PATTERNS OF MICROBIAL COMMUNITY DEVELOPMENT IN ISOLATED AQUATIC SYSTEMS by Paul V. McCormick Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE in

ZOOLOGY

APPROVED:

Dr. John Cairns, Jr., Chairman

Dr. Donalds. Cherry

Dr. Bruce c. Parker

May, 1986 Blacksburg, Virginia PATTERNS OF MICROBIAL COMMUNITY DEVELOPMENT

IN ISOLATED AQUATIC SYSTEMS

by Paul V. McCormick Committee Chairman: John Cairns, Jr. University Center for Environmental Studies and Department of Biology

(ABSTRACT) The purpose of this research was to compare the process of microbial community development in isolated aquatic systems to the MacArthur-Wilson equilibrium theory of island biogeography and suggest alternative models for the observed patterns. Water-filled plastic containers were used as aquatic islands to investigate the colonization dynamics of protozoan, algal and microinvertebrate taxa. Polyurethane foam (PF) artificial substrates were used as sampling devices in these systems. Distance from another aquatic system was found to have a significant effect colonization in these systems. Exclusion of macroscopic organisms, however, did not strongly affect the process of microbial community development in these systems. The results of short-term (36 day) and long-term (170 day) experiments suggest that no protracted species equilibrium is achieved in these systems; an initial asymptotic increase in species richness was followed by large oscillations in the number of species. There was no correlation between rates of species colonization and the number of species present. Rates of species extinction, however, increased with increasing species number. The estimated rate of species colonization was a non-monotonic function of time, increasing during the early stages of colonization and decreasing thereafter. These results, coupled with an analysis of temporal changes in species composition suggest that microbial community development in isolated systems is a deterministic process which may be best explained by an interactive model of species succession. Acknowledgements I would like to thank my advisor, Dr. John Cairns, Jr., for his support and advice throughout my years as an undergraduate and graduate researcher under him. I am also appreciative for the suggestions of my other committee members, Drs. Donald s. Cherry and Bruce c. Parker. Although not a formal member of my committee, Dr. Eric P. Smith provided immeasurable help with the development and analysis of the statistical component of this work. Darla Donald provided editorial help for the first chapter of the thesis, and Betty Higginbotham and Angela Miller typed the manuscript for that chapter. Nancy Pratt prepared the figures for the first chapter. Barbara Niederlehner has provided assistance and advice on statistical problems. I am indebted to these people for their time over the years. I would also like to thank my daily associates in the department for their advice, dissent and friendship. In particular, my office-mates Will Clements, Bob Genter, Kurt Pontasch and Michael Stewart have been helpful in various ways. Finally, I wish to give a special thanks to Dr. James R. Pratt. My four year association with him has been instrumental in the development of my research and problem- solving capabilities. I am grateful for his assistance and friendship over the years.

iv This research was supported in part by funds from the Virginia Academy of Science, The Graduate Student Assembly at VPI, and matching funds from Dr. E. R. Stout and the

Biology Department.

V TABLE OF CONTENTS

I. Abstract...... ii II. Acknowledgements...... iv

III. List of Tables...... vii

IV. List of Figures...... viii

V. Introduction ...... 1

VI • Chapter 1 • • • • • . • • • • • • • • • • • • • • • • • • • • • • • • . • • • • • • • • • • 2 9 Effect of Distance From a Source Pool on Protozoan Colonization of Isolated Aquatic Systems.

VI I • Chapter 2 . • • • • • • . . . • • • . . • • • • • . . • . . . • . • . • . • . . . • . . . 5 5 Microbial Colonization Dynamics in Isolated Aquatic Systems. VIII. Chapter 3...... 83 Microbial Succession in Isolated Aquatic Systems.

IX. Conclusion. • . . . . • • . . . . • . . . . . • . • • • • . . . . • • • • ...... • 96 X. Taxonomic References •...... •...... 100 XI. Appendix 1. Physico-chemical Measurements ...... 101

XI I • Vi ta ...... 112

vi List of Tables

Table Page 1. 1...... 45 Species observed on day 3 only in 100-m tubs. 1. 2...... 46 Results of nonlinear regression of number of species on time for tubs at each distance from the pond based on the MacArthur-Wilson equilibrium model. 1. 3...... 47 The observed species pool in the pond and in tubs at 10, 32, and 100m from the pond. 1.4...... 48 a) Matrix of similarity coefficients for species pools in the pond and at each distance. b) Number of species common to the pond and tubs at each distacne. 1. 5...... 49 a) Species found only in the pond and 10m tubs. b) Species common in the tubs but absent in the pond. 2.1...... 70 Summary of species pools for all samples in the four treatments. 2. 2...... 71 Results of nonlinear regression analysis fitting the MacArthur-Wilson colonization model to the species accrual data for the complete data set. 2. 3...... 72 Results of nonlinear regression analysis fitting the MacArthur-Wilson colonization model to the species accrual data for "resident" species.

vii List of Figures Figure Page

I . 1 ...... 6 The predicted shape of the species immigration and extinction curves as a function of the number of species present on an island.

I . 2 ...... • ...... • ...... 8 The predicted effect of distance from the species source pool and island size on the rates of species immigration and extinction. I. 3...... 10 The predicted species accrual, or colonization, curve for an island obtained from the integration of the difference between the species immigration and extinction curves as a function of time. 1. 1...... 50 Predicted MacArthur-Wilson colonization curves for tubs at the three distances from the pond. 1. 2...... 51 Mean rates of species immigration and extinction for each sa.I'l)pling interval in tubs 10-m from the pond. 1. 3...... 52 Mean rates of species immigration and extinction for each sampling interval in tubs 32-m from the pond. 1. 4...... 53 Mean rates of species immigration and extinction for each sampling interval in tubs 100-m from the pond. 1. 5...... 54 Mean number of species in different functional groups over time in tubs at the three distances. 2. 1...... 73 Mean number of species observed over time in each treatment for the full species data set.

viii 2. 2...... 74 Rates of species colonization as a function of estimated species number for the full species data set. 2. 3...... 75 Rates of species extinction as a function of estimated species number for the full species data set. 2.4...... 76 Rates of species colonization as a function of time for the full species data set. 2.5...... 77 Rates of species extinction as a function of time for the full species data set. 2. 6...... 78 Mean number of species observed over time for "resident" species.

2.7...... 79 Rates of species colonization as a function of estimated species number for "resident" species. 2. 8...... 80 Rates of species extinction as a function of estimated species number for "resident" species. 2. 9...... 81 Rates of species colonization as a function of time for "resident" species. 2. 10...... 82 Rates of species extinction as a function of time for "resident" species. 3. 1...... 93 Frequency of occurrence of dominant taxa over time in the isolated pools. 3. 2...... 94 Canonical discriminant analysis of "resident" species for the thirteen sampling dates. 3. 3...... 95 Number of occurrences and number of taxa over time for the four major functional groups in the isolated pools.

ix Introduction This research examined microbial colonization of artificial aquatic islands. Islands represent important units of study in ecology and evolution and have played an integral role in the development of important biological concepts, the most notable being the concept of natural selection (Darwin and Wallace, 1858; Darwin, 1859). The species assemblages of islands are relatively simple in comparison to mainland communities. As a result, species interactions are more readily identifiable (Simberloff, 1974). In addition, one can be more certain that an organism on an island is truly a part of the biotic community (Simberloff, 1974; Dickerson and Robinson, 1985). The spatial limits to the sampling area are also better defined on islands. The importance of insular biogeographic theory to ecological and evolutionary thought becomes clearer when one considers that many habitats are "islands"; a region need only be isolated from similar habitat by inhospitable terrain which can only be crossed with difficulty by the organisms being studied (Simberloff, 1974). In this sense, studies have been performed on insular faunas including rodents on mountaintops (Brown, 1971) and phytophagous insects on various tree species (Southwood, 1961; Strong 1973). Island theory has also become increasingly

1 2

important in the field of refuge management and species

conservation (Diamond and May, 1981).

The Equilibrium Theory of Island Biogeography

It has long been realized that species assemblages on

islands resulted from the interplay of several factors, including the biotic diversity of the source region, island size and the distance of the island from the source pool. Species richness was observed to increase with area on both island archipelagos (Gleason, 1922; Darlington 1957) and mainland areas (Gleason, 1922; Preston, 1962). Increasing distance of an island from the species source resulted in

an increasingly depauperate and disharmonic species assemblage (Mayr, 1940; Carlquist, 1974). Preston (1962) developed a relationship between the

area of an isolate and the number of species present of the

form: ( 1 ) where N = number of species, A= area of the isolate, K = a parameter which is affected by the taxon and the region

under study, and z = a value which is little affected by taxon or region and is approximately equal to 0.27 for true isolates. By calculating z values for both samples from mainland areas and islands of corresponding size he attempted to explain the observed impoverishment of island 3 biotas as resulting from differences in the value of z compared to mainland "samples". He concluded that most islands contain the correct number of species for their area, but samples from mainland areas are enriched in the species/individuals ratio as a result of the inclusion of nonresident species. Later work considered the influence of other variables, in addition to area, on the species richness of insular habitats. Koopman (1958) found that differences in ecology among islands could substantially distort the species area relationship for bats in the Carribean. Elevation and, in the case of the Galapagos islands, isolation were also suggested as important factors explaining variation in species number (Hamilton et al., 1964; Hamilton and Rubinoff, 1967). Many studies of this type employed multiple regression analysis, which is susceptible to the confounding effects of correlation between area and other factors, such as habitat diversity, which may be more important in influencing the number of species present on a given island (MacArthur and Wilson, 1967). Early qualitative explanations for the impoverishment of island biotas with increasing distance from the source pool considered only the length of time that an island had been available for colonization and the probability that species would reach islands of varying isolation. It was 4

assumed that, if given a sufficient amount of time, a more distant island would eventually contain as many species as a comparable island closer to the source pool. In contrast, MacArthur and Wilson (1963, 1967) proposed a quantitative model for species colonization of insular systems in which the number of species present on an island resulted from an equilibrium between the rate of immigration of new species to the island and the rate of extinction of species already present (fig. 1). The equilibrium theory of island biogeography generated several testable predictions with regard to the process of species accrual in insular habitats and raised insular biogeography from a purely descriptive to a predictive science. According to the theory, the rates of species immigration and extinction were related to the number of species present on an island at a given time. Immigration was a decreasing monotonic function of the number of species; as more species became established on an island, the chance of a subsequent colonist being a new species decreased. If all species were assumed to be equally adapted for dispersal then the immigration curve would be a simple linear function of number of species. The observation that some species were better dispersers than others was incorporated into the model by making the immigration curve concave (figure l); good dispersers 5

should quickly colonize an island, rapidly decreasing the

rate of immigration, while poor dispersers would reach the

island with longer intervals between successful

immigration, thereby reducing the rate of immigration more

slowly.

The rate of extinction was a decreasing monotonic

function of number of species; as more species colonized an

island the probability of species extinctions should

increase. As more species colonized an island, population

sizes would tend to be reduced, thereby increasing the

probability of local extinction for a given species as well. As a result, extinction was an increasing rate

function (figure 1). The equilibrium number of species for

a given island could be predicted from the intersection of

the immigration and extinction curves. Changes in the

immigration and extinction rate curves would affect this

predicted equilibrium. With increasing distance from the

source pool, the rate of immigration would be reduced since

fewer species would be able to reach the island. This would shift the immigration curve to the left, thereby decreasing

the equilibrium number of species on more distant islands

(figure 2). The rate of extinction was related to island

size. The size of species populations would tend to decrease with decreasing island size, so that the chance of a species becoming extinct would be increased. This would Rate

°'

Seq Number of species present

Figure 1. The predicted shape of the species immigration and extinction curves as a function of the number of species present on an island. 7 shift the extinction curve to the left, reducing the equilibrium number of species (figure 2). The equilibrium theory contained several nonobvious predictions which distingiushed it from earlier views of island biogeography. The effect of area on the equilibrium number of species should increase with increasing distance from the species source. Empirical evidence for this was given by MacArthur and Wilson (1963) for avian_species on the islands of Polynesia. Also, the effect of distance from the source pool should be greater for larger islands than smaller islands as a result of the increased rate of species extinction with decreasing area. Stepping stone islands and clustering of islands should affect colonization dynamics by their effect on the absissic and ordinate intercepts, respectively, of the immigration curve. As species colonized a given island, immigration would decrease and extinction increase in response to the increase in species number over time. Integrating the difference between the immigration and extinction curves over time would yield a species accrual, or colonization, curve (figure 3) of the form, -Gt st= Seq (1 - e ) ( 2 ) where = the number of species at time t, Seq = the equilibrium number of species and G = a fitted rate near small

Rate

00

Number of species present Figure 2. The predicted effect of distance from the species source pool and island size on the rates of species immigration and extinction. 9

constant. The increase in species numbers over time was, · therefore, asymptotic. The rate of immigration and extinction at this equilibrium were positive and equal. This turnover rate was homogeneous; all species contributed equally to rates of immigration and extinction. The turnover rate for a given island could be predicted from the equation:

X = 1.15 mean Seq ( 3 )

t90% where X = the turnover rate at equilibrium and t 90% = the time to ninety percent saturation. It was speculated that the turnover rate might decrease over time after the attainment of equilibrium as more persistant, co-adapted assemblages of species became present on an island.

