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LBA-ECO CD-08 Tropical Forest Ecosystem Respiration, Manaus, Brazil

Understanding how tropical forest carbon balance will respond to global change requires knowledge of individual heterotrophic and autotrophic respiratory sources, together with factors that control respiratory variability. We measured leaf, live wood (tree stem), and soil respiration, along with additional environmental factors over a 1-yr period in a Central Amazon terra firme forest. Scaling these fluxes to the ecosystem, and combining our data with results from other studies, we estimated an average total ecosystem respiration (R-eco) of 7.8 mumol(.)m(-2.)s(-1). Average estimates (per unit ground area) for leaf, wood, soil, total heterotrophic, and total autotrophic respiration were 2.6, 1.1, 3.2, 5.6, and 2.2 mumol(.)m(-2.)s(-1), respectively. Comparing autotrophic respiration with net primary production (NPP) estimates indicated that only similar to30% of carbon assimilated in photosynthesis was used to construct new tissues, with the remaining 70% being respired back to the atmosphere as autotrophic respiration. This low ecosystem carbon use efficiency (CUE) differs considerably from the relatively constant CUE of similar to0.5 found for temperate forests. Our R-eco estimate was comparable to the above-canopy flux (F-ac) from eddy covariance during defined sustained high turbulence conditions (when presumably F-ac = R-eco) of 8.4 (95% CI = 7.59.4). Multiple regression analysis demonstrated that similar to50% of the nighttime variability in Fa, was accounted for by friction velocity (u*, a measure of turbulence) variables. After accounting for u* variability, mean F-ac varied significantly with seasonal and daily changes in precipitation. A seasonal increase in precipitation resulted in a decrease in F-ac similar to our soil respiration response to moisture. The effect of daily changes in precipitation was complex: precipitation after a dry period resulted in a large increase in F-ac whereas additional precipitation after a rainy period had little effect. This response was similar to that of surface litter (coarse and fine), where respiration is greatly reduced when moisture is limiting, but increases markedly and quickly saturates with an increase in moisture.

NPP Multi-Biome: Grassland, Boreal Forest, and Tropical Forest Sites, 1939-1996, R1

This data set contains one data file (.csv format) that provides net primary productivity (NPP) estimates for 34 grasslands, 14 tropical forests, and 5 boreal forest sites distributed worldwide. The NPP data were compiled from published literature. In addition to above- and below-ground NPP, and total NPP estimates, the file includes site name and location, biome type, mean annual precipitation, and mean annual temperature, where available. Aboveground net primary production (ANPP), ranged from 35 to 2,320 g/m2/year, belowground net primary production (BNPP) ranged from 20 to 1,832 g/m2/year, and total net primary production (TNPP) ranged from 182 to 3,538 g/m2/year. Revision Notes: This data file has been revised to add a negative sign to south latitude and west longitude decimal degree coordinates, and the compass direction (N, S, E, W) for coordinates has been removed. NPP data for Vindhyan, India; Atherton, Australia; John Crow Ridge, Jamaica; and La Selva, Costa Rica, have been revised to correct previously reported values. Additional data references for Kuusamo, Finland, and La Selva, Costa Rica, have been added. Please see the Data Set Revisions section of this document for detailed information.

NPP Tropical Forest: Luquillo, Puerto Rico, 1946-1994, R1

This data set contains ten ASCII files (.txt format), one NPP file for each of the nine different montane tropical rainforest sites within the Luquillo Experimental Forest (LEF) of Puerto Rico and one file containing climate data. The NPP study sites are located along an environmental gradient of different soils, elevation (100-1,000 m), develop stage, and mean annual rainfall. Field measurements were carried out from 1946 through 1994.Estimates of above-ground net primary productivity (ANPP) in LEF are based on summation of litterfall accumulation, biomass increment, and herbivory estimates. ANPP values range from 370-1,950 g/m2/year, with ANPP decreasing with elevation. The lowest ANPP was in the Dwarf cloud rainforest. The Palm floodplain and Bisley (tabonuco) forests have the highest ANPP (1,950 g/m2/year and 1,630 g/m2/year, respectively). Below-ground NPP was measured at only two of the sites (Guzman and Bisley) and was estimated for the Dwarf site. TNPP estimates for these sites are 1,945, 2,160, and 383 g/m2/year, respectively.Climate data are available from a weather station at the El Verde forest study site.

