Identifying hotspots for biodiversity management using rank abundance distributions

Aim Identification of biodiversity hotspots has typically relied on species richness. We extend this approach to include prediction to regional scales of other attributes of biodiversity based on the prediction of Rank Abundance Distributions (RADs). This allows us to identify areas that have high numbers of rare species and areas that have a rare assemblage structure. Location Continental slope and shelf of south‐western Australia, between 20.5 and 30° S and depths of 100–1500 m. Methods We use a recently developed method to analyse RADs from biological surveys and predict attributes of RADs to regional scales from spatially abundant physical data for demersal fish and invertebrates. Predictions were made for total abundance (N), species richness (S) and relative evenness at 147,996 unsampled locations using data from two spatially limited surveys. The predictions for S and relative evenness were then independently split into categories, creating a bivariate distribution. The RAD categories are mapped spatially between 20.5 and 30° S to depths of 1500 m to allow identification of areas with rare species and assemblage structure across this region. Results Rank abundance distributions for demersal fish vary with large scale oceanographic patterns. Peaks in abundance and unevenness are found on the shelf break. The bivariate distributions for richness and evenness for both fish and invertebrates show that all assemblage structures are not equally likely. The RAD categories identify regions that have high numbers of rare species and areas with unique assemblage structure. Main conclusions Predicted RADs over large regions can be used to identify biodiversity hotspots in more detail than richness alone. Areas of rare species and rare assemblage structure identified from fish and invertebrates largely overlap, despite the underlying data coming from two different data sets with two different collection methods. This approach allows us to target conservation management at species that would otherwise be missed.     

Assessment of species diversity from species abundance distributions at different localities

We show how the spatial structure of species diversity can be analyzed using the correlation between the log abundances of the species in the communities, assuming that two communities at different localities can be described by a bivariate lognormal species abundance distribution. A useful property of this approach is that the log abundances of the species at two localities can be considered as samples from a bivariate normal distribution defined by only five parameters. The variances and the correlation can be estimated by maximum likelihood methods even if there is no information about the sampling intensity and the number of unobserved species. This method also enables estimation of over-dispersion in the sampling relative to a Poisson distribution that allows sampling adjustment of the estimate of β-diversity. Furthermore, we also obtain a partitioning of species diversity into additive components of α-, β- and γ-diversity. For instance, if the correlation between the log abundances of the species is close to one, the same species will be common and rare in the two communities and the β-diversity will be low. We illustrate this approach by analysing similarities of communities of rare and endangered species of oak-living beetles in south-eastern Norway. The number of recorded species was estimated to be only 48.1% of the total number of species actually present in these communities. The correlations among communities dropped rather quickly with distance with a scaling of order 200 km. This illustrates large spatial heterogeneity in species composition, which should be accounted for in the design of schemes of such devices for assessing species diversity in these habitat-types.     

Seasonality in bacterial diversity in north-west Mediterranean coastal waters: assessment through clone libraries, fingerprinting and FISH

We combined denaturing gradient gel electrophoresis (DGGE), catalysed reporter deposition-FISH (CARD-FISH) and clone libraries to investigate the seasonality of the bacterial assemblage composition in north-west Mediterranean coastal waters. DGGE analysis indicated that bacterial diversity changed gradually throughout the year, although with a clear distinction of the summer period. Alphaproteobacteria were the dominant group on an annual basis [29% of the DAPI (4',6-diamidino-2-phenylindole) counts by CARD-FISH, and 70% of the bacterial clones]. The SAR11 clade was most abundant during spring and summer (>20% of DAPI counts), while the Roseobacter clade was abundant primarily in winter and spring (up to 7% of DAPI counts). The phylum Bacteroidetes constituted the second most important group and was quantitatively uniform throughout the year (average 11% of the DAPI counts). Gammaproteobacteria showed a peak during summer (8% of DAPI counts), when most of them belonged to the NOR5 cluster. Clone libraries and CARD-FISH showed reasonable agreement in the quantitative proportions of Bacteroidetes and Gammaproteobacteria, but Alphaproteobacteria were overrepresented in clone libraries. Sequencing of the most predominant DGGE bands failed to detect the SAR11 group despite their high abundance. The combination of the three molecular approaches allowed a comprehensive assessment of seasonal changes in bacterial diversity.     

