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Comparative analysis shows that bacterivory, not viral lysis, controls the abundance of heterotrophic prokaryotic plankton
FEMS Microbiology Ecology 32 (2000) 157^165
www.fems-microbiology.org
Comparative analysis shows that bacterivory, not viral lysis, controls the abundance of heterotrophic prokaryotic plankton Carlos Pedro¨s-Alio¨ *, Juan I. Caldero¨n-Paz, Josep M. Gasol Departament de Biologia Marina i Oceanogra¢a, Institut de Cie©ncies del Mar, CSIC, Passeig Joan de Borbo¨ s/n, 08039 Barcelona, Spain Received 29 January 2000; accepted 16 March 2000
Abstract Empirical models derived from literature data were used to compare the factors controlling prokaryotic abundance (PN) and prokaryotic heterotrophic production (PHP) in solar salterns. These empirical relationships were generated as multiple linear regressions with PN or PHP as dependent variables, while the independent variables were chosen to reflect the likely sources of organic matter, inorganic nutrients and temperature. These variables were then measured in solar salterns and the predictions made by the general relationships were compared to actual saltern values of PN and PHP. Saltern ponds of salinity higher than 100x departed significantly from the general relationships, while the ponds of salinity lower than 100x fitted well within the range of values predicted by the general models. The most likely explanation for the discrepancy of the former was the absence of bacterivory. This hypothesis was tested with data from other very different aquatic systems: karstic lakes with anaerobic hypolimnia and two marine areas in the Mediterranean and the Southern Ocean. The anoxic regions of karstic lakes departed significantly from the predictions of the general model, while the oxic layers conformed to the predictions. As in the case of salterns, this difference could be explained by the presence of significant predation in the oxic, but not in the anoxic, layers of these lakes. Finally, two marine areas with similar predation pressure on prokaryotes but very different impacts of viral lysis were tested. In all cases, PN values conformed to the predictions, suggesting that lysis due to viruses is not the main factor controlling PN in aquatic systems, which is more likely to be determined by the balance between bacterivory and resource supply. The present work also demonstrates the usefulness of empirical comparative analyses to generate predictions and to draw inferences on the functioning of microbial communities. ß 2000 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved. Keywords : Multiple regression analysis; Solar saltern; Bacterivory; Viral lysis; Bacterial abundance
1. Introduction The current paradigm of planktonic food webs recognizes the fundamental role played by heterotrophic prokaryotes [1]. However, the mechanisms determining the actual values of abundance and activity of prokaryotes remain controversial and may be di¡erent in di¡erent ecosystems [2^5]. Accumulation of data from a wide range of aquatic ecosystems during the past two decades has allowed the comparative study of their microbial food webs [6^11]. One approach to this comparative study is the establishment of empirical relationships between prokaryotic plankton parameters and other environmental variables judged to be relevant. Thus, a relationship between bacterial abundance and chlorophyll a [8] is believed
* Corresponding author. Tel. : +34 (93) 221 6416; Fax: +34 (93) 221 7340; E-mail : [email protected]
to re£ect the strength of the link between bacteria and autotrophic phytoplankton across a wide range of aquatic systems. Likewise, a relationship between bacterial heterotrophic production and primary production has been found to be signi¢cant across systems [6]. These empirical relationships, usually in the form of linear regressions between exponentially transformed variables, de¢ne a region of probable values within the universe of all possible values [9]. One of the main weaknesses of this approach is that the addition of data from new environments may change the relationships. In fact, most data have been gathered in the ecosystems most accessible to well funded research institutions (see for example Fig. 1 in [10]). The whole range of existing systems with di¡erent values for the microbial parameters has not been explored. Outliers are usually eliminated from the analysis, for example, Lake Elmenteita was excluded from the regression by Bird and Kal¡ [8]. In fact, the Lake Elmenteita data on bacterial abundance [12] provided the ¢rst clue that hypersaline lakes might be
0168-6496 / 00 / $20.00 ß 2000 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved. PII: S 0 1 6 8 - 6 4 9 6 ( 0 0 ) 0 0 0 2 6 - X
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di¡erent from other more `common' systems. This impression was con¢rmed by the studies of Finlay et al. [13] and recently by Zinabu and Taylor [14], who found a regression line between bacterial abundance and chlorophyll a with a slope signi¢cantly di¡erent from those in the literature. To establish the limits of the general models and to check their underlying assumptions the outliers may be actually very helpful. We decided to use hypersaline ecosystems in a way analogous to that in which mutants are used in physiology: by studying systems in which a given function is impaired or exacerbated, the mechanisms regulating the `common' functioning of the system can be better appreciated. In this work we used the ponds in solar salterns as such outlier ecosystems. The analysis of salterns in a previous paper [15] suggested that predation was the most important cause for the di¡erences in prokaryotic plankton abundance in salterns versus other systems. Here we use karstic lakes to con¢rm this suggestion in a very di¡erent kind of environment, where water layers with and without predation were present. Finally, we use two di¡erent marine areas with a low and high impact of viral lysis to see whether this factor could also be important in regulating prokaryotic abundance (PN) in aquatic systems. Throughout the paper we use the terms prokaryotic plankton and prokaryotic heterotrophic production (PHP) instead of bacterioplankton and bacterial heterotrophic production. This is to recognize the presence and abundance of Archaea in all the sytems analyzed. Archaea are known to make up to 20% of the prokaryotic count in the ocean [16], between 80 and 90% in the high salinity
ponds in the salterns (Anto¨n and Rosello¨-Mora, personal communication) and they have been shown to be present in the anaerobic layers of karstic lakes [17]. However, we retain the term `bacterivory' for convenience, since any other alternative would be unnecessarily cumbersome and confusing. 2. Materials and methods 2.1. Environments studied The environments studied are summarized in Table 1. The data from salterns are those published in Pedro¨s-Alio¨ et al. [15] and Oren [18,19]. These salterns are formed by a series of connected ponds. As water evaporates and salinity increases, water is pumped or gravity-fed to the next pond, such that the salinity in each particular pond is kept, within narrow limits, essentially constant. The ponds provide a range of salinities from that of seawater to sodium chloride precipitation. Each pond can thus be considered at equilibrium and the biota in any given pond as a well-adapted and established community for that particular salinity. We have previously shown that viral lysis has a relatively low impact throughout the gradient [20]. Predation on prokaryotes, on the other hand, has a very strong impact at lower salinities, but completely disappears above 250x salinity [15]. The karstic lakes analyzed are located in two Spanish regions: Banyoles (Girona) and Cuenca. Information about these systems can be found in Miracle et al. [21]
Table 1 Some characteristics of the aquatic systems included in the analysis System
Salterns Bras del Port La Trinitat Eilat Karstic lakes Lake Ciso¨ Lake Vilar La Cruz El Tobar Arcas-2 El Tejo Open marine areas Mediterranean Sea Southern Ocean
Coordinates
Depth (m)
latitude
longitude
38³12PN 40³35PN 29³N
0³36PW 0³41PE 35³E
91 91 91
42³8PN 42³8PN 39³59PN 40³33PN 39³59PN 39³59PN
2³45PE 2³45PE 1³52PW 2³3PW 2³8PW 1³52PW
8 9 24 19.5 14.5 11
40^42³N 2^6³E 62^65³S 59^66³W
9 2000 9 4000
Temperature (³C)
Prokaryotic abundance (cells ml31 )
30
106 ^108
7^20
106 ^107
14^20 31.8/+4
104 ^105 104 ^105
Bacterivory
Viral lysis
Comments
Refs.
high to absent high to absent not determined
not determined lowa not determined not determined not determined not determined not determined not determined not determined not determined
[15] [15] [18,19] sul¢de in hypolimnion [37] anaerobic holomictic unpub.c biogenic meromictic unpub.c crenogenic meromictic unpub.c biogenic meromictic unpub.c aerobic holomictic unpub.c aerobic holomictic unpub.c
below detectione very highg
oligotrophic cold
high salinity
high to lowb
moderated moderatef
a
[20]. [23^25]. c Caldero¨n-Paz, J.I. (1997) Ph.D. thesis, University of Barcelona. d Marrase¨, C. and Vaque¨, D., in preparation. e [28]. f [29]. g [30]. b
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Fig. 1. Conceptual model for the analysis of the factors determining the abundance and production of prokaryotic plankton. The variables that were included in the regression analysis are shown in boldface. See text for explanation.
