Diversity

2572 KiB

Open AccessArticle

Diversity and Bioactivity of Marine Bacteria Associated with the Sponges Candidaspongia flabellata and Rhopaloeides odorabile from the Great Barrier Reef in Australia

by Candice M. Brinkmann, Philip S. Kearns, Elizabeth Evans-Illidge and D. İpek Kurtbӧke

Diversity 2017, 9(3), 39; https://doi.org/10.3390/d9030039 - 18 Sep 2017

Cited by 11 | Viewed by 6207

Abstract

Sponges and their associated microbial communities have sparked much interest in recent decades due on the abundant production of chemically diverse metabolites that in nature serve as functional compounds required by the marine sponge host. These compounds were found to carry therapeutic importance [...] Read more.

Sponges and their associated microbial communities have sparked much interest in recent decades due on the abundant production of chemically diverse metabolites that in nature serve as functional compounds required by the marine sponge host. These compounds were found to carry therapeutic importance for medicinal applications. In the presented study, 123 bacterial isolates from the culture collection of the Australian Institute of Marine Science (AIMS) previously isolated from two different sponge species, namely Candidaspongia flabellata and Rhopaloeides odorabile, originating from different locations on the Great Barrier Reef in Queensland, Australia, were thus studied for their bioactivity. The symbiotic bacterial isolates were first identified using 16S rRNA gene analysis and they were found to belong to five different dominating classes of Domain Bacteria, namely Alphaproteobacteria, Gammaproteobacteria, Flavobacteria, Bacilli and Actinobacteria. Following their taxonomical categorization, the isolates were screened for their antimicrobial activity against human pathogenic microbial reference strains: Escherichia coli (ATCC^® BAA-196™), E. coli (ATCC^® 13706™), E. coli (ATCC^® 25922™), Klebsiella pneumoniae (ATCC^® BAA-1705™), Enterococcus faecalis (ATCC^® 51575™), Bacillus subtilis (ATCC^® 19659™), Staphylococcus aureus (ATCC^® 29247™), Candida albicans (ATCC^® 10231™) and Aspergillus niger (ATCC^® 16888™). Over 50% of the isolates displayed antimicrobial activity against one or more of the reference strains tested. The subset of these bioactive bacterial isolates was further investigated to identify their biosynthetic genes such as polyketide synthase (PKS) type I and non-ribosomal peptide synthetase (NRPS) genes. This was done using polymerase chain reaction (PCR) with degenerate primers that have been previously used to amplify PKS-I and NRPS genes. These specific genes have been reported to be possibly involved in bacterial secondary metabolite production. In 47% of the bacterial isolates investigated, the PKS and NRPS genes were located. Some of the bacterial isolates were found to possess both gene types, which agrees with the previous reported biosynthetic ability of certain sponge-symbiotic bacteria such as the Actinobacteria or Gammaproteobacteria to produce secondary metabolites with antimicrobial activity. All these reported activities further confirm that sponge-symbiotic bacteria hold significant bioactivity with medicinal and biotechnological importance. Full article

(This article belongs to the Special Issue Diversity of Marine Invertebrate and Seaweed Symbiotic Bacteria)

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1310 KiB

Open AccessReview

NGS-Based Genotyping, High-Throughput Phenotyping and Genome-Wide Association Studies Laid the Foundations for Next-Generation Breeding in Horticultural Crops

by Nunzio D’Agostino and Pasquale Tripodi

Diversity 2017, 9(3), 38; https://doi.org/10.3390/d9030038 - 15 Sep 2017

Cited by 31 | Viewed by 11164

Abstract

Demographic trends and changes to climate require a more efficient use of plant genetic resources in breeding programs. Indeed, the release of high-yielding varieties has resulted in crop genetic erosion and loss of diversity. This has produced an increased susceptibility to severe stresses [...] Read more.

Demographic trends and changes to climate require a more efficient use of plant genetic resources in breeding programs. Indeed, the release of high-yielding varieties has resulted in crop genetic erosion and loss of diversity. This has produced an increased susceptibility to severe stresses and a reduction of several food quality parameters. Next generation sequencing (NGS) technologies are being increasingly used to explore “gene space” and to provide high-resolution profiling of nucleotide variation within germplasm collections. On the other hand, advances in high-throughput phenotyping are bridging the genotype-to-phenotype gap in crop selection. The combination of allelic and phenotypic data points via genome-wide association studies is facilitating the discovery of genetic loci that are associated with key agronomic traits. In this review, we provide a brief overview on the latest NGS-based and phenotyping technologies and on their role to unlocking the genetic potential of vegetable crops; then, we discuss the paradigm shift that is underway in horticultural crop breeding. Full article

(This article belongs to the Special Issue Plant Genetics and Biotechnology in Biodiversity)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Global scenario from domestication to modern agriculture. Wild species of tomato and pepper (Solanum habrochaites and Capsicum chacoense at the base of the triangle) are characterized by wide genetic variability, which can be used to improve modern varieties (Solanum lycopersicum and Capsicum annuum, at the top of the triangle). An intermediate step of domestication is represented by landraces which have a broadening genetic variation linked to adaptation to local environments. Transfer of alleles can be possible within gene pools (GP) [<a href="#B33-diversity-09-00038" class="html-bibr">33</a>]. Four different GP levels include: (i) species with easy crossing ability resulting in fruitful hybrids and fertile off-springs (primary gene pool, GP1); (ii) less closely related species that generates weak or sterile hybrids and are characterized by difficulty in obtaining advanced generations (secondary gene pool, GP2); (iii) species requiring sophisticated techniques for gene transfer such as embryo rescue, somatic fusion, grafting, and bridge species (tertiary gene pool, GP3); and, (iv) distantly-related species belonging to different families or kingdoms for which gene transfer is not possible sexually but through direct gene transfer by means of genetic engineering (fourth gene pool, GP4). Full article ">Figure 2
Genome-wide association studies (GWAS) are based on three pillars: (i) a high-density single nucleotide polymorphisms (SNP) catalogue derived from diversity assessment of individuals in a germplasm collection; (ii) knowledge on population structure and allele frequency spectrum (Q matrix); and, (iii) phenotypic data points for each individual within the population. Generally results from GWAS are displayed as Manhattan plots showing genome-wide p-values of SNP(s)-trait associations. GWAS allow the causal gene(s) or QTL(s) associated with the trait(s) of interest to be identified. Full article ">Figure 3
Large-scale phenotyping and its impact on plant breeding. Core collection (CC) and/or training populations (TP) developed in various fruit and leafy vegetable crops (in the figure from the top clockwise: pepper, tomato, eggplant, and rocket salad) can be deeply assessed through innovative phenotyping tools for different categories of traits. The integration with genotyping data lead to the identification of: (i) SNPs and alleles associated with target traits; (ii) accessions which can be used as parentals for future breeding programs; and, (iii) the basis of the genotype per environment (GxE) interaction. Full article ">

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Open AccessReview

Dispersal, Isolation, and Interaction in the Islands of Polynesia: A Critical Review of Archaeological and Genetic Evidence

by K. Ann Horsburgh and Mark D. McCoy

Diversity 2017, 9(3), 37; https://doi.org/10.3390/d9030037 - 14 Sep 2017

Cited by 13 | Viewed by 13154

Abstract

Integration of archaeology, modern genetics, and ancient DNA holds promise for the reconstruction of the human past. We examine the advances in research on the indigenous peoples of Polynesia to determine: (1) what do archaeological and genetic data (ancient and modern DNA) tell [...] Read more.

