Based on the data shown in the graph, when would the species richness of the ecosystem

Species Richness

Species richness (S) is the number of species within a defined region. The species richness of a region is obtained through sampling or via a census. Because “region” is defined by the observer, species richness has been further categorized into three components to account for changes in spatial scale.

Alpha diversity, sometimes referred to as point diversity, is the species richness that occurs within a given area within a region that is smaller than the entire distribution of the species. Beta diversity is the rate at which species richness increases as one moves in a straight line across a region from one habitat to another habitat. In other words, it is the rate of change in species richness that occurs with a change in spatial scale. Gamma diversity is the species richness within an entire region. As the area being surveyed approaches that of the entire region, alpha diversity approaches gamma diversity and beta diversity approaches zero.

Morphodynamic Effects

Species richness increases in response to increasing tide range, increasing wave energy, decreasing sand particle size and in flatter and wider beaches. This means that macrofaunal species richness increases as beaches become more dissipative (McLachlan and Dorvlo, 2005). For ocean beaches, the number of species recorded in a single transect survey ranges from 1 to 40 (if insects are excluded). More dissipative beaches and flats may harbor 20–40 species at least, as the shore flattens towards the dissipative or tidal flat extreme.

The best models fitted to 194 beaches based on data from five continents shows that the relationships between species richness and morphodynamic features of beach type are not linear. Summarizing global trends for a diverse range of exposed sandy beaches, Figure 5 shows that species richness: (1) increases asymptotically from reflective to dissipative beaches (Figure 5(a)), with increasing tide range (Figure 5(f)), and with a measure of intertidal area obtained by dividing tide range by the beach face slope (Figure 5(e)); and (2) decreases exponentially with increasing sand particle size (Figure 5(c)) and slope (Figure 5(d)). Moreover, species richness increases linearly with the Beach Index (Figure 5(b)), i.e. from microtidal reflective to macrotidal dissipative beaches.

Following the same trend as for species richness, macrofaunal abundance and biomass decrease exponentially with increasing sand particle size and slope (Figure 6). Thus, species richness in sandy beaches increases from microtidal reflective to macrotidal dissipative conditions, whereas abundance and biomass increase exponentially, leading to greater densities of organisms.

Abstract

Species richness is the simplest measure of species diversity and is either a count of the number of, or the list of, species inhabiting a given area or habitat. Measures of species diversity are formed from species richness by further classifying the species by attributes, such as abundance, size, or ecological role. Measures of species diversity link to many ecological and evolutionary processes such as population dynamics, competition, community dynamics, adaptive radiation, and the evolution of phenotypic plasticity. Species diversity is the basis for the diversity of higher taxa and ecological associations such as communities and biomes.

Richness

Species richness is the most widely used parameter for evaluating aspects of fungal biodiversity. It is deceptively simple to define species richness as an enumeration of the species that are associated with a particular sample, area, habitat, or substratum. In fact, three kinds of species richness can be distinguished: (1) numerical species richness, (2) species density, and (3) total species richness (Hurlbert 1971; Kempton 1979; Brown 1995; Rosenzweig 1995). The number of species in a sample in which the biomass or number of individuals has been standardized is numerical species richness. The number of species in a sample in which the area, volume, or weight of the sampling unit has been standardized is species density. Finally, the cumulative number of species based on a series of samples from a habitat or substratum is the total species richness.

Numerical species richness and species density are defined after complete enumeration of the taxa in a sample, whereas total species richness is estimated from a series of samples. The first two are measured without error, assuming that the size of the sample is sufficiently small and that the techniques of isolation and identification are developed sufficiently to allow an investigator to enumerate all taxa. It is important to note that the effects of natural or experimentally induced variation in environmental characteristics on species density and numerical species richness are scale-dependent—that is, the effect of environmental variation may not be the same for samples that differ in area, volume, weight, biomass, or number of individuals (Waide et al. 1999; Gross et al. 2000). In part, the scale-dependence of patterns occurs because the importance of different causal mechanisms depends on the scale at which data are collected. Most ecological studies of fungi cover species densities as a consequence of sampling design, although in most cases that aspect of diversity is not stated explicitly. We strongly recommend that future research always include an explicit definition of scale as well as of the attribute of richness that is being evaluated. Once a particular spatial scale has been selected for an investigation in which species density or numerical species richness is the characteristic of diversity under examination, classical statistical considerations of random sampling and power determine the efficacy of a research design.