and Pedro¨s-Alio¨ and Guerrero [22]. They are characterized by a very sharp strati¢cation that separates an oxic epilimnion from an anoxic hypolimnion. While predation on prokaryotes is relatively high in the oxic layers [23,24], it is much reduced in the anoxic layers [24,25]. The Mediterranean waters studied show strong vertical strati¢cation in the summer. The chlorophyll vertical pro¢le presents a deep maximum slightly above the thermocline [26]. We studied a transect o¡shore from Barcelona in June 1995 (boreal summer) that covered three di¡erent zones [27] : the coastal area on the Continental Shelf (depth 6 200 m), the Shelf break frontal area (depth 500^1500 m) and the deep open sea area (depth V2000 m). Predation in this area was moderate (Marrase¨ and Vaque¨, in preparation), while viral activity was below detection limits [28]. The area studied in the Southern Ocean included stations within the Gerlache and Brans¢eld Straits as well as in the Bellinghausen Sea, south of Drake Passage. The study was carried out during December 1995 (austral summer) and each zone had di¡erent strati¢cation characteristics as well as chlorophyll a concentrations. Bacterivory was uniformly moderate [29], while viral impact was rather high [30]. The conceptual model used is shown in Fig. 1. Prokaryotic plankton abundance is the result of a balance between loss factors (mostly viral lysis, bacterivory, advection and sedimentation) and PHP. PHP, in turn, is a consequence of the available carbon and physicochemical parameters such as temperature and inorganic nutrients that probably impose an upper limit to production. Finally, the available organic carbon depends on inputs from phytoplankton (represented by chlorophyll in the model), allochthonous carbon inputs and other sources, such as sloppy feeding or excretion by herbivores. We screened the literature for papers with values for as many variables as possible to build quantitative relationships able to predict prokaryotic
plankton abundance and production from the values of the other variables. We built multiple linear regressions with the choice of dependent and independent variables based on the model in Fig. 1. The predictions could then be compared to actual values in particular aquatic systems in which one of the factors was missing (for example predation in the high salinity ponds). We expected this analysis to reveal the relative importance of di¡erent factors in determining the actual values of prokaryotic plankton abundance and production. 2.2. Construction of the general relationships A database of microbial parameters in di¡erent aquatic systems was gathered from the literature (Caldero¨n-Paz, 1997, Ph.D. thesis, University of Barcelona). This database was created by combining those of White et al. [7] and Vaque¨ et al. [31], eliminating those studies in which some of the essential variables (PN and PHP, temperature and chlorophyll a) were missing and those where PHP was measured with techniques other than leucine or thymidine incorporation. In total, the database included 705 data points from 53 di¡erent studies. To compare values from systems with very di¡erent depths (from deep sea to shallow salterns), values for the respective photic zones were integrated and divided by the depth of integration, thus giving a weighted average of the photic zone. The values in this database were then used to derive general relationships between prokaryotic biomass and production and variables such as temperature and chlorophyll concentration. The purpose was to obtain empirical equations from the data gathered in many di¡erent systems that would re£ect the relationships shown in Fig. 1. Except for temperature, all the other variables were logarithmically transformed to satisfy the criteria of normality and homogeneity of variances required by regression analysis. Simple and multiple linear regressions were carried out by the
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Table 2 Simple linear regressions (Y = a+bX) for the prediction of PN and PHP Y Marine systems PHP PHP PN PN PN Freshwater systems PHP PHP PN PN PN
X
n
a þ 95% C.I.
b þ 95% C.I.
r2
Temperature Chla Temperature Chla PHP
244 94 244 90 244
30.10 þ 0.13 0.93 þ 0.09 30.20 þ 0.11 0.16 þ 0.03 30.32 þ 0.05
0.07 þ 0.01 0.34 þ 0.13 0.02 þ 0.01 0.24 þ 0.04 0.45 þ 0.05
0.52 0.22 0.11 0.61 0.55
Temperature Chla Temperature Chla PHP
242 143 217 159 201
0.60 þ 0.14 1.10 þ 0.15 0.24 þ 0.08 0.18 þ 0.08 0.12 þ 0.07
0.05 þ 0.01 0.38 þ 0.16 0.02 þ 0.01 0.36 þ 0.09 0.34 þ 0.05
0.33 0.13 0.21 0.27 0.50
All variables except temperature have been logarithmically transformed. Units for each variable are: PN = U109 cells l31 ; PHP = Wg C l31 day31 ; Chla = Wg l31 ; temperature = ³C. 95% C.I.: 95% con¢dence interval; n = number of cases used in the regression. All regressions were highly signi¢cant (P 6 0.001).
least squares method (model I), using backward elimination for the multiple regressions [32]. 3. Results 3.1. General models 3.1.1. Simple regressions Given that PN and PHP are commonly found to covary with chlorophyll a concentration (Chla) [6,8] and temperature [7], we ¢rst inspected the univariant relationships between these variables (Table 2). As expected, all these relationships were signi¢cant as were those of PN and PHP for both freshwater and marine systems. In marine data, PN was better related to Chla (r2 = 0.61) than to temperature (r2 = 0.11) while PHP was better correlated to temperature than to Chla (Table 2). In freshwater, however, both PN and PHP were better correlated to temperature than to Chla. PN and PHP covaried with an r2 of 0.5^0.6. 3.1.2. Multiple regressions Multiple regressions for marine and freshwater systems turned out to be signi¢cantly di¡erent (with an analysis of covariance). Following Billen et al. [4], Ducklow [5] and
White et al. [7], we expected PHP to depend on the sources of organic matter (with Chla as a surrogate variable) and temperature. PN was expected to depend on PHP as possibly modulated by temperature. Two empirical relationships were derived to predict bacterial abundance and production from other variables in marine systems (Table 3): Log10 PHP = 0.07Temp+0.40 Log10 Chla (r2 = 0.82, P 6 0.001, n = 94) Log10 PN = 30.18+0.58 Log10 PHP30.02Temp (r2 = 0.65, P 6 0.001, n = 249) where PN is the prokaryotic number in 109 cells per liter, PHP is the prokaryotic heterotrophic production in Wg C l31 day31 , Temp is temperature in ³C and Chla is chlorophyll a concentration in Wg l31 . The data obtained in our own studies of salterns and marine regions were then compared to these regressions. The regressions for the freshwater systems were (Table 3): Log10 PHP = 0.23+0.05Temp+0.43 Log10 Chla (r2 = 0.59, P 6 0.001, n = 133) Log10 PN = 0.04 Log10 PHP+0.01Temp+0.1 Log10 Chla (r2 = 0.70, P 6 0.001, n = 140) Note that for freshwater systems, PN was also a function of Chla.
Table 3 Multiple linear regressions (Y = a+bx1 X1 +T+bxn Xn ) for predictions of PN and PHP in marine and freshwater systems Predictions of PHP Freshwater Marine Predictions of PN Freshwater Marine
n
a
bTemp
bChla
bPHP
L1
L2
L3
Adjusted r2
133 94
0.23 þ 0.09 ^
0.05 þ 0.01 0.07 þ 0.00
0.43 þ 0.06 0.40 þ 0.04
^ ^
0.66 0.64
0.39 0.27
^ ^
0.59 0.82
140 249
^ 30.18 þ 0.04
0.01 þ 0.00 30.02 þ 0.00
0.10 þ 0.03 ^
0.32 þ 0.04 0.58 þ 0.03
0.15 30.32
0.15 ^
0.64 0.99
0.70 0.65
L1 , L2 and L3 are the standardized partial coe¤cients for variables Temp, Chla and PHP, respectively. All variables except temperature have been logarithmically transformed. Units for each variable are: PN = U109 cells l31 ; PHP = Wg C l31 day31 ; Chla = Wg l31 ; temperature = ³C. All regressions were highly signi¢cant (P 6 0.001).
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3.2. Comparison of data from the salterns to the general relationships To simplify comparisons of microbiological parameters with other marine and freshwater systems, we assigned individual ponds in the salterns to one of four groups according to their salinities. These groups correspond to the four biological domains established by Ort|¨-Cabo et al. [33] and Rodr|¨guez-Valera [34]: salinity less than 100x, salinity between 100 and 200x, salinity between 200 and 300x and salinity greater than 300x. The means and standard errors for each group were calculated for every variable measured [15]. These values could then be compared to the general relationships derived from the literature data. Fig. 2 shows the general relationship between PN and Chla (Fig. 2A) and between PHP and Chla (Fig. 2B). In addition to the regression line obtained with values from the literature, the 95% con¢dence limits for the prediction of the dependent variable are also shown as discontinuous lines. The length of the lines indicates the range of values of each variable that were included in the calculations that generated the regression (the data available in the data set). Values of Chla for the four saltern groups fell within the range of values in the database. Both PHP and PN values, however, were clearly above the highest value in the database (Fig. 2A,B). Thus, the extreme physico-chem-
Fig. 2. Linear regressions between PN (in 109 cells l31 , A) and PHP (in Wg C l31 day31 , B) with respect to Chla (in Wg l31 ). The continuous line is the regression derived from literature data. The discontinuous lines indicate the 95% con¢dence limits. The lines are drawn only across the range of values existing in the database used to derive the regressions. The ponds in the solar salterns have been grouped according to their salinity (see text). Mean values and standard errors within each group of ponds are shown.
