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Figures 38–43 in Diversity and distribution of species of the planktonic dinoflagellate genus Alexandrium (Dinophyta) from the tropical and subtropical Mexican Pacific Ocean

Figures 38–43: Alexandrium pseudogonyaulax, LM. (38) Cell in ventral view. (39) Empty cell in ventral view, showing 1′, 4′, 6″ and the large ventral pore (arrow). (40) Detail of Po with the foramen. (41–43) Epitheca in ventral view showing 1′, 4′, 6″, and ventral pore (arrow).

Figures 22–24 in Diversity and distribution of species of the planktonic dinoflagellate genus Alexandrium (Dinophyta) from the tropical and subtropical Mexican Pacific Ocean

Figures 22–24: Alexandrium margalefii, LM. (22) General outline of a cell. (23) An empty cell in ventral view showing 1′ and 6″ and the ventral pore (arrow) in the first apical plate (1′). (24) Hypotheca with plate tabulation.

Figures 12–19 in Diversity and distribution of species of the planktonic dinoflagellate genus Alexandrium (Dinophyta) from the tropical and subtropical Mexican Pacific Ocean

Figures 12–19: Alexandrium globosum, LM. (12) Cell outline, with the central nucleus arrowed. (13, 14) Two different cells in ventro-lateral and ventral views, respectively, showing some plates of the epitheca and the sulcus. (15) Epitheca with plate tabulation, arrow indicates the location of the ventral pore in the first apical plate (1′). (16) Hypotheca showing plate tabulation. (17) Po plate. (18) Posterior sulcal plate (Sp). (19) Detail of some precingular, cingular and sulcal plates.

Figures 53–66 in Diversity and distribution of species of the planktonic dinoflagellate genus Alexandrium (Dinophyta) from the tropical and subtropical Mexican Pacific Ocean

Figures 53–66: Alexandrium tamiyavanichii, LM and SEM. (53) Chain of 6 cells,LM. (54) Detail of two cells with cellular content of a chain, LM. (55) Cells in ventral view showing the anterior sulcal plate (Sa), LM. (56) Two cells slightly twisted in a chain, SEM. (57) Cell in ventral view showing plates of the ventral area, LM. (58) Empty cell in ventral view showing plate tabulation, the ventral pore is arrowed, LM. (59) Epitheca in ventro-lateral view with plate tabulation, the left sulcal list is arrowed, SEM. (60, 61) Hypotheca with plate tabulation and pore at the posterior sulcal plate (Sp),SEM.(62, 63) Po and plates around it; the ventral pore is arrowed, LM. (64) Posterior sulcal plate (Sp) with pore (arrow), LM. (65, 66) Anterior sulcal plate, LM.

Figure 71 in Diversity and distribution of species of the planktonic dinoflagellate genus Alexandrium (Dinophyta) from the tropical and subtropical Mexican Pacific Ocean

Figure 71: Maximum-likelihood (ML) tree inferred from ITS sequences of Alexandrium. ML bootstrap and Bayesian posterior probabilities values are shown at branches. Bold letters indicate newly generated sequences in this study. Bootstrap values <50 and posterior probabilities <0.50 are not shown.

Figure 70 in Diversity and distribution of species of the planktonic dinoflagellate genus Alexandrium (Dinophyta) from the tropical and subtropical Mexican Pacific Ocean

Figure 70: Maximum-likelihood (ML) tree inferred from D1-D2 LSU rDNA sequences of Alexandrium. ML bootstrap and Bayesian posterior probabilities values are shown at branches. Bold letters indicate newly generated sequences in this study. Bootstrap values <50 and posterior probabilities <0.50 are not shown.

opencc-by-4.0Dec 2018View details →

Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3).

opencc-by-4.0Aug 2021View details →

Plankton community composition and productivity near the Subantarctic Prince Edward Islands archipelago in autumn

<p>This data set, shows hydrographic (CTD) and biogeochemical (Chl-a, nitrate+nitrite, nitrite, ammonium and silicic acid concentrations) parameters, nitrate uptake rates and net primary production, egg production rates (<em>Calanus simillimus)</em>, Pheophytin-a concentrations in zooplankton guts, and phytoplankton and zooplankton abundances and biomass from the Prince Edward Islands archipelago in the Indian Sector of the Subantarctic Ocean, during April-May 2017.</p> <p> </p>

