Standard yet unusual mechanisms of long-distance dispersal: Seed dispersal of Corymbia torelliana by bees

Mellitochory, seed dispersal by bees, has been implicated in long-distance dispersal of the tropical rain forest tree, Corymbia torelliana (Myrtaceae). We examined natural and introduced populations of C. torelliana for 4 years to determine the species of bees that disperse seeds, and the extent and distance of seed dispersal. The mechanism of seed dispersal by bees was also investigated, including fruit traits that promote dispersal, foraging behaviour of bees at fruits, and the fate of seeds. The fruit structure of C. torelliana, with seed presented in a resin reward, is a unique trait that promotes seed dispersal by bees and often results in long-distance dispersal. We discovered that a guild of four species of stingless bees, Trigona carbonaria, T. clypearis, T. sapiens, and T. hockingsi, dispersed seeds of C. torelliana in its natural range. More than half of the nests found within 250 m of fruiting trees had evidence of seed transport. Seeds were transported minimum distances of 20-220 m by bees. Approximately 88% of seeds were dispersed by gravity but almost all fruits retained one or two seeds embedded in resin for bee dispersal. Bee foraging for resin peaked immediately after fruit opening and corresponded to a peak of seed dispersal at the hive. There were strong correlations between numbers of seeds brought in and taken out of each hive by bees (r = 0.753-0.992, P < 0.05), and germination rates were 95 ± 5%. These results showed that bee-transported seeds were effectively dispersed outside of the hive soon after release from fruits. Seed dispersal by bees is a non-standard dispersal mechanism for C. torelliana, as most seeds are dispersed by gravity before bees can enter fruits. However, many C. torelliana seeds are dispersed by bees, since seeds are retained in almost all fruits, and all of these are dispersed by bees.

Development of near infrared analysis of faeces to estimate non-grass proportions in diets selected by cattle grazing tropical pastures

Grass (monocots) and non-grass (dicots) proportions in ruminant diets are important nutritionally because the non-grasses are usually higher in nutritive value, particularly protein, than the grasses, especially in tropical pastures. For ruminants grazing tropical pastures where the grasses are C-4 species and most non-grasses are C-3 species, the ratio of C-13/C-12 in diet and faeces, measured as delta C-13 parts per thousand, is proportional to dietary non-grass%. This paper describes the development of a faecal near infrared (NIR) spectroscopy calibration equation for predicting faecal delta C-13 from which dietary grass and non-grass proportions can be calculated. Calibration development used cattle faeces derived from diets containing only C-3 non-grass and C-4 grass components, and a series of expansion and validation steps was employed to develop robustness and predictive reliability. The final calibration equation contained 1637 samples and faecal delta C-13 range (parts per thousand) of [12.27]-[27.65]. Calibration statistics were: standard error of calibration (SEC) of 0.78, standard error of cross-validation (SECV) of 0.80, standard deviation (SD) of reference values of 3.11 and R-2 of 0.94. Validation statistics for the final calibration equation applied to 60 samples were: standard error of prediction (SEP) of 0.87, bias of -0.15, R-2 of 0.92 and RPD of 3.16. The calibration equation was also tested on faeces from diets containing C-4 non-grass species or temperate C-3 grass species. Faecal delta C-13 predictions indicated that the spectral basis of the calibration was not related to C-13/C-12 ratios per se but to consistent differences between grasses and non-grasses in chemical composition and that the differences were modified by photosynthetic pathway. Thus, although the calibration equation could not be used to make valid faecal delta C-13 predictions when the diet contained either C-3 grass or C-4 non-grass, it could be used to make useful estimates of dietary non-grass proportions. It could also be ut :sed to make useful estimates of non-grass in mixed C-3 grass/non-grass diets by applying a modified formula to calculate non-grass from predicted faecal delta C-13. The development of a robust faecal-NIR calibration equation for estimating non-grass proportions in the diets of grazing cattle demonstrated a novel and useful application of NIR spectroscopy in agriculture.

Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy

The potential of near infra-red (NIR) spectroscopy for non-invasive measurement of fruit quality of pineapple (Ananas comosus var. Smooth Cayenne) and mango (Magnifera indica var. Kensington) fruit was assessed. A remote reflectance fibre optic probe, placed in contact with the fruit skin surface in a light-proof box, was used to deliver monochromatic light to the fruit, and to collect NIR reflectance spectra (760–2500 nm). The probe illuminated and collected reflected radiation from an area of about 16 cm2. The NIR spectral attributes were correlated with pineapple juice Brix and with mango flesh dry matter (DM) measured from fruit flesh directly underlying the scanned area. The highest correlations for both fruit were found using the second derivative of the spectra (d2 log 1/R) and an additive calibration equation. Multiple linear regression (MLR) on pineapple fruit spectra (n = 85) gave a calibration equation using d2 log 1/R at wavelengths of 866, 760, 1232 and 832 nm with a multiple coefficient of determination (R2) of 0.75, and a standard error of calibration (SEC) of 1.21 °Brix. Modified partial least squares (MPLS) regression analysis yielded a calibration equation with R2 = 0.91, SEC = 0.69, and a standard error of cross validation (SECV) of 1.09 oBrix. For mango, MLR gave a calibration equation using d2 log 1/R at 904, 872, 1660 and 1516 nm with R2 = 0.90, and SEC = 0.85% DM and a bias of 0.39. Using MPLS analysis, a calibration equation with R2 = 0.98, SEC = 0.54 and SECV = 1.19 was obtained. We conclude that NIR technology offers the potential to assess fruit sweetness in intact whole pineapple and DM in mango fruit, respectively, to within 1° Brix and 1% DM, and could be used for the grading of fruit in fruit packing sheds.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.