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Biodiversity patterns in protozoan communities: linking processes and scales 1
Yuri Mazei
Department of Zoology and Ecology, V.G. Belinsky State Pedagogical University,
Penza, Russia
Summary
One of the general questions in ecology is how patterns of species diversity change across spatial scales. In this study additive partitioning methodology, which allows estimating relative contributions of alpha and beta diversity components to total diversity, was applied to data on protozoan (heterotrophic flagellates and testate amoebae) communities from sphagnum bogs collected from a nested design consisting of five hierarchical levels. It allowed evaluating additive diversity partitioning on four spatial scales: 1) the Russian plain vs. ecoregions, 2) ecoregions vs. ecosystems, 3) ecosystems vs. sites, and 4) sites vs. samples. A significant percentage of total species richness was attributed to beta diversity between ecoregions and among ecosystems (different bogs) within ecoregions. Protozoan communities seem to be alpha-dominant at the broadest spatial scale and beta-dominant at finer scales. A switch in relative dominance from beta to alpha diversity with increasing spatial scale suggests scale transitions in ecological processes. This pattern is likely to be a result of different processes operating at different scales. At fine scales protozoan species interact directly, and niche partitioning is the strongest determinant of diversity, which results in differences between local communities. At broader spatial scales, where processes such as dispersal and colonization-extinction dynamics structure the communities, these interactions are probably not evident.
Keywords: biodiversity patterns, additive biodiversity partitioning, alpha-diversity, beta-di-versity, gamma-diversity, scale, microbial ecology, protozoa, heterotrophic flagellates, testate amoebae
Introduction
A central goal of ecology is to understand how biodiversity is generated and maintained. Spatial patterns of species diversity provide information about the mechanisms that regulate biodiversity at different scales (Levin, 1992; Gaston and Blackburn, 2000; Hillebrand and Blenckner, 2002; Brown et al.,
2002). For instance, these patterns can offer valuable clues to the relative influence of dispersal limitation, environmental heterogeneity, and environmental
and evolutionary change in shaping the structure of ecological communities (Green et al., 2004). Although spatial patterns have been documented in many studies of plant and animal diversity, such patterns are not as well documented in microbial species, i.e. those of Bacteria, Archaea, and microscopic Eukarya (Green and Bohannan, 2006). This is a serious omission given that microorganisms could comprise much of the biodiversity on Earth (Foissner, 1999; Torsvik et al., 2002) and have crucial roles in biogeochemical cycling and ecosystem func-
1 Materials presented on the V European Congress of Protistology (July 23-27, 2007, St. Petersburg, Russia).
© 2008 by Russia, Protistology
tioning (Gilbert et al., 1998; Morin and McGrady-Steed, 2004).
There are some reasons for our lack of understanding of the scaling of microbial diversity. Conceptually, it is assumed that microbes are different biologically from other forms of life so that their biodiversity scales in a fundamentally different way (Azovsky, 1996, 2000, 2002; Finlay et al., 1996, 1996a, 1999, 2004; Fenchel et al., 1997; Finlay, 1998, 2002; Finlay and Esteban, 1998; Finlay and Clarke, 1999; Finlay and Fenchel, 1999, 2004; Hillebrandt and Azovsky, 2001; Fenchel and Finlay, 2004, 2005). On the other hand, some of the recent research has challenged this conception, providing evidence of microbial endemism (Whittaker et al., 2003; Foissner, 2004, 2006; Mitchell and Meisterfeld, 2005), and also of a spatial patterning of microbial biodiversity (Chernov, 1993; Wilkinson, 1994, 2001; Green et al., 2004; Noguez et al., 2005; Bell et al., 2005; Smith et al., 2005) that is similar qualitatively to that of plants and animals.
So, the question remains open. A reasoning that could reconcile different views is that, since processes that produce biological diversities operate differently and at different rates according to the position of the biological phenomena along the scales of space and time, many theories and paradigms are probably more complementary than conflicting (Blondel, 1987).
