Journal Description
Diversity
Diversity
is a peer-reviewed, open access journal on the science of biodiversity from molecules, genes, populations, and species, to ecosystems and is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, GEOBASE, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Biodiversity Conservation) / CiteScore - Q2 (Agricultural and Biological Sciences (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.4 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Diversity is a companion journal of Fossil Studies.
Impact Factor:
2.1 (2023);
5-Year Impact Factor:
2.3 (2023)
Latest Articles
Correction: Slišković et al. A Systematic Analysis of the Mediterranean Sea (IHO Sea Area) in the WRiMS Database. Diversity 2024, 16, 358
Diversity 2024, 16(10), 646; https://doi.org/10.3390/d16100646 (registering DOI) - 17 Oct 2024
Abstract
In the published article [...]
Full article
Open AccessArticle
Dragonfly Functional Diversity in Dinaric Karst Tufa-Depositing Lotic Habitats in a Biodiversity Hotspot
by
Marina Vilenica, Vlatka Mičetić Stanković and Mladen Kučinić
Diversity 2024, 16(10), 645; https://doi.org/10.3390/d16100645 - 17 Oct 2024
Abstract
Functional diversity is a key component of biodiversity that reflects various dimensions of ecosystem functioning and the roles organisms play within communities and ecosystems. It is widely used to understand how ecological processes influence biotic assemblages. With an aim to increase our knowledge
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Functional diversity is a key component of biodiversity that reflects various dimensions of ecosystem functioning and the roles organisms play within communities and ecosystems. It is widely used to understand how ecological processes influence biotic assemblages. With an aim to increase our knowledge about dragonfly ecological requirements in tufa-depositing karst habitats, we assessed functional diversity of their assemblages, various life history traits (e.g., stream zonation preference, substrate preference, reproduction type), and relationship between functional diversity and physico-chemical water properties in three types of karst lotic habitats (springs, streams, and tufa barriers) in a biodiversity hotspot in the western Balkan Peninsula. Dragonfly functional diversity was mainly characterized by traits typical for lotic rheophile species with medium dispersal capacity. Among the investigated habitats, tufa barriers, characterized by higher (micro)habitat heterogeneity, higher water velocity, as well as lower conductivity and concentration of nitrates, can be considered as dragonfly functional diversity hotspots. Functional diversity and most of the life history traits were comparable among different substrate types in the studied habitats, indicating higher importance of habitat type in shaping dragonfly functional diversity patterns in karst lotic habitats. Our results should be considered in the management and conservation activities of vulnerable karst freshwater ecosystems and their dragonfly assemblages.
Full article
(This article belongs to the Section Freshwater Biodiversity)
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Figure 1
<p>Photo examples of study sites included in the study: springs: (<b>a</b>) Bijela rijeka spring, (<b>b</b>) Crna rijeka spring; streams (and small mountainous rivers): (<b>c</b>) Bijela rijeka middle reaches, (<b>d</b>) Crna rijeka middle reaches, (<b>e</b>) Crna rijeka lower reaches, (<b>f</b>) Plitvica, (<b>g</b>) Korana; tufa barriers: (<b>h</b>) Labudovac, (<b>i</b>) Kozjak–Milanovac, (<b>j</b>) Novakovića Brod.</p> Full article ">Figure 2
<p>Environmental variables at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean annual values with standard deviation, SD): (<b>a</b>) nitrate concentration, (<b>b</b>) pH, (<b>c</b>) oxygen saturation, (<b>d</b>) water velocity, (<b>e</b>) conductivity, and (<b>f</b>) alkalinity. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> < 0.05).</p> Full article ">Figure 3
<p>Environmental variables at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean annual values with standard deviation, SD): (<b>a</b>) water temperature, (<b>b</b>) oxygen concentration, and (<b>c</b>) ammonia concentration. Non-significant differences among the habitat types are indicated by the letter a (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> > 0.05).</p> Full article ">Figure 4
<p>Dragonfly functional diversity (RaoQ index) at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD). Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> < 0.05).</p> Full article ">Figure 5
<p>Dragonfly functional traits at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD): (<b>a</b>) body shape, (<b>b</b>) dispersal capacity, (<b>c</b>) stream zonation preference, (<b>d</b>) lateral connectivity preference, (<b>e</b>) current preference, (<b>f</b>) substrate type preference, and (<b>g</b>) reproduction type. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> < 0.05). Legend: DC = dispersal capacity; EUC = eucrenal, HYC = hypocrenal, ERH = epirhithral, MRH = metarhithral, HRH = hyporhithral, EPO = epipotamal, MPO = metapotamal, HPO = hypopotamal, LITT = littoral; EUP = eupotamon, PRP = parapotamon, PLP = plesiopotamon, PAP = palaeopotamon, TMP = temporary water bodies; LIP = limnophil, LRP = limno- to rheophil, RLP = rheo- to limnophil, RPH = rheophil; ARG = argyllal, PEL = pelal, PSA = psammal, AKA = akal, LITH = lithal, PHY = phytal, POM = particulate organic matter; ETS = eggs laid attached to substrate, EIS = eggs laid in substrate, SUB = eggs laid not attached to or in substrate, OWA = eggs laid in open water, IPL = eggs laid inside plant tissue, OPL = eggs laid onto plant material, IRS = eggs laid into submerged soil or onto submerged rock.</p> Full article ">Figure 6
<p>Redundancy analysis (RDA) ordination biplot showing the relationship between dragonfly functional traits and six significant environmental variables in Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia. Abbreviations of the functional (life history) traits are in <a href="#diversity-16-00645-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 7
<p>Dragonfly functional diversity (RaoQ index) at four main substrate types in three Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD) Non-significant differences among the habitat types are indicated by the letter a (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> > 0.05).</p> Full article ">Figure 8
<p>Dragonfly functional traits at four main substrate types in three Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD): (<b>a</b>) body shape, (<b>b</b>) dispersal capacity, (<b>c</b>) stream zonation preference, (<b>d</b>) lateral connectivity preference, (<b>e</b>) current preference, (<b>f</b>) substrate type preference, and (<b>g</b>) reproduction type. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons post hoc test, <span class="html-italic">p</span> < 0.05). Abbreviations of the functional (life history) traits are in <a href="#diversity-16-00645-f005" class="html-fig">Figure 5</a>.</p> Full article ">
<p>Photo examples of study sites included in the study: springs: (<b>a</b>) Bijela rijeka spring, (<b>b</b>) Crna rijeka spring; streams (and small mountainous rivers): (<b>c</b>) Bijela rijeka middle reaches, (<b>d</b>) Crna rijeka middle reaches, (<b>e</b>) Crna rijeka lower reaches, (<b>f</b>) Plitvica, (<b>g</b>) Korana; tufa barriers: (<b>h</b>) Labudovac, (<b>i</b>) Kozjak–Milanovac, (<b>j</b>) Novakovića Brod.</p> Full article ">Figure 2
<p>Environmental variables at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean annual values with standard deviation, SD): (<b>a</b>) nitrate concentration, (<b>b</b>) pH, (<b>c</b>) oxygen saturation, (<b>d</b>) water velocity, (<b>e</b>) conductivity, and (<b>f</b>) alkalinity. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> < 0.05).</p> Full article ">Figure 3
<p>Environmental variables at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean annual values with standard deviation, SD): (<b>a</b>) water temperature, (<b>b</b>) oxygen concentration, and (<b>c</b>) ammonia concentration. Non-significant differences among the habitat types are indicated by the letter a (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> > 0.05).</p> Full article ">Figure 4
<p>Dragonfly functional diversity (RaoQ index) at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD). Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> < 0.05).</p> Full article ">Figure 5
<p>Dragonfly functional traits at three Dinaric karst lotic habitat types in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD): (<b>a</b>) body shape, (<b>b</b>) dispersal capacity, (<b>c</b>) stream zonation preference, (<b>d</b>) lateral connectivity preference, (<b>e</b>) current preference, (<b>f</b>) substrate type preference, and (<b>g</b>) reproduction type. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> < 0.05). Legend: DC = dispersal capacity; EUC = eucrenal, HYC = hypocrenal, ERH = epirhithral, MRH = metarhithral, HRH = hyporhithral, EPO = epipotamal, MPO = metapotamal, HPO = hypopotamal, LITT = littoral; EUP = eupotamon, PRP = parapotamon, PLP = plesiopotamon, PAP = palaeopotamon, TMP = temporary water bodies; LIP = limnophil, LRP = limno- to rheophil, RLP = rheo- to limnophil, RPH = rheophil; ARG = argyllal, PEL = pelal, PSA = psammal, AKA = akal, LITH = lithal, PHY = phytal, POM = particulate organic matter; ETS = eggs laid attached to substrate, EIS = eggs laid in substrate, SUB = eggs laid not attached to or in substrate, OWA = eggs laid in open water, IPL = eggs laid inside plant tissue, OPL = eggs laid onto plant material, IRS = eggs laid into submerged soil or onto submerged rock.</p> Full article ">Figure 6
<p>Redundancy analysis (RDA) ordination biplot showing the relationship between dragonfly functional traits and six significant environmental variables in Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia. Abbreviations of the functional (life history) traits are in <a href="#diversity-16-00645-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 7
<p>Dragonfly functional diversity (RaoQ index) at four main substrate types in three Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD) Non-significant differences among the habitat types are indicated by the letter a (Kruskal–Wallis H test with multiple comparisons <span class="html-italic">post hoc</span> test, <span class="html-italic">p</span> > 0.05).</p> Full article ">Figure 8
<p>Dragonfly functional traits at four main substrate types in three Dinaric karst lotic habitats in the Plitvice Lakes NP, Croatia (shown as mean with standard deviation, SD): (<b>a</b>) body shape, (<b>b</b>) dispersal capacity, (<b>c</b>) stream zonation preference, (<b>d</b>) lateral connectivity preference, (<b>e</b>) current preference, (<b>f</b>) substrate type preference, and (<b>g</b>) reproduction type. Significant differences among the habitat types are indicated by different letters (Kruskal–Wallis H test with multiple comparisons post hoc test, <span class="html-italic">p</span> < 0.05). Abbreviations of the functional (life history) traits are in <a href="#diversity-16-00645-f005" class="html-fig">Figure 5</a>.</p> Full article ">
Open AccessArticle
Variation in the Biomass of Phragmites australis Across Community Types in the Aquatic Habitats of the Middle Volga Valley
by
Vladimir Papchenkov and Hana Čížková
Diversity 2024, 16(10), 644; https://doi.org/10.3390/d16100644 - 17 Oct 2024
Abstract
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Species composition and biomass are key indicators of vegetation performance. While Phragmites australis is extensively studied worldwide, data on its communities and biomass in natural habitats are limited in the European part of the Russian Federation. This study examines P. australis-dominated communities
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Species composition and biomass are key indicators of vegetation performance. While Phragmites australis is extensively studied worldwide, data on its communities and biomass in natural habitats are limited in the European part of the Russian Federation. This study examines P. australis-dominated communities and their biomass in wetlands along the Middle Volga River. P. australis was either the dominant or co-dominant species in seven community types. Their seasonal maximum aboveground biomass correlated with plant projective cover, being highest in Schoenoplecteto lacustris-Phragmitetum australis (mean 1.7 kg m−2), with nearly 100% cover, and lowest (0.5 kg m−2) in Spirodelo-Phragmitetum australis, with 50% cover. Compared with communities dominated by Glyceria maxima, Schoenoplectus lacustris, and Typha latifolia, those of P. australis had the highest seasonal maximum aboveground biomass in running waters (mean 1.32 kg m−2) but the lowest in standing waters of the Kuibyshev Reservoir (mean 0.70 kg m−2), likely reflecting nutrient availability. A similar pattern was observed for the dominant species alone. The mean belowground biomass of P. australis was 1.9 kg m−2, with a belowground/aboveground ratio of 1.5. Similar values were found for S. lacustris and T. latifolia. The community types and biomass values align with those found in other European regions with warm temperate climates.
Full article
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<p>Map of the Volga River catchment.</p> Full article ">Figure 2
<p>Seasonal maximum aboveground biomass of tall helophytes in running and standing water habitats in the Middle Volga Valley in different classes of projective cover (I: 0–30%, II: 30–60%, III: 60–90%, IV: 90–100%). <span class="html-italic">R</span><sup>2</sup> denote determination coefficients for exponential and linear fits for species cover in running and standing water habitats, respectively. The colors of fits correspond to the colors for species given in the legend.</p> Full article ">
<p>Map of the Volga River catchment.</p> Full article ">Figure 2
<p>Seasonal maximum aboveground biomass of tall helophytes in running and standing water habitats in the Middle Volga Valley in different classes of projective cover (I: 0–30%, II: 30–60%, III: 60–90%, IV: 90–100%). <span class="html-italic">R</span><sup>2</sup> denote determination coefficients for exponential and linear fits for species cover in running and standing water habitats, respectively. The colors of fits correspond to the colors for species given in the legend.</p> Full article ">
Open AccessArticle
Zoonotic Pathogens Isolated from an Introduced Population of Red Swamp Crayfish (Procambarus clarkii) in Tenerife (Canary Islands, Spain)
by
Néstor Abreu-Acosta, Natalia Martín-Carrillo and Pilar Foronda
Diversity 2024, 16(10), 643; https://doi.org/10.3390/d16100643 - 16 Oct 2024
Abstract
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The red swamp crayfish (Procambarus clarkii) is a widely distributed invasive species that is listed in the Delivering Alien Invasive Species Inventory for Europe. Native to North America, it has been introduced to numerous regions, such as the Canary Islands, Spain.
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The red swamp crayfish (Procambarus clarkii) is a widely distributed invasive species that is listed in the Delivering Alien Invasive Species Inventory for Europe. Native to North America, it has been introduced to numerous regions, such as the Canary Islands, Spain. Previous studies have confirmed the role of this crayfish in the maintenance of several foodborne pathogenic bacteria. Therefore, the aim of this study was to analyze the main zoonotic bacterial and parasitic pathogens present in a P. clarkii population introduced to the island of Tenerife, Canary Islands, and to assess the potential risk to public health and native fauna. A total of 22 crayfish from Tenerife were analyzed using Biofire FilmArray Gastrointestinal Panels and culture–PCR methods. The results show the presence of Plesiomonas shigelloides, Shigella/enteroinvasive Escherichia coli, enteropathogenic Escherichia coli, Salmonella ser. Enteritidis, Salmonella ser. Typhimurium, and Salmonella ser. Typhi. These results demonstrate the presence of a variety of pathogenic bacteria in the red swamp crayfish in Tenerife that represent a significant concern in terms of public health and conservation. Implementing educational campaigns to inform the community about the risks associated with handling and consuming contaminated crayfish, as well as initiatives for the restoration of the contaminated ecosystem, are necessary to prevent the transmission of the foodborne pathogens.
Full article
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<p>Sampling locations (in red) for <span class="html-italic">Procambarus clarkii</span> in El Cercado ravine (yellow), Tenerife (Canary Islands, Spain). Images captured from Google Earth Pro and edited with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (consulted on 27 May 2024).</p> Full article ">
<p>Sampling locations (in red) for <span class="html-italic">Procambarus clarkii</span> in El Cercado ravine (yellow), Tenerife (Canary Islands, Spain). Images captured from Google Earth Pro and edited with <a href="http://BioRender.com" target="_blank">BioRender.com</a> (consulted on 27 May 2024).</p> Full article ">
Open AccessArticle
Endangered Taxus wallichiana var. wallichiana—Its Forest Characteristics, Population Structure, and Regeneration Status in Yunnan, Southwestern China
by
Cindy Q. Tang, Qing Chen, You-Cai Shi, Qiao Li, Kang-Di Pei, Shuaifeng Li, Peng-Bin Han, Shu-Li Xiao, Min-Rui Du, Ming-Chun Peng and Chong-Yun Wang
Diversity 2024, 16(10), 642; https://doi.org/10.3390/d16100642 - 16 Oct 2024
Abstract
The survival of relict Taxus wallichiana var. wallichiana (Yunnan yew) is threatened by overexploitation for its quality wood and medicinal properties, particularly for taxol extraction. Understanding the current status of its communities and populations is crucial for protecting existing natural forest resources. We
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The survival of relict Taxus wallichiana var. wallichiana (Yunnan yew) is threatened by overexploitation for its quality wood and medicinal properties, particularly for taxol extraction. Understanding the current status of its communities and populations is crucial for protecting existing natural forest resources. We established 53 vegetation plots in Yunnan, southwestern China, where T. wallichiana var. wallichiana is the primary dominant species. These plots were classified into four forest types. The forests were multi-stratified, with T. wallichiana var. wallichiana frequently dominating the subcanopy and shrub layer. Species diversity indices did not significantly differ among the four forest types. The age structure of T. wallichiana var. wallichiana exhibited a multi-modal pattern, with a maximum age of 1165 years. Growth was slow, with an average radial growth rate of 0.78 mm/year. Despite its strong sprouting ability, the species had a poor seedling/sapling bank and suffered from inadequate regeneration. Its seedlings/saplings are shade-intolerant. This study provides a scientific basis for effective conservation strategies, emphasizing the need for in situ regeneration to ensure the survival of T. wallichiana var. wallichiana and its contributions to biodiversity and ecosystem services.
Full article
(This article belongs to the Special Issue Rare and Endemic Plant Conservation in the Context of Global Changes)
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<p>Study areas and plot locations in Yunnan, SW China.</p> Full article ">Figure 2
<p>Cluster analysis and height-class frequency distribution of woody species (height ≥ 1.3 m). (<b>A</b>) Cluster analysis of the 53 plots. (<b>B</b>) Height-class frequency distribution for each forest type. TW: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>; TD: <span class="html-italic">Tsuga dumosa</span>; Ae: <span class="html-italic">Abies ernestii</span> var. <span class="html-italic">salouenensis</span>; QS: <span class="html-italic">Quercus spinosa</span>. Type 1: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Tsuga dumosa</span> evergreen coniferous forest. Type 2: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> evergreen coniferous forest. Type 3: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Abies ernestii</span> var. <span class="html-italic">salouenensis</span> evergreen coniferous forest. Type 4: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Quercus spinosa</span> evergreen coniferous and broad-leaved mixed forest.</p> Full article ">Figure 3
<p>Representative forest profile of each forest type. (<b>A</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Tsuga dumosa</span> evergreen coniferous forest (Type 1) at 2964 m a.s.l. in Kenacun, Tachengxiang, Deqing County, Yunnan Province; (<b>B</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> evergreen coniferous forest (Type 2) at 3213 m in Fuhecun, Lajing Zhen, Lanping County, Yunnan Province. (<b>C</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Abies ernestii</span> var. <span class="html-italic">salouenensis</span> evergreen coniferous forest (Type 3) at 2613 m a.s.l. in Shirongcun, Xiruoxiang, Deqing County, Yunnan Province; (<b>D</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Quercus spinosa</span> evergreen coniferous and broad-leaved mixed forest (Type 4) at 3192 m in Haohuiwencun, ShunzhouZhen, Yongsheng County, Yunnan Province.</p> Full article ">Figure 3 Cont.
