Search

Aim: To derive null models for the expected number of species shared among multiple samples or habitat patches, allowing exploration of the geometric effects of subdivision on species diversity. Location: Global. Major taxa studied: Predominantly sessile organisms. Methods: The occurrence probability of a species in a subdivided area depends on its abundance and spatial pattern over a known habitat extent. The joint probability that two subareas share a species is the product of the probability of species occurrence in each subarea provided that the latter probability is independent. The sum of this probability over all species is the number of species the two subareas share, or zeta diversity of order 2. Generalizing from 2 to m subareas yields a null model for zeta diversity of order m. From zeta diversity, many metrics (e.g., beta and gamma diversity) for the m habitat patches can be calculated, revealing the effects of increasing habitat fragmentation. Results: The null models show the geometric effects of subdivision depend on patterns of spatial distribution of species within a landscape and evenness of species abundance distribution. For aggregated assemblages, increasing subdivision decreases shared species, increases beta diversity and results in higher total species richness in subdivided habitat than an equal contiguous area. Main conclusions: To correctly interpret diversity patterns in fragmented habitat the geometric effects of subdivision must be considered. Our models explain why fragmented habitat could have higher diversity than continuous habitat of equal area but predict a threshold patch‐size above which this will not occur (here c. 100 ha). Apparently positive diversity effects of subdivision, including more species in groups of small patches, are probable outcomes of spatial aggregation of assemblages. The shared species null models offer an analytical tool for exploring the geometric effects of subdivision on diversity while controlling for total habitat area. [ABSTRACT FROM AUTHOR]

We studied compositional turnover in communities of fleas and mites harboured by small mammals using zeta diversity metric (similarity between multiple communities) and asked whether the patterns of zeta diversity decline with an increase in the number of communities differ between taxa and hierarchical scales [infracommunities (parasite assemblages on individual hosts), component communities (parasite assemblages harboured by host populations), and compound communities (all parasite species in a locality)]. The average number of shared species declined with an increasing number of communities (zeta order). It attained zero at higher orders in infracommunities of both taxa with the shape of the zeta decline being best fitted by the negative exponential function, and the retention rate curves being modal. In contrast, zeta diversity values for compound communities of mites and fleas did not attain zero at higher zeta orders, and the form of the zeta decline was best fitted by the power-law function, whereas the retention rate curves were asymptotic. In component communities, the form of zeta decline was best fitted by either exponential or power-law function in dependence of whether communities were considered within a host across localities or across hosts within a locality and whether ubiquitous species were taken into account. Our main conclusions are that (a) the rules governing compositional turnover in parasite communities for the lowest and the highest hierarchical scales are taxon-invariant but scale-dependent and (b) species composition of infracommunities is mainly driven by stochastic assembly processed, whereas that of compound communities is mainly driven by niche-based processes. [ABSTRACT FROM AUTHOR]

Sonora has a rich natural diversity, including reptiles and amphibians. Sonora's location on the United States-Mexico border creates some unique conservation challenges for its wildlife. We compiled a list of the amphibian and reptile species currently known for Sonora, summarized the conservation status of these species, and compared our list of species with known species lists for adjacent states. The herpetofauna of Sonora comprises 200 species of amphibians and reptiles (38 amphibians and 162 reptiles). Overall, Sonora shares the most species with Chihuahua, Sinaloa, and Arizona. Approximately 11% of the amphibian and reptile species are IUCN listed, but 35.5% are placed in a protected category by SEMARNAT, and 32.6% are categorized as high risk by the Environmental Vulnerability Score. [ABSTRACT FROM AUTHOR]

A summary of the species of amphibians and reptiles of the state has been compiled, including their geographic distributions, habitats, and conservation statuses. The herpetofauna of San Luis Potosí consists of 41 species of amphibians and 141 species of reptiles. San Luis Potosí shares the highest number of species with Hidalgo and Tamaulipas, and the least number of species with Nuevo León. In San Luis Potosí, there are several taxa of particular conservation concern including salamanders, emydid and trionychid turtles, anguid and xenosaurid lizards, and natricid and colubrid snakes. [ABSTRACT FROM AUTHOR]

