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Supplentary materials for: "Long-term ecosystem monitoring along the Trabocchi coast (Chieti, Italy): insights from underwater visual surveys (2011-2024)"
<p>This collection of photos and videos showcases the rich biodiversity of marine fauna and flora along the Trabocchi coast reefs in the Chieti district of Italy.</p>
Replication Data for figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s
<p>Supporting data to reproduce figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s</p>
Data for: Long-term body size change in multiple landbird species, long-term change in temperature and precipitation as well as associations between temperature, precipitation, and morphological change in multiple landbird species, 2004 – 2019, 2021 - 2022.
<p>Six data sets used to look for long-term change in precipitation and temperature, body size change and possible environmental drivers of morphological change in birds captured during spring or fall migration in and around Lackawanna State Park, northeastern Pennsylvania, USA.</p> <p>The file labeled daily_temp_precip.csv contains daily precipitation and average daily temperature data from the Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA) and the file called daily_temp_precip_1400 contains daily precipitation and daily temperature data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude.</p> <p>The file called band_data_final.csv contains data collected from the first capture of individuals of multiple species during spring or fall migration, the file called all_hy_env_morph.csv contains temperature and precipitation anomaly data from Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA), as well as morphological data from the first capture of all fall migrating young of the year.</p> <p>The file called all_hy_env_morph_1400.csv contains temperature and precipitation anomaly data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude as well as morphological data from the first capture of all fall migrating young of the year while the file called local_hy_env_morph.csv contains temperature and precipitation anomaly data as well as first capture of local young of the year.</p>
LongPMInd: long-term (1980-2022) daily ground particulate matter datasets in India
<p>The LongPMInd dataset, including daily PM2.5 and PM10 concentration (10km) for India during 1980-2022, is publicly accessible. All data are provided with NetCDF format with a spatial resolution of 10 km.<br><br></p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 1982-2000
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks. We also </li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Skogaryd data used for the paper: Evaluation of long-term carbon dynamics in a drained forested peatland using the ForSAFE-Peat Model.
<p>Dataset of abiotic and carbon exchange variables for Skogaryd drained afforested peatland. The dataset include measurements of soil temperature, ground water level, and carbon exhange as well as modelled carbon fluxes performed with the model ForSAFE-Peat </p>
Supplementary data for: Chromosome-scale genome assemblies of aphids reveal extensively rearranged autosomes and long-term conservation of the X chromosome
<p><strong><em>Myzus persicae </em>clone O v2 frozen release</strong></p> <p>Genome assembly: Myzus_persicae_O_v2.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Myzus_persicae_O_v2.