<p>Representative forest profile of each forest type. (<b>A</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Tsuga dumosa</span> evergreen coniferous forest (Type 1) at 2964 m a.s.l. in Kenacun, Tachengxiang, Deqing County, Yunnan Province; (<b>B</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> evergreen coniferous forest (Type 2) at 3213 m in Fuhecun, Lajing Zhen, Lanping County, Yunnan Province. (<b>C</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Abies ernestii</span> var. <span class="html-italic">salouenensis</span> evergreen coniferous forest (Type 3) at 2613 m a.s.l. in Shirongcun, Xiruoxiang, Deqing County, Yunnan Province; (<b>D</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Quercus spinosa</span> evergreen coniferous and broad-leaved mixed forest (Type 4) at 3192 m in Haohuiwencun, ShunzhouZhen, Yongsheng County, Yunnan Province.</p> Full article ">Figure 4
<p><span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> and its representative forest stands and habitats. (<b>A</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> tree with a lot of sprouts growing in limestone habitat; (<b>B</b>) Sprouts of a <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> stump; (<b>C</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> forest with some logged <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> trees; (<b>D</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> forest on an upper slope; (<b>E</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> forest by a streamside; (<b>F</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> forest on a steep slope.</p> Full article ">Figure 5
<p>Diversity of woody species (height ≥ 1.3 m) in each forest type. (<b>A</b>) Average number of species in each forest type; (<b>B</b>) Shannon–Wiener diversity index in each forest type; (<b>C</b>) Pielou evenness index in each forest type; (<b>D</b>) Simpson diversity index in each forest type. Forests sharing the same letters do not differ significantly according to the non-parametric Kruskal–Wallis all-pairwise comparisons test (<span class="html-italic">p</span> < 0.05). Bars represent standard deviation.</p> Full article ">Figure 6
<p>Age–class structure of <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> across different forest types, as well as the combined structure for all four forest types.</p> Full article ">Figure 7
<p>Growth trends of <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> trees (height ≥ 1.3 m). (<b>A</b>) Changes in ring width with age. (<b>B</b>) Ring width for trees in three age groups (12–130, 130–240, and 240–312 years).</p> Full article ">Figure 8
<p>Variation in the density of seedlings and saplings of <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> across different height classes in various micro-habitats.</p> Full article ">
<p>Study areas and plot locations in Yunnan, SW China.</p> Full article ">Figure 2
<p>Cluster analysis and height-class frequency distribution of woody species (height ≥ 1.3 m). (<b>A</b>) Cluster analysis of the 53 plots. (<b>B</b>) Height-class frequency distribution for each forest type. TW: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>; TD: <span class="html-italic">Tsuga dumosa</span>; Ae: <span class="html-italic">Abies ernestii</span> var. <span class="html-italic">salouenensis</span>; QS: <span class="html-italic">Quercus spinosa</span>. Type 1: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Tsuga dumosa</span> evergreen coniferous forest. Type 2: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> evergreen coniferous forest. Type 3: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Abies ernestii</span> var. <span class="html-italic">salouenensis</span> evergreen coniferous forest. Type 4: <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Quercus spinosa</span> evergreen coniferous and broad-leaved mixed forest.</p> Full article ">Figure 3
<p>Representative forest profile of each forest type. (<b>A</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Tsuga dumosa</span> evergreen coniferous forest (Type 1) at 2964 m a.s.l. in Kenacun, Tachengxiang, Deqing County, Yunnan Province; (<b>B</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> evergreen coniferous forest (Type 2) at 3213 m in Fuhecun, Lajing Zhen, Lanping County, Yunnan Province. (<b>C</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Abies ernestii</span> var. <span class="html-italic">salouenensis</span> evergreen coniferous forest (Type 3) at 2613 m a.s.l. in Shirongcun, Xiruoxiang, Deqing County, Yunnan Province; (<b>D</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Quercus spinosa</span> evergreen coniferous and broad-leaved mixed forest (Type 4) at 3192 m in Haohuiwencun, ShunzhouZhen, Yongsheng County, Yunnan Province.</p> Full article ">Figure 3 Cont.
<p>Representative forest profile of each forest type. (<b>A</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Tsuga dumosa</span> evergreen coniferous forest (Type 1) at 2964 m a.s.l. in Kenacun, Tachengxiang, Deqing County, Yunnan Province; (<b>B</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> evergreen coniferous forest (Type 2) at 3213 m in Fuhecun, Lajing Zhen, Lanping County, Yunnan Province. (<b>C</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Abies ernestii</span> var. <span class="html-italic">salouenensis</span> evergreen coniferous forest (Type 3) at 2613 m a.s.l. in Shirongcun, Xiruoxiang, Deqing County, Yunnan Province; (<b>D</b>) <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span>—<span class="html-italic">Quercus spinosa</span> evergreen coniferous and broad-leaved mixed forest (Type 4) at 3192 m in Haohuiwencun, ShunzhouZhen, Yongsheng County, Yunnan Province.</p> Full article ">Figure 4
<p><span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> and its representative forest stands and habitats. (<b>A</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> tree with a lot of sprouts growing in limestone habitat; (<b>B</b>) Sprouts of a <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> stump; (<b>C</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> forest with some logged <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> trees; (<b>D</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> forest on an upper slope; (<b>E</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> forest by a streamside; (<b>F</b>) A <span class="html-italic">T. wallichiana</span> var. <span class="html-italic">wallichiana</span> forest on a steep slope.</p> Full article ">Figure 5
<p>Diversity of woody species (height ≥ 1.3 m) in each forest type. (<b>A</b>) Average number of species in each forest type; (<b>B</b>) Shannon–Wiener diversity index in each forest type; (<b>C</b>) Pielou evenness index in each forest type; (<b>D</b>) Simpson diversity index in each forest type. Forests sharing the same letters do not differ significantly according to the non-parametric Kruskal–Wallis all-pairwise comparisons test (<span class="html-italic">p</span> < 0.05). Bars represent standard deviation.</p> Full article ">Figure 6
<p>Age–class structure of <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> across different forest types, as well as the combined structure for all four forest types.</p> Full article ">Figure 7
<p>Growth trends of <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> trees (height ≥ 1.3 m). (<b>A</b>) Changes in ring width with age. (<b>B</b>) Ring width for trees in three age groups (12–130, 130–240, and 240–312 years).</p> Full article ">Figure 8
<p>Variation in the density of seedlings and saplings of <span class="html-italic">Taxus wallichiana</span> var. <span class="html-italic">wallichiana</span> across different height classes in various micro-habitats.</p> Full article ">
Open AccessArticle
Assessment of Plant Biodiversity and the Floristic Composition in the Black Irtysh River Valley (Kazakhstan)
by
Aliya Abitay, Elmira Imanova and Aidar Sumbembayev
Diversity 2024, 16(10), 641; https://doi.org/10.3390/d16100641 - 16 Oct 2024
Abstract
The Black Irtysh River, a major tributary of the Ob River, traverses diverse ecological zones, influencing the distribution and composition of its floodplain vegetation. This study focused on the Black Irtysh River valley, a key segment of the Irtysh basin, to assess the
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The Black Irtysh River, a major tributary of the Ob River, traverses diverse ecological zones, influencing the distribution and composition of its floodplain vegetation. This study focused on the Black Irtysh River valley, a key segment of the Irtysh basin, to assess the current state of its plant communities. To compile expedition routes and a preliminary floristic list, a critical revision of more than 1000 herbarium sheets was carried out in the herbarium collections of Kazakhstan (Altai Botanical Garden and Institute of Botany and Phytointroduction). During the field season, a study of plant biodiversity was carried out along the entire coastline of the Black Irtysh. As a result, 217 species of higher vascular plants were identified (55% of those previously found in herbarium archives) from 139 genera and 43 families. The habitats of two Red Book species were discovered: Tulipa patens and Tulipa uniflora. It was found that the flora of the Black Irtysh is similar to the flora of the entire Zaisan depression, and families Poaceae, Asteraceae, Amaranthaceae, Caryophyllaceae, Rosaceae, and Fabaceae are predominant. Geobotanical surveys revealed that the species composition of plant communities is poor and similar among survey points. Only the western part of the river delta is characterized by high rates of projective cover and reserves of forage plants. The main factors of anthropogenic influence are fires, livestock grazing, and deforestation.
Full article
(This article belongs to the Special Issue Plant Diversity Hotspots in the 2020s)
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<p>Field trip routes along Black Irtysh River with geobotanical profiles. Blue box denotes region of survey.</p> Full article ">Figure 2
<p>Rare species of early spring flora found in Black Irtysh valley: <span class="html-italic">Tulipa patens</span> C.Agardh ex Schult. Schult. (<b>A</b>), <span class="html-italic">Tulipa uniflora</span> (L.) Besser Backer (<b>B</b>), <span class="html-italic">Gagea fedschenkoana</span> Pasche (<b>C</b>), <span class="html-italic">Tulipa altaica</span> Pall.ex Spreng. (<b>D</b>).</p> Full article ">Figure 3
<p>Plant communities of Profile I (<b>A</b>), Profile II (<b>B</b>), and Profile III (<b>C</b>).</p> Full article ">Figure 4
<p>Community parameters: projective coverage at surveyed points (<b>A</b>), green mass yield (<b>B</b>), and estimated height of vegetation cover (<b>C</b>).</p> Full article ">Figure 5
<p>Correlations of the main factors and conditions of the habitat with the vegetation cover of the territory. Prof: profile; Alti: altitude; Soil: type of soil; ProC: projective coverage; MaxG: max grass layering; VegC: vegetation condition; and Yield: expected yield. Cells with significance at <span class="html-italic">p</span> < 0.05 are highlighted. The directions of correlations are denoted in color scale on the right, with red indicating negative and blue indicating positive.</p> Full article ">Figure 6
<p>Canonical correspondence analysis of the studied points.</p> Full article ">Figure 7
<p>NJ clustering of floristic profile points.</p> Full article ">Figure 8
<p>Main types of anthropogenic influence: uncontrolled grazing of livestock (<b>A</b>), numerous fires (<b>B</b>), illegal logging (<b>C</b>).</p> Full article ">
<p>Field trip routes along Black Irtysh River with geobotanical profiles. Blue box denotes region of survey.</p> Full article ">Figure 2
<p>Rare species of early spring flora found in Black Irtysh valley: <span class="html-italic">Tulipa patens</span> C.Agardh ex Schult. Schult. (<b>A</b>), <span class="html-italic">Tulipa uniflora</span> (L.) Besser Backer (<b>B</b>), <span class="html-italic">Gagea fedschenkoana</span> Pasche (<b>C</b>), <span class="html-italic">Tulipa altaica</span> Pall.ex Spreng. (<b>D</b>).</p> Full article ">Figure 3
<p>Plant communities of Profile I (<b>A</b>), Profile II (<b>B</b>), and Profile III (<b>C</b>).</p> Full article ">Figure 4
<p>Community parameters: projective coverage at surveyed points (<b>A</b>), green mass yield (<b>B</b>), and estimated height of vegetation cover (<b>C</b>).</p> Full article ">Figure 5
<p>Correlations of the main factors and conditions of the habitat with the vegetation cover of the territory. Prof: profile; Alti: altitude; Soil: type of soil; ProC: projective coverage; MaxG: max grass layering; VegC: vegetation condition; and Yield: expected yield. Cells with significance at <span class="html-italic">p</span> < 0.05 are highlighted. The directions of correlations are denoted in color scale on the right, with red indicating negative and blue indicating positive.</p> Full article ">Figure 6
<p>Canonical correspondence analysis of the studied points.</p> Full article ">Figure 7
<p>NJ clustering of floristic profile points.</p> Full article ">Figure 8
<p>Main types of anthropogenic influence: uncontrolled grazing of livestock (<b>A</b>), numerous fires (<b>B</b>), illegal logging (<b>C</b>).</p> Full article ">
Open AccessArticle
A Multi-Scale Species Distribution Model for Migrating and Overwintering Western Monarch Butterflies: Climate Is the Best Predictor
by
Ashley R. Fisher, William T. Bean and Francis X. Villablanca
Diversity 2024, 16(10), 640; https://doi.org/10.3390/d16100640 (registering DOI) - 15 Oct 2024
Abstract
Western Monarch butterflies (Danaus plexippus) migrate from inland breeding ranges to coastal overwintering grounds in California. Given that migratory individuals may make multi-scale habitat selection decisions, we considered a multi-scale species distribution model (SDM) using range-wide climatic and local landscape-level predictors of
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Western Monarch butterflies (Danaus plexippus) migrate from inland breeding ranges to coastal overwintering grounds in California. Given that migratory individuals may make multi-scale habitat selection decisions, we considered a multi-scale species distribution model (SDM) using range-wide climatic and local landscape-level predictors of migratory and overwintering habitat and community-science presence data. The range-wide model output was included as a predictor in the local-scale model, generating multi-scale habitat suitability. The top range-wide predictor was the minimum temperature in December, contributing 83.7% to the model, and was positively associated with presence. At the local scale, the strongest predictors of presence were the range-wide output and percent coverage of low and medium levels of development, contributing > 95%, with 61–63% from the range-wide output, with local-scale suitability coinciding with the California coastal zones. Development’s positive association with overwintering monarch presence was counterintuitive. It is likely that our local-scale model is overfit to these development zones, but it is unclear whether this overfitting resulted from modeler choices, monarchs overwintering close to human development, biased detection near human development, or a combination of these factors. Therefore, alternative approaches to collecting local-scale attribute data are suggested while recognizing the primacy of climate in restricting overwinter sites.
Full article
(This article belongs to the Collection Feature Papers in Animal Diversity)
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<p>FR and COR model extents and Western Monarch presence points. FR, or the population-wide level (hatched green and blue), extended to the land west of the Rocky Mountains, where the minimum temperature of the <span class="html-italic">warmest</span> month during migration (October) is above −6 °C. The COR model (blue) extended to the land west of the Rocky Mountains, where the minimum temperature of the <span class="html-italic">coldest</span> month (December) was above −6 °C. Both extents were constricted to the United States borders due to data limitations. Presence points in FR (both yellow and black) included all observations of adults and overwintering clusters from October to February. Presence points in the COR model (black only) include observations of adults and overwintering clusters during peak overwintering season only (November to February).</p> Full article ">Figure 2
<p>(<b>A</b>) FR (population-wide) model, and (<b>B</b>) COR (peak overwintering habitat) model predictive outputs. Values from 0 to 1 indicate the relative probability of presence for monarch butterfly overwintering sites. A higher-resolution view of <a href="#diversity-16-00640-f002" class="html-fig">Figure 2</a>A is presented in <a href="#diversity-16-00640-f003" class="html-fig">Figure 3</a>. The COR model shown is the output of the LT model. For a higher resolution view of <a href="#diversity-16-00640-f002" class="html-fig">Figure 2</a>B, see <a href="#app1-diversity-16-00640" class="html-app">Supplemental Material Figure S1</a>. See <a href="#app1-diversity-16-00640" class="html-app">Supplementary Materials (Table S4)</a> for a view of the LQT output (<a href="#app1-diversity-16-00640" class="html-app">Figure S9</a>), which performed equally well and is nearly identical.</p> Full article ">Figure 3
<p>FR predictive output. With (<b>A</b>–<b>E</b>) images at a larger scale (~1:4,000,000) for regions with high suitability estimates from the FR model (overwintering and migratory). With subpanels (<b>A</b>–<b>E</b>) corresponding with inset boxes (<b>A</b>–<b>E</b>).</p> Full article ">Figure 4
<p>Difference Map. Shows the difference in suitability between FR and COR, which was calculated by subtracting COR from the FR predicted suitability (values from 0 to 1). Subpanels (<b>A</b>–<b>C</b>) show different maps at higher resolution, with (<b>A</b>–<b>C</b>) corresponding with inset boxes. Areas that had higher suitability in FR than COR have positive values and are in red, orange, and yellow, whereas areas that had higher suitability in COR than FR have negative values and are in light and dark blue. Areas with the largest drop in suitability from FR to COR are Big Sur, the Santa Barbara Coast, and the Channel Islands, which all have low or relatively low human population density. Meanwhile, the suitability of COR increased in the urban areas of Las Vegas, Tucson, Phoenix, and Sacramento, as well as Stockton, Modesto, Fresno, and Bakersfield in the Central Valley of California. Presence points are in black.</p> Full article ">Figure 5
<p>Univariate response curves for each of the top predictors in the FR model. The predicted value represents the relative probability of presence (0 to 1) associated with the value of the given predictor. VPD = vapor pressure deficit.</p> Full article ">Figure 6
<p>Univariate response curves for each of the top predictors in the COR LT model. The LQT model had nearly identical results (see <a href="#app1-diversity-16-00640" class="html-app">Supplemental Material Figure S4</a>). The predicted value represents the relative probability of the presence of monarch butterflies associated with the value of the given predictor.</p> Full article ">
<p>FR and COR model extents and Western Monarch presence points. FR, or the population-wide level (hatched green and blue), extended to the land west of the Rocky Mountains, where the minimum temperature of the <span class="html-italic">warmest</span> month during migration (October) is above −6 °C. The COR model (blue) extended to the land west of the Rocky Mountains, where the minimum temperature of the <span class="html-italic">coldest</span> month (December) was above −6 °C. Both extents were constricted to the United States borders due to data limitations. Presence points in FR (both yellow and black) included all observations of adults and overwintering clusters from October to February. Presence points in the COR model (black only) include observations of adults and overwintering clusters during peak overwintering season only (November to February).</p> Full article ">Figure 2
<p>(<b>A</b>) FR (population-wide) model, and (<b>B</b>) COR (peak overwintering habitat) model predictive outputs. Values from 0 to 1 indicate the relative probability of presence for monarch butterfly overwintering sites. A higher-resolution view of <a href="#diversity-16-00640-f002" class="html-fig">Figure 2</a>A is presented in <a href="#diversity-16-00640-f003" class="html-fig">Figure 3</a>. The COR model shown is the output of the LT model. For a higher resolution view of <a href="#diversity-16-00640-f002" class="html-fig">Figure 2</a>B, see <a href="#app1-diversity-16-00640" class="html-app">Supplemental Material Figure S1</a>. See <a href="#app1-diversity-16-00640" class="html-app">Supplementary Materials (Table S4)</a> for a view of the LQT output (<a href="#app1-diversity-16-00640" class="html-app">Figure S9</a>), which performed equally well and is nearly identical.</p> Full article ">Figure 3
<p>FR predictive output. With (<b>A</b>–<b>E</b>) images at a larger scale (~1:4,000,000) for regions with high suitability estimates from the FR model (overwintering and migratory). With subpanels (<b>A</b>–<b>E</b>) corresponding with inset boxes (<b>A</b>–<b>E</b>).</p> Full article ">Figure 4
<p>Difference Map. Shows the difference in suitability between FR and COR, which was calculated by subtracting COR from the FR predicted suitability (values from 0 to 1). Subpanels (<b>A</b>–<b>C</b>) show different maps at higher resolution, with (<b>A</b>–<b>C</b>) corresponding with inset boxes. Areas that had higher suitability in FR than COR have positive values and are in red, orange, and yellow, whereas areas that had higher suitability in COR than FR have negative values and are in light and dark blue. Areas with the largest drop in suitability from FR to COR are Big Sur, the Santa Barbara Coast, and the Channel Islands, which all have low or relatively low human population density. Meanwhile, the suitability of COR increased in the urban areas of Las Vegas, Tucson, Phoenix, and Sacramento, as well as Stockton, Modesto, Fresno, and Bakersfield in the Central Valley of California. Presence points are in black.</p> Full article ">Figure 5
<p>Univariate response curves for each of the top predictors in the FR model. The predicted value represents the relative probability of presence (0 to 1) associated with the value of the given predictor. VPD = vapor pressure deficit.</p> Full article ">Figure 6
<p>Univariate response curves for each of the top predictors in the COR LT model. The LQT model had nearly identical results (see <a href="#app1-diversity-16-00640" class="html-app">Supplemental Material Figure S4</a>). The predicted value represents the relative probability of the presence of monarch butterflies associated with the value of the given predictor.</p> Full article ">
Open AccessArticle
Prevalence and Diversity of Plant Parasitic Nematodes in Irish Peatlands
by
Anusha Pulavarty, Tilman Klappauf, Ankit Singh, Patricia Molero Molina, Anique Godjo, Bastiaan Molleman, Douglas McMillan and Thomais Kakouli-Duarte
Diversity 2024, 16(10), 639; https://doi.org/10.3390/d16100639 (registering DOI) - 15 Oct 2024
Abstract
The prevalence of plant parasitic nematodes (PPN) in the Irish peatlands was investigated in five different peatland habitats—raised bog, cutover scrub/woodlands, fens and peat grasslands, which were further sub-categorised into fourteen different sub-habitats. Within the raised bog habitat were healthy bog hummock (HBH),
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The prevalence of plant parasitic nematodes (PPN) in the Irish peatlands was investigated in five different peatland habitats—raised bog, cutover scrub/woodlands, fens and peat grasslands, which were further sub-categorised into fourteen different sub-habitats. Within the raised bog habitat were healthy bog hummock (HBH), healthy bog lawn (HBL), degraded bog hummock (DBH) and degraded bog lawn (DBL) and the fen habitats were fen peat (FP) and rich fen peat (R-FP). Cutover scrub or woodland habitat included cutover scrub rewetted (C-RW), cutover scrub non-rewetted (C-NRW), woodlands rewetted (W-RW) and woodlands non-rewetted (W-NRW). Grassland included wasted peat (WP), rough grazing (RG-I) and improved fen peat grassland (IFPG-RW and IFPG-NRW). Soil samples from peatlands were all collected between July and December 2023 when the temperature ranged from 12 to 20 °C. One half of each sample was used for molecular nematode analysis and the other half for morphological identification of nematodes. For the morphological identification, a specific nematode extraction protocol was optimised for peatland soils, and the extracted nematodes were fixed onto slides to be studied under a high-power light microscope. Subsequently, the other part of the soil was processed to isolate total DNA, from which the 18S rRNA gene was sequenced for the identification of nematode taxa. The extracted DNA was also used for randomly amplified polymorphic DNA (RAPD) fingerprinting analysis to determine banding patterns that could classify different bog habitats based on PPN random primers. Compared to that in the climax habitats (HBH, HBL, DBH, DBL, FP, R-FP), PPN prevalence was recorded as being higher in grasslands (WP, RG-I, IFPG-RW and IFPG-NRW) and scrub/woodland ecosystems (C-RW, C-NRW, W-RW, W-NRW). The results indicate that nematode populations are different across the various bog habitats. Emerging and current quarantine PPN belonging to the families Pratylenchidae, Meloidogynidae, Anguinidae and Heteroderidae were noted to be above the threshold limits mentioned under EPPO guidelines, in grassland and wooded peatland habitats. Future actions for PPN management may need to be considered, along with the likelihood that these PPN might impact future paludiculture and other crops and trees growing in nearby agricultural lands.