NPP Tropical Forest: Kade, Ghana, 1957-1972, R1

This data set contains one NPP data file and two climate data files (ASCII .txt format). The NPP file contains above- and below- ground biomass, litterfall, standing litter crop, and nutrient content data for a moist semi-deciduous secondary tropical forest at the Kade Agricultural Research Station (6.15 N 0.92 W), Ghana, spanning several collections periods between 1957 and 1972. Climate data come from weather stations at Kade near the study site (1958-1997) and at Kumasi near Kade (1945-1990).The Kade study site is typical of an old secondary forest which has probably not been cultivated or harvested since around 1915-1925. Tree basal area measured in 1957 was quite high at 33.7 m2/ha; measurements in 1968 of a plot a few hundred meters away gave 30.6 m2/ha.Detailed above- and below-ground biomass data are provided from a single clear-felling made in 1957. Nutrient content for lianas, leaves and twigs, branches, large wood, standing dead wood, stumps, litter, and roots is also provided. Total live + dead biomass was 36,102 g/m2, of which 5,414 g/m2 (15%) was below-ground live biomass and 23,568 g/m2 was above-ground live biomass. Monthly litterfall is available for 26 months (1970-72).Total annual NPP was estimated in the late 1950s at about 2,400 g/m2/year based on litterfall of 1,054 g/m2/year plus rough estimates of timber fall (1,070-1,121 g/m2/year) and root production (258 g/m2/year). In the 1970s, NPP was recalculated at 2,200-2,500 g/m2/year based on additional measurements of litter, wood fall, and decomposition.

Forest Inventories at Burned and Unburned Tropical Forest Sites, Acre, Brazil, 2014

This dataset provides measurements for diameter at breast height (DBH) and species identification of trees for inventories taken at five tropical forest sites in Acre state, Brazil, in the southwestern Amazon region. The sites included one in a forest reserve (Reserva Bonal) and four within forest fragments situated on private property. The inventory sites included forests burned in 2005 and 2010 and also unburned forests. Surveys were conducted in July and August 2014.

NPP Tropical Forest: Khao Chong, Thailand, 1962-1965, R1

This data set contains one net primary productivity (NPP) data file and three climate data files (.txt format) for a fully closed tropical rainforest in the Khao Chong Reserve (7.58 N 99.8 E) in southern Thailand. The Reserve comprises 500 ha of well-preserved rainforest considered typical of the region, although maximum tree height (36 m) and biodiversity were less than in Malaysian forests. Net primary productivity (NPP) was estimated as the sum of annual net above-and below-ground biomass increase plus extrapolated annual litterfall and tree mortality. Biomass increment for trees > 4.5 cm diameter at breast height (DBH) was monitored between 1962 to 1965, and daily litterfall was measured for one month in 1962. Total NPP was estimated at 2,860 g/m2/year. This value includes a possible over-estimate of litterfall (2,330 g/m2/year) plus above-ground woody biomass increment and turnover as mortality combined (489 g/m2/year) plus below-ground woody biomass increment (41 g/m2/year). Fine root turnover and herbivory were not included in these estimates. Allometric relationships for estimating above-ground biomass were checked by destructive harvest. Leaf area index was relatively high at 11.4-12.3 m2/m2. Long-term climate data for Khao Chong are available from weather stations at Songkhla, Thailand (7.2 N 100.6 E) and Trang, Thailand (7.52 N 99.62 E). Depending on station location and temporal coverage, mean annual temperature is 27.2-27.4 C and mean annual precipitation is between 1,928 and 2,696 mm. Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 1998.