Functional gene diversity analysis in BTEX contaminated soils by means of PCR-SSCP DNA fingerprinting: comparative diversity assessment against bacterial isolates and PCR-DNA clone libraries

Developments in molecular biology based techniques have led to rapid and reliable tools to characterize microbial community structures and to monitor their dynamics under in situ conditions. However, there has been a distinct lack of emphasis on monitoring the functional diversity in the environment. Genes encoding catechol 2,3-dioxygenases (C23O), as key enzymes of various aerobic aromatic degradation pathways, were used as functional targets to assess the catabolic gene diversity in differentially BTEX contaminated environments by polymerase chain reaction-single-strand conformation polymorphism (PCR-SSCP). Site specific PCR-SSCP fingerprints were obtained, showing that gene diversity experienced shifts correlated to temporal changes and levels of contamination. PCR-SSCP enabled the recovery of predominant gene polymorphs, and results closely matched with the information retrieved from random sequencing of PCR-DNA clone libraries. A new method for isolating strains capable of growing on BTEX compounds was developed to diminish preselection or enrichment bias and to assess the function of predominant gene polymorphs. C23O abundance in isolates correlated with the levels of BTEX pollution in the soil samples analysed. Isolates harbouring C23O genes, identical to the gene polymorph predominant in all contaminated sites analysed, showed an unexpected benzene but not toluene mineralizing phenotype whereas isolates harbouring a C23O gene variant differing by a single point mutation and observed in highly polluted sites only, were capable, among some other isolates, to mineralize benzene and toluene, indicating a catabolically determined sharing of carbon sources on-site. The PCR-SSCP technique is thus a powerful tool for assessing the diversity of functional genes and the identification of predominant gene polymorphs in environmental samples as a prerequisite to understand the functioning of microbial communities.     

Office space bacterial abundance and diversity in three metropolitan areas

People in developed countries spend approximately 90% of their lives indoors, yet we know little about the source and diversity of microbes in built environments. In this study, we combined culture-based cell counting and multiplexed pyrosequencing of environmental ribosomal RNA (rRNA) gene sequences to investigate office space bacterial diversity in three metropolitan areas. Five surfaces common to all offices were sampled using sterile double-tipped swabs, one tip for culturing and one for DNA extraction, in 30 different offices per city (90 offices, 450 total samples). 16S rRNA gene sequences were PCR amplified using bar-coded "universal" bacterial primers from 54 of the surfaces (18 per city) and pooled for pyrosequencing. A three-factorial Analysis of Variance (ANOVA) found significant differences in viable bacterial abundance between offices inhabited by men or women, among the various surface types, and among cities. Multiplex pyrosequencing identified more than 500 bacterial genera from 20 different bacterial divisions. The most abundant of these genera tended to be common inhabitants of human skin, nasal, oral or intestinal cavities. Other commonly occurring genera appeared to have environmental origins (e.g., soils). There were no significant differences in the bacterial diversity between offices inhabited by men or women or among surfaces, but the bacterial community diversity of the Tucson samples was clearly distinguishable from that of New York and San Francisco, which were indistinguishable. Overall, our comprehensive molecular analysis of office building microbial diversity shows the potential of these methods for studying patterns and origins of indoor bacterial contamination. "[H]umans move through a sea of microbial life that is seldom perceived except in the context of potential disease and decay." - Feazel et al. (2009).     