Fig. 3. A: Linear regression between PHP (in Wg C l31 day31 ) and temperature (in ³C). B: Linear regression between PN (in 109 cells l31 ) and PHP (in Wg C l31 day31 ). The continuous lines are the regressions derived from literature data. The discontinuous lines indicate the 95% con¢dence limits. The lines are drawn only across the range of values existing in the database used to derive the regressions. The ponds in the solar salterns have been grouped according to their salinity (see text). Mean values and standard errors within each group of ponds are shown.
ical conditions of the salterns seem to result in very high values of PHP and extremely high values of PN with moderate values of Chla, in comparison to the `common' systems included in the database. Fig. 3A shows the relationship between PHP and temperature. Values of PHP fell within the 95% con¢dence interval of the predictive equation. This relationship, therefore, was not di¡erent in salterns and in more common systems. Likewise, the relationship between PN and PHP (Fig. 3B) correctly predicted the values found in the salterns, even though the PN values were much higher than any of those in the database. Values of PN and PHP could also be compared to the predictions from the multiple linear regressions derived from the literature data. Since showing graphs similar to those for simple linear regressions (e.g. those in Figs. 2 and 3) would require complex three dimensional graphs, we have plotted the residual of each value (that is, the di¡erence between the actual value and its prediction) against its prediction (Figs. 4 and 5). The 95% con¢dence limits for the predictions of Y are shown as discontinuous lines. A residual equal to zero (point on the horizontal continuous line) means that the prediction from the multiple regression model is perfect. This implies that the relationships between the variables used in the regression are similar in the tested and in the `common' systems. As
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were signi¢cantly larger than the values predicted by the relationship. This implies that the relationships among the variables involved in the regression (PN as dependent variable and PHP and temperature as independent variables) were di¡erent in salterns from those in most marine systems. Fig. 4B shows the equivalent graph for the prediction of PHP. Again, the values of PHP found in the salterns were larger than the largest values in the database. PHP values showed a tendency to be higher than their predictions (all were above the zero line). The residuals, however, were within the 95% con¢dence interval and, therefore, the predictions were very close to the actual values in all the saltern ponds. Thus, despite the di¡erence in absolute values, the relationships among the variables involved in the regression (PHP as dependent variable and temperature and Chla as independent variables) were not signi¢cantly di¡erent from those in most marine systems. Since the simple linear regression between PHP and chlorophyll a signi¢cantly underestimated PHP in the salterns (Fig. 2B), it must be concluded that temperature was the factor determining these high PHP values in comparison to `common' systems. This conclusion is in accordance with the relationship between PHP and temperature shown in Fig. 3A. 3.3. Comparison of data from the karstic lakes and marine areas to the general relationships Fig. 4. Comparison of values of PN (in 109 cells l31 , A) and PHP (in Wg C l31 day31 , B) from the salterns with their predictions from the general relationships (multiple linear regressions) derived from literature data. Symbols as in Figs. 2 and 3. In C, the same plot as in A is shown for values from the study of the Eilat salterns in Israel (data from [19,20]).
residuals move away from the zero line, the predictions become less accurate. Finally, if the residuals are beyond the 95% con¢dence lines, the prediction is signi¢cantly di¡erent from the actual values and, thus, it can be concluded that the relationships among the variables involved in the regression model are fundamentally di¡erent in the test and in the `common' systems. In Fig. 4A the predictions of PN are shown. First, all values were beyond the range of values used to derive the general relationship (they were all to the right of the discontinuous lines). This re£ects that PN in the salterns was much larger than in other marine systems. Second, the residuals for the lowest salinity ponds were within the 95% con¢dence interval and they were relatively small. This indicates that the ponds with salinities lower than 100x showed similar relationships among PN on the one hand, and temperature and PHP on the other. Finally, the residuals for the three groups of ponds with salinities higher than 100x were signi¢cantly beyond the upper 95% con¢dence limit. Thus, the actual values of PN
Fig. 5 shows the same kind of graph as Fig. 4 for the karstic lakes and the two marine areas studied. In the case of the karstic lakes the regression line used was that built with data from freshwater systems only (Table 3). Epilimnetic samples showed residuals that were not signi¢cantly di¡erent from zero (Fig. 5A). Hypolimnetic samples, on the contrary, had residuals above the 95% con¢dence limit. Metalimnetic samples were sometimes above and sometimes below the 95% con¢dence limit, re£ecting the intermediate characteristics of these layers between the oxic and anoxic layers of the lakes. The holomixis sample for Lake Ciso¨ was above the limit. This is in accordance with the fact that the whole lake is anoxic during holomixis [22]. The values of abundance for the two marine areas (northwestern Mediterranean and Southern Ocean) were at the lower range of the values in the database (Fig. 5B). However, they were all within the 95% con¢dence interval of their predictions. 4. Discussion One way to compare the microbial food webs of di¡erent systems is through the use of empirical models [4^11]. These models combine the data from a large number of di¡erent studies and look for trends in the relationships between variables. For example, there is a signi¢cant rela-
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Fig. 5. A: Comparison of values of PN (in 109 cells l31 ) from di¡erent karstic lakes with their predictions from the general relationships (multiple linear regressions) derived from literature data. Numbers indicate each lake: 1, Lake Ciso¨ ; 2, Lake Vilar ; 3, Lake La Cruz; 4, Lake Tobar and 5, Lake Lagunillo del Tejo. B: Comparison of values of PN (in 109 cells l31 ) from two marine areas: the northwestern Mediterranean Sea during June 1995 (¢lled symbols) and the Southern Ocean during December 1995 (empty triangles) with their predictions from the general relationship.
