478 research outputs found
Seal Molt Model
No full text<p>Programs for fitting progress of molt in elephant seals, written in C++. THe program should work on a Linux system as is, using the data included, but knowledge of C++ and Bayesian statistics are necessary to understand the output or adapt the code. The program fits a logistic curve to observations that increase from 0 to 100% molt for multiple individuals across multiple years. Individual logistic parameters are fitted to a hyperdistribution across years and animals. </p>
<p>Files with extension .h or .cpp contain the code:<br>1) modelMolt.h defines the molt class and runs all calculations, and includes subroutines in header files<br>2) utilities.cpp has commonly used subroutines<br>3) util.h has commonly used subroutines<br>4) Array2D.h is used within subroutines<br>5) likelihood.h has common likelihood functions for statistical calculations<br>6) statistics.h has common statistical functions<br>7) randomgenerator.h has a random number generator<br>8) modelMolt.cpp has the main function, accepting command-line parameters and executing a complete run</p>
<p><br>The file FemaleMolt.csv has sample data. It includes 4 tab-delimited columns:<br>1) animalID is an arbitrary identifier of individuals, always an integer<br>2) year is an integer<br>3) yday is day of the year, with 1=1Jan, treated as a double<br>4) molt is observation of the molt status of the given animal on the given day, always in [0,100], a double</p>
<p>The first 7 rows of data showing observed molt progress in animal 44168 in year 2016:<br>animalID year yday molt<br>44168 2016 126 0<br>44168 2016 129 0<br>44168 2016 130 0<br>44168 2016 137 0<br>44168 2016 139 5<br>44168 2016 146 15<br>44168 2016 171 100</p>
<p><br>To illustrate compilation, assume file #8 and the data file are in a folder FOLDERNAME. The following creates an executable program in FOLDERNAME:<br>> cd FOLDERNAME<br>> g++ -Wall modelMolt.cpp -o modelMolt.exe</p>
<p>Before compiling, edit lines 103-104 in modelMolt.h. They identify the folder to which results will be written. Those should be renamed as needed, and both folders must be created before executing. Then, to execute a run for 4000 steps, showing results every 500 steps, with output files named OUTPUT within those folders:<br>> ./modelMolt.exe FOLDERNAME FemaleMolt 4000 500 1 OUTPUT</p>
<p>There is one output file showing all parameter estimates of the Gibbs chain for every individual animal within the folder named 'pathFemale' (line 104). There are files of parameters for each year and grand hyperparameters within the folder named 'path' (line 103). Each file has 4000 rows for the 4000 steps chosen at run time.</p>
<p> </p>
A Cultivar Attribute Database Derived from Ira J. Condit (1955): “Fig Varieties: A Monograph”
No full textDuring California’s broad experiment with fig cultivation, Ira J. Condit became known as the “High Priest of the Fig.” Herein is a large set of fig attributes deemed agriculturally important by Ira J. Condit in his 1955 monograph. The attributes are divided into five categories and distributed across 717 cultivars. All data and software source code have been made available publicly on Figshare
Marker rescue mapping of the combined Condit/Dales collection of temperature-sensitive vaccinia virus mutants
No full textAbstractComplementation analysis of the combined Condit/Dales collection of vaccinia virus temperature-sensitive mutants has been reported (Lackner, C.A., D'Costa, S.M., Buck, C., Condit, R.C., 2003. Complementation analysis of the Dales collection of vaccinia virus temperature-sensitive mutants. Virology 305, 240–259), however not all complementation groups have previously been assigned to single genes on the viral genome. We have used marker rescue to map at least one representative of each complementation group to a unique viral gene. The final combined collection contains 124 temperature-sensitive mutants affecting 38 viral genes, plus five double mutants
Soil chemistry and dry season intensity, Panama Canal Area
No full textWoody plant species were surveyed at 72 locations near the Panama Canal, spanning geological formations and a rainfall gradient. Soil chemistry, and dry season intensity at all the sites. Response of tree species to environmental gradients was estimated. The soil and climate data are provided in a single table here. Tree distributions are published at Condit et al. (2013a), including a data archive in Condit et al. (2013b). Note that the PNAS article incorrectly cites the data archive; the link below is correct.Data are in a tab-delimited ascii file. There are 77 locations with latitude, longitude, and elevation given. See README file.
