Measuring richness and evenness

Surveying Natural Populations by L-A.C. Hayek and M.A. Buzas Columbia University Press, 1997. $69.00/￡48.00 hbk, $28.00/￡19.00 pbk (xvi+563 pages) ISBN 0 231 10240 2/0 231 10241 0.     

Habitat fragmentation and species richness

In a recent article in this journal, Fahrig (2013, Journal of Biogeography, 40 , 1649–1663) concludes that variation in species richness among sampling sites can be explained by the amount of habitat in the ‘local landscape’ around the sites, while the spatial configuration of habitat within the landscape makes little difference. This conclusion may be valid for small spatial scales and when the total amount of habitat is large, but modelling and empirical studies demonstrate adverse demographic consequences of fragmentation when there is little habitat across large areas. Fragmentation effects are best tested with studies on individual species rather than on communities, as the latter typically consist of species with dissimilar habitat requirements. The total amount of habitat and the degree of fragmentation tend to be correlated, which poses another challenge for empirical studies. I conclude that fragmentation poses an extra threat to biodiversity, in addition to the threat posed by loss of habitat area.     

Independence of total species richness and richness difference are not the same thing

Leprieur and Oikonomou (2014) criticized that a replacement index β_− 3, a partitioned component of Jaccard index β_jac, was not richness independent and should not be used in biogeographic and ecological studies. However, Leprieur and Oikonomou failed to recognize the difference between richness and richness difference. Independence of total species richness is not equal to independence of richness difference. Theoretically and ideally, it is true that β_− 3 (and β_jac as well) is not independent of richness difference while Simpson index (β_sim) is fully independent of richness difference. However, all these indices actually are independent of total species richness. At last, it is worth mentioning that the ideal independence studied here is easily violated in computational simulation and real-world settings.     

Different relationships between galling and non-galling herbivore richness and plant species richness: a meta-analysis

The plant richness hypothesis (PRH) is used to explain herbivorous insect richness based on the number of plant species, predicting a positive relationship. However, the influence of plant richness on insect distribution can become stronger with greater levels of specialization of herbivores. In this meta-analysis, I tested whether there is any difference in the correlation force recorded between studies that investigated endophagous versus exophagous herbivores, and galling versus non-galling guilds, in order to determine whether more specialized groups have a stronger relationship. Furthermore, I calculated whether effect sizes were homogeneous between galling studies carried out at local and regional scales, and between tropical and temperate regions. A total of 52 correlations were analyzed between plant species richness and herbivore species richness, with 18 correlations derived from galling herbivores and 34 from non-galling herbivores. The effect sizes were significant and positive in all studies, being higher for endophages than for exophages, and for galling than for non-galling studies. These results provide evidence that groups of insects with a higher level of host specialization and specificity (e.g., endophagous and galling) exhibit a greater dependence on plant richness. There was no difference in effect sizes for galling studies between the local and regional level or between tropical and temperate groups. Despite the large variability found for galling studies, effect sizes were consistent independently of climatic region and latitudinal variation. These results suggest that the PRH for galling insects can be generalized to most ecosystem and vegetation types.     

Plant species richness belowground: higher richness and new patterns revealed by next-generation sequencing

Variation in plant species richness has been described using only aboveground vegetation. The species richness of roots and rhizomes has never been compared with aboveground richness in natural plant communities. We made direct comparisons of grassland plant richness in identical volumes (0.1 × 0.1 × 0.1 m) above and below the soil surface, using conventional species identification to measure aboveground richness and 454 sequencing of the chloroplast trnL(UAA) intron to measure belowground richness. We described above- and belowground richness at multiple spatial scales (from a neighbourhood scale of centimetres to a community scale of hundreds of metres), and related variation in richness to soil fertility. Tests using reference material indicated that 454 sequencing captured patterns of species composition and abundance with acceptable accuracy. At neighbourhood scales, belowground richness was up to two times greater than aboveground richness. The relationship between above- and belowground richness was significantly different from linear: beyond a certain level of belowground richness, aboveground richness did not increase further. Belowground richness also exceeded that of aboveground at the community scale, indicating that some species are temporarily dormant and absent aboveground. Similar to other grassland studies, aboveground richness declined with increasing soil fertility; in contrast, the number of species found only belowground increased significantly with fertility. These results indicate that conventional aboveground studies of plant richness may overlook many coexisting species, and that belowground richness becomes relatively more important in conditions where aboveground richness decreases. Measuring plant belowground richness can considerably alter perceptions of biodiversity and its responses to natural and anthropogenic factors.     

