Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.
We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.
Published models indicate that phytoplankton density, and consequently water quality is dependent on grazing capacity by herbivore zooplankton (small and large Cladocera and Rotifera). Moreover, the top-down eco-force, cascading effect, produced by zooplanktivore fishes, Cyclopoid predator zooplankton, or both, attributes the principal pressure. Nevertheless, complex interactions within the ecosystem have also indicated an impact induced by other factors such as nutrients (bottom-up eco-force), which affect the major food resource of herbivore zooplankton, and grazeable algae (Chlorophyta, Diatoms) density. Temperature significantly affects zooplankton density as well as lake water residence time and Water Level. Two methods of statistical analyzes were utilized aimed at the evaluation of a multivariate comprised ecosystem: Principal Component Analyzes (PCA) and its illustrated plot (Biplot). Results conclusively indicate that zooplanktivore fishes (Sardines) and temperature are the Principal Components. Seclude of isolated single factor as a unique impacting parameter on zooplankton density, either predator Cyclopoida or Sardine fishes is therefore misleading.
MIGAL - Scientific Research Institute in Galilee, Kiryat Shmone, Israel
*Address all correspondence to: gophen@migal.org.il
1. Introduction
Statistical analyzes of Principal Component Analysis (PCA) and Graphical Representation of a PCA that combine both the scores and loading into a single plot (Biplot) was carried out using the software of STATA 17.0-Standard Edition, Statistics and Data Science, Copyright 1985–2021 StataCorp LLC, 4905 Lakeway Drive, 800-STATA-PC, Stata license: Single-user perpetual, Serial number: 401706315938, Licensed to Moshe Gophen, Migal. The life cycle of Copepoda within the zooplankton community comprised 10–12 stages, of which the first five are nauplii and taxonomic definitions were not applied in routine sample analysis. Within the next 10–11 copepodite stages the last four and five stages, as well as adults, were routinely defined as Cyclopoida. Nauplii and 1–3 of copepodite stages are herbivores, while 4–5 copepodite stages and adult copepods (termed as Cyclopoida) are predators. Consequently, the “Copepoda” variable includes all life cycle stages, and “Cyclopoida”—the predator stages. The fish, namely “Sardine” variable, includes two endemic bleak species, the most common fish in Lake Kinneret creating the heaviest predation pressure on zooplankton: Common Hebrew names are Lavnoon Kinneret and Lavnoon lisner and both are “Sardine” variable; Mirogrex terraesanctae and Acanthobrama lissneri. The Lake Kinneret limnological data sources were applied by the Lake Kinneret Data Base and annual reports of the Kinneret Limnological Laboratory, IOLR, 1969–2001. Information about the Sardine (Bleaks) annual harvest was applied by the annual reports of the Department of Fishery, Agriculture Ministry, Lake Kinneret Fishery Unit 1969–2001. Variable list and their units are given in Table 1.
Parameters
Units
Annual means, Rotifera, Cladocera, Copepoda density
G(ww)/m2
Predator Cyclopoida, herbivore Copepeoda
No/L
Small (1–3 neonates) and large (Adult) Cladocera
No/L
Small/large Cladocera ratio
No/L
Diaphanosoma sp., Bosmina spp., Ceridaphnia spp.
No/L
Herbivores zooplankton production
(gC/m2/month)
Herbivores zooplankton grazing
(gC/m2/month)
Chlorophyta, Diatoms, Peridinium density
g(ww)/ m2
TN, TP, TN/TP mass ratio
ppm; Ton
Water Level (annual mean) (higher value = lower level)
mbsl
Primary production
(gC/m2/day)
Annual mean epilimnion temperature
°C
Monthly mean residence time
Year values
Sardine annual harvest
Ton/Year
Annual means, Rotifera, Cladocera, Copepoda density
Eight different variable combinations of the environmental parameters (Table 1) were analyzed by Biplot and PCA and are presented in Figures 1–8.
Figure 1.
Densities (No/L) of total Rotifera plus Cladocera; herbivore Copepoda, predator Copepoda (Cyclopoida), and Sardines (ton, annual harvest).
Figure 2.
Lake load (ton) of total nitrogen (TN), total phosphorus (TP), and TN/TP mass ratio.
Figure 3.
Biomass (g(ww)/m2) of Copepoda, Cladocera and Rotifera.