NPP Tropical Forest: Barro Colorado, Panama, 1969-1990, R1

This data set contains three ASCII files (.txt format). One file provides net primary productivity (NPP) data for the moist lowland tropical forest on Barro Colorado Island, Panama. NPP estimates are based on field measurements of litterfall accumulation, tree growth and mortality, and herbivory. Above-ground biomass and LAI are also reported. The other two files provide climate data recorded onsite.Annual litterfall accumulation (leaf + twig + other litterfall) averaged 1,064 g/m2/year, excluding losses to herbivory, on the central plateau of the island and in the Lutz catchment (1969-1979) and 1,246 g/m2/year at Poacher's Peninsula (1986-1990). Herbivory due to insects (about 50 g/m2/year) was estimated from leaf litterfall (1974-1977) by measuring holes and gaps in fallen leaves. An additional 30 g/m2/year may be lost to vertebrate herbivores which leave no identifiable traces in litter traps. Coarse wood litterfall due to tree damage may represent an additional 46 g/m2/year. Above-ground biomass averaged 27,425 g/m2 based on inventory data collected every 5 years from 1985 to 2000 and allometric regression equations. Tree growth of 554 g/m2/year was based on above-ground biomass changes during the three census intervals. Tree mortality of 2-3% was estimated by recording dead or missing trees (1982-1990). LAI of 7.3 was based on the average area of leaves that fell per area of ground per year. Overall, above-ground NPP for Barro Colorado Island was estimated at 1,800 g/m2/year.

opencc-by-4.0Feb 2020View details →

Supporting Dataset for "Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests"