Here, I aimed to reveal spatial scaling of biodiversity patterns in different types of protozoan communities from different biotopes using original data collected within the European part of Russia. To discover the way in which total species diversity is partitioned into the alpha and beta components on different spatial scales, the additive partitioning methodology was applied (Lande, 1996; Loreau, 2000; Wagner et al., 2000; Gering, Crist, 2002; Veech et al., 2002; Lu et al., 2007). This approach allows linking biodiversity patterns with scales and processes operating at different scales (Whittaker et al., 2001).
Background
Species diversity and scale
Measuring species diversity is critical for ecological research and biodiversity conservation. In the ecological literature, many measures have been proposed to assess species diversity based on data on presence or abundance of species (Pielou, 1975; Magurran, 1988). Accordingly, a lot of terms have arose. The term species richness is used for the number of species in a sample. Species diversity is commonly used interchangeably for richness, but at local
scales of analysis it is often expressed as indices that weigh both the richness and equitability (evenness of abundance across species) of a sample. Moreover, some authors, in order to distinguish and underline certain spatial scale, have adopted the term species density for the number of species sampled in a standardized sample unit, e.g. per unit area (Whittaker, 1975; Lomolino, 2001). Others have used this protocol, i.e. holding area constant, but retain the terms diversity or richness rather than density (O'Brien, 1993; Fraser, 1998).
Species richness is the simplest and the most frequently used diversity measure. However, spe-cies-richness assessments are notoriously sensitive to scale, due to the species-area relationship (Palmer and White, 1994; Veech, 2000), and to sampling effort, due to the difficulty of obtaining complete species lists (Palmer, 1995). The two problems are closely related: the number of the species observed generally increases with the number of individuals sampled, and the number of individuals increases with the size of the sampling unit (Lu et al., 2007). Thus, an important starting point in analyzing spatial patterns in richness is to control the area: a step that is very often ignored or fudged in analysis, especially at coarser scales (Whittaker et al., 2001).
In order to consider scale in assessment of species diversity, Whittaker (1960, 1975) proposed scale-dependent species diversity terms. First, he designated inventory diversity, or simply richness, assessed at four scales: (1) point scale (2) alpha (3) gamma (landscape), and (4) epsilon (regional). Secondly, he described a separate phenomenon, compositional turnover. This he termed differentiation diversity, identifying three scales: (1) internal beta or pattern diversity, lying between the inventory scales of point and alpha; (2) beta diversity, between alpha and gamma scales, and (3) geographical differentiation or delta diversity, between gamma and epsilon scales. He thus subdivided diversity into seven categories in total. However, this scheme has not been widely adopted because of a limited number of scales. Most of the authors currently referring to the framework follow, in practice, the version proposed by Cody (1975). There is a general agreement over the terms alpha and beta in the two schemes. However, Cody’s gamma scale was intended to apply to the inventory diversity (species richness) of a whole landscape. Others adopted his scheme, but generally took gamma diversity to refer to areas of different scales, perhaps because Whittaker initially set no upper bound to the term.
What actual spatial scales do the terms alpha, beta, gamma translate to? Given that different taxa of terrestrial and aquatic creatures differ by many or-
ders of magnitude in body size, it is evident that the spatial scales at which alpha, beta and gamma should be operationalized can vary between taxa (Burkovsky et al., 1994; Azovsky, 2000, 2002; Whittaker et al., 2001). The precise scale chosen is often a matter of convenience relating to the scale at which species have been mapped (Linder, 1991). I favor (in the same way as O'Brien et al., 2000 and Whittaker et al., 2001) the use of the more intuitive (and intentionally imprecise) terms local-scale (micro-scale), landscape-scale (meso-scale), regional-scale (macro-scale), and geographical-scale (mega-scale). However, the distinction made by Whittaker (1977) between inventory and differentiation diversity is an important and useful one, as is the recognition that each of these concepts can be applied at different scales of analysis.