Integration of archaeology, modern genetics, and ancient DNA holds promise for the reconstruction of the human past. We examine the advances in research on the indigenous peoples of Polynesia to determine: (1) what do archaeological and genetic data (ancient and modern DNA) tell us about the origins of Polynesians; and, (2) what evidence is there for long-distance travel and contacts between Polynesians and indigenous populations of the Americas? We note that the general dispersal pattern of founding human populations in the remote islands of the Pacific and long-distance interaction spheres continue to reflect well-established models. New research suggests that the formation of an Ancestral Polynesia Culture in Western Polynesia may have involved differential patterns of dispersal followed by significant later migrations. It has also been suggested that the pause between the settlement of Western and Eastern Polynesia was centuries longer than currently thought, followed by a remarkably rapid pulse of island colonization. Long-distance travel between islands of the Pacific is currently best documented through the sourcing of artifacts, while the discovery of admixture of Native American DNA within the genome of the people from Easter Island (Rapa Nui) is strong new evidence for sustained contacts between Polynesia and the Americas. Full article

(This article belongs to the Special Issue Ancient DNA)

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Figure 1
The Human Colonization of the Islands of Polynesia. This map shows the general pattern of pulse-and-pause settlement of the remote islands of Polynesia: (1) movement of Lapita peoples with ancestry in Southeast Asia and New Guinea to the island groups that would become Western Polynesia (WP); (2) the dispersal from WP to Central Eastern Polynesia (CEP); and, (3) the settlement of the Polynesian Outliers (PO) in Melanesia and Micronesia from WP, and the settlement of islands of Marginal East Polynesia (MEP) from CEP. Islands by regions and sub-regions mentioned in the text (list is not exhaustive for sub-regions): Melanesia (Bougainville, Solomon Islands, New Britain, Fiji), Western Polynesia (Tonga, Sāmoa, Niue, Tuvalu, Wallis and Futuna), Polynesian Outliers (Tikopia), Central Eastern Polynesia (Society Islands, Cook Islands, Marquesas, Austral Islands, the Pitcairn Group), Marginal Eastern Polynesia (in the north, the Hawaiian Islands; in the east, Rapa Nui (Easter Island); in the southwest, Aotearoa (New Zealand), Chatham Islands, Norfolk Islands, Kermadec Islands, Auckland Islands). Source (base map): OpenStreetMap. Full article ">Figure 2
Modern human mtDNA (full-genome) haplogroup frequencies across the Pacific. Note the increased frequency of haplogroup-B (black) from west (left) to east (right). Full article ">Figure 3
Linages and Sublinages of haplogroup-B in modern human mtDNA (full-genome). The increased frequencies of some lineages (B4a1a1c; and B4a1a and B4a1a1a and sublineages) we attribute to a serial bottleneck with colonization of Eastern Polynesia. mtDNA reported in Benton et al. [<a href="#B87-diversity-09-00037" class="html-bibr">87</a>] and Knapp et al. [<a href="#B88-diversity-09-00037" class="html-bibr">88</a>] not shown here. Full article ">Figure 4
A model of cultural differentiation in Polynesia. In this general model, modified from Kirch [<a href="#B133-diversity-09-00037" class="html-bibr">133</a>], the sequence of colonization is shown beginning with movements of people with ancestry from Asia and Melanesia (Lapita Culture) to islands across the Pacific. An Ancestral Polynesian Culture formed in the island groups that would become Western Polynesia. A dispersal of people out from this region to Central Eastern Polynesia, Marginal Eastern Polynesia, and the Polynesian Outliers created an interaction sphere that spanned a vast region of the Pacific, and likely included the Pacific coast of the Americas. At the time of European contact, the farthest points in this network (Marginal Eastern Polynesia and the Americas) have dropped out. Later long-distance interaction is often attributed to the action of what has been called the Tongan ‘maritime polity’. Full article ">

7754 KiB

Open AccessArticle

In Situ Cultured Bacterial Diversity from Iron Curtain Cave, Chilliwack, British Columbia, Canada

by Soumya Ghosh, Elise Paine, Rob Wall, Gabrielle Kam, Tanna Lauriente, Pet-Chompoo Sa-ngarmangkang, Derrick Horne and Naowarat Cheeptham

Diversity 2017, 9(3), 36; https://doi.org/10.3390/d9030036 - 29 Aug 2017

Cited by 20 | Viewed by 7076

Abstract

The culturable bacterial diversity from Iron Curtain Cave, Chilliwack, British Columbia, Canada was examined. Sixty five bacterial isolates were successfully cultivated, purified, and identified based on 16S rRNA gene sequencing. Four distinguishable phyla, i.e., Actinobacteria (44.61%), Proteobacteria (27.69%), Firmicutes (20%) and Bacteroidetes (7.69%) [...] Read more.