Figure 4.
Sardine (ton, annual harvest), densities (No/L) of herbivore Copepoda, predator Copepoda (Cyclopoida), total Cladocera; biomass (g(ww)/m2) of Chloropyta and Diatoms, and primary production (gC/m2/day).
Figure 5.
Epilimnion temperature; lake residence time; herbivore grazing, and herbivore production (gC/m2/month).
Figure 6.
Densities (No/L): Small/large Cladocera ratio, all Cladocera, predator Copepoda (Cyclopoida), and Sardine (ton, annual harvest).
Epilimnion temperature, total phytoplankton biomass (g(ww)/m2); lake load (ton) of TN, TP, TN/TP mass ratio; Sardine (ton, annual harvest); densities (No/L) of small/large Cladocera, and Predator Copepoda (Cyclopoida).
Results given in Table 2 indicate a high level of explained variance by the two coordinates of the PCA analysis, as illustrated by the components in Biplots illustrations. Moreover, the higher the number of variables, the lower the value of the Total Explained Variance (V). Moreover, the assumption of top-down eco-force, as grazing capacity by herbivore zooplankton (small and large Cladocera and Rotifera) is just one variable (predator cyclopoids or Sardine fishes) attribution, the explained variance of the grazer’s density is high. Nevertheless, the complexity of interactions within the ecosystem justifies the involvement of surplus other factors which has an impact on the major food resource of herbivore zooplankton, grazeable algae (Chlorophyta, Diatoms) density. Consequently, several environmental factors (Table 2) were involved in PCA/Biplot analyzes (Figures 1–8; Table 1).
PCA/Biplot figure number
Number of variables
Explained variance by component (Coordinate)1
Explained variance by component (Coordinate)2
Total explained variance
1
4
0.6414
0.1866
0.8280
2
5
0.5606
0.1952
0.7558
3
3
0.5620
0.2704
0.8324
4
7
0.3750
0.2450
0.6200
5
4
0.4707
0.3878
0.8585
6
4
0.6015
0.2511
0.8526
7
6
0.5877
0.1855
0.7732
8
8
0.4735
0.1517
0.6252
Table 2.
Number of variables, values of explained variance by coordinate (Component) 1 and 2, and the total explained variance resulted by the PCA/Biplot.
Results of eight PCA analyzes and Biplot illustrations of paired variables that were randomly selected are presented in Figures 1–8. The positive and negative (−) Eigenvector values are given.
The negative (inverse) correlations were sorted into five classes (A) of correlated variables.
A: Lake loads of Nutrients, Lake Water Level, Lake Residence Time, and Epilimnetic Temperature correlated with Zooplankton communities.
B: Lake Nutrient Loads correlated with Phytoplankton.
E: Zooplankton and Phytoplankton correlated with Fish.
A summary of Eigenvectors that were calculated for each paired correlation sorted by class is shown in Table 3.
Class
Mean
SD
%
Minimum
Maximum
n
A
0.3032
0.1399
46
0.0443
0.4603
8
B
0.1997
0.0924
46
0.1182
0.3390
5
C
0.3345
0.2370
71
0.1752
0.6869
4
D
0.3347
0.2137
61
0.0458
0.7275
13
E
0.4492
0.1722
38
0.2212
0.6869
8
Table 3.
The class (A–E) averaged Eigenvalues of the negatively paired correlated (inversely) variables (see Figures 1–8) are summarized as mean class values (mean), SD’s, and its % (%), of the mean, maximum, minimum, and a number of parameters (n) are given.
Results given in Table 3 indicate a high level of data variability, as expressed by the 38–71% range of the SDs from the mean. The high level of variability resulting from these tested paired correlated parameters comprise the structured multivariate communities creating the Kinneret complex interaction system. Seclude of isolated single factor as a unique impacting parameter on zooplankton density, for example, either predator Cyclopoida or Sardine fishes is therefore misleading. Nevertheless, an indication that the Sardine factor, among others including Cyclopoida, is a dominant or principal component is justified.
