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46 results for “zooplankton species”
Crustacean Zooplankton Species Richness in 66 North American Lakes
Data from 66 North American lakes were collected to construct a model for predicting the number of crustacean zooplankton species expected in a lake. The chosen lakes have a range from 4 sq m to 80 x 10**9 sq m surface area, range from ultra-oligotrophic to hypereutrophic, and have zooplankton species lists based of several years of observation The number of crustacean zooplankton species in a lake is significantly correlated with lake size, average rate of photosynthesis (parabolic function) and the number of lakes within 20 km. A multiple linear regression model, using these three independent variables, explains approximately 75% of the variation in log species richness. Prediction of species richness is not enhanced by the knowledge of lake depth, salinity, elevation, latitude, longitude, or distance to nearest lake. The North American species area curve is statistically different from and steeper than the corresponding European curve. Number of sites: 69
ZooBase: A global synthesis of marine zooplankton species occurrences.
<p><em><strong>Description of the methods used to implement the present ZooBase dataset (extarct from Section A.2 from the Methods of Benedetti et al., 2021).</strong></em></p> <p>A new dataset of global zooplankton species occurrences was compiled in a comparable fashion to that put together for phytoplankton (Righetti et al., 2020). Prior to retrieving the occurrence data online, we first identified the phyla (Order/Class/Family) that comprise the bulk of extant oceanic zooplankton communities: Copelata (i.e. appendicularians), Ctenophora, Cubozoa (i.e. box jellyfish), Euphausiidae (i.e. krill), Foraminifera, Gymnosomata (i.e. sea angels, pteropods), Hydrozoa (i.e. jellyfish), Hyperiidea (i.e. amphipods), Myodocopina (i.e. ostracods), Mysidae (i.e. small pelagic shrimps resembling krill), Neocopepoda, Podonidae and <em>Penilia</em> <em>avirostris</em> (i.e. cladocerans), Sagittoidea (i.e. chaetognaths), Scyphozoa (i.e. jellyfish), Thaliacea (i.e. salps, doliolids and pyrosomes), Thecosomata (i.e. pteropods), and four families of pelagic Polychaeta (i.e. worms) that are often found in the zooplankton and whose species are known to display holoplanktonic lifecycles (Tomopteridae, Alciopidae, Lopadorrhynchidae, Typhloscolecidae). The presence data associated with species belonging to these groups were retrieved from OBIS and GBIF between the 12/04/2018 and the 18/04/2018 using online queries via the R packages RPostgreSQL, robis and rgbif. Since the Neocopepoda infra-class comprise several thousands of benthic and parasitic taxa (https://copepodes.obs-banyuls.fr/en/), a preliminary selection of the non-parasitic planktonic species had to be carried out prior to the online downloading using the species list of Razouls et al. (https://copepodes.obs-banyuls.fr/en/) as a reference. The spatial distributions of the groups cited above were first inspected using GBIF’s and OBIS’s online mapping tools to evaluate the potential number of overlapping observations between the two databases. As a result of their relatively low contributions to total observations/diversity, and very high overlap between databases, the occurrences of Cladocera and Polychaeta were retrieved from OBIS only (which usually harbours more occurrences). On top of the data collected from OBIS and GBIF, the copepod occurrences from Cornils et al. (2018) and the pteropod occurrences from the MAREDAT initiative (Buitenhuis et al., 2013) were added to the dataset. We discarded records that: (i) presented at least one missing spatial coordinate, (ii) were associated with an incomplete sampling date (d/m/y), (iii) were associated with a year of collection older than 1800, (iv) were not associated with any sampling depth, (v) were not identified down to the species level. Occurrences associated