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Data from: Drivers of global pre-industrial patterns of species turnover in planktonic foraminifera

<p>Anthropogenic climate change is altering global biogeographical patterns. However, it remains difficult to quantify how bioregions are changing because pre-industrial records of species distributions are rare. Marine microfossils, such as planktonic foraminifera, are preserved in seafloor sediments and allow the quantification of bioregions in the past. Using a recently compiled data set of pre-industrial species composition of planktonic foraminifera in 3802 worldwide seafloor sediments, we employed multivariate and statistical model-based approaches to study spatial turnover in order to 1) quantify planktonic foraminifera bioregions and 2) understand the environmental drivers of species turnover. Four latitudinally banded bioregions emerge from the global assemblage data. The polar and temperate bioregions are bi-hemispheric, supporting the idea that planktonic foraminifera species are not limited by dispersal. The equatorial bioregion shows complex longitudinal patterns and overlaps in sea surface temperature (SST) range with the tropical bioregion. Compositional-turnover models (Bayesian bootstrap generalised dissimilarity models) identify SST as the strongest driver of species turnover. The turnover rate is constant across most of the SST gradient, showing no SST threshold values with rapid shifts in species composition, but decelerates above 25°C, suggesting SST is less predictive of species composition in warmer waters. Other environmental predictors affect species turnover non-linearly, and their importance differs across regions. In the Pacific ocean, net primary productivity below 500 mgC m<sup>−2</sup> day<sup>−1</sup> drives fast compositional change. Water depth values below 3000 m (which affect calcareous microfossil preservation) increasingly drive changes in species composition among death assemblages in the Pacific and Indian oceans. Together, our results suggest that the dynamics of planktonic foraminifera bioregions are expected to be highly responsive to climate change; however, at lower latitudes, environmental drivers other than SST may affect these dynamics.</p>

opencc-zeroNov 2021View details →

opencc-by-4.0Feb 2022View details →

Plankton DSG Challenge

<p>This repository contains ResNet50 model weights and sample images required to demonstrate the Rapid Identification of Plankton using Machine Learning Project undertaken by Cefas, The Alan Turing Institute and Plankton Analytics Ltd. GitHub https://github.com/alan-turing-institute/plankton-dsg-challenge</p>

opencc-by-4.0Jun 2022View details →

Enzymatic digestion method development for long-term stored chitinaceous planktonic samples - Data

<table> <tbody> <tr> <td>Data for  Carrillo-Barragán, Priscilla, Heather Sugden, Catherine Scott, and Clare Fitzsimmons. 2022.<br>  “Enzymatic Digestion Method Development for Long-Term Stored Chitinaceous Planktonic Samples.”<br>  Marine Pollution Bulletin. </td> </tr> </tbody> </table>

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Integral projection model results of the planktonic foraminifer Trilobatus sacculifer

<p>Developmental plasticity, where traits change state in response to environmental cues, is well-studied in modern populations. It is also suspected to play a role in macroevolutionary dynamics, but due to a lack of long-term records the frequency of plasticity-led evolution in deep time remains unknown. Populations are dynamic entities, yet their representation in the fossil record is a static snapshot of often isolated individuals. Here, we apply for the first time contemporary integral projection models (IPMs) to fossil data to link individual development with expected population variation. IPMs describe the effects of individual growth in discrete steps on long-term population dynamics. We parameterize the models using modern and fossil data of the planktonic foraminifer <em>Trilobatus sacculifer</em>. Foraminifera grow by adding chambers in discrete stages and die at reproduction, making them excellent case studies for IPMs. Our results predict that somatic growth rates have almost twice as much influence on population dynamics than survival and more than eight times more influence than reproduction, suggesting that selection would primarily target somatic growth as the major determinant of fitness. As numerous palaeobiological systems record growth rate increments in single genetic individuals, and imaging technologies are increasingly available, our results open up the possibility of evidence-based inference of developmental plasticity spanning macroevolutionary dynamics. Given the centrality of ecology in palaeobiological thinking, our model is one approach to help bridge eco-evolutionary scales while directing attention towards the most relevant life-history traits to measure.</p>