References
Condit, R., B. M. J. Engelbrecht, D. Pino, R. Perez, and B. L. Turner. 2013a. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proceedings of the National Academy of Sciences 110:5064–5068.
Condit, R.; Engelbrecht, B.; Pino, D.; Turner, B.; Pérez, R. 2013b. Panama Tree Distribution Database. https://repository.si.edu/handle/10088/19529.
Mirabello, M. J., J. B. Yavitt, M. Garcia, K. E. Harms, B. L. Turner, and S. J. Wright. 2013. Soil phosphorus responses to chronic nutrient fertilisation and seasonal drought in a humid lowland forest, Panama. Soil Research 51:215.
Turner, B. L., T. Brenes-Arguedas, and R. Condit. 2018. Pervasive phosphorus limitation of tree species but not communities in tropical forests. Nature 555:367–370.
Turner, B. L., and S. Joseph Wright. 2014. The response of microbial biomass and hydrolytic enzymes to a decade of nitrogen, phosphorus, and potassium addition in a lowland tropical rain forest. Biogeochemistry 117:115–130.
Turner, B. L., J. B. Yavitt, K. E. Harms, M. N. Garcia, T. E. Romero, and S. J. Wright. 2013. Seasonal Changes and Treatment Effects on Soil Inorganic Nutrients Following a Decade of Fertilizer Addition in a Lowland Tropical Forest. Soil Science Society of America Journal 77:1357–1369.
Turner, B. L., J. B. Yavitt, K. E. Harms, M. N. Garcia, and S. J. Wright. 2015. Seasonal changes in soil organic matter after a decade of nutrient addition in a lowland tropical forest. Biogeochemistry 123:221–235.
Turner, B. L., P. Zalamea, R. Condit, K. Winter, S. J. Wright, and J. W. Dalling. 2017. No evidence that boron influences tree species distributions in lowland tropical forests of Panama. New Phytologist 214:108–119.Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 0948585Funding provided by: U.S. Department of DefenseCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000005Award Number: Funding provided by: United States Agency for International DevelopmentCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000200Award Number:The README file includes descriptions of columns in the data table. Details on methods for soil chemistry are given in references listed below. The Condit et al. (2013a) paper in PNAS has an accompanying data archive (Condit et al. 2013b), referred to below as PNAS archive
Data and code for: Partitioning mortality into growth-dependent and growth-independent hazards across 203 tropical tree species (PNAS).
No full textPartitioning mortality into growth-dependent and growth-independent hazards across 203 tropical tree speciesJames S Camac, Richard Condit, Richard G FitzJohn, Lachlan McCalman,Daniel Steinberg, Mark Westoby, Joe Wright, Daniel FalsterWe present a model that partitions rates of tropical tree mortality into growth-dependent and growth-independent hazards. This creates the opportunity to examine the relative contributions of within-species and across-species variation on tropical tree mortality rates, but also, how species traits affect each hazard. We parameterize this model using >400,000 observed survival records collected over a 15-year period at Barro Colorado Island from more than 180,000 individuals across 203 species. We show that marginal carbon budgets are a major contributor to tree death on Barro Colorado Island. Moreover, we found that while species' light demand, maximum dbh and wood density affected tree mortality in different ways, they explained only a small fraction of the total variability observed among species.This repository contains the data and code required to reproduce our entire workflow from data cleaning, rerunning the analysis, producing figures and reproducing the manuscript.## PublicationCamac, J.S., Condit, R., FitzJohn, R.G., McCalman, L., Steinberg, D., Westoby, M., Wright, S.J., Falster, D. (Accepted at PNAS) Partitioning mortality into growth-dependent and growth-independent hazards across 203 tropical tree species.</div
Census data from 65 tree plots in Panama, 1994-2015
No full textThese are data from 65 tree plots in Panama established over 1994-2014; 43 of the plots have been recensused, while 22 plots have just a single census. Details of census methods are described in Condit (1998) and Condit et al. (2013). The 65 plots here are mostly 1 ha in area, though several are 0.32 ha, one is 4 ha, and one is 6 ha. Those two larger censuses are the Sherman and Cocoli plots described in Condit et al. (2004). A companion data archive includes all data from the Barro Colorado 50-ha plot (Condit et al. 2019).