Estimating plant abundance using inflated beta distributions: Applied learnings from a lichen–caribou ecosystem

Quantifying abundance and distribution of plant species can be difficult because data are often inflated with zero values due to rarity or absence from many ecosystems. Terrestrial fruticose lichens (Cladonia and Cetraria spp.) occupy a narrow ecological niche and have been linked to the diets of declining caribou and reindeer populations (Rangifer tarandus) across their global distribution, and conditions related to their abundance and distribution are not well understood. We attempted to measure effects related to the occupancy and abundance of terrestrial fruticose lichens by sampling and simultaneously modeling two discrete conditions: absence and abundance. We sampled the proportion cover of terrestrial lichens at 438 vegetation plots, including 98 plots having zero lichens. A zero‐inflated beta regression model was employed to simultaneously estimate both the absence and the proportion cover of terrestrial fruticose lichens using fine resolution satellite imagery and light detection and ranging (LiDAR) derived covariates. The probability of lichen absence significantly increased with shallower groundwater, taller vegetation, and increased Sphagnum moss cover. Vegetation productivity, Sphagnum moss cover, and seasonal changes in photosynthetic capacity were negatively related to the abundances of terrestrial lichens. Inflated beta regression reliably estimated the abundance of terrestrial lichens (R² = .74) which was interpolated on a map at fine resolution across a caribou range to support ecological conservation and reclamation. Results demonstrate that sampling for and simultaneously estimating both occupancy and abundance offer a powerful approach to improve statistical estimation and expand ecological inference in an applied setting. Learnings are broadly applicable to studying species that are rare, occupy narrow niches, or where the response variable is a proportion value containing zero or one, which is typical of vegetation cover data.     

16S rDNA clone library-based bacterial phylogenetic diversity associated with three South China Sea sponges

Culture-independent molecular techniques, 16S rDNA clone library alongside RFLP and phylogenetic analysis, were applied to investigate the bacterial diversity associated with three South China Sea sponges, Stelletta tenui, Halichondria rugosa and Dysidea avara. A wide bacterial diversity was detected according to total genomic DNA-based 16S rDNA clone library, abundant clones with low identify with sequences retrieved from database were found as well as uncultured sponge symbionts. The phylogenetic analysis shows that the bacterial community structure of Stelletta tenui is similar to that of Halichondria rugosa comprising gamma-Proteobacteria and Firmicutes. Whereas, alpha-Proteobacteria, gamma-Protebacteria, Bacteroidetes and uncultured sponge symbionts were found in sponge Dysidea avara, suggesting that Dysidea avara has the highest bacteria diversity among these sponges. A specific sponge–microbe association is suggested based on the difference of bacterial diversity among these three sponges from the same geography location and the observed sponge species-specific bacteria.     

Estimating belowground plant abundance with DNA metabarcoding

Most work on plant community ecology has been performed above ground, neglecting the processes that occur in the soil. DNA metabarcoding, in which multiple species are computationally identified in bulk samples, can help to overcome the logistical limitations involved in sampling plant communities belowground. However, a major limitation of this methodology is the quantification of species’ abundances based on the percentage of sequences assigned to each taxon. Using root tissues of five dominant species in a semi‐arid Mediterranean shrubland (Bupleurum fruticescens, Helianthemum cinereum, Linum suffruticosum, Stipa pennata and Thymus vulgaris), we built pairwise mixtures of relative abundance (20%, 50% and 80% biomass), and implemented two methods (linear model fits and correction indices) to improve estimates of root biomass. We validated both methods with multispecies mixtures that simulate field‐collected samples. For all species, we found a positive and highly significant relationship between the percentage of sequences and biomass in the mixtures (R² = .44–.66), but the equations for each species (slope and intercept) differed among them, and two species were consistently over‐ and under‐estimated. The correction indices greatly improved the estimates of biomass percentage for all five species in the multispecies mixtures, and reduced the overall error from 17% to 6%. Our results show that, through the use of post‐sequencing quantification methods on mock communities, DNA metabarcoding can be effectively used to determine not only species’ presence but also their relative abundance in field samples of root mixtures. Importantly, knowledge of these aspects will allow us to study key, yet poorly understood, belowground processes.     

