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638 results for “biomes”
FIGURE 2 in A new species of Paspalum, Notata group (Poaceae, Paspaleae), from the Cerrado biome, Brazil: description, chromosome number, and leaf blade anatomy
FIGURE 2. Distribution map of Paspalum cerradoense in the Cerrado biome, Brazil.
FIGURE 27A–F in The spider genus Patrera Simon (Araneae: Dionycha, Anyphaeninae) in the Atlantic Forest biome, with the description of one new species from Brazil
FIGURE 27A–F. Distribution map of Patrera species.
Data from: Identification of habitat-specific biomes of aquatic fungal communities using a comprehensive nearly full-length 18S rRNA dataset enriched with contextual data
Molecular diversity surveys have demonstrated that aquatic fungi are highly diverse, and that they play fundamental ecological roles in aquatic systems. Unfortunately, comparative studies of aquatic fungal communities are few and far between, due to the scarcity of adequate datasets. We combined all publicly available fungal 18S ribosomal RNA (rRNA) gene sequences with new sequence data from a marine fungi culture collection. We further enriched this dataset by adding validated contextual data. Specifically, we included data on the habitat type of the samples assigning fungal taxa to ten different habitat categories. This dataset has been created with the intention to serve as a valuable reference dataset for aquatic fungi including a phylogenetic reference tree. The combined data enabled us to infer fungal community patterns in aquatic systems. Pairwise habitat comparisons showed significant phylogenetic differences, indicating that habitat strongly affects fungal community structure. Fungal taxonomic composition differed considerably even on phylum and class level. Freshwater fungal assemblage was most different from all other habitat types and was dominated by basal fungal lineages. For most communities, phylogenetic signals indicated clustering of sequences suggesting that environmental factors were the main drivers of fungal community structure, rather than species competition. Thus, the diversification process of aquatic fungi must be highly clade specific in some cases.The combined data enabled us to infer fungal community patterns in aquatic systems. Pairwise habitat comparisons showed significant phylogenetic differences, indicating that habitat strongly affects fungal community structure. Fungal taxonomic composition differed considerably even on phylum and class level. Freshwater fungal assemblage was most different from all other habitat types and was dominated by basal fungal lineages. For most communities, phylogenetic signals indicated clustering of sequences suggesting that environmental factors were the main drivers of fungal community structure, rather than species competition. Thus, the diversification process of aquatic fungi must be highly clade specific in some cases.
Figure 2 in Description and notes on the bionomics of a new species of Potamophilops Grouvelle, 1896 (Coleoptera: Elmidae: Larainae), from the Cerrado biome in Brazil
Figure 2. Potamophilops bragaorum sp. nov., holotype habitus. Scale bar 2.00 mm.
Figure 2 from: Gonçalves FMP, Goyder DJ (2016) A brief botanical survey into Kumbira forest, an isolated patch of Guineo-Congolian biome. PhytoKeys 65: 1-14. https://doi.org/10.3897/phytokeys.65.8679
Figure 2 - Turraea vogelii Hook.f. ex Benth, Cola welwitschii Exell & Mendonça ex R. Germ., Pancovia golungensis (Hiern) Exell & Mendonça, Cochlospermum angolense Welw. ex Oliv., Inga vera Willd. subsp. vera, Pavetta gossweileri Bremek, Pittosporum viridiflorum Sims, Clerodendrum poggei Gürke
Figure 1 from: Gonçalves FMP, Goyder DJ (2016) A brief botanical survey into Kumbira forest, an isolated patch of Guineo-Congolian biome. PhytoKeys 65: 1-14. https://doi.org/10.3897/phytokeys.65.8679
Figure 1 - Map of Angola and its provinces, Cuanza Sul province highlighted and Kumbira in Conda municipality (black dot). Kumbira forest (top), the forest with Njelo mountain in the background (bottom).
Fig 9 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 9 Scatter plot of the Principal Components Analysis (PCA) from Orphulellapunctata (De Geer, 1773) lateral head shape in populations collected in the Cerrado, Atlantic Forest, and Pantanal. A. Thin-plate spline of the positive (+) and B. negative (-) axes of PCA 2; C.PCA plot.
Fig 5 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 5 Scatter plot of the Principal Components Analysis (PCA) from Orphulellapunctata (De Geer, 1773) femur shape in populations collected in the Cerrado, Atlantic Forest, and Pantanal. A. Thin-plate spline of the positive (+) and B. negative (-) axes of PCA 1; C.PCA plot.
Fig 8 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 8 Similarity dendrogram for the head in dorsal view for Orphulellapunctata populations from the Cerrado, Atlantic Forest, and Pantanal by the UPGMA method. The permutation test was carried out with 10,000 replicates and a cophenetic correlation coefficient of 79.2%.
Fig 4 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 4 Similarity dendrogram for the pronotum in Orphulellapunctata populations from the Cerrado, Atlantic Forest, and Pantanal by the UPGMA method. The permutation test was carried out with 10,000 replicates and a cophenetic correlation coefficient of 97.1%.
Fig 1 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 1 Collection sites for Orphulellapunctata (De Geer, 1773): Cerrado, Atlantic Forest and Pantanal.
Fig 6 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 6 Similarity dendrogram for the femur from Orphulellapunctata populations from the Cerrado, Atlantic Forest, and Pantanal by the UPGMA method. The permutation test was carried out with 10,000 replicates and a cophenetic correlation coefficient of 86.83%.
Fig 11 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 11 Analysis of the size of the A. pronotum, B. femur, C. head in dorsal view and D. head in lateral view. Similar letters indicate that these biomes are statistically equivalent in relation to the size of the pronotum, femur, dorsal, and lateral view of the head by Tukey's test (p<0.05).