The PIs would like to be informed of papers resulting from the marena plot data. Depending on our level of interest and how much a paper depends on the plots, co-authorship might be requested.
References
Condit, R., Engelbrecht, B. M. J., Pino, D., Pérez, R., and Turner, B. L. 2013. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proceedings of the National Academy of Sciences 110:5064–5068. pdf http://conditdatacenter.org/pdfs/ConditEtAlPNAS2013.pdf.
Condit, R., 1998. Tropical Forest Census Plots: Methods and Results from Barro Colorado Island, Panama and a Comparison with Other Plots. Springer-Verlag, Berlin. pdf http://conditdatacenter.org/pdfs/Condit_1998_CensusPlotsmethodsBook.pdf.
Condit R., Perez, R., Aguilar, S., Lao, S., Foster, R., Hubbell, S.P. 2019. Complete data from the Barro Colorado 50-ha plot: 423617 trees, 35 years, 2019 version. https://doi.org/10.15146/5xcp-0d46.
Condit, R., Aguilar, S., Hernandez, A., Pérez, R., Lao, S., Angehr, G., Hubbell, S., and Foster, R. 2004. Tropical forest dynamics across a rainfall gradient and the impact of an El Niño dry season. Journal of Tropical Ecology 20:51–72. pdf http://conditdatacenter.org/pdfs/Condit%20et%20al_2004_J.of%20Trop.Eco.pdf.There are 42 tables in R format, with 21 tables in the tree format and 21 more in the stem format. The tree tables are divided into six sets:
The table marena1cns.tree1 has a record for every one of the 65 plots in one census (usually the most recent, but not always).
Tables marena2cns.tree1 and marena2cns.tree2 have a record for 43 plots having at least two censuses, with tree1 having the earlier census, tree2 a later census. The two tables have exactly the same set of trees, including those not yet present in the first census and dead trees in the second, and the rows of the two tables have exactly matching order. The two marena2cns.tree tables are thus effectively a single giant table, with columns for every bothcensuses. They are divided into separate tables for easier access.
Tables marena3cns.tree1, tree2, tree3 have records from the 11 plots having at least three censuses. As for marena2cns, every tree from all censuses is included, with rows in matching order.
Tables marena4cns.tree1, tree2, tree3, tree4 have records from the six plots having at least four censuses.
Tables marena5cns.tree1, tree2, tree3, tree4, tree5 have records from the one plot having at least five censuses.
Tables marena6cns.tree1, tree2, tree3, tree4, tree5, tree6 have records from the single plot having six censuses.
Note that the six sets of tables have many of the same records. So marena1cns has every tree, including all trees appearing in marena2cns etc. Likewise, the tables marena2cns include every tree appearing in marena3cnsetc. Most users will want just one of the five sets, depending on the specific question to be addressed. If only a single census is needed, then marena1cns should be used and there is no need to consult marena2cns and above. If exactly two censuses are needed, then marena2cns should be use and none of the others.
The 21 stem tables form a parallel set to the tree tables, divided into the same six groups, but having one row for every stem ever censused (instead of one per tree). The Barro Colorado data are organized in the same way (Condit et al. 2019).
In the tree tables, if stemID matches for a given tree in two censuses, then it is certain that the same stem was measured in both censuses. If the stemID differs between censuses, it often means that the first stem broke and a new stem was measured. However, in trees with 2 or more stems, it was not always possible to identify stems with certainty across censuses prior to 2010, because stem tags were not used. This means that there are cases where the stemID changes even though the stem(s) really did persist. The cleanest way to assess growth is to use only those cases where the stemID matches between censuses. It is also necessary to check the HOM (height-of-measurement) in case it changes between censuses. In the stem tables, records on matching rows are guaranteed to be the same stem, but the HOM might change.
