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Figure 5 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems

Figure 5. Occlusal view of the lower molars of the Sigmodontine rodents from Tyto furcata pellets from the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Brazil. (A) = Akodon azarae, UFSC-CF 43-2-2, right m1-m2. (B) = Akodon sp., UFSC-CF 32-4-2, right m1-m3; (C) = Bibimys sp., UFSC-CF 32-5-2, right m1-m3. (D) = Calomys sp., UFSC-CF 32-6-2, right m1-m3. (E) = Holochilus sp., UFSC-CF 43-4-2, left m1-m3. (F) = Lundomys molitor, UFSC-CF 32-9-1, left m1-m3. (G) = Nectomys squamipes, UFSC-CF 32-10-1, right m1-m3. (H) = Oligoryzomys sp., UFSC-CF 32-11-2, right m1-m3. (I) = Oxymycterus sp., UFSC-CF 32-12-2, right m1-m3. (J) = Sooretamys angouya, UFSC-CF 32-13-2, left m1-m2. (K) = Wilfredomys oenax, UFSC-CF 32-14-2, left m1-m3. Scale bars: H = 200 µm; A, B, C, D, G, I, J, K = 500 µm; E, F = 1 mm.

Figure 4 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems

Figure 4. Occlusal view of the upper molars of the Sigmodontine rodents from the Tyto furcata pellets from the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Brazil. (A) = Akodon azarae, UFSC-CF 43-2-1, left M1-M3. (B) = Akodon sp., UFSC-CF 32-4-1, left M1-M3. (C) = Bibimys sp., UFSC-CF 32-5-1, left M1-M3. (D) = Calomys sp., UFSC-CF 32-6-1, left M1-M3. (E) = Juliomys sp., UFSC-CF 32-7-1, left M1-M3. (F) = Holochilus sp., UFSC-CF 43-4-1, left M1-M3. (G) = Oligoryzomys sp., UFSC-CF 32-11-1, left M1-M3. (H) = Oxymycterus sp., UFSC-CF 32-12-1, left M1-M3. (I) = Sooretamys angouya, UFSC-CF 32-13-1, right M1-M3. (J) = Wilfredomys oenax, UFSC-CF 32-14-1, left M1-M3. Scale bars: C = 200 µm; A, B, D, E, F, G, H, I, J = 500 µm.

Figure 1 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems

Figure 1. Location of the sampling sites of the Tyto furcata pellets at the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Southern Brazil. BV1 = Boa Vista I; BV2 = Boa Vista II; BOQ = Boqueirão; CGA = Canta Galo; EV1 = Evaristo I; EV2 = Evaristo II; ANT = Picada das Antas; PF1 = Picada Feliz I; PF2 = Picada Feliz II; QV1 = Quevedos I; QV2 = Quevedos II; RES = Reserva. Map modified from MMA (1992).

Figure 3 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems

Figure 3. Chiroptera specimens from the Tyto furcata pellets from the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Brazil. (A) = ventral view of the skull of Sturnira lilium, UFSC-CF 40-5-1. (B) = labial view of the right dentary of Tadarida brasiliensis, UFSC-CF 42-11-1. Scale bars: 1 mm.

Figure 2 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems

Figure 2. Didelphimorphia specimens from the Tyto furcata pellets from the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Brazil. (A) = labial view of the left dentary of Cryptonanus guahybae, UFSC-CF 32-2-1. (B) = labial view of the right dentary of Gracilinanus microtarsus, UFSC-CF 42-10-1. Scale bars: 1 mm.