The study of ecological interactions is commonly disputed between two major concepts: (1) on an individual basis, specifically between one independent environmental parameter in relation to one or several dependent factors [1, 2, 3]. (2) Modeling construction which is supplied by experimental algorithms [4, 5, 6, 7, 8]. The first is limited between one influence and one or more influencers’ parameters. Models are evaluated by experimental algorithms that are changeable and might be therefore flexible. Moreover, an old discussion among limnologists was dedicated to the issue defined as each lake is different as phrased by the popular proverb “But in My Lake” [9]. A relevant case study was carried out in Lake Kinneret during the late 1990s to early 2000s. The lake suffered from overwhelmed zooplankton predation pressure by the most common fish in the lake. A recommendation was submitted to the governmental authorities to subsidize the removal of unwanted sardine fishes aimed at reducing predation pressure from herbivore Cladocera to enhance algal grazing capacity for the improvement of water quality. This recommendation was accepted and several thousand tons of noncommercial Sardines were removed. Nevertheless, besides the significant recovery of the Cladocera community, algal biomass did not decline. The reason was indicated; the reduction of Phosphorus inputs was not considered. The factor that enhanced the outbreak of Cyanobacteria in Lake Kinneret (1994) is not solely nitrogen deficiency [1, 2] or Phosphorus sufficiency [3] and algal biomass reduction is not only reduction of their grazing pressure and the density of herbivore Cladocera is not the only dependent of predator cyclopoids. The ecological instructive lesson given by those few case studies is that in the management of an ecosystem comprised of complex interaction, a multivariate evaluation is required and the PCA and Biplot analysis presented here is the answer. The utilization of the PCA and Biplot method avoidance of manipulated data management was carried out.
PCA analysis represents the directions of the loaded data that explain the maximum amount of variance. Those directions are presented as arrowed lines that capture most of the loaded data. The higher the variance included by a line, the larger the dispersion of the data points along it and the more information it has. This results in a better visibility of the differences between the observations. Moreover, the located position of the arrowed lines also indicates the correlation within variables: Positively correlated – the two tested variables are correlated, increase, or decrease together. Negatively or inversely correlated – among the two tested variables when one increases, the other decreases, and vice versa. The Eigenvector of the covariance matrix is a PCA component. The Eigenvalues are the coefficients attached to the Eigenvector which give the amount of variance carried in each Principal Component. The Eigenvector represents the direction of the axes where there is the most variance information. The Eigenvector value represents the best fit of the line direction, with maximum variance indicated. The Eigenvalue is the number representing the data spread on the line, the Eigenvector. The larger the Eigenvalue the higher the impact on the tested object. Data given in Table 3 consequently indicate a higher impact of Sardine fishes on herbivore zooplankton density than the impact of Cyclopoida predation.
Complex interactions research within a lake ecosystem is therefore an appropriate stimulator for comparative scientific bridging between versatile variables supported by multivariate evaluation. Positive correlations between paired parameters from different disciplines (nutrient, phytoplankton, zooplankton, fishes, physical trait etc.) that were indicated between variables within Figures 1–8 imply another factor such as temperature, mutual predator, or mutual food resource which has a similar impact on the two others. Nevertheless, negative (inverse) correlations between paired parameters from different disciplines (nutrient, phytoplankton, zooplankton, fishes, physical traits, etc.) point to contrasting relations implying environmental significance. The ecosystem’s complex interactions include interfingering through many interlocking processes which partly or completely overlap and therefore confound multifarious structures. The following inverse relations in paired variables with respect to the Figure illustrations were indicated.