The 21 tree tables are zipped into the single file marena.tree.zip, and the 21 stem tables into marena.stem.zip.
All species names, with additional taxonomic details, can be found in bci.spptable, archived with the BCI data at Condit et al. (2019). Though named for the BCI plot, it covers all species in the entire set of plots.
Columns in R Analytical Tables: Tree and Stem
treeID: The unique tree identifier in the database. Guarantees a tree match.
stemID: The unique stem identifier in the database. Guarantees a stem match.
tag: Tag number on the tree (occasionally negative where a tag was duplicated by mistake).
StemTag: Tag number on the individual stem, if present.
sp: The species mnemonic. See the R Species Table for full Latin names.
quadrat: Quadrat designation, as a 2-digit row number then 2-digit column number on a 20x20 m grid.
gx: The x coordinate within the plot, meters from the west border of the plot, always in [0,1000).
gy: The y coordinate, meters from the south border, always in [0,500).
dbh: Diameter (mm) of one stem on the tree, the stem whose stemID is given.
hom: The height-of-measure, meters above the ground, where the dbh was measured.
ExactDate: The date on which a tree was measured.
date: Integer date for easy calculation of time interval between censuses (the number of days since 1 Jan 1960).
codes: The codes describing the measurement as recorded in the field. See Condit (1998) for a description of field codes. For analyses, status codes should be used, not field codes.
status: A status code for the tree or stem. See the section below, Status Codes for Analyses.
DFstatus: Alternate stem status, redundant relative to status.
nostems: The number of living stems on the date of measurement.
agb: Above-ground-biomass of all stems on the tree, in Mg (= metric tons), or for the individual stem. Note that agb=0 for dead trees.
ba: Basal area of all stems on the tree, in square meters. Note that ba=0 for dead trees.
plot: Name of the plot.
census: Census number, 1-7.
plotID: A numeric plot identifier, not necessary for most users
Neighbours consistently influence tree growth and survival in a frequently burned open oak landscape
No full textThe 16-ha Cedar Creek plot had all trees at least 2 cm dbh measured every 5 years from 1990 to 2015. Additional censuses were carried out every year, but trees were only checked for survival in those intervening censuses. The six growth censuses are thus numbered 1, 6, 11, 16, 21, 26. Data included here were used in Davis and Condit (2022). More details on methods and other uses of the data appear Davis (2021) and Davis et al. (1997, 2005).
References
Davis, M. A., Duke, A., Ibsen, T., Tran, H., and Rhodes, R. 1997. Spatial Distribution of Penstemon grandiflorus(Nutt.) and Geomys bursarius in a fragmented oak woodland in Minnesota, USA. Natural Areas Journal 17:136-143.
Davis, M. A., Curran, C., Tietmeyer, A., and Miller, A. 2005. Dynamic tree aggregation patterns in a species-poor temperate woodland disturbed by fire. Journal of Vegetation Science 16: 167-174.
Davis, M. A. 2021. Twenty-five years of tree demography in a frequently burned oak woodland: implications for savanna restoration. Ecosphere 2(12):e03844. 10.1002/ecs2.3844.
Davis, M. A., Condit R. 2022. Neighbors consistently influence tree growth and survival in a frequently burned open oak landscape. Journal of Ecology, in press.The 16-ha Cedar Creek plot had all trees at least 2 cm dbh measured every 5 years from 1990 to 2015. Additional censuses were carried out every year, but trees were only checked for survival in those intervening censuses. The six growth censuses are thus numbered 1, 6, 11, 16, 21, 26.
Summary Results: We present four short tables in pdf format with median growth and survival rates as a function of density. Detailed legends for each table appear in the pdf.
Table 1. Median growth rate of bur oak vs. neighborhood density, both conspecific and heterospecific. Densities were binned into four categories, giving 16 combinations and clearly separating effects of conspecifics and heterospecifics. Growth rate decline consistently with density, moreso with conspecifics than heterospecifics. Growth is transformed with the equation given the the text.