The culturable bacterial diversity from Iron Curtain Cave, Chilliwack, British Columbia, Canada was examined. Sixty five bacterial isolates were successfully cultivated, purified, and identified based on 16S rRNA gene sequencing. Four distinguishable phyla, i.e., Actinobacteria (44.61%), Proteobacteria (27.69%), Firmicutes (20%) and Bacteroidetes (7.69%) were identified. Arthrobacter (21.53%) was identified as the major genus, followed by Sporosarcina (9.23%), Stenotrophomonas (9.23%), Streptomyces (6.15%), Brevundimonas (4.61%), and Crocebacterium (2.8%). Noteworthy, 12.3% of the population was recognized as unidentified bacteria. The isolates were evaluated for their potential antimicrobial activities against multidrug resistant microbial strains. Two species of the genus Streptomyces exhibited a wide range of antimicrobial activities against multidrug resistance (MDR) strains of Escherichia coli and Pseudomonas spp. along with non-resistant strains of Staphylococcus aureus and E. coli. However, all of the antimicrobial activities were only observed when the isolates were grown at 8 °C in different media. To the best of our knowledge, this is the first study conducted on the Iron Curtain Cave’s bacterial diversity, and reveals some bacterial isolates that have never been reported from a cave. Bacterial isolates identified with antimicrobial properties demonstrated that the Iron Curtain Cave can be further considered as a potential habitat for antimicrobial agents. Full article

(This article belongs to the Special Issue Microbial Diversity in Caves)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Map of Iron Curtain Cave in Chilliwack B.C. The numerals denote the locations of sampling points where media plates were left for nine months. #1 = Connection Room; near Squeeze dig around and Looking Pool, #2 = Upper section of The Gallery, near the climb to the Breakdown room, #3 = Entrance to side passage across from the Chocolate waterfall, #4 = Sitting area just beyond Lollipop Passage, #5 = Octopus Room, #6 = Mid passage to the Curtain, just past the Wishing Well, #7 = Chandelier Pit just below The Attic. Full article ">Figure 2
Iron Curtain Cave, Chilliwack, British Columbia, Canada. (A) Site #4: The Octopus Room; (B,C) Site #7: with two types of speleothem common to the cave; soda straws and bacon; (D) The Curtain made of calcium carbonate; (E) Soil sediment containing iron as well as some more geographical features unique to the cave; (F) The media plates are exposed for nine months in the cave; (G) The popcorn structures. Full article ">Figure 3
Distribution of bacterial isolates at different sampling points in Iron Curtain Cave, Chilliwack, BC, Canada. Full article ">Figure 4
(A) Taxonomical distribution of the bacterial isolates from Iron Curtain Cave by sampling sites. The vertical rectangular stalked columns represent the relative bacterial abundance. The asterisk (*) denotes the percentage of the bacteria that are not assigned with the gene accession numbers. (B) Percentage of total isolates from all sampling sites. Full article ">Figure 5
Evolutionary relationships of taxa. The 16S rRNA gene sequences obtained from the NCBI gene database were aligned by MUSCLE with default parameters. The phylogenetic tree was constructed using MEGA6 by neighbor-joining method, with a bootstrap test of 1000 replicates. The asterisk (*) denotes the sequences that were not assigned with a GenBank accession number. The red boxes represents the bacterial isolates that exhibited antimicrobial properties. The polygonal ( <img alt="Diversity 09 00036 i001" src="/diversity/diversity-09-00036/article_deploy/html/images/diversity-09-00036-i001.png"/>) represents the ICC1 having an identity of 73% to Streptomyces nojiriensis. Full article ">Figure 6
SEM images of actinobacterial-like structures from Iron Curtain Cave samples. (A) ICC1: Dense mass of rod-shaped and interwoven filaments with irregular rugose surface, Scale bars, 5 μm; (B) ICC4: Mass of hyphae with smoother surface, Scale bars, 3 μm. Full article ">

8072 KiB

Open AccessArticle

A Comparative Analysis of Viral Richness and Viral Sharing in Cave-Roosting Bats

by Anna R. Willoughby, Kendra L. Phelps, PREDICT Consortium and Kevin J. Olival

Diversity 2017, 9(3), 35; https://doi.org/10.3390/d9030035 - 28 Aug 2017

Cited by 38 | Viewed by 11167

Abstract

Caves provide critical roosting habitats for bats globally, but are increasingly disturbed or destroyed by human activities such as tourism and extractive industries. In addition to degrading the habitats of cave-roosting bats, such activities often promote contact between humans and bats, which may [...] Read more.

Caves provide critical roosting habitats for bats globally, but are increasingly disturbed or destroyed by human activities such as tourism and extractive industries. In addition to degrading the habitats of cave-roosting bats, such activities often promote contact between humans and bats, which may have potential impacts on human health. Cave-roosting bats are hosts to diverse viruses, some of which emerged in humans with severe consequences (e.g., severe acute respiratory syndrome coronavirus and Marburg virus). Characterizing patterns of viral richness and sharing among bat species are therefore important first steps for understanding bat-virus dynamics and mitigating future bat-human spillover. Here we compile a database of bat-virus associations and bat species ecological traits, and investigate the importance of roosting behavior as a determinant of viral richness and viral sharing among bat species. We show that cave-roosting species do not host greater viral richness, when accounting for publication bias, diet, body mass, and geographic range size. Our global analyses, however, show that cave-roosting bats do exhibit a greater likelihood of viral sharing, especially those documented in the literature as co-roosting in the same cave. We highlight the importance of caves as critical foci for bat conservation, as well as ideal sites for longitudinal surveillance of bat-virus dynamics. Full article

(This article belongs to the Special Issue Microbial Diversity in Caves)

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Host distribution maps for bat species in the viral database for (a) cave-roosting bats (n = 142) and (b) non-cave-roosting bats (n = 59). Bat species richness, regardless of roosting behavior, is highest in South and Central America, but richness of cave-roosting species is also high in Southeast Asia, western North America, and southern Europe. Four bat species in our database without IUCN spatial files are not included in this figure or our geographic trait analyses. Full article ">Figure 2
Host phylogeny pruned from the mammalian supertree of (a) bat species included in our database (n = 205), and insets shown for the genera: (b) Myotis, (c) Rhinolophus, and (d) Pteropus. Facultative cave-roosting bat species in light green, obligate cave-roosting species in dark green, and non-cave-roosting species in grey. A pie chart showcasing the proportion of viruses detected in each species via serology (white) or nucleic acid (blue) is next to each species. Full article ">Figure 3
Bipartite network of bat hosts (squares, n = 153 species) and PCR-detected viruses (circles, n = 82 species). Connected by 277 associations. Cave-roosting bats are shown in green (facultative, n = 102) and blue (obligate, n = 10), while non-cave-roosting species are shown in yellow. Viral abbreviations are listed with ICTV species name in <a href="#app1-diversity-09-00035" class="html-app">Supplementary Table S1</a>. Full article ">Figure 4
Coefficient plot for the best viral sharing generalized linear models for shared stringent viruses (Y/N) between (a) all bat species-pairs (n = 7864) and (b) co-roosting bat species-pairs (n = 1378). Each model includes publication count, phylogenetic distance, shared roosting behavior, and shared dietary niche. Spatial overlap is the best predictor for all species, while number of sympatric viral hosts performed better in the co-roosting species subset. All variables are significant. Full article ">

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Open AccessArticle

Millennia-Long Co-Existence of Two Major European Whitefish (Coregonus spp.) Lineages in Switzerland Inferred from Ancient Mitochondrial DNA

by José David Granado Alonso, Simone Häberle, Heidemarie Hüster Plogmann, Jörg Schibler and Angela Schlumbaum

Diversity 2017, 9(3), 34; https://doi.org/10.3390/d9030034 - 23 Aug 2017

Cited by 6 | Viewed by 4677

Abstract

Archaeological fish remains are an important source for reconstructing past aquatic ecosystems and ancient fishing strategies using aDNA techniques. Here, we focus on archaeological samples of European whitefish (Coregonus spp.) from Switzerland covering different time periods. Coregonus bones and scales are commonly [...] Read more.