Habitat patchiness and plant species richness

The pattern of woody species richness decline with a decrease in woody vegetation cover was studied within a tallgrass prairie. The decline in species richness is highly non-linear, with a well-defined threshold below which species richness collapses. This relationship can be understood after considering information on how landscape structure changes with woody vegetation cover, and how species richness is related to landscape structure.     

Energy input and zooplankton species richness

What are the relative contribution of temperature and solar irradiance as types of energy deliveries for species richness at the ecosystem level? In order to reveal this question in lake ecosystems, we assessed zooplankton species richness in 1891 Norwegian lakes covering a wide range in latitude, altitude, and lake area. Geographical variables could largely be replaced by temperature‐related variables, e.g. annual monthly maximum temperature or growth season. Multivariate analysis (PCA) revealed that not only maximum monthly temperature, but also energy input in terms of solar radiation were closely associated with species richness. This was confirmed by stepwise, linear regression analysis in which lake area was also found to be significant. We tested the predictive power of the “metabolic scaling laws” for species richness by regressing Ln of species richness over the inverse of the air temperature (in Kelvin), corrected for the activation energy (eV) as predicted by the Boltzmann constant. A significant, negative slope of 0.78 for ln richness over temperature, given as 1/kT, was found, thus slightly higher than the range of slopes predicted from the scaling law (0.60–0.70). Temperature basically constrained the upper bound of species number, but it was only a modest predictor of actual richness. Both PCA‐analysis and linear regression models left a large unexplained variance probably due to lake‐specific properties such as catchment influence, lake productivity, food‐web structure, immigration constraints or more stochastic effects.     

Sampling effort and parasite species richness

Comparative studies of parasite species richness among host taxa can be confounded by uneven sampling effort. Sampling ceases to be a confounding factor when extrapolation methods are used to estimate true species richness. Here, Bruno Walther and colleagues review examples of sampling bias and the use of extrapolation methods for circumventing it. They also discuss the confounding effects of phylogenetic association of estimates of species richness.     

Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal

Aim Species richness and endemic richness vary along elevation gradients, but not necessarily in the same way. This study tests if the maxima in gamma diversity for flowering plants and the endemic subset of these plants are coherent or not. Location The study was conducted in Nepal, between 1000 and 5000 m a.s.l. Methods We used published data on distribution and elevational ranges of the Nepalese flora to interpolate presence between maximum and minimum elevations. Correlation, regression and graphical analyses were used to evaluate the diversity pattern between 1000 and 5000 m a.s.l. Results The interval of maximum species endemic to Nepal or the Himalayas (3800–4200 m) is above the interval of maximum richness (1500–2500 m). The exact location of maximum species density is uncertain and its accuracy depends on ecologically sound estimates of area in the elevation zones. There is no positive statistically significant correlation between log‐area and richness (total or endemic). Total richness is positively correlated with log‐area‐adjusted, i.e. estimated area adjusted for the degree of topographic heterogeneity. The proportion of endemic species increases steadily from low to high elevations. The peak in endemism (c. 4000 m) corresponds to the start of a rapid decrease in species richness above 4000 m. This may relate to the last glacial maximum (equilibrium line at c. 4000 m) that penetrated down to 2500–3000 m. This dynamic hard boundary may have caused an increase in the extinction rate above 4000 m, and enhanced the probability of isolation and facilitated speciation of neoendemics, especially among genera with a high proportion of polyploids. Main conclusions The results reject the idea of corresponding maxima in endemic species and species richness in the lowlands tentatively deduced from Stevens’ elevational Rapoport effect. They confirm predictions based on hard boundary theory, but hard‐boundaries should be viewed as dynamic rather than static when broad‐scale biogeographical patterns with a historical component are being interpreted.     