Additive diversity partitioning
Although the hierarchical concept of diversity has a strong conceptual meaning for ecologists, it has lacked until recently the mathematical properties to make it useful in empirical or experimental settings. Whittaker (1960) originally developed a multiplicative formula to explain how alpha and beta contributed to gamma diversity (i.e. gamma = alpha x beta). The disadvantage of this relationship is that diversity components are not weighed equally when they are applied to more than one spatial scale (Gering and Crist, 2002). However, the additive relationship between the total diversity and its alpha and beta components (i.e. gamma = alpha + beta) modified Whittaker’s (1960) original formula. The additive approach was originally adopted 40 years ago (MacArthur et al., 1966; Levins, 1968; Pielou, 1969; Lewontin, 1972; Allan, 1975), but has only recently been evaluated (Lande, 1996; Veech et al., 2002) and applied to ecological phenomena (Wagner et al. 2000; Loreau, 2000; Fournier and Loreau, 2001; Gering and Crist, 2002; Ricotta, 2003; Crist et al., 2003; Gering et al., 2003; Martin et al, 2005; Stendera and Johnson, 2005; Chen et al., 2006; Lu et al., 2007). The additive approach treats alpha-diversity as the average with-in-unit diversity. Among-unit diversity (beta) is thus the average amount of diversity not found in a single, randomly chosen unit, and reflects the distinctiveness of all units. Therefore, alpha- and beta-diversity are commensurate and can be compared directly (Veech et al., 2002). Recently, Loreau (2000), Wagner et al. (2000), Fournier and Loreau (2001), and Veech et al. (2002) explicitly demonstrated how gamma-di-versity is partitioned into alpha- and beta-diversities at multiple spatial scales (Fig. 1).
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Fig. 1. The additive partitioning of total diversity into alpha and beta components at five nested spatial scales (modified from Veech et al., 2002). Mean diversity within samples at each scale (alpha1, alpha2, alpha3, alpha4 and alpha5) can be obtained on the basis of species richness in each sample. From these values, beta diversity at any scale is determined by subtracting the alpha diversity at that scale from the alpha diversity at the next highest scale (e.g. beta1= alpha2-alpha1). When there are three sampling scales, total = alpha1+beta1+beta2+beta3+ beta4; in a similar way, additive diversity partitioning can be extended to any number of scales. Converting each diversity component into a percentage is a convenient way of expressing its relative contribution to total diversity.
Species richness, scales and processes
There are numerous theories and hypotheses concerning spatial patterns of richness (Whittaker et al., 2001). It is proposed that at larger scales they collapse to dynamic hypotheses (based on climate, e.g. glaciation effect, dispersal, speciation rates), historical contingency, and available energy (partitioning of energy among species limits richness). Other hypotheses are largely operated in local-to-landscape scales of analysis. They are: (1) environmental stress (fewer species are physiologically equipped to tolerate harsh environments); (2) environmental stability (fewer species are physiologically equipped to tolerate varying environments); (3) disturbance (disturbance prevents competitive exclusion); (4) biological/
ecological interactions (competition and predation affect niche partitioning).
Thus, the species richness in local assemblages can be regulated by local factors (such as competition, disturbance, abiotic conditions) and by regional factors (such as history of climate, evolution and migration). Conceptually, the assembly of a local community can be visualized as species passing through a series of filters, which represent historical (e.g. dispersal, speciation) and ecological (e.g. competition, predation, disturbance, abiotic environmental factors) constraints on the arrival and survival of organisms at a certain locality (Zobel, 1997; Lawton, 1999). In this concept, the local diversity is related to the diversity of the regional pool if processes associated with the dispersal of organisms are mainly responsible for the assembly of local communities. A dominant impact of the local environment (abiotic and biotic) was supposed to lead to independence between local and regional species richness. Several contributions tried to disentangle the regional and the local constraints of local species richness (Cornell and Lawton, 1992; Ricklefs and Schluter, 1993; Cornell and Karlson 1996, 1997; Cornell, 1999; Srivastava, 1999; Shurin, 2001). Generally, these studies agree on an important influence of both regional and local factors, but the relative importance of these factors according to different scales and organisms’ body size is still uncertain (Hillebrand and Blencker, 2002). Some of these scales are difficult to manipulate or are not at all tractable, reducing the possibility to experimentally test the predictions on regional and local influence. Therefore, the importance of regional and local processes has been derived from the analysis of patterns.
