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opencc-by-4.0Jan 2016View details →

Fig. 2 in Individual Growth Rates of Nikolsky's Viper, Vipera berus nikolskii (Squamata, Viperidae)

Fig. 2. Individual growth curves of males and females of V. b. nikolskii approximated with the Von Bertalanffy growth curves. Solid lines connect observed SVL of recaptured specimens. Dashed lines represent the Von Bertalanffy growth curve. Horizontal solid line is the size of maturation. X-axis measures time in days, hibernations are indicated by numbers. The duration of active period before first hibernation was assumed to be 45 days; all consecutive active periods lasted 184 days, except for 164 days in adult males.

opencc-by-4.0Jan 2016View details →

Fig. 1 in Individual Growth Rates of Nikolsky's Viper, Vipera berus nikolskii (Squamata, Viperidae)

Fig. 1. Relationship between growth rates (dSVL/dt) and snout-ventral length (SVL) in males and females of V. b. nikolskii. Dashed lines show 95 % confidence intervals of the expected theoretical curve.

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An experimental test of the Growth Rate Hypothesis as a predictive framework for microevolutionary adaptation

<p><span>The growth rate hypothesis (GRH), a central concept of ecological stoichiometry, posits that the relative body phosphorus content of an organism is positively related to somatic growth rate as protein synthesis, which is necessary for growth, requires P-rich rRNA and has strong support at the interspecific level. Here, we explore the use of the GRH to predict microevolutionary responses in consumer body stoichiometry. For this, we subjected zooplankton populations to selection for fast population growth (PGR) in P-rich (HPF) and P-poor (LPF) food environments. With common garden transplant experiments, we demonstrate that in HP populations evolution towards increased PGR was concomitant with an increase in relative phosphorus content. In contrast, LP populations evolved higher PGR without an increase in relative phosphorus content. We conclude that the GRH has the potential to predict microevolutionary change, but that its application is contingent on the environmental context. Our results highlight the potential of cryptic evolution in determining the performance response of populations to elemental limitation of their food resources.</span></p>

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Figure 1 in Comparative biology and growth rate of the two predatory mites, Cydnoseius negevi and Neoseiulus californicus (Acari: Phytoseiidae), reared on two pea cultivars

Figure 1. Age-specific fecundity (mx) and survivorship (lx) of Cydnoseius negevi and Neoseiulus californicus reared on two pea cultivars fed on nymphal stages of Tetranychus urticae at 27 ± 1°C.

Рис. 6. Скорость роста мидий-сеголетков (мм/сутки) подвесного выраЩиваниЯ в б. Миноносок в 1975–1996 гг.: I – За времЯ от оседаниЯ до конца августа – начала сентЯбрЯ, II – За времЯ с начала сентЯбрЯ до середины октЯбрЯ, III – За времЯ с середины октЯбрЯ до конца ноЯбрЯ – начала декабрЯ, IV – За весь летне-осенний период. in Study on the growth rates of mollusks in hanging culture in Possjet Bay (Sea of Japan)

Рис. 6. Скорость роста мидий-сеголетков (мм/сутки) подвесного выраЩиваниЯ в б. Миноносок в 1975–1996 гг.: I – За времЯ от оседаниЯ до конца августа – начала сентЯбрЯ, II – За времЯ с начала сентЯбрЯ до середины октЯбрЯ, III – За времЯ с середины октЯбрЯ до конца ноЯбрЯ – начала декабрЯ, IV – За весь летне-осенний период.

Рис. 4. Скорость роста устриц-сеголетков (мм/сутки) в подвесного выраЩиваниЯ в б. Новгородской в 1975–1989 гг.: I – За времЯ от оседаниЯ до конца августа – начала сентЯбрЯ, II – За времЯ с начала сентЯбрЯ до середины октЯбрЯ, III – За времЯ с середины октЯбрЯ до конца ноЯбрЯ – начала декабрЯ, IV – За весь летне-осенний период. in Study on the growth rates of mollusks in hanging culture in Possjet Bay (Sea of Japan)

Рис. 4. Скорость роста устриц-сеголетков (мм/сутки) в подвесного выраЩиваниЯ в б. Новгородской в 1975–1989 гг.: I – За времЯ от оседаниЯ до конца августа – начала сентЯбрЯ, II – За времЯ с начала сентЯбрЯ до середины октЯбрЯ, III – За времЯ с середины октЯбрЯ до конца ноЯбрЯ – начала декабрЯ, IV – За весь летне-осенний период.