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Myzus_persicae_O_v2.0_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae de novo</em> repeat library: Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff.out</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae</em> <em>de novo r</em>epeat library (gff format): Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Acyrthosiphon pisum</em> clone JIC1 v1 frozen release</strong></p> <p>Genome assembly: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Acyrthosiphon_pisum_JIC1_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum</em> <em>de novo</em> repeat library: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.out</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum de novo</em> repeat library (gff format): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Rhodnius prolixus</em> DNA zoo chromosome-scale genome assembly annotation</strong></p> <p><em>R. prolixus </em>chromosome-scale genome assembly was obtained here: <a href="https://www.dnazoo.org/assemblies/Rhodnius_prolixus">https://www.dnazoo.org/assemblies/Rhodnius_prolixus</a>.</p> <p>Genome assembly: Rhodnius_prolixus-3.0.3_HiC.fasta</p> <p>BRAKER2 gene models: Rhodnius_prolixus-3.0.3_HiC.braker2.gff</p> <p>BRAKER2 protein sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.cds.fa</p> <p><strong><em>Triatoma rubrofasciata</em> chromosome-scale genome assembly annotation</strong></p> <p><em>T. rubrofasciata </em>chromosome-scale genome assembly was obtained here: <a href="http://dx.doi.org/10.5524/100614">http://dx.doi.org/10.5524/100614</a></p> <p>Genome assembly: zhuichun_assembly.fasta</p> <p>BRAKER2 gene models: zhuichun_assembly.braker2.gff</p> <p>BRAKER2 protein sequences: zhuichun_assembly.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): zhuichun_assembly.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: zhuichun_assembly.braker2.gff.cds.fa</p> <p><strong>Hemiptera orthogroups and species tree</strong></p> <p>OrthoFinder was used to cluster proteomes of 14 Hemiptera into orthogroups for phylogenomic analysis. All proteomes were reduced to the longest transcript per gene. See here for full details:</p> <p>Species included, taxon IDs and data source:</p> <p>Mcer = Myzus cerasi v1.1 (<a href="https://bipaa.genouest.org/sp/myzus_cerasi/">https://bipaa.genouest.org/sp/myzus_cerasi/</a>)</p> <p>MperO = Myzus persicae clone O v2 (This study)</p> <p>Dnox = Diuraphis noxia Thorpe et. al. gene predictions (<a href="https://bipaa.genouest.org/sp/diuraphis_noxia/">https://bipaa.genouest.org/sp/diuraphis_noxia/</a>)</p> <p>Apis = Acyrthosiphon pisum JIC1 v1 (This study)</p> <p>Pnig = Pentalonia nigronervosa (This study)</p> <p>Rmai = Rhopalosiphum maidis v0.1 (<a href="http://gigadb.org/dataset/100572">http://gigadb.org/dataset/100572</a>)</p> <p>Rpad = Rhopalosiphum padi v1.0 (<a href="https://bipaa.genouest.org/sp/rhopalosiphum_padi/">https://bipaa.genouest.org/sp/rhopalosiphum_padi/</a>)</p> <p>Agly = Aphis glycines biotype 4 v2.1 (<a href="https://zenodo.org/record/3453468#.XnpL5JOgLRY">https://zenodo.org/record/3453468#.XnpL5JOgLRY</a>)</p> <p>BtabMEAM1 = Bemissia tabacci MEAM1 v1.2 (<a href="http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi">http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi</a>)</p> <p>Trub = Triatoma rubrofasciata (This study)</p> <p>Rpro = Rhodnius prolixus (This study)</p> <p>Ofas = Oncopeltus fasciatus OGS v1.0 (<a href="https://i5k.nal.usda.gov/Oncopeltus_fasciatus">https://i5k.nal.usda.gov/Oncopeltus_fasciatus</a>)</p> <p>Sfuc = Sogatella furcifera v1 (<a href="http://dx.doi.org/10.5524/100255">http://dx.doi.org/10.5524/100255</a>)</p> <p>Nlug = Nilaparvata lugens (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42</a>)</p> <p>Files:</p> <p>Proteomes included in the analysis: proteomes.tar.gz</p> <p>Orthogroups: Orthogroups.txt</p> <p>Gene counts per orthogroup, per species: Orthogroups.GeneCount.csv</p> <p>Single copy conserved orthogroups used for species tree: SingleCopyOrthogroups.txt</p> <p>Species tree alignment: SpeciesTreeAlignment.fa</p> <p>r8s configuration file (includes time calibrations and OrthoFinder ML species tree with branch lengths): species_tree_rooted.r8s.nex</p> <p>r8s time calibrated species tree: r8s_tree.nwk</p>
Polyadenylation landscape of in vivo long-term potentiation in the rat brain