PCA analysis of the fish (Sardine) predation predator Cyclopoida impact on the density of herbivore Zooplankton (Figure 1) resulted in two inverse relations:
Sardine vs. densities of herbivore zooplankton (Rotifera plus Cladocera) and Herbivore zooplankton vs. Herbivore Copepods. It is suggested that zooplankton densities are dependent on Sardine biomass. Herbivore Copepoda is in the early life cycle stages of predator Cyclopoida. Therefore, the potential impact of other than fish predation on zooplankton is possible [4, 5, 7, 10, 11, 12, 13, 14, 15]. A principal issue in the complex interaction of freshwater ecosystems is who the dependents of phytoplankton biomass are. Nutrients (bottom-up) or grazers (top-down). During the 1990s, a significant change in the phytoplankton community structure occurred when Cyanobacteria replaced Peridinium as the dominant phytoplankton component. Consequently, the impact of total nitrogen and total phosphorus, as well as lake Water Level fluctuations on Cyanobacteria biomass, was analyzed (Figure 2). Peridinium is not edible to herbivore zooplankton and most of the Cyanobacteria as well. Nevertheless, Chlorophyta and Diatoms are edible and favored by herbivore zooplankton. Consequently, a PCA-Biplot analysis was carried out to evaluate the relations between Cyanobacteria and TP, TN, and Water Levels. Inverse relations of TN vs. TP, Cyanobacteria Biomass vs. TN, Water Level vs. TP, Cyanobacteria biomass vs. TN/TP mass ratio, and Tn/TP Mass ratio vs. Water Level. Earlier studies confirmed that the appearance and fluctuated biomass in Lake Kinneret are dependent on TN and TP lake loads regime and probably not affected by zooplankton grazing [1, 2, 3]. Nevertheless, Water Level is indirectly involved. The lower the level altitude (Figure 2): the higher the numbers below sea level, lake location) is the lower TP load as affected by changes in climate conditions (precipitation and river discharge decline) [8].
Among several prime issues that are predicted to be involved within the Kinneret ecosystem complexed interactions, the optional of intra-community predation was analyzed through biomass (g(ww)/m2) densities of Copepoda, Cladocera, and Rotifera (Figure 3). Copepoda was considered to include predator life cycle stages (Cyclopoida) and Cladocera and Rotifera as herbivores [16, 17, 18, 19, 20, 21, 22, 23]. PCA-Biplot analysis results have indicated a significant impact of the biomass of Copepoda on the biomass of Rotifera and, to a lesser extent, on Cladocera. It probably resulted from an indirect effect of top-down eco-force pressure. Several studies have documented the selectivity of large body size prey by visual predators by fishes (Sardine and young stages Tilapias) in Lake Kinneret. Large body zooplankters are preyed more efficiently [7, 11, 16, 17, 18, 19, 20, 21, 22, 23]. The decline of preferable consumption of large body size predator Cyclopoida was therefore accompanied by enhancement of smaller body size rotifers and, to a lesser extent, young neonates Cladocera [7].
A step forward was carried out as a multivariate internal correlation between phytoplankton, zooplankton, and fish variables (Figure 4):
The following inverse correlations between ecosystem variables were indicated: Between the biomass density of edible algal groups (Chlorophyta plus Diatoms) and Sardine, interpreted that if Sardine biomass was increased, resulted in an intensification of zooplankton predation created, grazing pressure was reduced and consequently algal biomass enhancement; An inverse correlation between the Primary Production (PP) of total Phytoplankton and the density of edible algal and Herbivore life stages of Copepoda (nauplii and copepodite stages) was indicated. Until the mid-1990s the dominant phytoplankter was the un-edible bloom-forming Peridinium and edible algal groups (Chlorophyta and Diatoms) were therefore suppressed. The PP enhancement is due to the Peridinium biomass while edible algal biomass was diminished.
Further inverse correlations were indicated between Sardine biomass vs. Herbivore Copepoda density and the density of Herbivore vs. Predator Copepoda; The density of Rotifera vs. Predator Copepoda; The density of Total Cladocera vs. Herbivore Copepoda.
The density of Total Cladocera vs. Predator Copepoda; The density of Total Cladocera vs. biomass density of edible phytoplankters (Chlorophyta plus Diatoms); Total Cladocera and Primary Production; Herbivore Copepoda density vs. biomass density of edible algal groups (Chlorophyta and Diatoms); Density of Total Cladocera vs. the biomass density of edible algal groups (Chlorophyta and Diatoms); and finally, between the Biomass density of edible algal groups (Chlorophyta and Diatoms) vs. Primary Production reflecting the positive correlation between total biomass and PP of Peridinium dominated Phytoplankton assemblages.
Inverse correlation between lake hydraulic residence time (RT) (years) and epilimnetic temperature vs. metabolic active capacities (grazing and production) of herbivore and predator Cyclopoida zooplankton were identified (Figure 5): Residence Time vs. herbivore grazing capacity (gC/m2/month); Residence Time vs. predator Cyclopoida production.
Herbivores grazing capacity vs. predator Production. It is concluded that these inverse correlations indicate that RT prolongation was accompanied by temperature increase and temperature elevation enhanced metabolic activity of zooplankton (herbivores and predators). Temperature increase is likely a result of RT prolongation as a consequence of seasonality [14, 16, 17, 24, 25, 26, 27, 28].