Full article
(This article belongs to the Section Biodiversity Conservation)
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<p>(<b>a</b>) Approximate site location in the Republic of Ireland; (<b>b</b>) Bog sampling location and bog habitats in each location, (i) 53°01′14.2″ N and 7°57′15.5″ W, (ii) 53°05′14.01″ N and 7°87′69.96″ W, (iii) 53°06′08.4″ N and 7°80′08.4″ W; source Google Maps.</p> Full article ">Figure 2
<p>RAPD profile of peat habitats: (<b>a</b>) Healthy bog lawn (HBH), (<b>b</b>) Rich Fen peat (R-FP) obtained with primers A5, A6, A7, A9, A10, A12, A13, A15, A16, A18, A19, A20, A22, A24. M = Molecular weight marker (Promega 1 Kb Ladder (G571A)).</p> Full article ">Figure 3
<p>Dendrogram showing the proximity distance between various peatland habitats based on RAPD index data (constructed using IBM SPSS (version 29.0.1.0 (171)).</p> Full article ">Figure 4
<p>Heat map showing the abundance of different nematode families detected in various peat habitats. The PPN families are highlighted using red ovals.</p> Full article ">Figure 5
<p>Relative abundance of PPN (%) in different peatland habitats (molecular data). Values represented by similar letters are not significantly different from each other in terms of PPN % (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">
<p>(<b>a</b>) Approximate site location in the Republic of Ireland; (<b>b</b>) Bog sampling location and bog habitats in each location, (i) 53°01′14.2″ N and 7°57′15.5″ W, (ii) 53°05′14.01″ N and 7°87′69.96″ W, (iii) 53°06′08.4″ N and 7°80′08.4″ W; source Google Maps.</p> Full article ">Figure 2
<p>RAPD profile of peat habitats: (<b>a</b>) Healthy bog lawn (HBH), (<b>b</b>) Rich Fen peat (R-FP) obtained with primers A5, A6, A7, A9, A10, A12, A13, A15, A16, A18, A19, A20, A22, A24. M = Molecular weight marker (Promega 1 Kb Ladder (G571A)).</p> Full article ">Figure 3
<p>Dendrogram showing the proximity distance between various peatland habitats based on RAPD index data (constructed using IBM SPSS (version 29.0.1.0 (171)).</p> Full article ">Figure 4
<p>Heat map showing the abundance of different nematode families detected in various peat habitats. The PPN families are highlighted using red ovals.</p> Full article ">Figure 5
<p>Relative abundance of PPN (%) in different peatland habitats (molecular data). Values represented by similar letters are not significantly different from each other in terms of PPN % (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">
Open AccessArticle
Quantitative Analysis about the Spatial Heterogeneity of Water Conservation Services Function Using a Space–Time Cube Constructed Based on Ecosystem and Soil Types
by
Yisheng Liu, Peng Hou, Ping Wang, Jian Zhu, Jun Zhai, Yan Chen, Jiahao Wang and Le Xie
Diversity 2024, 16(10), 638; https://doi.org/10.3390/d16100638 (registering DOI) - 14 Oct 2024
Abstract
Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values
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Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.
Full article
(This article belongs to the Special Issue Habitat Assessment and Conservation Strategies)
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<p>Spatial distribution of the ecosystem and elevation in the study area.</p> Full article ">Figure 2
<p>The flowchart of investigation.</p> Full article ">Figure 3
<p>Schematic diagram of ESM: runoff depth and comprehensive runoff index (Ri) calculation principles.</p> Full article ">Figure 4
<p>Schematic diagram of construction principle of the space–time cube.</p> Full article ">Figure 5
<p>The main types of spatial relationship conceptualizations in EHSA, including (<b>a</b>) Fixed Distance, (<b>b</b>) K Nearest Neighbors, (<b>c</b>) Contiguity Edge Only, and (<b>d</b>) Contiguity Edges Corners.</p> Full article ">Figure 6
<p>Spatial distribution of average annual (<b>a</b>) precipitation, (<b>b</b>) actual evapotranspiration, and WCF from (<b>c</b>) 2012 to (<b>d</b>) 2022.</p> Full article ">Figure 7
<p>Spatial distribution of WCF importance grade of the YRB. Grade Ⅰ: generally important 0–223 mm; grade Ⅱ: slightly important, 223–278 mm; grade Ⅲ: moderately important, 278–325 mm; grade Ⅳ: highly important, 325–378 mm; and grade Ⅴ: extremely important, >378 mm (378–538 mm).</p> Full article ">Figure 8
<p>Inter-annual WCF variation averaged from 2012 to 2022 over the sub-watershed.</p> Full article ">Figure 9
<p>Slope of WCF variation from 2012 to 2022.</p> Full article ">Figure 10
<p>The parameters’ sensitivity analysis by EFAST and Sobol’ method, including (<b>a</b>) the Major (First order) Sensitivity Index and (<b>b</b>) the Total Sensitivity Index.</p> Full article ">Figure 11
<p>Spatiotemporal heterogeneity of (<b>a</b>) WCF and (<b>b</b>) specific proportion of 17 EHSA patterns.</p> Full article ">Figure 12
<p>(<b>a</b>) The spatial heterogeneity and (<b>b</b>) transfer characteristics of each pattern from 100 to 150, 150 to 200, 200 to 250 m.</p> Full article ">Figure 13
<p>(<b>a</b>) Spatial distribution and (<b>b</b>) major patterns’ proportions of 4 types of spatial relationships conceptualization.</p> Full article ">Figure 13 Cont.
<p>(<b>a</b>) Spatial distribution and (<b>b</b>) major patterns’ proportions of 4 types of spatial relationships conceptualization.</p> Full article ">
<p>Spatial distribution of the ecosystem and elevation in the study area.</p> Full article ">Figure 2
<p>The flowchart of investigation.</p> Full article ">Figure 3
<p>Schematic diagram of ESM: runoff depth and comprehensive runoff index (Ri) calculation principles.</p> Full article ">Figure 4
<p>Schematic diagram of construction principle of the space–time cube.</p> Full article ">Figure 5
<p>The main types of spatial relationship conceptualizations in EHSA, including (<b>a</b>) Fixed Distance, (<b>b</b>) K Nearest Neighbors, (<b>c</b>) Contiguity Edge Only, and (<b>d</b>) Contiguity Edges Corners.</p> Full article ">Figure 6
<p>Spatial distribution of average annual (<b>a</b>) precipitation, (<b>b</b>) actual evapotranspiration, and WCF from (<b>c</b>) 2012 to (<b>d</b>) 2022.</p> Full article ">Figure 7
<p>Spatial distribution of WCF importance grade of the YRB. Grade Ⅰ: generally important 0–223 mm; grade Ⅱ: slightly important, 223–278 mm; grade Ⅲ: moderately important, 278–325 mm; grade Ⅳ: highly important, 325–378 mm; and grade Ⅴ: extremely important, >378 mm (378–538 mm).</p> Full article ">Figure 8
<p>Inter-annual WCF variation averaged from 2012 to 2022 over the sub-watershed.</p> Full article ">Figure 9
<p>Slope of WCF variation from 2012 to 2022.</p> Full article ">Figure 10
<p>The parameters’ sensitivity analysis by EFAST and Sobol’ method, including (<b>a</b>) the Major (First order) Sensitivity Index and (<b>b</b>) the Total Sensitivity Index.</p> Full article ">Figure 11
<p>Spatiotemporal heterogeneity of (<b>a</b>) WCF and (<b>b</b>) specific proportion of 17 EHSA patterns.</p> Full article ">Figure 12
<p>(<b>a</b>) The spatial heterogeneity and (<b>b</b>) transfer characteristics of each pattern from 100 to 150, 150 to 200, 200 to 250 m.</p> Full article ">Figure 13
<p>(<b>a</b>) Spatial distribution and (<b>b</b>) major patterns’ proportions of 4 types of spatial relationships conceptualization.</p> Full article ">Figure 13 Cont.
<p>(<b>a</b>) Spatial distribution and (<b>b</b>) major patterns’ proportions of 4 types of spatial relationships conceptualization.</p> Full article ">
Open AccessCorrection
Correction: Müller et al. Henneguya correai n. sp. (Cnidaria, Myxozoa) Parasitizing the Fins of the Amazonian Fish Semaprochilodus insignis. Diversity 2023, 15, 702
by
Maria I. Müller, Rayline T. A. Figueredo, Stephen D. Atkinson, Jerri L. Bartholomew and Edson A. Adriano
Diversity 2024, 16(10), 637; https://doi.org/10.3390/d16100637 (registering DOI) - 14 Oct 2024
Abstract
In the published publication [...]
Full article
(This article belongs to the Special Issue Diversity, Taxonomy and Systematics of Fish Parasites)
Open AccessArticle
Metazoan Parasites of Antimora rostrata (Günther, 1878) (Gadiformes: Moridae) from the Deep Sea in the Southeastern Pacific Ocean
by
Luis A. Ñacari, Ruben Escribano and Marcelo E. Oliva
Diversity 2024, 16(10), 636; https://doi.org/10.3390/d16100636 (registering DOI) - 12 Oct 2024
Abstract
A total of 127 specimens of the “Blue Antimora” Antimora rostrata (Günther, 1878) were obtained from 2015 to 2019 as bycatch from the artisanal fishery of the Patagonian toothfish (Dissostichus eleginoides (Smitt, 1898)) at depths between 1000 and 2200 m in Northern
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A total of 127 specimens of the “Blue Antimora” Antimora rostrata (Günther, 1878) were obtained from 2015 to 2019 as bycatch from the artisanal fishery of the Patagonian toothfish (Dissostichus eleginoides (Smitt, 1898)) at depths between 1000 and 2200 m in Northern Chile (app. 22° S 70° W). All individuals were examined for parasites. A total of seventeen parasite taxa, two Copepoda, two Monogenea, seven Digenea, three Nematoda, and three Cestoda, were found, and twelve taxa were found as adults while five taxa were found at the larval stage. Anisakis sp. (Nematoda) and Trypanorhyncha gen. sp. (Cestoda) were the predominant species with a prevalence of 53.5% and 11.8%, respectively. The high prevalence of Anisakis sp. (>50%) suggests that A. rostrata may play a significant role in the life cycle of Anisakis sp. in the southeastern Pacific Ocean. The detected parasite community, consisting predominantly of parasites from pelagic environments rather than benthopelagic, suggests that A. rostrata may fulfill a crucial role as a predator of pelagic organism communities. Additionally, it may undertake vertical migrations in the southeastern Pacific Ocean.
Full article
(This article belongs to the Special Issue Taxonomy, Biodiversity and Ecology of Parasites of Aquatic Organisms—2nd Edition)
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Open AccessArticle
Geographical Variation in the Sand Cat, Felis margarita (Carnivora: Felidae)
by
Andrew C. Kitchener, Alexei V. Abramov, Géraldine Veron, Lisa Banfield, Helen Senn, Nobuyuki Yamaguchi and Andrey Yu. Puzachenko
Diversity 2024, 16(10), 635; https://doi.org/10.3390/d16100635 - 11 Oct 2024
Abstract
Sand cats, Felis margarita, range from northern Africa and the Arabian Peninsula to Central Asia. Their apparently discontinuous distribution is recognized as comprising four subspecies. Recent genetic research found little differentiation between subspecies except for the North African form. In this study,
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Sand cats, Felis margarita, range from northern Africa and the Arabian Peninsula to Central Asia. Their apparently discontinuous distribution is recognized as comprising four subspecies. Recent genetic research found little differentiation between subspecies except for the North African form. In this study, 90 skins and 88 skulls were analyzed from the four subspecies. A discriminant function analysis of the scores, ranging from 1 to 4, of four pelage characteristics revealed differentiation between putative subspecies, except between Turkmenian and Pakistani sand cats. Northern African and Arabian sand cats tend to be spotted and striped, while Turkmenian and Pakistani sand cats are less spotted and have a dorsal crest of fur. Nonmetric multidimensional scaling (NMDS) models generated from 21 skull measurements revealed an overlap in morphospace between all subspecies, except for larger Turkmenian sand cats; northern African sand cats were smallest. Therefore, both pelage characteristics and skull morphometrics support up to three subspecies. However, considering recent genetic research, it is likely that two subspecies should be recognized, F. m. margarita from northern Africa and F. m. thinobia from the Arabian Peninsula, and Southwest and Central Asia. Widening of the dataset and nuclear DNA evidence are required to increase our understanding of geographical variation in this little studied species.