Tests and Extensions of the Equilibrium Theory One of the first critical tests of the equilibrium theory (Simberloff and Wilson, 1969, 1970; Simberloff, 1969; Wilson and Simberloff, 1969) observed arthropod colonization of seven defaunated mangrove islands at various distances from the Florida keys. Several lines of evidence suggested that an equilibrium was being observed on these islands. The number of species on two control islands did not change appreciably during the one year study and during this period the number of species on the '"141. 1G~ l)Otv a. w a...... Cl) <( a: 0 I-' z I / 0 ~c,,o~ €,i-''

TIME TIME

Figure 3. The predicted species accrual, or colonization, curve for an island obtained from the integration of the difference between the species immigration and extinction curves as a function of time. 11

experimental islands returned to a level similar to that found in pre-defaunation surveys. The number of species found at this equilibrium was similar for islands of comparable size and distance from the mainland. The equilibrium which was observed was termed a noninteractive equilibrium and was proceeded by a decrease in species richness to an "interactive" equilibrium. It was postulated that increases in extinction would occur at this time as a result of negative interactions (i.e. competition, predation) between species as population sizes increased. Evidence for such interactions included observations of predation and exclusion. The most distant island did not exhibit this pattern, possibly because of the reduced frequency of colonization, which provided time for established species to build up population densities which would allow for significant interactions during the accrual phase. Over a longer period of time an "assortative" equilibrium with a lower turnover rate might be achieved which had a greater number of species than the original interactive equilibrium (Wilson, 1969). This would result from the eventual accumulation of longer-lived species and a constant propagule invasion rate, and eventually cause a convergence of species composition toward that present before defaunation (Simberloff and Wilson, 1970). Heatwole and Levins (1972) subsequently showed that the trophic 12

structure of these assemblages also converged to

predefaunation conditions. The time period between censuses precluded accurate

predictions of the exact shapes of the immigration and

extinction curves. However, turnover rates at equilibrium

approximated those predicted by equilibrium theory ( .1 to

1.0 species /day as calculated from equation [3]). The rate

of extinction was explained adequately in terms of the

intrinsic probability of a species going extinct without the inclusion of a species-dependent or density-dependent factor. It was suggested that this resulted from the large number of transient species present on these islands which exhibited rapid extinction, and the low population sizes observed during most of the accrual phase.

Simberloff (1969) extended the equilibrium theory by

developing a model of species accrual for insular systems which considered only the changes in invasion and

extinction through time rather than relying on a relationship between rates and the number of species.

Simberloff criticized the uniqueness of extinction rates

for a given species number using three lines of reasoning:

1) every possible grouping of n species need not produce

the same extinction rate since interactions among different

species would likely be different, 2) the intrinsic rate of extinction is not equal for al~ species, and 3) extinction 13 as a function of species number fails to take into account population structure. In contrast, extinction and immigration as a function of time were unambiguous for a particular island and could be used to derive a colonization curve for that island. More recent tests of equilibrium theory have used the terrestrial arthropod fauna of defaunated Spartina islands in northern Florida (Strong and Rey, 1982; Rey, 1981; Rey, 1984). The confounding factor of seasonality was much more pronounced for these islands than for the previous experiments in the Florida keys. This was accounted for by comparing rates on experimental islands to those of control islands and the mainland (Rey, 1981). The results of these studies corroborated the earlier work of Simberloff and Wilson. Avian assemblages have been used extensively to test and extend predictions of the equilibrium theory. Diamond (1969), using a 1968 survey and an earlier survey (Howell, 1917) of the birds of the channel islands off the coast of California, concluded that a dynamic equilibrium did exist; species numbers on the islands changed little between censuses but the composition was quite different. In contrast to MacArthur-Wilson predictions, turnover rates were not strongly correlated with distance or area, possibly as a result of the dispersal capacity of species in the pool of colonists and the variation in habitat 14 diversity among islands. It was argued that the results corroborated equilibrium theory when habitat heterogeneity was taken into account. A second study (Diamond, 1971) using similar methods compared the colonization dynamics of the island of Karkar near New Guinea to one of the channel islands having a similar area and distance from the mainland. Although extinction rates were similar for the two islands, immigration, expressed as a percent of the mainland species pool, was higher for the temperate island. These studies were subsequently used as strong supportive evidence of the equilibrium model. Lynch and Johnson (1974) reassessed much of the previous work on bird communities, redefining true avian colonizers as only those species which are found breeding on an island. Since transient species (i.e. those which immigrate and quickly emmigrate) are normally much more abundant than actual colonizers, the inclusion of these species in the analysis leads to the artifactual result that immigration is equal to extinction. They argued that earlier work (Diamond, 1969) was seriously flawed and provided no evidence for a dynamic equilibrium because of: 1) the inadequacy of the earlier survey (Howell, 1917), 2) the criteria used to define true immigration and extinction, and 3) the effect of human and natural perturbations on the habitat and species composition of 15 these islands. The results of studies on tropical islands (Diamond, 1971; Terborgh and Faaborg, 1973) were rejected on similar grounds. The equilibrium theory has been widely applied to habitat islands. Janzen (1968, 1973) suggested that the theory of island biogeography would be applicable to plant species as islands for phytophagous insects. Although it has been suggested that these islands are never in equilibrium (Whittaker, 1969) and that species richness is nonasymptotic (Southwood, 1961), Strong (1974) provided evidence for the attainment of equilibrium by insect species in ecological time, although these may be protracted noninteractive equilibria (Strong, 1984).

Similarly, aquatic arthropods in caves have also been shown to follow equilibrium theory (Culver, 1970). Brown and Kodric-Brown (1977) used thistle plants as host islands for arthropod species. Their results largely corroborated equilibrium predictions except that turnover rates were positively rather than inversely correlated with distance. High immigration rates would also imply high invasion rates of propagules of species already present, which would decrease the probability of the extinction of residents. Termed the rescue effect, this interaction between immigration and extinction would result in the highest turnover rates being observed on islands at intermediate distances. 16

High altitude peaks of a mountain range can be considered as habitat islands for boreal mammals (Brown, 1971). Although a strong species-area relationship was found for such habitats in the Great Basin, no dynamic equilibrium appeared to exist; extinctions but no immigrations have occurred since the Pleistocene when the present-day montane habitat was continuous throughout much of the Southwestern U.S. This situation may also be true for fish in isolated aquatic systems in the Great Basin (Hubbs and Miller, 1948), but it is in marked contrast to studies of taxa possessing strong dispersal abilities (e.g. Vuilleumier, 1970). Experiments purported to confirm equilibrium theory have been criticized on the basis of poor methodology and misinterpretation of results (see Sauer, 1969; Lynch and Johnson, 1974; Gilbert, 1980). Many tests, especially those pertaining to species-area relations, have employed multiple regression analysis which provides correlations, but cannot implicitly show causation (Simberloff, 1976; Gilbert, 1980). Many such analyses are based on too few data points and may be seriously compromised by influential data points. The generality of experimental evidence has been questioned, especially the extrapolation of results from small islands with homogeneous habitats to larger, more 17

complex islands (Whitehead and Jones, 1969). Sauer (1969) and Lack (1970) suggested a more individualistic approach to insular biogeography, and Sauer (1969) criticized the equilibrium theory for it's oversimplified approach in treating islands and species as interchangeable units. Even former advocates of the theory questioned application of the theory based on available evidence (Simberloff and Abele, 1976a, 1976b).

The Use of Microbes for Testing Equilibrium Theory If an ecological theory is to have general relevance it's predictive ability should transcend taxonomic as well as geographic boundaries. Unanimous agreement on confirmations of the equilibrium theory, however, rests only with one experiment, that of Simberloff and Wilson (1969, 1970). Results of tests using higher organisms have been dismissed for several reasons, many of which relate to the attributes of these organisms. The use of microbial assemblages in ecological research, particularly in testing equilibrium theory, can eliminate several of these problems. Because of the short generation times of these organisms, experiments can be performed quickly, thereby minimizing the effects of environmental changes and anthropogenic disturbances and eliminating problems of inter-researcher taxonomic bias associated with many studies on higher organisms such as birds. The design of 18 experiments can be better controlled through the use of artificial systems, thereby allowing for the isolation of a specific effect and increased replicability among sampling units.

Few biogeographic studies have been performed using microorganisms. Patrick (1967), using diatom colonization of glass slides in lotic systems, concluded that species richness and diversity were positively correlated with the area of the substrate, the available species pool and the invasion rate. Diatom communities on the island of Dominica were found to be depauperate compared to communities in similar habitats on the continental U.S ..

Cairns and co-workers (1969) used polyurethane foam artificial substrates as islands for protozoan colonization in a lentic system. Although the composition of the developing community was not identical among substrates, the colonization process was repeatable. The rate of colonization decreased and extinction increased over time.

Species accrual was found to occur in two stages, suggesting the possibility for positive interactions among colonizing species. Colonization was not found to be significantly affected by either depth in a lake (Cairns and Yongue, 1974) or overall placement of the artificial islands within the system under study (Cairns et al.,

1976). A species-volume effect has been observed for these artificial islands (Cairns and Ruthven, 1970); a positive 19 linear relationship was found between the number of colonizing protozoan species and the logarithm of substrate volume up to a certain critical substrate size. Microbial colonization in small water-filled glass beakers in the field was studied by Maguire (1963, 1977) at several locations. The process of species accrual and the rate of colonization followed MacArthur-Wilson predictions. No volume effect was seen for species richness between 250 and 500 ml beakers. Although distance from a natural aquatic system was found to affect species richness in some experiments, it was not shown conclusively. Laboratory studies on the effect of island size and invasion rate were performed by Dickerson and Robinson (1985) using protozoan, algal and rotifer species as colonizers of glass beakers under controlled environmental conditions. A dynamic equilibrium was shown to occur, but smaller (400 ml) islands had a higher species richness than did larger (1200 ml) islands. Species numbers did increase with increasing invasion rate, however. The results suggested that at least two stable equilibria were possible for the species pool used. Objectives and Hypotheses The primary objective of the following studies was to test specific predictions of the MacArthur-Wilson equilibrium theory using microbial colonization of 20 replicable, artificial isolated aquatic systems. Organisms identified in the experiments included protozoans, algae and microinvertebrates. Identification of all organisms to the species level is not feasible in an ecological study of this type. Therefore, the number of species present in these systems could only be estimated. The systems used in this study were small, water- filled plastic containers. These represent ideal units for ecological studies since their physico-chemical history can be known and replication is easily acheived. The predictions to be tested were: 1) the rate of species colonization is a monotonic decreasing function of the number of species present on an island. 2) the rate of species extinction is a monotonic increasing function of the number of species present on an island. 3) Over time, an island community will reached a dynamic equilibrium; some constant level of species richness will be achieved within ecological time but the identity of the resident species will continue to change after the attainment of this equilibrium. 4) Natural aquatic systems can act as a source pool of colonists for isolated aquatic systems, and, as such, the rate of colonization and the number of species at equilibrium should decrease within increasing distance from 21

the source pool. 5) Access of animals such as mammals, birds and insects increases the microbial species pool available for colonization of isolated &quatic systems and, as such, influences the process of community development. A second goal of this research was to provide evidence for the influence of deterministic and stochastic forces in shaping the process of microbial community development. The following hypothesis is examined in chapter 3: 6) The process of microbial community development in isolated aquatic systems is a repeatable process involving distinct temporal patterns of species occurrences.

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Simberloff, D. s. and L. G. Abele. 1976a. Island biogeography theory and conservation practice. Science 191:285-286. 27

Simberloff, D. s. and L. G. Abele. 1976b. Reply to critics. Science 193:1032.

Simberloff, D. s. and E. o. Wilson. 1969. Experimental zoogeography of islands: the colonization of empty

islands. Ecology 50:278-296.

Simberloff, D. s. and E. o. Wilson. 1970. Experimental zoogeography of islands: a two year record of

colonization. Ecology 51:934-937.

Southwood, T. R. E. 1961. The number of species of insect associated with various trees. J. Anim. Ecol. 30:1-8.

Strong, D.R. 1974. Asymptotic species richness models and insects of British trees. Proc. Nat. Acad. Sci. 71(7):2766-2769.

Strong, D. R. and J. R. Rey. 1982. Testing for MacArthur-

Wilson equilibrium with the arthropods of the

miniature Spartina archipelago at Oyster Bay, Florida. Amer. Zool. 22:355-360.

Terborgh, J. and J. Faaborg. 1973. Turnover and ecological

release in the avifauna of Mona Island, Puerto Rico.

Auk 90:759-779.

Vuilleumier, F. 1970. Insular biogeography on continental

regions. I. The northern Andes of South America. Am. 28

Nat. 104:373-388.

Whitehead, D. R. and c. F. Jones. 1969. Small islands and the equilibrium theory of insular biogeography. Evolution 23:171-179.

Whittaker, R. H. 1969. Evolution of Diversity in Plant Communities. p. 264 In: Diversity and Stability in Ecological Systems, G. M. Woodwell and H. H. Smith (eds.). Brookhaven Symposia in Biology No. 22.

Wilson, E. o. 1969. The Species Equilibrium. p. 38 In: Diversity and Stability in Ecological Systems, G. M. Woodwell and H. H. Smith (eds.). Brookhaven Symposia in Biology No. 22.

Wilson, E. o. and D. s. Simberloff. 1969. Experimental zoogeography of islands: defaunation and monitoring techniques. Ecology 50:267-278. Chapter 1

Effect of Distance From a Source Pool on Microbial

Colonization of Isolated Aquatic Systems

Introduction This investigation used protozoan colonization of replicable aquatic microcosms in the field {a) to test for the effect of distance from a postulated source pool, a nearby pond, on colonization and {b) to compare the observed patterns of early development in isolated aquatic systems to the MacArthur-Wilson colonization model. It was hypothesized that increasing distance from a species pool, a nearby natural aquatic system, would result in a decreased rate of species colonization and a lower number of species at equilibrium as predicted by MacArthur and

Wilson (1967). Furthermore, species immigration and extinction curves were expected to be monotonic, decreasing with time for the former and increasing for the latter. If the pond were acting as a species source, then the theory of island biogeography would predict that the rate of immigration would decrease with increasing distance from the pond, resulting in a reduction in the equilibrium number of species.