Archaeological fish remains are an important source for reconstructing past aquatic ecosystems and ancient fishing strategies using aDNA techniques. Here, we focus on archaeological samples of European whitefish (Coregonus spp.) from Switzerland covering different time periods. Coregonus bones and scales are commonly found in archaeological assemblages, but these elements lack species specific features and thus inhibit morphological species identification. Even today, fish taxonomy is confusing and numerous species and ecotypes are recognized, and even more probably existed in the past. By targeting short fragments of the mitochondrial d-loop in 48 morphologically identified Coregonus scales and vertebrae from 10 archaeological sites in Switzerland, endogenous d-loop sequences were found in 24 samples from one Neolithic, two Roman, and four Medieval sites. Two major mtDNA clades, C and N, known from contemporary European whitefish populations were detected, suggesting co-occurrence for at least 5000 years. In the future, NGS technologies may be used to explore Coregonus or other fish species and ecotype diversity in the past to elucidate the human impact on lacustrine/limnic environments. Full article

(This article belongs to the Special Issue Ancient DNA)

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Figure 1
(a) Switzerland within Europe and (b) showing an enlarged part of Switzerland with the locations of cities and archaeological sites (numbers in parenthesis) providing the samples for this study. Main lakes and rivers are in italics. Number of fish symbols corresponds to number of archaeological samples tested per site. Pie charts represent extant samples from Lake Lucerne (n = 49), Lake Zürich (n = 20), Lake Walen (n = 23), and Lake Constance (n = 42) (as listed in <a href="#app1-diversity-09-00034" class="html-app">Table S2</a>). Red coloured fish symbols and pie charts denote C lineage; blue coloured fish symbols and pie charts denote N lineage; empty fish symbols denote no aDNA detected. Number of fish symbols corresponds to number of archaeological samples tested per site. (1) Stansstad-Kehrsiten (3500–3400 BC); (2) Arbon Bleiche 3 (3384–3370 BC); (3) Windisch Breite (1st century AD); (4) Windisch Römerblick (1st century AD); (5) Neftenbach (3th/4th century AD); (6) Tomils (7th century AD); (7) Zürich Fraumünsterstrasse (1010–1160 AD); (8) Basel Bäumleingasse 14 (13th century AD); (9) Basel Museum der Kulturen, Im Schürhof (15th/16th century AD); (10) Weesen Rosengärten (14th century AD). (Map (b) © swisstopo). Full article ">Figure 2
Median-joining network of concatenated Coregonus d-loop sequences (140–141 bp) displaying lineage distribution of archaeological samples from Switzerland (n = 24) (solid black) compared to published modern sequences from the European pre-Alpine/Alpine region including France (n =12), Switzerland (n = 270) and South-Germany (n = 57) (backward diagonal lines), and Northern Europe (n = 34) (cross lines). Size of nodes are proportional to haplotype frequencies. Numbers denote number of mutations between nodes. Full article ">

204 KiB

Open AccessArticle

An eDNA-Based SNP Assay for Ungulate Species and Sex Identification

by Ruth V. Nichols and Göran Spong

Diversity 2017, 9(3), 33; https://doi.org/10.3390/d9030033 - 22 Aug 2017

Cited by 9 | Viewed by 5210

Abstract

Many processes in wild populations are difficult to study. Genetic data, often non-invasively collected, may provide a solution to these difficulties and are increasingly used to study behavioral, demographic, ecological, and evolutionary processes. Moreover, the improved sensitivity of genetic methods now allows analyses [...] Read more.

Many processes in wild populations are difficult to study. Genetic data, often non-invasively collected, may provide a solution to these difficulties and are increasingly used to study behavioral, demographic, ecological, and evolutionary processes. Moreover, the improved sensitivity of genetic methods now allows analyses of trace amounts of DNA left by animals in their environment (e.g., saliva, urine, epithelial cells). Environmental DNA (eDNA) thus offers new opportunities to study a range of historic and contemporary questions. Here, we present a species and sex diagnostic kit for studying browsing in a multispecies temperate ungulate assemblage. Using mitochondrial sequences deposited in Genbank, we developed four single nucleotide polymorphisms (SNPs) for identifying four temperate ungulate species. We also sequenced portions of the Amelogenin gene on the X- and Y-chromosomes and developed six SNPs (three on the X-chromosome and three on the Y-chromosome) for sex determination. We tested the SNP assays on high and low quality/quantity DNA samples. Full article

(This article belongs to the Special Issue Application of Environmental DNA for Biological Conservation)

6822 KiB

Open AccessArticle

Venetian Local Corn (Zea mays L.) Germplasm: Disclosing the Genetic Anatomy of Old Landraces Suited for Typical Cornmeal Mush Production

by Fabio Palumbo, Giulio Galla, Liliam Martínez-Bello and Gianni Barcaccia

Diversity 2017, 9(3), 32; https://doi.org/10.3390/d9030032 - 16 Aug 2017

Cited by 20 | Viewed by 5222

Abstract

Due to growing concern for the genetic erosion of local varieties, four of the main corn landraces historically grown in Veneto (Italy)—Sponcio, Marano, Biancoperla and Rosso Piave—were characterized in this work. A total of 197 phenotypically representative plants collected from field populations were [...] Read more.