Table 2. Growth rate of pin oak vs. neighborhood density, as in Table 1. Growth rate declined with conspecific density, but not heterospecific.
Table 3. Five-year survival rates of bur oak vs. neighborhood density, as in Tables 1-2. Survival rates increased sharply with density of neighbors, especially conspecifics, and trees in the highest density categories had extremely low death rates.
Table 4. Five-year survival rates of pin oak vs. neighborhood density, as in Table 3. Survival rates increased sharply with density of neighbors, especially heterospecifics, and trees in the highest density categories had extremely low death rates.
FullPlot Data: The main data includes tables of all trees, stems, and environmental variables. These were the basis of all analyses in Davis and Condit (2022), as well as Tables 1-4 above. The three tables are in tab-delimited ascii format: 1) Trees, 2) Stems, 3) Environment. The README file has a full description of every column in the tables.Funding provided by: Division of Environmental BiologyAward Number: BSR/8717847Funding provided by: Division of Environmental BiologyAward Number: DEB/0208125Funding provided by: Division of Environmental BiologyAward Number: DEB/9419922Funding provided by: Division of Environmental BiologyAward Number: DEB/9873673See references under abstract
Connections: A Journal of Public Education Advocacy - Fall 2002, Vol. 9, No. 2
No full textPresident's Message - Wendy D. Puriefoy sees education as the universal liberator and children as our nation's most valuable resource.Richard Riley on Transforming American Education - Don't shortchange adolescents, urges Richard W. Riley, Clinton administration secretary of education, as we build for a knowledge-driven economy.Q&A: Bob Moses - Civil rights activist Bob Moses promotes math literacy as the key to education and economic access.Making It Happen - Phyllis McClure, Title I expert, alerts parents and communities to valuable NCLB-mandated information on schools, districts, and states coming their way.Viewpoint - Boeing Company CEO Philip M. Condit links the need for a worldclass workforce to the need for quality public education.Conversations - Grassroots organizer Donna Cooper, Maryland lawmaker Pete Rawlings, and New York attorney Michael Rebell discuss accountability, adequacy, and fiscal equity as long-term investments in the future of our nation.End Notes - William Novelli, CEO, on how AARP members put lifelong learning into action
Complete Tree Species of Panama
No full text<p>1 June 2020</p>
<p>This data archive presents a complete compilation of the tree species of Panama, including the full geographic range and local abundance of each. The species list is based on the most recent monographs, especially the Flora Mesoamericana, along with herbarium records, especially those online at the Missouri Botanic Garden, and our tree census plots, mostly in the forests around the Panama Canal, including the 50 ha plot at Barro Colorado. The full set of occurrence records was collected from the BIEN database and from Tropicos (Missouri Botanical Garden), plus our tree plots, and a geographic range was calculated as the minimum convex polygon around all occurrences. Details of methods can be found in Condit et al. (2020), and that paper includes a complete bibliography of the taxonomic literature consulted.</p>
<p>Species were included based if they root in the ground as free-standing woody plants, and reach a height of at least 3 m as reproductives. Epiphytes, lianas, and herbs were excluded. Species were included broadly, thus even if only occasionally terrestrial and free-standing while usually not; this aspect forces a loose gray area between trees and non-trees. The category shrub or descriptor shrubby were never considered, as they are poorly defined and used in inconsistent ways. An online version of the database (http://conditdatacenter.tech/PanamaTrees/) includes range maps and notes on growth form for marginal species. Tables of all coordinates from BIEN and Tropicos used in the estimates of range size can be downloaded from http://conditdatacenter.tech/TreeMap/txt.</p>
<p>References</p>
<ul>