Figure 6 in Small mammal diversity in Semi-deciduous Seasonal Forest of the southernmost Brazilian Pampa: the importance of owl pellets for rapid inventories in human-changing ecosystems

Figure 6. Occlusal view of the upper and lower molars of the Muridae and Caviidae rodents fromTytofurcata pellets from the Municipality of São Lourenço do Sul, State of Rio Grande do Sul, Brazil.(A) = Mus musculus, UFSC-CF 32-15-1, left M1-M3. (B) = Rattus rattus, UFSC-CF 35-9-1, right M1-M3. (C) = Cavia aperea, UFSC-CF 43-8-1, left P4-M3. (D) = M. musculus, UFSC-CF 32-15-2, right m1-m3. (E) = R. rattus, UFSC-CF 35-9-2, right m1-m3. (F) = C. aperea, UFSC-CF 43-8-2, right p4-m3. Scale bars A, D = 200 µm; B, C, E, F = 1 mm.

opencc-zeroJun 2021View details →

Latitudinal gradient in the intensity of biotic interactions in terrestrial ecosystems: Sources of variation and differences from the diversity gradient revealed by meta-analysis

The Latitudinal Biotic Interaction Hypothesis (LBIH) states that the intensity of biotic interactions increases from high to low latitudes. This hypothesis, which may partly explain latitudinal gradients in biodiversity, remains hotly debated, largely due to variable outcomes of published studies. We used meta-analysis to identify the scope of the LBIH in terrestrial ecosystems. For this purpose, we explored the sources of variation in the strength of latitudinal changes in herbivory, carnivory, and parasitism (119 publications) and compared these gradients with gradients in the diversity of the respective groups of animals (102 publications). Overall, both herbivory and carnivory decreased towards the poles, while parasitism increased. The latitudinal gradient in herbivory and carnivory was threefold stronger above 50–60º than at lower latitudes and was significant due to interactions involving ectothermic consumers, studies using standardized prey (i.e. prey lacking local anti-predator adaptations) and studies aimed at testing LBIH. The poleward decrease in biodiversity did not differ between ectothermic and endothermic animals or among climate zones and was four-fold stronger than decrease in herbivory and carnivory. The discovered differences between the gradients in biotic interactions and biodiversity suggest that these two global macroecological patterns are likely shaped by different factors.

opencc-zeroDec 2017View details →

Data from: Ecosystem context illuminates conflicting roles of plant diversity in carbon storage

Plant diversity can increase biomass production in plot‐scale studies, but applying these results to ecosystem carbon (C) storage at larger spatial and temporal scales remains problematic. Other ecosystem controls interact with diversity and plant production, and may influence soil pools differently from plant pools. We integrated diversity with the state‐factor framework, which identifies key controls, or 'state factors', over ecosystem properties and services such as C storage. We used this framework to assess the effects of diversity, plant traits and state factors (climate, topography, time) on live tree, standing dead, organic horizon and total C in Québec forests. Four patterns emerged: (1) while state factors were usually the most important model predictors, models with both state and biotic factors (mean plant traits and diversity) better predicted C pools; (2) mean plant traits were better predictors than diversity; (3) diversity increased live tree C but reduced organic horizon C; (4) different C pools responded to different traits and diversity metrics. These results suggest that, where ecosystem properties result from multiple processes, no simple relationship may exist with any one organismal factor. Integrating biodiversity into ecosystem ecology and assessing both traits and diversity improves our mechanistic understanding of biotic effects on ecosystems.

opencc-by-4.0Nov 2023View details →

Influences of Satellite Sensor and Scale on Derivation of Ecosystem Functional Types and Diversity

These are the datasets where were generated for paper "Influences of Satellite Sensor and Scale on Derivation of  Ecosystem Functional Types and Diversity"

opencc-zeroDec 2023View details →

Tree diversity across multiple scales and environmental heterogeneity promote ecosystem multifunctionality in a large temperate forest region