Methods

Small, plastic tubs (30 litre) were used as isolated

29 30 aquatic islands for colonization by protozoan species. The species source was a vernal pond east of the Virginia Polytechnic Institute and State University airport, Blacksburg, Virginia. In July 1984, water was obtained from the pond and pasteurized by heating to 70 C for 30 minutes to remove protozoan species. To verify. that no species survived pasteurization, an initial amount of water was first pasteurized and used to fill three 30 litre plastic tubs in the laboratory. The PF substrates were placed in these containers for 7 d under daylight equivalent illumination (Durotest Vitalites) and then sampled. A second portion of water was pasteurized and used to fill triplicate, 30-litre containers placed 10, 32, and 100 m from the pond. Ten PF substrates were placed in each tub to serve as replicate collectors of colonizing species (Cairns and Henebry 1982). All tubs were covered with fine mesh netting to reduce interference from organisms such as birds, mammals, and insects that have been implicated in microbial dispersal (Maguire 1977; Parsons et. al. 1966; Schlichting 1960; Stewart and Schlichting 1966). On days 3, 9, 15, 22, 29, and 36, a single PF substrate was collected from each tub and transported to the laboratory in a sterile collecting bag. Contents of each substrate were squeezed into the bags in the 31

laboratory, and, after settling, the number and kinds of

protozoan species were recorded following the method of

Cairns and Henebry (1982). Protozoan colonization was also

followed in the pond using PF substrates as sampling devices.

Species data collected for the pond and the tubs at each distance were fitted to the MacArthur-Wilson equilibrium model -Gt st= Seq (1-e ) where = number of species at a given time, Seq = equilibrium species number, G = colonization rate, and t = time, using nonlinear regression analysis with Marquardt methods of estimation (Draper and Smith 1981) to predict Seq and G values. Lack-of-fit tests were performed to assess the appropriateness of the model. Dummy variable analysis (Kleinbaum and Kupper 1978) was used to test for statistical differences in Seq and G values among distances.

The total species pool in the pond and in tubs at each distance was estimated from collection data. A Jonckheere ordered alternative (Hollander and Wolfe 1973) was used to test for a decrease in species pools with increasing distance from the pond. Similarities among distances and the pond were calculated using the Jaccard coefficient of similarity (Sokal and Sneath 1973): 32

J = ____ A ___ _ A + B + C where A= the number of species common to the two species

pools, B = the number of species present in the first

species pool but not in the second, and C = the number present in the second species pool but absent in the first.

Extinction and immigration rates were calculated for each time period and averaged for the three replicates at each distance. Since the majority of protozoan species is capable of asexual reproduction (Sleigh 1973), the presence of only one individual in a sample was regarded as a

successful colonization. Immigration and extinction curves were fitted to the data as a function of time using simple linear regression. Lack-of-fit tests were performed to assess the appropriateness of the linear model in explaining data.

RESULTS

Predicted colonization curves for the three distances are shown in fig. 1. No significant lack-of-fit was found for any of the curves (p>0.05, F), suggesting that the

MacArthur-Wilson equilibrium model was consistent with the process of species accrual in the pond and the containers.

No difference in G was found among distances (p>0.05, dummy variable analysis), but the number of species at equilibrium was significantly decreased at 100 (p<0.05).

Contrary to expectation, the 100-m tubs had the highest 33 colonization rate of any distance. This may have resulted from disturbance by mowing near this distance on day O, causing an input of species not observed at other distances (table 1). The results of these analyses are summarized in table 2. The observed species pool (table 3) decreased with increasing distance from the pond and was suggestive of significance (p=0.06, Jonckheere); the three 10-m tubs had the highest species pools of any tubs. The matrix of similarity coefficients for the pond and the three distances is shown in table 4. Similarity with the pond tended to decrease with increasing distance from the pond. Table S(a) lists the species common to the pond and tubs at 10 m but never present at greater distances. Also shown are species that commonly occurred in tubs at all distances but were never present in samples from the pond (Table S(b)). The majority of species in the second group has been identified as common soil forms (Bamforth 1980, 1985) or has been cultured from aerial samples (Brown et al. 1964; Schlichting 1964). All these species belong to taxonomic groups where encystment is known (Kudo 1966; Corliss and Esser 1974). Species colonizing the tubs were classified into three groups based on morphology and functional status. The 34 groups included heterotrophic microflagellates (Bodo spp., Anthophysis vegetans), autotrophic flagellates (Chlamydomonas spp., Chromulina spp., Ochromonas sp.), and high-volume bactivores including ciliates and sarcodines. Figure 2 shows the number of species in each group through time at each distance. At all distances, flagellated heterotrophs were the first to colonize the containers. As population densities and species numbers within this group began declining, autotrophic flagellates became the dominant organisms. Bactivores became prominent as the number of species in the containers reached an asymptote (see Fig. 1). Bactivore numbers in the 100-m containers were elevated on the first sampling day, due to the presence of several species which likely came from soil blown into the tubs by mowing on day O near this distance. Extinction and immigration curves for the three distances were plotted as a function of time (fig. 3). The linear model was significant (p<0.05, F) and appropriate (p>0.05, LOF) for extinction rate versus time in the 10-m containers. The slope was positive and a zero intercept could not be rejected. No significant regression was found for rates in containers at 32 m and 100 m (p>>0.05, F). 35

DISCUSSION Data presented here support certain predictions of the equilibrium theory of island biogeography. Protozoan species accrual at all distances was similar to that predicted by theory. A significant reduction in the predicted equilibrium number of species was found at 100 m from the pond compared to nearer tubs, and, consistent with this result, there was a decrease in the total number of colonizing species with increasing distance from the pond. The decrease in species similarity with the pond with increasing distance also suggests that the pond was acting as a source of colonists and that a distance effect was actually being observed. In many experiments, the species pool of interest (e.g., the mainland) can be considered the sole source of species for island colonization. Viable protozoan cells, however, are present in the atmosphere (Schlichting 1964) and soil (Bamforth 1980). Therefore, invasion pressure was occurring from more than one source such that the effects of species inputs from the pond was less pronounced than if it had been the sole source of propagules. All organisms require water for growth and reproduction. Some protozoan forms, however, have evolved mechanisms for encystment that allow the cells to remain viable in the absence of water. The predominant colonizers initially consisted of aerial and soil forms, which have a 36

high probability of traversing inhospitable territory as a result of their encystment ability. Most of these were present in the 10-m tubs by day 9 of the experiment. Pond taxa were not observed in the 10-m tubs until day 15 or after. The difference in times of colonization between the two groups may, therefore, result from differential dispersal abilities. A second explanation for the observed patterns of colonization is that species capable of surviving in harsh environments, such as the soil or atmosphere, are better suited for colonizing barren habitats. In contrast, some community structure or physico-chemical change may be necessary before pond forms, less suited for extreme environmental conditions, can invade. The second argument hypothesizes the existence of some form of interaction among colonizing species. Although this was not directly tested, it was evident from the data that colonization by pond species was concurrent with the extinction or reduction in population densities of soil and aerial forms. Patterns of extinction and colonization observed here are consistent with a colonizing strategy proposed by Diamond (1975) where certain species (supertramps) with high disperal abilities and unspecialized habitat preferences are able to dominate species-poor environments, but are excluded from species- 37

rich environments as a result of species interactions. The observed patterns of extinction and immigration suggest that species accrual in these systems is an interactive process. Although an asymptote in species numbers was observed by the end of the experiment, the rate of immigration did not decrease mono~onically with time. Assuming a finite species pool, the probability that a given species would be a successful colonist increased as more species colonized the island. These results are similar to those found for diatom assemblages by Patrick (1967); immigration curves, rather than decreasing monotonically, increased initially during the process of species accrual. Except in the 10-m containers, extinction rates did not increase monotonically with time. Therefore, the probability of extinction for a given species decreased as more species colonized an island. The increased extinction in the 10-m tubs, as mentioned above, resulted mainly from the loss of soil and aerial forms and was concurrent with the colonization of pond taxa not found at farther distances. The immigration and extinction curves suggest that in some cases the presence of some species on the islands increases rather than decreases the probability that other species will successfully colonize. Although this appears inconsistent with current ideas of community ecology, which 38

emphasize negative species interactions, a reasonable explanation for this phenomenon can be developed using ideas from succession theory. In particular, the idea that certain "pioneer" species must colonize newly formed barren patches before other species can become established may explain the developmental process in microbial communities. Yongue and Cairns (1978) found that small flagellates dominate protozoan assemblages during early stages of development on PF substrates placed in natural lentic systems. In the present study, this was also found to be true, with many of the same taxa found in the previous experiment also dominating the isolated containers (e.g., Bodo, Cyathomonas, Ochromonas). Although these species were only dominant during the early stages of colonization, they often persisted at very low population densities throughout the experiment, hence the depression of the extinction rate. The succession of species observed in this study is similar to that in other experiments performed in Northern Michigan (Pratt, pers. comm.) and Southwest Virginia (McCormick, unpublished; McCormick and Cairns, in prep.). This not only suggests that species accrual is a nonrandom process but also that some species of protozoans exhibit a cosmopolitan distribution, which is maintained by an r- selected strategy of high dispersal rates and opportunistic 39 colonization. A strong interdependence among species is suggested from the process of species accrual. In contrast, the MacArthur-Wilson theory predicts a noninteractive process of species accrual. Although this may be appropriate for organisms with slow reproductive rates, the short generation time of protozoans allows for colonizing species to dominant the system quickly during the process of species accrual. The potential for species interactions, either directly or through effects on the physico-chemical environment, under these conditions would be likely. That many previous studies have seemingly provided evidence for MacArthur-Wilson theory may be the result of the taxonomic bias of the investigators. The use of guilds or specific groups of organisms, such as birds, insects, or lizards, is often justified by the argument of strong interaction within the unit of study. However, close taxonomic affinities do not necessarily imply strong interactions. In some cases, the strongest interactions within sympatric groups of species may be found between phylogenetically distinct species with similar functional roles (e.g., Brown et al. 1979). The MacArthur-Wilson theory and most colonization studies disregard important functional aspects of community development that may have a controlling function in the process of species immigration and extinction. The protozoan associations in the present 40 study represent a broad taxonomic group encompassing several trophic levels and appear to suggest a different pattern for community development on islands, one in which colonizing species have an important role in determining the future composition of the developing community during the process of species accrual.

Literature Cited Bamforth, s. s. 1980. Terrestrial Protozoa. J. Protozool. 27(1):33-36.

Bamforth, s. s. 1985. Ecology of Protozoa, p. 8 In An illustrated guide to the Protozoa. J. J. Lee, s. H. Hunter, and E. c. Bovee (ed.). Allen Press, Inc., Lawrence, KS.

Brown, J. H., D. W. Davidson, and o. J. Reichman. 1979. An experimental study of competition between seed- eating desert rodents and ants. Am. Zool. 19:1129- 1143.

Brown, R. M., Jr., D. A. Larson, and H. C. Bold. 1964. Airborne algae: their abundance and heterogeneity. Science 143:583-585.

Cairns, J., Jr. and M. s. Henebry. 1982. Interactive and noninteractive protozoan colonization processes, p. 23 In: Artificial substrates. J. Cairns, Jr. (ed.). Ann 41

Arbor Science Publishers, Inc., Ann Arbor, MI.

Cairns, J., Jr., M. L. Dahlberg, K. L. Dickson, N. Smith, and w. T. Waller. 1969. The relationship of fresh- water protozoan communities to the MacArthur-Wilson equilibrium model. Am. Nat. 103:439-454.

Corliss, J. O. and S. C. Esser. 1974. Comments on the role of the cyst in the life cycle and survival of free-living Protozoa. Trans. Am. Micros. Soc. 93(4) :578-593.

Diamond, J. M. 1969. Avifaunal equilibria and species turnover on the Channel Islands of California. Proc. Nat. Acad. Sci. 64:57-63.

Diamond, J.M. 1975. Assembly of species communities, p. 342 In: Ecology and evolution of communities. M. L. Cody and J. H. Diamond (ed.). Harvard University Press, Cambridge, MA.

Diamond, J. M. and R. M. May. 1981. Island biogeography and the design of natural reserves, p. 228 In: Theoretical ecology: principles and applications. R. M. May (ed.). Blackwell Scientific, Oxford.

Dickerson, J. E. and J. V. Robinson. 1985. Microcosms as islands: a test of the MacArthur-Wilson equilibrium 42

theory. Ecology 66(3):966-980.

Draper, N. R. and H. Smith. 1981. Applied regression

analysis. John Wiley, N.Y.

Gilbert, F. S. 1980. The equilibrium theory of island

biogoegraphy: fact or fiction? J. Biogeog. 7:209-

235.

Hollander, M. and D. Wolfe. 1973. Nonparametric statistical methods. John Wiley, N.Y.

Howell, A. B. 1917. Birds of the islands off the coast of southern California. Pac. Coast Avifauna 12:1-127.

Kleinbaum, D. and L. Kupper. 1978. Applied regression analysis and other multivariable methods. Duxbury

Press, North Scituate, MA.

Kudo, R. R. 1966. Protozoology, 5th ed. Charles c. Thomas, Springfield, IL.

MacArthur, R. H. and E. o. Wilson. 1963. An equilibrium theory of insular zoology. Evolution 17:373-387.

MacArthur, R. H. and E. o. Wilson. 1967. The theory of island biogeography. Princeton University Press,

Princeton, N.J. 43

Maguire, B., Jr. 1963. The passive dispersal of small aquatic organisms and their colonization of isolated bodies of water. Ecol. Monogr. 33:161185.

Maguire, B., Jr. 1977. Community structure of protozoans and algae with particular emphasis on recently colonized bodies of water, p. 355 In: Aquatic microbial communities. J. Cairns, Jr. (ed.). Garland Publishing, Inc., N.Y.

