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348 results for “tree diversity”
How ancient forest fragmentation and riparian connectivity generate high levels of genetic diversity in a micro-endemic Malagasy tree
<p>This repository contains all the scripts and most of the intermediary files necessary to replicate the analyses of the preprint "<strong>How ancient forest fragmentation and riparian connectivity generate high levels of genetic diversity in a micro-endemic Malagasy tree</strong>" submitted to Molecular Ecology and available at:</p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.11.25.394544v1">https://www.biorxiv.org/content/10.1101/2020.11.25.394544v5</a></p> <p>Within each of the different zipped folders a readme.txt file briefly explains how the analyses are organized.</p> <p>This version of the dataset has been revised in agreement with the manuscript revision to answer the comments of the first two rounds of reviews in Peer Community In Evolutionary Biolology (PCI-EvolBiol; <a href="https://evolbiol.peercommunityin.org/">https://evolbiol.peercommunityin.org/</a>) by M. Navascues (Recommender), Katharina Budde (reviewer) and Yurena Arjona (reviewer), as well as two rounds of reviews in Molecular Ecology. All PCIevolbiol comments, response and changes are documented on the PCIevolbiol website.</p>
Data from: Temporal changes in taxonomic and functional alpha and beta diversity across tree communities in subtropical Atlantic forests
<h2><strong>The study is published in Oikos and available at: <a href="https://doi.org/10.1111/oik.10961">https://doi.org/10.1111/oik.10961</a></strong></h2> <p>Here we aim to assess temporal taxonomic and functional alpha and beta diversity of adult and juvenile tree communities across 11 sites in the subtropical Brazilian Atlantic Forest to infer about trends and drivers of biodiversity change. The tree communities were evaluated for temporal changes in: (1) taxonomic and functional alpha diversity, (2) taxonomic and functional composition (beta diversity), and (3) identifying potential abiotic and biotic drivers of these changes, considering three censuses across a period of 10 years.</p> <p> </p> <h2>Files description:</h2> <p><strong>traits-adults.csv</strong> - adult tree species and their functional traits values.</p> <p><strong>traits-juveniles.csv</strong> - juvenile tree species and their functional trait values.</p> <p><strong>abundance-adults_synthesis.csv</strong> - adult tree species abundance over the three time periods of forest surveys (T1, T2, and T3). Raw data on tree individual level is available at ForestPlots.net database (<a href="https://forestplots.net/">https://forestplots.net/</a>) under request.</p> <p><strong>abundance-juveniles_synthesis.csv</strong> - juvenile tree species abundance over the three time periods of forest surveys (T1, T2, and T3). Raw data on tree individual level is available at ForestPlots.net database (<a href="https://forestplots.net/">https://forestplots.net/</a>) under request.</p> <p>Functional traits abbreviations are defined as follows: LA = leaf area; SLA = specific leaf area; WD = wood density; SM = seed mass; range_temp = range of mean annual temperature; range_CWD = range of climatological water deficit; and biomes_distrib = number of Brazilian biomes that the species occur according to Flora and Funga do Brazil.</p> <p> </p> <h2><strong>Acknowledgments</strong></h2> <p>This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) – Finance Code 001, through Portal de Periódicos and scholarships granted to JMFK, JK and RCP. The fieldwork was supported by Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS grant numbers 2218 – 2551/12-2 and 19/2551-0001698-0), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq/FAPERGS/PELD number 441590/2020-9), and the Instituto Nacional de Ciência e Tecnologia (INCT) in Ecology, Evolution and Biodiversity Conservation, supported by MCTIC/CNPq (grant number 465610/2014-5). KMB gratefully acknowledge the financial support by the National Institute of Science and Technology in Low Carbon Emission Agriculture (INCT-ABC) sponsored by Brazil’s National Council for Scientific and Technological Development (CNPq, grant no. 406635/2022-6), the Foundation for Research Support of the State of Rio Grande do Sul (Fapergs, grant no. 22/2551-0000392-3), and the Ministry of Agriculture (MAPA). SCM is supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; grant number 309659/2019-1.</p> <p> </p> <h3><strong>Please find below the data used in the study.</strong></h3>
Fig.2. The phylogenetic tree for 72 in Genetic Diversity Of (Brassica Napus L.) Spring Oilseed Rape
Fig.2. The phylogenetic tree for 72 individual of Brassica napus constructed on the basis of RAPD data: M - 'Maskot, S - 'Sw Savan', H -'Heros', U -'Ural', L -'Landmark'
FIGURE 6. Classification Tree results and predictions for the five fossil localities. A in What are the best modern analogs for ancient South American mammal communities? Evidence from ecological diversity analysis (EDA)
FIGURE 6. Classification Tree results and predictions for the five fossil localities. A) Results and predictions for CT1, vegetative cover. B) Results and predictions for CT2, biogeographic realm. Abbreviations: LV, La Venta; QH, Quebrada Honda; RU, Rümikon; SC, Santa Cruz; TG, Tinguiririca.