A summary of the species of amphibians and reptiles of Durango, as well as their geographic distributions, habitat, and conservation status have been compiled. The herpetofauna of Durango consists of 36 species of amphibians and 120 species of reptiles. Durango shares the most species with Chihuahua (74.0%), and shares fewer species with Sinaloa (48.0%), Nayarit (48.7%), and Coahuila (48.0%). Arid-semiarid and Sierras habitat types have the most species, with valleys and Quebradas habitat types having fewer species. In Durango, there are several taxa of particular conservation concern including eleutherodactylid frogs, eublepharid, iguanid, phrynosomatid, and xantusid lizards, boid, colubrid, and natricid snakes, and emydid and testudinid turtles. [ABSTRACT FROM AUTHOR]

In extensive low input farming and in agroforestry systems, the importance for biodiversity of managed productive fields with respect to unmanaged marginal habitats that occupy a low proportion of farm surface, is still poorly understood, contrasting with the well-known key importance of marginal habitats in intensive systems. We analyzed the importance of open and wood pastures and marginal habitats for species richness of Iberian dehesas in Central-Western Spain. We sampled 155 plots classified into 9 general habitat categories: wood pastures (n = 41 plots); open pastures dominated by annual plants (n = 11), by perennial plants (n = 15) and co-dominated by annuals and perennial plants (n = 16); shrublands (n = 19); agricultural crops (n = 12); herbaceous strips (n = 10); woody strips (n = 11); and water bodies (n = 10). In each plot we measured the abundance and species richness of four taxonomic groups: vascular plants, bees, spiders, and earthworms. We detected 431 plant species (37 ± 2.5 CI in 100 m on average), 60 bee species (3.1 ± 1.1 in 600 m), 128 spider species (7.4 ± 1.2 in 1.5 m) and 18 earthworm species (2.5 ± 1.0 in 0.27 m) in 145 sampling plots. Wood pastures supported fewer species of spiders and earthworms at the plot level, but more plants and earthworm species at the landscape level than open pastures. The low proportion of shared species among habitats and among plots within each habitat type, and the high proportion of species found in unique plots or habitats indicated that every habitat contributes to farm biodiversity. Overall, our extensive survey confirms the hypothesis that the high diversity of dehesas depends on the coexistence within farms of a wide mosaic of habitats, including marginal habitats, which seemed to harbor a disproportionately high number of species as compared to their small extent. Results support policy measures for the maintenance of farm keystone structures such as linear features, small wood/shrub patches and ponds, and reveal that these measures should not be exclusively applied to more intensive farming systems. [ABSTRACT FROM AUTHOR]

A sequence of jackknife estimators is developed to estimate the number of shared species in two communities. The estimators have simple and explicit formulae. A sequential testing criterion is also developed to determine a proper order for these jackknife estimators. The performance of the estimators is evaluated using empirical data on two forests from Malaysia, where 209 shared species present in both forests, and using simulated data. Results for the empirical data and simulated scenarios (for sampling fraction ranging from 0.5 to 20 %) show that the jackknife estimator, compared with other existing estimators, has a smaller bias and provides more reliable interval estimation in most cases. Additionally, two avian datasets from Taiwan and Hong Kong are used to demonstrate the proposed method. To extend the proposed method to three communities, we also list the first six orders of the jackknife estimators explicitly. [ABSTRACT FROM AUTHOR]

A number of species richness estimators have been developed under the model that individuals (or sampling units) are sampled with replacement. However, if sampling is done without replacement so that no sampled unit can be repeatedly observed, then the traditional estimators for sampling with replacement tend to overestimate richness for relatively high-sampling fractions (ratio of sample size to the total number of sampling units) and do not converge to the true species richness when the sampling fraction approaches one. Based on abundance data or replicated incidence data, we propose a nonparametric lower bound for species richness in a single community and also a lower bound for the number of species shared by multiple communities. Our proposed lower bounds are derived under very general sampling models. They are universally valid for all types of species abundance distributions and species detection probabilities. For abundance data, individuals' detectabilities are allowed to be heterogeneous among species. For replicated incidence data, the selected sampling units (e.g., quadrats) need not be fully censused and species can be spatially aggregated. All bounds converge correctly to the true parameters when the sampling fraction approaches one. Real data sets are used for illustration. We also test the proposed bounds by using subsamples generated from large real surveys or censuses, and their performance is compared with that of some previous estimators. [ABSTRACT FROM AUTHOR]

