Data CitationPlease cite this dataset asSvenning, J.-C., D. A. Kinner, R. F. Stallard, B. M. J. Engelbrecht, and S. J. Wright. 2024. Data on environmental characteristics and plant abundances associated with trail segments on Barro Colorado Island, Panama, for 2000-2001. Smithsonian Figshare. https://doi.org/10.25573/data.25331362License and usage termsThis data is licensed as CC BY 4.0 and is thus freely available for reuse with proper citation. We ask that data users share any resulting publications, preprints, associated analysis code, and derived data products with us by emailing Jens-Cristian Svenning at svenning@bio.au.dk. We are open to contributing our expert knowledge of the study site and datasets to projects that use these data; please direct queries regarding potential collaboration to Jens-Cristian Svenning at svenning@bio.au.dk.Data descriptionThis is a dataset on the abundances of particular plant taxa and on environmental conditions on trail segments on Barro Colorado Island, Panama. These data are associated with the following journal publication:Svenning, J.-C., D. A. Kinner, R. F. Stallard, B. M. J. Engelbrecht, and S. J. Wright. 2004. Ecological determinism in plant community structure across a tropical forest landscape. Ecology 85: 2526-2538. https://doi.org/10.1890/03-0396Additional online supplemental material for the article is available at the following link:https://figshare.com/collections/ECOLOGICAL_DETERMINISM_IN_PLANT_COMMUNITY_STRUCTURE_ACROSS_A_TROPICAL_FOREST_LANDSCAPE/3298217Files included in this repository:finaldata_BCIdist_350p_5mb.txt Tab-delimited text file version of the full dataset.finaldata_BCIdist_350p_5mb_metadata.rtf Rich text format file containing the metadata including column definitions, methods details, and references.finaldata_BCIdist_350p_5mb_plus.xls Excel file containing the full dataset as the first worksheet, as well as column definitions, methods info, and references in additional worksheets.Column definitions for the dataset:Column names definitions (see methods for details)trail_code code for the relevant trail segment on BCI and letters indicate the trail, the numbers give the start and end of the trail section; for example, AHC_0102 indicates trail AHC between markers 1 and 2.Segment length The length of the trail segment in metersnumGAP123 Gaps in the canopy were measured with the following ordinal index: 0, no large overhead or lateral gaps; 1, either exposed to very large lateral gap or 1–4 m of the trail exposed to the sky directly overhead as part of a major gap; or 2, >=5 m of the trail exposed to the sky directly overhead and opening part of major gap (July–August 2000). Stream Stream presence/absence was determined by streams crossing the trail and estimated to flow throughout the wet seasonMean %water(DW) Soil moisture was determined by collecting samples of the upper 10 cm of the soil using standard soil corers during the late dry season (28 March–3 April 2001). for3cat 12 Percentage of area in old-growth forest, as quantified by the darkest of three grey-scale class in a 1927 aerial photo. for3cat 45 Percentage of area in shorter secondary forest, as quantified by the lightest of three grey-scale classes in a 1927 aerial photo. Mean Slope Mean slope (maximum rate of change in elevation ["] between a 1-m2 cell and its eight neighbors; the variable used was the mean over all 1-m2 cells in a given trail segment).Mean lna/tanb Hydrologic index (log[A/tan (Beta)]) represents topographic runoff potential of a stratified soil. Mean soil type The trail segment mean soil type for its 1-m2 cells, each assigned 1 or 2 according to soil type. Lith 1 Percentage of area in lithology 1 (Basalt/andesite flows). Lith3 Percentage of area in lithology 3 (Caimito Volcanic).Lith4 Percentage of area in lithology 4 (Caimito Marine). ln_dist_shore Natural-log-transformed distance to shore, in meters. Mean distance between the GIS cells of a trail segment and the lake edge.Y centered N-S geographical coordinatesY2 Y squaredY3 Y cubedX Centered E-W geographical coordinatesXY The product of X and YXY2 The product of X and Y squaredX2 X squaredX2Y The product of X squared and YX3 X cubedAll further columns are abundances of particular plant taxa along the relevant trail segmentFull taxonomic names are given inhttps://wiley.figshare.com/articles/dataset/Appendix_A_A_total_species_list_including_family_growth_form_and_voucher_number_/3523688AcknowledgmentsWe thank Sebastian Bernal for doing most of the woody plant inventory, Maria del Carmen Ruiz and David Galvéz for soil sampling and processing, and Andrés Hernandez, Osvaldo Calderón, Rolando Pérez, and Salomon Aguilar for taxonomic help. We acknowledge economic support from The Carlsberg Foundation (grants 990086/20 and 990576/20 to J.-C. Svenning), The Danish Natural Science Research Council (grants 9901835, 51-00-0138, and 21-01-0415 to J.-C. Svenning), the Andrew W. Mellon Foundation (to S. J. Wright and B. M. J. Engelbrecht), the U.S. Geological Survey WEBB Project, and the Smithsonian Terrestrial Environmental Sciences Program. All GIS analyses were completed at the Environmental Imaging and Computation Facility at the University of Colorado.ReferencesAs of the date of this data publication (2024), these data have been used for the following publications:Svenning, J.-C., D. A. Kinner, R. F. Stallard, B. M. J. Engelbrecht, and S. J. Wright. 2004. Ecological determinism in plant community structure across a tropical forest landscape. Ecology 85: 2526-2538. https://doi.org/10.1890/03-0396Svenning, J.-C., B. M. J. Engelbrecht, D. A. Kinner, T. A. Kursar, R. F. Stallard, and S. J. Wright. 2006. The relative roles of environment, history and local dispersal in controlling the distributions of common tree and shrub species in a tropical forest landscape, Panama. Journal Of Tropical Ecology 22: 575-586. https://doi.org/10.1017/s0266467406003348Muller-Landau, H. C., and J.-C. Svenning. 2024. An Introduction to Landscape-Level Variation Across the Barro Colorado Nature Monument. Chapter 2 in The First 100 Years of Research on Barro Colorado: Plant and Ecosystem Science, ed. H. C. Muller-Landau and S. J. Wright. Smithsonian Institution Scholarly Press.R code for reproducing the analyses in Muller-Landau and Svenning 2024 is published atMuller-Landau, H.C. and J.-C. Svenning. 2024. Chapter 02 - An Introduction to Landscape-level variation across the Barro Colorado Nature Monument - Appendix S1 - R code and data files to construct Figure 2.Smithsonian Figshare. https://doi.org/10.25573/data.22779260