tionship between bacterial number and Chla when data from many di¡erent systems are combined and a regression is performed. This signi¢cant regression re£ects a relationship between the two variables and, thus, a characteristic of microbial communities. Although we assume that the relationship should be `causal', the models do not require this to be the case. If data from a particular system, such as the solar salterns, are compared to this general relationship, they may either conform to the regression or show statistically signi¢cant di¡erences. In the former case, one may reason that the relationships between the examined variables are the same in the particular system and in the systems used to derive the regression. In the latter case, however, the relationship should be di¡erent. In the case of PN (Fig. 4A) the group of ponds with salinities lower than 100x showed values very close to the predictions provided by temperature and PHP. Thus, we conclude that the mechanisms regulating PN in these ponds are the same as those in most marine systems. All the other ponds, however, showed values signi¢cantly higher than predicted ones, indicating the existence of differences in the regulation of PN. The PN in any given system is the result of a balance between growth and losses. In most marine systems, and in the lower salinity group of ponds, the ¢nal balance is the same. However, in
163
the ponds with salinities higher than 100x the balance is displaced towards higher biomass values. This could be the result of either faster growth or lower loss factors. Since growth rates are actually slower in the ponds with higher salinities [15], the reason for this higher abundance must be the reduced losses. We have shown that viral impact is low throughout the salinity gradient [20]. Therefore, viral lysis cannot be the reason for these changes as salinity increases. Bacterivory, on the other hand, is much reduced or non-existent in the ponds with higher salinities [15] and, therefore, it seems to be the main factor responsible for the di¡erences between salterns and other systems. Even though values of PHP were always higher than their predictions (Fig. 4B), these di¡erences were never signi¢cant: sometimes the residuals were very close to the upper 95% con¢dence limit but always below it. Therefore, we must conclude that the main factors regulating PHP are temperature and resource supply (as represented by Chla), just as in most other environments. Thus, salinity per se does not play a role in limiting PHP. The organisms present at each salinity, therefore, must be well adapted to the ambient salinity. To see whether these results were also true for salterns in other parts of the world, we screened the literature for other studies of solar salterns. Only the work of Oren [18,19] in the Eilat salterns (Israel) included the necessary variables. Unfortunately, there was no data for Chla and, thus, the comparison could only be carried out with the prediction of PN, but not with that of PHP. The ponds in Oren's study were assigned to the same four groups of salinity and the multiple linear regression was used to obtain predictions of prokaryotic number. As can be seen in Fig. 4C the results were exactly as those found in the two Spanish salterns : the ponds with salinities lower than 100x had very low residuals and all the other ponds were above the upper 95% con¢dence limit. Thus, the pattern found is fairly robust, since it has been found in three di¡erent solar salterns. To con¢rm the importance of bacterivory in determining the PN in aquatic systems, it had to be shown that other environments without bacterivory also had abundances larger than the predictions. Small karstic lakes in northeastern Spain are ideal systems to test this point. These lakes tend to be sharply strati¢ed [21]. While the epilimnion is oxic and has a microbial food web similar to that of most other lakes, the hypolimnion is generally anoxic with large concentrations of sul¢de. This alters many characteristics of the microbial community. Bacteria tend to be more abundant and greater in size [25,35] and the abundance of bacterivores is clearly reduced [25]. As a consequence, the impact of bacterivory on the prokaryotic assemblage is low [25]. If our contention that bacterivory is the most important factor determining PN were true, the predictions from the general relationships should fall within the 95% con¢dence interval for the oxic layers and
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outside this interval for the anoxic layers of these lakes. This is exactly what happened (Fig. 5A): all the epilimnetic samples were within the 95% con¢dence interval and all the hypolimnetic samples were beyond the upper 95% con¢dence interval. It could be argued that some other di¡erence between epilimnetic and hypolimnetic samples (such as light regime for example) was the cause of these di¡erent results. However, when Lake Ciso¨ mixes in the winter it becomes completely anoxic [22] and the microbial community for the whole water column is very similar to that of the anoxic hypolimnion during strati¢cation [24]. In accordance with our hypothesis the holomixis samples from Lake Ciso¨ were also beyond the upper 95% con¢dence limit (Fig. 5A). As a ¢nal test of our hypothesis, we checked the possible in£uence of di¡erent degrees of viral lysis on the abundance of prokaryotic plankton. Part of the behavior of our data could have been the result of concerted changes in the importance of viral lysis. This loss factor has been shown to account for up to 50% of total bacterioplankton losses in a coastal environment [36]. However, we have found viral lysis to be very low in the salterns [20]. Thus, we decided to test systems with high and low impact of viral lysis but similar values of bacterivory. The northwestern Mediterranean and some areas of the Southern Ocean were shown to have moderate but similar levels of bacterivory (Marrase¨ and Vaque¨, in preparation, [29]). The former system was sampled during a cruise in June 1995 and viral lysis was found to be undetectable [28]. The water in the Gerlache and Brans¢eld Straits was sampled during a cruise in December 1995 and viral lysis was found to be more important than bacterivory in all stations where both values could be compared directly. In fact, viral lysis accounted for 50^100% of PHP in di¡erent stations [30]. If viral lysis were an important factor determining PN, we should see di¡erences between these two systems in their respective residual plots. As can be seen in Fig. 5B, all the samples were within the 95% con¢dence interval and, therefore, viral lysis was not an important factor in the determination of the balance between prokaryotic growth and losses that ultimately determines the PN found in any given aquatic system. In conclusion, the method of comparing results from a given system to an empirically derived general relationship seems to be very e¡ective in revealing whether the functioning of the microbial food web is similar to that of other systems. The analysis of `peculiar' systems such as the solar salterns clearly identi¢ed bacterivory as the factor responsible for maintaining the PN values usually found in `common' systems such as the coastal oceans. Conversely, this analysis showed that PHP was regulated by the same factors (temperature and resource supply) as in most `common' aquatic environments. The analysis of karstic lakes and two di¡erent marine zones con¢rmed that bacterivory and not viral lysis is the main factor de-
termining the actual abundance of prokaryotic plankton in a wide range of aquatic systems. Acknowledgements This study was supported by DGICYT Grant PB950222-C02-01. We thank Mr. Juan Duch from INFOSA SA and Mr. Miguel Cuervo-Arango for permission to work at La Trinitat and Bras del Port salterns, respectively. We thank Paul A. White for access to the data of [7]. J.I.C.-P. was a recipient of a scholarship from the Generalitat de Catalunya.
References [1] Azam, F., Fenchel, T., Field, J.G., Gray, J.S., Meyer-Reil, L.-A. and Thingstad, F. (1983) The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257^263. [2] Wright, R.R. (1988) Methods for evaluating the interaction of substrate and grazing as factors controlling planktonic bacteria. Arch. Hydrobiol. Beih. Ergeb. Limnol. 31, 229^242. [3] Pace, M.L. (1988) Bacterial mortality and the fate of bacterial production. Hydrobiologia 159, 41^50. [4] Billen, G., Servais, P. and Becquevort, S. (1990) Dynamics of bacterioplankton in oligotrophic and eutrophic aquatic environments: bottom-up or top-down control ? Hydrobiologia 207, 37^42. [5] Ducklow, H.W. (1992) Factors regulating bottom-up control of bacterial biomass in ocean plankton communities. Arch. Hydrobiol. Beih. Ergeb. Limnol. 37, 207^217. [6] Cole, J.J., Findlay, S. and Pace, M.L. (1988) Bacterial production in fresh and saltwater ecosystems : a cross-system overview. Mar. Ecol. Prog. Ser. 43, 1^10. [7] White, P.A., Kal¡, J., Rasmussen, J.B. and Gasol, J.M. (1991) The e¡ect of temperature and algal biomass on bacterial production and speci¢c growth rate in freshwater and marine habitats. Microb. Ecol. 21, 99^118. [8] Bird, D.F. and Kal¡, J. (1984) Empirical relationship between bacterial abundance and chlorophyll concentration in fresh and marine waters. Can. J. Fish. Aquat. Sci. 41, 1015^1023. [9] Peters, R.H. (1991) A Critique for Ecology. Cambridge University Press, Cambridge, MA. [10] Pedro¨s-Alio¨, C. and Guerrero, R. (1991) Abundance and activity of bacterioplankton in warm lakes. Verh. Int. Ver. Limnol. 24, 1212^ 1219. [11] Gasol, J.M. and Duarte, C.M. (2000) Comparative analysis in aquatic microbial ecology: how far do they go? FEMS Microbiol. Ecol., in press. [12] Kilham, P. (1981) Pelagic bacteria: extreme abundances in African soda lakes. Naturwissenschaften 68, 380^381. [13] Finlay, B., Curds, C.R., Bamforth, S.S. and Bamfort, J.M. (1987) Ciliated protozoa and other microorganisms from two African soda lakes (Lake Nakuru and Lake Simbi, Kenya). Arch. Protistenkd. 133, 81^91. [14] Zinabu, G.M. and Taylor, W.D. (1997) Bacteria^chlorophyll relationships in Ethiopian lakes of varying salinity : are soda lakes di¡erent? J. Plankton Res. 19, 647^654. [15] Pedro¨s-Alio¨, C., Caldero¨n-Paz, J.I., MacLean, M.H., Medina, G., Marrase¨, C., Gasol, J.M. and Guixa-Boixereu, N. (2000) The microbial food web along salinity gradients. FEMS Microbiol. Ecol. 32, 143^155. [16] Massana, R., Taylor, L.T., Murray, A.E., Wu, K.Y., Je¡rey, W.H.
