Resource Use Assessment of Shrimp,Litopenaeus vannamei and Penaeus monodon,Production in Thailand and Vietnam

Resource use was investigated at 34 Litopenaeus vannamei and five Penaeus monodon farms in Thailand and 30 L. vannamei and 24 P. monodon farms in Vietnam. Farms varied in water surface areas for production, reservoirs, canals, and settling basins; in pond size and depth; and in water management, stocking density, feeding rate, amendment input, aeration rate, crop duration, and crops per year. Production of L. vannamei averaged 17.3 and 10.9 m.t./ha/yr, and feed conversion ratio averaged 1.49 and 1.33 in Thailand and Vietnam, respectively. On average, production of 1 m.t. of L. vannamei required 0.58 ha land, 5,400 m³ water, 60 GJ energy, and 1218 kg wildfish in Thailand and 1.76 ha land, 15,100 m³ water, 33.7 GJ energy, and 1264 kg wildfish in Vietnam. Resource use per metric ton of shrimp declined with greater production intensity. In Thailand, P. monodon was produced at 0.2–0.4 m.t./ha/yr, with no inputs but water and postlarvae. In Vietnam, P. monodon production averaged 3.60 m.t./ha/yr. Production of 1 m.t. of P. monodon required 0.80 ha land, 36,000 m³ water, 47.8 GJ energy, and 1180 kg wildfish, and resource use per ton production declined with increasing production intensity.     

Veterinary ocular microbiome: Lessons learned beyond the culture

Ocular pathogens cause many painful and vision‐threatening diseases such as infectious keratitis, uveitis, and endophthalmitis. While virulent pathogens and pathobionts play important roles in disease pathogenesis, the scientific community has long assumed disruption of the ocular surface occurs prior to microbial colonization and subsequent infection. While nonpathogenic bacteria are often detected in corneal and conjunctival cultures from healthy eyes, cultures also frequently fail to yield growth of common ocular pathogens or nonpathogenic bacteria. This prompts the following question: Is the ocular surface populated by a stable microbial population that cannot be detected using standard culture techniques? The study of the microbiome has recently become a widespread focus in physician and veterinary medicine. Research suggests a pivotal symbiotic relationship with these microbes to maintain healthy host tissues, and when altered is associated with various disease states (“dysbiosis”). The microbiota that lives within and on mammalian bodies have long been known to influence health and susceptibility to infection. However, limitations of traditional culture methods have resulted in an incomplete understanding of what many now call the “forgotten organ,” that is, the microbiome. With the introduction of high‐throughput sequencing, physician ophthalmology has recognized an ocular surface with much more diverse microbial communities than suspected based on traditional culture. This article reviews the salient features of the ocular surface microbiome and highlights important future applications following the advent of molecular techniques for microbial identification, including characterizing ocular surface microbiomes in our veterinary species and their potential role in management of infectious and inflammatory ocular diseases.     

A field guide for aging passerine nestlings using growth data and predictive modeling

Background: Accurate nestling age is valuable for studies on nesting strategies, productivity, and impacts on repro-ductive success. Most aging guides consist o...     

Determining minimum sample size for the conditioned Latin hypercube sampling algorithm

In digital soil mapping (DSM), a fundamental assumption is that the spatial variability of the target variable can be explained by the predictors or environmental covariates. Strategies to adequately sample the predictors have been well documented, with the conditioned Latin hypercube sampling (cLHS) algorithm receiving the most attention in the DSM community. Despite advances in sampling design, a critical gap remains in determining the number of samples required for DSM projects. We propose a simple workflow and function coded in R language to determine the minimum sample size for the cLHS algorithm based on histograms of the predictor variables using the Freedman-Diaconis rule for determining optimal bin width. Data preprocessing was included to correct for multimodal and non-normally distributed data, as these can affect sample size determination from the histogram. Based on a user-selected quantile range (QR) for the sample plan, the densities of the histogram bins at the upper and lower bounds of the QR were used as a scaling factor to determine minimum sample size. This technique was applied to a field-scale set of environmental covariates for a well-sampled agricultural study site near Guelph, Ontario, Canada, and tested across a range of QRs. The results showed increasing minimum sample size with an increase in the QR selected. Minimum sample size increased from 44 to 83 when the QR increased from 50% to 95% and then increased exponentially to 194 for the 99% QR. This technique provides an estimate of minimum sample size that can be used as an input to the cLHS algorithm.     

Production Response of Freshwater Prawns Macrobrachium rosenbergii to Increasing Amounts of Artificial Substrate in Ponds

Abstract.— The response of freshwater prawns Macrobrachium rosenbergii to increasing amounts of artificial substrate was evaluated in ponds. Juvenile prawns (0.24 ± 0.13 g) were stocked into nine 0.04-ha ponds at 74,000/ha. Three control ponds received no artificial substrate while artificial substrate in the form of horizontal strips of polyethylene "construction fence" was added to the six treatment ponds to produce 40% or 80% increases in available surface area. Increasing availability of surface area produced a direct linear increase ( P < 0.05, r ²= 0.89) in total production with no significant change in average weight ( P > 0.05). There was an inverse linear relationship between available surface area and feed conversion ratios ( P < 0.01, r ² = 0.66) likely indicating increased availability of natural foods or reduced stress among animals. There was a direct linear increase in the percentage of females which achieved sexual maturity ( P < 0.01, r ²= 0.71) as the amount of added substrate was increased. Size and number of other sexual morphotypes were not significantly affected. These responses are consistent with those that would be expected if stocking densities were decreased. These data indicate that prawn production increases in direct relation to the amount of added substrate while utilizing feed more efficiently. The effect of substrate orientation on its functionality should be evaluated to allow further increases in substrate inclusion amounts for additional production intensification.