Aim: Biodiversity across different scales provides multidimensional insurance for ecosystem functioning. Although the positive effects of local scale (α-diversity) biodiversity on ecosystem multifunctionality are widely accepted, species turnover across communities (β-diversity) which is often an important driver of ecosystem functioning did not receive the same attention. This study broadens the understanding of how multiple attributes of biodiversity maintain ecosystem multifunctionality from local to regional scales, across diverse environmental gradients. Location: North-eastern China. Time period: 2017. Major taxa studied: Woody plants. Methods: We estimate ecosystem multifunctionality using both averaging and modified multiple thresholds (50%, 70% and 90%) approaches. Multiple dimensions of biodiversity across varying spatial scales were measured within the framework of Hill‒Chao numbers. Linear and nonlinear models were used to evaluate the optimal patterns of multifunctionality and biodiversity along the latitude. Using variance decomposition, structural equation modeling and linear mixed models, we explored how multiple attributes of tree diversity at varying spatial scales affect multifunctionality, and how these relationships are modulated by environmental drivers. Results: Our results show that multifunctionality decreased with increasing latitude, mirroring the pattern of tree diversity along latitudinal gradients. Phylogenetic β-diversity and species α-diversity emerged as crucial diversity indices for sustaining multifunctionality in these temperate forests. Soil and climatic conditions had either direct effects on multifunctionality, or indirect ones mediated by tree diversity. Environmental heterogeneity played a pivotal role in maintaining high levels of multifunctionality, exerting influence both directly and indirectly via phylogenetic β-diversity. Main conclusions: This study underscores the positive effects of biodiversity on multifunctionality across multiple dimensions. Based on our findings, we conclude that any design of a forested landscape that is aimed at maximizing multifunctionality should consider maintaining high local diversity as well as forest community heterogeneity at varying scales.

opencc-by-4.0Sep 2024View details →

Exploring the global metaplasmidome: unravelling plasmid landscapes and the spread of antibiotic resistance genes across diverse ecosystems

