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Species richness and distribution patterns of wood-inhabiting fungi and mycetozoans (slime moulds) were investigated in the canopy of a Central European temperate mixed deciduous forest. Species richness was described with diversity indices and species-accumulation curves. Nonmetrical multidimensional scaling was used to assess fungal species composition on different tree species. Different species richness estimators were used to extrapolate species richness beyond our own data. The reliability of the abundance-based coverage estimator, Chao, Jackknife and other estimators of species richness was evaluated for mycological surveys. While the species-accumulation curve of mycetozoans came close to saturation, that of wood-inhabiting fungi was continuously rising. The Chao 2 richness estimator was considered most appropriate to predict the number of species at the investigation site if sampling were continued. Gray's predictor of species richness should be used if statements of the number of species in larger areas are required. Multivariate analysis revealed the importance of different tree species for the conservation and maintenance of fungal diversity within forests, because each tree species possessed a characteristic fungal community. The described mathematical approaches of estimating species richness possess great potential to address fungal diversity on a regional, national, and global scale.

Most of accumulation curves tend to underestimate species richness, as they do not consider spatial heterogeneity in species distribution, or are structured to provide lower bound estimates and limited extrapolations. The total‐species (T–S) curve allows extrapolations over large areas while taking into account spatial heterogeneity, making this estimator more prone to attempt upper bound estimates of regional species richness. However, the T–S curve may overestimate species richness due to (1) the mismatch among the spatial units used in the accumulation model and the actual units of variation in β‐diversity across the region, (2) small‐scale patchiness, and/or (3) patterns of rarity of species. We propose a new framework allowing the T–S curve to limit overestimation and give an application to a large dataset of marine mollusks spanning over 11 km2 of subtidal bottom (W Mediterranean). As accumulation patterns are closely related across the taxonomic hierarchy up to family level, improvements of the T–S curve leading to more realistic estimates of family richness, that is, not exceeding the maximum number of known families potentially present in the area, can be considered as conducive to more realistic estimates of species richness. Results on real data showed that improvements of the T–S curve to accounts for true variations in β‐diversity within the sampled areas, small‐scale patchiness, and rarity of families led to the most plausible richness when all aspects were considered in the model. Data on simulated communities indicated that in the presence of high heterogeneity, and when the proportion of rare species was not excessive (>2/3), the procedure led to almost unbiased estimates. Our findings highlighted the central role of variations in β‐diversity within the region when attempting to estimate species richness, providing a general framework exploiting the properties of the T–S curve and known family richness to estimate plausible upper bounds in γ‐diversity. Using known family richness, we showed that improvements to the T–S curve to accounts for true variations in beta‐diversity within the region, small‐scale patchiness, and rarity of families led to the most plausible richness estimates when all aspects were considered in the model. As accumulation patterns are closely related across the taxonomic hierarchy up to family level, improvements leading to more realistic estimates of family richness can be considered as conducive to more realistic estimates of species richness. Our approach represents a first attempt allowing a context‐specific assessment of estimates when information on true species richness lacks and that, by exploiting the properties of the T–S curve and known higher‐taxon richness, may lead to estimate plausible upper limits in species richness over large areas.

Aim To investigate how plant diversity of whole islands (‘gamma’) is related to alpha and beta diversity patterns among sampling plots within each island, thus exploring aspects of diversity patterns across scales. Location Nineteen islands of the Aegean Sea, Greece. Methods Plant species were recorded at both the whole‐island scale and in small 100 m2 plots on each island. Mean plot species richness was considered as a measure of alpha diversity, and six indices of the ‘variation’‐type beta diversity were also applied. In addition, we partitioned beta diversity into a ‘nestedness’ and a ‘replacement’ component, using the total species richness recorded in all plots of each island as a measure of ‘gamma’ diversity. We also applied 10 species–area models to predict the total observed richness of each island from accumulated plot species richness. Results Mean alpha diversity was not significantly correlated with the overall island species richness or island area. The range of plot species richness for each island was significantly correlated with both overall species richness and area. Alpha diversity was not correlated with most indices of beta diversity. The majority of beta diversity indices were correlated with whole‐island species richness, and this was also true for the ‘replacement’ component of beta diversity. The rational function model provided the best prediction of observed island species richness, with Monod’s and the exponential models following closely. Inaccuracy of predictions was positively correlated with the number of plots and with most indices of beta diversity. Main conclusions Diversity at the broader scale (whole islands) is shaped mainly by variation among small local samples (beta diversity), while local alpha diversity is not a good predictor of species diversity at broader scales. In this system, all results support the crucial role of habitat diversity in determining the species–area relationship.