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[17]
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and DeLong, E.F. (1998) Vertical distribution and temporal variation of marine planktonic archaea in the Gerlache Strait, Antarctica, during early spring. Limnol. Oceanogr. 43, 607^617. Casamayor, E.O., Scha«fer, H., Ban¬eras, L., Pedro¨s-Alio¨, C. and Muyzer, G. (2000) Identi¢cation and spatio-temporal di¡erences of microbial assemblages of two neighboring sulfureous lake : a comparison of microscopy and DGGE. Appl. Environ. Microbiol. 66, in press. Oren, A. (1990) Thymidine incorporation in saltern ponds of di¡erent salinities: estimation of in situ growth rates of halophilic archaeobacteria and eubacteria. Microb. Ecol. 19, 43^51. Oren, A. (1990) The use of protein synthesis inhibitors in the estimation of the contribution of halophilic archaebacteria to bacterial activity in hypersaline environments. FEMS Microbiol. Ecol. 73, 187^ 192. Guixa-Boixereu, N., Caldero¨n-Paz, J.I., Heldal, M., Bratbak, G. and Pedro¨s-Alio¨, C. (1996) Viral lysis and bacterivory as prokaryotic loss factors along a salinity gradient. Aquat. Microb. Ecol. 11, 215^227. Miracle, M.R., Vicente, E. and Pedro¨s-Alio¨, C. (1992) Biological studies of Spanish meromictic and strati¢ed lakes. Limnetica 8, 59^ 77. Pedro¨s-Alio¨, C. and Guerrero, R. (1993) Microbial ecology in Lake Ciso¨. Adv. Microb. Ecol. 13, 155^209. Ju«rgens, K., Gasol, J.M., Massana, R. and Pedro¨s-Alio¨, C. (1994) Control of heterotrophic bacteria and protozoans by Daphnia pulex in the epilimnion of Lake Ciso¨. Arch. Hydrobiol. 131, 55^78. Massana, R., Garc|¨a-Cantizano, J. and Pedro¨s-Alio¨, C. (1996) Components, structure and £uxes of the microbial food web in a small, strati¢ed lake. Aquat. Microb. Ecol. 11, 279^288. Massana, R. and Pedro¨s-Alio¨, C. (1994) Role of anaerobic ciliates in planktonic food webs: abundance, feeding and impact on bacteria in the ¢eld. Appl. Environ. Microbiol. 60, 1325^1334. Estrada, M. (1996) Primary production in the northwestern Mediterranean. Sci. Mar. 60 ((Suppl. 2)), 55^64. Pedro¨s-Alio¨, C., Caldero¨n-Paz, J.I., Guixa-Boixereu, N., Estrada, M. and Gasol, J.M. (1999) Bacterioplankton and phytoplankton biomass and production during summer strati¢cation in the northwestern Mediterranean Sea. Deep-Sea Res. 46, 985^1019.
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[28] Guixa-Boixereu, N., Gasol, J.M., Vaque¨, D. and Pedro¨s-Alio¨, C. (1999) Distribution of viruses and their potential e¡ect on bacterioplankton in an oligotrophic marine system. Aquat. Microb. Ecol. 19, 205^213. [29] Vaque¨, D., Gasol, J.M., Guixa-Boixereu, N. and Pedro¨s-Alio¨, C. (2000) Heterotrophic protist biomass and bacterivory during spring and summer 1995^96 in Antarctic ecosystems. Deep-Sea Res. II, in press. [30] Guixa-Boixereu, N., Vaque¨, D., Gasol, J.M., Sa¨nchez-Ca¨mara, J. and Pedro¨s-Alio¨, C. (2000) Viral distribution and activity in Antarctic waters. Deep-Sea Res. II, in press. [31] Vaque¨, D., Gasol, J.M. and Marrase¨, C. (1993) Grazing rates on bacteria: the signi¢cance of methodology and ecological factors. Mar. Ecol. Prog. Ser. 109, 263^274. [32] Sokal, R.R. and Rohlf, J. (1995) Biometry. The Principles and Practice of Statistics in Biological Research, 3rd edn. Freeman, New York. [33] Ort|¨-Cabo, F., Pueyo Mur, J.J. and Truc, G. (1984) Las salinas mar|¨timas de Santa Pola (Alicante, Espan¬a). Breve introduccio¨n al estudio de un medio natural controlado por sedimentacio¨n evapor|¨tica somera. Rev. Invest. Geol. 38/39, 9^29. [34] Rodr|¨guez-Valera, F. (1988) Characteristics and microbial ecology of hypersaline environments. In: Halophilic Bacteria (Rodr|¨guez-Valera, F., Ed.), Vol. I, pp. 3^30. CRC Press, Boca Raton, FL. [35] Cole, J.J., Pace, M.L., Caraco, N.F. and Steinhart, G.J. (1993) Bacterial biomass and cell size distributions in lakes: more and larger cells in anoxic waters. Limnol. Oceanogr. 38, 1627^1632. [36] Fuhrman, J.A. (1995) Viruses and protists cause similar bacterial mortality in coastal seawater. Limnol. Oceanogr. 40, 1236^1242. [37] Garc|¨a-Cantizano, J., Caldero¨n-Paz, J.I. and Pedro¨s-Alio¨, C. (1994) Thymidine incorporation in Lake Ciso¨ : problems in estimating bacterial secondary production across oxic-anoxic interfaces. FEMS Microbiol. Ecol. 14, 53^64. [38] Pedro¨s-Alio¨, C., Vaque¨, D., Guixa-Boixereu, N. and Gasol, J.M. (2000) Bacterioplankton biomass and heterotrophic production in western Brans¢eld Strait, southern Drake Passage and Gerlache Strait, Antarctica, during the 1995^96 Austral summer. Deep-Sea Res. II, in press.
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Comparative analysis shows that bacterivory, not viral lysis, controls the abundance of heterotrophic prokaryotic plankton Carlos Pedro¨s-Alio¨ *, Juan I. Caldero¨n-Paz, Josep M. Gasol Departament de Biologia Marina i Oceanogra¢a, Institut de Cie©ncies del Mar, CSIC, Passeig Joan de Borbo¨ s/n, 08039 Barcelona, Spain Received 29 January 2000; accepted 16 March 2000
Abstract Empirical models derived from literature data were used to compare the factors controlling prokaryotic abundance (PN) and prokaryotic heterotrophic production (PHP) in solar salterns. These empirical relationships were generated as multiple linear regressions with PN or PHP as dependent variables, while the independent variables were chosen to reflect the likely sources of organic matter, inorganic nutrients and temperature. These variables were then measured in solar salterns and the predictions made by the general relationships were compared to actual saltern values of PN and PHP. Saltern ponds of salinity higher than 100x departed significantly from the general relationships, while the ponds of salinity lower than 100x fitted well within the range of values predicted by the general models. The most likely explanation for the discrepancy of the former was the absence of bacterivory. This hypothesis was tested with data from other very different aquatic systems: karstic lakes with anaerobic hypolimnia and two marine areas in the Mediterranean and the Southern Ocean. The anoxic regions of karstic lakes departed significantly from the predictions of the general model, while the oxic layers conformed to the predictions. As in the case of salterns, this difference could be explained by the presence of significant predation in the oxic, but not in the anoxic, layers of these lakes. Finally, two marine areas with similar predation pressure on prokaryotes but very different impacts of viral lysis were tested. In all cases, PN values conformed to the predictions, suggesting that lysis due to viruses is not the main factor controlling PN in aquatic systems, which is more likely to be determined by the balance between bacterivory and resource supply. The present work also demonstrates the usefulness of empirical comparative analyses to generate predictions and to draw inferences on the functioning of microbial communities. ß 2000 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved. Keywords : Multiple regression analysis; Solar saltern; Bacterivory; Viral lysis; Bacterial abundance
1. Introduction The current paradigm of planktonic food webs recognizes the fundamental role played by heterotrophic prokaryotes [1]. However, the mechanisms determining the actual values of abundance and activity of prokaryotes remain controversial and may be di¡erent in di¡erent ecosystems [2^5]. Accumulation of data from a wide range of aquatic ecosystems during the past two decades has allowed the comparative study of their microbial food webs [6^11]. One approach to this comparative study is the establishment of empirical relationships between prokaryotic plankton parameters and other environmental variables judged to be relevant. Thus, a relationship between bacterial abundance and chlorophyll a [8] is believed
* Corresponding author. Tel. : +34 (93) 221 6416; Fax: +34 (93) 221 7340; E-mail : [email protected]
to re£ect the strength of the link between bacteria and autotrophic phytoplankton across a wide range of aquatic systems. Likewise, a relationship between bacterial heterotrophic production and primary production has been found to be signi¢cant across systems [6]. These empirical relationships, usually in the form of linear regressions between exponentially transformed variables, de¢ne a region of probable values within the universe of all possible values [9]. One of the main weaknesses of this approach is that the addition of data from new environments may change the relationships. In fact, most data have been gathered in the ecosystems most accessible to well funded research institutions (see for example Fig. 1 in [10]). The whole range of existing systems with di¡erent values for the microbial parameters has not been explored. Outliers are usually eliminated from the analysis, for example, Lake Elmenteita was excluded from the regression by Bird and Kal¡ [8]. In fact, the Lake Elmenteita data on bacterial abundance [12] provided the ¢rst clue that hypersaline lakes might be
0168-6496 / 00 / $20.00 ß 2000 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved. PII: S 0 1 6 8 - 6 4 9 6 ( 0 0 ) 0 0 0 2 6 - X