Plasmid content was predicted from assembled data already publicly available or constructed from reads for this study. The assembled data supplied by Pasolli and colleagues (Pasolli et al., 2019) , metasub consortium (Danko et al., 2020) and TARA ocean (Tully et al., 2018) were used for the human microbiome, the built environment and the marine ecosystem respectively. For assembly in the current study, reads from metagenomes were selected from two main databases. For the soil ecosystem, the metagenomes were selected from the dedicated curated database “TerrestrialMetagenomeDB” (Corrêa et al., 2020).  If the metagenomes were not assembled, reads were assembled by using megahit 1.2.9 with the metalarge option (Li et al., 2015) after cleaning the data with bbduk2 (qtrim=rl trimq=28 minlen=25 maq=20 ktrim=r k=25 mink=11 and a list of adapters to remove) from the bbtools suite (<a href="https://jgi.doe.gov/data-and-tools/software-tools/bbtools/">https://jgi.doe.gov/data-and-tools/software-tools/bbtools/</a>). Plasmids were predicted for each assembly by using both reference-based and reference-free approaches as described in previous works (Hilpert et al., 2021; Hennequin et al., 2022) and available on the github website (https://github.com/meb-team/PlasSuite/). The databases used for the first approach included those for chromosomes (archaea and bacteria) and plasmids from RefSeq, as well as the MOB-suite tool (Robertson and Nash, 2018), SILVA (Quast et al., 2013) and phylogenetic markers hosted by chromosomes (Wu et al., 2013). The database created for this purpose is available at this address <a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-</a><a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">databases</a>. Two reference-free methods were applied to contigs that were not affiliated with chromosomes (discarded) or plasmids (retained in the first step): PlasFlow (Krawczyk et al., 2018) and PlasClass (Pellow et al., 2020). Previously undetected viruses were removed by using ViralVerify (<a href="https://github.com/ablab/viralVerify">https://github.com/ablab/viralVerify</a>)(Antipov et al., 2020) that provides in parallel plasmid/non-plasmid classification. This step would also remove potential plasmid-phage elements as described by Pfeifer et al.  (Pfeifer et al., 2021), but would minimise false positives. Eukaryotic contamination was removed by aligning the sequences against the NT database and human chromosomes (GRCh38) using minimap2 (Li, 2018) with -x asm5 option. Contigs mapping with 95% identity for at least 80% coverage were removed. The predicted plasmids, hereafter referred as plasmid-like sequences (PLSs), were grouped by "scientific names" (i.e. 27) such as defined in the SRA metadata (air, lake, wetland…) and subsequently named ecosystems. These ecosystems were grouped in 9 biomes (Tab Supplementary 4). The data were then dereplicated by ecosystems using cd-hit-est with a threshold of 99%. The dereplicated PLSs were then clustered using MMseqs2 (Steinegger and Söding, 2017) with 80% of coverage an 90% of identity (--min-seq-id 0.90 -c 0.8 --cov-mode 1 --cluster-mode 2 --alignment-mode 3 --kmer-per-seq-scale 0.2) to define plasmid-like clusters (PLCs). <div> The PLC sequences are included in the file "predicted_PLC.fasta" and the main features are dercribed in the file "metadata_PLC.tsv" <ul> <li>fasta_id: fasta identification of the PLC</li> <li>ecosystem: ecosystem from which the PLC originates</li> <li>biome: biome of the ecosystem</li> <li>latitude, longitude: GPS coordinate of the ecosystem</li> <li>length: PLC length</li> <li>map_markers: plasmid marker genes detected by PlasSuite (Hilpert et al., 2021)</li> <li>map_ncbi: PLCs present in the RefSeq plasmid database(Hilpert et al., 2021)</li> <li>nb_genes: Number of genes detected by Prokka implemented in PlasSuite</li> <li>nb_args: ARGs detected by PlasSuite</li> <li>plascad: results from plascad (Che et al., 2021)</li> </ul>     Antipov, D., Raiko, M., Lapidus, A., and Pevzner, P.A. (2020) MetaviralSPAdes: assembly of viruses from metagenomic data. Bioinformatics 36: 4126–4129. Che, Y., Yang, Y., Xu, X., Břinda, K., Polz, M.F., Hanage, W.P., and Zhang, T. (2021) Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes. Proceedings of the National Academy of Sciences 118: e2008731118. Corrêa, F.B., Saraiva, J.P., Stadler, P.F., and da Rocha, U.N. (2020) TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes. Nucleic Acids Res 48: D626–D632. Danko, D., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D.J., et al. (2020) Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance. bioRxiv 724526. Hennequin, C., Forestier, C., Traore, O., Debroas, D., and Bricheux, G. (2022) Plasmidome analysis of a hospital effluent biofilm: Status of antibiotic resistance. Plasmid 122: 102638. Hilpert, C., Bricheux, G., and Debroas, D. (2021) Reconstruction of plasmids by shotgun sequencing from environmental DNA: which bioinformatic workflow? Briefings in Bioinformatics 22: bbaa059. Krawczyk, P.S., Lipinski, L., and Dziembowski, A. (2018) PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res 46: e35. Li, D., Liu, C.-M., Luo, R., Sadakane, K., and Lam, T.-W. (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31: 1674–1676. Li, H. (2018) Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34: 3094–3100. Pasolli, E., Asnicar, F., Manara, S., Zolfo, M., Karcher, N., Armanini, F., et al. (2019) Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle. Cell 176: 649-662.e20. Pellow, D., Mizrahi, I., and Shamir, R. (2020) PlasClass improves plasmid sequence classification. PLOS Computational Biology 16: e1007781. Pfeifer, E., Moura de Sousa, J.A., Touchon, M., and Rocha, E.P.C. (2021) Bacteria have numerous distinctive groups of phage–plasmids with conserved phage and variable plasmid gene repertoires. Nucleic Acids Res 49: 2655–2673. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41: D590–D596. Robertson, J. and Nash, J.H.E. (2018) MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. Microbial Genomics 4:. Steinegger, M. and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology. Tully, B.J., Graham, E.D., and Heidelberg, J.F. (2018) The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Scientific Data 5: 170203. Wu, D., Jospin, G., and Eisen, J.A. (2013) Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. PLoS One 8:. </div>

opencc-zeroAug 2022View details →

Phylogenetic data for: High diversity of new and known Phytophthora species from phylogenetic Clade 10 in natural ecosystems of Asia, Europe and the Americas