We propose an integrated sampling, rarefaction, and extrapolation methodology to compare species richness of a set of communities based on samples of equal completeness (as measured by sample coverage) instead of equal size. Traditional rarefaction or extrapolation to equal-sized samples can misrepresent the relationships between the richnesses of the communities being compared because a sample of a given size may be sufficient to fully characterize the lower diversity community, but insufficient to characterize the richer community. Thus, the traditional method systematically biases the degree of differences between community richnesses. We derived a new analytic method for seamless coverage-based rarefaction and extrapolation. We show that this method yields less biased comparisons of richness between communities, and manages this with less total sampling effort. When this approach is integrated with an adaptive coverage-based stopping rule during sampling, samples may be compared directly without rarefaction, so no extra data is taken and none is thrown away. Even if this stopping rule is not used during data collection, coverage-based rarefaction throws away less data than traditional size-based rarefaction, and more efficiently finds the correct ranking of communities according to their true richnesses. Several hypothetical and real examples demonstrate these advantages.

I challenge (1) the assumption that habitat patches are natural units of measurement for species richness, and (2) the assumption of distinct effects of habitat patch size and isolation on species richness. I propose a simpler view of the relationship between habitat distribution and species richness, the 'habitat amount hypothesis', and I suggest ways of testing it. The habitat amount hypothesis posits that, for habitat patches in a matrix of non-habitat, the patch size effect and the patch isolation effect are driven mainly by a single underlying process, the sample area effect. The hypothesis predicts that species richness in equal-sized sample sites should increase with the total amount of habitat in the 'local landscape' of the sample site, where the local landscape is the area within an appropriate distance of the sample site. It also predicts that species richness in a sample site is independent of the area of the particular patch in which the sample site is located (its 'local patch'), except insofar as the area of that patch contributes to the amount of habitat in the local landscape of the sample site. The habitat amount hypothesis replaces two predictor variables, patch size and isolation, with a single predictor variable, habitat amount, when species richness is analysed for equal-sized sample sites rather than for unequal-sized habitat patches. Studies to test the hypothesis should ensure that 'habitat' is correctly defined, and the spatial extent of the local landscape is appropriate, for the species group under consideration. If supported, the habitat amount hypothesis would mean that to predict the relationship between habitat distribution and species richness: (1) distinguishing between patch-scale and landscape-scale habitat effects is unnecessary; (2) distinguishing between patch size effects and patch isolation effects is unnecessary; (3) considering habitat configuration independent of habitat amount is unnecessary; and (4) delineating discrete habitat patches is unnecessary.

1. For managed temperate forests, conservationists and policymakers favour finegrained uneven-aged (UEA) management over more traditional coarse-grained even-aged (EA) management, based on the assumption that within-stand habitat heterogeneity enhances biodiversity. There is, however, little empirical evidence to support this assumption. We investigated for the first time how differently grained forest management systems affect the biodiversity of multiple above- and below-ground taxa across spatial scales. 2. We sampled 15 taxa of animals, plants, fungi and bacteria within the largest contiguous beech forest landscape of Germany and classified them into functional groups. Selected forest stands have been managed for more than a century at different spatial grains. The EA (coarse-grained management) and UEA (fine-grained) forests are comparable in spatial arrangement, climate and soil conditions. These were compared to forests of a nearby national park that have been unmanaged for at least 20 years. We used diversity accumulation curves to compare γ-diversity for Hill numbers ⁰D (species richness), ¹D (Shannon diversity) and ²D (Simpson diversity) between the management systems. Beta diversity was quantified as multiplesite dissimilarity. 3. Gamma diversity was higher in EA than in UEA forests for at least one of the three Hill numbers for six taxa (up to 77%), while eight showed no difference. Only bacteria showed the opposite pattern. Higher γ-diversity in EA forests was also found for forest specialists and saproxylic beetles. 4. Between-stand β-diversity was higher in EA than in UEA forests for one-third (all species) and half (forest specialists) of all taxa, driven by environmental heterogeneity between age-classes, while α-diversity showed no directional response across taxa or for forest specialists. 5. Synthesis and applications. Comparing EA and uneven-aged forest management in Central European beech forests, our results show that a mosaic of different ageclasses is more important for regional biodiversity than high within-stand heterogeneity. We suggest reconsidering the current trend of replacing even-aged management in temperate forests. Instead, the variability of stages and stand structures should be increased to promote landscape-scale biodiversity.