<li>Condit, R., Perez, R., Aguilar, S. 2020. Tree species of Panama: a complete checklist with every geographic range. In press.</li>
<li>Condit, R., Engelbrecht, B. M. J., Pino, D., Pérez, R., and Turner, B. L. 2013. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proceedings of the National Academy of Sciences 110:5064–5068. pdf http://conditdatacenter.org/pdfs/ConditEtAlPNAS2013.pdf</li>
<li>Condit, R., Chisholm, R. A., and Hubbell, S. P. 2012. Thirty years of forest census at Barro Colorado and the importance of immigration in maintaining diversity. PLoS ONE 7:e49826. pdf http://conditdatacenter.org/pdfs/ConditChisholmHubbell2012.pdf</li>
<li>Condit, R., Pérez, R., Aguilar, S., Lao, S., and Hubbell, S. P. 2017. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. Forest Ecosystems 4:1–13. pdf http://conditdatacenter.org/pdfs/Condit_et_al-2017-Forest_Ecosystems.pdf</li>
<li>Davidse, G. M.; Sousa, M. S.; Knapp, S.; Chiang, F. & Ulloa, C. U. (Eds.) 1994-2018. Flora Mesoamericana (6 Volumes).</li>
</ul><p>Table 1. PanamaTreeSpecies.tsv. A tab-delimited ascii table including a record for 3045 tree species we consider native to Panama. Taxa in the table are identified by the Family, Latin name, and taxonomic authority, and include the following columns of data:</p>
<ul>
<li>countries -- Number of countries observed, ostensibly in a natural state (countries)</li>
<li>minLat -- Minimum latitude of species occurrences</li>
<li>maxLat -- Maximum latitude</li>
<li>minLong -- Minimum longitude</li>
<li>maxLong -- Maximum longitude</li>
<li>Npan -- Number of those records in Panama</li>
<li>N -- Total number of records in combined BIEN and Tropicos databases, counting only unique locations</li>
<li>range -- Extent of range, in thousands of square km, calculated as minimum convex polygon around BIEN records</li>
<li>plots -- Number of our tree plots in Panama in which the species was observed</li>
<li>dens -- Mean density in the plots (including zeroes) </li>
<li>inventories -- Number of our tree inventories in Panama in which the species was observed</li>
<li>maxht -- Maximum height in meters according to taxonomic monographs</li>
</ul>
<p>Table 2. PanamaTreeNameLookup.tsv. A tab-delimited ascii table including a record for 4497 Latin names which we found to be associated with the native tree species of Panama, including the 3045 currently accepted names plus 1452 alternate names. These are the most relevant recent synonyms, those appearing in recent treatments, at BIEN, or at Tropicos. For the 3045 current names, the column ValidLatin matches the column Latin. For the other 1452, the name in the column Latin is not valid in Panama and should be replaced by ValidLatin throughout the country. The column Scope applies to those 1452 cases: where Scope = everywhere, Latin should be replaced by ValidLatin everywhere the species is found, and Latin is now an obsolete name. In the remained 62 cases, where Scope = only Panama, the name Latin is misused in Panama and should be replaced there, but not elsewhere.</p>
<p>Table 3. PanamaTreeBIENError.tsv. A tab-delimited ascii table including 1035 tree species names appearing incorrectly in the BIEN database with records in Panama. All of these are based either on erroneous records, ie in the wrong country, or misidentified, or are based on cultivated specimens of species not native to Panama. </p>
Complete Tree Species of Panama
No full text<p>1 June 2020</p>
<p>This data archive presents a complete compilation of the tree species of Panama, including the full geographic range and local abundance of each. The species list is based on the most recent monographs, especially the Flora Mesoamericana, along with herbarium records, especially those online at the Missouri Botanic Garden, and our tree census plots, mostly in the forests around the Panama Canal, including the 50 ha plot at Barro Colorado. The full set of occurrence records was collected from the BIEN database and from Tropicos (Missouri Botanical Garden), plus our tree plots, and a geographic range was calculated as the minimum convex polygon around all occurrences. Details of methods can be found in Condit et al. (2020), and that paper includes a complete bibliography of the taxonomic literature consulted.</p>