<h3><strong>This repository contains data published along our corresponding manuscript and additional data resources generated/used throughout this work. <br>______________________________________________________________________________</strong></h3> <h2><strong>Source data accompanying the manuscript:<br></strong></h2> <p><strong>Supplementary Table 1. Key resource table. (A) </strong>Characteristics of analyzed material (e.g. sample identifiers, number of animals used, reads produced, accession numbers). <strong>(B)</strong> Key resources (antibodies, reagents, software).<strong><br><br>Supplementary Table 2. Summary of dentate gyri DRS data per gene. (A)</strong> Differential expression and differential adenylation data for 10 min timepoint. <strong>(B)</strong> Differential expression and differential adenylation data for 60 min timepoint. <strong>(C)</strong> GO-terms for genes with significantly elongated poly(A) tails in 10 min timepoint. <strong>(D)</strong> GO-terms for genes with significantly elongated poly(A) tails in 60 min timepoint. <strong>(E)</strong> GO-terms for upregulated genes in 10 min timepoint. <strong>(F)</strong> GO-terms for upregulated genes in 60 min timepoint. <strong>(G)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 10 min timepoint. <strong>(H)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 60 min timepoint.</p> <p><strong>Supplementary Table 3. Summary of dentate gyri cDNA data per gene. (A)</strong> Differential expression and differential adenylation data for 10 min timepoint. <strong>(B)</strong> Differential expression and differential adenylation data for 60 min timepoint. <strong>(C)</strong> GO-terms for genes with significantly elongated poly(A) tails in 10 min timepoint. <strong>(D)</strong> GO-terms for genes with significantly elongated poly(A) tails in 60 min timepoint. <strong>(E)</strong> GO-terms for upregulated genes in 10 min timepoint. <strong>(F)</strong> GO-terms for upregulated genes in 60 min timepoint. <strong>(G)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 10 min timepoint. <strong>(H)</strong> GO-terms for upregulated genes with CPEB-binding motifs in 60 min timepoint.</p> <p><strong>Supplementary Table 4.</strong> <strong>High-confidence PASs.</strong> <strong>(A)</strong> PASs predicted for datasets obtained 10 min after LTP induction by TAPAS. <strong>(B)</strong> High confidence poly(A) clusters predicted by LAPA for datasets obtained 10 min after LTP induction. <strong>(C)</strong> PASs predicted for datasets obtained 60 min after LTP induction. <strong>(D)</strong> High confidence poly(A) clusters predicted by LAPA for datasets obtained 60 min after LTP induction.</p> <p><strong>Supplementary Table 5.</strong> <strong>Nonadenosine profiling upon LTP induction. (A)</strong> Summary of Ninetails pipeline for dentate gyrus. <strong>(B)</strong> List of genes containing semi-templated poly(A) tails with their adjacent nucleotide contexts.</p> <p><strong>Supplementary Table 6. Summary of synaptoneurosomal DRS/cDNA data per gene.</strong> <strong>(A)</strong> Differential expression and differential adenylation data for unfractionated synaptoneurosomes DRS sequencing. (B) <strong> </strong>Summary of Ninetails pipeline for unfractionated synaptoneurosomes DRS sequencing. (C) Differential expression and differential adenylation data for monoribosome-bound mRNA synaptoneurosomes cDNA sequencing. (D) Differential expression and differential adenylation data for polyribosome-bound mRNA synaptoneurosomes cDNA sequencing. (E) Differential expression and differential adenylation data for unfractionated synaptoneurosomes cDNA sequencing.<br><br><strong>Supplementary Information</strong> - supplementary figures and captions.<br><br><strong>______________________________________________________________________________</strong></p> <h2><strong>Additional data resources:</strong></h2> <p><strong>CPEB1_motif.meme </strong>- CPE1 motif sequence represented as position-dependent letter-probability matrice required by FIMO to make predictions.<br><strong><br>CPEB2_4_motif.meme</strong> - CPE2,4 motif sequence represented as position-dependent letter-probability matrice required by FIMO to make predictions.