Full article
(This article belongs to the Special Issue Ecology and Evolution of Mammals)
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<p>Distribution map of the sand cat, <span class="html-italic">Felis margarita</span>, and its putative subspecies with locality records of the specimens used in this study. In several cases, multiple specimens were recorded from the same localities. Black dots show precise specimen localities, white dots show estimated locations based on regional names, etc. Distribution map from [<a href="#B11-diversity-16-00635" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>Scatter plot of sand cat pelage character scores based on the extracted discriminant functions, Function 1 and Function 2.</p> Full article ">Figure 3
<p>Grouped morphometric separation drawings for all the skulls from the four putative subspecies of <span class="html-italic">F. margarita</span>. (<b>A</b>,<b>B</b>) Projections of the SZM morphospace on the coordinates E1 and E2 in males (<b>A</b>) and females (<b>B</b>); (<b>C</b>,<b>E</b>,<b>F</b>) radial phenograms (Euclidean distance) of the putative subspecies’ centroids based on means of E1–E3, K2, K3 ((<b>C</b>), males), E1,E2, K2 ((<b>D</b>), females), and K1–K3 (males (<b>E</b>), females (<b>F</b>)).</p> Full article ">Figure 4
<p>Plots of zygomatic width and greatest length of skull (<b>a</b>), upper carnassial (P<sup>4</sup>) length and greatest length of skull (<b>b</b>), lower carnassial (M<sub>1</sub>) length and greatest length of skull (<b>c</b>), and occiput height and greatest length of skull (<b>d</b>) of putative subspecies of sand cat.</p> Full article ">Figure 5
<p>Plots of auditory bulla width and auditory bulla length (<b>a</b>), auditory bulla height and auditory bulla width (<b>b</b>), and auditory bulla height and auditory bulla length (<b>c</b>) of putative subspecies of sand cat.</p> Full article ">
<p>Distribution map of the sand cat, <span class="html-italic">Felis margarita</span>, and its putative subspecies with locality records of the specimens used in this study. In several cases, multiple specimens were recorded from the same localities. Black dots show precise specimen localities, white dots show estimated locations based on regional names, etc. Distribution map from [<a href="#B11-diversity-16-00635" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>Scatter plot of sand cat pelage character scores based on the extracted discriminant functions, Function 1 and Function 2.</p> Full article ">Figure 3
<p>Grouped morphometric separation drawings for all the skulls from the four putative subspecies of <span class="html-italic">F. margarita</span>. (<b>A</b>,<b>B</b>) Projections of the SZM morphospace on the coordinates E1 and E2 in males (<b>A</b>) and females (<b>B</b>); (<b>C</b>,<b>E</b>,<b>F</b>) radial phenograms (Euclidean distance) of the putative subspecies’ centroids based on means of E1–E3, K2, K3 ((<b>C</b>), males), E1,E2, K2 ((<b>D</b>), females), and K1–K3 (males (<b>E</b>), females (<b>F</b>)).</p> Full article ">Figure 4
<p>Plots of zygomatic width and greatest length of skull (<b>a</b>), upper carnassial (P<sup>4</sup>) length and greatest length of skull (<b>b</b>), lower carnassial (M<sub>1</sub>) length and greatest length of skull (<b>c</b>), and occiput height and greatest length of skull (<b>d</b>) of putative subspecies of sand cat.</p> Full article ">Figure 5
<p>Plots of auditory bulla width and auditory bulla length (<b>a</b>), auditory bulla height and auditory bulla width (<b>b</b>), and auditory bulla height and auditory bulla length (<b>c</b>) of putative subspecies of sand cat.</p> Full article ">
Open AccessArticle
Alternative DNA Markers to Detect Guam-Specific CRB-G (Clade I) Oryctes rhinoceros (Coleoptera: Scarabaeidae) Indicate That the Beetle Did Not Disperse from Guam to the Solomon Islands or Palau
by
Wee Tek Tay, Sean D. G. Marshall, Angel David Popa-Baez, Glenn F. J. Dulla, Andrea L. Blas, Juniaty W. Sambiran, Meldy Hosang, Justine Bennette H. Millado, Michael Melzer, Rahul V. Rane, Tim Hogarty, Demi Yi-Chun Cho, Jelfina C. Alouw, Muhammad Faheem and Benjamin D. Hoffmann
Diversity 2024, 16(10), 634; https://doi.org/10.3390/d16100634 - 10 Oct 2024
Abstract
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A partial mitochondrial DNA Cytochrome Oxidase subunit I (mtCOI) gene haplotype variant of the coconut rhinoceros beetle (CRB) Oryctes rhinoceros, classed as ‘CRB-G (clade I)’, has been the focus of much research since 2007, with reports of invasions into new
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A partial mitochondrial DNA Cytochrome Oxidase subunit I (mtCOI) gene haplotype variant of the coconut rhinoceros beetle (CRB) Oryctes rhinoceros, classed as ‘CRB-G (clade I)’, has been the focus of much research since 2007, with reports of invasions into new Pacific Island locations (e.g., Guam, Hawaii, Solomons Islands). For numerous invasive species, inference of invasion biology via whole genome is superior to assessments via the partial mtCOI gene. Here, we explore CRB draft mitochondrial genomes (mitogenomes) from historical and recent collections, with assessment focused on individuals associated within the CRB-G (clade I) classification. We found that all Guam CRB individuals possessed the same mitogenome across all 13 protein-coding genes and differed from individuals collected elsewhere, including ‘non-Guam’ individuals designated as CRB-G (clade I) by partial mtCOI assessment. Two alternative ATP6 and COIII partial gene primer sets were developed to enable distinction between CRB individuals from Guam that classed within the CRB-G (clade I) haplotype grouping and CRB-G (Clade I) individuals collected elsewhere. Phylogenetic analyses based on concatenated ATP6–COIII genes showed that only Guam CRB-G (clade I) individuals clustered together, and therefore Guam was not the source of the CRB that invaded the other locations in the Pacific assessed in this study. The use of the mtCOI and/or mtCOIII genes for initial molecular diagnosis of CRB remained crucial, and assessment of more native CRB populations will further advance our ability to identify the provenance of CRB invasions being reported within the Pacific and elsewhere.
Full article
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<p>Phylogenetic analysis using (<b>a</b>) partial mt<span class="html-italic">COI</span> gene sequence (676 bp) and (<b>b</b>) concatenated partial <span class="html-italic">APT6</span> (446 bp) and partial <span class="html-italic">COIII</span> (422 bp) gene sequences. A phylogram based on concatenated <span class="html-italic">ATP6–COIII</span> partial gene sequences and the haplotype network are also presented in (<b>c</b>) and (<b>d</b>), respectively. The <span class="html-italic">Oryctes narsicornis</span> sample (OK484312) was included as an outgroup.</p> Full article ">
<p>Phylogenetic analysis using (<b>a</b>) partial mt<span class="html-italic">COI</span> gene sequence (676 bp) and (<b>b</b>) concatenated partial <span class="html-italic">APT6</span> (446 bp) and partial <span class="html-italic">COIII</span> (422 bp) gene sequences. A phylogram based on concatenated <span class="html-italic">ATP6–COIII</span> partial gene sequences and the haplotype network are also presented in (<b>c</b>) and (<b>d</b>), respectively. The <span class="html-italic">Oryctes narsicornis</span> sample (OK484312) was included as an outgroup.</p> Full article ">
Open AccessBrief Report
Fungal Diversity Detected by ITS-5.8S from Coffea arabica Leaves Infected by Rust (Hemileia vastatrix) in Southern Ecuador
by
Darío Cruz, Andrea Jaramillo-Riofrío, Paulo Herrera, Ruth Aguinsaca and Marianela Chamba
Diversity 2024, 16(10), 633; https://doi.org/10.3390/d16100633 - 10 Oct 2024
Abstract
Coffee production worldwide is affected by the pathogen Hemileia vastatrix, which causes the “coffee rust” disease and may be associated with other fungi. Ecuador lacks studies on fungal diversity associated with coffee rust, which could potentially control or escalate pathogen activity. Using
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Coffee production worldwide is affected by the pathogen Hemileia vastatrix, which causes the “coffee rust” disease and may be associated with other fungi. Ecuador lacks studies on fungal diversity associated with coffee rust, which could potentially control or escalate pathogen activity. Using the ITS-5.8S nrDNA region, we randomly detected a small preliminary fungi diversity related to coffee rust in Ecuador, which we report here for the first time. Ten coffee farms (four in Loja, three in Calvas, and three in Quilanga) from the Loja Province were sampled to analyze the genetic diversity of the pathogen Hemileia vastatrix in rust lesions on coffee leaves. A high number of selected sequences (Sanger sequencing) showed the presence of 48 OTUs (Operational Taxonomic Units) or “hypothetical species” of Ascomycetes and Basidiomycetes distributed across all the sampled farms. The genera Akanthomyces, Ceramothyrium, Cladosporium, Didymella, Fusarium, Mycosphaerella, Neoceratosperma, and Trichothecium of Ascomycetes, as well as Bulleribasidium, Hannaella, and Meira of Basidiomycetes, were the most abundant. To avoid taxonomic conflict, some sequences were placed into Capnodiales (Ascomycetes) and Tremelalles (Basidiomycetes) without a genus definition. A new phylogenetic group of sequences is considered Incertae Sedis from Basidiomycetes. Additionally, morphospecies of Akanthomyces (synonymous with some Lecanicillium species) and Colletotrichum were observed macroscopically and microscopically growing closely with rust. Most of the OTUs probably correspond to rust mycoparasites, as previously reported in the literature. However, this study is limited by the number of sequences analyzed phylogenetically, which may hinder the discovery of significant insights. Future studies are needed to determine whether this preliminary fungal diversity is associated with the rust fungus or corresponds to ubiquitous airborne fungi. Furthermore, research into the function of these species may reveal whether they promote rust pathogenicity or enhance plant responses by activating resistance mechanisms.
Full article
(This article belongs to the Special Issue Fungal Diversity)
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<p>Topographic map showing the elevation and geographical location of the coffee farms sampled in the cantons of Calvas, Loja, and Quilanga in Loja Province.</p> Full article ">Figure 2
<p>General coffee-growing ecosystems from the sampled farms in the different cantons: (<b>A</b>) cultivation with bananas and other plants (Calvas: Jiropamba and Cango Bajo; and Loja: El Cristal); (<b>B</b>) growing in the shade of eucalyptus (black arrowheads) (Quilanga); (<b>C</b>) growing in more open areas (Loja: San Pedro de Vilcabamba); and (<b>D</b>) growing along with “porotillo” plants (black arrowhead) (Quilanga).</p> Full article ">Figure 3
<p>(<b>A</b>) Leaf of <span class="html-italic">Coffea arabica</span> L. ‘red Bourbon’ with foliar symptoms (yellow spots caused by rust). Bar = 1 cm; (<b>B</b>) uredospores generating germinating hyphae (black arrowhead) along leaf cells and stomata (grey arrowheads); (<b>C</b>) group of uredospores with thickened and warty upper walls (black arrowheads), with internal granules of orange–yellow carotenoid lipids (grey arrowheads); (<b>D</b>) mass of uredospores (blue arrowheads) close to hyphae and pigmented conidia belonging to other fungi (black arrowhead); (<b>E</b>) conidia and hyphae of <span class="html-italic">Cladosporium</span> spp. (black arrowheads) next to a rust lesion (orange zone); (<b>F</b>) conidia and hyphae of <span class="html-italic">Cladosporium</span> spp. (black arrowhead) colonizing the mealybug <span class="html-italic">Planococcus lilacinus</span> Risso (grey arrowhead) found next to a rust lesion. Bars in Figures (<b>B</b>–<b>F</b>) = 20 µm.</p> Full article ">Figure 4
<p>Neighbor-Joining phylogenetic tree for Ascomycetes, with bootstrap values ≥ 50 corresponding to Neighbor-Joining and Maximum Likelihood bootstrap, respectively. The tree is rooted with the outgroup <span class="html-italic">Hemileia vastatrix</span>. Here, 3% represents the applied threshold. OTU numbers are listed under the percentages. Colored circles show the origin of the samples: Calvas = green, Quilanga = blue, and Loja = red.</p> Full article ">Figure 5
<p>Neighbor-Joining phylogenetic tree for Basidiomycetes, with bootstrap values > 50 corresponding to Neighbor-Joining and Maximum Likelihood bootstrap, respectively. The tree is rooted with the outgroup <span class="html-italic">Mycosphaerella yunnanensis</span>. Here, 3% represents the applied threshold. OTU numbers are listed under the percentages. Colored circles show the origin of the samples: Calvas = green, Quilanga = blue, and Loja = red.</p> Full article ">
<p>Topographic map showing the elevation and geographical location of the coffee farms sampled in the cantons of Calvas, Loja, and Quilanga in Loja Province.</p> Full article ">Figure 2
<p>General coffee-growing ecosystems from the sampled farms in the different cantons: (<b>A</b>) cultivation with bananas and other plants (Calvas: Jiropamba and Cango Bajo; and Loja: El Cristal); (<b>B</b>) growing in the shade of eucalyptus (black arrowheads) (Quilanga); (<b>C</b>) growing in more open areas (Loja: San Pedro de Vilcabamba); and (<b>D</b>) growing along with “porotillo” plants (black arrowhead) (Quilanga).</p> Full article ">Figure 3
<p>(<b>A</b>) Leaf of <span class="html-italic">Coffea arabica</span> L. ‘red Bourbon’ with foliar symptoms (yellow spots caused by rust). Bar = 1 cm; (<b>B</b>) uredospores generating germinating hyphae (black arrowhead) along leaf cells and stomata (grey arrowheads); (<b>C</b>) group of uredospores with thickened and warty upper walls (black arrowheads), with internal granules of orange–yellow carotenoid lipids (grey arrowheads); (<b>D</b>) mass of uredospores (blue arrowheads) close to hyphae and pigmented conidia belonging to other fungi (black arrowhead); (<b>E</b>) conidia and hyphae of <span class="html-italic">Cladosporium</span> spp. (black arrowheads) next to a rust lesion (orange zone); (<b>F</b>) conidia and hyphae of <span class="html-italic">Cladosporium</span> spp. (black arrowhead) colonizing the mealybug <span class="html-italic">Planococcus lilacinus</span> Risso (grey arrowhead) found next to a rust lesion. Bars in Figures (<b>B</b>–<b>F</b>) = 20 µm.</p> Full article ">Figure 4
<p>Neighbor-Joining phylogenetic tree for Ascomycetes, with bootstrap values ≥ 50 corresponding to Neighbor-Joining and Maximum Likelihood bootstrap, respectively. The tree is rooted with the outgroup <span class="html-italic">Hemileia vastatrix</span>. Here, 3% represents the applied threshold. OTU numbers are listed under the percentages. Colored circles show the origin of the samples: Calvas = green, Quilanga = blue, and Loja = red.</p> Full article ">Figure 5
<p>Neighbor-Joining phylogenetic tree for Basidiomycetes, with bootstrap values > 50 corresponding to Neighbor-Joining and Maximum Likelihood bootstrap, respectively. The tree is rooted with the outgroup <span class="html-italic">Mycosphaerella yunnanensis</span>. Here, 3% represents the applied threshold. OTU numbers are listed under the percentages. Colored circles show the origin of the samples: Calvas = green, Quilanga = blue, and Loja = red.</p> Full article ">
Open AccessArticle
The Radiation of Landhoppers (Crustacea, Amphipoda) in New Zealand
by
Olivier J.-P. Ball, Alan A. Myers, Stephen R. Pohe and Lara D. Shepherd
Diversity 2024, 16(10), 632; https://doi.org/10.3390/d16100632 - 10 Oct 2024
Abstract
A synopsis of current knowledge of the diversity of the New Zealand landhopper fauna is provided. A combination of morphological and molecular analysis was employed on material from across New Zealand. Thirteen new endemic genera soon to be formally described have been discovered,
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A synopsis of current knowledge of the diversity of the New Zealand landhopper fauna is provided. A combination of morphological and molecular analysis was employed on material from across New Zealand. Thirteen new endemic genera soon to be formally described have been discovered, including four belonging to the widespread families Talitridae and Arcitalitridae. These are families that had not been previously reported from New Zealand. We document the existence of at least 48 new provisional native species. This number far exceeds the 28 species currently described. Some described species are now shown to be species complexes, and a few of these are very diverse with numerous cryptic species. Six changes to the existing taxonomy are proposed. Dallwitzia simularis (Hurley, 1957) is transferred from Makawidae Myers & Lowry, 2020 to Talitridae Rafinesque, 1815; Kellyduncania hauturu (Duncan, 1994) is reinstated as a member of Dana Lowry, 2011; Kellyduncania (Lowry & Myers, 2019) is relegated to a synonym of Dana Lowry, 2011; Kanikania Duncan, 1994 is transferred from Makawidae Myers & Lowry, 2020 to Arcitalitridae Myers & Lowry, 2020; Parorchestia longicornis is transferred to Kanikania Duncan, 1994; Waematau kaitaia (Duncan, 1994) is transferred to Kohuroa Lowry, Myers & Nakano, 2019; and Waematau unuwhao (Duncan, 1994) is transferred to Omaiorchestia Lowry & Myers, 2019. This reduces the number of described New Zealand genera from 17 to 16.
Full article
(This article belongs to the Special Issue Diversity and Evolution within the Amphipoda)
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<p>Map of New Zealand’s main islands showing area codes used to describe landhopper distribution records [<a href="#B12-diversity-16-00632" class="html-bibr">12</a>]. Inset map shows the New Zealand subregion including nearshore and offshore islands. Crosby area codes for the North Island: AK = Auckland; BP = Bay of Plenty; CL = Coromandel; GB = Gisborne; HB = Hawke’s Bay; ND = Northland; RI = Rangitikei; TK = Taranaki; TO = Taupo; WA = Wairarapa; WI = Whanganui; WN = Wellington; WO = Waikato. Crosby area codes for the South Island and Stewart Island: BR = Buller; CO = Central Otago; DN = Dunedin; FD = Fiordland; KA = Kaikoura; MB = Marlborough; MC = Mid Canterbury; MK = Mackenzie; NC = North Canterbury; NN = Nelson; OL = Otago Lakes; SC = South Canterbury; SD = Marlborough Sounds; SI = Stewart Island; SL = Southland; WD = Westland.</p> Full article ">Figure 2
<p>Plot of transitions (s) and transversions (v) at the 16S locus versus F84 genetic distance. The 16S locus is saturated with transversions exceeding transitions, indicating that some nucleotides are likely to have experienced multiple substitutions.</p> Full article ">Figure 3
<p>Phylogeny of Makawidae, Arcitalitridae and Talitridae present in New Zealand based on mitochondrial 16S DNA sequences. The phylogeny is rooted with <span class="html-italic">Apohyale papanuiensis</span> (Hyalidae). Support values are shown in the order Bayesian posterior probability (PP)/maximum likelihood bootstrap support (BS). Only nodes with >0.95 PP or 70% BS are shown.</p> Full article ">Figure 4
<p>Examples of colour patterns in New Zealand landhoppers following preservation in ethanol. The landhoppers are illustrated to scale. (Note: colour patterns generally fade after several weeks or months in ethanol.)</p> Full article ">
<p>Map of New Zealand’s main islands showing area codes used to describe landhopper distribution records [<a href="#B12-diversity-16-00632" class="html-bibr">12</a>]. Inset map shows the New Zealand subregion including nearshore and offshore islands. Crosby area codes for the North Island: AK = Auckland; BP = Bay of Plenty; CL = Coromandel; GB = Gisborne; HB = Hawke’s Bay; ND = Northland; RI = Rangitikei; TK = Taranaki; TO = Taupo; WA = Wairarapa; WI = Whanganui; WN = Wellington; WO = Waikato. Crosby area codes for the South Island and Stewart Island: BR = Buller; CO = Central Otago; DN = Dunedin; FD = Fiordland; KA = Kaikoura; MB = Marlborough; MC = Mid Canterbury; MK = Mackenzie; NC = North Canterbury; NN = Nelson; OL = Otago Lakes; SC = South Canterbury; SD = Marlborough Sounds; SI = Stewart Island; SL = Southland; WD = Westland.</p> Full article ">Figure 2
<p>Plot of transitions (s) and transversions (v) at the 16S locus versus F84 genetic distance. The 16S locus is saturated with transversions exceeding transitions, indicating that some nucleotides are likely to have experienced multiple substitutions.</p> Full article ">Figure 3
<p>Phylogeny of Makawidae, Arcitalitridae and Talitridae present in New Zealand based on mitochondrial 16S DNA sequences. The phylogeny is rooted with <span class="html-italic">Apohyale papanuiensis</span> (Hyalidae). Support values are shown in the order Bayesian posterior probability (PP)/maximum likelihood bootstrap support (BS). Only nodes with >0.95 PP or 70% BS are shown.</p> Full article ">Figure 4
<p>Examples of colour patterns in New Zealand landhoppers following preservation in ethanol. The landhoppers are illustrated to scale. (Note: colour patterns generally fade after several weeks or months in ethanol.)</p> Full article ">
Open AccessInteresting Images
Gone with the Wind: Disappearance of Ulva-Driven Green Tides with Super Typhoons in Jeju Waters, South Korea
by
Sun Kyeong Choi, Kyeonglim Moon, Taihun Kim, Young Baek Son and Sang Rul Park
Diversity 2024, 16(10), 631; https://doi.org/10.3390/d16100631 - 10 Oct 2024
Abstract
Jeju Island, located in the northern East China Sea, is experiencing a rapid rise in water temperature due to climate change. This has led to the increased activity of subtropical species and extreme fluctuations in coastal ecosystems, such as macroalgal blooms and coral
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Jeju Island, located in the northern East China Sea, is experiencing a rapid rise in water temperature due to climate change. This has led to the increased activity of subtropical species and extreme fluctuations in coastal ecosystems, such as macroalgal blooms and coral bleaching. Additionally, the region is experiencing more frequent and intense typhoons. This study investigated the green tides caused by Ulva, particularly Ulva ohnoi, a subtropical species, and the effects of typhoons on these blooms through photographic analysis of the Jeju coastline. The study area was consistently covered by Ulva species every August from 2020 to 2022. Super typhoons struck Jeju Island every September during the study period, with wind speeds exceeding 20 m/s. In 2020 and 2022, the green tides largely dissipated following the typhoons. This ironic outcome highlights how climate-driven increases in subtropical Ulva biomass are being mitigated by the increasing frequency of super typhoons. However, despite the impact of super typhoon Chanthu in September 2021, there was no significant reduction in the Ulva bloom area. This anomaly may be attributable to the dominant easterly wind system in 2021, as the study area faces east, preventing the typhoon from influencing the distribution of Ulva blooms. These findings suggest that the wind intensity and direction of annual typhoons play a critical role in determining the resolution of green tide outbreaks.