with grid cells shallower than 10m were removed (bathymetry data from ETOPOv1 at a 15min resolution, downloaded using the 'marmap' R package). Finally, every species name was then carefully examined and compared to the taxonomic reference list of the World Register of Marine Species (WoRMS; <a href="http://www.marinespecies.org">http://www.marinespecies.org</a>) for all taxa. The AphiaID and the Status were retreived from WoRMS based on the ScientificName. To remove the duplicate occurrences due to the highly overlapping source archives (GBIF and OBIS), a unique occurrenceID was given to each record based on rounded spatial coordinates (closest 0.1°x0.1°), rounded depth layer (10m depth layers), month and year of the occurrence and the acccpted species name (e.g., AphiaID).</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Species Portfolio Effects Dominate Seasonal Zooplankton Stabilization Within a Large Temperate Lake
<p>The raw data file is available online for public access (<a href="https://data.ontario.ca/dataset/lake-simcoe-monitoring">https://data.ontario.ca/dataset/lake-simcoe-monitoring</a>). Download the 1980-2019 csv files and open up the file named "Simcoe_Zooplankton&Bythotrephes.csv". Copy and paste the zooplankton sheet into a new excel file called "Simcoe_Zooplankton.csv". The column ZDATE in the excel file needs to be switched from GENERAL to SHORT DATE so that the dates in the ZDATE column read "YYYY/MM/DD". Save as .csv in appropriate R folder. The data file "simcoe_manual_subset_weeks_5" is the raw data that has been subset for the main analysis of the article using the .R file "Simcoe MS - 5 Station Subset Data". The .csv file produced from this must then be manually edited to remove data points that do not have 5 stations per sampling period as well as by combining data points that should fall into a single week. The "simcoe_manual_subset_weeks_5.csv" is then used for the calculation of variability, stabilization, asynchrony, and Shannon Diversity for each year in the .R file "Simcoe MS - 5 Station Calculations". The final .R file "Simcoe MS - 5 Station Analysis contains the final statistical analyses as well as code to reproduce the original figures. Data and code for main and supplementary analyses are also available on GitHub (https://github.com/reillyoc/ZPseasonalPEs). </p> <p> </p>
Рис. 2. ЭкоΛого-географическая характеристика зоопΛанктона техногенных воΑоемов Fig. 2. Ecological and geographical characteristics of zooplankton in technogenic reservoirs in Zooplankton species diversity in technogenic reservoirs of the Southeastern Transbaikalia
Рис. 2. ЭкоΛого-географическая характеристика зоопΛанктона техногенных воΑоемов Fig. 2. Ecological and geographical characteristics of zooplankton in technogenic reservoirs
Рис. 1. Daphnia (D.) curvirostris. Партеногенетическая самка: А — гоΛова с боковой стороны, Б — постабΑомен Fig. 1. Daphnia (D.) curvirostris. Parthenogenetic female: А — head, lateral view, Б — postabdomen, lateral view in Zooplankton species diversity in technogenic reservoirs of the Southeastern Transbaikalia
Рис. 1. Daphnia (D.) curvirostris. Партеногенетическая самка: А — гоΛова с боковой стороны, Б — постабΑомен Fig. 1. Daphnia (D.) curvirostris. Parthenogenetic female: А — head, lateral view, Б — postabdomen, lateral view
Рис. 3. ÀенΑрограмма биоценотического схоΑства зоопΛанктона техногенных воΑоемов: 4–6 — ШерΛовогорское месторожΑение:4 — ШГ-10 — карьерное озеро, 5 — ШГ-8 — озеро поΑ отваΛами руΑного карьера, 6 — ШГ-9 — поΑпруΑное озеро у пгт. ШерΛовая Гора;7–8 — ОрΛовское месторожΑение: 7 — ОР-1, ОР-3 — хвостохраниΛище, 8 — ОР-7 — озеро ниже хвостохраниΛища; 9 — МаΛокуΛунΑинское месторожΑение: МК-2 — поΑпруΑное озеро р. МаΛая КуΛинΑа; 10 — Спокойнинское месторожΑение: ОР-8 — хвостохраниΛище; 11 — Жипкошинское месторожΑение: ЖП-2 — карьер Fig. 3. Dendrogram of zooplankton biocenotic similarity in technogenic reservoirs: 4–6 — Sherlovogorskoye deposit: 4 — ShG-10 pit lake, 5 — ShG-8, a lake under the dumps of an ore quarry, 6 — ShG-9 dammed lake near the village of Sherlovaya Gora;7 –8 — Orlovskoye deposit: O R-1, OR-3 — tailing dump, OR-7— lake below the tailing dump; 9 — Malokulundinskoye deposit: MK-2 — dammed lake on the Malaya Kulinda River; 10 — Spokoininskoye deposit: OR-8 — tailing dump; 11 — Zhipkoshinskoye deposit; ZhP-2 — pit lake in Zooplankton species diversity in technogenic reservoirs of the Southeastern Transbaikalia