A central method used in this discussion is the regression of local species richness on regional one (Lawton, 1999; Srivastava, 1999). Significant linear regressions are interpreted as an indication of the high impact of regional factors on local diversity, whereas saturating or nonlinear functions would indicate an upper limit of local species richness set by ecological interactions (Cornell and Lawton, 1992). However, this approach has some limitation in terms of scaling (Hillebrand and Blencker, 2002). Another possibility of revealing processes operating at different scales is to examine the way in which gamma diversity is partitioned into alpha and beta components (Loreau, 2000; Gering and Crist, 2002). A switch in relative dominance from beta to alpha diversity with changing spatial scale suggests scale transitions in ecological processes (local vs. regional).
Such analyses have been done in terrestrial, marine and freshwater systems, mainly for vertebrates,
insects and host-specific organisms like parasites. However, there is no information about diversity patterns in protozoan communities in this context, although these tiny creatures could provide new insights into the understanding of mechanisms shaping communities of living things.
Aim and hypothesis
The aim of this study is to investigate how the contributions of alpha and beta to regional diversity change as a function of spatial scale. Documenting the scale dependence (if any) of alpha and beta to gamma diversity would be helpful in determining the processes that produce a pattern of species richness at a given spatial scale (Loreau, 2000; Scheiner et al., 2000).
Different hypotheses could be developed representing a broad range of possible scenarios. Scale independence in alpha and beta could occur only if the relationship of alpha and beta to regional diversity remained unchanged across spatial scales. This scenario is analogous to a null model (i.e. no change in alpha and beta across scales), but is the least likely, because processes that determine community structure change across spatial scales (Burkovsky et al., 1994; Peterson and Parker, 1998; Huston, 1999; Azovsky, 2000, 2002) and subsequently affect the balance between alpha and beta (Loreau, 2000). Alternatively, alpha and beta could exhibit constant scale dependence, under which there would be a constant decrease (or increase) in the contribution of alpha or beta to regional diversity as the spatial scale is decreased (or increased). However, it is unclear whether these changes occur in a constant manner or in an irregular manner, so I also considered a situation where alpha and beta diversity would exhibit irregular scale dependence. This could occur if abrupt transition zones were encountered across the range of spatial scales. Transition zones represent boundaries between scale domains, or ranges of spatial scales that are dominated by particular ecological processes (Wiens, 1989; King et al., 1991; Levin, 1992). Finally, it is unclear whether alpha or beta diversity will contribute more to the regional diversity across the range of spatial scales, although Huston
(1999) predicts that alpha diversity should contribute less to regional diversity as spatial scale decreases because direct interactions are more common at fine spatial scales. In any case, we have included both alpha-dominant and beta-dominant scenarios.
I tested these hypotheses using two types of protozoan communities (testate amoebae and heterotrophic flagellates from sphagnum bogs) from a hierarchical-
ly nested design that encompassed four spatial scales: ecoregions (different continental native zones; megascale), ecosystems (different bogs within one region; macro-scale), parts of ecosystems (different sites [ e.g. hummocks, lawns and hollows in bogs] within one ecosystem; meso-scale), and samples (within macro-scopically homogeneous microbiotopes) within the European part of Russia (the Russian plain).
Material and Methods
Sampling design and study sites
I used a hierarchically nested design to sample protozoans (testate amoebae and heterotrophic flagellates) from sphagnum bogs of forest-steppe (Middle Volga; Sura river basin; Penza region), southern taiga (Upper Volga; Latka river basin; surroundings of Borok village) and northern taiga (Nothern Karelia;
Chernaya river basin; surroundings of Chernaya river village) regions (Fig. 2). The following hierarchical levels (corresponding to spatial scales) were represented in the design: the Russian plain, ecoregions, ecosystems, sites and samples. The highest level (broadest spatial scale) was represented by three ecoregions (forest-steppe, southern taiga and northern taiga; the distance between them is measured as thousands of kilometers) situated within the Russian plain and differing in their present-day climate and vegetation. Four bogs (ecosystems) were nested within each ecoregion (the distance between bogs is measured as tens of kilometers; size of bogs varies from 2000 to 5000 m2). Within each bog, five sites were selected that represented typical xeric, mesic and humidic parts of ecosystems (the distance between them is measured as tens of meters; size of sites varies from 1 to 4 m2). From those sites three samples were taken (their size was the size of one stem of
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Fig. 2. Geographic position of the study sites (black circles).
