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151 results for “population tree”
Long-term (1935-2019) tree population data from remeasurements of a large network of permanent study plots in old-growth forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 0.2-acre (~0.08 ha) permanent monitoring (CFI) plots. This package includes tree censuses for subsets of CFI plots conducted in 1935, 1948, and 1974-1980, and repeated censuses with mapped stems from 1989 to 2019. This 84-year record constitutes one of the longest repeated-measurement, permanent-plot data-sets for old-growth temperate forest.
Inferring whole-genome histories in large population datasets: inferred tree sequences for 1000 Genomes
<p>Tree sequences inferred for the 1000 Genomes phase 3 autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip 1kg_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("1kg_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("1kg_chr1.trees") 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 class="language-python">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> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project
<p>Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can be decompressed as follows:</p> <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>. </p> <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") 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 class="language-python">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> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.</p>
Long-term (1993-2019) dynamics of tree populations on a mapped 3-ha permanent plot in old-growth northern hardwood forest, Huron Mts., Marquette Co., MI, USA
This data-set includes multiple remeasurements, over 25 years, of all woody stems >2 cm diameter (total of 2125 stems) on a 2.72-ha stem-mapped plot in old-growth northern hardwood forest in the Huron Mountains region of northern Marquette County, MI. The plot and surrounding forest is dominated by sugar maple (Acer saccharum) and eastern hemlock (Tsuga canadensis). Among secondary species, yellow birch (Betula alleghaniensis) and basswood (Tilia americana) are most common. Soils (identified as Kalkaska series) are developed on deep sandy glacial outwash. The plot is within a much larger region of old-growth forest, protected since ca. 1880, with only minimal disturbance associated with access tracks and trails. Numerous other forest community and dendrochronological studies support the interpretation that the area around the study plot has not experienced stand-initiating disturbance for at least 400 years. Initial mapping and measurements (1993-1995 for 2.52 ha; an additional 0.2 ha added in 1999) used a 20x20 m grid established in a near-level area of uniform substrate. All stems were identified to species, mapped on polar coordinates from the center of each grid cell (including, at first measurement, identifiable dead trees, standing and down), and diameter at breast height (dbh) measured to nearest 0.1 cm. All stems were remeasured on a five-year cycle 1999-2019, and new mortality was recorded at each remeasurement. New recruits > 2 cm dbh were added at each remeasurement.
Long-term (1993-2019) tree population measurements from a mapped 2.9-ha permanent plot in old-growth northern hardwood forest, Dukes Research Natural Area, Marquette Co., MI, USA
The Dukes Research Natural Area (Hiawatha National Forest, Marquette Co., MI) amounts to ca. 100 ha of minimally disturbed original forests, including a mix of mesic 'hemlock-northern hardwood' types and peaty wetlands dominated by several species of swamp conifers and black ash (Fraxinus nigra). The RNA hosts a regular grid of 250 permanent monitoring plots (data to be provided in a separate package). In 1993-95, a macroplot of 2.91 ha was established in a mixed mesic upland forest area within the RNA, in which all woody stems >2 cm diameter at breast height (DBH) were identified, measured, and mapped. In 1999 and again every five years subsequently through 2019, the macroplot was recensused; all stems were remeasured, stems newly recruited (>2 cm DBH) were measured and mapped, and any mortality since previous census was noted and described. A severe storm in 2002 resulted in extensive mortality throughout the RNA, particularly in the area in and around the macroplot.
Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves
<p>We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format). </p> <p>The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions.</p> <p>Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.</p>
Fig. 1 in Distribution Pattern, Nest-Tree Features And Breeding Performance Of Population Of The Black Stork, Ciconia Nigra (Ciconiiformes, Ciconiidae), In Northwestern Serbia
Fig. 1. The proportion of tree species picked for nest placement by the Black Stork (Ciconia nigra) in Northwestern Serbia (n = 44).
Figure 1 in Description, biology and conservation of a new species of Australian tree frog (Amphibia: Anura: Hylidae: Litoria) and an assessment of the remaining populations of Litoria genimaculata Horst, 1883: systematic and conservation implications of an unusual speciation event
Figure 1. Distribution of Litoria myola sp. nov. (formerly termed iS) and the northern (N) and southern (S) lineages of Litoria genimaculata in the Wet Tropics, northeast Queensland. CT, Carbine Tableland; BMC, Black Mountain Corridor; LR, Lamb Range; BK, Bellenden Ker Range; AT, Atherton Tableland; MT, Malbon Thompson Range; GR, Graham Range.
Figure 2 in Description, biology and conservation of a new species of Australian tree frog (Amphibia: Anura: Hylidae: Litoria) and an assessment of the remaining populations of Litoria genimaculata Horst, 1883: systematic and conservation implications of an unusual speciation event
Figure 2. Litoria myola, sp. nov., males (A, a pale individual; B, a heavily marked individual), Kuranda, north-east Queensland.
