Suwanvecho et al.Gibbon foods_2006-2011

This file contains data on fruit species eaten by the gibbon study group A during April-May follows for the six years 2006-2011. It contains for each visit to a fruit source: the tree species and tree number visited, date, time and fruit species code; and a summary of sources, no. of visits and no. of days for each species. The full species names are given in a separate sheet, copied from the SI on line file associated with Suwanvecho et al. 2017 in Biotropica

Data from: High interannual variation in the diet of a tropical forest frugivore (Hylobates lar)

Frugivores must deal with seasonal changes in fruit availability and changes from year to year, as most species of tropical forest fruiting trees have considerable interannual variation in phenology and many are mast fruiters. We quantified seasonal and interannual changes in the fruit diet in a frugivore and important seed disperser, the white-handed gibbon, Hylobates lar, in Thailand. We used 40-day following data during April and May replicated in six consecutive years to study interannual variability in the diet, and compared it with seasonal changes measured in monthly samples of the same size collected in three successive years. The 40-day periods of following also allowed us to measure the decline in dietary similarity with time over a finer scale. We measured fruit diet similarity between replicated 5-day periods using the percentage overlap (Renkonen’s) index, and Jaccard’s similarity index. Seasonally, average dietary overlap between adjacent months was low, and similarity approached zero after four months. Average rate of decline in similarity exceeded 20 percent per five day period. Variation in fruit species in the diet between years was high, and was correlated with interannual variation in fruiting phenology. The strongest correlation occurred in the case of Nephelium melliferum, a highly preferred species that dominated the diet in good fruiting years. It is difficult to separate changes in food-species preference from changes in availability from year to year. We devised a relative measure of preference that depends on the degree to which the gibbons rely on prior knowledge to find sources

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha-1) at spatial scales ranging from 5 to 250 m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise. © Author(s) 2014