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di¡erent from other more `common' systems. This impression was con¢rmed by the studies of Finlay et al. [13] and recently by Zinabu and Taylor [14], who found a regression line between bacterial abundance and chlorophyll a with a slope signi¢cantly di¡erent from those in the literature. To establish the limits of the general models and to check their underlying assumptions the outliers may be actually very helpful. We decided to use hypersaline ecosystems in a way analogous to that in which mutants are used in physiology: by studying systems in which a given function is impaired or exacerbated, the mechanisms regulating the `common' functioning of the system can be better appreciated. In this work we used the ponds in solar salterns as such outlier ecosystems. The analysis of salterns in a previous paper [15] suggested that predation was the most important cause for the di¡erences in prokaryotic plankton abundance in salterns versus other systems. Here we use karstic lakes to con¢rm this suggestion in a very di¡erent kind of environment, where water layers with and without predation were present. Finally, we use two di¡erent marine areas with a low and high impact of viral lysis to see whether this factor could also be important in regulating prokaryotic abundance (PN) in aquatic systems. Throughout the paper we use the terms prokaryotic plankton and prokaryotic heterotrophic production (PHP) instead of bacterioplankton and bacterial heterotrophic production. This is to recognize the presence and abundance of Archaea in all the sytems analyzed. Archaea are known to make up to 20% of the prokaryotic count in the ocean [16], between 80 and 90% in the high salinity
ponds in the salterns (Anto¨n and Rosello¨-Mora, personal communication) and they have been shown to be present in the anaerobic layers of karstic lakes [17]. However, we retain the term `bacterivory' for convenience, since any other alternative would be unnecessarily cumbersome and confusing. 2. Materials and methods 2.1. Environments studied The environments studied are summarized in Table 1. The data from salterns are those published in Pedro¨s-Alio¨ et al. [15] and Oren [18,19]. These salterns are formed by a series of connected ponds. As water evaporates and salinity increases, water is pumped or gravity-fed to the next pond, such that the salinity in each particular pond is kept, within narrow limits, essentially constant. The ponds provide a range of salinities from that of seawater to sodium chloride precipitation. Each pond can thus be considered at equilibrium and the biota in any given pond as a well-adapted and established community for that particular salinity. We have previously shown that viral lysis has a relatively low impact throughout the gradient [20]. Predation on prokaryotes, on the other hand, has a very strong impact at lower salinities, but completely disappears above 250x salinity [15]. The karstic lakes analyzed are located in two Spanish regions: Banyoles (Girona) and Cuenca. Information about these systems can be found in Miracle et al. [21]
Table 1 Some characteristics of the aquatic systems included in the analysis System
Salterns Bras del Port La Trinitat Eilat Karstic lakes Lake Ciso¨ Lake Vilar La Cruz El Tobar Arcas-2 El Tejo Open marine areas Mediterranean Sea Southern Ocean
Coordinates
Depth (m)
latitude
longitude
38³12PN 40³35PN 29³N
0³36PW 0³41PE 35³E
91 91 91
42³8PN 42³8PN 39³59PN 40³33PN 39³59PN 39³59PN
2³45PE 2³45PE 1³52PW 2³3PW 2³8PW 1³52PW
8 9 24 19.5 14.5 11
40^42³N 2^6³E 62^65³S 59^66³W
9 2000 9 4000
Temperature (³C)
Prokaryotic abundance (cells ml31 )
30
106 ^108
7^20
106 ^107
14^20 31.8/+4
104 ^105 104 ^105
Bacterivory
Viral lysis
Comments
Refs.
high to absent high to absent not determined
not determined lowa not determined not determined not determined not determined not determined not determined not determined not determined
[15] [15] [18,19] sul¢de in hypolimnion [37] anaerobic holomictic unpub.c biogenic meromictic unpub.c crenogenic meromictic unpub.c biogenic meromictic unpub.c aerobic holomictic unpub.c aerobic holomictic unpub.c
below detectione very highg
oligotrophic cold
high salinity
high to lowb
moderated moderatef
a
[20]. [23^25]. c Caldero¨n-Paz, J.I. (1997) Ph.D. thesis, University of Barcelona. d Marrase¨, C. and Vaque¨, D., in preparation. e [28]. f [29]. g [30]. b
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Fig. 1. Conceptual model for the analysis of the factors determining the abundance and production of prokaryotic plankton. The variables that were included in the regression analysis are shown in boldface. See text for explanation.
and Pedro¨s-Alio¨ and Guerrero [22]. They are characterized by a very sharp strati¢cation that separates an oxic epilimnion from an anoxic hypolimnion. While predation on prokaryotes is relatively high in the oxic layers [23,24], it is much reduced in the anoxic layers [24,25]. The Mediterranean waters studied show strong vertical strati¢cation in the summer. The chlorophyll vertical pro¢le presents a deep maximum slightly above the thermocline [26]. We studied a transect o¡shore from Barcelona in June 1995 (boreal summer) that covered three di¡erent zones [27] : the coastal area on the Continental Shelf (depth 6 200 m), the Shelf break frontal area (depth 500^1500 m) and the deep open sea area (depth V2000 m). Predation in this area was moderate (Marrase¨ and Vaque¨, in preparation), while viral activity was below detection limits [28]. The area studied in the Southern Ocean included stations within the Gerlache and Brans¢eld Straits as well as in the Bellinghausen Sea, south of Drake Passage. The study was carried out during December 1995 (austral summer) and each zone had di¡erent strati¢cation characteristics as well as chlorophyll a concentrations. Bacterivory was uniformly moderate [29], while viral impact was rather high [30]. The conceptual model used is shown in Fig. 1. Prokaryotic plankton abundance is the result of a balance between loss factors (mostly viral lysis, bacterivory, advection and sedimentation) and PHP. PHP, in turn, is a consequence of the available carbon and physicochemical parameters such as temperature and inorganic nutrients that probably impose an upper limit to production. Finally, the available organic carbon depends on inputs from phytoplankton (represented by chlorophyll in the model), allochthonous carbon inputs and other sources, such as sloppy feeding or excretion by herbivores. We screened the literature for papers with values for as many variables as possible to build quantitative relationships able to predict prokaryotic
plankton abundance and production from the values of the other variables. We built multiple linear regressions with the choice of dependent and independent variables based on the model in Fig. 1. The predictions could then be compared to actual values in particular aquatic systems in which one of the factors was missing (for example predation in the high salinity ponds). We expected this analysis to reveal the relative importance of di¡erent factors in determining the actual values of prokaryotic plankton abundance and production. 2.2. Construction of the general relationships A database of microbial parameters in di¡erent aquatic systems was gathered from the literature (Caldero¨n-Paz, 1997, Ph.D. thesis, University of Barcelona). This database was created by combining those of White et al. [7] and Vaque¨ et al. [31], eliminating those studies in which some of the essential variables (PN and PHP, temperature and chlorophyll a) were missing and those where PHP was measured with techniques other than leucine or thymidine incorporation. In total, the database included 705 data points from 53 di¡erent studies. To compare values from systems with very di¡erent depths (from deep sea to shallow salterns), values for the respective photic zones were integrated and divided by the depth of integration, thus giving a weighted average of the photic zone. The values in this database were then used to derive general relationships between prokaryotic biomass and production and variables such as temperature and chlorophyll concentration. The purpose was to obtain empirical equations from the data gathered in many di¡erent systems that would re£ect the relationships shown in Fig. 1. Except for temperature, all the other variables were logarithmically transformed to satisfy the criteria of normality and homogeneity of variances required by regression analysis. Simple and multiple linear regressions were carried out by the
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Table 2 Simple linear regressions (Y = a+bX) for the prediction of PN and PHP Y Marine systems PHP PHP PN PN PN Freshwater systems PHP PHP PN PN PN
X
n
a þ 95% C.I.
b þ 95% C.I.
r2
Temperature Chla Temperature Chla PHP
244 94 244 90 244
30.10 þ 0.13 0.93 þ 0.09 30.20 þ 0.11 0.16 þ 0.03 30.32 þ 0.05
0.07 þ 0.01 0.34 þ 0.13 0.02 þ 0.01 0.24 þ 0.04 0.45 þ 0.05
0.52 0.22 0.11 0.61 0.55
Temperature Chla Temperature Chla PHP
242 143 217 159 201
0.60 þ 0.14 1.10 þ 0.15 0.24 þ 0.08 0.18 þ 0.08 0.12 þ 0.07
0.05 þ 0.01 0.38 þ 0.16 0.02 þ 0.01 0.36 þ 0.09 0.34 þ 0.05
0.33 0.13 0.21 0.27 0.50
All variables except temperature have been logarithmically transformed. Units for each variable are: PN = U109 cells l31 ; PHP = Wg C l31 day31 ; Chla = Wg l31 ; temperature = ³C. 95% C.I.: 95% con¢dence interval; n = number of cases used in the regression. All regressions were highly signi¢cant (P 6 0.001).