Due to growing concern for the genetic erosion of local varieties, four of the main corn landraces historically grown in Veneto (Italy)—Sponcio, Marano, Biancoperla and Rosso Piave—were characterized in this work. A total of 197 phenotypically representative plants collected from field populations were genotyped at 10 SSR marker loci, which were regularly distributed across the 10 genetic linkage groups and were previously characterized for high polymorphism information content (PIC), on average equal to 0.5. The population structure analysis based on this marker set revealed that 144 individuals could be assigned with strong ancestry association (>90%) to four distinct clusters, corresponding to the landraces used in this study. The remaining 53 individuals, mainly from Sponcio and Marano, showed admixed ancestry. Among all possible pairwise comparisons of individual plants, these two landraces exhibited the highest mean genetic similarity (approximately 67%), as graphically confirmed through ordination analyses based on PCoA centroids and UPGMA trees. Our findings support the hypothesis of direct gene flow between Sponcio and Marano, likely promoted by the geographical proximity of these two landraces and their overlapping cultivation areas. Conversely, consistent with its production mainly confined to the eastern area of the region, Rosso Piave scored the lowest genetic similarity (<59%) to the other three landraces and firmly grouped (with average membership of 89%) in a separate cluster, forming a molecularly distinguishable gene pool. The elite inbred B73 used as tester line scored very low estimates of genetic similarity (on average <45%) with all the landraces. Finally, although Biancoperla was represented at K = 4 by a single subgroup with individual memberships higher than 80% in almost all cases (57 of 62), when analyzed with an additional level of population structure for K = 6, it appeared to be entirely (100%) constituted by individuals with admixed ancestry. This suggests that the current population could be the result of repeated hybridization events between the two accessions currently bred in Veneto. The genetic characterization of these heritage landraces should prove very useful for monitoring and preventing further genetic erosion and genetic introgression, thus preserving their gene pools, phenotypic identities and qualitative traits for the future. Full article

(This article belongs to the Special Issue Plant Genetics and Biotechnology in Biodiversity)

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Figure 1
Two-dimensional centroids derived from genetic similarity estimates computed among the 197 accessions in all possible pairwise comparisons using the SSR marker data set. Only the names of those genotypes with unclear membership to one of the four subgroups are reported. Full article ">Figure 2
Constrained UPGMA tree of genetic similarity estimates computed among pairwise comparisons of corn accessions using the SSR marker data set, with nodes supported by bootstrap values. Black circle: bootstrap values ≥ 90%; red circle: 70% ≤ bootstrap values < 90%; green circle: 50% ≤ bootstrap values < 70%. The color scheme for the text is the same as for the symbols described in <a href="#diversity-09-00032-f001" class="html-fig">Figure 1</a> (black = Sponcio, blue = Marano, magenta = Biancoperla and green = Rosso Piave). Full article ">Figure 3
Definition of the number of ancestral corn populations based on the SSR marker dataset. Mean LnP(D) ± SD over 10 runs is a function of K, as L′(K) = ΔLnP(D). Mean ΔK is calculated as ∣L′′(K)∣/(SD(L(K)) following Evanno et al. [<a href="#B35-diversity-09-00032" class="html-bibr">35</a>]. ΔK values are represented by the orange line, while the blue points indicate the mean LnP(D) ± SD values. Full article ">Figure 4
Population genetic structure of the four main corn landraces in Veneto (N = 197) as estimated by STRUCTURE using the SSR marker data set. Each sample is represented by a vertical histogram partitioned into K = 4 (panel a) or K = 6 (panel b) colored segments that represent the estimated membership. The proportion of ancestry (%) is reported on the ordinate axis and the identification number of each accession is reported below each histogram. The color scheme for the figure is the same as for the symbols and the text described in <a href="#diversity-09-00032-f001" class="html-fig">Figure 1</a> and <a href="#diversity-09-00032-f002" class="html-fig">Figure 2</a>, respectively. Green = Rosso Piave, black = Sponcio, blue = Marano and magenta = Biancoperla. For K = 6 two shades of red are used for the two clusters of Biancoperla and the third new cluster between Sponcio and Marano is marked in grey. Full article ">

1122 KiB

Open AccessArticle

16S rRNA Gene-Based Metagenomic Analysis of Ozark Cave Bacteria

by Cássia Oliveira, Lauren Gunderman, Cathryn A. Coles, Jason Lochmann, Megan Parks, Ethan Ballard, Galina Glazko, Yasir Rahmatallah, Alan J. Tackett and David J. Thomas

Diversity 2017, 9(3), 31; https://doi.org/10.3390/d9030031 - 15 Aug 2017

Cited by 25 | Viewed by 8801

Abstract

The microbial diversity within cave ecosystems is largely unknown. Ozark caves maintain a year-round stable temperature (12–14 °C), but most parts of the caves experience complete darkness. The lack of sunlight and geological isolation from surface-energy inputs generate nutrient-poor conditions that may limit [...] Read more.

The microbial diversity within cave ecosystems is largely unknown. Ozark caves maintain a year-round stable temperature (12–14 °C), but most parts of the caves experience complete darkness. The lack of sunlight and geological isolation from surface-energy inputs generate nutrient-poor conditions that may limit species diversity in such environments. Although microorganisms play a crucial role in sustaining life on Earth and impacting human health, little is known about their diversity, ecology, and evolution in community structures. We used five Ozark region caves as test sites for exploring bacterial diversity and monitoring long-term biodiversity. Illumina MiSeq sequencing of five cave soil samples and a control sample revealed a total of 49 bacterial phyla, with seven major phyla: Proteobacteria, Acidobacteria, Actinobacteria, Firmicutes, Chloroflexi, Bacteroidetes, and Nitrospirae. Variation in bacterial composition was observed among the five caves studied. Sandtown Cave had the lowest richness and most divergent community composition. 16S rRNA gene-based metagenomic analysis of cave-dwelling microbial communities in the Ozark caves revealed that species abundance and diversity are vast and included ecologically, agriculturally, and economically relevant taxa. Full article

(This article belongs to the Special Issue Microbial Diversity in Caves)

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1426 KiB

Open AccessReview

Biodiversity Dynamics on Islands: Explicitly Accounting for Causality in Mechanistic Models

by Ludwig Leidinger and Juliano Sarmento Cabral

Diversity 2017, 9(3), 30; https://doi.org/10.3390/d9030030 - 7 Aug 2017

Cited by 16 | Viewed by 7174

Abstract

Island biogeography remains a popular topic in ecology and has gained renewed interest due to recent theoretical development. As experimental investigation of the theory is difficult to carry out, mechanistic simulation models provide useful alternatives. Several eco-evolutionary mechanisms have been identified to affect [...] Read more.