A critical issue was tested through the correlation between fish (Sardine) biomass and the density of the total number of Cladocera which resulted in significant inverse relations (Figure 6). A low statistical probability of the correlation between the density of the total number of Cladocera and Small/Large Cladocera ratio vs. predator Cyclopoida was also indicated. The correlation between fish biomass and Cladocera density was significantly high. Consequently, the intensive fish predation pressure on Cladocera which is more efficient than that of Cyclopoida pressure on Cladocera was confirmed. The known cascading top-down fish predation pressure accompanied by lower pressure of Cyclopoida on Cladocera is confirmed.
The required sensible step forward is aimed at the test of indicative Cladocera prey selectivity by predator fish or Inverse correlation Cyclopoida as well as an in-between crustacean (Bosmina spp., Ceriodaphnia spp., Diaphanosoma sp., and predator Cyclopoida) community densities (Figure 7). Significant inverse correlations of the following were indicated: Sardine vs. Diaphanosoma sp., Sardine vs. Bosmina spp., Bosmina spp. vs. Diaphanosoma sp., Ceripodaphnia spp. vs. Diaphanosoma sp., and Cyclopoida vs. Bosmina spp. It has to be considered that food item selection by visual attackers zooplantivore fishes is dependent on body size and highly affected by skillful escapability. Therefore, Diaphanosoma and Cyclopoida are better escapers and less vulnerable than Bosmina and Ceriodaphnia. Moreover, the inverse correlation that was indicated between Cyclopoida and Bosmina might be a result of an indirect predation effect where Sardin visual attack predation selectively preyed more Bosmina and Ceriodaphnia while Diaphanosoma and Cyclopoida were therefore enhanced. A microscopical survey confirmed negligible residual fragments of Bosmina in the Cyclopoida gut content [16].
The complexity of the Kinneret ecosystem comprises verified variables of nutrients (TN, TP, TN/TP mass ratio) zooplankton densities, (Cladocera, Small/Large body size ratio of Cladocera, Cyclopoida), phytoplankton biomass distribution, and epilimnetic temperature (Figure 8) are presented as the following inverse correlations: Temperature vs. Phytoplankton, TP, Small/Large; Phytoplankton vs. epilimnetic temperature; Phytoplankton vs. TN, TP, Sardine, Small/Large cladoceran life cycle neonates; TN vs. TP, and Cyclopoida density; TN/TP mass ratio vs. Phytoplankton, TP, Sardine, and Cyclopoida density; Sardine vs. Phytoplankton, TN/TP, and Cyclopoida density; Small/Large of cladoceran life cycle neonates vs. Phytoplankton and TN; and finally Cyclopoida density vs. TN, Small/Large cladoceran life cycle stages ratio.
Those indicated inverse correlations reflect the temporal regular dynamics sequence pattern in the Kinneret ecosystem prior to the outbreak of Cyanobacteria (1994). Starting during the second half of the winter after heavy external inputs of nitrogen (TN) during the winter which induce the formation of the dominant Peridinium bloom creating a high biomass of total phytoplankton which transfer Phosphorus from bottom sediments through the germination of dormant Cysts into free-swimming vegetative cells. Nevertheless, due to the second part of the winter q continuation of a spawning season of the Sardine fishes, their zooplankton predation is not yet enhanced while for the Cladocera it is the optimal season for reproduction resulting in high densities of young neonates and high-value of Small/Large body size ratio [5, 17, 29].
The basic assumption attached to the utilized statistical method of PCA/Biplot was: positive relation between two environmental variables within the complexity of the Kinneret ecosystem are mutual dependents of one or more factors. Whereas, if the correlation is inverse, these two (or more) variables might have an impact of one on the other, and if the correlation is positive, there is an involvement of another variable on both. Moreover, the higher the significance values, the stronger the impact of one on other factors. Paired variables were randomly selected within eight groups of PCA/Biplot analysis. The bilateral impact between two inversed variables maybe done through a third (or more) variable indirectly. An inverse correlation between the Cladocera Bosmina and predator Cyclopoida is created through the selectivity of the predator by Sardine. Seasonal or temporal elevation of epilimnetic temperature and/or climate condition changes clearly affect the metabolic activity and population density of Cladocera, and also nutrient inputs (TN, TP) into lake Kinneret and consequently replacement of dominant algal bloom of Peridinium by Cyanophyta.