Full article
(This article belongs to the Section Marine Diversity)
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<p>Image of <span class="html-italic">Ulva ohnoi</span> found at Jeju Island in March 2014. The quadrat in the center of the image measures 50 × 50 cm.</p> Full article ">Figure 2
<p>Green tide in Shiheung, Jeju Island. (<b>A</b>) <span class="html-italic">Ulva</span> mats along the shoreline. (<b>B</b>) Fresh mats in the upper section and decomposed ones in the lower section. (<b>C</b>) Sacks of <span class="html-italic">Ulva</span> collected for removal. (<b>D</b>) Accumulated and decaying <span class="html-italic">Ulva</span> biomass.</p> Full article ">Figure 3
<p>Map of the study site. (<b>A</b>) Typhoon tracks affecting Jeju Island, Korea, from 2020 to 2022. Each colored circle indicates the point when the typhoon was closest to Jeju Island. (<b>B</b>) A map of Jeju Island, Korea. (<b>C</b>) Shiheung, located in the western part of Jeju Island (Source: Google Maps; Imagery ©2024 TerraMetric. Map data ©2024). The white dashed box indicates the study site, and the yellow circle marks the wind observation point from the Korea Hydrographic and Oceanographic Agency.</p> Full article ">Figure 4
<p>(<b>A</b>) Monthly orthomosaic images of Shiheung from August to October 2020–2022. (<b>B</b>) Object classification images derived from (<b>A</b>). Drone images were taken on 21 August, 18 September, and 18 October 2020; 26 August, 7 September, and 9 October 2021; 13 August, 26 September, and 22 October 2022.</p> Full article ">Figure 5
<p>The distributional area of <span class="html-italic">Ulva</span> in Shiheung from August to October 2020–2022. The gray box indicates the 5 days of maximum impact of each typhoon (from 31 August to 4 September 2020; from 15 to 19 September 2021; from 4 to 8 September 2022).</p> Full article ">Figure 6
<p>Wind direction and speed of typhoons near the study site during the 5 days of maximum impact from 2020 to 2022 (data from the Korea Hydrographic and Oceanographic Agency). (<b>A</b>) Maysack, (<b>B</b>) Chanthu, (<b>C</b>) Hannamnor. The gray capital letter and bold number indicate wind direction (unit: °) and italic number present wind speed (unit: m/s).</p> Full article ">
<p>Image of <span class="html-italic">Ulva ohnoi</span> found at Jeju Island in March 2014. The quadrat in the center of the image measures 50 × 50 cm.</p> Full article ">Figure 2
<p>Green tide in Shiheung, Jeju Island. (<b>A</b>) <span class="html-italic">Ulva</span> mats along the shoreline. (<b>B</b>) Fresh mats in the upper section and decomposed ones in the lower section. (<b>C</b>) Sacks of <span class="html-italic">Ulva</span> collected for removal. (<b>D</b>) Accumulated and decaying <span class="html-italic">Ulva</span> biomass.</p> Full article ">Figure 3
<p>Map of the study site. (<b>A</b>) Typhoon tracks affecting Jeju Island, Korea, from 2020 to 2022. Each colored circle indicates the point when the typhoon was closest to Jeju Island. (<b>B</b>) A map of Jeju Island, Korea. (<b>C</b>) Shiheung, located in the western part of Jeju Island (Source: Google Maps; Imagery ©2024 TerraMetric. Map data ©2024). The white dashed box indicates the study site, and the yellow circle marks the wind observation point from the Korea Hydrographic and Oceanographic Agency.</p> Full article ">Figure 4
<p>(<b>A</b>) Monthly orthomosaic images of Shiheung from August to October 2020–2022. (<b>B</b>) Object classification images derived from (<b>A</b>). Drone images were taken on 21 August, 18 September, and 18 October 2020; 26 August, 7 September, and 9 October 2021; 13 August, 26 September, and 22 October 2022.</p> Full article ">Figure 5
<p>The distributional area of <span class="html-italic">Ulva</span> in Shiheung from August to October 2020–2022. The gray box indicates the 5 days of maximum impact of each typhoon (from 31 August to 4 September 2020; from 15 to 19 September 2021; from 4 to 8 September 2022).</p> Full article ">Figure 6
<p>Wind direction and speed of typhoons near the study site during the 5 days of maximum impact from 2020 to 2022 (data from the Korea Hydrographic and Oceanographic Agency). (<b>A</b>) Maysack, (<b>B</b>) Chanthu, (<b>C</b>) Hannamnor. The gray capital letter and bold number indicate wind direction (unit: °) and italic number present wind speed (unit: m/s).</p> Full article ">
Open AccessArticle
Assessment of the Impact of Land Use on Biodiversity Based on Multiple Scenarios—A Case Study of Southwest China
by
Yingzhi Kuang, Hao Zhou and Lun Yin
Diversity 2024, 16(10), 630; https://doi.org/10.3390/d16100630 - 10 Oct 2024
Abstract
The main causes of habitat conversion, degradation, and fragmentation—all of which add to the loss in biodiversity—are human activities, such as urbanization and farmland reclamation. In order to inform scientific land management and biodiversity conservation strategies and, therefore, advance sustainable development, it is
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The main causes of habitat conversion, degradation, and fragmentation—all of which add to the loss in biodiversity—are human activities, such as urbanization and farmland reclamation. In order to inform scientific land management and biodiversity conservation strategies and, therefore, advance sustainable development, it is imperative to evaluate the effects of land-use changes on biodiversity, especially in areas with high biodiversity. Using data from five future land-use scenarios under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), this study systematically assesses the characteristics of land-use and landscape pattern changes in southwest China by 2050. This study builds a comprehensive biodiversity index and forecasts trends in species richness and habitat quality using models like Fragstats and InVEST to evaluate the overall effects of future land-use changes on biodiversity. The research yielded the subsequent conclusions: (1) Grasslands and woods will continue to be the primary land uses in southwest China in the future. But the amount of grassland is expected to decrease by 11,521 to 102,832 km2, and the amounts of wasteland and urban area are expected to increase by 8130 to 16,293 km2 and 4028 to 19,677 km2, respectively. Furthermore, it is anticipated that metropolitan areas will see an increase in landscape fragmentation and shape complexity, whereas forests and wastelands will see a decrease in these aspects. (2) In southwest China, there is a synergistic relationship between species richness and habitat quality, and both are still at relatively high levels. In terms of species richness and habitat quality, the percentage of regions categorized as outstanding and good range from 71.63% to 74.33% and 70.13% to 75.83%, respectively. The environmental circumstances for species survival and habitat quality are expected to worsen in comparison to 2020, notwithstanding these high levels. Western Sichuan, southern Guizhou, and western Yunnan are home to most of the high-habitat-quality and species-richness areas, while the western plateau is home to the majority of the lower scoring areas. (3) The majority of areas (89.84% to 94.29%) are forecast to undergo little change in the spatial distribution of biodiversity in southwest China, and the general quality of the ecological environment is predicted to stay favorable. Except in the SSP1-RCP2.6 scenario, however, it is expected that the region with declining biodiversity will exceed those with increasing biodiversity. In comparison to 2020, there is a projected decline of 1.0562% to 5.2491% in the comprehensive biodiversity index. These results underscore the major obstacles to the conservation of biodiversity in the area, highlighting the need to fortify macro-level land-use management, put into practice efficient regional conservation plans, and incorporate traditional knowledge in order to save biodiversity.
Full article
(This article belongs to the Special Issue Biodiversity Conservation Planning and Assessment)
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<p>Research area.</p> Full article ">Figure 2
<p>Chord diagram of land-use transitions in southwest China in 2050 under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 3
<p>Spatial distribution and change in major land-use types in southwest China in 2050 under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 4
<p>Variations in landscape indices for land use in the southwest region by 2050, under different SSP–RCP scenarios: (<b>a</b>) variation in the number of patches; (<b>b</b>) variation in patch density; (<b>c</b>) variation in landscape shape indices.</p> Full article ">Figure 5
<p>Proportional chart of the habitat quality classification under different SSP–RCP scenarios in southwest China.</p> Full article ">Figure 6
<p>The percentage changes in the habitat quality indexes for southwest China by 2050 under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 7
<p>Mean habitat quality index for southwest China and its provinces in 2050 under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 8
<p>Proportional chart of the biological richness classification under the different SSP–RCP scenarios in southwest China.</p> Full article ">Figure 9
<p>The percentage change in the biodiversity richness index in southwestern China by 2050 under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 10
<p>Integrated biodiversity index for Southwestern China and its provinces in 2050 under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 11
<p>Spatial variation in the comprehensive biodiversity index in southwest China in 2050 under different SSP–RCP scenarios.</p> Full article ">Figure 12
<p>Percentage of areas with different levels of changes in the comprehensive biodiversity index in southwest China and each province in 2050 under different SSP–RCP scenarios.</p> Full article ">
<p>Research area.</p> Full article ">Figure 2
<p>Chord diagram of land-use transitions in southwest China in 2050 under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 3
<p>Spatial distribution and change in major land-use types in southwest China in 2050 under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 4
<p>Variations in landscape indices for land use in the southwest region by 2050, under different SSP–RCP scenarios: (<b>a</b>) variation in the number of patches; (<b>b</b>) variation in patch density; (<b>c</b>) variation in landscape shape indices.</p> Full article ">Figure 5
<p>Proportional chart of the habitat quality classification under different SSP–RCP scenarios in southwest China.</p> Full article ">Figure 6
<p>The percentage changes in the habitat quality indexes for southwest China by 2050 under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 7
<p>Mean habitat quality index for southwest China and its provinces in 2050 under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 8
<p>Proportional chart of the biological richness classification under the different SSP–RCP scenarios in southwest China.</p> Full article ">Figure 9
<p>The percentage change in the biodiversity richness index in southwestern China by 2050 under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 10
<p>Integrated biodiversity index for Southwestern China and its provinces in 2050 under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs).</p> Full article ">Figure 11
<p>Spatial variation in the comprehensive biodiversity index in southwest China in 2050 under different SSP–RCP scenarios.</p> Full article ">Figure 12
<p>Percentage of areas with different levels of changes in the comprehensive biodiversity index in southwest China and each province in 2050 under different SSP–RCP scenarios.</p> Full article ">
Open AccessReview
The Portofino Promontory: 200 Years of History of Marine Biology
by
Giorgio Bavestrello, Federico Betti, Carlo Nike Bianchi, Valentina Cappanera, Mariachiara Chiantore, Nicola Corradi, Monica Montefalcone, Mauro Giorgio Mariotti, Carla Morri, Paolo Povero, Giulio Relini, Stefano Schiaparelli and Marzia Bo
Diversity 2024, 16(10), 629; https://doi.org/10.3390/d16100629 - 10 Oct 2024
Abstract
This paper outlines the history of scientific research developed in the Portofino Promontory, located in the centre of the Ligurian Sea. The chronicles span over two centuries, from the late 18th century to the present day. Portofino is now recognised as one of
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This paper outlines the history of scientific research developed in the Portofino Promontory, located in the centre of the Ligurian Sea. The chronicles span over two centuries, from the late 18th century to the present day. Portofino is now recognised as one of the best-known areas in the world regarding marine biological communities and their temporal dynamics, particularly in relation to current climate changes. In addition, since 1999, with the establishment of the Marine Protected Area, significant research related to marine environment conservation has developed in Portofino. The role of the University of Genoa, the Natural History Museum, other important institutions, and the researchers involved in the Portofino area has been outlined.
Full article
(This article belongs to the Section Marine Diversity)
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<p>The Portofino Promontory. (<b>a</b>) View of the western side of the Gulf of Tigullio and the Portofino Promontory; (<b>b</b>,<b>c</b>) the two ends of the promontory: Punta del Faro to the east (<b>b</b>) and Punta Chiappa to the west (<b>c</b>); (<b>d</b>) representation of the Portofino Promontory (A, puddingstone, B, fucoid limestone) (from [<a href="#B6-diversity-16-00629" class="html-bibr">6</a>]); (<b>e</b>) historical nautical chart of the Gulf of Tigullio.</p> Full article ">Figure 2
<p>History. (<b>a</b>–<b>c</b>) Three young zoologists from the University of Turin, Lorenzo Camerano (<b>a</b>), Mario G. Peracca (<b>b</b>), and Daniele Rosa (<b>c</b>); (<b>d</b>,<b>e</b>) the marine biology laboratory in Rapallo in 1898; (<b>f</b>) Ernst Haeckel during his last stay in Rapallo in 1907; (<b>g</b>,<b>h</b>) Raffaele Issel and the original edition of his famous Hoepli manual of Marine Biology published in 1918.</p> Full article ">Figure 3
<p>Marine biology, the beginnings. (<b>a</b>,<b>b</b>) Two pioneers of marine biology research on the Portofino Promontory: Enrico Tortonese (<b>a</b>), director of the Giacomo Doria Natural History Museum in Genoa from 1955 to 1976, and Lucia Rossi (<b>b</b>), professor of zoology at the University of Turin; (<b>c</b>) Duilio Marcante, pioneer of scuba diving; (<b>d</b>) Marcante (crouched) and Tortonese (second from left) with a group of divers on a Camogli dock; (<b>e</b>) the first zonation scheme of benthic communities along a vertical transect in Cala Dell’Oro [<a href="#B24-diversity-16-00629" class="html-bibr">24</a>]; (<b>f</b>) the first Italian diving guide regarding Portofino published by Giulio Melegari in 1973; (<b>g</b>,<b>h</b>) photograph of Punta Chiappa with the main shallow-water biocenoses (1, supralittoral; 2, upper midlittoral; 3 lower midlittoral); (<b>g</b>) and zonation scheme (<b>h</b>) (from [<a href="#B24-diversity-16-00629" class="html-bibr">24</a>]).</p> Full article ">Figure 4
<p>Genoese sponge research. (<b>a</b>) Michele Sarà, director of the Institute of Zoology of the University of Genoa, and (<b>b</b>) Gustavo Pulitzer-Finali studied the temporal changes of the shallow-water sponge communities; (<b>c</b>) temporal maps of the encrusting sponge communities observed on the cliffs of Cala dell’Olivetta obtained by tracing the contours of the specimens on transparent plexiglass tablets (drawing by Roberto Pronzato); (<b>d</b>,<b>e</b>) sponges (<b>d</b>) settled on the cliffs of Cala dell’Olivetta (<b>e</b>).</p> Full article ">Figure 5
<p><b>Pleustonic organisms</b>. (<b>a</b>) A colony of the pleustonic hydrozoan <span class="html-italic">Velella velella</span>, known as by-the-wind sailor, photographed in front of the Portofino coast; (<b>b</b>) seasonal mass stranding of <span class="html-italic">V. velella</span> along the coast of Santa Margherita Ligure; (<b>c</b>) drawing of a colony of <span class="html-italic">V. velella</span> (from [<a href="#B92-diversity-16-00629" class="html-bibr">92</a>]); (<b>d</b>) photograph of a huge stranding, along Sturla beach, occurred in 1923 (from [<a href="#B92-diversity-16-00629" class="html-bibr">92</a>]); (<b>e</b>) tens of thousands of specimens of the pleustonic gastropod <span class="html-italic">Janthina pallida</span> stranded on the Sturla beach in 2017; (<b>f</b>) a specimen of <span class="html-italic">J. pallida</span> and (<b>g</b>) the rare Atlantic goose barnacle <span class="html-italic">Dosima fascicularis</span> pictured in the port of Bogliasco and (<b>h</b>) in the 2017 stranding.</p> Full article ">Figure 6
<p>Algal reforestation. (<b>a</b>) An algal fringe dominated by <span class="html-italic">Ericaria amentacea</span>; (<b>b</b>) a close-up view of the algae; (<b>c</b>) one of the terracotta supports with young propagules used for the restoration.</p> Full article ">Figure 7
<p><span class="html-italic">Posidonia</span> meadows<b>.</b> (<b>a</b>) <span class="html-italic">Posidonia oceanica</span> in Paraggi Bay; (<b>b</b>) detail of an inflorescence; (<b>c</b>) <span class="html-italic">Cymodocea nodosa</span> along the eastern front of the promontory; (<b>d</b>) water–colour drawing of <span class="html-italic">P. oceanica</span> by C. Parona (<b>e</b>) diagram of the <span class="html-italic">P. oceanica</span> meadows visited by Issel [<a href="#B17-diversity-16-00629" class="html-bibr">17</a>] on the western side of the Tigullio Gulf and studied again by Bavestrello [<a href="#B131-diversity-16-00629" class="html-bibr">131</a>]; (<b>f</b>) the Bay of Prelo, home to a long-studied meadow; (<b>g</b>) isolines of the cover of <span class="html-italic">P. oceanica</span> in the Prelo Bay (T, matte terraces and R, high cover areas) (from [<a href="#B136-diversity-16-00629" class="html-bibr">136</a>]); (<b>h</b>) scheme by Roberto Pronzato illustrating the semi-quantitative method used to study the fauna associated with rhizomes and leaves.</p> Full article ">Figure 8