<strong><br></strong></p> <p><strong>mRatBN7.2_TAPAS_ref_flat.txt</strong> - mRatBN7.2 reference annotation in format required by TAPAS<br><br><strong>mRatBN7.2_LAPA.gtf </strong>- mRatBN7.2 reference annotation in format required by LAPA</p> <p><strong>FIMO_output.zip</strong> - compressed folder with motif predictions provided by FIMO software.<strong><br><br>LAPA_output_dentate_gyrus.zip </strong>- compressed folder with poly(A) clusters predicted by LAPA software. Each timepoint is represented by separate output. <br><strong><br>TAPAS_output_dentate_gyrus.zip </strong>- compressed folder with raw outputs produced by TAPAS software. Each timepoint is represented by separate output. <strong><br><br>Ninetails_output_dentate_gyrus.zip </strong>- compressed folder with raw outputs produced by Ninetails software for samples from dentate gyrus. Subfolders are named according to the sample identifiers provided in Supplementary Table 1. For each sequencing run, 2 tsv files are produced: read classification and nonadenosine residue classification.<br><strong><br>Ninetails_output_synaptoneurosomes.zip </strong>- compressed folder with raw outputs produced by Ninetails software for samples from synaptoneurosomes. Subfolders are named according to the sample identifiers provided in Supplementary Table 1. For each sequencing run, 2 tsv files are produced: read classification and nonadenosine residue classification.<strong><br><br>PolyA_clusters_high_confident.bed </strong>- High-confidence poly(A) clusters annotation in bed format.<strong><br></strong></p>
ChinaWheat30L: Long-term winter wheat maps of China at 30-m resolution from 2000 to 2023
<p>This is the long-term winter wheat map product of China at 30-m resolution from 2000 to 2023 (ChinaWheat30L). The product was generated using a knowledge-guided machine learning approach and the integration of satellite remote sensing and environmental datasets.</p>
Long-term litter fall data series from 34 boreal forest stands in Finland
<p><strong>Introduction</strong></p> <p>Litter fall data were collected on a network of 34 forest sampling plots in Finland from late 1950s to 2010s. The data collection spanned different time periods in different sampling plots. The data have been used for studies on the flowering and seed crop of forest trees, air quality, and insect damage (see list of publications in the end of this document). They have been used to develop seed production and needle litter fall models for Scots pine (<em>Pinus sylvestris</em>) and Norway spruce (<em>Picea abies</em>), a branch litter model for pine, and total litter fall models used in greenhouse gas inventories.</p> <p><strong>Data collection</strong></p> <p>The litter fall collection was set up in mature, single species stands. The focal tree species include Scots pine (<em>Pinus sylvestris</em>), Norway spruce (<em>Picea abies</em>), Silver birch (<em>Betula pendula</em>), Downy birch (<em>Betula pubescens</em>), Grey alder (<em>Alnus</em> <em>incana</em>), European rowan (<em>Sorbus aucuparia</em>), European larch (<em>Larix decidua</em>), and Siberian larch (<em>Larix sibirica</em>). The sampling plots varied in shape and in size with a typical area of 0.1-0.25 ha. Between 6 and 30 litter collecting funnels were used per plot. The funnels were made of galvanized sheet metal and attached to cloth bags to collect the falling litter. The sampling sites were monitored for changes in conditions, such as natural disturbances, tree harvesting, forestry operations, or construction on the plot or in its immediate proximity (none observed).</p> <p>The litter samples were collected from the sampling plots, usually four to six times per year in spring to autumn. The samples were dried in room temperature (except male flowers in 1960s – 1970s, see note in Table 1) and stored in paper bags. The dried samples were sorted into litter fractions (Table 1). These fractions varied between tree species and between years to some extent. Cones and seeds were counted, and all other litter fractions were weighed to the nearest milligram.