Parsons, w. M., H. E. Schlichting, and K. W. Stewart. 1966. In-flight transport of algae and Protozoa by selected Odonata. Trans. Am. Microsc. Soc. 85(4):520-527.

Patrick, R. 1967. The effect of invasion rate, species pool, and size of area on the structure of the diatom community. Proc. Nat. Acad. Sci. 58:1335-1342.

Schlichting, H. E., Jr. 1960. The role of waterfowl in

the dispersal of algae. Trans. Am. Microsc. Soc. 79:160-166.

Schlichting, H. E., Jr. 1964. Meteorological conditions affecting the dispersal of airborne algae and Protozoa. Lloydia 27(1):64-78.

Simberloff, D. s. 1974. The equilibrium theory of island biogeography and ecology. Ann. Rev. Ecol. Syst. 44

5:161-183.

Simherloff, D. S. and L. G. Abele. 1976a. Island

biogeography theory and conservation practice.

Science 191:285-286.

Simherloff, D. S. and L. G. Abele. 1976b. Reply to critics. Science 193:1030.

Simberloff, D. s. and E. o. Wilson. 1970. Experimental zoogeography of islands: a two-year record of

colonization. Ecology 51(5):934-937.

Sleigh, M. A. 1973. The biology of Protozoa. American Elsevier Publishing Co., Inc., N.Y.

Sokal, R. R. and P. H. Sneath. 1973. Principles of numerical taxonomy. Freeman, London.

Stewart, K. w. and H. E. Schlichting. 1966. Dispersal of algae and Protozoa by selected aquatic insects. J. Ecol. 54:551-562.

Yongue, W. H., Jr. and J. Cairns, Jr. 1978. The role of flagellates in pioneer protozoan colonization of artificial substrates. Pol. Arch. Hydrobiol.

25(4):787-801. 45

Table 1. Species observed on day 3 only in 100 m tubs. Acanthamoeba spl. Balladyna parvula Chilodonella capucina Colpoda cucullus C. steini Vahlkampfia spl. 46

Table 2. Results of nonlinear regression of number of species on time for tubs at each distance from the pond based on the MacArthur-Wilson equilibrium model.

Estimates

Distance Seq (ASE) G (ASE) LOF F r2

10 m 21.5 ( 2 • 2 ) 0.10 (0.03) 0.799 0.95 0.75>p>0.50

32 m 17.6 ( 5 . 9 ) 0.04 (0.03 1.074 0.92 0.S0>p>0.25

100 m 15.3 (1.6) 0.13 (0.05) 2.763 0.93 0.l0>p>0.05 47

Table 3. The observed species pool in the pond and in the

three tubs at each distance. Numbers in parentheses are the

species pools for individual tubs.

Unit Species Pool

Pond 117

10 m 89 (47,58,49)

32 m 59 (23,31,45)

100 m 58 (32,37,42) 48

Table 4. (a) Matrix of similarity coefficients for species pools in pond and ·at each distance. (b) Number of species found in both the pond and at each distance.

(a) Pond 10 m 32 m 100 m

Pond 0.2625 0.1586 0.1610 10 m 0.4141 0.4388 32 m 0.5493 100 m

( b) 10 m and Pond 32 m and Pond 100 m and Pond common species 42 23 22 49

Table 5. (a) Species found only in the pond and 10-m tubs. (b) Species common in the tubs but absent in the pond. Number in parentheses is first sampling day observed.

(a) Pond and 10 m only Acanthocystis aculeata (15) Holophyra sp. (29) Aspidisca lynceus (15) Oikomonas termo (36) Eudorina elegans (29) Pandorina morum (15) Euglena pisciformis (15) Petalomonas minuta (22) Glenodinium sp. (22) Spathidium sp. (22) Halteria grandinella (15) Urotricna sp. (15) (b) Common in tubs, absent in pond Acanthamoeba sp. (15)a Naegleria sp. (3)a,c Anthophysis vegetans (3)a Ochromonas sp. (3)a Chlamydomonas sp.l (9)a,b Vahlkampfia sp. (9)a,c Colpoda aspera (9)a,b,c Vannella platyPodia (9)c Spharellopsis sp. (15)a

a - encystment known within family (Kudo 1966) b - airborne organism (Brown et al. 1964; Schlichting 1964) c - soil organism (Bamforth 1985) 24 10 m

32 m 16 --. - . - . -11-. - . - . -II- . -1 en ...... Q) ,, ....---·- ., • ., .,--- 100 m 0 ., Q) ; a. ; ; Cl) ; 8 ; . " " U1 .,. 0 " ,,, " "

3 9 15 22 29 36 · Days

Figure 1. Predicted MacArthur-Wilson colonization curves for tubs at the three distances from the pond. - 10-rn, - 32-rn, - 100-rn. 2.5 -

- - >- -cu -- -- -0 -- 1.5 -- . 0. en - -- rt .... - .....-Q) -- -~ cu .. 0: --.. V1 0.5 I-' tt

I I 3 9 15 22 29 36 (0-3) (3-9) (9-15) (15-22) (22-29) (29-36) Time Period (days)

Figure 2. Hean rates of species immigration and extinction for each sampling interval in tubs 10-m from the pond. Error bars represent standard errors of the means. 2.0

(/) ->, ro u 0. - 1.0 .._.-(1) ca a: V1 N

3 9 15 22 29 36 (0-3) (3-9) (9-15) ( 15-22) (22-29) (29-36)

Time Period (days)

Figure 3. Hean rates of species immigration and extinction for each sampling interval in tubs 32-m from the pond. Error bars represent standard errors of the means. 3.0

-;. 2.0 cu -0 ...... a. a. Cl) .....-(1) cu a: 1.0

Vt (.,..)

3 9 15 22 29 36 (0-3) (3-9) (9-15) ( 15-22) (22-29) (29-36)

Time Period (days)

Figure 4. Mean rates of species immigration and extinction for each sampling interval in tubs 100-m from the pond. Error bars represent standard errors of the means. 54

10 m . - . - ·•. ' . . ' , ------1------. ·-·-·. ., ..... :.---- ... -

32 m -·•

10 100 m (/J Q) - ·•· - . - u Q) 5 a. Cf)

3 9 15 22 29 36 Days

Figure 5. Mean number of species in different functional groups over time in tubs at the three distances. - microflagellates, - autotrophic flagellates, - bactivorous ciliates and sarcodines. Chapter 2 Microbial Colonization Dynamics in Isolated Aquatic Systems

Introduction This study used isolated aquatic mesocosms to assess the applicability of the MacArthur-Wilson theory of island biogeography (MacArthur and Wilson, 1963; 1967) to long- term patterns of microbial colonization. According to this theory, patterns of species accrual on islands are determined by rates of species immigration and extinction which are, respectively, decreasing and increasing monotonic functions of the number of species present on an island. The interaction of these two processes result in the attainment of an equilibrium number of species specific for the size and remoteness of a given island. As the number of species on an island increases over time, the rate of immigration of new species to the island should decrease and the rate of extinction should increase until the two are equal. An early extension of the theory (Simberloff and Wilson, 1969) suggested that an intial asymptote in species richness, resulting from noninteractive species accrual, would be followed by a decrease to a new equilibrial value determined by interactions among species populations.

55 56

Extinction rates, therefore, would exceed immigration for a time after initial equilibration. Invasion of isolated aquatic systems by microbes can occur by various pathways. The atmosphere harbors a variety of species (Overeem, 1936; 1937; Gregory et al., 1955; Schlichting 1961; Stevenson and Collier, 1962), some of which may be in a vegetative state (Parker, 1970). Several studies (Proctor, 1959; Maguire, 1959; Schlichting, 1960) have found viable protistan species attached to the outer body parts and in the digestive tract and feces of birds. Insects are also important in the passive dispersal of microorganisms as shown by Stewart and Schlichting (1966) and Parsons et. al. (1966). Maguire (1977) studied microbial colonization of water-filled glass beakers and found a positive correlation between the number of species in a given container and the number of muddy racoon footprints around it; one taxon, the protozoan Paramecium, was only found in beakers so disturbed. The objectives of this study were: 1) to compare the process of microbial colonization in isolated aquatic systems to the predictions of the equilibrium theory of insular biogeography and 2) to assess the effects of dispersal vectors on rates of species colonization and the species pool available for colonization in such systems. 57

Methods One-hundred liter plastic swimming pools were used as aquatic islands in this experiment. Sixteen pools were sunk into the ground in an open field in late March, 1985. Four treatments consisting of four replicates each were used to vary invasion pressure including: 1) chicken wire enclosures with fine mesh nylon netting over the pool to exclude all potential animal dispersal vectors except for small insects, 2) chicken wire enclosures alone to only allow access to insects, 3) fences to exclude only terrestrial vertebrates and 4) pools with no enclosures around them. All pools were rinsed with alcohol on day Oto ensure that no viable cysts or organisms were present. Chlorinated tap water from the laboratory was used to fill the pools, thereby ensuring that the water was organism- free. Later in the day, the water in two of the pools was tested for free residual chlorine and the results were negative. Polyurethane foam (PF) substrates were used as sampling devices of microbial species in the pools. These devices have been shown to be effective, replicable collectors of protozoans, algae and benthic microinvertebrates in natural systems (Cairns and Henebry, 1982; McCormick et. al., in prep) and minimize the destructiveness of sampling in the small systems used in this experiment. On each collection day a composite sample 58

(two PF substrates) was taken from each pool and squeezed

into a sterile collecting bag. The collector's hands were

rinsed in alcohol after sampling each pool to avoid cross- contamination among pools.

Thirteen samples were collected from each pool on specified days over a 170 day period. Protistan and

microinvertebrate taxa were identified from two subsamples taken from each sample at 100-450x following the method of Cairns and Henebry (1982). In addition, on days 6, 78 and 170 benthic samples were taken from each pool to insure that certain taxa were not being missed as a result of the sampling method.

The species data from each treatment was fitted to the MacArthur-Wilson equilibrium model: S = S (1-e-Gt) t eq where = the number of species at time t, = the equilibrium number of species, G = the rate of colonization and t = time using nonlinear regression procedures with Marquardt methods of estimation to obtain Seq and G values

(Draper and Smith, 1981). Lack-of-fit tests were performed on all curves to assess the appropriatness of the model in explaining the data (Kleinbaum and Kupper, 1978). 95% asymptotic confidence intervals were used to detect differences in colonization rate and the equilibrium number of species between the various treatments. 59

Colonization and extinction rates were computed for each time period in each pool and plotted as a function of time and number of species for each treatment. The number of species used for plotting rate values was the average of the number of species present at the beginning and end of the census interval. Some of the taxa observed during the study were uncommon or had very short residency times in the pools. In order to eliminate at least some of these "transient" species the species data set was truncated by eliminating 1) species which occurred ten times or less during the study or 2) species which quickly colonized and went extinct, as determined by the formula: RC= total no. occurrences

total no. colonizations where RC was termed the residency coefficient. Species with a coefficient less than 1.25 (i.e. were present in a pool for an average of less than 1.25 sampling periods before extinction) were eliminated from the truncated data set. Species included in the reduced data set were termed "resident" species. Previous analyses were also performed on this data set. Results Although there was a decrease in the total number of species observed during the experiment with increasing enclosure, (table 1) the results were not statistically 60

significant using a distribution-free rank procedure. The only strong difference among the treatments was the decrease in the number of algal taxa found in those treatments for which access to birds was prevented. Colonization curves for the total species data set for each treatment are shown in figure 1. Variability was high, but only the caged treatment had significant lack-of-fit (table 2). No differences were found among treatments for G or Seq (table 2). An initial species accrual phase, similar to that predicted by equilibrium theory, was observed during the first month of sampling. Access to dispersal vectors had little affect on this process; all treatments attained an asymptote near fifteen species and changes in species composition were occurring in a similar manner among treatments. After the initial period of species accrual, variability in species numbers within treatments increased. There were large fluctuations in species numbers over time for individual containers as well, exceeding that which might be explained by insufficient sampling. Although changes in species composition over time were similar among treatments, the rate at which these changes were occurring were different for individual containers. Although theory predicts that changes in species composition (turnover) will occur at equilibrium, it was not apparent that an 61 equilibrium was being achieved. During the initial rise in species numbers in the first month of the experiment, certain taxa were numerically dominant in all the containers: Bodo minimus during the first two weeks and Chlarnydomonas spp. during the following two weeks. Other species (Acanthocystis sp., Anthophysis vegetans, and Sennia parvula) were also very common in several containers during this period. It is probable, therefore, that these taxa influenced other colonizing species, either directly or indirectly by their influence on the physico-chemical environment. There were no strong differences among treatments for colonization and extinction as a function of the number of species (fig. 2-3) or time (fig. 4-5) despite differences in the species pools among treatments (see table 1). Using the full species data set, extinction rates appeared to be consistent with equilibrium theory; an exponential increase in the rate of species extinction was observed as the number of species increased. Colonization, however, was not a monotonic function of number of species. As the number of species increased at low levels of species richness so did rates of colonization. As the number of species continued to increase colonization became a decreasing function of number of species. Variability was quite high for both rates of extinction and colonization. Colonization of resident species (fig. 6) appeared to 62

follow the same general patterns as for the complete data set but only the no enclosure treatment did not have a significant lack-of-fit (table 3). The same was true for rates of extinction and colonization as a function of number of species (fig. 7-8). Rates of extinction and colonization as a function of time showed a pattern of nonmonotonicity for colonization and an asymptotic increase for extinction (fig. 9-10). The asymptotic increase in extinction found over time is consistent with earlier work performed by Cairns and co-workers (1969) in natural aquatic systems.