<p><span class="html-italic">Posidonia oceanica</span> reforestation. (<b>a</b>) Reforestation activities of the <span class="html-italic">Posidonia oceanica</span> meadow in Punta Pedale; (<b>b</b>) anchoring the cuttings to the biomattes.</p> Full article ">Figure 9
<p>Caves. (<b>a</b>) Diagram of a portion of Punta Carega with the Marcante Cave [<a href="#B24-diversity-16-00629" class="html-bibr">24</a>]; (<b>b</b>) profile of the Marcante Cave from [<a href="#B25-diversity-16-00629" class="html-bibr">25</a>] re–drawn by Carlo Nike Bianchi; (<b>c</b>) survey carried out by Carlo Nike Bianchi of the Tortonese Cave; (<b>d</b>) location of the main marine caves of the Portofino Promontory, and diagrams of some of them (from [<a href="#B174-diversity-16-00629" class="html-bibr">174</a>]); (<b>e</b>) the Dragone Tunnel and some typical sciaphilic organisms, such as the scleractinians <span class="html-italic">Leptopsammia pruvoti</span> and <span class="html-italic">Madracis pharensis</span>, and (<b>f</b>) the cardinal fish <span class="html-italic">Apogon imberbis</span>.</p> Full article ">Figure 10
<p>Coralligenous reefs: past and present. (<b>a</b>) Coralligenous shelf from the Altare dive spot at 35 m; (<b>b</b>) Marco Bertolino and Giorgio Bavestrello sampling coralligenous blocks for the study of paleospicules; (<b>c</b>) core of the coralligenous bioconstruction; (<b>d</b>) diagram of the sediment trap used to study the detritus rolling down along the Paraggi coralligenous cliff (from [<a href="#B183-diversity-16-00629" class="html-bibr">183</a>]; (<b>e</b>) Giorgio Bavestrello during the installation of the traps.</p> Full article ">Figure 11
<p>Red coral: a key species. (<b>a</b>) Riccardo Cattaneo-Vietti, who stimulated the study of <span class="html-italic">Corallium rubrum</span> in Portofino and to whom this paper is dedicated; (<b>b</b>,<b>c</b>) the two volumes on red coral published by the Ministry of Agricultural and Forestry Resources [<a href="#B202-diversity-16-00629" class="html-bibr">202</a>,<a href="#B203-diversity-16-00629" class="html-bibr">203</a>]; (<b>d</b>) diagram by Roberto Pronzato illustrating the method used to study the relationship between red coral and the scleractinian <span class="html-italic">Leptopsammia pruvoti</span> at Punta Torretta; (<b>e</b>) population of <span class="html-italic">C. rubrum</span> on the Altare cliff at 35 m; (<b>f</b>) evolution of the population structure of <span class="html-italic">C. rubrum</span> on the Portofino Promontory from the 1950s to today (from [<a href="#B196-diversity-16-00629" class="html-bibr">196</a>]).</p> Full article ">Figure 12
<p>Soft bottoms. (<b>a</b>) Distribution map of surface sediments between Portofino and La Spezia (from [<a href="#B212-diversity-16-00629" class="html-bibr">212</a>]); (<b>b</b>,<b>c</b>) two of the main representatives of the burrowing macrofauna of Portofino, the bivalve <span class="html-italic">Lucinella divaricata</span> (<b>b</b>) and the polychaete <span class="html-italic">Sternaspis scutata</span> (<b>c</b>); (<b>d</b>) the serpulid <span class="html-italic">Filograna implexa</span>; (<b>e</b>) the echiuran <span class="html-italic">Bonellia viridis</span>; (<b>f</b>) the pennatulacean <span class="html-italic">Veretillum cynomorium</span>.</p> Full article ">Figure 13
<p>Deep sea: first explorations. (<b>a</b>) Bathymorphology of the seabed of the central–eastern Ligurian Sea (from EMODNET); (<b>b</b>) map of the seabed in front of Portofino and Sestri Levante, with the trawl track Di Terra Le Rame (from [<a href="#B221-diversity-16-00629" class="html-bibr">221</a>]); (<b>c</b>) specimens of the rare gorgonian <span class="html-italic">Placogorgia coronata</span> found in the trawling discard (from [<a href="#B221-diversity-16-00629" class="html-bibr">221</a>]); (<b>d</b>) map of the sampling stations visited during the oceanographic campaign carried out by the Calypso in the Gulf of Genoa [<a href="#B214-diversity-16-00629" class="html-bibr">214</a>]; (<b>e</b>) dead fragment of <span class="html-italic">Desmophyllum pertusum</span> from the Portofino thanatocoenosis [<a href="#B32-diversity-16-00629" class="html-bibr">32</a>]; (<b>f</b>) Leonardo Tunesi together with Ifremer’s bathyscaphe Cyana on the deck of the oceanographic vessel Le Suroit, about to dive in front of Punta del Faro; (<b>g</b>) bathyscaphe Cyana about to dive; (<b>h</b>) sample collection filmed by the on–board camera.</p> Full article ">Figure 14
<p>Deep shoal of Portofino. (<b>a</b>) Carlo Cerrano sampling anthozoans during a technical dive on the deep shoal of Portofino; (<b>b</b>) the ROV Pollux off Punta del Faro; (<b>c</b>) Marzia Bo with a sample of <span class="html-italic">Antipathella subpinnata</span> just collected by ROV; (<b>d</b>) Giorgio Bavestrello (<b>left</b>) and Maurizio Pansini (<b>right</b>) on the ISPRA oceanographic vessel Astrea in June 2012; (<b>e</b>) biocoenotic cartography of the deep rocky outcrops of Punta del Faro (from [<a href="#B234-diversity-16-00629" class="html-bibr">234</a>]), updated in 2021 (<b>f</b>) (by [<a href="#B244-diversity-16-00629" class="html-bibr">244</a>]); (<b>g</b>) three–dimensional bathymorphology of the deep shoal of Punta del Faro obtained through multibeam echo sounder (from [<a href="#B245-diversity-16-00629" class="html-bibr">245</a>]); (<b>h</b>) first photograph of the black coral <span class="html-italic">A. subpinnata</span> from Portofino (from [<a href="#B246-diversity-16-00629" class="html-bibr">246</a>]); (<b>i</b>) a colony of Ligurian <span class="html-italic">A. subpinnata</span> (from [<a href="#B41-diversity-16-00629" class="html-bibr">41</a>]); (<b>j</b>) the same species photographed in Portofino by ISPRA’s ROV Pollux in June 2012.</p> Full article ">Figure 15
<p>Portofino bioherm. (<b>a</b>) The catamaran <span class="html-italic">Daedalus</span> of engineer Guido Gay, operational base for numerous explorations in the Ligurian Sea through the use of the ROV Multipluto designed by Gay himself; (<b>b</b>) close–up view of the ROV Multipluto; (<b>c</b>) Side Scan Sonar map (a) and ROV footage of the Portofino bioherm (b–c) (from [<a href="#B221-diversity-16-00629" class="html-bibr">221</a>]); (<b>d</b>) a forest of large colonies of the gorgonian <span class="html-italic">Placogorgia coronata</span> on the white coral thanatocoenosis in front of the promontory (from [<a href="#B260-diversity-16-00629" class="html-bibr">260</a>]); (<b>e</b>) a colony of the white coral <span class="html-italic">Desmophyllum pertusum</span> taken by the ROV at over 700 m depth in Portofino; (<b>f</b>) large living colonies of the white coral <span class="html-italic">Desmophyllum pertusum</span> (from [<a href="#B260-diversity-16-00629" class="html-bibr">260</a>]).</p> Full article ">Figure 16
<p>Underwater group of the University of Genoa. (<b>a</b>) Carlo Nike Bianchi and Carla Morri on board the first vessel of the diving group of the Institute of Zoology; (<b>b</b>) the first operational base of the diving team in the port of Santa Margherita Ligure since 1983; (<b>c</b>) Ferdinando Boero working on the minimum sampling area on the Portofino coralligenous cliff; (<b>d</b>) Roberto Pronzato during a photographic sampling with the famous Hasselmar 500 C/M (Hasselblad, Göteborg, Sweden); (<b>e</b>) Roberto Pronzato and Renata Manconi analyse the diversity of the coralligenous; (<b>f</b>) Giorgio Bavestrello evaluates the volume of a massive sponge with a specially built instrument.</p> Full article ">Figure 17
<p>Study of the benthic communities: the Aurora Transect. (<b>a</b>) The northern side of the Portofino Promontory where the Aurora Transect is located; (<b>b</b>) Federico Betti sampling hydrozoans in 2018 along the historic transect (photo by Alessandro Grasso); (<b>c</b>) the restaurant Aurora above the cliff where the homonymous transect is located; (<b>d</b>) two researchers position the panels on the steel frame; (<b>e</b>) the panels in position; (<b>f</b>) Carlo Nike Bianchi photographs the panels; (<b>g</b>) the frame still visible today on the bottom; (<b>h</b>) diagram by Roberto Pronzato showing the distribution of the panels along the transect.</p> Full article ">Figure 18
<p>Temporal dynamics of the benthic communities: the hydrozoans. (<b>a</b>,<b>b</b>) Drawings of the polyp and the colony of the hydrozoan <span class="html-italic">Eudendrium glomeratum</span> (<b>a</b>) and its seasonal trend (<b>b</b>) (from [<a href="#B268-diversity-16-00629" class="html-bibr">268</a>]); (<b>c</b>) polyp and medusa of the thecate hydrozoan <span class="html-italic">Anthohebella parasitica</span> (from [<a href="#B269-diversity-16-00629" class="html-bibr">269</a>]); (<b>d</b>) polyp and medusa of the athecate hydrozoan <span class="html-italic">Turritopsis dohrnii</span>. The jellyfish, at the end of the cycle, transforms back into a polyp (from [<a href="#B270-diversity-16-00629" class="html-bibr">270</a>]).</p> Full article ">Figure 19
<p>Underwater photography. (<b>a</b>) Scheme by Roberto Pronzato illustrating the methodology for studying the temporal variations of the sponge communities; (<b>b</b>) Maurizio Pansini collecting all the sponge specimens present within a standard surface; (<b>c</b>) two drawings by Roberto Pronzato showing the dimensional changes of two sponges, one year apart (from [<a href="#B278-diversity-16-00629" class="html-bibr">278</a>]); (<b>d</b>) Riccardo Cattaneo-Vietti carries out a photographic survey in Paraggi; (<b>e</b>) the underwater time-lapse camera built by Roberto Pronzato and Fabio Cicogna used to study the opening rhythms of the polyps of the gorgonian <span class="html-italic">Eunicella cavolini</span>; (<b>f</b>) the same colony with contracted (<b>left</b>) and expanded (<b>right</b>) polyps (from [<a href="#B282-diversity-16-00629" class="html-bibr">282</a>]).</p> Full article ">Figure 20
<p>Fishing biology. (<b>a</b>) The trawling vessel Lavoratore II of Benedetto Paccagnella used for a long time by Giulio Relini and Lidia Orsi Relini to study the shrimp fishing grounds; (<b>b</b>) Benedetto Paccagnella surrounded by students from the University of Genoa, some of whom continued their scientific careers in marine biology (Mario Mori, Mario Petrillo, Paolo Povero, Leonardo Tunesi); (<b>c</b>) some crustaceans obtained during scientific fishing campaigns (from [<a href="#B225-diversity-16-00629" class="html-bibr">225</a>]); (<b>d</b>) deep–water food web scheme for the Ligurian trawling grounds (from [<a href="#B223-diversity-16-00629" class="html-bibr">223</a>]); (<b>e</b>) bathymetric map of the Ligurian Sea showing the main trawling fishing areas of the purple shrimp <span class="html-italic">Aristeus antennatus</span> (from [<a href="#B220-diversity-16-00629" class="html-bibr">220</a>]); (<b>f</b>) a shrimp haul (in the foreground a blackmouth catshark, <span class="html-italic">Galeus melastomus</span>); (<b>g</b>) graphs showing the quantities (kg/km<sup>2</sup>) of the trawl target species detected during the GRUND scientific fishing campaigns (from [<a href="#B220-diversity-16-00629" class="html-bibr">220</a>]).</p> Full article ">Figure 21
<p>The tuna trap of Camogli. (<b>a</b>) Preparation of the nets used in the <span class="html-italic">tonnarella</span>; (<b>b</b>) the <span class="html-italic">tonnarella</span> in action; (<b>c</b>,<b>d</b>) historical and recent hauling of the nets; (<b>e</b>) school of bullet tunas <span class="html-italic">Auxis rochei</span> inside the death chamber; (<b>f</b>–<b>i</b>) historical photographs of exceptional catches of large pelagic animals: (<b>f</b>) great white shark, <span class="html-italic">Carcharodon carcharias</span>, (<b>g</b>) great white shark photographed on the pier of the port of Portofino by Baron von Mümm, (<b>h</b>) devil fish, <span class="html-italic">Mobula mobular</span>, and (<b>i</b>) leatherback turtle, <span class="html-italic">Dermochelys coriacea</span> (from [<a href="#B297-diversity-16-00629" class="html-bibr">297</a>]).</p> Full article ">Figure 22
<p>Portofino Marine Protected Area. (<b>a</b>) Ministerial map indicating the boundaries of the Portofino Marine Protected Area; (<b>b</b>) map indicating the zonation and regulation of the activities; (<b>c</b>) map of the main benthic biocoenoses (from [<a href="#B253-diversity-16-00629" class="html-bibr">253</a>]); (<b>d</b>) map of the area extrapolated from the WebGIS Maciste.</p> Full article ">Figure 23
<p>Anthropic impacts on the benthic communities. (<b>a</b>–<b>c</b>) Effects of entangled fishing lines on the gorgonians: the lines, moved by the current, scrape the coenenchyma (<b>a</b>,<b>b</b>), enhancing the settlement of epibiotic organisms on the denuded portions (<b>c</b>) (from [<a href="#B334-diversity-16-00629" class="html-bibr">334</a>]); (<b>d</b>–<b>f</b>) the monitoring activity of the health status of the gorgonians carried out in 2016 shows numerous colonies of <span class="html-italic">Paramuricea clavata</span> enveloped by lost gear, lines (<b>d</b>), and nets (<b>e</b>), which cause abrasion of the living tissues of the gorgonians, sometimes leading to the death of the colonies (<b>f</b>) ((<b>d</b>,<b>f</b>), from [<a href="#B296-diversity-16-00629" class="html-bibr">296</a>]); (<b>g</b>) the same survey showed <span class="html-italic">Corallium rubrum</span> colonies entangled in lost fishing gear; (<b>h</b>) accumulation of fragments of benthic organisms, including <span class="html-italic">C. rubrum</span>, at the base of cliffs with high diving frequentation.</p> Full article ">Figure 24
<p>Diseases and mass mortalities<b>.</b> (<b>a</b>) The first Mediterranean report of a disease of <span class="html-italic">Eunicella cavolini</span> occurred in 1985 on the cliff of Castello di Paraggi (from [<a href="#B356-diversity-16-00629" class="html-bibr">356</a>]); (<b>b</b>) mass mortality of the gorgonian <span class="html-italic">Paramuricea clavata</span> observed at the end of summer 1993 on the Punta del Faro cliff (from [<a href="#B358-diversity-16-00629" class="html-bibr">358</a>]); (<b>c</b>) colonies of <span class="html-italic">P. clavata</span> affected by the mass mortality event of 1999 (from [<a href="#B359-diversity-16-00629" class="html-bibr">359</a>]); (<b>d</b>) necrosis in the gorgonian <span class="html-italic">Eunicella cavolini</span> during an episode of mass mortality; (<b>e</b>) high coverage of epibionts on <span class="html-italic">E. cavolini</span> following the loss of coenenchyme; (<b>f</b>,<b>g</b>) significant quantities of mucilage covering the seabed of the promontory and enveloping the colonies of <span class="html-italic">P. clavata</span> and <span class="html-italic">E. cavolini</span>; (<b>h</b>) detail of on a branch of <span class="html-italic">P. clavata</span> covered by mucilage; (<b>i</b>) colonies of <span class="html-italic">E. cavolini</span> covered in mucilage.</p> Full article ">Figure 25
<p>Effects of climate changes on the benthic and fish communities. (<b>a</b>,<b>b</b>) Change in benthic coverage recorded on the Paraggi cliff from November 1987–1988 (<b>a</b>) to November 2012–2013 (<b>b</b>) (from [<a href="#B381-diversity-16-00629" class="html-bibr">381</a>]); (<b>c</b>,<b>d</b>) the thermophilic hydrozoans <span class="html-italic">Corydendrium parasiticum</span> (<b>c</b>) and <span class="html-italic">Pennaria disticha</span> (<b>d</b>) increasingly observed during the summer months along the cliffs of the promontory; (<b>e</b>–<b>g</b>) thermophilic species expanding their geographic distribution in the Ligurian Sea: the ornate wrasse <span class="html-italic">Thalassoma pavo</span> (<b>e</b>), the yellow-mouth barracuda <span class="html-italic">Sphyraena viridensis</span> (<b>f</b>), and the mottled grouper <span class="html-italic">Mycteroperca rubra</span> (<b>g</b>); (<b>h</b>–<b>j</b>) non-indigenous species recorded in Portofino including the algae <span class="html-italic">Caulerpa cylindracea</span> (<b>h</b>), the calcareous sponge <span class="html-italic">Paraleucilla magna</span> (<b>i</b>), and the splendid Alfonsino <span class="html-italic">Beryx splendens</span> (the latter from [<a href="#B384-diversity-16-00629" class="html-bibr">384</a>]).</p> Full article ">Figure 26
<p>The storm of 29–30 October 2018. (<b>a</b>,<b>b</b>) Punta del Faro before (<b>a</b>) and after (<b>b</b>) the storm showing evident changes in the cliff morphology (dotted parts); (<b>c</b>) large rocky boulders fallen from the cliff or overturned by the storm waves; (<b>d</b>) the sandy seafloor of Paraggi lowered more than a metre by the waves (all images from [<a href="#B64-diversity-16-00629" class="html-bibr">64</a>]); (<b>e</b>) temperature (°C) profile in Lighthouse Cape station on 25 October 2018 (in red) compared to the average profiles of the second half of October from 2000 to 2017 (in black; horizontal bars denote standard deviations) (from [<a href="#B377-diversity-16-00629" class="html-bibr">377</a>]); (<b>f</b>) annual trend of the seawater surface temperature showing the progressive increase.</p> Full article ">
<p>The Portofino Promontory. (<b>a</b>) View of the western side of the Gulf of Tigullio and the Portofino Promontory; (<b>b</b>,<b>c</b>) the two ends of the promontory: Punta del Faro to the east (<b>b</b>) and Punta Chiappa to the west (<b>c</b>); (<b>d</b>) representation of the Portofino Promontory (A, puddingstone, B, fucoid limestone) (from [<a href="#B6-diversity-16-00629" class="html-bibr">6</a>]); (<b>e</b>) historical nautical chart of the Gulf of Tigullio.</p> Full article ">Figure 2
<p>History. (<b>a</b>–<b>c</b>) Three young zoologists from the University of Turin, Lorenzo Camerano (<b>a</b>), Mario G. Peracca (<b>b</b>), and Daniele Rosa (<b>c</b>); (<b>d</b>,<b>e</b>) the marine biology laboratory in Rapallo in 1898; (<b>f</b>) Ernst Haeckel during his last stay in Rapallo in 1907; (<b>g</b>,<b>h</b>) Raffaele Issel and the original edition of his famous Hoepli manual of Marine Biology published in 1918.</p> Full article ">Figure 3