Рис. 3. ÀенΑрограмма биоценотического схоΑства зоопΛанктона техногенных воΑоемов: 4–6 — ШерΛовогорское месторожΑение:4 — ШГ-10 — карьерное озеро, 5 — ШГ-8 — озеро поΑ отваΛами руΑного карьера, 6 — ШГ-9 — поΑпруΑное озеро у пгт. ШерΛовая Гора;7–8 — ОрΛовское месторожΑение: 7 — ОР-1, ОР-3 — хвостохраниΛище, 8 — ОР-7 — озеро ниже хвостохраниΛища; 9 — МаΛокуΛунΑинское месторожΑение: МК-2 — поΑпруΑное озеро р. МаΛая КуΛинΑа; 10 — Спокойнинское месторожΑение: ОР-8 — хвостохраниΛище; 11 — Жипкошинское месторожΑение: ЖП-2 — карьер Fig. 3. Dendrogram of zooplankton biocenotic similarity in technogenic reservoirs: 4–6 — Sherlovogorskoye deposit: 4 — ShG-10 pit lake, 5 — ShG-8, a lake under the dumps of an ore quarry, 6 — ShG-9 dammed lake near the village of Sherlovaya Gora;7 –8 — Orlovskoye deposit: O R-1, OR-3 — tailing dump, OR-7— lake below the tailing dump; 9 — Malokulundinskoye deposit: MK-2 — dammed lake on the Malaya Kulinda River; 10 — Spokoininskoye deposit: OR-8 — tailing dump; 11 — Zhipkoshinskoye deposit; ZhP-2 — pit lake
Figure 3b in Species diversity and community structure of zooplankton in three different types of water body within the Sakarya River Basin, Turkey
Figure 3b. CCA biplot diagram with three lakes (all seasons and stations) and 81 species (Rot: Rotifera, Cla: Cladocera, Cop: Copepoda, species abbreviations are listed in Table 2).
Figure 1 in Species diversity and community structure of zooplankton in three different types of water body within the Sakarya River Basin, Turkey
Figure 1. Species richness, evenness, and diversity boxplots in each lake. The horizontal thick black band represents the median value, and the boxplot margins indicate first and third quartiles.
Figure 2 in Species diversity and community structure of zooplankton in three different types of water body within the Sakarya River Basin, Turkey
Figure 2. nMDS plots between lakes in terms of zooplankton species composition and abundance (a: all zooplankton species, b: rotifers, c: cladocerans.), Triangle: Lake Poyrazlar, Square: Çubuk II Reservoir, Circle: Sorgun Pond.
Figure 3a in Species diversity and community structure of zooplankton in three different types of water body within the Sakarya River Basin, Turkey
Figure 3a. CCA biplot diagram with three lakes (all seasons and stations), and eight environmental variables. For sample abbreviations, first letter indicates water body; s: Sorgun, p: Poyrazlar, c: Çubuk II; letters between 2 and 4 indicate the seasons: spr: spring, sum: summer, win: winter; numerical variables indicate sampling stations.
Fig. 4 in Quantifying zooplankton species: use of richness estimators
Fig. 4. Species accumulation curves, uniques and duplicates for the BA1 station of Furnas reservoir, state of Minas Gerais, Brazil, from March 2011 to February 2012.
Fig. 6 in Quantifying zooplankton species: use of richness estimators
Fig. 6. Species accumulation curves, uniques and duplicates for the BA3 station of Furnas reservoir, state of Minas Gerais from March 2011 to February 2012.
Fig 1 in Quantifying zooplankton species: use of richness estimators
Fig 1. Sampling stations in the Hydroelectric Power Plant of Furnas reservoir, state of Minas Gerais, Brazil (A, Barranco Alto region; B, junction of rivers Verde and Sapucai - VSJ).
Fig. 5 in Quantifying zooplankton species: use of richness estimators
Fig. 5. Species accumulation curves, uniques and duplicates for the BA2 station of Furnas reservoir, state of Minas Gerais, Brazil from March 2011 to February 2012.