During extensive surveys of Phytophthora diversity, 14 new species were detected in natural ecosystems in Chile, Louisiana, Sweden, Ukraine, Vietnam and Indonesia. Multigene phylogeny based on the nuclear LSU, rpl10, ITS, ßtub, enl, hsp90, tef-1α, ras-ypt1 and tigA and the mitochondrial cox1, nadh1 and rps10 gene sequences demonstrated that they belong to phylogenetic Clade 10 which is structured into three subclades. Subclades 10a and 10b comprise soil- and waterborne species with nonpapillate sporangia and variable breeding systems, including the known P. afrocarpa, P. gallica and P. intercalaris and the new P. ludoviciana, P. procera, P. pseudogallica, P. scandinavica, P. subarctica, P. tenuimura, P. tonkinensis and P. ukrainensis. In contrast, all species in Subclade 10c are airborne with papillate sporangia and homothallic breeding system, including the known P. boehmeriae, P. kernoviae and P. morindae and the new P. celebensis, P. chilensis, P. javanensis, P. multiglobulosa, P. pseudochilensis and P. pseudokernoviae. All new species differed from each other and from related species by a unique combination of morphological characters, the breeding system, cardinal temperatures and growth rates. The biogeography and evolutionary history of Clade 10 are discussed and the hypothesis put forward that the extant subclades originate from early divergences of pre-Gondwanan ancestors (>175 Mya) into water-/soilborne and airborne lineages which during their global spread experienced multiple allopatric and sympatric radiations.

opencc-zeroJun 2024View details →

Diverse host-parasite interactions mediate seasonal ecosystem linkages

Nematomorph parasites manipulate terrestrial arthropods, such as crickets and ground beetles, to enter streams where the parasites reproduce. These manipulated arthropods become a substantial prey subsidy for stream salmonids, causing cross-ecosystem energy flow. Diverse nematomorph-arthropod interactions are known to underlie the energy flow. However, whether and how they can mediate the magnitude and temporal attributes of energy flow remains largely unknown. Here, we investigated whether distinct species or phylogenetic groups of nematomorphs respectively manipulate different arthropod hosts, and how the diverse nematomorph-arthropod interactions, if any, mediate seasonal prey subsidy for stream salmonids. We found that distinct phylogenetic groups of Gordionus and Gordius nematomorphs infected ground beetle and orthopteran hosts, respectively. The Gordionus nematomorphs led their ground beetle hosts to enter streams in spring, subsidizing salmonid individuals during that season. By contrast, the Gordius nematomorphs manipulated orthopterans in autumn, causing the prey subsidy for salmonid individuals during that time. Maintaining the two distinct nematomorph-arthropod interactions, thus, resulted in the parasite-mediated prey subsidy in both spring and autumn in the study streams. Manipulative parasites are common, and they often associate with a range of host lineages, suggesting that similar effects of diverse host-parasite interactions on energy flow might be widespread in nature.

opencc-by-4.0Nov 2021View details →

Fig. 1 in Multiple Environmental Factors Increase the Niche Complexity and Species Diversity of Brachyuran Crabs in an Intertidal Algal Reef Ecosystem in Northwestern Taiwan.

Fig. 1. The six study sites in the present study.

opencc-by-4.0Oct 2019View details →

Low redundancy and complementarity shape ecosystem functioning in a low-diversity ecosystem

Data used in the publication of "Low redundancy and complementarity shape ecosystem functioning in a low-diversity ecosystem" in the Journal of Animal Ecology.

opencc-by-4.0Jan 2014View details →

Figure 6 in Biological diversity and seasonal variation of mesozooplankton in the southeastern Black Sea coastal ecosystem

Figure 6. Anchovy production during the sampling period.

opencc-by-4.0Dec 2020View details →

Figure 1 in Diversity of Orthopteran insects and their role in Tea Agro-Ecosystem of West Bengal

Figure 1. District-wise sampling sites (tea gardens) in West Bengal.

opencc-by-4.0Sep 2016View details →

Figure 3 in Diversity of zooplankton in municipal wastewater-contaminated urban pond ecosystems of the lower Gangetic plains

Figure 3. Proportional abundance of different representative groups of zooplankton at study sites.

opencc-by-4.0Sep 2016View details →

Figure 1 in Diversity of zooplankton in municipal wastewater-contaminated urban pond ecosystems of the lower Gangetic plains

Figure 1. Map showing the study sites (Site 1–5) within Hooghly-Chinsurah Municipality.