It would appear that an unambiguous and straightforward index of total species richness, S, would be the cumulative number of unique species present in a series of samples. The magnitude of S, however, depends on the size, number, and dispersion of samples in a particular habitat, biome, or area. Indeed, three mathematical relations (Power, Exponential, and Logistic) have been championed in the literature to predict the way in which S increases with effort, A (number, area, or volume of samples). In both Power (S = CAz; Arrhenius 1921) and Exponential (S = C + z ln A; Gleason 1922, 1925) models, S increases monotonically as effort increases (Fig. 5.1 middle and lower graphs). Those functions are most appropriate when heterogeneous landscapes are sampled and increases in effort result in increases in the heterogeneity of habitats that are included in samples. All three relations are members of the same family of curves (He and Legendre 1996). Consequently, both Power and Exponential models figure prominently in the theory and practice of island biogeography and conservation biology. In contrast, when the domain of interest is circumscribed geographically and random sampling occurs within the borders of that domain, the logistic relation (S = B/(C + A)−z; Archibold 1949) more likely characterizes the manner in which S increases with effort. Unlike the other two species-effort curves, the logistic relation predicts that S eventually will reach a plateau or asymptote (Fig. 5.1 upper graph). The value of S at this asymptote is an accurate estimate of the true species richness of the domain (inference space) of interest. The effort required to attain asymptotic values for particular taxa, however, is likely specific to particular substrata, habitats, or biomes. Consequently, comparisons of S at levels of effort not associated with the asymptote can lead to spurious conclusions.

Given the dependence of S on collection effort and the fact that limited financial resources, personnel, or logistic support often prevent the collection of samples sufficient to attain asymptotic values, S is of limited value as a comparative index (Ludwig and Reynolds 1988). Consequently, a number of indices that are independent of the number of samples taken have been developed to estimate species richness. Those indices are based on the relationship between S and n, the total number of individuals in the collection of samples. Ludwig and Reynolds (1988) also cautioned that two well-known species-richness indices, the Margalef index (1958) and the Menhinick index (1964), make specific assumptions concerning the relationship between S and n (S = kn0.5, where k is a constant). In many cases, those assumptions may not hold, and as a consequence, the utility of the indices is limited.

Direct counts of species numbers in samples of equal size (i.e., equal number of individuals) may provide an informative alternative to indices of species richness. Implicit in this approach, however, is the assumption that the collector's curves for the treatments of concern are coincident (or at least nonintersecting). For enumeration of fungi from leaf disks or root surfaces, the approach has merit. However, it is not always possible to ensure that all sample sizes are equal. In situations in which the sample size is not constant, rarefaction, a quantitative method (Magurran 1988), facilitates comparison of species richness among areas or habitats as if they were based on a standardized sample size. The number of species that can be expected in a sample of n individuals is:

(3)E (S)=Σ{1-[(N-Ni n)/(Nn)]}

where E(S) is the expected number of species in a rarified sample, n is the standardized (rarified) sample size (usually chosen to be equal to the smallest sample available for an area or habitat), N is the total number of isolates (individuals) recorded in the set of samples, and Ni is the number of isolates (individuals) in the species. To calculate the expected number of species in a rarified sample, the abundance of each species is inserted into the quantity defined by braces ({}) in formula (3) and summed to provide the expected number of species. To assist in the computation recall that:

(4) (Nn)=N!n!(N-n)!

where the exclamation point (!) indicates a mathematical operation termed a factorial. Worked examples can be found in Magurran (1988) and Krebs (1989). Polishook and colleagues (1996) used rarefaction to determine the expected number of species of fungi from decaying leaves from a Puerto Rican rain forest.

Species Richness and Lake Trophy

Species richness increases with habitat area and heterogeneity. The increase in species richness (S) with habitat area (A) is often predicted by the equation S = constant × Az. The z value depends on the organisms, the spatial range of the study, and the habitats, and there is no single mechanism that can account for the variability of z. However, low z values are consistent with high immigration rates, low extinction rates, and a low rate of increase in additional habitat with increasing area. As a consequence, very small z values are predicted for bacteria, microalgae, protozoa, and zooplankton. Indeed, the z values predicting crustacean zooplankton species richness are only 0.054 for European and 0.094 for North American lakes. A low z value of 0.10 for submerged macrophytes in Scandinavian lakes is also consistent with their widespread occurrence.

In contrast, species richness of fish in lakes of four different regions in Canada and the United States shows much higher z values (0.16, 0.22, 0.36, and 0.37) consistent with the more restricted geographic range size, smaller local abundance, and greater risk of extinction of fish following disturbance or restriction of natural habitats. As a consequence, risks of local, regional, and global extinction are high among fish and low among microorganisms and plankton organisms, with aquatic plants and insects probably holding an intermediate position. Some large crustaceans and molluscs presumably resemble the fish by having a low rate of dispersal and including several species of restricted geographic distribution (e.g., the freshwater crabs and prawns on tropical oceanic islands).