Island biogeography remains a popular topic in ecology and has gained renewed interest due to recent theoretical development. As experimental investigation of the theory is difficult to carry out, mechanistic simulation models provide useful alternatives. Several eco-evolutionary mechanisms have been identified to affect island biodiversity, but integrating more than a few of these processes into models remains a challenge. To get an overview of what processes mechanistic island models have integrated so far and what conclusions they came to, we conducted an exhaustive literature review of studies featuring island-specific mechanistic models. This was done using an extensive systematic literature search with subsequent manual filtering. We obtained a list of 28 studies containing mechanistic island models, out of 647 total hits. Mechanistic island models differ greatly in their integrated processes and computational structure. Their individual findings range from theoretical (such as humped-shaped extinction rates for oceanic islands) to system-specific dynamics (e.g., equilibrium and non-equilibrium dynamics for Galápagos’ birds). However, most models so far only integrate theories and processes pair-wise, while focusing on hypothetical systems. Trophic interactions and explicit micro-evolution are largely underrepresented in models. We expect future models to continue integrating processes, thus promoting the full appraisal of biodiversity dynamics. Full article

(This article belongs to the Special Issue Selected Papers from The ISLAND BIOLOGY–2016 - An International Conference on Island Evolution, Ecology, and Conservation (18-22 July 2016))

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Number of paper hits from the literature search over publication years. (A) Total (raw) numbers of all paper types; (B) numbers of studies containing mechanistic simulation models. Note the different y-axis scales of both graphs. Full article ">Figure 2
Multidimensional scaling of the refined list of papers according to the model characteristics (<math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>t</mi> <mi>r</mi> <mi>e</mi> <mi>s</mi> <mi>s</mi> <mo>−</mo> <mn>1</mn> <mo>=</mo> <mn>0.144</mn> </mrow> </semantics> </math>, better than permutation-based null solutions with <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.05</mn> </mrow> </semantics> </math>). Blue arrows represent category axes with significant importance (<math display="inline"> <semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.05</mn> </mrow> </semantics> </math>). Arrow directions are from the absence to the presence of a property or in increasing order (number of niche axes and focal level). Clusters show groupings of papers based on whether models consider niche differences between species (green cluster) and whether they employ evolution in any form (orange cluster). Meaning of arrow labels: neutral: whether the model follows neutral theory [<a href="#B30-diversity-09-00030" class="html-bibr">30</a>]; evolution: whether the model employs evolution; stochastic: whether the model architecture is deterministic (zero) or stochastic (one); spatially.explicit: whether the model explicitly considers space; no.niche.axes: the number of parameters that relate to biological differences between species; static.environment: whether the model arena is subject to change (zero) or static (one); agent.level: the organizational level at which the model processes act (from individual, one, to population, two, to species, three). The underlying data are shown in <a href="#diversity-09-00030-t001" class="html-table">Table 1</a>. Note that for the creation of the plot, a jitter was applied to the data to make points better distinguishable. Full article ">Figure 3
Multidimensional scaling of the model properties in the refined list of papers (<math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>t</mi> <mi>r</mi> <mi>e</mi> <mi>s</mi> <mi>s</mi> <mo>−</mo> <mn>1</mn> <mo>=</mo> <mn>0.126</mn> </mrow> </semantics> </math>). The closer two properties are, the more often these properties are implemented simultaneously in models. The underlying data are shown in <a href="#diversity-09-00030-t001" class="html-table">Table 1</a>. ind.based: individual-based; pop.based: population-based; sp.based: species-based; other properties as in <a href="#diversity-09-00030-f002" class="html-fig">Figure 2</a>. For this analysis, we used the same data as for producing <a href="#diversity-09-00030-f002" class="html-fig">Figure 2</a> . Full article ">Figure 4
Summary of typical processes and drivers affecting island biodiversity implemented in the models with their assumed causal relationships (<a href="#diversity-09-00030-t001" class="html-table">Table 1</a>, columns “processes”, “focal level”, “agent level”, “investigated patterns”). Round and square boxes denote drivers and processes, respectively. The bottom row of processes represents processes usually acting on individual-/population-levels, the top row of processes metacommunity-level processes. Black boxes and text mark factors and relationships regularly integrated in models, while grey arrows stand for thus far under- or un-explored relationships. Note that authors may opt to implement models that skip certain organizational levels, for example for investigating the direct effect of isolation on extinction as predicted by the ETIB. Additional relationships not explicitly stated in the chart include rescue-effects [<a href="#B53-diversity-09-00030" class="html-bibr">53</a>] between isolation and extinction and target effects [<a href="#B54-diversity-09-00030" class="html-bibr">54</a>] from area to immigration. For clarity, these kinds of relationships have been excluded from the graphic. Furthermore, “growth” combines both birth and death processes, while “interactions” include positive, neutral and negative interactions, for example competition or trophic interactions, and “differentiation” encompasses micro-evolutionary processes, such as mutation and gene flow. For a more complete overview of processes, patterns and organizational levels, the reader may refer to <a href="#diversity-09-00030-f005" class="html-fig">Figure 5</a>. Full article ">Figure 5
Representation of exemplary integrated processes and drivers in island models (bottom) and emerging variables and patterns (top). Model complexity, and thus generality [<a href="#B20-diversity-09-00030" class="html-bibr">20</a>], increases from left to right. We assume that processes and drivers add up from left to right. Thus, a given model representation includes also all processes and drivers of the less complex models to its left and is therefore also able to produce the respective patterns. Mechanisms and drivers are closely related to different theories (exemplary theories are shown in brackets at the bottom), e.g., colonization and extinction as the fundamental rates of the equilibrium theory of island biogeography (ETIB). As can be seen, some emergent patterns of more complex models are the same as the drivers for simpler models. For instance, the second model on the right produces colonization rates as an emergent pattern, which at the same time are necessary input parameters for the far left model. AE: archipelago endemics; ind.: individual. MIE: multiple island endemics; SIE: single island endemics; sp.: species; GDM: general dynamic model. Full article ">

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Open AccessReview

A Unique Coral Community in the Mangroves of Hurricane Hole, St. John, US Virgin Islands

by Caroline S. Rogers

Diversity 2017, 9(3), 29; https://doi.org/10.3390/d9030029 - 4 Aug 2017

Cited by 18 | Viewed by 7179

Abstract

Corals do not typically thrive in mangrove environments. However, corals are growing on and near the prop roots of red mangrove trees in Hurricane Hole, an area within the Virgin Islands Coral Reef National Monument under the protection of the US National Park [...] Read more.