Discussion It has been suggested (Simberloff and Wilson, 1970) that the initial process of species accrual on islands is determined by the colonizing potentials of individual species and not by interactions between species, such as competition and predation. These density-dependent forces should only become important in structuring the community as population sizes increase after an initial noninteractive equilibrium is reached. It would be expected, therefore, that the number of species might decrease following this point. Microbial communities may not adhere to this noninteractive model of colonization, which was developed from data on macroscopic communities. Because of their rapid generation times relative to the 63

process of colonization, microbial species are capable of quickly reaching very large population sizes. This strongly suggests that certain species affect the colonizing potential of other species either through direct interaction or through their effect on the physico-chemical environment. Therefore, the initial asymptote in species richness was likely determined by interactive as well as noninteractive forces. The lack of a noninteractive equilibrium has also been suggested for arthropod colonization of remote islands (Simberloff and Wilson, 1970); due to the reduced rate of colonization on such islands, the generation times of the organisms are faster relative to the time to equilibrium. Earlier studies of protozoan colonization in natural systems (Cairns et al., 1969; Pratt and Cairns, 1985) provided evidence for the attainment of an equilibrium in the number of species over time, albeit with some fluctuation in species richness about this point (see Cairns et al., 1971) .. This point is generally reached within three to seven days in lotic systems, but may take as long as five weeks in lentic systems (Cairns and Henebry, 1982). In the present study the oscillation in species numbers after an initial increase suggests that no equilibrium had been reached. It is possible that the time to equilibrium in these systems is greater than 170 days, 64

since the invasion pressure is likely to be substantially less than in the previously cited studies. The initial environment in these systems is also much harsher and, therefore, may have further slowed the attainment of some equilibrium. MacArthur and Wilson (1963, 1967) assumed that the species pool capable of colonizing an island remained constant through time and, therefore, the chance of a colonist being a new species should decrease as more species became present on the island. The data presented here suggest that, although the pool of potential immigrants may remain fairly constant through time, the number and kinds of species capable of successfully colonizing an island changes as the environment on the island changes. In particular, the pool of potentially successful colonists for a very barren habitat, in this case a pool filled with organism-free tap water, is probably very limited. As a few species colonize the environment, the group of po~entially successful colonizers may increase rather rapidly as a result of changes in the physico-chemical environment or increased habitat heterogeneity resulting from the presence of these "pioneer" species. The enhancement of colonization by the presence of certain species has also been suggested by Cairns et. al. (1969). Immigration may not be a function only of number of species, but is probably also influenced 65

by several endogenous and exogenous factors, such as the identity of the species present and the environmental state of the system. Nonmonotonic colonization curves have also been reported as a function of time (Patrick, 1967). Extinction would be expected to increase with number of species since there are more chances for an extinction to occur as more species occupy an island. It is likely that this process would also be influenced by microbial interactions which might affect a species intrinsic rate of extinction. There are two possible reasons why non-monotonic immigration curves have not been reported. First, many colonization studies (e.g Simberloff and Wilson, 1970; Strong and Rey, 1982) have dealt with recolonization of habitats where only a specific taxonomic component (e.g. terrestrial arthropods) had been removed. Few studies have documented the colonization process on truly "barren, inhospitable" habitats. Additionally, since microbes have a closer affinity to the physico-chemical environment than most macroscopic species, certain aspects of colonization may be quite different. The data presented in this chapter suggest that the process of microbial colonization in isolated aquatic systems is a strongly interactive process. The process of colonization, although similar to that of macroscopic 66

assemblages, may differ as a result of the population dynamics of microbial species and the close contact between microorganisms and the environment. As such, microbial assemblages appear to have a structure which may be heavily

influenced by direct and indirect effects between species.

Literature Cited

Cairns, J., Jr., M. L. Dahlberg, K. L. Dickson, N. Smith

and W. T. Waller. 1969. The relationship of fresh-

water protozoan communities to the MacArthur-Wilson

equilibrium model. Am. Nat.103:439-454.

Cairns, J., Jr., K. L. Dickson and W. H. Yongue, Jr. 1971. The consequences of nonselective periodic removal of portions of freshwater protozoan communities. Trans. Amer. Microsc. Soc. 90(1):71-80.

Cairns, J., Jr. and M. s. Henebry. 1982. Interactive and Noninteractive Protozoan Colonization Processes, p. 23

In: Artificial Substrates. J. Cairns, Jr. (ed.). Ann Arbor Science Publishers, Inc., Ann Arbor, MI. 67

Draper, N. R. and H. Smith. 1981. Applied Regression Analysis. John Wiley, N.Y.

Kleinbaum, D. and L. Kupper. 1978. Applied Regression Analysis and Other Multivariable Methods. Duxbury Press, North Scituate, MA.

MacArthur, R. H. and E. o. Wilson. 1963. An equilibrium theory of insular zoology. Evolution 17:373-387.

MacArthur, R. H. and E. o. Wilson. 1967. The Theory of Island Biogeography. Princeton University Press, Princeton, N.J.

Maguire, B., Jr. 1959. Passive Overland Transport of Small Aquatic Organisms. Ecology 40(2):312.

Maguire, B., Jr. 1977. Community Structure of Protozoans and Algae with Particular Emphasis on Recently Colonized Bodies of Water. p. 355 In: Aquatic Microbial Communities. J. Cairns, Jr. (ed.). Garland Publishers, Inc., N.Y.

McCormick, P. v., J. R. Pratt, D. G. Jenkins and J. Cairns, Jr. In prep. A Comparison of Protozoan, Algal and Microinvertebrate Colonization of Artificial Substrates of Differing Size. 68

Overeem, M. A. van. 1936. A sampling apparatus for

aeroplankton. Proc. Roy. Acad. Amsterdam 34:981-990.

Overeem, M. A. van. 1937. On green organisms occurring in

the lower trophosphere. Rec. Trav. Botan. Neerl.

34:389-439.

Parker, B. C. 1970. Life in the Sky. Nat. Hist. 79(8):54- 59.

Parson, W. M., H. E. Schlichting and K. w. Stewart. 1966. In-flight transport of Algae and Protozoa by Selected Odonata. Trans. Amer. Microsc. Soc. 85(4):520-527.

Patrick, R. 1967. The effect of invasion rate, species pool, and size of area on the structure of the diatom community. Proc. Nat. Acad. Sci. 58:1335-1342.

Pratt, J. R. and J. Cairns, Jr. 1985. Long term patterns of

protozoan colonization in Douglas Lake, Michigan.

J. Protozool. 32(1):95-98.

Proctor, V. W. 1959. Dispersal of Fresh-Water Algae by Migratory Water Birds. Science 130:623-624.

SAS Institute. 1985. SAS User's Guide: Statistics, Version

5 Edition. SAS Institute Inc., Cary, N.C.

Schlichting, H. E. 1960. Role of Waterfowl in the Dispersal of Algae. Trans. Amer. Microsc. Soc. 79:160-166. 69

Schlichting, H. E. 1961. Viable Species of Algae and Protozoa in the Atmosphere. Lloydia 24(2):81-88.

Simberloff, D. s. 1974. The equilibrium theory of island biogeography and ecology. Ann. Rev. Ecol. Syst. 5:161- 183.

Simberloff, D. s. and E. O Wilson. 1970. Experimental Zoogeography of Islands: A Two Year Record of Colonization. Ecology 51(5):934-937.

Stevenson, R. E. and A. Collier. 1962. Preliminary

observations on occurrence of air-borne marine phytoplankton. Lloydia 25:89-93.

Stewart, K. w. and H. E. Schlichting. 1966. Dispersal of Algae and Protozoa by Selected Aquatic Insects. J.

Ecol. 54:551-562.

Strong, D. R. and J. R. Rey. 1982. Testing for MacArthur- Wilson Equilibrium with the Arthropods of the Miniature Spartina Archipelago at Oyster Bay, Florida. Amer. Zool. 22:355-360. 70

Table 1 - Summary of species pools for all samples in the four treatments.

Total Species Pool For Experiment No. Protozoan Taxa 174

No. Algal Taxa 36 No. Microinvertebrate Taxa ,10

No. Taxa

Treatment Protozoa Algae Other Total

No access 124 20 5 149

Cages only 114 23 8 145

Fences only 115 34 6 155

No enclosure 131 28 5 164 71

Table 2 - Results of nonlinear regression analysis fitting the MacArthur-Wilson colonization model to the species accrual data for the complete data set ((ASE)= asymptotic standard error).

Estimates

Treatment Seq (ASE) G (ASE) LOF p

No access 19.19 (0.98) 0.05 (0.01) 0.25 > p > 0.10 Cages only ------p = 0.025 Fences only 21.52 (1.00) 0.05 (0.01) 0.25 > p > 0.10 No enclosure 21.12 (0.80) 0.06 (0.01) 0.25 > p > 0.10 72

Table 3 - Results of nonlinear regression analysis fitting the MacArthur-Wilson colonization model to the species

accrual data for "resident" species ((ASE) = asymptotic

standard error).

Estimates Treatment Seq (ASE) G (ASE) LOF p

No access ------0.01 > p > 0.005 Cages only ------p < 0.001 Fences only ------0.05 > p > 0.025 No enclosure 16.38 (0.66) 0.05 (0.01) 0.50 > p > 0.25 CAGES + NETTING CAGES

00 ::: !! f ft I jf h b,,!irqi () 101. I '- w . ! a.. • Cl) LL 0 a: FENCES NO ENCLOSURE w

-..J I::l t! t dI Hd tf qh dtt! w 10~ ! ! 0 60 120 180 °~ 60 120 180

TIME (DAYS)

Figure 1. Mean number of species observed over time in each treatment for the full species data set. Error bars are the ranges in species number. CAGES + NETTING I CAGES • 2.0 • • • • >- • • -<( • • 0 • • • • ...... I\ • • •• 1.0 • ••••• ·, .2 • Cl) • • • • UJ •• I\ • JJ • • .,-. . • {~)( I • 0 • • • • ..•. . e2 , w • f#: I,,• ' ' a. • '., .. Cl)

-UJ I- <( I FENCES I NO ENCLOSURE a: z 2.0 • 0 • • I- • • • • ...... <( • • • • _p.. N • • • •• • • • • z 1.0 • • • ,- • • ·' 0 • ·-c-• •• • • ...... J ,, .., , • •.. • \e 0 • . ., ~,.. s ,~ • ' 0 el • • . . •• • 0 20 40 0 20 40

NUMBER OF SPECIES

Figure 2. Rates of species colonization as a function of estimated species number for the full species data set. - 1 and 2 week sampling intervals. - 3 week sampling intervals. 2.0r CAGES + NETTING CAGES

• • • ->- ••• •• < 1.0 • • • • •• C...... ~·· ... • •• • • • 0 ••o• • .. . • "'UJ i • 00 .r,~._ • 0 •• oe • fu 0 • •• ) UJ • • • .,. • Q. •• . -.. • • "' -. -UJ I- < 2.0 FENCES NO ENCLOSURE a: • • z I • 0 • • I- • • • 0 • --..J z 1.0 • . ' V, I- , 0 • . .•.. X • • • • UJ • • ••• t? rx•• • • 1l'l, -~ • () .. 0 • 0 0 eo- • =,· • •• • • • • 0 20 40 0 20 ----40

NUMBER OF SPECIES

Figure 3. Rates of species extinction as a function of estimated species number for the full species data set. - 1 and 2 week sampling intervals. - 3 week sampling intervals. CAGES + NETTING L CAGES • 2.0 • 0 D 60 >- o• -< 0 • • ...... 111P • • 1.0 0 I'! " • •D • en • D • • j • w • 8 0• 0 Iii B i • D 0 • • D Ill •0 0 0 8 • B 0 w 111P f} ij a. • D ij "' • en 0 • • • • • w 0 -I- <0: z I- FENCES L NO ENCLOSURE 0 i= 2.0 •

TIME (DAYS)

Figure 4. Rates of species colonization as a function of time for the full species data set. Different symbols for each day represent replicate pools. CAGES + NETTING r CAGES

2.0 I

>- 0 • - D• (y < 0 0 0 1.0 i • 61 ...... D (/) • • D • 0 ·Ii • 0 Ii 0 0 • w 0 0 D• • j D i • 0 i a i 8 0 • i 0 D 0 'D D ij w D 0.. ~@ 0 (/) -w t- < a: r FENCES .- NO ENCLOSURE z 0 D i=u D z 2.°t 0 i= 0 -.J X -.J w • 0 1.0 •0 D D• i D 0 • ii 0 0 0 • • • • D I • 0 Ii i i I •D • D 0 e 0 • • e --13 D •D 0 D i ii D Iii! I I I I o' 90 180 0 90 " 180

TIME (DAYS)

Figure 5. Rates of species extinction as a function of time for the full species data set. Different symbols for each day represent replicate pools. CAGES + NETTING CAGES

20 t ! ~- i i l I f ! t p Cl) 10 .! !. w 0 w a.. t ! Cl) u. • • 0 a: w FENCES NO ENCLOSURE m :l? z:::> 20 f' ql, If tft "ex:, 10 t! ,i!J dPI ! I

0 60 120 180 O • 60 120 180

TIME (DAYS)

Figure 6. Hean number of species observed over time for "resident" species. Error bars are the ranges in species number. 2.0 CAGES + NETTING CAGES

• • • >- • -< 1.0 • • • 0 -• • • • • • en • -w • •• ••• • • • • •• 0 - •• •21"·· • 0 .2 .... • 00 ••, ... w • I • oo • • l't<»o ••• 0 a. •• 0 •• • o. en •' o. •' -._w < a: 2.or z FENCES r NO ENCLOSURE 0 • • • < • • N • z • • • ...... 0 1.0 • \0 ....I • • •• 0 • • • • u • • •• • -• • ••2 - • • • .,.0 - 12 =- • • •o•• • 'I-. ,, 0 • •• •oo.»e-~ o~ • • • ef!b. g •' 0 •' - • 0 10 20 30 0 10 20 30