Trait-based species richness: ecology and macroevolution

Understanding the origins of species richness patterns is a fundamental goal in ecology and evolutionary biology. Much research has focused on explaining two kinds of species richness patterns: (i) spatial species richness patterns (e.g. the latitudinal diversity gradient), and (ii) clade-based species richness patterns (e.g. the predominance of angiosperm species among plants). Here, I highlight a third kind of richness pattern: trait-based species richness (e.g. the number of species with each state of a character, such as diet or body size). Trait-based richness patterns are relevant to many topics in ecology and evolution, from ecosystem function to adaptive radiation to the paradox of sex. Although many studies have described particular trait-based richness patterns, the origins of these patterns remain far less understood, and trait-based richness has not been emphasised as a general category of richness patterns. Here, I describe a conceptual framework for how trait-based richness patterns arise compared to other richness patterns. A systematic review suggests that trait-based richness patterns are most often explained by when each state originates within a group (i.e. older states generally have higher richness), and not by differences in transition rates among states or faster diversification of species with certain states. This latter result contrasts with the widespread emphasis on diversification rates in species-richness research. I show that many recent studies of spatial richness patterns are actually studies of trait-based richness patterns, potentially confounding the causes of these patterns. Finally, I describe a plethora of unanswered questions related to trait-based richness patterns.     

Kin selection,species richness and community

Can evolutionary and ecological dynamics operating at one level of the biological hierarchy affect the dynamics and structure at other levels? In social insects, strong hostility towards unrelated individuals can evolve as a kin-selected counter-adaptation to intraspecific social parasitism. This aggression in turn might cause intraspecific competition to predominate over interspecific competition, permitting coexistence with other social insect species. In other words, kin selection—a form of intra-population dynamics—might enhance the species richness of the community, a higher-level structure. The converse effect, from higher to lower levels, might also operate, whereby strong interspecific competition may limit the evolution of selfish individual traits. If the latter effect were to prove more important, it would challenge the common view that intra-population dynamics (via individual or gene selection) is the main driver of evolution.     

Plant species richness under

Exotic pine plantations constitute a significant landscape feature in the North Island of New Zealand but their conservation value for native plant species is not often documented. Pine stem density, height and basal area of nine plantations of Pinus radiata ranging in age from 6 to 67 years in Kinleith Forest was determined. Pines reached heights of 60 m, and stand basal areas up to 183 ± 14 m(2)ha(-1). The abundance of woody shrubs, tree ferns and ground ferns was assessed in each stand. Understorey composition of shrubs and ferns was reflected on the first two axes of DCA ordinations and correlated with the age of the pines. Adventive shrubs predominated in stands < 20 years old. Light-demanding native shrubs with bird dispersed fruits predominated in older stands, with more shade-tolerant species in the oldest site. Species richness increased rapidly in the first 11 years, but thereafter more slowly. Twelve native shrub species and 22 ferns were recorded from the most diverse stands. Richness and species composition were related to stand age, and probably also to topographical heterogeneity and aspect. Tree ferns reached densities of 2000—2500 ha(-1) and basal areas of 20—30 m(2)ha(-1) in the older stands. Initially the tree fern population was strongly dominated by Dicksonia squarrosa, which comprised 84% of individuals overall. Five species were present by 29 years. The faster growing Cynthea medullaris and C. smithii achieved greater heights than the Dicksonia spp., and their relative biomass was greatest in the oldest stands.     