Fig 7 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 7 Scatter plot of the Principal Components Analysis (PCA) from Orphulellapunctata (De Geer, 1773) dorsal head shape in populations collected in the Cerrado, Atlantic Forest, and Pantanal. A. Thin-plate spline of the positive (+) and B. negative (-) axes of PCA 2; C.PCA plot.
Fig 3 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 3 Scatter plot of the Principal Components Analysis (PCA) of Orphulellapunctata (De Geer, 1773) pronotum shape in populations collected from the Cerrado, Atlantic Forest, and Pantanal. A. Thin-plate spline of the positive (+) and B. negative (-) axes of PCA 2; C.PCA plot.
Fig 10 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 10 Similarity dendrogram for the lateral view of the head in Orphulellapunctata populations from the Cerrado, Atlantic Forest, and Pantanal by the UPGMA method. The permutation test was carried out with 10,000 replicates and a cophenetic correlation coefficient of 98.8%.
Fig 2 from: Silva ACS, Nunes LA, Batista WL, Lhano MG (2018) Morphometric variation among males of Orphulella punctata (De Geer, 1773) (Acrididae: Gomphocerinae) from different biomes in Brazil. Journal of Orthoptera Research 27(2): 163-171. https://doi.org/10.3897/jor.27.21203
Fig 2 Lateral view of Orphulellapunctata (De Geer, 1773). A. Pronotum: 10 anatomical points; B. Femur: 18 anatomical points; C. Lateral view of the head: 16 anatomical points; D. Dorsal view of the head: 18 anatomical points. Black circles represent the landmarks and white circles represent the semi-landmarks.
FIGURE 2 in Contributions to the knowledge and distribution of Pucciniales (rust fungi) in three Brazilian biomes
FIGURE 2. Distribution of Pucciniales species in each genus.
Biome classification influences the projected rate of future biome transitions
<p><strong>Aim. </strong>Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.</p> <p><strong>Location.</strong> Africa, Australia, Tropical Asia</p> <p><strong>Time period.</strong> 2000-2099</p> <p><strong>Major taxa studied.</strong> Tropical vegetation</p> <p><strong>Methods.</strong> We used adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of functional types, (2) a cluster analysis based on the cover of functional types, and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation-based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.</p> <p><strong>Results.</strong> As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data-model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.</p> <p><strong>Main conclusions. </strong> Studies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.</p>
Anthropogenic pressure index on biomes (APIB): scenarios for Brazil 2050
<p>Anthropogenic transformations in the terrestrial biosphere have become increasingly significant and concerning. Whether through agriculture, silviculture, industrialization, and/or urbanization, these disturbances alter fundamental biogeochemical cycles and contribute to the addition or removal of genetically distinct species and populations from or to habitats in most terrestrial ecosystems, thus compromising the sustainability of ecological processes and the provision of ecosystem goods and services. In the short term, the primary threats to biodiversity arising from human activities include habitat loss and fragmentation. According to global assessments, the number of species at risk of extinction has increased, and the size of species populations has decreased.</p><p>Understanding the future of land use and land cover changes in Brazil and their impact on ecosystems is essential for the future of climate and biodiversity. Therefore, an Anthropogenic Pressure Index on Biomes (APIB) was developed to establish the level and distribution of anthropogenic pressure on biodiversity in Brazilian territories. This allowed for the analysis of the dynamics of anthropogenic pressure on biodiversity based on future scenarios of land use and land cover change and the identification of regions that are internally homogeneous and heterogeneous regarding the dynamics of this pressure in different scenarios.</p><p>For the development of APIB, the following spatially explicit factors related to anthropogenic pressure on biomes were considered: a) land use and land cover (forest vegetation, grassland vegetation, planted pasture, agriculture, mosaic of occupation, and forestry); b) rivers; c) protected areas; d) agricultural establishments; e) highways; and f) hydroelectric projects. Three scenarios of land use and land cover change were considered: a) the sustainable development scenario (SSP1), combined with a strict climate policy (RCP 1.9); b) the intermediate road development scenario (SSP2 and RCP 4.5); and c) the scenario of high inequality, which associates SSP3 with RCP 7.0.</p><p> </p><p><strong>Data</strong></p><p>Anthropogenic Pressure Index on Biomes (APIB) value.</p><p> </p><p><strong>Spatial resolution</strong></p><p>The data is available at a spatial resolution of 0.083º x 0.083º (~100 km²) and covers the entire Brazilian territory.</p><p> </p><p><strong>Temporal resolution </strong></p><p>Period of observed data: 2000 and 2014</p><p>Scenario Period: 2050</p><p> </p><p><strong>Coordinate reference system</strong> </p><p>Geographic Coordinate System with Datum SIRGAS 2000 (EPSG:5880)</p><p> </p><p><strong>Data format</strong></p><p>Data is provided as Shapefile.</p><p> </p><p><strong>Dataset usage</strong> </p><p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p><p>F. G. S. Bezerra, <i>et al.</i>, Spatio-temporal analysis of dynamics and future scenarios of anthropic pressure on biomes in Brazil. <i>Ecol Indic</i> <strong>137</strong> (2022). https://doi.org/10.1016/j.ecolind.2022.108749</p><p> </p><p><strong>Publication & further information</strong></p><p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p><p> </p><p><strong>Acknowledgments</strong></p><p>The authors would like to thank the São Paulo Research Foundation (FAPESP, project number 2017/22269-2 and Nexus Project) for their support in the development of this study.</p>
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Allen Brain Atlas
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