AIM: A species–accumulation curve may represent a direct expression of β‐diversity, the rate at which diversity increases from local to regional scale. Patterns of variation in β‐diversity tend to be consistent when measured across lower levels of the Linnaean taxonomic hierarchy (i.e. using species, genera or families). Our aim was to assess the relationships between species–accumulation curves and β‐diversity at different taxonomic levels and to combine the logic of species–accumulation curves with taxonomic surrogacy to provide a new approach for cost‐effective and reliable estimates of large‐scale species richness (γ‐diversity). LOCATION: Mediterranean, N Atlantic and SW Pacific. METHODS: We provide here a novel framework to extrapolate quantitative measures of species richness in large areas from accumulation curves based on extensive sampling at the family level coupled with estimation of species‐to‐family ratios from a subset of sampling units where organisms are identified to the species level. We demonstrated the effectiveness of the approach by analysing six datasets of diverse marine molluscan assemblages from different biogeographical regions and habitat types. RESULTS: The approach proposed here can be used successfully to gain substantial efficiencies in sampling, potentially reducing the number of sampling units in which organisms have to be identified at species level between 50 and 75%, while still allowing reliable estimates of regional species richness. MAIN CONCLUSIONS: Our results highlight the potential of this approach to improve the general exploration of biodiversity, especially for large‐scale monitoring programs. The method we propose differs from previously described approaches by taking into account the spatial heterogeneity of species distributions within the sampled area and also by relying on estimates of species‐to‐family ratios obtained directly from the specific area of interest.

Aim: Recent advances in the availability of species distributional and high-resolution environmental data have facilitated the investigation of species richness—environment relationships. However, even exhaustive distributional databases are prone to geographical sampling bias. We aim to quantify the inventory incompleteness of vascular plant data across 2377 Chinese counties and to test whether inventory incompleteness affects the analysis of richness—environment relationships and spatial predictions of species richness. Location: China. Methods: We used the most comprehensive database of Chinese vascular plants, which includes county-level occurrences for 29,012 native species derived from 4,236,768 specimen and literature records. For each county, we computed smoothed species accumulation curves and used the mean slope of the last 10% of the curves as a proxy for inventory incompleteness. We created a series of data subsets with different levels of inventory incompleteness by excluding successively more under-sampled counties from the full data set. We then applied spatial and non-spatial regression models to each of these subsets to investigate relationships between the species richness of subsets and environmental factors, and to predict spatial patterns of vascular plant species richness in China. Results: Log 10 -transformed numbers of records and documented species were strongly correlated (r = 0.97). In total, 91% of Chinese counties were identified as under-sampled. After controlling for inventory incompleteness, the overall explanatory power of environmental factors markedly increased, and the strongest predictor of species richness switched from elevational range to annual wet days. Environmental models calibrated with more complete inventories yielded better spatial predictions of species richness. Main conclusions: Our results indicate that inventory incompleteness strongly affects the explanatory power of environmental factors, the main determinants of species richness obtained from regression analyses, and the reliability of environment-based spatial predictions of species richness. We conclude that even large distributional databases are prone to geographical sampling bias, with far-reaching implications for the perception of and inferences about macroecological patterns.

Scheiner (2003) presented a classification of species-area curves into six types based on the pattern of sampling and how the data are combined to form the curves. Gray et al. (2004) contended that five of those types should be termed 'species-accumulation curves', reserving 'species-area curve' for those based on island-type data. Their proposition contradicts 70 years of usage and confounds curves that are area-explicit with those that are area-undefined. In exploring these issues, I highlight additional aspects of species-area and species-accumulation curves, including the assumption of nesting in Type IV (island) curves, how to convert area-unspecified curves into area curves, and the effects of the grain of the analysis on the properties of the curve. Further exploration, theoretical development, and dialogue are needed before we will understand all the biology that species-area curves summarize.

Aim Biodiversity inventories are not yet complete, with potentially thousands of species unknown to science. This paper aims to (1) elucidate the historical discovery rate of endemic southern African marine fishes and (2) to estimate the number of potentially undescribed endemic marine fishes in the region using a statistical model and which factors (e.g. size/depth) contribute to unknown diversity. Thirdly, all species described globally for eight families (Blennidae, Clinidae, Gobiidae, Lutjanidae, Rajidae, Sciaenidae, Scyliorhinidae, Sparidae) were analysed to elucidate unknown diversity in these groups. Location The oceans of southern Africa, including Namibia, South Africa and Mozambique, encompassing sections of the south-eastern Atlantic Ocean and the western Indian Ocean. Methods Literature surveys from a number of sources were carried out to compile lists of species descriptions from 1758 onwards. Species-accumulation curves were plotted for all endemic species, as well as for different components of size and depth. A maximum likelihood model was used to estimate the number of undescribed endemic marine fishes in southern Africa, as well as for all other categories, including global families. Results Fish discovery rates in southern Africa have varied with time. Estimates suggest that at least 25% of the total endemic fish fauna remain unknown and that size is the greatest predictor of whether a fish has been described. Smaller-sized fishes and those inhabiting shallow areas are most likely to be undersampled. It is likely that most undescribed fishes inhabit the subtropical/tropical waters of the east coast. Main conclusions At the current rate of fish description, it will take at least 50 years to describe the total endemic fish fauna. However, estimates of unknown species are probably higher because of cryptic species within currently recognized taxa. Climate change may also contribute to range expansions of marine species, further complicating the status of endemicity in southern African marine fishes.