Corals do not typically thrive in mangrove environments. However, corals are growing on and near the prop roots of red mangrove trees in Hurricane Hole, an area within the Virgin Islands Coral Reef National Monument under the protection of the US National Park Service in St. John, US Virgin Islands. This review summarizes current knowledge of the remarkable biodiversity of this area. Over 30 scleractinian coral species, about the same number as documented to date from nearby coral reefs, grow here. No other mangrove ecosystems in the Caribbean are known to have so many coral species. This area may be a refuge from changing climate, as these corals weathered the severe thermal stress and subsequent disease outbreak that caused major coral loss on the island’s coral reefs in 2005 and 2006. Shading by the red mangrove trees reduces the stress that leads to coral bleaching. Seawater temperatures in these mangroves are more variable than those on the reefs, and some studies have shown that this variability results in corals with a greater resistance to higher temperatures. The diversity of sponges and fish is also high, and a new genus of serpulid worm was recently described. Continuing research may lead to the discovery of more new species. Full article

(This article belongs to the Special Issue Tropical Marine Biodiversity)

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Open AccessArticle

Spacio-Temporal Distribution and Tourist Impact on Airborne Bacteria in a Cave (Škocjan Caves, Slovenia)

by Janez Mulec, Andreea Oarga-Mulec, Samo Šturm, Rok Tomazin and Tadeja Matos

Diversity 2017, 9(3), 28; https://doi.org/10.3390/d9030028 - 1 Aug 2017

Cited by 27 | Viewed by 5992

Abstract

(1) Background: Airborne microbes are an integral part of a cave ecosystem. Cave allochtonous airborne microbiota, which occurs mainly during aerosolization from an underground river, from animals, and from visitors, is particularly pronounced in show caves. The impacts of tourists and natural river [...] Read more.

(1) Background: Airborne microbes are an integral part of a cave ecosystem. Cave allochtonous airborne microbiota, which occurs mainly during aerosolization from an underground river, from animals, and from visitors, is particularly pronounced in show caves. The impacts of tourists and natural river aerosolization on the cave air were estimated in large cave spaces within the Škocjan Caves; (2) Methods: Simultaneously with the measurements of atmospheric parameters, cultivable airborne bacteria were impacted, counted and identified using MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry); (3) Results: A mix of bacteria typically associated with humans and with natural habitats, including a large percentage of non-identified isolates, was found in the cave air. Few of the isolates were attributed to Risk Group 2. A strong positive correlation between tourist numbers and the rise in the concentration of airborne bacteria was indicated. Concentration of airborne bacteria rises to particularly high levels close to the underground river during periods of high discharge. A 10-times lower discharge reflected an approximately 20-times lower concentration of airborne bacteria; (4) Conclusions: Caves that are open and visited contain a diverse airborne microbiota originating from different sources. Enormous cave chambers that display relatively dynamic cave climate conditions do not normally support the enhancement of airborne bacterial concentrations. Full article

(This article belongs to the Special Issue Microbial Diversity in Caves)

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Map of the Škocjan Caves (a) with the designated locations of bioaerosol sampling sites in the Tiha jama section (Šotor, Orjak) along the tourist footpath (tourist impact, (b)); and in the Šumeča jama section (Prevoj, Ponvice, Belveder, with the Reka River impact, (c)). The ground plan is from the Cave cadastre of the Karst Research Institute ZRC SAZU (drawn by Franjo Drole). Full article ">Figure 2
Relationship between tourist counts (n = 7) and the increase in concentration of airborne bacteria from their background level at the Šotor sampling site (increase in biomass expressed in CFU/m3 = 0.94 × tourist count—45.28; p = 0.001; blue—off-season, orange—shoulder season, red—peak season; see Material and Methods and <a href="#diversity-09-00028-t001" class="html-table">Table 1</a> for details). Full article ">Figure 3
Estimated bacterial species richness after six sampling campaigns at the Šotor and Orjak sampling stations before, and during, tourist visits in 2014. Full article ">Figure 4
Atmospheric conditions and concentrations of airborne bacteria at three sampling locations Belveder (Be), Ponvice (Po), and Prevoj (Pr) along the underground channel of the Reka River, with different discharges in November 2014 (~200 m3/s), August 2015 (~0.2 m3/s), and November 2016 (~20 m3/s). Full article ">

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Open AccessArticle

Putting Plant Genetic Diversity and Variability at Work for Breeding: Hybrid Rice Suitability in West Africa

by Raafat El-Namaky, Mamadou M. Bare Coulibaly, Maji Alhassan, Karim Traore, Francis Nwilene, Ibnou Dieng, Rodomiro Ortiz and Baboucarr Manneh

Diversity 2017, 9(3), 27; https://doi.org/10.3390/d9030027 - 10 Jul 2017

Cited by 5 | Viewed by 5187

Abstract

Rice is a staple food in West Africa, where its demand keeps increasing due to population growth. Hence, there is an urgent need to identify high yielding rice cultivars that fulfill this demand locally. Rice hybrids are already known to significantly increase productivity. [...] Read more.

Rice is a staple food in West Africa, where its demand keeps increasing due to population growth. Hence, there is an urgent need to identify high yielding rice cultivars that fulfill this demand locally. Rice hybrids are already known to significantly increase productivity. This study evaluated the potential of Asian hybrids with good adaptability to irrigated and rainfed lowland rice areas in Mali, Nigeria, and Senegal. There were 169 hybrids from China included in trials at target sites during 2009 and 2010. The genotype × environment interaction was highly significant (p < 0.0001) for grain yield indicating that the hybrids’ and their respective cultivar checks’ performance differed across locations. Two hybrids had the highest grain yield during 2010 in Mali, while in Nigeria, four hybrids in 2009 and one hybrid in 2010 had higher grain yield and matured earlier than the best local cultivar. The milling recovery, grain shape and cooking features of most hybrids had the quality preferred by West African consumers. Most of the hybrids were, however, susceptible to African rice gall midge (AfRGM) and Rice Yellow Mottle Virus (RMYV) isolate Ng40. About 60% of these hybrids were resistant to blast. Hybrids need to incorporate host plant resistant for AfRGM and RYMV to be grown in West Africa. Full article

(This article belongs to the Special Issue Plant Genetics and Biotechnology in Biodiversity)

5909 KiB

Open AccessArticle

Does Stream Size Really Explain Biodiversity Patterns in Lotic Systems? A Call for Mechanistic Explanations

by Ross Vander Vorste, Philip McElmurray, Spencer Bell, Kevin M. Eliason and Bryan L. Brown

Diversity 2017, 9(3), 26; https://doi.org/10.3390/d9030026 - 8 Jul 2017

Cited by 33 | Viewed by 7908

Abstract

Understanding drivers of biodiversity is a long-standing goal of basic and applied ecological research. In riverine systems, there remains a critical need to identify these drivers as efforts to manage and protect rivers grow increasingly desperate in the face of global change. We [...] Read more.