A richness that cures

A study in Nature by Fischer et al. shows that environmental enrichment or increasing histone acetylation rescue the ability to form new memories and re-establish access to remote memories even in the presence of brain degeneration. Chromatin remodeling may be the final gate environmental enrichment opens to enhance plasticity and represents a promising target for therapeutical intervention in neurodegenerative diseases.     

Estimating lacustrine zooplankton species richness and complementarity

Literature and original information reveals that lakes at any latitude may be expected to lodge + 50 spp. of cladocerans, against + 150 spp. of rotifers in the temperate, and + 210 spp. in the tropical zone. Collector's curves can be used to estimate the number of species present at any point in time in a lake. Hyperbolic regression and Chao's non-parametric estimator were used to extrapolate from species numbers observed to true numbers present. Estimates for rotifers were better (had lower variances) than for cladocerans, and both were better in the temperate than in the tropical zone, where more species co-exist than in the temperate zone but where many more species are rare. Approximate numbers of samples required to approach true instantaneous species richness were calculated. However, a test in a (sub)tropical lake in Brasil where such an asymptotic number of samples was collected and examined failed to reduce the variance, while recording a number of species higher than predicted. We conclude that seasonal succession was still significant here, and that more research is needed to determine the minimum number of sampling repeats needed for a full census.Lakes with an ATBI (All Taxa Biological Inventory) for rotifers and cladocerans were compared by a complementarity index. This revealed geographic gradients between lakes, strong for cladocerans, but less so for rotifers. It is argued that this mainly reflects a difference in the state of taxonomic advancement between these two groups, and that the theory of cosmopolitanism must be abandoned for both.     

Species richness, area and climate correlates

Aim Species richness–area theory predicts that more species should be found if one samples a larger area. To avoid biases from comparing species richness in areas of very different sizes, area is often controlled by counting the numbers of co‐occupying species in near‐equal area grid cells. The assumption is that variation in grid cell size accrued from working in a three‐dimensional world is negligible. Here we provide a first test of this idea. We measure the surface area of c. 50 × 50 km and c. 220 × 220 km grid cells across western Europe. We then ask how variation in the area of grid cells affects: (1) the selection of climate variables entering a species richness model; and (2) the accuracy of models in predicting species richness in unsampled grid cells. Location Western Europe. Methods Models are developed for European plant, breeding bird, mammal and herptile species richness using seven climate variables. Generalized additive models are used to relate species richness, climate and area. Results We found that variation in the grid cell area was large (50 × 50 km: 8–3311 km²; 220 × 220: 193–55,100 km²), but this did not affect the selection of variables in the models. Similarly, the predictive accuracy was affected only marginally by exclusion of area within models developed at the c. 50 × 50 km grid cells, although predictive accuracy suffered greater reductions when area was not included as a covariate in models developed for c. 220 × 220 km grid cells. Main conclusions Our results support the assumption that variation in near‐equal area cells may be of second‐order importance for models explaining or predicting species richness in relation to climate, although there is a possibility that drops in accuracy might increase with grid cell size. The results are, however, contingent on this particular data set, grain and extent of the analyses, and more empirical work is required.     

Species richness and evenness in Australian birds

Species richness and evenness are the two major components of biodiversity, but the way in which they are interrelated is a subject of contention. We found a negative relationship between the two variables for bird communities at 92 woodland sites across Australia and sought an explanation. Actual evapotranspiration (AET) was by far the best predictor of species richness. When AET was controlled for, the relationship between richness and evenness became nonsignificant. Richness is greater at sites with higher AET because such sites support a greater number of individuals. However, such sites have a greater number of rare species, resulting in lower evenness. A complicating factor is that evenness is best predicted by degree of vegetation cover, with sparsely vegetated sites having significantly lower evenness. We conclude that there are two competing ecological processes, related to energy and water availability, that determine richness and evenness. The first drives total abundance (leading to high richness, low evenness), while the second drives productivity and niche availability (leading to high richness, high evenness). The relative strength of these two processes and the observed relationship between richness and evenness are likely to depend on the scale of the analysis and the species and range of habitats studied.