NUMBER OF SPECIES

Figure 7. Rates of species colonization as a function of estimated species number for "resident" species. - 1 and 2 week sampling intervals. - 3 week sampling intervals. 2.0 CAGES + NETTING CAGES

NUMBER OF SPECIES

Figure 8. Rates of species extinction as a function of estimated species number for "resident" species. - 1 and 2 week sampling intervals. - 3 week sampling intervals. 2.0 CAGES + NETTING CAGES

•o >- (j OD• -<( 1.0 • Ii> ...... 0 •• • U) • • i • i fi • 0 w § • 0 I • • Ii> 0 ij • • 0 •(j ti ti jl i w 0 • • Ill> " 8 • a. i 8 0 • • U) •• 0 • • i • fl i 0 • • .J -._w 0 <( a: z 2.or FENCES r NO ENCLOSURE 0 .== <( N • 0. z 00 r0. .. 0 ...... I 1.0 0 • 0 • 0 •0 f} u il • 0 0 0 i 0 • 0 • • 0 I I • •f} • 0 8 • • • 0 • • 0 • 0 • ii 0 0 i • • 0 • f • 0 • 0 i • 0 90 180 90 180

TIME (DAYS)

Figure 9. Rates of species colonization as a function of time for "resident" species. Different symbols for each day represent replicate pools. 2.0 CAGES + NETTING CAGES

>- -< Q 0 • • 0 --... 0 D (/) 0 • • D 0 D D 0 • D I • w D • D 1) . i D • e D • • 0 • 0 • D D 8• • '1 • w D• Iii 0 Q I Cl. • Iii• • • • e 0 = •0 (/) 0 r1) n I I I 0 ~) -w I- < a: 2.01 FENCES NO ENCLOSURE z r 0 ui== z i== 0 D X 1.0 00 w • • N • D 0 • n 0 i 0 • • • 8 I • ii • D • e • I i 0 a ! I 0 0 •• C0 • " I 8 0 • D i Ol5 I I I • J • 90 180 90 180

TIME (DAYS)

Figure 10. Rates of species extinction as a function of time for "resident" species. Different symbols for each day represent replicate pools. Chapter 3 Microbial Succession in

Isolated Aquatic Systems

Introduction The previous chapters dealt with the applicability of the MacArthur-Wilson equilibrium theory to the process of microbial colonization in isolated aquatic systems. This chapter will focus on temporal changes in species composition and trophic structure in these systems. In addition to an approach toward an equilibrium in species richness over time, species assemblages on islands have also been shown to establish a stable trophic structure during the process of colonization (Heatwole and Levins, 1972). Most studies of this nature have been performed using islands which have had one taxonomic component removed; the process of trophic change and succession may be quite different on truly barren islands (e.g. Dammerman, 1948). Heatwole and Levins (1972) used the species data of Simberloff and Wilson (1969, 1970) for arthropod colonization of defaunated mangrove islands to study the change in trophic structure as colonization proceeded. Despite differences in faunistic composition among islands, the relative contribution of different trophic levels to

83 84

the composition of the community became fairly constant over time. The time to equilibrium for different groups varied; scavengers reached a species equilibrium in only 64 . days but higher trophic levels took longer to equilibrate. Unlike the previous study, where higher trophic levels were present very early in the recolonization process, Dammerman (1948) found a more distinct establishment sequence for trophic levels on islands made barren by the explosion of the volcano Krakatoa. Scavengers were the first to colonize, followed by omnivores, herbivores and eventually predators and parasites. Microbial assemblages include species from several trophic levels including producers, bactivores, omnivores, raptors, and saprovores (Pratt and Cairns, 1985). Changes in the trophic structure of these communities occur as a result of colonization and seasonal effects (Pratt and Cairns, 1985). Although the process of seasonal succession in aquatic microbial communities has been well documented, the process of species and trophic succession from an initially barren state has received less attention. The studies of Cairns and co-workers (1969, 1971), using protozoan colonization of polyurethane foam (PF) substrates, and Patrick (1967), using diatom colonization of glass slides, support the notion that compositional changes in microbial assemblages are a relatively orderely, 85 repeatable process. There is also evidence for a stabilization in the trophic structure of these communities (Pratt and Cairns, 1985). The purpose of this chapter is to analyze changes in species composition and functional groups during the process of colonization in barren, isolated aquatic systems. In particular, these analyses were used to provide evidence that:

1) The change in species composition over time in aquatic microbial communities is an orderly, repeatable process.

2) The relative proportion of functional groups in a developing microbial community becomes constant over time.

Methods The truncated data set obtained from the experiment described in chapter 2 was used to analyze the changes in species composition and trophic structure over time in the isolated systems. All sixteen pools were used in the analyses since there were no strong treatment effects (see Chapter 2). The number of occurrences on each sampling day (maximum= 16) was tabulated for each species, and plotted as a function of time for those taxa which were numerically dominant in several pools for at least a few sampling days. A canonical discriminant analysis was performed to evaluate temporal trends in the species composition of the 86

pools. This multivariate procedure reduces the dimensionality of a data set by deriving linear combinations of several variables (e.g. species) to achieve maximum separation among groups of observations (e.g. sampling days). Species were placed into functional groups using the scheme presented by Pratt and Cairns (1985) and personal observations on feeding habits during sampling. The groups identified in the data set included: 1) heterotrophic microflagellates, 2) photosynthetic autotrophs, including both motile and nonmotile forms, 3) bactivorous ciliates and sarcodines and 4) omnivorous species. The total number of occurrences and the total number of taxa observed on each sampling day was plotted as a function of time for each of these groups.

Results Most dominant taxa had fairly distinct temporal distributions (fig. 1), dominating an assemblage during a certain stage of the developmental process. In particular, several microflagellates were dominant during the beginning or end of the period, and algal taxa became dominant as the experiment progressed. A few taxa maintained a moderate frequency of occurrence throughout much of the experiment, although their abundance patterns changed. The results of the canonical discriminant analysis are 87 given in fig. 2. Early sampling days were well separated with respect to both canonical axes. A strong directional trend in community development was evident for later sampling dates as well, although the rate of species changed varied among pools so that adjacent sampling dates were not strongly separated. It should be noted that a strong nonlinear relationship was evident from the plot. This has been termed the "arch" or "horseshoe" effect (Pielou, 1984). Although there are procedures for removing this mathematical artifact (Hill and Gauch, 1980), its presence does not exclude a valid, nonstatistical analysis. The trophic structure of the developing community (fig. 3) underwent a rapid transition from a stage dominated by small heterotrophic flagellates to one in which autotrophs were numerically dominant. Larger bactivores and omnivores made up an increasing proportion of the community after the first month. Although there was a second increase in the occurrence and richness of microflagellates during ~he second part of the experiment, the dominant species were different from earlier species. By the end of the experiment, the autotrophic and microflagellate component of the systems remained at a fairly constant level. Fluctuations were still evident in other groups. 88

Discussion Most studies of the microbial component of aquatic systems utilize a functional approach and tend to consider the microbial component to be a "black box" with measurable inputs and outputs. Such studies have contributed much to the understanding of ecosystem processes. However, studies on the structure or microbial communites have been lacking, thereby propagating the belief that microbial assemblages are the products of random forces and do not exhibit structural properties of macroscopic communities. Cairns and Yongue (1973) found that, given a similar set of initial conditions, the process of microbial community development occurred in a fairly repeatable manner. The work performed here with isolated systems supports this result. Chance dispersal events would affect the composition of the species pool available for invasion and decrease similarity among the pools. The succession of dominant species, however, was similar for all the pools. This repeatable pattern su~gests that there is a strong deterministic component in this process. The similarity in the pattern of species succession among experiments provides even stronger evidence that microbial community development is a .·pt.edictable, repeatable phenomenon. Previous experiments (McCormick et al., in review; Pratt, pers. comm.) have found very similar patterns to the ones presented here for the temporal 89 distribution of species during colonization. The experiments differed in the size of the "islands", the source of water and the location of the "islands". Clearly, more work is needed to understand what factors are important in determining the pathway of succession. In plant succession, the observed climax community has stable parameters through time. It is likely that the dynamics of microbial succession in initially barren aquatic systems are quite different from those of higher plants. However, it does appear that a stable structure can be achieved, although there would certainly be a strong seasonal component to any "climax" community that developed. Unlike work in plant succession, this study encompassed several trophic levels. It appears that stability is first reached at the lowest trophic levels. Indeed, by 170 days there were still few or no higher order predators in some of the pools. With low invasion rates, the attainment of a stable structure throughout the community may take much longer and encompass several stable points through time (Maguire, 1977). The work presented here supports the idea that microbial communities are not random assemblages of species. It is suggested that: 1) Species are not distributed randomly through time during the process of microbial community development. 90

2) Microbial succession in barren systems moves in an orderly, repeatable direction through time; in a certain

place at a certain season the process of community development progresses toward the same state in independent sampling units.

3) Trophic stability appears to be achieved in these test systems although the species identities continue to

change. This process may occur more rapidly at lower

trophic levels than for higher order predators.

These results are similar to those found in previous

ecological studies of macroscopic communities. Although

this does not warrant the use of microbial assemblages as

"surrogates" for macroscopic communities, it does suggest

that these communities have a structure which is at least

superficially similar to other groups.

Literature Cited

Cairns, J., Jr., M. L. Dahlberg, K. L. Dickson, N. Smith and W. T. Waller. 1969. The relationship of fresh- water protozoan communities to the MacArthur-Wilson equilibrium model. Am. Nat. 103:439-454.

Cairns, J., Jr., K. L. Dickson and w. H. Yongue, Jr. 1971. The consequences of nonselective periodic removal of

portions of fresh-water protozoan communities. Trans.

Amer. Microsc. Soc. 90(1):71-80. 91

Cairns, J., Jr. and W. H. Yongue. 1973. The effect of an influx of new species on the diversity of protozoan

communities. Revista de Biologia 9(1-4):187-206.

Dammerman, K. W. 1948. The fauna of Krakatau 1883-1933.

Verhandelingen der Koninklijke Nederlandsche Akademie

van Wetenschappen, afd. Natuurkunde 44:1-594.

Hill, M. o. and H. G. Gauch. 1980. Detrended correspondence analysis: an improved ordination technique. Vegetatio

42:47-58.

Heatwole, H. and R. Levins. 1972. Trophic structure stability and faunal change during recolonization. Ecology 53(3):531-534.

McCormick, P. V., P. M. Stewart and J. Cairns, Jr. In review. The effect of distance from a source pool on

protozoan colonization of isolated aquatic systems. J. Freshwater Ecol.

Patrick, R. 1967. The effect of invasion rate, species pool, and size of area on the structure of the diatom community. Proc. Nat. Acad. Sci. 58:1335-1342.

Pielou, E. c. 1984. The Interpretation of Ecological Data: A Primer on Classification and Ordination. John Wiley,

N.Y. 92

Pratt, J. R. and J. Cairns, Jr. 1985. Functional groups in the protozoa: Roles in differing ecosystems. J. Protozool 32(3):415-423.

Simberloff, D. s. and E. o. Wilson. 1969. Experimental zoogeography of islands: the colonization of empty islands. Ecology 50:278-296.

Simberloff, D. s. and E. o. Wilson. 1970. Experimental zoogeography of islands: A two year record of colonization. Ecology 51(5):934-937. 93

B. minimus A. vegetans - C. long1cauda

Monas sp.

N. apocamptus

M. robusta

Trichamoeba sp. 1/) Q) u Vahlkampfia sp. c:: ,_Q) :5 Thecamoeba sp. u u 0 E. gasterosteus 0 z L. sphagnetorum

Chlamydomonas sp.