least squares method (model I), using backward elimination for the multiple regressions [32]. 3. Results 3.1. General models 3.1.1. Simple regressions Given that PN and PHP are commonly found to covary with chlorophyll a concentration (Chla) [6,8] and temperature [7], we ¢rst inspected the univariant relationships between these variables (Table 2). As expected, all these relationships were signi¢cant as were those of PN and PHP for both freshwater and marine systems. In marine data, PN was better related to Chla (r2 = 0.61) than to temperature (r2 = 0.11) while PHP was better correlated to temperature than to Chla (Table 2). In freshwater, however, both PN and PHP were better correlated to temperature than to Chla. PN and PHP covaried with an r2 of 0.5^0.6. 3.1.2. Multiple regressions Multiple regressions for marine and freshwater systems turned out to be signi¢cantly di¡erent (with an analysis of covariance). Following Billen et al. [4], Ducklow [5] and
White et al. [7], we expected PHP to depend on the sources of organic matter (with Chla as a surrogate variable) and temperature. PN was expected to depend on PHP as possibly modulated by temperature. Two empirical relationships were derived to predict bacterial abundance and production from other variables in marine systems (Table 3): Log10 PHP = 0.07Temp+0.40 Log10 Chla (r2 = 0.82, P 6 0.001, n = 94) Log10 PN = 30.18+0.58 Log10 PHP30.02Temp (r2 = 0.65, P 6 0.001, n = 249) where PN is the prokaryotic number in 109 cells per liter, PHP is the prokaryotic heterotrophic production in Wg C l31 day31 , Temp is temperature in ³C and Chla is chlorophyll a concentration in Wg l31 . The data obtained in our own studies of salterns and marine regions were then compared to these regressions. The regressions for the freshwater systems were (Table 3): Log10 PHP = 0.23+0.05Temp+0.43 Log10 Chla (r2 = 0.59, P 6 0.001, n = 133) Log10 PN = 0.04 Log10 PHP+0.01Temp+0.1 Log10 Chla (r2 = 0.70, P 6 0.001, n = 140) Note that for freshwater systems, PN was also a function of Chla.
Table 3 Multiple linear regressions (Y = a+bx1 X1 +T+bxn Xn ) for predictions of PN and PHP in marine and freshwater systems Predictions of PHP Freshwater Marine Predictions of PN Freshwater Marine
n
a
bTemp
bChla
bPHP
L1
L2
L3
Adjusted r2
133 94
0.23 þ 0.09 ^
0.05 þ 0.01 0.07 þ 0.00
0.43 þ 0.06 0.40 þ 0.04
^ ^
0.66 0.64
0.39 0.27
^ ^
0.59 0.82
140 249
^ 30.18 þ 0.04
0.01 þ 0.00 30.02 þ 0.00
0.10 þ 0.03 ^
0.32 þ 0.04 0.58 þ 0.03
0.15 30.32
0.15 ^
0.64 0.99
0.70 0.65
L1 , L2 and L3 are the standardized partial coe¤cients for variables Temp, Chla and PHP, respectively. All variables except temperature have been logarithmically transformed. Units for each variable are: PN = U109 cells l31 ; PHP = Wg C l31 day31 ; Chla = Wg l31 ; temperature = ³C. All regressions were highly signi¢cant (P 6 0.001).
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3.2. Comparison of data from the salterns to the general relationships To simplify comparisons of microbiological parameters with other marine and freshwater systems, we assigned individual ponds in the salterns to one of four groups according to their salinities. These groups correspond to the four biological domains established by Ort|¨-Cabo et al. [33] and Rodr|¨guez-Valera [34]: salinity less than 100x, salinity between 100 and 200x, salinity between 200 and 300x and salinity greater than 300x. The means and standard errors for each group were calculated for every variable measured [15]. These values could then be compared to the general relationships derived from the literature data. Fig. 2 shows the general relationship between PN and Chla (Fig. 2A) and between PHP and Chla (Fig. 2B). In addition to the regression line obtained with values from the literature, the 95% con¢dence limits for the prediction of the dependent variable are also shown as discontinuous lines. The length of the lines indicates the range of values of each variable that were included in the calculations that generated the regression (the data available in the data set). Values of Chla for the four saltern groups fell within the range of values in the database. Both PHP and PN values, however, were clearly above the highest value in the database (Fig. 2A,B). Thus, the extreme physico-chem-
Fig. 2. Linear regressions between PN (in 109 cells l31 , A) and PHP (in Wg C l31 day31 , B) with respect to Chla (in Wg l31 ). The continuous line is the regression derived from literature data. The discontinuous lines indicate the 95% con¢dence limits. The lines are drawn only across the range of values existing in the database used to derive the regressions. The ponds in the solar salterns have been grouped according to their salinity (see text). Mean values and standard errors within each group of ponds are shown.
Fig. 3. A: Linear regression between PHP (in Wg C l31 day31 ) and temperature (in ³C). B: Linear regression between PN (in 109 cells l31 ) and PHP (in Wg C l31 day31 ). The continuous lines are the regressions derived from literature data. The discontinuous lines indicate the 95% con¢dence limits. The lines are drawn only across the range of values existing in the database used to derive the regressions. The ponds in the solar salterns have been grouped according to their salinity (see text). Mean values and standard errors within each group of ponds are shown.
ical conditions of the salterns seem to result in very high values of PHP and extremely high values of PN with moderate values of Chla, in comparison to the `common' systems included in the database. Fig. 3A shows the relationship between PHP and temperature. Values of PHP fell within the 95% con¢dence interval of the predictive equation. This relationship, therefore, was not di¡erent in salterns and in more common systems. Likewise, the relationship between PN and PHP (Fig. 3B) correctly predicted the values found in the salterns, even though the PN values were much higher than any of those in the database. Values of PN and PHP could also be compared to the predictions from the multiple linear regressions derived from the literature data. Since showing graphs similar to those for simple linear regressions (e.g. those in Figs. 2 and 3) would require complex three dimensional graphs, we have plotted the residual of each value (that is, the di¡erence between the actual value and its prediction) against its prediction (Figs. 4 and 5). The 95% con¢dence limits for the predictions of Y are shown as discontinuous lines. A residual equal to zero (point on the horizontal continuous line) means that the prediction from the multiple regression model is perfect. This implies that the relationships between the variables used in the regression are similar in the tested and in the `common' systems. As
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were signi¢cantly larger than the values predicted by the relationship. This implies that the relationships among the variables involved in the regression (PN as dependent variable and PHP and temperature as independent variables) were di¡erent in salterns from those in most marine systems. Fig. 4B shows the equivalent graph for the prediction of PHP. Again, the values of PHP found in the salterns were larger than the largest values in the database. PHP values showed a tendency to be higher than their predictions (all were above the zero line). The residuals, however, were within the 95% con¢dence interval and, therefore, the predictions were very close to the actual values in all the saltern ponds. Thus, despite the di¡erence in absolute values, the relationships among the variables involved in the regression (PHP as dependent variable and temperature and Chla as independent variables) were not signi¢cantly di¡erent from those in most marine systems. Since the simple linear regression between PHP and chlorophyll a signi¢cantly underestimated PHP in the salterns (Fig. 2B), it must be concluded that temperature was the factor determining these high PHP values in comparison to `common' systems. This conclusion is in accordance with the relationship between PHP and temperature shown in Fig. 3A. 3.3. Comparison of data from the karstic lakes and marine areas to the general relationships Fig. 4. Comparison of values of PN (in 109 cells l31 , A) and PHP (in Wg C l31 day31 , B) from the salterns with their predictions from the general relationships (multiple linear regressions) derived from literature data. Symbols as in Figs. 2 and 3. In C, the same plot as in A is shown for values from the study of the Eilat salterns in Israel (data from [19,20]).