This data set is a supplement for: Longo, M., S. S. Saatchi, M. Keller, K. W. Bowman, A. Ferraz, P. R. Moorcroft, D. Morton, D. Bonal, P. Brando, B. Burban, G. Derroire, M. N. dos-Santos, V. Meyer, S. R. Saleska, S. Trumbore, and G. Vin- cent, 2020: Impacts of degradation on water, energy, and carbon cycling of the Amazon tropical forests. J. Geophys. Res.-Biogeosci., 125 (8), e2020JG005 677, doi:<a href="http://dx.doi.org/10.1029/2020JG005677">10.1029/2020JG005677</a>. This data set contains the following files (which should be all downloaded and uncompressed in the same root directory): <ul> <li>00_SiteLidar.zip – R scripts to process forest inventory plots and Airborne LiDAR point clouds.  Sub-directories contains a directory Template, which should be copied for each site for which data are to be processed.</li> <li>01_LidarSynthesis.zip – R scripts to fit the statistical models of aggregated properties, and to evaluate both the statistical model and the prediction of Airborne LiDAR profiles to be used to initialize ED-2.2.</li> <li>02_model_eval.zip – R scripts to compare the ED-2.2 model output and evaluate the model against tower observations.</li> <li>03_degrad_mtr – R scripts to visualize the ED-2.2 simulation results.</li> <li>InputData – Miscellaneous data to be used by the scripts.</li> <li>Util – Additional R scripts <ul> <li>Rsc – Mostly R functions, which may be called by other R scripts</li> <li>OutsideLAS – List of plots that were not fully overlapped by the Airborne LiDAR surveys</li> <li>GenMERRA2_ED2 – Utility scripts to process MERRA-2 to generate the met drivers needed by ED-2.2</li> <li>GenMSWEP2_ED2 – Utility scripts to process MSWEP-2.2 to generate the met drivers needed by ED-2.2 </li> </ul> </li> <li>ED2IN_Config – list of ED2IN files used in the runs.</li> </ul>   To see the input data used for this analysis, load any of the objects available in 01_LidarSynthesis/01_eval_multivar, and look for the following structures: <table align="left"> <caption>List of variables and units of data structure census[[1]], rlidar[[1]], and tchdat[[1]].</caption> <thead> <tr> <th scope="col">Variable</th> <th scope="col">Structure</th> <th scope="col">Description</th> <th scope="col">Units</th> </tr> </thead> <tbody> <tr> <th scope="row">identifier</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Plot identifier. This always has the site identifier (see below), the area within each site, the nominal year of the campaign, the unique sub-plot ID (Pxx_Byy for rectangular plots, and Txx_Pyy for long transects) </td> <td> </td> </tr> <tr> <th scope="row">iata</th> <td> census[[1]], rlidar[[1]], tchdat[[1]] </td> <td> Site identifier: <ul> <li>115: Km 115 of BR-163 highway, PA, BRA</li> <li>ana: Anambé, PA, BRA</li> <li>and: Fazenda Andiroba, PA, BRA</li> <li>bon: Fazenda Bonal, AC, BRA</li> <li>cau: Fazenda Cauaxi, PA, BRA</li> <li>duc: Reserva Ducke, AM, BRA</li> <li>fc2: Feliz Natal (zone C, area 2), MT, BRA</li> <li>fd1: Feliz Natal (zone D, area 1), MT, BRA</li> <li>fd2: Feliz Natal (zone D, area 2), MT, BRA</li> <li>fd3: Feliz Natal (zone D, area 3), MT, BRA</li> <li>fn2: Feliz Natal (long transect 2), MT, BRA</li> <li>fna: Feliz Natal (zone A), MT, BRA</li> <li>fst: Saracá-Taquera National Forest, PA, BRA</li> <li>gf1: Paracou (Guyaflux plots), GUF</li> <li>gf2: Paracou (Logging experiment plots), GUF</li> <li>hum: Fazenda Humaitá, AC, BRA</li> <li>jm2: Jamari National Forest (area 2), RO, BRA</li> <li>jm3: Jamari National Forest (area 3), RO, BRA</li> <li>par: Fazenda Nova Neonita, PA, BRA</li> <li>sbe: </li> </ul> </td> <td> </td> </tr> <tr> <th scope="row">local</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td> Region identifier (used for regional cross-validation): <ul> <li>bte: Belterra, PA, BRA</li> <li>duc: Manaus (Reserva Ducke), AM, BRA</li> <li>fst: Saracá-Taquera National Forest, PA, BRA</li> <li>fzn: Feliz Natal, MT, BRA</li> <li>gyf: Paracou, GUF</li> <li>jam: Jamari National Forest, RO, BRA</li> <li>prg: Paragominas, PA, BRA</li> <li>rib: Rio Branco, AC, BRA</li> <li>sfx: São Félix do Xingu, PA, BRA</li> <li>tan: Tanguro, MT, BRA</li> <li>sbe: Southeastern Belterra, PA, BRA</li> <li>sx1: São Félix do Xingu (area 1), PA, BRA</li> <li>sx2: São Félix do Xingu (area 2), PA, BRA</li> <li>tac: Tomé-Açu, PA, BRA</li> <li>tal: Fazenda Talismã, AC, BRA</li> <li>tn1: Fazenda Tanguro (Sustainable Landscapes transects), MT, BRA</li> <li>tn2: Fazenda Tanguro (fire experiment transects), MT, BRA</li> <li>tp1: Tapajós National Forest, PA, BRA</li> <li>tp2: São Jorge (area 2), PA, BRA</li> <li>tp3: São Jorge (area 3), PA, BRA</li> </ul> </td> <td> </td> </tr> <tr> <th scope="row">poi</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Nominal size of each plot</td> <td> </td> </tr> <tr> <th scope="row">when</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Date of measurement</td> <td> </td> </tr> <tr> <th scope="row">col</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Colour associated with plot (for plotting only)</td> <td> </td> </tr> <tr> <th scope="row">pch</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Symbol associated with plot (for plotting only)</td> <td> </td> </tr> <tr> <th scope="row">dist.key</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td> Disturbance flag: <ul> <li>bnm: Burnt multiple times</li> <li>bno: Burnt once</li> <li>cvl: Conventional logging</li> <li>int: Intact (minimally disturbed) forest</li> <li>lbn: Logged and burnt once</li> <li>lth: Logged and thinned</li> <li>ril: Reduced-impact logging</li> <li>sbn: Secondary growth then burnt</li> <li>sec: Secondary growth</li> <li>ukn: Unknown/Unclassified</li> </ul> </td> <td> </td> </tr> <tr> <th scope="row">dist.age</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Age since last disturbance</td> <td>yr</td> </tr> <tr> <th scope="row">dist.col</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Colour associated with disturbance (for plotting only)</td> <td> </td> </tr> <tr> <th scope="row">dist.pch</th> <td>census[[1]], rlidar[[1]], tchdat[[1]]</td> <td>Symbol associated with disturbance (for plotting only)</td> <td> </td> </tr> <tr> <th scope="row">agb.std</th> <td>census[[1]]</td> <td>Above-ground biomass of individuals with DBH ≥ 10 cm</td> <td>kgC m−2</td> </tr> <tr> <th scope="row">ba.std</th> <td>census[[1]]</td> <td>Basal area of individuals with DBH ≥ 10 cm</td> <td>cm2 m−2</td> </tr> <tr> <th scope="row">lai.std</th> <td>census[[1]]</td> <td>Potential (allometry-based) leaf area index of individuals with DBH ≥ 10 cm</td> <td>m2 m−2</td> </tr> <tr> <th scope="row">nplant.std</th> <td>census[[1]]</td> <td>Stem number density of individuals with DBH ≥ 10 cm</td> <td>m−2</td> </tr> <tr> <th scope="row">elev.mean</th> <td>rlidar[[1]]</td> <td>Mean elevation of point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.sdev</th> <td>rlidar[[1]]</td> <td>Standard deviation of point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.skew</th> <td>rlidar[[1]]</td> <td>Skewness of point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.kurt</th> <td>rlidar[[1]]</td> <td>Kurtosis of point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p01</th> <td>rlidar[[1]]</td> <td>1st percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p05</th> <td>rlidar[[1]]</td> <td>5th percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p10</th> <td>rlidar[[1]]</td> <td>10th percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p25</th> <td>rlidar[[1]]</td> <td>25th percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p50</th> <td>rlidar[[1]]</td> <td>50th percentile (median) of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p75</th> <td>rlidar[[1]]</td> <td>75th percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p90</th> <td>rlidar[[1]]</td> <td>90th percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p95</th> <td>rlidar[[1]]</td> <td>95th percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.p99</th> <td>rlidar[[1]]</td> <td>99th percentile of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.iqr</th> <td>rlidar[[1]]</td> <td>Interquartile range of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">elev.max</th> <td>rlidar[[1]]</td> <td>Maximum of the point cloud return distribution (all returns)</td> <td>m</td> </tr> <tr> <th scope="row">fcan.elev.1.0.to.2.5.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 1.0 and 2.5 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.2.5.to.5.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 2.5 and 5.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.5.0.to.7.5.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 5.0 and 7.5 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.7.5.to.10.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 7.5 and 10.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.10.0.to.15.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 10.0 and 15.0 m </td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.15.0.to.20.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 15.0 and 20.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.20.0.to.25.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 20.0 and 25.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.25.0.to.30.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns between 25.0 and 30.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.1.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 1.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.2.5.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 2.5 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.5.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 5.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.7.5.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 7.5 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.10.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 10.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.15.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 15.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.20.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 20.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.25.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 25.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">fcan.elev.above.30.0.m</th> <td>rlidar[[1]]</td> <td>Fraction of returns above 30.0 m</td> <td>fraction [0-1]</td> </tr> <tr> <th scope="row">ztch</th> <td>tchdat[[1]]</td> <td>Mean top canopy height (0.25ha average from 1-m pixels)</td> <td>m</td> </tr> </tbody> </table>