Changes in ionic composition and nutrient concentrations of freshwaters are superimposed on changes in habitat area and heterogeneity. Water chemistry is not independent of habitat area and heterogeneity, since small lakes include a higher proportion of low pH, low-calcium, and nutrient-poor waters than do large lakes. In many studies of the influence of pH and lake trophy on species richness, it is not possible to fully compensate for the influence of habitat area and heterogeneity.

The most diverse conditions and highest species richness often are found under mesotrophic conditions, where many species can coexist at different sites and depths within the lakes. Thus, high habitat heterogeneity is probably important for the common peak of species richness under mesotrophic conditions. For example, many free-living and attached species adapted to different conditions of light, temperature, and exposure can replace each other along depth gradients from shallow to deep waters. Under very oligotrophic conditions, many algae and plant species are nutrient limited, and low primary production places an energetic restriction on the diversity and density of invertebrates and fish. In contrast, under hypereutrophy, many species are restricted by the lack of light and oxygen in deep waters, and special adaptations are required for survival in very muddy sediments.

Two other conditions contribute to the decline of species diversity under hypereutrophic conditions. First, the natural local variability between unfertilized and fertilized sites disappears with the overall nutrient enrichment at all sites accompanying eutrophication. Second, hypereutrophy is exceptional for most species, which have evolved over many millions of years of no or weak human impact. Species richness is expected to be highest in those common habitat types that have had the most widespread and long-term natural occurrence, because there has been time and room for speciation. Hypereutrophic lakes, which are common today, have been exceedingly rare during most of the development of freshwater species.

It is noteworthy that lake ecosystems are much more sensitive to environmental deterioration and catastrophic declines of organisms by eutrophication than terrestrial ecosystems for at least four reasons. First, nutrients tend to stay in circulation within the lake boundaries. Second, phytoplankton responds by a steep increase in biomass and productivity. Third, photosynthesis and growth of algae and plants are restricted to the uppermost surface waters because of the impoverished light conditions. Fourth, the risk of oxygen depletion is very high because of low oxygen solubility in the water. Therefore, species richness within many groups of organisms declines in hypereutrophic lakes because of reduced habitat heterogeneity, restriction to the distribution of organisms, and development of stressful and highly variable environmental conditions with respect to light, oxygen, pH, and sulfide.

Factors driving diversity

Species richness/alpha- and beta-diversity in saline lakes decrease with increasing salinity till few, if any, macroinvertebrates and algae are present beyond ca. 250 g L− 1. This relationship is often expressed linearly and the slope may change with drought conditions which affect insects far more than crustaceans (Fig. 5). In reality, once halophiles dominate, the decrease beyond 50 g L− 1 is appreciably less in the mesosaline and particularly the hyposaline range (Williams et al., 1990).

While salinity per se is the major influencing factor (Timms, 1997), acidity adversely affects diversity in many mesosaline lakes in southern Western Australia (Timms, 2009), so that only one to two species are present at pH 3, just as in euhypersaline conditions (salinity > 250 g L− 1). Another factor is heterogeneity of shores—in inland Australia beta-diversity is promoted by many small lakes vis-a-vis large windswept lakes like Lake Eyre (Timms, 2021). This effect can be counterbalanced in deep vs. shallow lakes due to the addition of a distinct benthic community in the former. Diversity can be influenced by season as seen in lakes with irregular filling regimes, for insects are adversely affected in winter fills verses summer inundations (Timms, 2018). Another complication is provided by relict species of ultimate marine origin that may inflate species richness at higher salinities as in the coastal lakes in Crimea (Anufriineva and Shadrin, 2018). Details are given in Timms (2021).

Functional Diversity

Although species richness has been most often used as a metric of diversity, functional group richness has been invoked as a better predictor of ecosystem service as traits of organisms more strongly relate to functions than do taxonomic classifications (Tilman et al., 1997; Diaz and Cabido, 2001). A functional group is a grouping of species based on similarity in behavioral, morphological, physiological, or resource use traits (Petchy and Gaston, 2006; Philpott et al., 2009). Functional diversity of natural enemy characteristics, then, might be more important to consider than the taxonomic richness of predators itself, as this relates more strongly to ecosystem function (Hooper et al., 2005).

Functional diversity, especially of important predators, may be more strongly affected by agricultural intensification than species richness. For example, Flynn et al. (2009) found that bird functional diversity declined with agricultural intensification more quickly than did species richness. Further, Schweiger et al. (2007) found that declines in specialist parasitoids were stronger than generalist parasitoids with habitat degradation. This may be especially important if species complementarity is an important mechanism maintaining positive effects of predator diversity on pest control. Philpott et al. (2009) examined patterns behind significant positive relationships between richness of insectivorous birds and arthropod removal in tropical agroforestry systems. They divided birds into functional groups based on characteristics related to predatory function (e.g., body size, diet, foraging strategy, and strata) and then correlated changes in functional richness with both species richness and pest control function. Species richness and functional richness were highly correlated across the nine study sites examined, and functional richness correlated significantly with arthropod removal. However, simple species richness remained a better predictor of ecosystem function than functional richness either because the traits included were not sufficient to explain all variation, or because presence of important predator species played a more important role.