sphagnum moss (2-3 cm2) and the distance between them is measured as tens of centimeters) to represent the lowest hierarchical level (i.e., finest spatial scale) in the study. Thus, this sampling design consisted of five hierarchical levels, which allowed me to evaluate additive diversity partitioning on four spatial scales: 1) the Russian plain vs. ecoregions, 2) ecoregions vs. ecosystems, 3) ecosystems vs. sites, and 4) sites vs. samples.
Protozoan sampling and processing
I sampled the protozoan communities from three ecoregions during the period from 15 June to 15 July, 2004.
During sampling of testate amoebae a part of sphagnum was taken from moss carpet; one plant were picked out, placed in 10-ml plastic vessels and fixed by 4-% formalin. To extract testate amoebae from the moss, samples were thoroughly shaken and stirred for 10 min in distilled water. The suspension without sphagnum stems was poured off to a Petri dish; live amoebae and empty tests were distinguished and counted separately in one-tenth field of vision of stereomicroscope MBS-9 (Russia) at a magnification of x60. The amounts of cells obtained were evaluated to 1 gram of absolute dry sphagnum weight. If necessary, the tests were transferred, with the help of a thin pipette, to an object-plate, placed in a drop of glycerin and investigated at a magnification of x150 or x300 with the use of BIOMED-2 microscope (Russia).
For sampling heterotrophic flagellates, the samples containing peat water with organic debris and live sphagnum stems, were placed in 10-ml plastic vessels and conserved at a temperature of 3°C during transportation to the laboratory. In the laboratory, samples 5 cm3 in volume were split into two equal parts and put into Petri dishes (that is, each sample was analyzed in two replicates). To each dish
0.15 ml of a suspension of the bacteria Pseudomonas fluorescens containing approximately 25 mln bacterial cells was added. First, natural (non-enriched) samples were examined; then, after feeding them with bacteria, enriched samples were analyzed three, six, and nine days later. This allowed us to more adequately estimate the species richness. In order to reduce the number of photosynthesizing species and to enchance the development of the heterotrophic organisms, the Petri dishes containing the samples were kept in the dark in a thermostat at the temperature of 20°C. BIOLAM-I microscope (Russia) with KF-5 transmission light-contrast devices and water-immersion lenses (total magnification x700), and
REICHERT microscope (Austria) with interference-contrast nozzles and oil-immersion lenses (x1000) were used for light microscopic examination. The microscopes were compounded with an AVT HORN MC-1009/S analog video camera. To increase distinctness of identification of flagellates, the images were recorded on a Panasonic NV-HS 850 recorder in VHS and S-VHS formats with subsequent digitization and saving of video-film fragments AVI files. Heterotrophic flagellates were identified by means of observations of living cells, with the exception of scale-bearing species. Drops of suspended scale-bearing cells were placed on copper grids coated with Formvar film and prepared as whole mounts by the method described by Moestrup and Thomsen (1980). Grids were shadowed with tungsten oxide, and were observed with a JEM-100C transmission electron microscope.
Data analysis
I use additive partitioning methodology to understand how components of species diversity (in this study I estimate diversity as species richness, i.e. number of species at different scales) and richness contribute to different scales and then to hypothesize about the processes that produce a pattern of species richness at a given spatial scale (Allan, 1975; Lande, 1996; Loreau, 2000; Scheiner et al., 2000; Gering et al., 2003). Within the context of this study, alpha and beta diversity components maintain their traditional interpretations (Whittaker, 1960, 1977) as within-unit diversity (alpha-component) and between-unit diversity (beta-component) on a given scale. Since alpha diversity at a given scale is the sum of the alpha and beta diversity at the next lowest scale ( e.g., alpha2 = alpha1 + beta1; Allan, 1975; Lande, 1996), the overall protozoan diversity in this study can be described by the following formula: alpha1 + beta1 + beta2 + beta3 + beta4 (Fig. 3).