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Data from: NDVI is not reliable as a surrogate of forage abundance for a large herbivore in tropical forest habitat

Remotely-sensed vegetation indices are increasingly being used in wildlife studies but field-based support for their utility as a measure of forage availability come largely from open-canopy habitats. We assessed whether normalized difference vegetation index (NDVI) represents forage availability for Asian elephants in a southern Indian tropical forest. We found that the number of food species was a small percentage of all plant species. NDVI was not a good measure of food abundance in any vegetation category partly because of (a) small to moderate proportional abundances of food species relative to the total abundance of all species in that category (herbs and shrubs), (b) abundant overstorey vegetation resulting in low correlations between NDVI and food abundance, despite a high proportional abundance of food species and a concordance between total abundance and food species abundance (graminoids), and (c) the relevant variables measured and important as food at the ground level (count and GBH) not being related to primary productivity (trees and recruits). NDVI had a negative relationship with the total abundance of graminoids, which represent a bulk of elephant and other herbivore diet, because of negative interaction with other vegetation and canopy cover that positively explained NDVI. Spatially interpolated total graminoid abundance modelled from field data outperformed NDVI in predicting total graminoid abundance, although interpolation models of food graminoid abundance were not satisfactory. Our results reject the utility of NDVI in mapping elephant forage abundance in tropical forests, a finding that has implications for studies of other herbivores also.