Figure 3 in Description, biology and conservation of a new species of Australian tree frog (Amphibia: Anura: Hylidae: Litoria) and an assessment of the remaining populations of Litoria genimaculata Horst, 1883: systematic and conservation implications of an unusual speciation event
Figure 3. Spectrogram of the courtship call of Litoria myola sp. nov. (recorded at an air temperature of 25 °C). The spectrogram displays a single courtship call consisting of six notes ('tocs'). The degree of shading displays call intensity.
Figure 7 in Description, biology and conservation of a new species of Australian tree frog (Amphibia: Anura: Hylidae: Litoria) and an assessment of the remaining populations of Litoria genimaculata Horst, 1883: systematic and conservation implications of an unusual speciation event
Figure 7. Rainforest stream habitat, with rocky (A) and sandy (B) substrate, Kuranda, north-east Queensland. Litoria myola sp. nov. and Litoria genimaculata are present at both sites.
Data from: Winter-moth populations are isolated on co-occurring tree species with contrasting budburst-phenology
<p>Differences between neighbouring tree species in phenology could isolate populations of host-plant generalists that depend on matching the phenology of their host. We studied the relationship between the budburst phenology of two co-occurring tree species with early (hornbeams) and late (oaks) budburst, and the egg-hatching date of associated winter moths (<em>Operophtera brumata</em>) during two seasons (autumns starting in 2020 and 2021)<em>.</em> A previous study in spring 2019 had found no winter moth larvae on the focal oaks, while we found them mainly on oaks with hornbeam neighbours in 2022. Congruently, adult winter moths were rarely encountered during the autumns of 2018 and 2019 and sparsely in 2020 and 2021, and then mainly near hornbeams.The vast majority of winter moths had early-hatching eggs when both hornbeams and oaks were present, matching the hornbeams rather than the oaks. Where hornbeam was absent in the neighbourhood, the few winter moths had late-hatching eggs.These results suggest that winter moth populations on hornbeam and oak are to some degree isolated from each other despite spatial proximity, so that recolonization of oaks by populations synchronized with budburst of hornbeam is slow. Therefore, trees would benefit from differing from their neighbours in budburst phenology with respect to herbivore damage.</p>
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
<p>Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (<em>Pinus pinaster</em> Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.</p>
Fig. 3 in Repeatability Analysis Of Egg Shape In A Wild Tree Sparrow (Passer Montanus) Population: A Sensitive Method For Egg Shape Description
Fig. 3. The effect of egg-photographing on the description of outline. Panel a shows ten outlines described following the photos of ten randomly chosen eggs, panel b shows ten outlines described fol-
Рис. 1. a — кΛаΔки; б — кокон; в — гнезΔо на ствоΛе Δерева; г — гусеницы (фото автора) Fig. 1. a — clutches; б — cocoon; в — nest on the tree trunk; г — caterpillars (photos by the author) in Lackey Moth (Malacosoma Neustria L., Lasiocampidae, Lepidoptera) Population During The Eruptive Phase
Рис. 1. a — кΛаΔки; б — кокон; в — гнезΔо на ствоΛе Δерева; г — гусеницы (фото автора) Fig. 1. a — clutches; б — cocoon; в — nest on the tree trunk; г — caterpillars (photos by the author)
Fig. 1. Species trees and alternative constraint convergence topologies. H0 in Positive association between PTN polymorphisms and schizophrenia in Northeast Chinese Han population.
Fig. 1. Species trees and alternative constraint convergence topologies. H0 is the well-accepted species tree. H1, H2, and H3 are three alternate echolocator-converged topologies. H1-control, H2-control and H3-control refer to the constraint convergent topologies of representative non-echolocators (cow, hedgehog, and non-echolocating bats).
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. Maximum-likelihood trees for J in Mitochondrial evidence indicates a shallow phylogeographic structure for Jaculus blanfordi (Murray, 1884) populations (Rodentia: Dipodidae)
Figure 2. Maximum-likelihood trees for J. blanfordi mtDNA haplotypes in different datasets for cyt b (1110 bp), COI (618 bp), and COI + cyt b (313 bp + 284 bp). The numbers next to the nodes indicate the bootstrap (>50%) and posterior probability (>0.50) values obtained by maximum-likelihood and Bayesian inference, respectively. The trees are rooted with haplotypes from J. orientalis and J. jaculus. See Table 1 and Figure 1 for the haplotype designations and corresponding localities.
Fig. 2 in First record of Heteropsylla caldwelli Burckhardt (Hemiptera: Psyllidae) from Brazil and its population dynamics on earpod tree in Rio Grande do Sul
Fig. 2. Population dynamics of Heteropsylla caldwelli (bars) and mean air temperature variation (in ◦C) in a seven-year-old plantation of Enterolobium contortisiliquum in an abandoned open-pit coal mine in Candiota, RS, between May 2014 and April 2016.
Fig. 1. Psyllids sampling with a in First record of Heteropsylla caldwelli Burckhardt (Hemiptera: Psyllidae) from Brazil and its population dynamics on earpod tree in Rio Grande do Sul
Fig. 1. Psyllids sampling with a beating net in the middle in a seven-year-old plantation of Enterolobium contortisiliquum in an abandoned open-pit coal mine in Candiota, RS, between May 2014 and April 2016.
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OpenNeuro
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