Nigeria NG: Tariff Rate: Applied: Simple Mean: All Products data was reported at 12.440 % in 2016. This records an increase from the previous number of 11.270 % for 2015. Nigeria NG: Tariff Rate: Applied: Simple Mean: All Products data is updated yearly, averaging 23.000 % from Dec 1988 (Median) to 2016, with 23 observations. The data reached an all-time high of 87.190 % in 1995 and a record low of 9.940 % in 2009. Nigeria NG: Tariff Rate: Applied: Simple Mean: All Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Trade Tariffs. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

Uzbekistan UZ: Tariff Rate: Applied: Weighted Mean: All Products data was reported at 8.730 % in 2015. This records an increase from the previous number of 6.830 % for 2014. Uzbekistan UZ: Tariff Rate: Applied: Weighted Mean: All Products data is updated yearly, averaging 7.125 % from Dec 2001 (Median) to 2015, with 8 observations. The data reached an all-time high of 8.730 % in 2015 and a record low of 5.830 % in 2001. Uzbekistan UZ: Tariff Rate: Applied: Weighted Mean: All Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uzbekistan – Table UZ.World Bank.WDI: Trade Tariffs. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

The EOS-WEBSTER VEMAP2 Data Collection contains several datasets which provide historical and future climate variables, and monthly and annual biogeochemical model outputs.

Two Tclimate + TScenario Datasets provide annual mean historical and model-predicted climate data from 1895 to 2100 for a subset of the variable available in the monthly dataset. These variables include Tmin, Tmax, Precipitation and Solar Radiation.

A dataset is provided for each GCM climate model scenarios:

1) TClimate + TScenario-CGCM1 - historical climate (1895 - 1993) + Canadian Climate Center (see above) predicted future values (1994 - 2100).