Filamentous green

H. pluvialis

S. acutus

Oocystis sp. 16

Bdelloid rotifer 0 6 1320 27 48 64 78 92 115 129 143 157 170

Time (Days)

Figure 1. Frequency of occurrence of dominant taxa over time in the isolated pools. 6 6

5 66 76 5 6 7 5 6 6 67 6 6 8 5 4 5 5 66 6 4 555 555 7 7 75 8 4 5 5 7 777 444 8 77 7 8 5 6 8 9 9 44 444 5 87 8 10 4 4 1 888,89 9 4 4 I 9 9 910 9 1010 (\I 9 9 109 ,~g 3 10 9 1010 1110 10 z 3 9 11

CAN1

Figure 2. Canonical discriminant analysis of "resident" species for the thirteen sampling times. 90 Flagellates 12 spp. 15 12or Bactivores 24 spp. 730 Cl) Cl) w w 0 0 z 60 <( <( w 10 X X a: <( <( a: I- I- :::, !aot 0 0 8 30 5 z o 40 10 z 0 0 0 r 0 z z 0 0 0 0

150 Producers 28 spp. 30 I Omnivores 6 spp. Cl) Cl) w 0 ~30 6 <( z ffi 100 20 X w <( a: <( a: X I.O a: I- a: 20 4 <( VI :::, :::, I- 0 0 0 0 50 10 z 0 0 0 0 10 2 z 0 0 z z 0 0 0 0 50 100 150 50 100 150 TIME (DAYS) TIME (DAYS)

Figure 3. tJumber of occurrences and number of taxa over time for the four major functional groups in the isolated pools. - number of occurrences. - number of taxa. Conclusion Except for the work of a few investigators, the vast majority of research concerning the microbial component of aquatic systems has been functional in nature. The taxa present are rarely considered; rather the assemblage is viewed as a processing unit with inputs and outputs. Studies of this type are invaluable for providing information on large scale system processes and, indeed, are scarce relative to studies on various macroscopic components. The systems level approach to microbial ecology, however, has perpetuated the belief that there is little repeatable structure to microbial assemblages. A general ignorance of these organisms only heightens this belief. The results of this study provide evidence which corroborates the conclusion of earlier work (Picken, 1937; Yongue, 1972), suggesting that there is a repeatable pattern to the structure of microbial assemblages and that this may be endogenously controlled. Studies which disregard the identity of the microbial taxa present should not be construed as providing evidence for the interchangeability of species. Although many protistan species may be regarded as functional equivalents (Cairns and Yongue, 1977), each species likely has its own feeding strategy, environmental optima, and reproductive and colonizing potentials just as is true for species in

96 97

macroscopic communities. Unfortunately very little work has been performed to substantiate such claims. A recent study (Stewart et al., in press) has shown that protistan species can respond to certain physico-chemical environmental gradients. More detailed studies are needed, not only on the microenvironment of these organisms, but on the importance of interactions (e.g. competition, resource partitioning, predation) between species within these associations. The focus of this study was to compare the process of microbial community development in isolated aquatic systems to theories developed using macroscopic organisms. The MacArthur-Wilson equilibrium theory of island biogeography has been extensively, but not critically, tested with macroscopic communities. As reviewed in the introductory chapter, there have been many studies which have purported to confirm equilibrium predictions although an increasing number of researchers have dismissed this evidence as inconclusive. The dynamics of microbial colonization in isolated systems appears to be somewhat different than that predicted by theory. In these studies it was found that: 1) Natural aquatic systems can act as a source of species for the colonization of isolated aquatic systems; a distance effect was seen with regard to the equilibrium 98

number of species, similarity with the pond, and the total number of species observed during the experiment. Rates of species immigration were not appreciably affected by the distances used from this source. 2) Access to potential microbial dispersal vectors including mammals, birds and insects did not significantly affect microbial colonization of these systems. 3) The rate of species colonization is not strongly correlated with either number of species or time, but appears to be non-monotonic with respect to the latter. 4) The rate of species extinction is positively correlated with species numbers and time in a manner similar to that predicted by theory. 5) Although an initial accrual phase was observed during the early stages of community development, after this point, individual pools exhibited unique patterns of fluctuations in species numbers. 6) Changes in trophic structure occur during the process of community development. Over time the trophic structure may stabilize, beginning with lower trophic levels. 7) Species are not distributed randomly through time, but predominate at specific points in the successional process. The process of species change through time occurs in an orderly, predictable manner both within and among 99 experiments.

It appears that the process of microbial community development is a repeatable process influenced by species interactions, either direct or indirect via effects on the physico-chemical environment. Additionally, this work corroborates and extends earlier cited work on microbial assemblages which supports the use of these communities for the testing of contemporary ecological theories.

Literature Cited Cairns, J., Jr and w. H. Yongue, Jr. 1977. Factors Affecting the Number of Species in Freshwater

Protozoan Communities p. 257 In: Aquatic Microbial

Communities. J. Cairns, Jr. (ed.). Garland Publishing,

Inc., N.Y.

Picken, L. R. E. 1937. The structure of some protozoan

communities. Jour. Ecol. 25:368-384.

Stewart, P. M., E. P. Smith, J. R. Pratt, P. V. McCormick and J. Cairns, Jr. In press. Multivariate analysis of protistan communities in lentic systems. J. Protozool.

Yongue, w. H., Jr. 1972. The Structure of Fresh-water Protozoan Communities . PhD. dissertation, Virginia

Polytechnic Institute and State University,

Blacksburg, VA. Taxonomic References

Kahl, A. 1930. Urtiere oder Protozoa. Jena.

Kudo, R.R. 1966. Protozoology, 5th ed. Charles C. Thomas.

Leidy, J. 1879. Freshwater Rhizopods of North America. U.S.

Geol. Survey, Washington.

Page, F. C. 1976. An Illustrated Key to Freshwater and Soil

Amoebae. Freshwater Biol. Assoc., Cumbria.

Pascher, A. 1913, -14, -27. Die Susswasser-Flora. Jena.

Patrick, R. and C. W. Reimer. 1966. The Diatoms of the

United States. Vol 1. Acad. Nat Sci., Philadelphia.

Pennak, Robert W. 1953. Fresh-Water Invertebrates of the

United States. Ronald Press Co.

Prescott, G. w. 1954. How to Know the Fresh-Water Algae. Wm. C. Brown Co.

Prescott, G. w. 1962. Algae of the Western Great Lakes Area. Wm. c. Brown Co.

Whitford, L. A. and G. J. Schumacher. 1973. A Manual of

Fresh-Water Algae. Sparks Press, Raleigh, NC.

100 Appendix 1 - Water chemistry data for experimental work presented in chapters 2 and 3. Treatments: 1 - cages and netting, 2 - cages, 3 - fences, 4 - no enclosure. Letters following treatment number distinguish replicate pools.

Nitrate (ppm)

Day

6 13 20 27 48 64 78 92 115 129 143 157 170 la .81 .72 .83 .00 .03 .03 .13 .18 .29 .18 .03 .23 .07 lb .80 .83 1.09 .00 .oo .08 .10 .88 .36 .57 .05 .01 .02 le .99 1.00 1.30 .28 .03 .10 .15 .41 .16 .26 .00 .13 .00 ld 1.02 1.00 .96 .77 .05 .06 .11 .02 .00 .22 .00 .34 .00 2a .89 .84 .95 .00 .00 .10 .06 .17 1.80 .25 .06 .22 .00 2b .85 1.66 1.12 .00 .00 .15 .21 .19 .52 .28 .00 .04 .oo 2c .94 .93 1.17 .78 .10 .10 .11 .32 .04 .16 .00 .20 .00 2d .90 .84 1.03 .00 .14 .13 .02 .23 .22 .39 .00 .oo .00 3a .77 .77 .92 .00 .09 .12 .06 .27 .12 .12 .02 .13 .00 3b .83 .87 .74 .oo .00 .31 .08 .08 .50 .18 .00 .11 .00 3c .98 .93 .98 .00 .04 .22 .09 .09 1.40 .53 .00 .05 .00 3d .89 .94 .95 .00 .10 .13 .06 .16 .06 .30 .00 .02 .00 4a .81 .90 .66 .00 .00 .08 .15 .25 .28 .39 .00 .19 .00 4b .80 .76 .94 .00 .00 .07 .06 .08 .16 .56 .00 .29 .00 4c .86 .77 .27 .00 .18 .07 .35 .24 .52 .16 .oo .00 .34 4d .87 .91 .92 .00 .00 .30 .02 .08 .10 .27 .01 .04 .03

101 102

Nitrite (ppm)

Day 6 13 20 27 48 64 78 92 115 129 143 157 170 la .06 .22 .08 .22 .04 .04 .04 .oo .00 .oo .10 .05 .07 lb .06 .22 .06 .24 .so .06 .05 .00 .00 .00 .07 .06 .09 le .06 .20 .10 .61 .08 .05 .05 .00 .00 .00 .12 .07 .16 ld .06 .21 .04 .47 .07 .04 .05 .00 .00 .00 .08 .14 .26 2a .09 .26 .30 .16 .05 .06 .09 .00 .00 .00 .10 .07 .13 2b .08 .26 .08 .25 .03 .00 .05 .00 .00 .00 .19 .10 .37 2c .08 .23 .06 .57 .05 .00 .04 .oo .00 .00 .34 .20 .41 2d .07 .24 .11 .50 .07 .00 .06 .oo .00 .00 .10 .10 .15 3a .08 .23 .16 .09 .05 .07 .04 .00 .00 .00 .06 .04 .12 3b .07 .20 .15 .09 .07 .06 .06 .oo .00 .oo .11 .11 .12 3c .08 .23 .14 .12 .05 .00 .04 .00 .00 .00 .11 .23 .08 3d .07 .21 .07 .14 .06 .00 .05 .oo .00 .00 .08 .10 .09 4a .07 .21 .15 .10 .18 .00 .07 .00 .00 .00 .09 .05 .07 4b .06 .24 .22 .09 .39 .00 .06 .00 .00 .00 .07 .OS .04 4c .08 .24 .29 .12 .21 .oo .11 .00 .00 .00 .31 .82 .04 4d .08 .21 .03 .15 .07 .00 .07 .00 .00 .oo .08 .08 .06 103

Ammonia (ppm)

Day

6 13 20 27 48 64 78 92 115 129 143 157 170 la .00 .17 .24 .24 .00 .44 .22 .oo .26 .15 .10 .24 .00 lb .oo .09 .24 .07 .55 .00 .23 .oo .28 .00 .16 .19 .00 le .oo .13 .51 .27 .18 .00 .00 .00 .15 .00 .35 .15 .00 ld .oo .16 .35 .34 .00 .00 .00 .oo .21 .00 .33 .10 .00 2a .00 .22 .42 .34 .00 .00 .00 .00 .17 .00 .00 .29 .00 2b .oo .10 .25 .04 .00 .47 .00 .00 .34 .00 .30 .16 .00 2c .oo .09 .24 .16 .00 .00 .00 .00 .34 .oo .31 .30 .00 2d .oo .16 .32 .30 .00 .00 .00 .00 .36 .oo .13 .09 .oo 3a .oo .12 .20 .34 .00 .00 .30 .oo .22 .oo .00 .04 .00 3b .oo .09 .16 .70 .00 .00 .00 .oo .17 .oo .29 .00 .00 3c .00 .13 .16 .55 .00 .00 .34 .00 .30 .00 .00 .04 .00 3d .00 .13 .18 .80 .00 .35 .00 .00 .15 .oo .13 .17 .00 4a .oo .13 .20 .50 .08 .22 .00 .00 .25 .00 .00 .oo .00 4b .oo .20 .48 .33 .52 .50 .00 .oo .34 .oo .00 .00 .00 4c .oo .18 .24 .07 .02 .00 .00 .00 .44 .oo .28 .04 .00 4d .oo .12 .16 .00 .04 .00 .39 .00 .32 .oo .00 .oo .00 104

Dissolved Orthophosphate (ppm) Day

6 13 20 27 48 64 78 92 115 129 143 157 170 la .00 .oo .00 .00 .00 .oo .06 .00 .12 .00 .00 .oo .oo lb .00 .oo .00 .00 .02 .09 .08 .oo .00 .00 .oo .00 .00 le .00 .00 .00 .oo .00 .02 .00 .oo .04 .oo .00 .00 .00 ld .oo .00 .00 .00 .oo .02 .60 .00 .04 .00 .00 .00 .00 2a .07 .oo .00 .00 .00 .00 .17 .oo .13 .00 .oo .00 .00 2b .08 .00 .00 .00 .00 .02 .oo .00 .00 .00 .00 .00 .00 2c .06 .00 .00 .00 .00 .02 .08 .00 .00 .00 .00 .00 .00 2d .00 .00 .00 .00 .00 .00 .oo .00 .05 .00 .oo .00 .00 3a .24 .oo .00 .00 .00 .00 .17 .00 .04 .oo .oo .00 .00 3b .06 .00 .00 .00 .00 .03 .00 .00 .00 .00 .oo .00 .00 3c .06 .oo .00 .00 .oo .00 .00 .00 .00 .00 .oo .00 .00 3d .07 .00 .00 .00 .00 .09 .00 .00 .14 .00 .oo .oo .oo 4a .10 .00 .oo .00 .00 .15 .42 .00 .39 .00 .00 .00 .oo 4b .12 .00 .00 .00 .00 .09 .00 .00 .01 .00 .oo .00 .00 4c .08 .00 .00 .00 .00 .04 .00 .00 .00 .oo .oo .00 .00 4d .04 .00 .05 .00 .00 .00 .00 .oo .00 .00 .00 .00 .00 105

Sulfate (ppm) Day 6 13 20 27 48 64 78 92 115 129 143 157 170 la 14.7 17.5 20.2 18.9 36.4 18.2 17.6 26.2 20.9 18.4 15.7 7.6 5.7 lb 13.7 18.3 18.2 18.9 35.2 24.0 18.2 25.3 20.6 16.6 17.0 8.5 8.4 le 12.1 16.3 13.8 16.6 33.3 18.5 15.2 29.7 19.3 15.3 16.0 6.4 5.5 ld 12.5 16.1 13.7 17.2 31.9 16.2 13.3 24.6 18.5 13.9 12.5 5.3 6.3 2a 14.9 17.3 18.0 21.4 37.7 16.5 14.0 25.3 22.1 18.3 20.4 7.8 7.2 2b 12.7 18.6 18.4 18.6 36.8 19.3 18.9 27.4 19.8 15.9 16.6 14.9 12.0 2c 11.9 17.9 13.9 17.1 34.6 22.2 13.6 27.1 21.9 18.2 18.3 7.8 8.9 2d 12.7 17.9 15.1 17.8 39.3 16.0 17.1 27.8 20.2 15.9 14.4 4.8 5.7 3a 12.7 16.5 18.7 19.6 43.2 14.5 15.7 23.6 20.2 27.6 16.6 9.8 7.2 3b 12.7 16.3 16.4 22.9 37.2 15.4 12.4 25.5 20.9 17.8 17.2 9.8 4.7 3c 11.9 15.7 14.4 17.3 37.4 17.4 10.3 23.4 20.0 13.3 14.2 7.0 6.8 3d 13.2 16.1 13.3 16.3 32.1 16.5 11.4 24.8 19.1 15.9 16.0 6.3 8.4 4a 13.4 18.5 18.6 18.7 46.9 16.7 17.1 31.8 20.4 15.5 12.1 3.2 4.7 4b 14.4 18.9 18.4 20.1 52.2 21.4 17.9 32.0 20.4 19.6 19.2 18.5 10.5 4c 13.5 15.3 13.4 19.6 40.7 4.5 7.1 18.8 6.5 3.0 o.o o.o 0.0 4d 12.9 17.7 14.2 17.3 43.8 17.6 15.8 29.5 20.2 18.2 12.7 4.9 0.0 106