opencc-zeroJun 2022View details →

openother-openOct 2022View details →

Planktonic functional diversity changes in synchrony with lake ecosystem state

<p><strong>Abstract</strong></p> <p>Managing ecosystems to effectively preserve function and services requires reliable tools that can infer changes in the stability and dynamics of a system. Conceptually, functional diversity (FD) appears a sensitive and viable monitoring metric stemming from suggestions that FD is a universally important measure of biodiversity and has a mechanistic influence on ecological processes. It is however unclear whether changes in FD consistently occur prior to state responses or vice versa, with no current work on the temporal relationship between FD and state to support a transition towards trait-based indicators. There is consequently a knowledge gap regarding when functioning changes relative to biodiversity change and where FD change falls in that sequence. We therefore examine the lagged relationship between planktonic FD and abundance-based metrics of system state (e.g. biomass) across five highly monitored lake communities using both correlation and cutting edge non-linear empirical dynamic modelling approaches. Overall, phytoplankton and zooplankton FD display synchrony with lake state but each lake is idiosyncratic in the strength of relationship. It is therefore unlikely that changes in plankton FD are identifiable before changes in more easily collected abundance metrics. These results highlight the power of empirical dynamic modelling in disentangling time lagged relationships in complex multivariate ecosystems, but suggest that FD cannot be generically viable as an early indicator. Individual lakes therefore require consideration of their specific context and any interpretation of FD across systems requires caution. However, FD still retains value as an alternative state measure or a trait representation of biodiversity when considered at the system level.</p> <p><strong>Dataset</strong></p> <p>The deposited dataset contains scripts used in functional diversity, cross correlation and convergent cross mapping analysis, the generation of figures and the custom functions underpinning the work. Raw plankton data is not provided but links to publicly available data portals and maintainer contact details are provided.</p>

Рис. 2. Распределение Значений биомассы и численности Macoma balthica по станциЯм отбора проб. Fig. 2. Distribution of the Macoma balthica biomass and abundance values at sampling stations. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer

Рис. 2. Распределение Значений биомассы и численности Macoma balthica по станциЯм отбора проб. Fig. 2. Distribution of the Macoma balthica biomass and abundance values at sampling stations.

Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer

Рис. 4. Распределение станций отбора проб по глубине и типу грунта (круЖком обведены станции, на которых макробентос не обнаруЖен; БО – биогенные остатки, ГМ – галька мелкаЯ, Гр – гравий, И – ил, П – песок). Fig. 4. Distribution of sampling stations by depth and type of bottom sediments (circles are around the stations where no macrobenthos was detected; БО – biogenic residues, ГМ – pebbles, Гр – gravel, И – silt, П – sand).

Рис. 5. АналиЗ линейной коррелЯции параметров макробентоса от доминируюЩей фракции в пробе грунта (А, Б) и глубины (В, Г). Fig. 5. Analysis of the linear correlation of macrobenthos parameters with the dominant fraction in the bottom sample (А, Б) and depth (В, Г). in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer

Рис. 5. АналиЗ линейной коррелЯции параметров макробентоса от доминируюЩей фракции в пробе грунта (А, Б) и глубины (В, Г). Fig. 5. Analysis of the linear correlation of macrobenthos parameters with the dominant fraction in the bottom sample (А, Б) and depth (В, Г).

Рис. 3. Карта-схема пространственного распределениЯ биомассы Macoma balthica. Fig. 3. A schematic map of the spatial distribution of the Macoma balthica biomass. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer

Рис. 3. Карта-схема пространственного распределениЯ биомассы Macoma balthica. Fig. 3. A schematic map of the spatial distribution of the Macoma balthica biomass.

Рис.1. Карта-схема района исследований. ● – станции отбора планктонных и бентосных проб. Fig.1. A schematic map of the studied area. ● – sampling stations. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer

Рис.1. Карта-схема района исследований. ● – станции отбора планктонных и бентосных проб. Fig.1. A schematic map of the studied area. ● – sampling stations.

opencc-by-4.0Dec 2018View details →

Рис. 8. СвяЗь меЖду количеством личинок гребешка в планктоне раЗмером 250–275 мкм и количеством спата на коллекторах. Fig. 8. The relationship between the number of the Japanese scallop larvae in plankton with a size of 250 to 275 µm and the number of spat on collectors. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 8. СвяЗь меЖду количеством личинок гребешка в планктоне раЗмером 250–275 мкм и количеством спата на коллекторах. Fig. 8. The relationship between the number of the Japanese scallop larvae in plankton with a size of 250 to 275 µm and the number of spat on collectors.