Climatic considerations

Zooplankton species richness and composition have been shown to vary with elevation and latitude; patterns that suggest a role for climatic factors in determining diversity. For example, maximal richness values decline at high latitude and elevation across Norway (Hessen et al., 2006), cladoceran species richness declines from south-to-north throughout Canada (Henriques-Silva et al., 2016), and zooplankton compositional change occurs among fishless North American lakes spanning an elevation gradient (Symons and Shurin, 2016). Changes in zooplankton community composition have also been more directly correlated with large-scale temperature and precipitation gradients (Loewen et al., 2019). Lower local or regional-scale species richness in locations with lower temperatures and incoming solar radiation suggest that zooplankton communities may follow the predictions of the species-energy hypothesis, which posits that higher energy inputs support a greater number of species, by stimulating ecosystem productivity, and thereby reducing the chances of species’ populations declining to extinction (Pinel-Alloul et al., 2013; Lyons and Vinebrooke, 2016).

It has also been suggested that temperature and diversity may be linked through overlaps in species distributions. This would result in a hump-shaped relationship between species richness and temperature, with maximum species richness at intermediate temperatures, and declines in cooler or warmer systems (Patalas, 1990). The proposed mechanism is that, over the intermediate temperature range, southern warm-water and northern cool-water species would potentially coexist, resulting in a comparatively high diversity. Interestingly, it may not be just average temperature that is correlated with zooplankton species richness, but also temporal variability in water temperature (Shurin et al., 2010). It is thought that lakes that demonstrate strong temperature variation over time allow larger numbers of species to coexist, since those species can occupy niches that occur at different points in time, during periods of either water column mixing or stable thermal stratification.

Species Richness of Vertebrates and Invertebrates

The species richness of vertebrates recorded in a plant community is positively related to the species richness of vascular plants found in the understory stratum (Figure 7(a) and 7(b)), as indeed is the number of frog and snake species (Braithwaite et al., 1985; P. C. Catling, in Specht, 1988b; Cody, 1994a and Cody, 1994b; Specht, 1994; Specht and Specht, 1999; Specht and Tyler, 2010). These may also be related to potential energy-fixation. An inverse correlation has been observed, however, for lizards, presumably reflecting their food and habitat preference.

It would also appear from the limited data available that the species richness of epigaeic invertebrates collected in pit-fall traps decreases as the annual input of leaf litter (Specht and Brouwer, 1975) from the overstory decreases from the humid to the semi-arid climatic zone in the Mediterranean-type climate of southern Australia (Table 3; after Greenslade and Majer, 1985, and Specht, 1988).

Table 3. Number of groupsa of epigaeic invertebrates collected (throughout the year) in pitfall traps established within four vegetation formations typical of humid to semiarid mediterranean-type climates of southern Australiab

I.C The Problem

Species richness, the number of species present, is conceptually the most straightforward of diversity measures. Measuring richness, however, is not so simple. The number of species observed in a sample, Sobs, will always tend to underestimate the true species richness because we lack the resources for exhaustive sampling of communities. Even if we had such resources, properties of community structure may change during the course of sampling. This problem is exacerbated by the fact that many biological communities have a large number of rare species, which are unlikely to be detected by sampling efforts. These issues are not unique to ecology.

The problem of determining the number of classes of objects in a collection has a long history. Many different disciplines have sought ways to estimate the number of undiscovered classes from the classes already observed. For example, how many dies were used to mint a collection of ancient coins? Or, how many undiscovered bugs remain in a large computer program? No one method has been successful at all such problems. This is because we often find that we have the most information where it is the least useful. That is, a few classes account for most of the observations while a few observations are scattered over most of the classes. So, we wind up with small sample sizes for the rare observations where we need to characterize variation the most. In the most extreme case, imagine a community where every species is represented by just one individual. Then, for any sample size below the true number of species we shall always observe less than the true number of species. Of course, in this case we could observe that the number of species was a linear function of the number of individuals. This might suggest to us a way to estimate the number of undetected species if we knew how many individuals were present in the study area. This example illustrates the strategy followed by richness estimators: model the regularity in the behavior of the number of species detected as a function of sample size. Then, use this model to predict the number of species when the sample size becomes large. The regularity could involve a clear relationship between the average sample size and the average number of species observed; this is the basis of extrapolation-based estimators. On the other hand, the regularity could mean that we can replace a complicated sampling process with a more tractable model; estimators based on sampling theory follow this approach.