Community structure and diversity of biofilms from a beer bottling plant as revealed using 16S rRNA gene clone libraries

The microbial composition of biofilms from a beer bottling plant was analyzed by a cultivation independent analysis of the 16S rRNA genes. Clone libraries were differentiated by amplified 16S rRNA gene restriction analysis and representative clones from each group were sequenced. The diversity of the clone libraries was comparable with the diversity found for environmental samples. No evidences for the presence of strictly anaerobic taxa or important beer spoilers were found, indicating that biofilms developed for more than 6 months at the plant formed no appropriate habitat for those microorganisms. The genus Methylobacterium was one of the dominating groups of the clone libraries. The size of this population was assessed by fluorescence in situ hybridization and fatty acid analysis. In addition, considerable numbers of clones were assigned to uncultivated organisms.     

Estimating mountain goat abundance using DNA from fecal pellets

Non-invasive collection of tissue samples to obtain DNA for microsatellite genotyping required to estimate population size has been used for many wildlife species but rarely for ungulates. We estimated mountain goat (Oreamnos americanus) population size on a mountain complex in southwestern British Columbia by identification of individuals using DNA obtained from fecal pellet and hair samples collected during 3 sampling sessions. We identified 55 individuals from 170 samples that were successfully genotyped, and estimated a population of 77 mountain goats (SE = 7.4). Mean capture probability was 0.38 (SE = 0.037) per session. Our technique provides one of the first statistically rigorous estimates of abundance of an ungulate species using DNA derived primarily from fecal pellets. Our technique enables managers to obtain minimum counts or population estimates of ungulates in areas of low sightability that can be used for conservation and management. © 2011 The Wildlife Society.     

Estimating changes in species abundance from occupancy and aggregation

Predicting the change in abundance is pivotal for evaluating species’ current conservation status and population viability. Empirical works have suggested that species with an increasing abundance have a more aggregated distribution than those with a declining abundance (namely, the change-aggregation hypothesis, CAH). Here we introduced an improved negative binomial distribution model of the occupancy-abundance relationship (OAR) to estimate the change in abundance from changes in occupancy or aggregation. Analysis of the model suggests that (i) in general the change in abundance is synchronized with the change in occupancy when the level of environmental heterogeneity remains constant, and (ii) there could exist a threshold of the population density above which the CAH is no longer valid. Tests using data of epigaeic ants in Fynbos of South Africa collected from different seasons and macro-invertebrates from different localities in streams of central Spain verified these model propositions and thus support the use of this model as a monitoring method for assessing species persistence. Results suggest that the change in abundance can be estimated from the change in occupancy often obtained from cost-efficient presence-absence records, and a revision of the traditional CAH is necessary to capture the threshold phenomenon in the change-aggregation relationship. This work thus signifies the use of the three distinct but related concepts of population structure (i.e. occupancy, abundance and aggregation) in conservation biology.     

Estimating Alpine ibex Capra ibex abundance from photographic sampling

Monitoring procedures for Alpine ibex Capra ibex are limited in habitats with reduced visibility and when physical capture and marking of the animals is not intended. Photographic sampling, involving using camera‐trap data and identifying ibex from natural markings, was adopted with capture‐recapture models to estimate the abundance of ibex in Austria. The software CAPTURE's model produced an average capture probability of 0.44 with an estimate of 34–51 ibex and a mean population size of 38 ibex. This first study showed the applicability of photographic capture‐recapture techniques to estimate the abundance of ibex based on their natural markings.     

Estimating abundance of mountain lions from unstructured spatial sampling

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km²) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km² (95% CI 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182–451) to 529 (95% CI 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km² for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

Species-area curves, diversity indices, and species abundance distributions: a multifractal analysis

Although fractals have been applied in ecology for some time, multifractals have, in contrast, received little attention. In this article, we apply multifractals to the species-area relationship and species abundance distributions. We highlight two results: first, species abundance distributions collected at different spatial scales may collapse into a single curve after appropriate renormalization, and second, the power-law form of the species-area relationship and the Shannon, Simpson, and Berger-Parker diversity indices belong to a family of equations relating the species number, species abundance, and area through the moments of the species abundance-probability density function. Explicit formulas for these diversity indices, as a function of area, are derived. Methods to obtain the multifractal spectra from a data set are discussed, and an example is shown with data on tree and shrub species collected in a 50-ha plot on Barro Colorado Island, Panama. Finally, we discuss the implications of the multifractal formalism to the relationship between species range and abundance and the relation between the shape of the species abundance distribution and area.     