Four model types aimed at comprehensive (physical and biological traits) structuring of the ecological complexity of the Kinneret ecosystem were published: (1) The Carbon flow pattern [5]; (2) A numerical simulation of the role of zooplankton in C, N, and P cycling [6]; (3) ECOPATH [4]; and (4) Intraguild predation dynamics based on a coupled Hydrodynamic-Ecological model [7]. Three of the models, 1st–3rd, postited zooplankton predation by Sardine as a solid cardinal status within the Kinneret ecosystem. The 4th model type attributed the major impact of Cyclopoida on zooplankton consumption. The model presented in this paper eliminated the usage of metabolic variables and ecological value algorithms, and the only usage of statistical distribution was evaluated. The level or value ranges of eco-physiological traits were replaced by statistical distribution through the PCA/Biplot method. Conclusively, the present study is in agreement with conclusions presented by 1–3 models and disagrees with the conclusion of model No. 4. Nevertheless, this conclusive summary does not confound the side effect of other variables on the distribution of zooplankton communities in Lake Kinneret.
Sincere appreciation is given to staff members and field data assistance of Kinneret Limnological Laboratory, Israel Oceanographic and Limnological Research Co. Ltd., Haifa, Lake Kinneret Data Base, and The Kinneret and Drainage Authority for availabilities of Data records, Migal-scientific research Institute in the Galilee, and Mekorot Water Co. Ltd.
The data presented in this study are available on request from the corresponding author.
References
1.Gophen M, Smith VH, Nishri A, Threlkeld ST. Nitrogen deficiency, phosphorus sufficiency, and the invasion of lake Kinneret, Israel, by N2-fixing cyanobacterium Aphanizomenon ovalisporum. Aquatic Sciences. 1999;1:1-14
2.Smith VH. Low nitrogen to phosphorus ratios favor dominance by blue-green algae in lake phytoplankton. Science. 1983;221:669-671
3.Hadas O, Pinkas R, Malinsky-Rushansky N, Nishri A, Kaplan A, Rimmer A, et al. Appearance and establishment of diazotrophic cyanobacteria in lake Kinneret, Israel. Freshwater Biology. 2012;57:1214-1227. DOI: 10.1111/j.1365-2427.2012.02792.x
4.Walline P, Pizanty S, Gophen M, Berman T. The Ecosystem of Lake Kinneret, Israel. ICES.C.M. 1990/L:39. 1990. pp. 1-8
5.Serruya C, Gophen M, Pollingher U. Lake Kinneret: Carbon flow patterns and ecosystem management. Archiv für Hydrobiologie. 1980;88(3):265-302
6.Bruce LC, Hamilton D, Imberger J, Gal G, Gophen M, Zohary T, et al. A numerical simulation of the role of zooplankton in C, N, and P, cycling in lake Kinneret, Israel. Ecological Modelling. 2006;193(2006):412-436
7.Makler V, Hipsey MR, Zohary T, Carmel Y, Gal G. Intraguild predation dynamics in a lake ecosystem based on a coupled hydrodynamic-ecological model: The example of lake Kinneret (Israel); MDPI. Biology. 2017, 2017;6(2):22
8.Gal G, Skerianer M, Tanasova N. Fluctuations in water level and the dynamics of zooplankton: A data driven modeling approach. Freshwater Biolgy. 2013;58(4):800-816. DOI: 10.1111/fwb.12087
9.Carpenter SR, editor. Complex Interactions in Lake Communities. New York Berlin Heildelberg London Tokyo: Springer-Verlag; 1988. 283 p
10.Gophen M. The impact of zooplankton status on the management of lake Kinneret (Israel). Hydrobiologia. 1984;113:249-258
11.Gophen M. Effect of fish predation on size class distribution of Cladocerans in lake Kinneret. Verhandlungen des Internationalen Verein Limnologie. 1985;22:3104-3108
12.Gophen M. Fisheries management in lake Kinneret (Israel). In: Proc. Annual Meeting North American Lake Management Society; 13-16 November 1985; Lake Geneva, Wisconsin, USA. Wisconsin: NALMS; 1986. pp. 327-332