Results
General community patterns
During this study 103 heterotrophic flagellate and 130 testate amoebae species were identified. The species diversity of cercomonads, euglenids, and kineto-plastids is the highest. Spumella sp., Paraphysomonas vestita, Bodo saltans, B. designis, Goniomonas trun-cata, Heteromita minima, H. reniformis, Cercomonas radiatus, C. longicauda, Dimastigella mimosa, Helkesimastix faecicola, and Spongomonas uvella are the most common heterotrophic flagellate species.
Fig. 3. Relationships among hierarchical levels in additive partitioning mode, applied in this study, Whittaker’s (1960) terminology (in parentheses), and MacArthur’s (1965) designations (in brackets) of diversity (slightly modified after Gering et al., 2003). Because of the additive relationship between levels (e.g., point + pattern = alpha, alpha + beta = gamma), we can use substitution among levels to arrive at the following equation (illustrated by arrows and mathematical operators) to describe the total (i.e., regional or alpha5) diversity: alpha1 + beta1 + beta2 + beta3 + beta4.
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Fig. 4. Percentage of total protozoan species richness explained by alpha and beta components of diversity on different spatial scales. The contributions to the total richness for each scale were determined by the additive partitioning of diversity.
Among testate amoebae the most species-rich are families Arcellidae, Centropyxidae, Difflugiidae, Nebelidae, and Euglyphidae. The most common species are Assulina muscorum, Archerella flavum, Nebela tincta, N. t. major, Phryganella hemisphaeri-ca, Hyalosphenia papilio, H. elegans, Euglypha laevis, Arcella arenaria, and A. catinus. The more detailed data on species composition and protozoan community structure in sphagnum bogs investigated are given in previous publications (Mazei and Bubnova, 2007; Mazei and Tsyganov, 2007, 2007a; Mazei et al., 2007; Tikhonenkov and Mazei, 2007; Tsyganov and Mazei, 2007).
Additive biodiversity partitioning
The most noticeable result from the additive partitioning is the highest contribution of beta3 and beta4 components into total regional species richness both for heterotrophic flagellates and for testate amoebae (Fig. 4).
Another point is that protozoan communities from sphagnum bogs seem to be rather beta-domi-
nant at lower spatial scales and distinctly alpha-dominant at the broadest (ecoregional) spatial scale (Fig. 5).
Discussion
Additive partitioning methodology is simply a mathematical approach to describing the relative contributions of components to a sum total. It can be used on a variety of metrics and can quantify spatial and temporal patterns of diversity as well as other ecological data (Gering et al., 2003). In practice, however, this approach has not been applied to many ecological phenomena. The exceptions include study of benthic insect diversity in an alpine stream (Allan, 1975), plant species richness in agricultural landscapes (Wagner et al., 2000), temporal and spatial patterns of butterfly diversity in rainforests (DeVries and Walla, 2001), arboreal beetle diversity in eastern deciduous forests in the USA (Gering and Crist, 2002; Gering et al., 2003). Even so, it has considerable potential because it allows one to understand the contributions of alpha and beta diversity to the
Fig. 5. Percentage into which total species richness is partitioned by alpha and beta components on different spatial scales. The percentages of alpha and beta were determined by applying additive partitioning to the total species richness within an individual spatial scale.
total diversity (and, thus, the processes that produce a pattern of species richness at a given spatial scale; Loreau, 2000; Scheiner et al., 2000; Gering et al.,
2003) over a range of user-defined spatial scales.
The result of this study - that broad-scale beta components of diversity make a greater contribution to the regional diversity - indicates the role of the ecoregions structure’ as well as that of the structure of different bog ecosystems in forming species richness and composition of testate amoebae and hetero-trophic flagellate communities of boreal sphagnum biotopes. This pattern is similar to those obtained from arboreal beetle communities in North America (Gering et al., 2003).