opencc-zeroDec 2018View details →

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Data from: A highly-resolved food web for insect seed predators in a species-rich tropical forest

The top-down and indirect effects of insects on plant communities depend on patterns of host use, which are often poorly documented, particularly in species-rich tropical forests. At Barro Colorado Island, Panama, we compiled the first food web quantifying trophic interactions between the majority of co-occurring woody plant species and their internally-feeding insect seed predators. Our study is based on more than 200,000 fruits representing 478 plant species, associated with 369 insect species. Insect host-specificity was remarkably high: only 20% of seed predator species were associated with more than one plant species, while each tree species experienced seed predation from a median of two insect species. Phylogeny, but not plant traits, explained patterns of seed predator attack. These data suggest that seed predators are unlikely to mediate indirect interactions such as apparent competition between plant species, but are consistent with their proposed contribution to maintaining plant diversity via the Janzen-Connell mechanism.

opencc-zeroSep 2019View details →

restrictedcc-by-4.0Dec 2023View details →

Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests

Species mean information for the six leaf water-related traits used in the paper titled: Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests.

opencc-by-4.0Aug 2024View details →

Selected data sets for Marsh et al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'

Data sets used in the for the manuscript Marsh et al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'. The DOIs that link to all other data sets used in the publication are available in Tables S2-5 of the supplementary information. The z-score standardised data, and outputs of RMarkdown documents outline all the steps in the processing and analysis of the data are available at https://zenodo.org/uploads/13161799.   This repository contains data used for: <h3>Mean canopy height</h3> Canopy height and vertical profiles of forest structure were compiled using airborne remote sensing with LiDAR collected by NERC’s Airborne Research Facility (ARF) in November 2014, using a Leica ALS50-II LiDAR. A Beer-Lambert approximation was used to convert point clouds to plant area density (PAD) distributions, a similar measure to leaf-area index, but where methods do not distinguish between leaves and branches or trunks. LiDAR measurements for the carbon plots were converted to rasters with 0.5 × 0.5 m cell size. Plots were rotated to a North-South axis if necessary <h3> Spectral diversity</h3> Spectral measurements were made on five leaves attached to tree branches used to measure leaf chemical traits. Leaves were randomly selected but we avoided damaged and young plant material to avoid potential confounding factors. Reflectance spectra (350–2500 nm) were acquired using a FieldSpec 4, produced by Analytical Spectral Devices (ASD, Boulder, Colorado, USA). The spectroradiometer's contact probe was mounted on a clamp and firmly pushed down onto the sample against a black background so that no extraneous light was included in the measurement. Spectral measurements were taken halfway between the petiole and leaf tip, and between the main vein and the leaf edge, with the abaxial surface pointing towards the probe. The readings were calibrated against a Spectralon white reference panel every five samples. Leaf reflectance measured at 430 nm, 660 nm, 1450, 1980 nm and 2350 nm align closely with absorption features for pigments, water content, proteins and cellulose. Spectral diversity calculated from these absorption features can provide an integrated measure of the functional trait variability within plant communities and may be used as a proxy for functional diversity.   <h3>Liana abundance</h3> Percentage liana cover for large canopy and emergent trees. The four quadrants of the canopy were scored as 0 (no lianas), 1 (1-20%), 2 (20-40%), 3 (40-60%), 4 (60-80%) and 5 (80-100%).   <h3>Leaf-area index </h3> Leaf area index (LAI) for carbon plots was derived from hemispherical photos (Sigma 8mm SRL fish eye lens and Canon EOS 600D digital camera, mounted on a tripod at 1 m height). Between 5-27 photos were taken over time in each subplot. Images were processed with Hemisfer® software (www.wsl.ch/dienstleistungen/produkte/software/hemisfer/index_EN). LAI was calculated with the method by Thimonier et al. (2010) European Journal of Forest Research 129, 543–562 (2010), with a canopy clumping correction applied from Chen & Cihlar (1995) IEEE Transactions on Geoscience and Remote Sensing 33, 777–787.   <h2>Funding</h2> Analyses were carried out, and data were collected, as part of the BALI (Biodiversity And Land-use Impacts on tropical ecosystem function) using the following funding: <ul> <li>NERC's Human Modified Tropical Forests research programme (grant number NE/K016377/1 awarded to the BALI consortium)</li> <li>MHN was supported by a PhD scholarship from the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq, grant No. 201516/2014-4) from Brazil</li> </ul>