The article presents a nonparametric lower bound for the number of species shared by multiple communities. Based on sample species abundance or replicated incidence records from each of the N communities, it derives a nonparametric approach to constructing a lower bound for the number of species shared by N communities. It also presents simulation results to examine the performance of the lower bounds.

Abstract: Deforestation and degradation of tropical old-growth forests has the potential to cause catastrophic species extinctions. In this review, we assess whether regenerating secondary forests (SF) can support species typically found in old-growth forest (OG) and so prevent extinctions. We review 65 studies that compare faunal diversity in SF and corresponding OG, and compare the similarity of species composition both within and between these two forest types using the Sorensen, Morisita–Horn and Sorensen–Chao indices. Comparisons between traditional similarity indices and Sorensen–Chao, which minimizes sampling biases, indicated that limited sampling effort consistently reduced apparent similarity between SF and OG and that similarity between SF and OG is actually higher than previously appreciated. Similarity, which ranges from 0 to 1, varied from 0.49 to 0.92 between replicate OG sites and was correlated with similarity between SF and OG. This correlation might be an artefact of variation among studies in sampling effort, especially for vertebrates where small samples reduce apparent similarity across all comparisons, as well as a real effect of variation among studies in landscape heterogeneity and the presence of species with patchy distributions. Therefore, similarity between SF and OG cannot be interpreted without an understanding of background variation in OG. Similarity between different SF sites provided no evidence that disturbance specialists dominate SF. Similarity to OG increased rapidly with SF age; when SF was contiguous with OG; when SF was growing in small clearings; and after low intensity land uses including clearing only, shifting agriculture and tree plantations. This describes the most frequently observed tropical SF; isolated from roads and on hilly terrain unsuitable for mechanized agriculture. Thus, our analyses indicate that tropical SF can play an important role in biodiversity conservation particularly when OG forests are nearby. An important caveat remains, however. Abundance, geographic range and levels of habitat specialization are often related. Widespread, abundant, habitat generalists might dominate similarity analyses even when relatively rare OG specialists are present. Additional species-level analyses of habitat specialization will be needed before the conservation value of tropical SF is fully understood. [Copyright &y& Elsevier]

A traditional approach for assessing similarity among N ( N > 2) communities is to use multiple pairwise comparisons. However, pairwise similarity indices do not completely characterize multiple-community similarity because the information shared by at least three communities is ignored. We propose a new and intuitive two-stage probabilistic approach, which leads to a general framework to simultaneously compare multiple communities based on abundance data. The approach is specifically used to extend the commonly used Morisita index and NESS (normalized expected species shared) index to the case of N communities. For comparing N communities, a profile of N− 1 indices is proposed to characterize similarity of species composition across communities. Based on sample abundance data, nearly unbiased estimators of the proposed indices and their variances are obtained. These generalized NESS and Morisita indices are applied to comparison of three size classes of plant data (seedling, saplings, and trees) within old-growth and secondary rain forest plots in Costa Rica. [ABSTRACT FROM AUTHOR]