Data & Analysis Script for: Phylogenetic relatedness to native congeners drives insect abundance and diversity hosted by non-native trees
<p>The dataset contains all necessary data to reproduce the findings presented in Schweiger et al. 2023 - Phylogenetic relatedness to native congeners drives insect abundance and diversity hosted by non-native trees (submitted).</p> <p>The code necessary to reproduce the findings is included within this repository. The code contains comments. Please note, if you want to reproduce the findings you will have to change file path information matching your personal computer to be able to re-run the code.</p> <p>This data includes the biodiversity raw data collected for the manuscript. It <strong>does not </strong>include data used to calculate geographic, climatic or phylogenetic distances, as these data are freely available and necessary information to reproduce calculations are given within the Material & Methods section.</p> <p>All data is provided within one Excel file. Please, pay attention to the provided ReadMe sheet containing metadata information on the dataset.</p> <p>Please carefully read provided information within ReadMe, Metadata and Code description.</p>
Fig. 2 in Diversity of Hemiptera (Arthropoda: Insecta) and their natural enemies on Caryocar brasiliense (Malpighiales: Caryocaraceae) trees in the Brazilian Cerrado
Fig. 2. Correlation of soil pH, soil aluminum level, tree height, and crown width with diversity indices, numbers of individuals, and numbers of species of sucking insects and their natural enemies per Caryocar brasiliense tree in the 3 areas of Montes Claros, Minas Gerais, Brazil. Symbols represent the mean values.
Fig. 1 in Diversity of Hemiptera (Arthropoda: Insecta) and their natural enemies on Caryocar brasiliense (Malpighiales: Caryocaraceae) trees in the Brazilian Cerrado
Fig. 1. Correlation of diversity indices, numbers of individuals, and numbers of species between sucking insects and their natural enemies; between number of sucking insects and total numbers of ants, green lacewings, predatory thrips, and lady beetles per Caryocar brasiliense tree in the 3 areas of Montes Claros, Minas Gerais, Brazil. Symbols represent the mean values.
Fig. 4 in Diversity of Hemiptera (Arthropoda: Insecta) and their natural enemies on Caryocar brasiliense (Malpighiales: Caryocaraceae) trees in the Brazilian Cerrado
Fig. 4. Correlation between tree height and crown width with total numbers of ants, predator thrips, and lady beetles per Caryocar brasiliense tree in the 3 areas of Montes Claros, Minas Gerais, Brazil. Symbols represent the mean values.
Fig. 3 in Diversity of Hemiptera (Arthropoda: Insecta) and their natural enemies on Caryocar brasiliense (Malpighiales: Caryocaraceae) trees in the Brazilian Cerrado
Fig. 3. Correlation of soil pH, soil aluminum level, tree height, and crown width with numbers of Dikrella caryocar and Aphis gossypii individuals per Caryocar brasiliense tree in the 3 areas of Montes Claros, Minas Gerais, Brazil. Symbols represent the mean values.