opencc-by-4.0Apr 2018View details →

Figure 1 from: Osorio-Beristain M, Rodríguez A, Martínez-Garza C, Alcalá RE (2018) Relating flight initiation distance in birds to tropical dry forest restoration. Zoologia 35: 1-7. https://doi.org/10.3897/zoologia.35.e12642

Figure 1 Mean (± SE) flight initiation distance scored in the three avian species evaluated.

Figure 5 from: Mata-Silva V, Rocha A, Ramírez-Bautista A, Berriozabal-Islas C, Wilson LD (2019) A new species of forest snake of the genus Rhadinaea from Tropical Montane Rainforest in the Sierra Madre del Sur of Oaxaca, Mexico (Squamata, Dipsadidae). ZooKeys 813: 55-65. https://doi.org/10.3897/zookeys.813.29617

Figure 5 Habitat where holotype of Rhadinaeaeduardoi was found.

Figure 1 from: Mata-Silva V, Rocha A, Ramírez-Bautista A, Berriozabal-Islas C, Wilson LD (2019) A new species of forest snake of the genus Rhadinaea from Tropical Montane Rainforest in the Sierra Madre del Sur of Oaxaca, Mexico (Squamata, Dipsadidae). ZooKeys 813: 55-65. https://doi.org/10.3897/zookeys.813.29617

Figure 1 Map depicting the site (star) where Rhadinaeaeduardoi was found.

Figure 4 from: Mata-Silva V, Rocha A, Ramírez-Bautista A, Berriozabal-Islas C, Wilson LD (2019) A new species of forest snake of the genus Rhadinaea from Tropical Montane Rainforest in the Sierra Madre del Sur of Oaxaca, Mexico (Squamata, Dipsadidae). ZooKeys 813: 55-65. https://doi.org/10.3897/zookeys.813.29617

Figure 4 Doral and ventral views of the preserved holotype of Rhadinaeaeduardoi.

Figure 3 from: Mata-Silva V, Rocha A, Ramírez-Bautista A, Berriozabal-Islas C, Wilson LD (2019) A new species of forest snake of the genus Rhadinaea from Tropical Montane Rainforest in the Sierra Madre del Sur of Oaxaca, Mexico (Squamata, Dipsadidae). ZooKeys 813: 55-65. https://doi.org/10.3897/zookeys.813.29617

Figure 3 Holotype of Rhadinaeaeduardoi in life.

Figure 2 from: Mata-Silva V, Rocha A, Ramírez-Bautista A, Berriozabal-Islas C, Wilson LD (2019) A new species of forest snake of the genus Rhadinaea from Tropical Montane Rainforest in the Sierra Madre del Sur of Oaxaca, Mexico (Squamata, Dipsadidae). ZooKeys 813: 55-65. https://doi.org/10.3897/zookeys.813.29617

Figure 2 Head and anterior portion of body of holotype of Rhadinaeaeduardoi.

opencc-by-4.0Oct 2019View details →

Figure 1 from: Norhazrina N, Syazwana N, Aisyah M, Aznani H, Maideen H, Nizam MS (2019) Mosses of Gunung Senyum Recreational Forest, a tropical limestone forest in Pahang, Peninsular Malaysia. PhytoKeys 128: 57-72. https://doi.org/10.3897/phytokeys.128.33860

Figure 1 Map of Gunung Senyum Recreational Forest, Jengka Forest Reserve, Pahang.