Data on environmental conditions and previous land use
DR Danaë M. A. Rozendaal  FB Frans Bongers TA T. Mitchell Aide EA Esteban Alvarez-Dávila NA Nataly Ascarrunz PB Patricia Balvanera JB Justin M. Becknell TB Tony V. Bentos PB Pedro H. S. Brancalion GC George A. L. Cabral SC Sofia Calvo-Rodriguez JC Jerome Chave RC Ricardo G. César RC Robin L. Chazdon RC Richard Condit JD Jorn S. Dallinga JA Jarcilene S. de Almeida-Cortez BJ Ben de Jong AO Alexandre de Oliveira JD Julie S. Denslow DD Daisy H. Dent SD Saara J. DeWalt JD Juan Manuel Dupuy SD Sandra M. Durán LD Loïc P. Dutrieux ME Mario M. Espírito-Santo MF María C. Fandino GF G. Wilson Fernandes BF Bryan Finegan HG Hernando García NG Noel Gonzalez VM Vanessa Granda Moser JH Jefferson S. Hall JH José Luis Hernández-Stefanoni SH Stephen Hubbell CJ Catarina C. Jakovac AH Alma Johanna Hernández AJ André B. Junqueira DK Deborah Kennard DL Denis Larpin SL Susan G. Letcher JL Juan-Carlos Licona EL Edwin Lebrija-Trejos EM Erika Marín-Spiotta MM Miguel Martínez-Ramos PM Paulo E. S. Massoca JM Jorge A. Meave RM Rita C. G. Mesquita FM Francisco Mora SM Sandra C. Müller RM Rodrigo Muñoz SN Silvio Nolasco de Oliveira Neto NN Natalia Norden YN Yule R. F. Nunes SO Susana Ochoa-Gaona EO Edgar Ortiz-Malavassi RO Rebecca Ostertag MP Marielos Peña-Claros EP Eduardo A. Pérez-García DP Daniel Piotto JP Jennifer S. Powers JA José Aguilar-Cano SR Susana Rodriguez-Buritica JR Jorge Rodríguez-Velázquez MR Marco Antonio Romero-Romero JR Jorge Ruíz AS Arturo Sanchez-Azofeifa AA Arlete Silva de Almeida WS Whendee L. Silver NS Naomi B. Schwartz WT William Wayt Thomas MT Marisol Toledo MU Maria Uriarte ES Everardo Valadares de Sá Sampaio MB Michiel van Breugel HW Hans van der Wal SM Sebastião Venâncio Martins MV Maria D. M. Veloso HV Hans F. M. Vester AV Alberto Vicentini IV Ima C. G. Vieira PV Pedro Villa GW G. Bruce Williamson KZ Kátia J. Zanini JZ Jess Zimmerman LP Lourens Poorter
This protocol is extracted from research article:
Biodiversity recovery of Neotropical secondary forests
Sci Adv, Mar 6, 2019; DOI: 10.1126/sciadv.aau3114

Average annual rainfall (in mm year−1) was obtained from the nearest weather station for each site. As seasonality in water availability is a stronger determinant of species richness and composition than annual rainfall (16), we obtained CWA (in mm year−1) from http://chave.ups-tlse.fr/pantropical_allometry.htm (where CWA is referred to as “climatic water deficit”). CWA indicates the amount of water lost by the environment during dry months, that is, the months in which evapotranspiration is larger than rainfall. CWA is, by definition, negative, and sites with a maximum CWA of 0 do not experience seasonal drought stress. For one site for which CWA was not available (Providencia Island; table S1), we estimated CWA from a linear regression between CWA and rainfall based on the other chronosequence sites (CWA = −822 + 0.203 × rainfall; n = 55, P < 0.0001, R2 = 0.49). Topsoil CEC [in cmol(+) kg−1] over the first 30 cm of the soil was used as an indicator of soil nutrient availability. We preferably included data from old-growth forest plots because soil fertility is expected to recover over the course of succession. CEC represents the amount of exchangeable cations [Ca2+, Mg2+, K+, Na+, Al3+, and H+ in cmol(+) kg−1]. A high CEC can therefore also result from high acidity or aluminum toxicity and may not only reflect soil fertility. For 39 sites for which no local CEC data were available, CEC was obtained from the SoilGrids database (34). SoilGrids did not contain data on soil nitrogen and phosphorus. Phosphorus is thought to limit plant growth in highly weathered tropical soils and may therefore be strongly correlated with the biodiversity recovery of tropical forests. We obtained total exchangeable bases (TEB) from the World Harmonized Soil Database (35), as this variable was not included in SoilGrids, for 55 sites for which data were available (Providencia Island was not included in the database). CEC was significantly, positively correlated with TEB (Pearson’s r = 0.67, P < 0.0001, n = 55), which indicates that, for our dataset, CEC likely reflected soil fertility rather than the degree of aluminum toxicity or acidity. Therefore, we included CEC in the analyses, as for part of the sites, locally measured values were available, while no local data were available for TEB.

Biodiversity recovery will likely be highest when seed sources and seed dispersal agents are nearby, thus with high forest cover and forest quality in the landscape. For each site, percentage of forest cover was calculated for each of the plots within circular buffers with radii of 500, 1000, and 5000 m using a remote sensing–based tree cover map for the year 2000 (24). For 11 chronosequence sites, (part of) the fieldwork was conducted in the 1990s, and for the other sites, the fieldwork was conducted from the year 2000 onward. This does mean that landscape forest cover in the year 2000 generally reflects the landscape matrix for the younger secondary forest plots. Therefore, our estimate of landscape forest cover is ecologically relevant, as it reflects the landscape conditions experienced by younger secondary forests (<20 years), when most of the recovery of species richness and species composition occurs (Fig. 2).

Tree cover data were available at a resolution of 30 m by 30 m (24) and included any type of tree cover (e.g., old-growth forest, secondary forest, and plantations). A threshold of 30% tree cover was applied per pixel to distinguish between forest and nonforest land cover types, and forest cover was calculated on the basis of the number of pixels covered by forest versus nonforest land cover types. For eight sites without individual plot-level coordinates, we similarly calculated percentage of forest cover in circular buffers with radii of 500, 1000, and 5000 m based on just the average coordinates of the site. Landscape-scale forest cover was estimated as percentage of forest cover in the total area covered by a union of circular buffers with radii of 500, 1000, or 5000 m of all individual plots within a chronosequence site. Thus, areas in which circular buffers overlapped were included only once in the calculation of percentage of forest cover in the landscape. In addition, we estimated percentage of old-growth forest and secondary forest cover in the landscape matrix (i.e., in a radius of 1 km around the area that comprises all plots of a chronosequence site) for 45 of our sites (15).

Biodiversity recovery depends on forest legacies that accelerate secondary forest succession, such as the presence of a soil seed bank, resprouts from tree roots or stumps, or remnant forest trees. Both remaining legacies and environmental conditions that influence regeneration, such as soil nutrient availability and soil structure, are partly driven by previous land use (13, 18). We distinguished three types of land use before abandonment (shifting cultivation, pasture, and a combination of these in the landscape) based on interviews with local landowners. Land-use intensity is generally lowest under shifting cultivation, resulting in faster forest recovery in abandoned agricultural fields than abandoned pastures.

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