Understanding drivers of biodiversity is a long-standing goal of basic and applied ecological research. In riverine systems, there remains a critical need to identify these drivers as efforts to manage and protect rivers grow increasingly desperate in the face of global change. We explored one commonly cited potential driver of riverine biodiversity, stream size (e.g., stream order, watershed area, width), using a systematic literature review paired with an analysis of broad-scale macroinvertebrate and fish communities. Of the 165 papers reviewed, we found mostly positive, but no universal, relationship between biodiversity and stream size despite inconsistent use of over 30 measures of stream size. One-third of studies failed to report explanatory mechanisms driving biodiversity–stream size relationships. Across over 4000 macroinvertebrate and fish samples from 1st–8th order streams in the contiguous USA, our analysis showed biodiversity (Shannon diversity, functional diversity, beta diversity) generally increased with measures of stream size. However, because of inconsistent and generally weak relationships between biodiversity and stream size across organismal groups, we emphasize the need to look beyond simple physical stream size measures to understand and predict riverine biodiversity, and strongly suggest that studies search for more mechanistic explanations of biodiversity patterns in lotic systems. Full article

(This article belongs to the Special Issue Aquatic Biodiversity Conservation, Aquatic Natural Resources and Management)

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Map of macroinvertebrate and fish community collection sites from Maryland Biological Stream Survey (MBSS, upper right, n = 952 sites) and North Carolina Basinwide Monitoring Program (NCBMP, lower right, n = 1222 sites) and the contiguous USA (USEPA, center, n = 2123 sites). Colors indicate Strahler stream order from 1st order to 8th order. Map not to scale. Full article ">Figure 2
Proportion of reviewed publications utilizing alpha-diversity (81%), beta-diversity (37%), and gamma-diversity (4%) as a scale for biodiversity comparison (a). Overlap of circles representing diversity types indicates the number of studies that used multiple metrics. Alpha-diversity refers to local diversity, beta-diversity as turnover between local communities, and gamma-diversity as regional diversity. Measures of biodiversity used to infer relationships between stream size and biodiversity (b). Measures of beta-diversity include dissimilarity and turnover. Other includes measures such as biomass, genetic diversity, nestedness, and occurrence. Full article ">Figure 3
Measures of stream size used to infer biodiversity–stream size relationships (a) Network measures includes measures such as link distance from source, downstream, links magnitude, and several other measures. Geographic scale of studies inferring relationships between stream size and biodiversity (b) The category “other” includes continental, global, and single reach. Full article ">Figure 4
Types of organisms assessed in studies inferring biodiversity–stream size relationships. Full article ">Figure 5
Relationship between Shannon diversity and Strahler stream order for macroinvertebrate (a,c,e) and fish (b,d,f) communities collected from Maryland (MBSS), North Carolina (NCBMP) and contiguous USA (USEPA). Error bars represent standard deviation and different letters indicate significant differences between orders determined using Tukey’s HSD multiple comparisons. Full article ">Figure 6
Relationship between functional diversity (Rao Q) and Strahler stream order for macroinvertebrate (a,c,e) and fish (b,d,f) communities collected from Maryland (MBSS), North Carolina (NCBMP) and contiguous USA (USEPA). Error bars represent standard deviation and different letters indicate significant differences between orders determined using Tukey’s HSD multiple comparisons. Full article ">Figure 7
Relationship between Shannon diversity and watershed area (log transformed) for macroinvertebrate (a,c,e) and fish (b,d,f) communities collected from Maryland (MBSS), North Carolina (NCBMP) and contiguous USA (USEPA). Lines, R2 and p-values indicate significant positive relationships determined by linear models. Full article ">Figure 8
Relationship between functional diversity (Rao Q) and watershed area (log transformed) for macroinvertebrate (a,c,e) and fish (b,d,f) communities collected from Maryland (MBSS), North Carolina (NCBMP) and contiguous USA (USEPA). Lines, R2 and p-values indicate significant positive relationships determined by linear models. Full article ">Figure 9
Relationship between beta diversity and watershed area (log transformed) for macroinvertebrate (a,c,e) and fish (b,d,f) communities collected from Maryland (MBSS), North Carolina (NCBMP) and contiguous USA (USEPA). Lines, R2 and p-values indicate significant positive relationships determined by linear models. Full article ">Figure 10
Relationship between Shannon diversity and stream width (log+1 transformed) for macroinvertebrate (a,c,e) and fish (b,d,f) communities collected from Maryland (MBSS), North Carolina (NCBMP) and contiguous USA (USEPA). Lines, R2 and p-values indicate significant positive relationships determined by linear models. Full article ">Figure 11
Relationship between functional diversity (Rao Q) and stream width (log+1 transformed) for macroinvertebrate (a,c,e) and fish (b,d,f) communities collected from Maryland (MBSS), North Carolina (NCBMP) and contiguous USA (USEPA). Lines, R2 and p-values indicate significant positive relationships determined by linear models. Full article ">

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Open AccessArticle

Species Richness and Relative Abundance of Reef-Building Corals in the Indo-West Pacific

by Lyndon DeVantier and Emre Turak

Diversity 2017, 9(3), 25; https://doi.org/10.3390/d9030025 - 29 Jun 2017

Cited by 48 | Viewed by 13465

Abstract

Scleractinian corals, the main framework builders of coral reefs, are in serious global decline, although there remains significant uncertainty as to the consequences for individual species and particular regions. We assessed coral species richness and ranked relative abundance across 3075 depth-stratified survey sites, [...] Read more.

Scleractinian corals, the main framework builders of coral reefs, are in serious global decline, although there remains significant uncertainty as to the consequences for individual species and particular regions. We assessed coral species richness and ranked relative abundance across 3075 depth-stratified survey sites, each < 0.5 ha in area, using a standardized rapid assessment method, in 31 Indo-West Pacific (IWP) coral ecoregions (ERs), from 1994 to 2016. The ecoregions cover a significant proportion of the ranges of most IWP reef coral species, including main centres of diversity, providing a baseline (albeit a shifted one) of species abundance over a large area of highly endangered reef systems, facilitating study of future change. In all, 672 species were recorded. The richest sites and ERs were all located in the Coral Triangle. Local (site) richness peaked at 224 species in Halmahera ER (IWP mean 71 species Standard Deviation 38 species). Nineteen species occurred in more than half of all sites, all but one occurring in more than 90% of ERs. Representing 13 genera, these widespread species exhibit a broad range of life histories, indicating that no particular strategy, or taxonomic affiliation, conferred particular ecological advantage. For most other species, occurrence and abundance varied markedly among different ERs, some having pronounced “centres of abundance”. Conversely, another 40 species, also with widely divergent life histories, were very rare, occurring in five or fewer sites, 14 species of which are ranked as “Vulnerable” or “Endangered” on the International Union for Conservation of Nature (IUCN) Red List. Others may also qualify in these Threatened categories under criteria of small geographic range and population fragmentation, the utility of which is briefly assessed. Full article

(This article belongs to the Special Issue Tropical Marine Biodiversity)

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