 mL of diplonemid culture (10⁵ cells mL⁻¹) for 5 days, after which their fecal pellets were collected under a dissection microscope and immediately analyzed by Raman microscopy (as specified below).

Pilátová J., Tashyreva D., Týč J., Vancová M., Bokhari S.N., Skoupý R., Klementová M., Küpper H., Mojzeš P, & Lukeš J. (2023). Massive Accumulation of Strontium and Barium in Diplonemid Protists. mBio, 14(1), e03279-22.

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Publication 2023

Cells Copepoda Dissection Feces Microscopy Pellets, Drug Plankton Zooplankton

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What are the different types of zooplankton?

Zooplankton encompasses a diverse array of organisms, including microscopic crustaceans (such as copepods and cladocerans), rotifers, and the larval stages of many larger animals like fish and invertebrates. These zooplankton vary significantly in size, shape, and behaviors, playing crucial roles in aquatic food webs as a vital food source for larger marine creatures.

How do zooplankton contribute to aquatic ecosystems?

Zooplankton play a crucial role in aquatic food webs, serving as a vital food source for larger marine creatures. They help drive nutrient cycling and energy flow within aquatic ecosystems, supporting the overall health and productivity of these environments. By studying zooplankton, scientists can gain valuable insights into topics like trophic interactions, the impacts of environmental changes, and the dynamics of aquatic life.

What are some common challenges in zooplankton research?

One of the key challenges in zooplankton research is ensuring the accuracy and reproducibility of studies. Zooplankton can exhibit significant variability in their characteristics and behaviors, making it crucial to identify the most effective protocols for sampling, identification, and analysis. Inefficient screening of protocol literature can lead to suboptimal choices, potentially compromising the quality and reliability of research findings.

How can PubCompare.ai assist with zooplankton research?

PubCompare.ai can revolutionize zooplankton research by helping scientists more efficiently screen protocol literature and leveraging AI to pinpoint critical insights. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to identify the most optimal protocols for their specific goals. This can enhance the reproducibility and accuracy of zooplankton studies, ensuring your research is optimized for success. By utilizing PubCompare.ai, you can save time, improve the quality of your findings, and drive your zooplankton research to new heights.

What are some unique applications of zooplankton research?

Beyond their role in aquatic food webs, zooplankton research has a wide range of applications. For example, the study of zooplankton communities can provide valuable insights into the overall health and functioning of aquatic ecosystems. Researchers may also leverage zooplankton as bioindicators, using their presence and abundance to monitor environmental changes and assess the impact of human activities on waterways. Additionally, zooplankton research can contribute to the development of sustainable fishery management strategies and the understanding of the effects of climate change on aquatic life.

How can PubCompare.ai help optimize zooplankton research protocols?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoint critical insights that can enhance the effectiveness of their zooplankton studies. The platform's AI-driven analysis can help identify the most optimal protocols for your specific research goals, ensuring the reproducibility and accuracy of your findings. By utilizing PubCompare.ai, you can save time, improve the quality of your work, and drive your zooplankton research to new heights of success.

More about "Zooplankton"

Tiny Aquatic Drifters: Exploring the Crucial Role of Zooplankton in Aquatic Ecosystems Zooplankton, the minuscule denizens of our waterways, play a vital role in the delicate balance of aquatic food webs.
These tiny, drifting creatures, which include microscopic crustaceans, rotifers, and the larval stages of larger marine animals, serve as a crucial food source for larger aquatic creatures, from small fish to towering whales.
Studying the dynamics of these aquatic microorganisms is essential for understanding the intricate workings of aquatic ecosystems.
Researchers utilize a range of specialized tools and techniques to explore the diverse array of zooplankton species, their sizes, shapes, and behaviors.
Tools like the RNAlater preservation solution, the CKX41 inverted microscope, and the Axio Vert.A1 microscope system help scientists analyze and document the intricate details of these tiny organisms.
But the study of zooplankton goes beyond mere observation.
Researchers also employ advanced analytical techniques, such as the ConFlo IV system for stable isotope analysis and the Flash EA 1112 Series elemental analyzer, to unravel the complex nutrient cycling and food web interactions that underpin the health of aquatic environments.
By understanding the role of zooplankton in aquatic ecosystems, scientists can better explore the impacts of environmental changes, such as pollution, climate shifts, and habitat degradation, on the delicate balance of aquatic life.
The Thiosulfate Citrate Bile Sucrose agar plates (TCBS) can be used to assess the presence of harmful bacteria that may threaten zooplankton populations.
Optimizing zooplankton research is a key priority for scientists, and tools like PubCompare.ai can help researchers locate the best protocols and methods from the scientific literature, ensuring reproducibility and accuracy in their work.
With the right tools and techniques, researchers can unlock the secrets of these tiny aquatic drifters and safeguard the health of our precious waterways for generations to come.
So dive into the fascinating world of zooplankton and discover how these microscopic marvels play a vital role in sustaining the delicate balance of aquatic ecosystems.
Whether you're studying nutrient cycling, food web dynamics, or the impacts of environmental change, the insights gained from zooplankton research can be truly transformative.