13.Gophen M, Scharf A. Food and feeding habits of Mirogrex fingerlings in lake Kinneret (Israel). Hydrobiologia. 1981;78(1):3-9
14.Gophen M, Serruya S. Fish predation effects on plankton community structure in large lakes: Lake Kinneret. In: Tilzer M, Serruya C, editors. Ecological Structure and Function in Large Lakes. Berlin, Heidelberg, New-York, London, Paris, Tokyo, Hong Kong: Springer-Verlag; 1990. pp. 645-660
15.Easton J, Gophen M. Trophic relations between zooplankton and bleaks (Acanthobrama spp.) in lake Kinneret (Israel). Verhandlungen des Internationalen Verein Limnologie. 2002;28:1258-1261
16.Gophen M. The metabolism of adult Mesocyclops leuckarti populations in lake Kinneret (Israel) during 1969-1978. Verhandlungen des Internationalen Verein Limnologie. 1981a;21:1568-1572
17.Gophen M. Metabolic activity of herbivorous zooplankton in lake Kinneret (Israel) during 1972-1977. Journal of Plankton Research. 1981b;3(1):15-24
18.Gophen M. Population dynamics of cyclopoid copepods in lake Kinneret (Israel). Open Journal of Modern Hydrology. 2016;6:212-221
19.Gophen M, Threlkeld ST. An experimental study of zooplankton consumption by the lake Kinneret Sardine. Archiv für Hydrobiologie. 1989;115:91-95
20.Drenner RW, Vinyard GL, Gophen M, McComas SR. Feeding behaviour of the cichlid Sarotherodon galilaeus selective predation on lake Kinneret Zooplankton. Hydrobiologia. 1982;87(1):17-20
21.Drenner RW, Hambright JD, Vinyard GL, Gophen M, Pollingher U. Experimental study of size-selective phytoplankton grazing by a filter-feeding cichlid and the cichlid's effects on plankton community structure. Limnology and Oceanography. 1987;32(5):1138-1144
22.Vinyard GL, Drenner RW, Gophen M, Pollingher U, Winkleman DL, Hambright KD. An experimental study of the plankton community impacts of two filter-feeding cichlids, the Galilee Saint Peter's fish (Sarotherodon galilaeus) and blue tilapia (Tilapia aurea). Canadian Journal of Fisheries and Aquatic Sciences. 1988;45(4):685-690
23.Landau R, Gophen M, Walline P. Larval Mirogrex terraesanctae (Cyprinidae) of lake Kinneret (Israel): Growth rate, plankton selectivities, consumption rates and interactions with rotifers. Hydrobiologia. 1988;169:91-106
24.Gophen M. Food and feeding habits of Mesocyclops leuckarti (Claus) in lake Kinneret (Israel). Freshwater Biology. 1977;7:513-518
25.Gophen M. Artemia nauplii as a food source for cyclopoids: Extrapolation of experimental measurements to the metabolic activities of copepods in lake Kinneret, Israel. In: Persoone G, Sorgeloos P, Roels O, Jaspers E, editors. The Brine Shrimp Artemia. Vol. 3. Wetteren, Belgium: Universe Press; 1980. pp. 67-76
26.Gophen M, Azoulay B. The trophic status of zooplankton communities in lake Kinneret (Israel). Verhandlungen des Internationalen Verein Limnologie. 2002;28:836-839
27.Gophen M, Serruya S, Spataru P. Zooplankton community changes in lake Kinneret (Israel), during 1969-1985. In: Biro P, Talling JF, editors. Trophic Relationships in Inland Waters. Hydrobiologia. Vol. 191. 1990. pp. 39-46
28.Gophen M, Serruya S. Effects of fish and nutrients on long-term changes of plankton communities in lake Kinneret (Israel). In: Schroder JH, Bauer J, Schartl M, editors. Trends in Ichthyology, an International Perspectives. Munich: Blackwell Scientific Publications; 1993. pp. 371-380
29.Gophen M. Long-term changes of plankton-communities in lake Kinneret, Israel. Asian Fisheries Science. 1992;5:291-302
Written By
Moshe Gophen
Submitted: 28 June 2024Reviewed: 28 June 2024Published: 25 July 2024
Open access peer-reviewed chapter - ONLINE FIRST