</p> <table> <caption>Table 1. Litter fractions and their codes. The code of the litter fraction is used in the data files.</caption> <thead> <tr> <th scope="col">Code</th> <th scope="col">Litter fraction</th> <th scope="col">Description / note</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Male flowers</td> <td>In the 1960s – 1970s male flowers were dried in 105°C for 24 hours (Sarvas 1962, 1968).</td> </tr> <tr> <td>2</td> <td>Seeds</td> <td>Seed wings and seeds from species other than the focal one were included into the “other litter” fraction.</td> </tr> <tr> <td>3</td> <td>Female flowers</td> <td> </td> </tr> <tr> <td>4</td> <td>Needles</td> <td> </td> </tr> <tr> <td>5</td> <td>Insects and their faeces</td> <td> </td> </tr> <tr> <td>6</td> <td>Other litter</td> <td> </td> </tr> <tr> <td>7</td> <td>Lichens, branches, and tree bark</td> <td>In some years lichens, branches, and bark were combined in the same fraction, and in some they were separated in their own fractions (numbers 12-14 below).</td> </tr> <tr> <td>8</td> <td>Small cones (1 year)</td> <td>For pine stands, cones were separated into one-year old small cones and large cones. Cones were counted.</td> </tr> <tr> <td>9</td> <td>Cones</td> <td> </td> </tr> <tr> <td>10</td> <td>Cones and loose scales</td> <td>In some years loose scales were included in the same fraction as cones, and in some they were included into the “other litter” fraction.</td> </tr> <tr> <td>11</td> <td>Shifting dust</td> <td> </td> </tr> <tr> <td>12</td> <td>Branches</td> <td> </td> </tr> <tr> <td>13</td> <td>Lichens</td> <td> </td> </tr> <tr> <td>14</td> <td>Tree bark</td> <td> </td> </tr> <tr> <td>15</td> <td>Leaves</td> <td>For deciduous stands, leaves were separated into small (diameter < 1 cm) and large leaves (diameter > 1 cm), while for conifer stands all leaves were included into the “other litter” fraction.</td> </tr> <tr> <td>16</td> <td>Berries</td> <td> </td> </tr> </tbody> </table> <p>In addition to litter fall data, tree stand data were collected on most of the plots in some years. In the tree stand inventories, all trees in the sampling plot with diameter at breast height ≥ 7 cm were mapped, and all trees were counted and measured for diameter (at breast height and at 6 meters), total height, and height to first living branches. Stand basal area and dominant diameter and height were calculated. Stand age was estimated based on core samples from five trees outside but representative of the sample plot. Crown coverage was estimated with a Cajanus tube.</p> <p><strong>Description of the data files</strong></p> <p>The litter fall data is in eight csv-files, one per tree species. The files are named “Litter_Tree_species.csv”, for example “Litter_Betula_pendula.csv”.</p> <p>Variables (in columns) are consistent across the files (explained in Table 2), but note that there are varying numbers of columns between the tables in the files, as each litter collecting funnel has its own column and different maximum numbers of funnels were used in different sampling sites and tree species (see row “S1 – S30” in Table 2 for more details).</p> <table> <caption>Table 2. Variables included in the litter fall data files.</caption> <thead> <tr> <th scope="col">Variable name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Form</td> <td>Number identifying the original paper form.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Tree species code: 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch, 5 = Grey alder, 6 = Siberian larch, 7 = European larch, 8 = European rowan.</td> </tr> <tr> <td>LitterFraction</td> <td>Code for the litter fraction (1-16), explained in Table 1.</td> </tr> <tr> <td>Coefficient</td> <td>Coefficient used to transform the weight of the litter (g) to weight per square meter (g m<sup>-2</sup>). The coefficient is based on the number and area of the collection funnels.