The major objective of this paper was to investigate how the contributions of alpha and beta to the total diversity of protozoan communities from sphagnum bogs change as a function of spatial scale. There are few predictions about how alpha and beta diversity change across spatial scales (Gering and Crist, 2002). Scale independence of alpha and beta is unlikely because ecological processes are scale-dependent and have transitions which could in turn affect the balance between alpha and beta diversity on a given scale (Wiens, 1989; Burkovsky et al., 1994; Peterson and Parker, 1998; Azovsky, 2000, 2002). Constant scale-dependence is also unlikely unless there were gradual transitions in ecological processes that could generate constant and predictable changes in the contribution of alpha and beta to regional richness (Gering and Crist, 2002). Irregular scale dependence of alpha diversity is the most likely of the three possibilities and has already been alluded to by other authors (Wiens, 1989; Gering and Crist, 2002). Wiens (1989), for example, conceptualized scale domains, or spatial scales over which ecological patterns and processes do not change or change monotonically. Scale domains are separated by abrupt scale transitions that occur when a set of ecological patterns and processes are replaced by another set of patterns and processes. It is at these transition points where non-monotonic changes are evident. Across the range of scales in this study, these transitions could result in a pattern similar to the irregular scale dependence (Fig. 5).
I found that alpha richness accounted for a significantly larger portion (60.8-64.1 %) of the regional richness at the broadest scale. Moreover, my empirical data indicate a clear shift in dominance between alpha and beta components across the range of spatial scales (Fig.5). The analogous results were obtained during the study of arboreal beetle communities (Gering and Crist, 2002). This switch in dominance has been theorized by other authors. Loreau
(2000), for instance, stated that alpha richness should
decrease at fine spatial scales because the number of individuals is reduced and strong direct interactions could dominate the community, thereby increasing beta richness. The reverse is also true: the importance of alpha richness to overall regional richness should be more important at broader scales because local interactions are less important or undetectable (Huston, 1999; Loreau, 2000).
These explanations are realistic for protozoan communities because there is evidence that inter specific interactions (e.g. competition, facilitation, and resource sharing) among protozoan species occur within rather small sites in the ecosystems (Fenchel, 1969; Burkovsky, 1984, 1987, 1992; Azovsky, 1989, 1989a). It has also been pointed out (Shmida and Wilson, 1985) that niche relations are the strongest determinant of diversity at fine spatial scales (<10m2). However, these interactions are probably not evident at broader spatial scales, where processes such as dispersal and colonization-extinction dynamics structure the communities. In fact, dispersal of species into sites where they cannot be self-maintaining almost always results in an increased alpha diversity and is one mechanism, among others, that operates at broad spatial scales (>103 m2).
In summary, I have documented empirical evidence of irregular scale dependence in alpha richness (and therefore beta richness) and found that diversity components could switch dominance over the range of spatial scales. There is considerable indirect evidence to suggest that this pattern may be related to changes in dominant ecological processes such as interspecific interactions (at finer spatial scales) and coloniza-tion-extinction dynamics (at broader spatial scales). However, as it was noted in some papers (Gering and Crist, 2002; Hillebrand and Blenckner, 2002), the scale dependence of diversity components has not been well explored, so I cannot eliminate the possibility that sampling phenomena and/or statistical properties of hierarchical data could also generate the patterns observed. Further studies of scale dependence of diversity components will strengthen our understanding about the additivity and scale dependence of species diversity in biological communities.
Acknowledgements
I would like to thank D.V. Tikhonenkov, A.N. Tsyganov and O.A. Bubnova for their help in collecting and processing protozoan samples. The work was supported by the Russian Foundation for Basic Research (grant no. 07-04-00185) and grant from the President of the Russian Federation (MK-7388.2006.04).
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Address for correspondence. Yuri Mazei. Department of Zoology and Ecology, V.G. Belinsky State Pedagogical University, Penza, 440026 Russia. E-mail: yurimazei@mail.ru