Abstract: Soil biodiversity varies through space as influenced by habitat features and land-use history. The performance of any sampling strategy highly depends on its relevance with regards to this pattern. We surveyed the soil macrofaunal species richness in the pastures of the Benfica Field Station (Eastern Amazonia, State of Pará, Brazil) and described its variability in 4 independent replicate plots. We designed a within-plot sampling scheme that accounted for the soil spatial variation (stratified sampling). Replicated pasture plots had different species richness (49–65) corresponding to a low proportion (40–53%) of the total number of species (123). Pairs of replicated plots showed an outstandingly low number of shared species (28–41% of the species pool). Likewise, different classes of soil thickness, corresponding to a Ferralsol–Cambisol sequence, had different species richness (12–44) and exhibited a very low proportion of shared species (15–29%). The proportion of rare species, i.e. singletons, ranged from 40–51% of the total species richness depending on the plot considered. We used the abundance-based coverage estimator of species richness (ACE) and the Chao shared species estimator that provides a correction based on the relative abundance of rare species. These indices also showed both a high between plots dissimilarity and a substantial within plot variability of species composition. Because of the high proportion of rare species, the rarefaction curves failed to reach any asymptote in all replicated plots. Bootstrap resampling showed that less than 5 samples per stratum (class of soil thickness) provided inconsistent species richness values. We simulated the efficiency of sampling strategies that included our 4 replicate plots and the 3 classes of soil thickness but with varying sampling effort within each stratum. The results indicated that a fairly large (74%) proportion of species would be recorded if strata were sampled using 5 sampling units (hence 15 samples per plot for a total of 4×15=60 samples). This study showed the need for adequate plot replication in soil macrofaunal biodiversity studies. Also, the main relevant factors of within-replicate plot spatial heterogeneity (e.g. soil, vegetation) should be accounted for through stratified sampling. The results showed that there is no way of reducing the local sampling effort below a certain level (here, 5 sampling units per stratum). [Copyright &y& Elsevier]

The classic Jaccard and Sørensen indices of compositional similarity (and other indices that depend upon the same variables) are notoriously sensitive to sample size, especially for assemblages with numerous rare species. Further, because these indices are based solely on presence–absence data, accurate estimators for them are unattainable. We provide a probabilistic derivation for the classic, incidence-based forms of these indices and extend this approach to formulate new Jaccard-type or Sørensen-type indices based on species abundance data. We then propose estimators for these indices that include the effect of unseen shared species, based on either (replicated) incidence- or abundance-based sample data. In sampling simulations, these new estimators prove to be considerably less biased than classic indices when a substantial proportion of species are missing from samples. Based on species-rich empirical datasets, we show how incorporating the effect of unseen shared species not only increases accuracy but also can change the interpretation of results. [ABSTRACT FROM AUTHOR]

• Dissimilarity indices differ in the relative weight given to rare species. • Indices based on dominant species may produce inaccurate estimates. • Heavy-weighting of rare species improves the ability to detect multivariate groups. • Heavy-weighting rare species may be achieved using specific formulae or coded data. Dissimilarity indices differ in the relative weight given to rare species. Heavy-weighting of rare species may be justified in terms of sampling. An index may erroneously estimate high dissimilarity between two identical communities if they are composed of many rare species and the sampling effort is insufficient to observe most of them in both samples. Heavy-weighting of rare species is thought to compensate for this negative bias. I evaluated two quantitative indices that heavy-weight rare species, NNESS (New Normalized Expected Species Shared) and Goodall, and two probability versions of the Sørensen index, one that takes into account shared unseen rare species and the other that does not. They were compared against the widely used Bray-Curtis (or Sørensen quantitative) and the Morisita-Horn. Indices were computed using raw abundance data or coded data that heavy-weight rare species (frequency in sample units, log-transformation and standardization by the maximum abundance within species). Indices were evaluated for their ability to distinguish, using distance-based MANOVA, season-defined (summer, winter) groups of samples of stream macroinvertebrates and groups of samples obtained by simulation. Sørensen corrected for unseen shared species performed poorly in the empirical study and intermediate in the simulations. NNESS was good in the empirical study and intermediate in the simulations. Goodall scored inversely as NNESS, being intermediate in the empirical assessment and very good in the simulations. The Sørensen uncorrected for unseen shared species, Bray-Curtis and the Morisita-Horn presented poor or intermediate results using raw abundance data. Their performance, however, improved consistently using coded data that heavy-weight rare species and made them good or very good. I conclude that heavy-weighting rare species improves the ability to detect multivariate groups. Heavy-weighting of rare species may be achieved either by using specific formulae (NNESS, Goodall) or using coded data. [ABSTRACT FROM AUTHOR]