Fig. 2 Maximum likelihood phylogenetic tree constructed using the mitochondrial cox1 gene for 103 in Genetic diversity and population genetics of large lungworms (Dictyocaulus, Nematoda) in wild deer in Hungary
ƒFig. 2 Maximum likelihood phylogenetic tree constructed using the mitochondrial cox1 gene for 103 Dictyocaulus lungworms originating from Hungary and five lungworms from GenBank indicated by their accession numbers (one dictyocaulid worm of red deer in New Zealand and four sequences of D. viviparus). Lungworms were collected from hunted deer (fallow, red and roe deer), indicated by triangle, square and circle, respectively. Geographical collecting regions are indicated for each sample
Figure 2 in Preliminary study of mosquitoes' diversity (Diptera: Culicidae) in tree holes in an Atlantic Forest reserve in Rio Grande do Norte, Brazil
Figure 2. Detailed steps of the collection. (a) Measurement of circumference with a tape measure. (b) Removal of water with an artisanal siphon. (c) Basin for sorting immatures. / Figura 2. Detalle de las etapas de recolección. (a) Medición de la circunferencia con cinta métrica. (b) Extracción del agua con sifón artesanal. (c) Recipiente para la clasificación de inmaduros.
Figure 1 in Preliminary study of mosquitoes' diversity (Diptera: Culicidae) in tree holes in an Atlantic Forest reserve in Rio Grande do Norte, Brazil
Figure 1. Location of the study area with identification of the states of Rio Grande do Norte and ParaÍba, municipality of Baia Formosa, area of the Mata Estrela Private Natural Heritage Reserve (RPPN) and transects of the investigated hollows. Sentinel-2 images and land use and land cover classes from MapBiomas were used. / Figura 1. Ubicación del área de estudio con la identificación de los estados de Rio Grande do Norte y ParaÍba, el municipio de Baia Formosa, el área de la RPPN Mata Estrela y los transectos de las cavidades investigadas se muestran utilizando imágenes del Sentinel-2 y las clases de uso y cobertura del suelo del MapBiomas.
Fig. 2. Majority rule tree from the 73 in The European Early Cretaceous cryptodiran turtle Chitracephalus dumonii and the diversity of a poorly known lineage of turtles
Fig. 2. Majority rule tree from the 73 most parsimonious trees produced by the cladistic analysis of Chitracephalus dumonii using the modified data set of Joyce (2007) proposed in Pérez−García et al. (2012). Retention index (RI) = 0.872 and consistency index (CI) = 0.567. Values refer to percentages under 100% obtained in the majority rule analysis; those with values below 50% are collapsed. Letters refer to the nodes mentioned in the text.
Low-input breeding in stone pine, a multipurpose forest tree with low genome diversity
<p><span>Stone pine (<em>Pinus pinea</em> L.) is an emblematic tree species within the Mediterranean basin, with high ecological and economic relevance due to the production of edible nuts. Breeding programmes to improve pine nut production started decades ago in Southern Europe but have been hindered by the near absence of polymorphisms in the species genome and the lack of suitable genomic tools. In this study, we assessed new stone pine’s genomic resources and their utilisation in breeding and sustainable use, by using a commercial SNP-array (5,671 SNPs). Firstly, we confirmed the accurate clonal identification and identity check of 99 clones from the Spanish breeding programme. Secondly, we successfully estimated genomic relationships in</span><span> clonal collections, an information needed for </span><span>low-input breeding and genomic prediction. Thirdly, we applied this information to genomic prediction for total number of cones unspoiled by pests and their weight measured in three Spanish clonal tests. Genomic prediction accuracy depends on the trait under consideration and possibly on the number of genotypes included in the test. Predictive ability (<em>r</em><sub><span>y</span></sub>) was significant for the mean cone weight measured in the three clonal tests, while solely significant for the number of cones in one clonal test. The combination of a new SNP-array together with the phenotyping of relevant commercial traits into genomic prediction models, proved to be very promising to identify superior clones for cone weight. This approach opens new perspectives for early selection. </span></p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture.</p> <p>Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1 and <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4, as described in <a href="http://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. The files were compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>import json site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
Raw data for: Habitat edges affect tree diversity more than biomass regeneration in a reforested wet neotropical timber plantation
<p>Raw data for: Habitat edges affect tree diversity more than biomass regeneration in a reforested wet neotropical timber plantation. Code hosted on GitHub/Zenodo.</p>