Fig. 3 in Study on the growth rates of mollusks in hanging culture in Possjet Bay (Sea of Japan)

Fig. 3. Growth of young of the under-yearling oysters in hanging culture in Novgorodskaya Bay (Possjet Bay) during 1975–1989.

Fig. 2 in Study on the growth rates of mollusks in hanging culture in Possjet Bay (Sea of Japan)

Fig. 2. Growth rate of the under-yearling scallops (mm/day) of hanging cultivation in Minonosok Bay during 1970–2011: I – the period from settlement to the end of August – early September, II – the period from early September to mid-October, III – the period from mid-October to the end of November – early December, IV – the entire summer-autumn period.

Fig. 1 in Study on the growth rates of mollusks in hanging culture in Possjet Bay (Sea of Japan)

Fig. 1. Growth of the under-yearling scallops in hanging culture in Minonosok Bay (Possjet Bay) during 1970–2011.

Fig. 6 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 6: Maxiumum growth rate determination of all temperatures, light intensities and nutrient concentrations.

Fig. 5 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 5: Entomoneis sp biomass (Chl a, µg /L) under different N/P ratios and light intensities (a) representing growth under T1°C (b) T2°C (c) and T3°C.

Fig. 2 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 2: 3D response surface plot and contour line of Box– Behnken Design showing the mutual effect of temperature and light intensity on chlorophyll a concentration (µg/L) of Entomoneis sp. using an N/P ratio of 11.

Fig. 3 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 3: 3D response surface plot and contour line of Box– Behnken Design showing the mutual effect of temperature and light intensity on growth rate (day-1) of Entomoneis sp. using an N/P ratio of 4.4.

Fig. 4 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 4: 3D response surface plot and contour line of Box– Behnken Design showing the mutual effect of temperature and light intensity on growth rate (day-1) of Entomoneis sp. using an N/P ratio of 27.

opencc-by-4.0Dec 2022View details →

Fig 2 in Growth performance, nutrient utilization and survival rate of Clarias gariepinus fed varied inclusion of processed Moringa oleifera diets

Fig 2: Biweekly growth performance of Clarias gariepinus fed 3% and 5% inclusion of Moringa Processed diets

Fig. 2 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes

Fig. 2. Growth curves of Arcella intermedia and Pyxidicula operculata in the monospecific culture experiments (three replicates each). Dots represent the raw sampled data; colored intervals represent the 95% credibility intervals of cell counts from the Bayesian model fitting.

Fig. S2 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes

Fig. S2. Posterior distributions of the logistic model parameters. The values of K are in cells cm–2, r = d–1. P is the detection probability. P has a fixed range between 0.9 and 1. Color lines represents each one of the single-species experiments, color legend is in the right corner of the figure. A.intermedia experiments are Arc 1, 2 and 3. P.operculata experiments are Pyx 1, 2 and 3.

Fig. S1 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes

Fig. S1. Overview of data collection design. Microcosms are assembled and sampled by a sub- sampling strategy where the organisms are counted by eye. Model adjustment considers both the system dynamics and the sampling level.

Fig. S4. Growth curves for A.intermedia when started the experiment with a in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes

Fig. S4. Growth curves for A.intermedia when started the experiment with a single cell. Color points represents each one of the single-cell experiments, color legend is in the left corner of the figure. Black line correspond to the average growth between experiments.

Fig. S3 in Growth Rate Modulation Enables Coexistence in a Competitive Exclusion Scenario Between Microbial Eukaryotes

Fig. S3. Posterior distributions of the competition model parameters for the species Arcella intermedia (A) and Pyxidicula operculata (P). Each colored line represent one of the replicates of the competition experiment (color legend shown in the last figure). The values of k are in a logarithmic scale of cells cm-2, r are in days–1. aAP is the competition coefficient of the influence of A species on P (Eq. 3), whereas aPA is the competition coefficient of the influence of P on A (Eq. 4).