DOI: 10.5061/dryad.51c59zw8n
title: Two decades of annual landscape-scale tree growth and dynamics in old-growth tropical rainforest in the CARBONO Project, La Selva Biological Station, 1997-2018

description: Here we present the complete data series from a 21-yr study of the annual
 growth and dynamics of trees, palms and lianas in the old-growth tropical
 rainforest at the La Selva Biological Station in Costa Rica. These
 observations were part of the CARBONO Project, a multidisciplinary team
 study of forest carbon cycling.  The project was designed to
 assess forest processes at the landscape scale by sampling with
 replication across the within-landscape edaphic heterogeneity typical of
 tropical forests.  Through more than two decades, forest growth
 and dynamics were assessed annually.  The annual time-step used
 in the CARBONO Project effectively captured forest responses to major
 disturbances and to interannual and climatic variation.  Annual
 measurements also enhanced the accuracy and long-term consistency of the
 data.  To our knowledge, the resulting records are unique for
 tropical forests, where the dominant approach to studying the dynamics of
 a given forest has been to use a single plot and multi-year inter-census
 intervals. To date these CARBONO Project data have revealed: multi-decadal
 forest stability in spite of the short-term changes in forest structure
 resulting from major natural disturbances (e.g., the 1997-1998 Strong El
 Niño, and the extreme windstorm of May 2018); the dynamics and importance
 of large trees; and the responses of a major component of ecosystem
 productivity,  aboveground wood production, to interannual and
 long-term climatic and atmospheric change.  These data have also
 contributed to many remote-sensing studies. The data set consists of
 annual observations through the period 1997–2018 of the floristics,
 survival, recruitment, and growth of all woody stems (diameter &gt; 10
 cm) in a landscape-scale plot network.  At completion of the
 study, the data spanned 6705 individuals and 21 years. The data set is
 complete and has been through extensive internal checks for quality
 assurance.   Detailed data documentation and an emphasis
 on measurement repeatability were prioritized through the study. 
 The metadata include an extensive README file describing the data files
 and the methods, a document detailing the data management and qa/qc, and
 the scanned original field data-sheets for the 22 annual censuses. We
 gratefully acknowledge the careful long-term field work and data entry and
 checking by paraforesters Leonel Campos Otoya and William Miranda Conejo.
  Logistical support and the long-term protection of the La Selva
 reserve were provided by the Organization for Tropical Studies. The
 Ministerio de Ambiente y Energía of Costa Rica granted permits to carry
 out this study through the years of the study (most recently: Resolución
 No. 037-2018-ACCVC-PI).
description: A network of 18 0.5-ha forest inventory plots was sited across
 the upland old-growth landscape using the soil and topographic coverages
 of La Selva’s Geographic Information System.  The plots were sited using a
 random-block design.  The plot network sampled the three dominant edaphic
 conditions (younger-oxisol alluvial terrace, older-oxisol plateau, and
 older-oxisol slope), with replication (six plots in each edaphic
 type). During plot establishment, all live individuals
 were mapped, tagged and identified.  In Sept.-Dec. 1997 and annually
 thereafter, all trees were censused for survival and their diameter
 measured using standardized protocols, and the recruits to all plots
 (stems newly reaching 10 cm diameter) were identified, tagged, and
 measured.  At the outset of each year’s census, a large sample of trees
 were measured and then re-measured after multiple days to quantify the
 repeatability of the measurements (made by the same two paraforesters over
 the entire study).  The two project PIs supervised each annual census on a
 daily basis and, after data digitization by the paraforesters, carried out
 all data management, with an emphasis on data qa/qc throughout the
 study.   
description: The field methods and all variables and calculated data in the
 data files are explained in detail in the included README file.