Estimating receptive fields from responses to natural stimuli with asymmetric intensity distributions

The reasons for using natural stimuli to study sensory function are quickly mounting, as recent studies have revealed important differences in neural responses to natural and artificial stimuli. However, natural stimuli typically contain strong correlations and are spherically asymmetric (i.e. stimulus intensities are not symmetrically distributed around the mean), and these statistical complexities can bias receptive field (RF) estimates when standard techniques such as spike-triggered averaging or reverse correlation are used. While a number of approaches have been developed to explicitly correct the bias due to stimulus correlations, there is no complementary technique to correct the bias due to stimulus asymmetries. Here, we develop a method for RF estimation that corrects reverse correlation RF estimates for the spherical asymmetries present in natural stimuli. Using simulated neural responses, we demonstrate how stimulus asymmetries can bias reverse-correlation RF estimates (even for uncorrelated stimuli) and illustrate how this bias can be removed by explicit correction. We demonstrate the utility of the asymmetry correction method under experimental conditions by estimating RFs from the responses of retinal ganglion cells to natural stimuli and using these RFs to predict responses to novel stimuli.     

Inferring recent historic abundance from current genetic diversity

Recent historic abundance is an elusive parameter of great importance for conserving endangered species and understanding the pre‐anthropogenic state of the biosphere. The number of studies that have used population genetic theory to estimate recent historic abundance from contemporary levels of genetic diversity has grown rapidly over the last two decades. Such assessments often yield unexpectedly large estimates of historic abundance. We review the underlying theory and common practices of estimating recent historic abundance from contemporary genetic diversity, and critically evaluate the potential issues at various estimation steps. A general issue of mismatched spatio‐temporal scales between the estimation itself and the objective of the estimation emerged from our assessment; genetic diversity–based estimates of recent historic abundance represent long‐term averages, whereas the objective typically is an estimate of recent abundance for a specific population. Currently, the most promising approach to estimate the difference between recent historic and contemporary abundance requires that genetic data be collected from samples of similar spatial and temporal duration. Novel genome‐enabled inference methods may be able to utilize additional information of dense genome‐wide distributions of markers, such as of identity‐by‐descent tracts, to infer recent historic abundance from contemporary samples only.     

Estimating cell lineage from distributions of randomly introduced markers

Cell lineage of a multicellular organism has been analysed by introducing a genetic or chemical marker that is inherited from a cell to its daughter cells and is detectable even after several cell divisions. To construct a complete cell lineage, all the cells at different developmental stages need to be identified, and then the intracellular marker must be introduced to each cell. In this paper, I study a new method of estimating cell lineage based on distributions of intercellular markers observed at a single stage, which are introduced randomly at earlier stages. Assumptions are: (1) cell lineage is invariant between embryos; (2) a small number of cells are marked in each experiment; and (3) the total number of replicate experiments is sufficiently large. Then we identify the most likely cell lineage pattern (or tree topology) as the one that requires the least marker insertions to be compatible with the observed distributions of cell markers. This method is essentially the same as the principle of persimony widely used for ancestral phylogeny reconstruction in evolutionary biology. When the total number of cells is small, we can generate all the possible cell lineages and calculate the minimum number of marker insertions for each candidate, and then choose the cell lineage that requires the least marker insertions. If the number of cells is large, we can use clustering method in which a pair of cells with the highest correlation in marker labelling are merged sequentially. The efficiency of the clustering method in estimating the correct cell lineage is confirmed by computer simulations. Finally, the clustering method is applied to reconstruct the cell lineage of ascidian from experimental data.