</td> </tr> <tr> <td>Date</td> <td>Date of sample collection.</td> </tr> <tr> <td>Period</td> <td>Variable used to define the time of data collection as calendar year or phenological year. The variable is based on the schedule of data collection in different years and on the focal tree species so that it corresponds to the species-specific litter fall schedule. For spruce, the peak needle fall is in the spring, so the calendar year is appropriate for describing the temporal variation in litter fall. For pine, the peak needle fall is in August–September, so a phenological year defined as July 1<sup>st</sup> – June 30<sup>th</sup> is appropriate for describing the temporal variation in litter fall. Period = -1 means the values in the row are allocated to the previous calendar year; period = 0 means the values are allocated to the current calendar year; and period = 1 means the values are allocated to the next calendar year.</td> </tr> <tr> <td>S1 – S30</td> <td>Columns S1 – S30 refer to litter collection funnels 1 – 30. The maximum number of funnels varies between tree species: for <em>silver birch</em> up to 30 funnels were used per plot, for downy birch up to 20 funnels, for spruce up to 10 funnels, for pine up to 15 funnels, for rowan 10 funnels, and for both larch species and alder 8 funnels. Missing values (NA) mean that the funnel was not used in the plot. Value -1 means that the funnel was used but the sample was missing. The values give the dry weight of the litter in mg.</td> </tr> <tr> <td>Combined</td> <td>For some samples, litter collected by different funnels has been combined and the total weight is shown in column S1. In this column, 0 = values have not been combined, and 1 = values have been combined.</td> </tr> <tr> <td>TotalWeight</td> <td>Total weight of the litter (mg).</td> </tr> <tr> <td>TotalWeightArea</td> <td>Total weight of the litter per area (mg m<sup>-2</sup>). Value is same as TotalWeight × Coefficient.</td> </tr> <tr> <td>Note</td> <td>Note</td> </tr> </tbody> </table> <p>Tree stand data is in one csv-file, named “Tree_stand_data.csv”, and can be combined with the litter fall data based on the sample plot abbreviations (variable “PlotAbbr” in both litter fall data files and the tree stand data file). Note that there is no tree stand data available for all the same years as litter fall data. There is no tree stand data available at all for one downy birch site (abbreviation HEI568), one spruce site (NOO85), one pine site (HEI566), and the alder, rowan, and larch sites. Tree stand data variables are explained in Table 3.</p> <table> <caption>Table 3. Variables included in the tree stand data file.</caption> <tbody> <tr> <td>Variable name</td> <td>Description</td> </tr> <tr> <td>PlotName</td> <td>Name of sampling plot.</td> </tr> <tr> <td>PlotAbbr</td> <td>Abbreviation of sampling plot name.</td> </tr> <tr> <td>Year</td> <td>Year of data collection.</td> </tr> <tr> <td>TreeSpecies</td> <td>Dominant tree species. 1 = Scots pine, 2 = Norway spruce, 3 = Silver birch, 4 = Downy birch.</td> </tr> <tr> <td>Age</td> <td>Stand age (years).</td> </tr> <tr> <td>SiteType</td> <td>Forest site type describing site productivity. 2 = xeric heath forest, 3 = sub-xeric heath forest, 4 = mesic heath forest, 5 = herb-rich heath forest. </td> </tr> <tr> <td>North</td> <td>North coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>East</td> <td>East coordinate (m), coordinate system ETRS-TM35FIN.</td> </tr> <tr> <td>Elevation</td> <td>Elevation (m above sea level).</td> </tr> <tr> <td>N</td> <td>Stem number (ha<sup>-1</sup>).</td> </tr> <tr> <td>BA</td> <td>Basal area (m<sup>2</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>DomD</td> <td>Diameter (cm) of dominant trees.</td> </tr> <tr> <td>DomH</td> <td>Height (m) of dominant trees.</td> </tr> <tr> <td>CrownLength</td> <td>Crown length (m).</td> </tr> <tr> <td>V</td> <td>Stem volume (m<sup>3</sup> ha<sup>-1</sup>).</td> </tr> <tr> <td>CrownCover</td> <td>Crown coverage (%).