<p>Species were included based if they root in the ground as free-standing woody plants, and reach a height of at least 3 m as reproductives. Epiphytes, lianas, and herbs were excluded. Species were included broadly, thus even if only occasionally terrestrial and free-standing while usually not; this aspect forces a loose gray area between trees and non-trees. The category shrub or descriptor shrubby were never considered, as they are poorly defined and used in inconsistent ways. An online version of the database (http://conditdatacenter.tech/PanamaTrees/) includes range maps and notes on growth form for marginal species. Tables of all coordinates from BIEN and Tropicos used in the estimates of range size can be downloaded from http://conditdatacenter.tech/TreeMap/txt.</p>
<p>References</p>
<ul>
<li>Condit, R., Perez, R., Aguilar, S. 2020. Tree species of Panama: a complete checklist with every geographic range. In press.</li>
<li>Condit, R., Engelbrecht, B. M. J., Pino, D., Pérez, R., and Turner, B. L. 2013. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proceedings of the National Academy of Sciences 110:5064–5068. pdf http://conditdatacenter.org/pdfs/ConditEtAlPNAS2013.pdf</li>
<li>Condit, R., Chisholm, R. A., and Hubbell, S. P. 2012. Thirty years of forest census at Barro Colorado and the importance of immigration in maintaining diversity. PLoS ONE 7:e49826. pdf http://conditdatacenter.org/pdfs/ConditChisholmHubbell2012.pdf</li>
<li>Condit, R., Pérez, R., Aguilar, S., Lao, S., and Hubbell, S. P. 2017. Demographic trends and climate over 35 years in the Barro Colorado 50 ha plot. Forest Ecosystems 4:1–13. pdf http://conditdatacenter.org/pdfs/Condit_et_al-2017-Forest_Ecosystems.pdf</li>
<li>Davidse, G. M.; Sousa, M. S.; Knapp, S.; Chiang, F. & Ulloa, C. U. (Eds.) 1994-2018. Flora Mesoamericana (6 Volumes).</li>
</ul><p>Table 1. PanamaTreeSpecies.tsv. A tab-delimited ascii table including a record for 3045 tree species we consider native to Panama. Taxa in the table are identified by the Family, Latin name, and taxonomic authority, and include the following columns of data:</p>
<ul>
<li>countries -- Number of countries observed, ostensibly in a natural state (countries)</li>
<li>minLat -- Minimum latitude of species occurrences</li>
<li>maxLat -- Maximum latitude</li>
<li>minLong -- Minimum longitude</li>
<li>maxLong -- Maximum longitude</li>
<li>Npan -- Number of those records in Panama</li>
<li>N -- Total number of records in combined BIEN and Tropicos databases, counting only unique locations</li>
<li>range -- Extent of range, in thousands of square km, calculated as minimum convex polygon around BIEN records</li>
<li>plots -- Number of our tree plots in Panama in which the species was observed</li>
<li>dens -- Mean density in the plots (including zeroes) </li>
<li>inventories -- Number of our tree inventories in Panama in which the species was observed</li>
<li>maxht -- Maximum height in meters according to taxonomic monographs</li>
</ul>
<p>Table 2. PanamaTreeNameLookup.tsv. A tab-delimited ascii table including a record for 4497 Latin names which we found to be associated with the native tree species of Panama, including the 3045 currently accepted names plus 1452 alternate names. These are the most relevant recent synonyms, those appearing in recent treatments, at BIEN, or at Tropicos. For the 3045 current names, the column ValidLatin matches the column Latin. For the other 1452, the name in the column Latin is not valid in Panama and should be replaced by ValidLatin throughout the country. The column Scope applies to those 1452 cases: where Scope = everywhere, Latin should be replaced by ValidLatin everywhere the species is found, and Latin is now an obsolete name. In the remained 62 cases, where Scope = only Panama, the name Latin is misused in Panama and should be replaced there, but not elsewhere.</p>
<p>Table 3. PanamaTreeBIENError.tsv. A tab-delimited ascii table including 1035 tree species names appearing incorrectly in the BIEN database with records in Panama. All of these are based either on erroneous records, ie in the wrong country, or misidentified, or are based on cultivated specimens of species not native to Panama. </p>
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