Population dynamics of Dasiops inedulis (Diptera: Lonchaeidae) and its biotic and abiotic mortality factors in Colombian sweet passionfruit orchards

Throughout South America, the lonchaeid flies Dasiops spp. are important herbivores of passionfruit crops. However, little is known on the biology and ecology of these insects, resulting in inadequate pest management schemes. In this study, we describe Dasiops inedulis population dynamics in Colombian sweet passionfruit (SP; Passiflora ligularis Juss.) and elucidate biotic mortality factors at different fly developmental stages. From August 2009 to July 2010, D. inedulis and Dasiops spp. abundance was assessed through monthly McPhail bait trapping and collection of SP flower buds, flowers, and immature fruits. Mortality levels of D. inedulis were determined for early instar larvae by ovary dissection and for late-instar larvae or pupae by prey removal trials. Maximum infestation reached 80 % in fruits and flower buds, and bud infestation correlated with precipitation during the previous month. Two days after oviposition, 8.2 ± 2.3 (mean ± SD) Dasiops sp. eggs were found in SP ovaries and 4.4 ± 1.2 late-instar larvae were recovered from immature fruits at day 14. Upon larval drop on the orchard soil, 74.8 % larvae burrowed within the soil within 9 min, while 12.5 % larvae were attacked by ants. In-field mortality of young pupae amounted to 75.3 ± 7.0 %, with vertebrate predators likely causing 12.1 ± 6.0 % mortality. Late-instar larvae and pupae appear highly vulnerable to natural enemy action, with the ground-foraging predator community mainly composed of ants (80.37 %) and ground beetles (9.17 %). Our findings should help develop integrated pest management (IPM) tactics for SP crops.     

Tree diversity and composition in Mexican traditional smallholder cocoa agroforestry systems

Agroforestry Systems - The cultivation of cocoa is a commodity of great importance worldwide. In Mexico, cocoa is grown in Tabasco and Chiapas states in agroforestry systems since pre-Hispanic...     

Additions of landscape metrics improve predictions of occurrence of species distribution models

Species distribution models are used to aid our understanding of the processes driving the spatial patterns of species’ habitats. This approach has received criticism, however, largely because it neglects landscape metrics. We examined the relative impacts of landscape predictors on the accuracy of habitat models by constructing distribution models at regional scales incorporating environmental variables (climate, topography, vegetation, and soil types) and secondary species occurrence data, and using them to predict the occurrence of 36 species in 15 forest fragments where we conducted rapid surveys. We then selected six landscape predictors at the landscape scale and ran general linear models of species presence/absence with either a single scale predictor (the probabilities of occurrence of the distribution models or landscape variables) or multiple scale predictors (distribution models + one landscape variable). Our results indicated that distribution models alone had poor predictive abilities but were improved when landscape predictors were added; the species responses were not, however, similar to the multiple scale predictors. Our study thus highlights the importance of considering landscape metrics to generate more accurate habitat suitability models.     

Lumber volume modeling of Amazon Brazilian species

Accurate estimates of lumber volume become an important indicator of production and monetary value for a sawmill. However, such estimates are only obtained directly after logging, and no accurate predictions are made for the Amazonian commercial species. In this sense, the objective was to generate equations of lumber volume for commercial species in Amapá (a state in northern Brazil) by adjusting and selecting regression models. The data of 50 logs processed from 10 commercial species were collected, as well as the quantity and volume of sawn products. Sixteen (16) statistical models were adjusted and statistical weights were performed to evaluate the quality of the estimates and to select the best equation by species. In summary, precise estimates of lumber volume can be obtained by the nº16 model for Carapa guianensis, while the models nº13 and nº15 are the most recommended for Dinizia excelsa and Hymenolobium petraeum, respectively. Model 7 presented the best adjustments for Hymenaea courbaril and Vochysia guianensis. Equations using only the log diameter variable suggest less precise estimates. Also, the log volume should be considered as an important predictor variable to obtain the serrated/lumber volume for the different Amazonian commercial species.     

Role of Starch Granule Characteristics (volume fraction,rigidity, and fractal dimension) on Rheology of Starch Dispersions With and Without Amylose

The link between rheological behavior and morphological‐structural characteristics of gelatinized starch granules has been studied in two starch dispersions (SDs): a cross‐linked waxy maize (CLWM), and tapioca starch, a tuber starch with 19.3 % amylose. Based on the power law relationship between the elastic modulus and volume fraction of the granules predicted by scaling theory, fractal dimension values were obtained for CLWM starch D = 2.81, and tapioca starch D = 2.79, suggesting that both starch granules have highly convoluted surfaces. However, the preexponential coefficient (G′_⊘=1) for CLWM SDs was an order of magnitude greater than that for tapioca SDs, in the same range of volume fractions. The G′_⊘=1 was mainly dependent on the granule rigidity, and the amylose content in the continuous phase played only a minor role in the rheological behavior.