Fig. 3 in Quantifying zooplankton species: use of richness estimators
Fig. 3. Species accumulation curves, uniques and duplicates for the VSJ station in Furnas reservoir, state of Minas Gerais, Brazil, collected with vertical hauls.
Data from: Sampling effects drive the species-area relationship in lake zooplankton
<p><b>The Island Species-Area relationship (ISAR) describes how the numbers of species increases with increasing size of an island (or island-like habitat, such as lakes), and is one of the oldest laws in ecology. Despite its conceptual importance, there remains a great deal of ambiguity regarding the ISAR and its underlying processes. We compiled data from sampled zooplankton assemblages from several hundred lakes in North America and Europe to examine the influence of the three main hypothesized mechanisms leading to ISARs - passive sampling, disproportionate effects, and habitat heterogeneity. We compiled data on lake zooplankton assemblages that reported sample-level and lake level species richness estimates, as well as relative abundance data. In both North American and European lakes, we found a consistent and strong increase in total species richness with increasing lake area. However, when we compared the number of species standardized by number of individuals, there was no relationship between lake area and sample-level species richness or an estimate of species relative abundances, calculated as the Probability of Interspecific Encounter (PIE; a measure of evenness). This was true even when multiple samples were taken across lakes and combined, reducing the likelihood that habitat heterogeneity was driving the results. Overall, our results suggest that the ISAR of zooplankton in these lakes was most likely determined by sampling effects rather than disproportionate effects or habitat heterogeneity leading to more species in larger lakes. Understanding the mechanisms driving ISAR results such as ours can also help us develop predictions for biodiversity change when the area of these habitats changes. </b></p>
Fig. 1 in Zooplankton species composition of Cladocera and Cyclopoida (Copepoda) in the Vaya Lake
Fig. 1. Map of the Vaya Lake with the location of the stations.
The old and the large may suffer disproportionately during episodes of high temperature: evidence from a keystone zooplankton species
<p class="TBBody1">Widespread declines in the body size of aquatic ectotherms have been attributed to the poorer ability of older, larger individuals to tolerate high temperature. Here, using the thermal death time curve framework, we investigate the relationship between temperature tolerance and size/age by measuring the change in heat tolerance of the keystone zooplankton species <i>Daphnia magna</i> across a range of temperature intensities (and hence exposures of varying duration) among individuals that differed up to three-fold in size and thus varied in age also. Across the gradient of exposure temperatures, younger, smaller individuals were more tolerant than older, larger individuals. This suggests that the young and the small may be better equipped to withstand temperature challenges that are both intense/brief and more moderate/prolonged. Our study generalizes results obtained from more acute tolerance assays, providing physiological evidence consistent with the observed reductions in ectotherm body size as a response to warming in aquatic systems.</p>
Borealization of Arctic zooplankton—smaller and less fat zooplankton species in Disko Bay, Western Greenland
<p>Data for chlorophyll a and copepods in Figure 2 in the publication, doi: 10.1002/lno.11380</p>
Higher predation rate need not and did not lead to higher risk-induced trait responses in related zooplankton species
<p>Predators can directly affect prey populations both through predation (consumption of prey) and risk-induced trait responses (RITRs) that reduce predation risk but are often associated with a fitness cost. Thousands of studies make clear that RITRs (also termed anti-predator or defensive traits) including changes in behavior, morphology, and life history, are employed by numerous taxa across diverse ecological systems, and there is large variation in their magnitude. A natural goal is to elucidate the species and circumstances for which and to what magnitude RITRs are expected. A candidate hypothesis is that prey species that experience higher mortality from a predator will exhibit a higher RITR. This hypothesis is an intuitive extension of the fact that invulnerable animals are not expected to exhibit an RITR, while vulnerable species are. We present an example that clarifies why this relationship is not always expected and when it is expected. Other factors may influence the level of the RITR leading to the possibility that a positive relationship is not expected. We elucidate this problem using a mesocosm experiment with a fish-cladoceran system in which there is large variability in the predation rate on different cladoceran species. Results not only did not show a positive relationship but rather a negative trend between predation rate and the RITR. In fact, highly-preyed-upon taxa did not respond, while the least-preyed-upon taxa had the largest responses. These results clarify how the level of predation risk interacts with many factors to determine the RITR of prey.</p>
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OpenNeuro
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