</td> </tr> </tbody> </table> <p> </p> <p><strong>List of publications </strong></p> <p>Hilli, A., Hokkanen, T., Hyvönen, J. & Sutinen, M.-L. 2008. Long-term variation in Scots pine seed crop size and quality in northern Finland. Scandinavian Journal of Forest Research 23(5): 395-403. </p> <p>Koski, V. & Tallqvist, R. (1978). Results of long-time measurements of the quantity of flowering and seed crop of forest trees (in Finnish with English summary). Folia Forestalia, 364, 1-60. </p> <p>Kouki, J. & Hokkanen, T. 1992. Long-term needle litterfall of a Scots pine Pinus sylvestris stand: relation to temperature factors. Oecologia 89: 176-181. </p> <p>Lehtonen, A., Lindholm, M., Hokkanen, T., Salminen, H. & Jalkanen, R. 2008. Testing dependence between growth and needle litterfall in Scots pine - a case study in northern Finland. Tree Physiology 28(11): 1741-1749. </p> <p>Lehtonen, A., Sievänen, R., Mäkelä, A., Mäkipää, R., Korhonen, K.T. & Hokkanen, T. 2004. Potential litterfall of Scots pine branches in southern Finland. Ecological Modelling 180(2-3): 305-315. </p> <p>Leikola, M., Raulo, J. & Pukkala, T. (1982). Prediction of the variations of the seed crop of Scots pine and Norway spruce (in Finnish with English summary). Folia Forestalia, 537, 1-43. </p> <p>Niemistö, P., Hokkanen T. & Varama, M. 2004. Karikemäärän muutokset 1982–2001 ja puiden kunto lumi- ja hallamittariesiintymän vaivaamissa koivikoissa Noormarkussa. Metsätieteen aikakauskirja 1/2004: 21–41. </p> <p>Poikolainen, J. & Kuusinen, M. 2000. Abundance of epiphytic lichens in litterfall during 1967-1994. In: Forest condition in a changing environment - the Finnish case. Forestry Sciences, Vol. 65. Kluwer Academic Publishers / Ed. Mälkönen, E. Sivut: 171-172. </p> <p>Pukkala, T. 1987a. A model for predicting the seed crop of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 135-144. </p> <p>Pukkala, T. 1987b. Effect of seed production on the annual growth of Picea abies and Pinus sylvestris (in Finnish with English abstract). Silva Fennica 21(2): 145-158. </p> <p>Pukkala, T., Hokkanen, T. & Nikkanen, T. 2010. Prediction models for the annual seed crop of Norway spruce and Scots pine in Finland. Silva Fennica 44(4): 629-642. </p> <p>Ranta, E., Lindström, J., Kaitala, V., Crone, E., Lundberg, P., Hokkanen, T. & Kubin, E. 2010. Life history mediated responses to weather, phenology and large-scale population patterns. In: Hudson, I. L & Keatley, M. R. (eds.). Phenological Research. Springer, Dordrecht Heidelberg London New York, Netherlands. p. 321-338. </p> <p>Raulo, J. & Hokkanen, T. 1989. Litter fall of Alnus incana and Alnus glutinosa (in Finnish with English summary). Folia Forestalia 738. 25 s.</p> <p>Saarsalmi, A., Starr, M., Hokkanen, T., Ukonmaanaho, L., Kukkola, M., Nöjd, P. & Sievänen, R. 2007. Predicting annual canopy litterfall production for Norway spruce (Picea abies (L.) Karst.) stands. Forest Ecology and Management 242(2-3): 578-586. </p> <p>Sarvas, R. (1962). Investigations on the flowering and seed crop of Pinus Silvestris. Communicationes Instituti Forestalis Fenniae, 53, 1-198. </p> <p>Sarvas, R. 1968. Investigation on the flowering and seed crop of Picea abies. Communicationes Instituti Forestalis Fenniae 67.5. 84 pp. </p> <p>Starr, M., Saarsalmi, A., Hokkanen, T., Merilä, P. & Helmisaari, H.-S. 2005. Models of litterfall production for Scots pine (Pinus sylvestris L.) in Finland using stand, site and climate factors. Forest Ecology and Management 205: 215-225. </p> <p>Ťupek, B., Mäkipää, R., Heikkinen J., Peltoniemi, M., Ukonmaanaho, L., Hokkanen, T., Nöjd, P., Nevalainen, S., Lindgren, M. & Lehtonen, A. 2015: Foliar turnover rates in Finland — comparing estimates from needle-cohort and litterfall-biomass methods. Boreal Environment Research 20: 283–304</p>
Fig. 1 in A Long-Term Comparison Of Laying Date And Clutch Size In The Red-Backed Shrike (Lanius Collurio) In Silesia, Southern Poland