residuals move away from the zero line, the predictions become less accurate. Finally, if the residuals are beyond the 95% con¢dence lines, the prediction is signi¢cantly di¡erent from the actual values and, thus, it can be concluded that the relationships among the variables involved in the regression model are fundamentally di¡erent in the test and in the `common' systems. In Fig. 4A the predictions of PN are shown. First, all values were beyond the range of values used to derive the general relationship (they were all to the right of the discontinuous lines). This re£ects that PN in the salterns was much larger than in other marine systems. Second, the residuals for the lowest salinity ponds were within the 95% con¢dence interval and they were relatively small. This indicates that the ponds with salinities lower than 100x showed similar relationships among PN on the one hand, and temperature and PHP on the other. Finally, the residuals for the three groups of ponds with salinities higher than 100x were signi¢cantly beyond the upper 95% con¢dence limit. Thus, the actual values of PN
Fig. 5 shows the same kind of graph as Fig. 4 for the karstic lakes and the two marine areas studied. In the case of the karstic lakes the regression line used was that built with data from freshwater systems only (Table 3). Epilimnetic samples showed residuals that were not signi¢cantly di¡erent from zero (Fig. 5A). Hypolimnetic samples, on the contrary, had residuals above the 95% con¢dence limit. Metalimnetic samples were sometimes above and sometimes below the 95% con¢dence limit, re£ecting the intermediate characteristics of these layers between the oxic and anoxic layers of the lakes. The holomixis sample for Lake Ciso¨ was above the limit. This is in accordance with the fact that the whole lake is anoxic during holomixis [22]. The values of abundance for the two marine areas (northwestern Mediterranean and Southern Ocean) were at the lower range of the values in the database (Fig. 5B). However, they were all within the 95% con¢dence interval of their predictions. 4. Discussion One way to compare the microbial food webs of di¡erent systems is through the use of empirical models [4^11]. These models combine the data from a large number of di¡erent studies and look for trends in the relationships between variables. For example, there is a signi¢cant rela-
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Fig. 5. A: Comparison of values of PN (in 109 cells l31 ) from di¡erent karstic lakes with their predictions from the general relationships (multiple linear regressions) derived from literature data. Numbers indicate each lake: 1, Lake Ciso¨ ; 2, Lake Vilar ; 3, Lake La Cruz; 4, Lake Tobar and 5, Lake Lagunillo del Tejo. B: Comparison of values of PN (in 109 cells l31 ) from two marine areas: the northwestern Mediterranean Sea during June 1995 (¢lled symbols) and the Southern Ocean during December 1995 (empty triangles) with their predictions from the general relationship.
tionship between bacterial number and Chla when data from many di¡erent systems are combined and a regression is performed. This signi¢cant regression re£ects a relationship between the two variables and, thus, a characteristic of microbial communities. Although we assume that the relationship should be `causal', the models do not require this to be the case. If data from a particular system, such as the solar salterns, are compared to this general relationship, they may either conform to the regression or show statistically signi¢cant di¡erences. In the former case, one may reason that the relationships between the examined variables are the same in the particular system and in the systems used to derive the regression. In the latter case, however, the relationship should be di¡erent. In the case of PN (Fig. 4A) the group of ponds with salinities lower than 100x showed values very close to the predictions provided by temperature and PHP. Thus, we conclude that the mechanisms regulating PN in these ponds are the same as those in most marine systems. All the other ponds, however, showed values signi¢cantly higher than predicted ones, indicating the existence of differences in the regulation of PN. The PN in any given system is the result of a balance between growth and losses. In most marine systems, and in the lower salinity group of ponds, the ¢nal balance is the same. However, in
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the ponds with salinities higher than 100x the balance is displaced towards higher biomass values. This could be the result of either faster growth or lower loss factors. Since growth rates are actually slower in the ponds with higher salinities [15], the reason for this higher abundance must be the reduced losses. We have shown that viral impact is low throughout the salinity gradient [20]. Therefore, viral lysis cannot be the reason for these changes as salinity increases. Bacterivory, on the other hand, is much reduced or non-existent in the ponds with higher salinities [15] and, therefore, it seems to be the main factor responsible for the di¡erences between salterns and other systems. Even though values of PHP were always higher than their predictions (Fig. 4B), these di¡erences were never signi¢cant: sometimes the residuals were very close to the upper 95% con¢dence limit but always below it. Therefore, we must conclude that the main factors regulating PHP are temperature and resource supply (as represented by Chla), just as in most other environments. Thus, salinity per se does not play a role in limiting PHP. The organisms present at each salinity, therefore, must be well adapted to the ambient salinity. To see whether these results were also true for salterns in other parts of the world, we screened the literature for other studies of solar salterns. Only the work of Oren [18,19] in the Eilat salterns (Israel) included the necessary variables. Unfortunately, there was no data for Chla and, thus, the comparison could only be carried out with the prediction of PN, but not with that of PHP. The ponds in Oren's study were assigned to the same four groups of salinity and the multiple linear regression was used to obtain predictions of prokaryotic number. As can be seen in Fig. 4C the results were exactly as those found in the two Spanish salterns : the ponds with salinities lower than 100x had very low residuals and all the other ponds were above the upper 95% con¢dence limit. Thus, the pattern found is fairly robust, since it has been found in three di¡erent solar salterns. To con¢rm the importance of bacterivory in determining the PN in aquatic systems, it had to be shown that other environments without bacterivory also had abundances larger than the predictions. Small karstic lakes in northeastern Spain are ideal systems to test this point. These lakes tend to be sharply strati¢ed [21]. While the epilimnion is oxic and has a microbial food web similar to that of most other lakes, the hypolimnion is generally anoxic with large concentrations of sul¢de. This alters many characteristics of the microbial community. Bacteria tend to be more abundant and greater in size [25,35] and the abundance of bacterivores is clearly reduced [25]. As a consequence, the impact of bacterivory on the prokaryotic assemblage is low [25]. If our contention that bacterivory is the most important factor determining PN were true, the predictions from the general relationships should fall within the 95% con¢dence interval for the oxic layers and
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outside this interval for the anoxic layers of these lakes. This is exactly what happened (Fig. 5A): all the epilimnetic samples were within the 95% con¢dence interval and all the hypolimnetic samples were beyond the upper 95% con¢dence interval. It could be argued that some other di¡erence between epilimnetic and hypolimnetic samples (such as light regime for example) was the cause of these di¡erent results. However, when Lake Ciso¨ mixes in the winter it becomes completely anoxic [22] and the microbial community for the whole water column is very similar to that of the anoxic hypolimnion during strati¢cation [24]. In accordance with our hypothesis the holomixis samples from Lake Ciso¨ were also beyond the upper 95% con¢dence limit (Fig. 5A). As a ¢nal test of our hypothesis, we checked the possible in£uence of di¡erent degrees of viral lysis on the abundance of prokaryotic plankton. Part of the behavior of our data could have been the result of concerted changes in the importance of viral lysis. This loss factor has been shown to account for up to 50% of total bacterioplankton losses in a coastal environment [36]. However, we have found viral lysis to be very low in the salterns [20]. Thus, we decided to test systems with high and low impact of viral lysis but similar values of bacterivory. The northwestern Mediterranean and some areas of the Southern Ocean were shown to have moderate but similar levels of bacterivory (Marrase¨ and Vaque¨, in preparation, [29]). The former system was sampled during a cruise in June 1995 and viral lysis was found to be undetectable [28]. The water in the Gerlache and Brans¢eld Straits was sampled during a cruise in December 1995 and viral lysis was found to be more important than bacterivory in all stations where both values could be compared directly. In fact, viral lysis accounted for 50^100% of PHP in di¡erent stations [30]. If viral lysis were an important factor determining PN, we should see di¡erences between these two systems in their respective residual plots. As can be seen in Fig. 5B, all the samples were within the 95% con¢dence interval and, therefore, viral lysis was not an important factor in the determination of the balance between prokaryotic growth and losses that ultimately determines the PN found in any given aquatic system. In conclusion, the method of comparing results from a given system to an empirically derived general relationship seems to be very e¡ective in revealing whether the functioning of the microbial food web is similar to that of other systems. The analysis of `peculiar' systems such as the solar salterns clearly identi¢ed bacterivory as the factor responsible for maintaining the PN values usually found in `common' systems such as the coastal oceans. Conversely, this analysis showed that PHP was regulated by the same factors (temperature and resource supply) as in most `common' aquatic environments. The analysis of karstic lakes and two di¡erent marine zones con¢rmed that bacterivory and not viral lysis is the main factor de-
termining the actual abundance of prokaryotic plankton in a wide range of aquatic systems. Acknowledgements This study was supported by DGICYT Grant PB950222-C02-01. We thank Mr. Juan Duch from INFOSA SA and Mr. Miguel Cuervo-Arango for permission to work at La Trinitat and Bras del Port salterns, respectively. We thank Paul A. White for access to the data of [7]. J.I.C.-P. was a recipient of a scholarship from the Generalitat de Catalunya.
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