<p>Marine biology, the beginnings. (<b>a</b>,<b>b</b>) Two pioneers of marine biology research on the Portofino Promontory: Enrico Tortonese (<b>a</b>), director of the Giacomo Doria Natural History Museum in Genoa from 1955 to 1976, and Lucia Rossi (<b>b</b>), professor of zoology at the University of Turin; (<b>c</b>) Duilio Marcante, pioneer of scuba diving; (<b>d</b>) Marcante (crouched) and Tortonese (second from left) with a group of divers on a Camogli dock; (<b>e</b>) the first zonation scheme of benthic communities along a vertical transect in Cala Dell’Oro [<a href="#B24-diversity-16-00629" class="html-bibr">24</a>]; (<b>f</b>) the first Italian diving guide regarding Portofino published by Giulio Melegari in 1973; (<b>g</b>,<b>h</b>) photograph of Punta Chiappa with the main shallow-water biocenoses (1, supralittoral; 2, upper midlittoral; 3 lower midlittoral); (<b>g</b>) and zonation scheme (<b>h</b>) (from [<a href="#B24-diversity-16-00629" class="html-bibr">24</a>]).</p> Full article ">Figure 4
<p>Genoese sponge research. (<b>a</b>) Michele Sarà, director of the Institute of Zoology of the University of Genoa, and (<b>b</b>) Gustavo Pulitzer-Finali studied the temporal changes of the shallow-water sponge communities; (<b>c</b>) temporal maps of the encrusting sponge communities observed on the cliffs of Cala dell’Olivetta obtained by tracing the contours of the specimens on transparent plexiglass tablets (drawing by Roberto Pronzato); (<b>d</b>,<b>e</b>) sponges (<b>d</b>) settled on the cliffs of Cala dell’Olivetta (<b>e</b>).</p> Full article ">Figure 5
<p><b>Pleustonic organisms</b>. (<b>a</b>) A colony of the pleustonic hydrozoan <span class="html-italic">Velella velella</span>, known as by-the-wind sailor, photographed in front of the Portofino coast; (<b>b</b>) seasonal mass stranding of <span class="html-italic">V. velella</span> along the coast of Santa Margherita Ligure; (<b>c</b>) drawing of a colony of <span class="html-italic">V. velella</span> (from [<a href="#B92-diversity-16-00629" class="html-bibr">92</a>]); (<b>d</b>) photograph of a huge stranding, along Sturla beach, occurred in 1923 (from [<a href="#B92-diversity-16-00629" class="html-bibr">92</a>]); (<b>e</b>) tens of thousands of specimens of the pleustonic gastropod <span class="html-italic">Janthina pallida</span> stranded on the Sturla beach in 2017; (<b>f</b>) a specimen of <span class="html-italic">J. pallida</span> and (<b>g</b>) the rare Atlantic goose barnacle <span class="html-italic">Dosima fascicularis</span> pictured in the port of Bogliasco and (<b>h</b>) in the 2017 stranding.</p> Full article ">Figure 6
<p>Algal reforestation. (<b>a</b>) An algal fringe dominated by <span class="html-italic">Ericaria amentacea</span>; (<b>b</b>) a close-up view of the algae; (<b>c</b>) one of the terracotta supports with young propagules used for the restoration.</p> Full article ">Figure 7
<p><span class="html-italic">Posidonia</span> meadows<b>.</b> (<b>a</b>) <span class="html-italic">Posidonia oceanica</span> in Paraggi Bay; (<b>b</b>) detail of an inflorescence; (<b>c</b>) <span class="html-italic">Cymodocea nodosa</span> along the eastern front of the promontory; (<b>d</b>) water–colour drawing of <span class="html-italic">P. oceanica</span> by C. Parona (<b>e</b>) diagram of the <span class="html-italic">P. oceanica</span> meadows visited by Issel [<a href="#B17-diversity-16-00629" class="html-bibr">17</a>] on the western side of the Tigullio Gulf and studied again by Bavestrello [<a href="#B131-diversity-16-00629" class="html-bibr">131</a>]; (<b>f</b>) the Bay of Prelo, home to a long-studied meadow; (<b>g</b>) isolines of the cover of <span class="html-italic">P. oceanica</span> in the Prelo Bay (T, matte terraces and R, high cover areas) (from [<a href="#B136-diversity-16-00629" class="html-bibr">136</a>]); (<b>h</b>) scheme by Roberto Pronzato illustrating the semi-quantitative method used to study the fauna associated with rhizomes and leaves.</p> Full article ">Figure 8
<p><span class="html-italic">Posidonia oceanica</span> reforestation. (<b>a</b>) Reforestation activities of the <span class="html-italic">Posidonia oceanica</span> meadow in Punta Pedale; (<b>b</b>) anchoring the cuttings to the biomattes.</p> Full article ">Figure 9
<p>Caves. (<b>a</b>) Diagram of a portion of Punta Carega with the Marcante Cave [<a href="#B24-diversity-16-00629" class="html-bibr">24</a>]; (<b>b</b>) profile of the Marcante Cave from [<a href="#B25-diversity-16-00629" class="html-bibr">25</a>] re–drawn by Carlo Nike Bianchi; (<b>c</b>) survey carried out by Carlo Nike Bianchi of the Tortonese Cave; (<b>d</b>) location of the main marine caves of the Portofino Promontory, and diagrams of some of them (from [<a href="#B174-diversity-16-00629" class="html-bibr">174</a>]); (<b>e</b>) the Dragone Tunnel and some typical sciaphilic organisms, such as the scleractinians <span class="html-italic">Leptopsammia pruvoti</span> and <span class="html-italic">Madracis pharensis</span>, and (<b>f</b>) the cardinal fish <span class="html-italic">Apogon imberbis</span>.</p> Full article ">Figure 10
<p>Coralligenous reefs: past and present. (<b>a</b>) Coralligenous shelf from the Altare dive spot at 35 m; (<b>b</b>) Marco Bertolino and Giorgio Bavestrello sampling coralligenous blocks for the study of paleospicules; (<b>c</b>) core of the coralligenous bioconstruction; (<b>d</b>) diagram of the sediment trap used to study the detritus rolling down along the Paraggi coralligenous cliff (from [<a href="#B183-diversity-16-00629" class="html-bibr">183</a>]; (<b>e</b>) Giorgio Bavestrello during the installation of the traps.</p> Full article ">Figure 11
<p>Red coral: a key species. (<b>a</b>) Riccardo Cattaneo-Vietti, who stimulated the study of <span class="html-italic">Corallium rubrum</span> in Portofino and to whom this paper is dedicated; (<b>b</b>,<b>c</b>) the two volumes on red coral published by the Ministry of Agricultural and Forestry Resources [<a href="#B202-diversity-16-00629" class="html-bibr">202</a>,<a href="#B203-diversity-16-00629" class="html-bibr">203</a>]; (<b>d</b>) diagram by Roberto Pronzato illustrating the method used to study the relationship between red coral and the scleractinian <span class="html-italic">Leptopsammia pruvoti</span> at Punta Torretta; (<b>e</b>) population of <span class="html-italic">C. rubrum</span> on the Altare cliff at 35 m; (<b>f</b>) evolution of the population structure of <span class="html-italic">C. rubrum</span> on the Portofino Promontory from the 1950s to today (from [<a href="#B196-diversity-16-00629" class="html-bibr">196</a>]).</p> Full article ">Figure 12
<p>Soft bottoms. (<b>a</b>) Distribution map of surface sediments between Portofino and La Spezia (from [<a href="#B212-diversity-16-00629" class="html-bibr">212</a>]); (<b>b</b>,<b>c</b>) two of the main representatives of the burrowing macrofauna of Portofino, the bivalve <span class="html-italic">Lucinella divaricata</span> (<b>b</b>) and the polychaete <span class="html-italic">Sternaspis scutata</span> (<b>c</b>); (<b>d</b>) the serpulid <span class="html-italic">Filograna implexa</span>; (<b>e</b>) the echiuran <span class="html-italic">Bonellia viridis</span>; (<b>f</b>) the pennatulacean <span class="html-italic">Veretillum cynomorium</span>.</p> Full article ">Figure 13
<p>Deep sea: first explorations. (<b>a</b>) Bathymorphology of the seabed of the central–eastern Ligurian Sea (from EMODNET); (<b>b</b>) map of the seabed in front of Portofino and Sestri Levante, with the trawl track Di Terra Le Rame (from [<a href="#B221-diversity-16-00629" class="html-bibr">221</a>]); (<b>c</b>) specimens of the rare gorgonian <span class="html-italic">Placogorgia coronata</span> found in the trawling discard (from [<a href="#B221-diversity-16-00629" class="html-bibr">221</a>]); (<b>d</b>) map of the sampling stations visited during the oceanographic campaign carried out by the Calypso in the Gulf of Genoa [<a href="#B214-diversity-16-00629" class="html-bibr">214</a>]; (<b>e</b>) dead fragment of <span class="html-italic">Desmophyllum pertusum</span> from the Portofino thanatocoenosis [<a href="#B32-diversity-16-00629" class="html-bibr">32</a>]; (<b>f</b>) Leonardo Tunesi together with Ifremer’s bathyscaphe Cyana on the deck of the oceanographic vessel Le Suroit, about to dive in front of Punta del Faro; (<b>g</b>) bathyscaphe Cyana about to dive; (<b>h</b>) sample collection filmed by the on–board camera.</p> Full article ">Figure 14
<p>Deep shoal of Portofino. (<b>a</b>) Carlo Cerrano sampling anthozoans during a technical dive on the deep shoal of Portofino; (<b>b</b>) the ROV Pollux off Punta del Faro; (<b>c</b>) Marzia Bo with a sample of <span class="html-italic">Antipathella subpinnata</span> just collected by ROV; (<b>d</b>) Giorgio Bavestrello (<b>left</b>) and Maurizio Pansini (<b>right</b>) on the ISPRA oceanographic vessel Astrea in June 2012; (<b>e</b>) biocoenotic cartography of the deep rocky outcrops of Punta del Faro (from [<a href="#B234-diversity-16-00629" class="html-bibr">234</a>]), updated in 2021 (<b>f</b>) (by [<a href="#B244-diversity-16-00629" class="html-bibr">244</a>]); (<b>g</b>) three–dimensional bathymorphology of the deep shoal of Punta del Faro obtained through multibeam echo sounder (from [<a href="#B245-diversity-16-00629" class="html-bibr">245</a>]); (<b>h</b>) first photograph of the black coral <span class="html-italic">A. subpinnata</span> from Portofino (from [<a href="#B246-diversity-16-00629" class="html-bibr">246</a>]); (<b>i</b>) a colony of Ligurian <span class="html-italic">A. subpinnata</span> (from [<a href="#B41-diversity-16-00629" class="html-bibr">41</a>]); (<b>j</b>) the same species photographed in Portofino by ISPRA’s ROV Pollux in June 2012.</p> Full article ">Figure 15
<p>Portofino bioherm. (<b>a</b>) The catamaran <span class="html-italic">Daedalus</span> of engineer Guido Gay, operational base for numerous explorations in the Ligurian Sea through the use of the ROV Multipluto designed by Gay himself; (<b>b</b>) close–up view of the ROV Multipluto; (<b>c</b>) Side Scan Sonar map (a) and ROV footage of the Portofino bioherm (b–c) (from [<a href="#B221-diversity-16-00629" class="html-bibr">221</a>]); (<b>d</b>) a forest of large colonies of the gorgonian <span class="html-italic">Placogorgia coronata</span> on the white coral thanatocoenosis in front of the promontory (from [<a href="#B260-diversity-16-00629" class="html-bibr">260</a>]); (<b>e</b>) a colony of the white coral <span class="html-italic">Desmophyllum pertusum</span> taken by the ROV at over 700 m depth in Portofino; (<b>f</b>) large living colonies of the white coral <span class="html-italic">Desmophyllum pertusum</span> (from [<a href="#B260-diversity-16-00629" class="html-bibr">260</a>]).</p> Full article ">Figure 16
<p>Underwater group of the University of Genoa. (<b>a</b>) Carlo Nike Bianchi and Carla Morri on board the first vessel of the diving group of the Institute of Zoology; (<b>b</b>) the first operational base of the diving team in the port of Santa Margherita Ligure since 1983; (<b>c</b>) Ferdinando Boero working on the minimum sampling area on the Portofino coralligenous cliff; (<b>d</b>) Roberto Pronzato during a photographic sampling with the famous Hasselmar 500 C/M (Hasselblad, Göteborg, Sweden); (<b>e</b>) Roberto Pronzato and Renata Manconi analyse the diversity of the coralligenous; (<b>f</b>) Giorgio Bavestrello evaluates the volume of a massive sponge with a specially built instrument.</p> Full article ">Figure 17
<p>Study of the benthic communities: the Aurora Transect. (<b>a</b>) The northern side of the Portofino Promontory where the Aurora Transect is located; (<b>b</b>) Federico Betti sampling hydrozoans in 2018 along the historic transect (photo by Alessandro Grasso); (<b>c</b>) the restaurant Aurora above the cliff where the homonymous transect is located; (<b>d</b>) two researchers position the panels on the steel frame; (<b>e</b>) the panels in position; (<b>f</b>) Carlo Nike Bianchi photographs the panels; (<b>g</b>) the frame still visible today on the bottom; (<b>h</b>) diagram by Roberto Pronzato showing the distribution of the panels along the transect.</p> Full article ">Figure 18
<p>Temporal dynamics of the benthic communities: the hydrozoans. (<b>a</b>,<b>b</b>) Drawings of the polyp and the colony of the hydrozoan <span class="html-italic">Eudendrium glomeratum</span> (<b>a</b>) and its seasonal trend (<b>b</b>) (from [<a href="#B268-diversity-16-00629" class="html-bibr">268</a>]); (<b>c</b>) polyp and medusa of the thecate hydrozoan <span class="html-italic">Anthohebella parasitica</span> (from [<a href="#B269-diversity-16-00629" class="html-bibr">269</a>]); (<b>d</b>) polyp and medusa of the athecate hydrozoan <span class="html-italic">Turritopsis dohrnii</span>. The jellyfish, at the end of the cycle, transforms back into a polyp (from [<a href="#B270-diversity-16-00629" class="html-bibr">270</a>]).</p> Full article ">Figure 19
<p>Underwater photography. (<b>a</b>) Scheme by Roberto Pronzato illustrating the methodology for studying the temporal variations of the sponge communities; (<b>b</b>) Maurizio Pansini collecting all the sponge specimens present within a standard surface; (<b>c</b>) two drawings by Roberto Pronzato showing the dimensional changes of two sponges, one year apart (from [<a href="#B278-diversity-16-00629" class="html-bibr">278</a>]); (<b>d</b>) Riccardo Cattaneo-Vietti carries out a photographic survey in Paraggi; (<b>e</b>) the underwater time-lapse camera built by Roberto Pronzato and Fabio Cicogna used to study the opening rhythms of the polyps of the gorgonian <span class="html-italic">Eunicella cavolini</span>; (<b>f</b>) the same colony with contracted (<b>left</b>) and expanded (<b>right</b>) polyps (from [<a href="#B282-diversity-16-00629" class="html-bibr">282</a>]).</p> Full article ">Figure 20
<p>Fishing biology. (<b>a</b>) The trawling vessel Lavoratore II of Benedetto Paccagnella used for a long time by Giulio Relini and Lidia Orsi Relini to study the shrimp fishing grounds; (<b>b</b>) Benedetto Paccagnella surrounded by students from the University of Genoa, some of whom continued their scientific careers in marine biology (Mario Mori, Mario Petrillo, Paolo Povero, Leonardo Tunesi); (<b>c</b>) some crustaceans obtained during scientific fishing campaigns (from [<a href="#B225-diversity-16-00629" class="html-bibr">225</a>]); (<b>d</b>) deep–water food web scheme for the Ligurian trawling grounds (from [<a href="#B223-diversity-16-00629" class="html-bibr">223</a>]); (<b>e</b>) bathymetric map of the Ligurian Sea showing the main trawling fishing areas of the purple shrimp <span class="html-italic">Aristeus antennatus</span> (from [<a href="#B220-diversity-16-00629" class="html-bibr">220</a>]); (<b>f</b>) a shrimp haul (in the foreground a blackmouth catshark, <span class="html-italic">Galeus melastomus</span>); (<b>g</b>) graphs showing the quantities (kg/km<sup>2</sup>) of the trawl target species detected during the GRUND scientific fishing campaigns (from [<a href="#B220-diversity-16-00629" class="html-bibr">220</a>]).</p> Full article ">Figure 21
<p>The tuna trap of Camogli. (<b>a</b>) Preparation of the nets used in the <span class="html-italic">tonnarella</span>; (<b>b</b>) the <span class="html-italic">tonnarella</span> in action; (<b>c</b>,<b>d</b>) historical and recent hauling of the nets; (<b>e</b>) school of bullet tunas <span class="html-italic">Auxis rochei</span> inside the death chamber; (<b>f</b>–<b>i</b>) historical photographs of exceptional catches of large pelagic animals: (<b>f</b>) great white shark, <span class="html-italic">Carcharodon carcharias</span>, (<b>g</b>) great white shark photographed on the pier of the port of Portofino by Baron von Mümm, (<b>h</b>) devil fish, <span class="html-italic">Mobula mobular</span>, and (<b>i</b>) leatherback turtle, <span class="html-italic">Dermochelys coriacea</span> (from [<a href="#B297-diversity-16-00629" class="html-bibr">297</a>]).</p> Full article ">Figure 22
<p>Portofino Marine Protected Area. (<b>a</b>) Ministerial map indicating the boundaries of the Portofino Marine Protected Area; (<b>b</b>) map indicating the zonation and regulation of the activities; (<b>c</b>) map of the main benthic biocoenoses (from [<a href="#B253-diversity-16-00629" class="html-bibr">253</a>]); (<b>d</b>) map of the area extrapolated from the WebGIS Maciste.</p> Full article ">Figure 23
<p>Anthropic impacts on the benthic communities. (<b>a</b>–<b>c</b>) Effects of entangled fishing lines on the gorgonians: the lines, moved by the current, scrape the coenenchyma (<b>a</b>,<b>b</b>), enhancing the settlement of epibiotic organisms on the denuded portions (<b>c</b>) (from [<a href="#B334-diversity-16-00629" class="html-bibr">334</a>]); (<b>d</b>–<b>f</b>) the monitoring activity of the health status of the gorgonians carried out in 2016 shows numerous colonies of <span class="html-italic">Paramuricea clavata</span> enveloped by lost gear, lines (<b>d</b>), and nets (<b>e</b>), which cause abrasion of the living tissues of the gorgonians, sometimes leading to the death of the colonies (<b>f</b>) ((<b>d</b>,<b>f</b>), from [<a href="#B296-diversity-16-00629" class="html-bibr">296</a>]); (<b>g</b>) the same survey showed <span class="html-italic">Corallium rubrum</span> colonies entangled in lost fishing gear; (<b>h</b>) accumulation of fragments of benthic organisms, including <span class="html-italic">C. rubrum</span>, at the base of cliffs with high diving frequentation.</p> Full article ">Figure 24