Calcium (ppm)

Day

6 13 20 27 48 64 78 92 115 129 143 157 170 la 8.9 17.5 25.4 21.0 28.9 14.4 14.0 13.0 13.8 12.8 19.7 13.4 16.6 lb 3.2 17.4 20.0 20.5 28.8 12.1 12.9 12.3 12.0 11.2 16.1 10.4 12.4 le 2.3 17.0 16.2 19.7 28.5 14.5 12.4 13.3 13.2 11.0 17.0 10.6 12.2 ld 8.8 17.4 17.1 20.5 27.8 12.9 12.9 12.4 12.2 10.2 15.1 10.2 12.2 2a 10.0 18.5 35.7 23.5 34.6 13.4 13.6 13.9 13.7 12.3 18.5 12.3 14.4 2b 9.4 18.5 24.5 22.1 33.8 16.1 14.5 14.7 15.0 14.1 3.8 14.0 16.4 2c 10.0 18.5 17.9 22.3 36.3 13.8 14.2 14.5 14.4 12.5 19.7 11.1 14.4 2d 9.9 19.1 18.1 22.7 36.6 13.7 14.0 12.2 12.3 10.0 16.7 10.2 13.3 3a 9.8 18.8 20.9 23.6 36.3 13.2 13.4 12.7 13.2 11.4 16.5 9.1 12.6 3b 10.2 18.6 21.2 21.6 32.8 13.3 15.3 14.3 14.1 13.6 19.7 12.0 14.6 3c 8.6 18.4 17.2 23.4 34.9 12.9 14.2 14.1 15.3 13.6 20.9 14.9 16.6 3d 9.6 18.5 17.2 22.7 31.6 18.6 16.1 14.2 15.0 12.5 20.0 13.1 15.1 4a 9.2 18.8 22.4 24.7 36.1 15.7 16.2 15.2 14.6 12.6 20.2 12.0 12.4 4b 8.4 19.3 22.4 22.9 44.0 15.8 16.2 17.3 16.4 15.0 22.5 15.5 18.5 4c 9.2 18.5 18.0 22.9 38.8 12.9 13.0 14.6 15.0 11.8 13.0 12.6 13.5 4d 6.7 18.3 18.1 22.6 39.3 15.1 14.8 13.2 15.2 11.8 20.7 12.0 12.0 107

Magnesium (ppm)

Day

6 13 20 27 48 64 78 92 115 129 143 157 170 la 2.4 3.8 4.1 3.6 5.2 2.5 2.7 1.6 2.0 2.4 4.7 3.5 4.4 lb 3.0 3.8 4.2 3.5 4.5 1.5 1.9 1.2 1.7 1.9 4.0 2.9 3.7 le 1.8 3.6 3.3 3.1 4.4 2.2 2.7 1.9 2.2 2.0 4.1 2.9 3.5 ld 2.1 3.6 3.4 3.2 3.8 2.0 2.2 1.6 1.9 1.7 3.5 2.4 3.3 2a 2.6 4.1 4.4 4.4 5.2 1.8 2.2 1.7 2.3 2.4 4.6 3.8 4.6 2b 2.6 3.9 4.4 3.9 6.2 2.6 3.2 2.0 2.8 3.0 2.8 3.7 4.5 2c 2.4 3.8 3.7 3.8 7.0 2.4 2.9 2.0 2.6 2.5 4.6 3.3 4.4 2d 2.0 3.8 3.4 3.8 6.5 2.4 3.2 1.7 2.0 1.7 4.0 2.8 3.9 3a 2.5 4.0 4.4 3.9 4.7 2.0 2.5 1.8 2.9 2.7 4.6 3.0 4.3 3b 2.5 3.8 4.2 3.5 2.8 1.3 2.7 2.0 3.0 3.4 5.2 3.7 4.5 3c 2.4 3.7 3.7 3.7 5.1 2.4 3.2 2.2 3.2 3.1 4.9 3.8 4.5 3d 2.3 3.8 3.5 3.2 2.4 2.2 3.3 2.2 3.1 2.5 4.6 3.7 4.0 4a 2.7 4.0 4.5 3.7 4.0 3.1 1.1 3.1 3.7 3.2 5.2 3.9 4.0 4b 2.6 4.1 4.6 3.1 3.8 2.4 0.9 3.0 3.1 3.0 5.1 4.2 5.1 4c 2.1 3.7 3.5 2.9 3.4 1.9 0.6 2.9 3.4 2.6 3.3 3.7 4.1 4d 2.3 3.8 3.6 3.0 3.7 2.3 0.8 1.8 3.3 2.2 4.8 3.4 3.4 108

Alkalinity (ppm)

Day

6 13 20 27 48 64 78 92 115 129 143 157 170 la 43.8 42.5 49.4 60.0 51.2 22.5 23.8 25.0 30.0 32.5 36.2 25.0 33.8 lb 43.8 42.5 47.5 65.0 48.8 22.5 22.5 22.5 26.9 26.2 30.0 16.9 25.0 le 41.2 41.2 36.2 41.2 43.8 21.2 25.0 22.5 36.2 30.0 33.1 18.8 21.2 ld 42.5 42.5 37.5 43.8 42.S 20.0 23.8 22.5 27.5 27.5 23.1 21.2 22.5 2a 45.0 45.0 so.a 52.5 62.5 23.8 27.5 28.1 32.5 32.5 36.2 25.0 31.2 2b 43.8 45.0 51.9 53.1 61.2 25.0 30.0 36.2 36.2 35.6 38.8 23.8 30.0 2c 43.8 45.0 41.9 48.8 58.8 23.8 28.1 30.0 32.5 31.2 33.8 21.2 24.4 2d 42.5 42.5 42.5 52.5 65.0 23.8 25.0 23.8 26.2 25.0 30.6 21.2 26.2 3a 43.8 45.0 52.5 48.8 53.8 21.2 25.0 25.0 15.0 21.2 23.8 16.2 20.6 3b 43.8 44.4 51.2 47.5 so.a 21.2 27.5 28.8 32.5 35.0 37.5 25.0 30.0 3c 43.8 42.5 41.2 48.1 56.2 25.0 28.8 26.2 37.5 36.2 41.2 27.5 31.2 3d 43.8 43.1 40.6 43.8 41.2 22.5 30.0 28.8 36.2 27.5 36.2 21.2 30.0 4a 44.4 41.2 58.8 46.2 55.0 25.0 30.0 31.2 33.8 31.2 33.8 21.9 22.5 4b 43.1 46.2 53.8 47.5 62.S 25.0 33.8 23.8 35.0 34.4 37.5 27.5 35.6 4c 45.0 44.4 43.8 45.0 48 .. 8 22.5 33.8 31.2 37.5 33.8 39.4 23.8 31.2 4d 46.2 42.5 42.5 46.2 53.8 22.5 31.2 28.8 32.5 26.2 33.8 23.8 30.0 109

pH

Day

6 13 20 27 48 64 78 92 115 129 143 157 170 la 7.45 7.40 7.60 9.20 8.30 7.00 7.70 7.60 7.70 7.15 7.50 7.00 7.25 lb 7.65 7.40 7.50 9.30 7.35 7.50 7.80 7.40 7.50 6.95 7.50 6.75 7.10 le 7.40 7.35 6.85 9.05 8.80 7.00 7.20 7.95 7.70 7.00 7.30 6.90 7.00 ld 7.45 7.55 7.10 9.40 9.15 7.60 8.40 6.20 6.95 7.10 7.25 7.05 7.70 2a 7.90 7.25 7.70 9.20 8.10 7.95 7.65 7.20 7.40 7.30 7.20 6.80 7.20 2b 7.50 7.35 7.60 8.90 9.10 7.40 7.20 8.30 7.50 7.25 7.50 7.10 7.40 2c 7.70 7.50 7.45 7.90 8.20 8.65 7.35 7.60 7.50 6.75 7.30 7.20 7.30 2d 7.15 7.75 7.30 9.15 7.65 7.25 7.40 7.25 7.70 7.20 6.90 6.75 6.75 3a 7.75 7.40 7.80 9.45 8.50 7.50 7.30 7.40 7.10 7.00 7.20 6.70 7.10 3b 7.60 7.40 7.50 9.20 8.90 6.95 7.35 7.30 7.25 6.90 6.95 6.50 6.80 3c 7.60 7.50 7.60 8.90 7.80 7.15 7.45 7.40 7.50 7.45 9.15 8.40 8.20 3d 7.72 7.50 7.55 9.90 7.85 6.70 9.10 8.15 7.15 6.60 7.15 6.55 7.40 4a 7.90 7.30 7.55 9.70 7.20 6.90 6.90 7.60 7.80 6.90 7.55 7.00 6.90 4b 7.50 7.60 7.80 9.70 7.50 6.80 7.65 7.40 8.10 7.00 7.35 7.15 7.10 4c 7.15 7.50 7.55 9.40 7.45 6.85 6.95 6.95 8.20 7.20 7.35 7.25 7.40 4d 7.30 7.50 7.50 9.65 8.25 6.75 7.30 8.10 7.30 6.90 7.45 8.40 8.80 110

Dissolved Silica (ppm)

Day

6 13 20 27 48 64 78 92 115 129 143 157 170 la 9.0 6.0 9.5 11.0 9.5 3.8 6.1 6.6 6.3 6.8 7.0 5.5 5.2 lb 5.2 8.2 11.3 7.5 6.3 2.7 6.1 6.0 9.5 8.2 7.0 4.5 4.5 le 6.8 6.2 7.0 12.0 17.0 4.5 7.8 10.0 10.5 8.5 10.5 8.1 7.1 ld 6.5 6.3 5.6 10.0 13.5 3.0 7.1 6.5 8.1 8.0 8.5 6.2 6.8 2a 8.3 11.5 9.0 12.2 9.5 3.5 6.2 5.9 10.5 11.0 11.0 8.9 11.0 2b 5.9 11.1 12.8 12.7 17.0 4.0 7.8 8.3 11.5 10.0 9.5 6.9 7.0 2c 9.9 9.0 4.1 12.5 26.0 4.5 8.1 12.0 16.0 12.9 11.0 8.5 7.5 2d 6.9 5.8 12.1 13.7 18.0 1.0 1.0 4.2 8.0 7.9 6.0 4.3 6.8 3a 6.0 5.1 14.9 13.0 16.7 4.2 6.8 7.6 8.2 4.8 1.9 2.5 3.8 3b 7.5 8.3 11.9 5.3 6.7 3.2 6.7 11.5 21.0 17.5 19.0 12.0 14.0 3c 9.2 11.0 11.2 13.3 12.5 1.3 2.1 4.1 7.5 8.3 10.5 6.5 7.2 3d 11.0 12.1 6.8 13.0 12.0 2.9 6.5 9.7 12.3 8.9 9.7 8.0 7.1 4a 6.9 7.5 13.0 12.0 6.4 4.2 9.0 19.7 23.5 22.0 21.0 11.3 12.2 4b 8.6 9.0 4.2 13.0 3.3 3.9 8.9 11.2 16.0 12.2 5.9 4.5 4.1 4c 9.3 7.0 11.0 14.2 14.9 2.8 7.5 18.8 24.0 18.5 21.0 12.5 13.1 4d 10.5 6.3 5.5 13.0 14.4 1.2 0.1 5.0 9.0 9.2 8.9 7.5 7.1 111

Conductivity (mV)

Day

6 13 20 27 48 64 78

92 115 129 143 157 170 la 104.3 108.9 138.1 135.5 186.0 104.4 119.0 116.2 ·139.5 147.6 144.1 102.5 121.7 lb 104.3 106.6 136.7 132.7 202.0 92.6 105.1 109.9 128.0 128.1 125.6 89.1 104.5 le 94.1 111.4 121.1 131.3 195.0 103.8 101.9 103.0 134.0 130.7 120.8 93.5 107.9 ld 93.2 113.4 118.8 129.1 182.0 95.6 98.8 104.1 122.1 123.6 118.9 86.8 101.3 2a 104.0 103.8 146.2 138.6 227.0 95.6 109.9 114.6 134.7 132.7 139.0 106.7 124.9 2b 102.6 110.7 144.1 140.1 225.0 107.5 126.4 124.5 149.0 155.5 154.0 106.5 125.2 2c 94.2 117.6 118.9 144.2 228.0 87.0 104.4 99.2 146.6 136.1 140.0 93.8 118.2 2d 93.4 115.2 118.7 141.8 242.0 97.4 99.2 98.5 120.8 121.2 124.4 88.6 104.4 3a 104.6 106.5 143.5 135.1 225.0 94.7 114.6 106.2 137.0 137.0 133.3 91.6 110.2 3b 104.0 106.1 139.3 134.0 203.0 94.0 117.4 119.2 141.8 152.5 156.4 110.9 126.5 3c 94.4 115.9 121.0 135.3 229.0 96.9 94.6 100.3 132.8 134.4 129.8 96.6 111.3 3d 92.5 116.1 121.6 130.7 188.0 103.9 107.9 117.3 156.6 146.0 149.1 107.2 126.5 4a 103.3 100.3 146.4 136.6 245.0 111.2 130.0 139.9 159.5 161.5 155.0 102.1 115.5 4b 100.8 107.1 148.4 137.2 275.0 118.5 140.8 153.0 164.5 159.7 159.5 109.2 140.7 4c 94.4 116.6 117.0 140.2 251.0 96.8 107.9 128.3 161.2 144.3 152.8 96.3 125.3 4d 92.6 117.4 116.1 136.8 241.0 109.7 121.2 117.1 140.1 110.0 123.3 83.9 107.4 The 4 page vita has been removed

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