Fig. 1. The number of Red-backed Shrike clutches laid during successive five-day periods (1 = 6–10 May) in the two study periods. For sample size – see text
Fig. 10 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 10. Trends in the relative abundance of trophic guilds in Lake Fenéki (Piscivores: y = 0.06 + 0.003x; R2 = 0.757; P> 0.00001)
Fig. 9 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 9. Proportion of each species in the cumulative abundance of non-native fish species in Lake Fenéki
Fig. 7 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 7. PCA biplot of the arcsin-square root transformed relative abundance data of the whole sampling period (1992–2011) (Variables: Sampling years; Objects: Relative abundances) (abbreviations were constructed from the Latin names of the species, using the first 3 characters of genus and species
Fig. 5 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 5. Estimated number of species (SD's ignored in order to improve visibility) as a function of number of individuals collected in each sampling year
Fig. 8 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 8. PCA biplot of the arcsin-square root transformed relative abundance data of the period 1994–2011 (Variables: Sampling years; Objects: Relative abundances)
Fig. 4 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 4. Relationships between the age of Lake Fenéki and the Shannon–Weaver index (y = 0.414ln(x) + 0.852; R2 = 0.772; P <0.0001)
Fig. 3 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 3. Relationships between the age of Lake Fenéki and the number of fish species (y = 4.141ln(x) + 3.807; R2 = 0.759; P <0.0001)
Fig. 1 in Long-Term Development Of Fish Assemblage In Lake Fenéki (Kis-Balaton Water Protection System, Hungary): Succession, Invasion And Stabilization
Fig. 1. Overlooking map of the Balaton-catchment, with the sampling site (Dark rectangle marked by the arrow indicates the flooded area of Lake Fenéki)
Data from: The impact of long-term azithromycin on antibiotic resistance in HIV-associated chronic lung disease
<p><b>Background</b>: Selection for resistance to azithromycin (AZM) and other antibiotics such as tetracyclines and lincosamides remains a concern with long-term AZM use for treatment of chronic lung diseases (CLD). We investigated the impact of 48 weeks of AZM on the carriage and antibiotic resistance of common respiratory bacteria among children with HIV-associated CLD.</p> <p><b>Methods</b>: Nasopharyngeal (NP) swabs and sputa were collected at baseline, 48 and 72 weeks from participants with HIV-associated CLD randomised to receive weekly AZM or placebo for 48 weeks and followed post-intervention until 72 weeks. The primary outcomes were prevalence and antibiotic resistance of <i>Streptococcus pneumoniae</i> (SP), <i>Staphylococcus aureus </i>(SA), <i>Haemophilus influenzae </i>(HI), and <i>Moraxella catarrhalis </i>(MC) at these timepoints. Mixed-effects logistic regression and Fisher's exact test were used to compare carriage and resistance respectively.</p> <p><b>Results</b>: Of 347 (174 AZM, 173 placebo) participants (median age 15 years [IQR =13–18], females 49%),NP carriage was significantly lower in the AZM (n=159) compared to placebo (n=153) arm for SP (18% vs 41%, <i>p</i><0.001)<i>, </i>HI (7% vs 16%, p=0.01)<i>, </i>and MC (4% vs 11%, <i>p</i>=0.02); SP resistance to AZM (62% [18/29] vs 13%[8/63], <i>p</i><0.0001) or tetracycline (60%[18/29] vs 21%[13/63], <i>p</i><0.0001) were higher in the AZM arm. Carriage of SA resistant to AZM (91% [31/34] vs 3% [1/31],<i> p</i><0.0001), tetracycline (35% [12/34] vs 13% [4/31],<i> p</i>= 0.05) and clindamycin (79% [27/34] vs 3% [1/31],<i> p</i><0.0001) was also significantly higher in the AZM arm and persisted at 72 weeks. Similar findings were observed for sputa.</p> <p><b>Conclusions</b>: The persistence of antibiotic resistance and its clinical relevance for future infectious episodes requiring treatment needs further investigation.</p>
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