Scale-dependent diversity-biomass relationships can be driven by tree mycorrhizal association and soil fertility
<p><span>Diversity–biomass relationships (DBRs) often vary with spatial scale in terrestrial ecosystems, but the mechanisms driving these scale-dependent patterns remain unclear, especially for highly heterogeneous forest ecosystems. This study explores how mutualistic associations between trees and different mycorrhizal fungi (i.e., arbuscular mycorrhizal (AM) vs. ectomycorrhizal (EM) association) modulate scale-dependent DBRs. We hypothesized that in soil-heterogeneous forests with a mixture of AM and EM tree species, (i) AM and EM tree species respond in contrasting ways (i.e., positively vs. negatively respectively) to increasing soil fertility, (ii) AM tree dominance contributes to higher tree diversity and EM tree dominance contributes to greater standing biomass and that as a result, (iii) mycorrhizal associations exert an overall negative effect on DBRs across spatial scales. To empirically test these hypotheses, we collected </span><span>detailed tree distribution and soil information (nitrogen, phosphorus, organic matter, pH, etc.) from seven temperate and subtropical AM-EM mixed forest mega-plots (16–50 ha). Using spatial codispersion null model and structural equation modeling, we identified the relationships among AM or EM tree dominance, soil fertility, tree species diversity and biomass, and thus DBRs across 0.01–1 ha scales. We found first evidence overall supporting the above three hypotheses in these AM-EM mixed forests: (i) In most forests, with increasing soil fertility tree communities changed from EM-dominated to AM-dominated. (ii) Increasing AM tree dominance had an overall positive effect on tree diversity and a negative effect on biomass, even after controlling for soil fertility and number of trees. Together, (iii) the changes in mycorrhizal dominance along soil fertility gradients weakened the positive DBR observed at 0.01–0.04 ha scales in nearly all forests and drove negative DBRs at 0.25–1 ha scales in four out of seven forests. Hence, this study highlights a soil-related mycorrhizal dominance mechanism that could partly explain why in many natural forests, biodiversity-ecosystem functioning (BEF) relationships shift from positive to negative with increasing spatial scale.</span></p>
Data for: The interactive effects of soil fertility and tree mycorrhizal association explain spatial variation of diversity-biomass relationships in a subtropical forest
<p><span>Observed biodiversity-ecosystem function (BEF) relationships are highly variable, particularly in natural forests. However, our understanding of the factors that generate these often contradictory patterns, especially the role of different mycorrhizal associations, is still limited. By relating tree species richness and aboveground biomass (AGB) in a fully-mapped 24-ha subtropical forest dynamics plot, we evaluated the impacts of soil fertility and tree mycorrhizal type in mediating BEF relationships at multiple spatial scales. Our results demonstrate a highly positive total richness effect on AGB for arbuscular mycorrhizal (AM) trees but a negative effect on AGB for ectomycorrhizal (EcM) trees, and their relationships were highly spatial scale dependent. However, the observed BEF relationships turned into positive at small spatial scales (i.e., 10 m and 20 m) after controlling for other confounding factors (i.e., topography, soil fertility, and AM proportion). In addition, we found significant interactions between soil fertility and species richness on AGB. Specifically, the positive effect of total species richness on AGB for major mycorrhizal types gradually weakened with increasing soil fertility, while the positive effect of EcM species richness on AM AGB gradually enhanced at small spatial scales, suggesting the observed diversity effects can be largely attributed to resource niche complementarity and the role of EcM fungi.</span></p> <p><span>Synthesis. We conclude that the variable BEF relationships among forest communities could be explained by spatial variation in abiotic environments and community mycorrhizal composition because different types of symbionts perform different nutrient uptake strategies and ability in protection from antagonists. Our findings provide novel insights into the understanding of the variation in the shape of BEF relationships in natural forests, which is critical for forest management, conservation, and restoration in a changing world.</span></p>
Data for: The interactive effects of soil fertility and tree mycorrhizal association explain spatial variation of diversity-biomass relationships in a subtropical forest
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Scale-dependent diversity-biomass relationships can be driven by tree mycorrhizal association and soil fertility
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