Canadian Climate Center -- Model Name: CCCma -CGCM1; Experiment: GHG+A 1; 1% per year compounded increase in equivalent CO2 plus IS92A sulphate aerosols; Ensemble 1.1994-2100. Release: r4.

2) TClimate + TScenario-HadCM2 - historical climate (1895 - 1993) + UKMO/Hadley predicted future values (1994 - 2099)

UKMO/Hadley -- Model Name: HadCM2; Experiment: HadCM2GSa1; 1% per year compounded increase in equivalent CO2 plus IS92A sulphate aerosols; Ensemble 1. 1994 - 2099. Release: r3.

Data provided by the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) at the National Center for Atmospheric Research (NCAR) are gridded monthly time series climate data for the Conterminous United States at 0.5 x 0.5 degree spatial resolution. Visit the VEMAP2 website for complete information about the VEMAP2 project and datasets.

The VEMAP2 Collection contains the following dataset groups.

1) TClimate - monthly historical climate dataset from 1895 to 1993. Release: r3.

2) TScenario - monthly climate data of possible future values based on different model scenarios.

3) TClimate + TScenario - annual historical climate data + possible future values based on the above different model scenarios.

4) TResults - annual biogeography/biogeochemical model estimates of ecosystem response from 1895 to 2100.

Please see the individual dataset group DIFs, for more detailed information.

United Kingdom UK: Tariff Rate: Applied: Weighted Mean: All Products data was reported at 1.960 % in 2016. This records an increase from the previous number of 1.890 % for 2015. United Kingdom UK: Tariff Rate: Applied: Weighted Mean: All Products data is updated yearly, averaging 2.270 % from Dec 1988 (Median) to 2016, with 29 observations. The data reached an all-time high of 6.280 % in 1995 and a record low of 1.310 % in 2012. United Kingdom UK: Tariff Rate: Applied: Weighted Mean: All Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s UK – Table UK.World Bank: Trade Tariffs. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

The benchmark interest rate in China was last recorded at 3.10 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN FOR ROUND 4 OF THE GLSS A nationally representative sample of households was selected in order to achieve the survey objectives.

Sample Frame For the purposes of this survey the list of the 1984 population census Enumeration Areas (EAs) with population and household information was used as the sampling frame. The primary sampling units were the 1984 EAs with the secondary units being the households in the EAs. This frame, though quite old, was considered inadequate, it being the best available at the time. Indeed, this frame was used for the earlier rounds of the GLSS.

Stratification In order to increase precision and reliability of the estimates, the technique of stratification was employed in the sample design, using geographical factors, ecological zones and location of residence as the main controls. Specifically, the EAs were first stratified according to the three ecological zones namely; Coastal, Forest and Savannah, and then within each zone further stratification was done based on the size of the locality into rural or urban.

SAMPLE SELECTION EAs A two-stage sample was selected for the survey. At the first stage, 300 EAs were selected using systematic sampling with probability proportional to size method (PPS) where the size measure is the 1984 number of households in the EA. This was achieved by ordering the list of EAs with their sizes according to the strata. The size column was then cumulated, and with a random start and a fixed interval the sample EAs were selected.

It was observed that some of the selected EAs had grown in size over time and therefore needed segmentation. In this connection, such EAs were divided into approximately equal parts, each segment constituting about 200 households. Only one segment was then randomly selected for listing of the households.

Households At the second stage, a fixed number of 20 households was systematically selected from each selected EA to give a total of 6,000 households. Additional 5 households were selected as reserve to replace missing households. Equal number of households was selected from each EA in order to reflect the labour force focus of the survey.

NOTE: The above sample selection procedure deviated slightly from that used for the earlier rounds of the GLSS, as such the sample is not self-weighting. This is because, 1. given the long period between 1984 and the GLSS 4 fieldwork the number of households in the various EAs are likely to have grown at different rates. 2. the listing exercise was not properly done as some of the selected EAs were not listed completely. Moreover, it was noted that the segmentation done for larger EAs during the listing was a bit arbitrary.

Mode of data collection

Face-to-face [f2f]

The Consumer Price Index in the United States increased 0.20 percent in February of 2025 over the previous month. This dataset provides - United States Inflation Rate MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.