<p>Diseases and mass mortalities<b>.</b> (<b>a</b>) The first Mediterranean report of a disease of <span class="html-italic">Eunicella cavolini</span> occurred in 1985 on the cliff of Castello di Paraggi (from [<a href="#B356-diversity-16-00629" class="html-bibr">356</a>]); (<b>b</b>) mass mortality of the gorgonian <span class="html-italic">Paramuricea clavata</span> observed at the end of summer 1993 on the Punta del Faro cliff (from [<a href="#B358-diversity-16-00629" class="html-bibr">358</a>]); (<b>c</b>) colonies of <span class="html-italic">P. clavata</span> affected by the mass mortality event of 1999 (from [<a href="#B359-diversity-16-00629" class="html-bibr">359</a>]); (<b>d</b>) necrosis in the gorgonian <span class="html-italic">Eunicella cavolini</span> during an episode of mass mortality; (<b>e</b>) high coverage of epibionts on <span class="html-italic">E. cavolini</span> following the loss of coenenchyme; (<b>f</b>,<b>g</b>) significant quantities of mucilage covering the seabed of the promontory and enveloping the colonies of <span class="html-italic">P. clavata</span> and <span class="html-italic">E. cavolini</span>; (<b>h</b>) detail of on a branch of <span class="html-italic">P. clavata</span> covered by mucilage; (<b>i</b>) colonies of <span class="html-italic">E. cavolini</span> covered in mucilage.</p> Full article ">Figure 25
<p>Effects of climate changes on the benthic and fish communities. (<b>a</b>,<b>b</b>) Change in benthic coverage recorded on the Paraggi cliff from November 1987–1988 (<b>a</b>) to November 2012–2013 (<b>b</b>) (from [<a href="#B381-diversity-16-00629" class="html-bibr">381</a>]); (<b>c</b>,<b>d</b>) the thermophilic hydrozoans <span class="html-italic">Corydendrium parasiticum</span> (<b>c</b>) and <span class="html-italic">Pennaria disticha</span> (<b>d</b>) increasingly observed during the summer months along the cliffs of the promontory; (<b>e</b>–<b>g</b>) thermophilic species expanding their geographic distribution in the Ligurian Sea: the ornate wrasse <span class="html-italic">Thalassoma pavo</span> (<b>e</b>), the yellow-mouth barracuda <span class="html-italic">Sphyraena viridensis</span> (<b>f</b>), and the mottled grouper <span class="html-italic">Mycteroperca rubra</span> (<b>g</b>); (<b>h</b>–<b>j</b>) non-indigenous species recorded in Portofino including the algae <span class="html-italic">Caulerpa cylindracea</span> (<b>h</b>), the calcareous sponge <span class="html-italic">Paraleucilla magna</span> (<b>i</b>), and the splendid Alfonsino <span class="html-italic">Beryx splendens</span> (the latter from [<a href="#B384-diversity-16-00629" class="html-bibr">384</a>]).</p> Full article ">Figure 26
<p>The storm of 29–30 October 2018. (<b>a</b>,<b>b</b>) Punta del Faro before (<b>a</b>) and after (<b>b</b>) the storm showing evident changes in the cliff morphology (dotted parts); (<b>c</b>) large rocky boulders fallen from the cliff or overturned by the storm waves; (<b>d</b>) the sandy seafloor of Paraggi lowered more than a metre by the waves (all images from [<a href="#B64-diversity-16-00629" class="html-bibr">64</a>]); (<b>e</b>) temperature (°C) profile in Lighthouse Cape station on 25 October 2018 (in red) compared to the average profiles of the second half of October from 2000 to 2017 (in black; horizontal bars denote standard deviations) (from [<a href="#B377-diversity-16-00629" class="html-bibr">377</a>]); (<b>f</b>) annual trend of the seawater surface temperature showing the progressive increase.</p> Full article ">
Open AccessArticle
Substrate Preferences and Interspecific Affinities of Antarctic Macroalgae: Insights from Maxwell Bay, King George Island
by
Young Wook Ko, Kwon Mo Yang and Han-Gu Choi
Diversity 2024, 16(10), 628; https://doi.org/10.3390/d16100628 - 10 Oct 2024
Abstract
This study investigates the diversity and ecological dynamics of macroalgae in Maxwell Bay, King George Island, Antarctica, focusing on species distribution, substrate composition, and interspecific interactions. Across nine survey sites, 31 macroalgal species were recorded, with 12 species identified as significant due to
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This study investigates the diversity and ecological dynamics of macroalgae in Maxwell Bay, King George Island, Antarctica, focusing on species distribution, substrate composition, and interspecific interactions. Across nine survey sites, 31 macroalgal species were recorded, with 12 species identified as significant due to their considerable relative frequency, coverage, and biomass. Palmaria decipiens was the most dominant species in terms of frequency and coverage, while Desmarestia anceps had the highest biomass. The study revealed distinct substrate preferences, with P. decipiens favoring cobble and mud substrates, and Himantothallus grandifolius associating predominantly with pebble substrates. A core group of species, including Plocamium sp., H. grandifolius, Picconiella plumosa, Iridaea sp., and Trematocarpus antarcticus, exhibited strong ecological interactions characterized by high substrate similarity and mutual affinity. In contrast, pioneer species like P. decipiens and Monostroma hariotii showed lower affinity with other species, reflecting their early successional roles. These findings enhance our understanding of the complex interspecific relationships within Antarctic macroalgal assemblage and provide valuable baseline data for future ecological studies in the region.
Full article
(This article belongs to the Section Marine Diversity)
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Figure 1
Figure 1
<p>The nine survey sites in Maxwell Bay, King George Island, Antarctica. The red arrow indicates King George Island in the South Shetland Islands, located at the tip of the Antarctic Peninsula, representing the area in the upper right map section. The map was created using a basemap provided by the British Antarctic Survey Geophysics Data Portal (BAS−GDP; <a href="http://geoportal.nerc-bas.ac.uk/GDP" target="_blank">http://geoportal.nerc-bas.ac.uk/GDP</a>; accessed on 8 August 2024) and utilizing QGIS version 3.24 (<a href="https://qgis.org/" target="_blank">https://qgis.org/</a>; accessed on 8 August 2024).</p> Full article ">Figure 2
<p>Relative substrate coverage and species occurrence by depth for significant species. Sub-figures (<b>A</b>–<b>L</b>) represent the significant species individually (<b>A</b>) <span class="html-italic">Palmaria decipiens</span>; (<b>B</b>) Crustose coralline algae; (<b>C</b>) <span class="html-italic">Plocamium</span> sp.; (<b>D</b>) <span class="html-italic">Himantothallus grandifolius</span>; (<b>E</b>) <span class="html-italic">Desmarestia anceps</span>; (<b>F</b>) <span class="html-italic">Picconiella plumosa</span>; (<b>G</b>) Desmarestia menziesii; (<b>H</b>) <span class="html-italic">Iridaea</span> sp.; (<b>I</b>) <span class="html-italic">Sarcopeltis antarctica</span>; (<b>J</b>) <span class="html-italic">Monostroma hariotii</span>; (<b>K</b>) <span class="html-italic">Trematocarpus anstarcticus</span>; (<b>L</b>) <span class="html-italic">Pantoneura plocamioides</span>. The bar charts show the relative coverage of each substrate (mean ± S.E.), while the pie charts depict the relative occurrence frequency of each species by water depth. The red dotted line overlaid on the bar charts represents the average substrate cover across the entire study area.</p> Full article ">Figure 3
<p>Segmented bubble plot for nMDS with superimposed vector plot showing correlation with substrate. ASC: average substrate cover; PAL: <span class="html-italic">Palmaria decipiens</span>; CCA: Crustose coralline algae; PLO: <span class="html-italic">Plocamium</span> sp.; HIM: <span class="html-italic">Himantothallus grandifolius</span>; DAN: <span class="html-italic">Desmarestia anceps</span>; PIC: <span class="html-italic">Picconiella plumosa</span>; DME: <span class="html-italic">Desmarestia menziesii</span>; IRI: <span class="html-italic">Iridaea</span> sp.; SAR: <span class="html-italic">Sarcopeltis antarctica</span>; MON: <span class="html-italic">Monostroma hariotii</span>; TRE: <span class="html-italic">Trematocarpus antarcticus</span>; PAN: <span class="html-italic">Pantoneura plocamioides</span>.</p> Full article ">Figure 4
<p>Polar plots of interspecies affinity based on neighbor species occurrence frequency in the presence of host species. High affinity (blue circle): greater than twice the expected frequency; Medium affinity (light blue circle): greater than the expected frequency; Low affinity (light red circle): less than the expected frequency; Very low affinity (red circle): less than half the expected frequency. (<b>A</b>) PAL (<span class="html-italic">Palmaria decipiens</span>); (<b>B</b>) CCA (Crustose coralline algae); (<b>C</b>) PLO (<span class="html-italic">Plocamium</span> sp.); (<b>D</b>) HIM (<span class="html-italic">Himantothallus grandifolius</span>); (<b>E</b>) DAN (<span class="html-italic">Desmarestia anceps</span>); (<b>F</b>) PIC (<span class="html-italic">Picconiella plumosa</span>); (<b>G</b>) DME (<span class="html-italic">Desmarestia menziesii</span>); (<b>H</b>) IRI (<span class="html-italic">Iridaea</span> sp.); (<b>I</b>) SAR (<span class="html-italic">Sarcopeltis antarctica</span>); (<b>J</b>) MON (<span class="html-italic">Monostroma hariotii</span>); (<b>K</b>) TRE (<span class="html-italic">Trematocarpus antarcticus</span>); (<b>L</b>) PAN (<span class="html-italic">Pantoneura plocamioides</span>).</p> Full article ">Figure 5
<p>Substrate—affinity matrix for interaction between all combinations of significant species. The double arrow indicates a two-way interaction where both species act as host and neighbor, while the single arrow represents a one-way interaction where the species at the head of the arrow is the host and the species at the tail is the neighbor. Only the two-way interaction is highlighted in bold. PAL: <span class="html-italic">Palmaria decipiens</span>; CCA: Crustose coralline algae; PLO: <span class="html-italic">Plocamium</span> sp.; HIM: <span class="html-italic">Himantothallus grandifolius</span>; DAN: <span class="html-italic">Desmarestia anceps</span>; PIC: <span class="html-italic">Picconiella plumosa</span>; DME: <span class="html-italic">Desmarestia menziesii</span>; IRI: <span class="html-italic">Iridaea</span> sp.; SAR: <span class="html-italic">Sarcopeltis antarctica</span>; MON: <span class="html-italic">Monostroma hariotii</span>; TRE: <span class="html-italic">Trematocarpus antarcticus</span>; PAN: <span class="html-italic">Pantoneura plocamioides</span>.</p> Full article ">
<p>The nine survey sites in Maxwell Bay, King George Island, Antarctica. The red arrow indicates King George Island in the South Shetland Islands, located at the tip of the Antarctic Peninsula, representing the area in the upper right map section. The map was created using a basemap provided by the British Antarctic Survey Geophysics Data Portal (BAS−GDP; <a href="http://geoportal.nerc-bas.ac.uk/GDP" target="_blank">http://geoportal.nerc-bas.ac.uk/GDP</a>; accessed on 8 August 2024) and utilizing QGIS version 3.24 (<a href="https://qgis.org/" target="_blank">https://qgis.org/</a>; accessed on 8 August 2024).</p> Full article ">Figure 2
<p>Relative substrate coverage and species occurrence by depth for significant species. Sub-figures (<b>A</b>–<b>L</b>) represent the significant species individually (<b>A</b>) <span class="html-italic">Palmaria decipiens</span>; (<b>B</b>) Crustose coralline algae; (<b>C</b>) <span class="html-italic">Plocamium</span> sp.; (<b>D</b>) <span class="html-italic">Himantothallus grandifolius</span>; (<b>E</b>) <span class="html-italic">Desmarestia anceps</span>; (<b>F</b>) <span class="html-italic">Picconiella plumosa</span>; (<b>G</b>) Desmarestia menziesii; (<b>H</b>) <span class="html-italic">Iridaea</span> sp.; (<b>I</b>) <span class="html-italic">Sarcopeltis antarctica</span>; (<b>J</b>) <span class="html-italic">Monostroma hariotii</span>; (<b>K</b>) <span class="html-italic">Trematocarpus anstarcticus</span>; (<b>L</b>) <span class="html-italic">Pantoneura plocamioides</span>. The bar charts show the relative coverage of each substrate (mean ± S.E.), while the pie charts depict the relative occurrence frequency of each species by water depth. The red dotted line overlaid on the bar charts represents the average substrate cover across the entire study area.</p> Full article ">Figure 3
<p>Segmented bubble plot for nMDS with superimposed vector plot showing correlation with substrate. ASC: average substrate cover; PAL: <span class="html-italic">Palmaria decipiens</span>; CCA: Crustose coralline algae; PLO: <span class="html-italic">Plocamium</span> sp.; HIM: <span class="html-italic">Himantothallus grandifolius</span>; DAN: <span class="html-italic">Desmarestia anceps</span>; PIC: <span class="html-italic">Picconiella plumosa</span>; DME: <span class="html-italic">Desmarestia menziesii</span>; IRI: <span class="html-italic">Iridaea</span> sp.; SAR: <span class="html-italic">Sarcopeltis antarctica</span>; MON: <span class="html-italic">Monostroma hariotii</span>; TRE: <span class="html-italic">Trematocarpus antarcticus</span>; PAN: <span class="html-italic">Pantoneura plocamioides</span>.</p> Full article ">Figure 4
<p>Polar plots of interspecies affinity based on neighbor species occurrence frequency in the presence of host species. High affinity (blue circle): greater than twice the expected frequency; Medium affinity (light blue circle): greater than the expected frequency; Low affinity (light red circle): less than the expected frequency; Very low affinity (red circle): less than half the expected frequency. (<b>A</b>) PAL (<span class="html-italic">Palmaria decipiens</span>); (<b>B</b>) CCA (Crustose coralline algae); (<b>C</b>) PLO (<span class="html-italic">Plocamium</span> sp.); (<b>D</b>) HIM (<span class="html-italic">Himantothallus grandifolius</span>); (<b>E</b>) DAN (<span class="html-italic">Desmarestia anceps</span>); (<b>F</b>) PIC (<span class="html-italic">Picconiella plumosa</span>); (<b>G</b>) DME (<span class="html-italic">Desmarestia menziesii</span>); (<b>H</b>) IRI (<span class="html-italic">Iridaea</span> sp.); (<b>I</b>) SAR (<span class="html-italic">Sarcopeltis antarctica</span>); (<b>J</b>) MON (<span class="html-italic">Monostroma hariotii</span>); (<b>K</b>) TRE (<span class="html-italic">Trematocarpus antarcticus</span>); (<b>L</b>) PAN (<span class="html-italic">Pantoneura plocamioides</span>).</p> Full article ">Figure 5
<p>Substrate—affinity matrix for interaction between all combinations of significant species. The double arrow indicates a two-way interaction where both species act as host and neighbor, while the single arrow represents a one-way interaction where the species at the head of the arrow is the host and the species at the tail is the neighbor. Only the two-way interaction is highlighted in bold. PAL: <span class="html-italic">Palmaria decipiens</span>; CCA: Crustose coralline algae; PLO: <span class="html-italic">Plocamium</span> sp.; HIM: <span class="html-italic">Himantothallus grandifolius</span>; DAN: <span class="html-italic">Desmarestia anceps</span>; PIC: <span class="html-italic">Picconiella plumosa</span>; DME: <span class="html-italic">Desmarestia menziesii</span>; IRI: <span class="html-italic">Iridaea</span> sp.; SAR: <span class="html-italic">Sarcopeltis antarctica</span>; MON: <span class="html-italic">Monostroma hariotii</span>; TRE: <span class="html-italic">Trematocarpus antarcticus</span>; PAN: <span class="html-italic">Pantoneura plocamioides</span>.</p> Full article ">
Open AccessArticle
Assessing Mercury Contamination Levels in the Sediments of Two Pyrenean Lakes
by
Cristian Yoel Quintero-Castañeda, Luis Roberto Hernández-Angulo, Daniel Tobón-Vélez, Anamaría Franco-Leyva and María Margarita Sierra-Carrillo
Diversity 2024, 16(10), 627; https://doi.org/10.3390/d16100627 - 10 Oct 2024
Abstract
Mercury, a trace metal, is a persistent environmental pollutant that can be detected even in remote regions, including high-mountain lakes. This study examined mercury concentrations in the sediment of two lakes in the French Pyrenees, the Legunabens and Labant lakes. Sediment samples were
[...] Read more.
Mercury, a trace metal, is a persistent environmental pollutant that can be detected even in remote regions, including high-mountain lakes. This study examined mercury concentrations in the sediment of two lakes in the French Pyrenees, the Legunabens and Labant lakes. Sediment samples were collected using a Hon-Kajak Sediment Corer, and mercury concentrations were measured following the EPA 7473 method with a direct mercury analyzer (DMA-80). Mercury levels reached up to 283 ng g−1 in the Legunabens lake and up to 110 ng g−1 in the Labant lake, possibly linked to the mining history of the Ariège department and atmospheric deposition from distant sources. These findings indicate significant contamination, ranging from an 8% to 42% probability of generating adverse biological effects according to Canadian standards, and approximately 90% higher concentrations compared to average mercury concentrations in other Pyrenean lakes. Such contamination poses potential risks to aquatic life and the environment due to mercury’s toxicity and bioaccumulation in microorganisms.
Full article
(This article belongs to the Special Issue High-Mountain Lakes, Indicators of Global Change: Ecological Characterization and Environmental Pressures-2nd Edition)
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Show Figures
Figure 1
Figure 1
<p>Locations of lakes: Labant (<b>a</b>) and Legunabens (<b>b</b>).</p> Full article ">Figure 2
<p>Mercury concentrations (ng g<sup>−1</sup>; dry weight) as a function of depth (cm) of the core collected in Labant lake.</p> Full article ">Figure 3
<p>Mercury concentrations (ng g<sup>−1</sup>; dry weight) as a function of the depth (cm) of the core collected in the Legunabens lake.</p> Full article ">
<p>Locations of lakes: Labant (<b>a</b>) and Legunabens (<b>b</b>).</p> Full article ">Figure 2
<p>Mercury concentrations (ng g<sup>−1</sup>; dry weight) as a function of depth (cm) of the core collected in Labant lake.</p> Full article ">Figure 3
<p>Mercury concentrations (ng g<sup>−1</sup>; dry weight) as a function of the depth (cm) of the core collected in the Legunabens lake.</p> Full article ">
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