Resource Theft in Tropical Forest Communities: Implications for Non-timber Management, Livelihoods, and Conservation

Increased devolution of forest ownership and management rights to local control has the potential to promote both conservation and livelihood development in remote tropical regions. Such shifts in property rights, however, can generate conflicts, particularly when combined with rapidly increasing values of forest resources. We explored the phenomenon of Brazil nut (Bertholletia excelsa) theft in communities in Western Amazonia. Through interviews with 189 Brazil nut collectors in 12 communities in Bolivia and Brazil and participation in the 2006 and 2007 harvests, we quantified relative income derived from Brazil nuts, reported nut thefts, and nut collection and management practices. We found a much greater incidence of reported Brazil nut thefts in Pando, Bolivia than in the adjacent state of Acre, Brazil. Our analyses suggest that three factors may have affected nut thefts in the forest: (1) contrasts in the timing and process of formally recognizing property rights, (2) different historic settlement patterns, and (3) varying degrees of economic dependence on Brazil nuts. Threat of theft influenced Brazil nut harvest regimes, with potentially long-term implications for forest-based livelihoods, and management and conservation of Brazil nut-rich forests in Western Amazonia

Permethrin-Treated Clothing as Protection against the Dengue Vector, Aedes aegypti: Extent and Duration of Protection.

INTRODUCTION: Dengue transmission by the mosquito vector, Aedes aegypti, occurs indoors and outdoors during the day. Personal protection of individuals, particularly when outside, is challenging. Here we assess the efficacy and durability of different types of insecticide-treated clothing on laboratory-reared Ae. aegypti. METHODS: Standardised World Health Organisation Pesticide Evaluation Scheme (WHOPES) cone tests and arm-in-cage assays were used to assess knockdown (KD) and mortality of Ae. aegypti tested against factory-treated fabric, home-dipped fabric and microencapsulated fabric. Based on the testing of these three different treatment types, the most protective was selected for further analysis using arm-in cage assays with the effect of washing, ultra-violet light, and ironing investigated using high pressure liquid chromatography. RESULTS: Efficacy varied between the microencapsulated and factory dipped fabrics in cone testing. Factory-dipped clothing showed the greatest effect on KD (3 min 38.1%; 1 hour 96.5%) and mortality (97.1%) with no significant difference between this and the factory dipped school uniforms. Factory-dipped clothing was therefore selected for further testing. Factory dipped clothing provided 59% (95% CI = 49.2%- 66.9%) reduction in landing and a 100% reduction in biting in arm-in-cage tests. Washing duration and technique had a significant effect, with insecticidal longevity shown to be greater with machine washing (LW50 = 33.4) compared to simulated hand washing (LW50 = 17.6). Ironing significantly reduced permethrin content after 1 week of simulated use, with a 96.7% decrease after 3 months although UV exposure did not reduce permethrin content within clothing significantly after 3 months simulated use. CONCLUSION: Permethrin-treated clothing may be a promising intervention in reducing dengue transmission. However, our findings also suggest that clothing may provide only short-term protection due to the effect of washing and ironing, highlighting the need for improved fabric treatment techniques

Personal Protection of Permethrin-Treated Clothing against Aedes aegypti, the Vector of Dengue and Zika Virus, in the Laboratory.

BACKGROUND: The dengue and Zika viruses are primarily transmitted by Aedes aegypti mosquitoes, which are most active during day light hours and feed both in and outside of the household. Personal protection technologies such as insecticide-treated clothing could provide individual protection. Here we assessed the efficacy of permethrin-treated clothing on personal protection in the laboratory. METHODS: The effect of washing on treated clothing, skin coverage and protection against resistant and susceptible Ae. aegypti was assessed using modified WHO arm-in-cage assays. Coverage was further assessed using free-flight room tests to investigate the protective efficacy of unwashed factory-dipped permethrin-treated clothing. Clothing was worn as full coverage (long sleeves and trousers) and partial coverage (short sleeves and shorts). Residual permethrin on the skin and its effect on mosquitoes was measured using modified WHO cone assays and quantified using high-pressure liquid chromatography (HPLC) analysis. RESULTS: In the arm-in-cage assays, unwashed clothing reduced landing by 58.9% (95% CI 49.2-66.9) and biting by 28.5% (95% CI 22.5-34.0), but reduced to 18.5% (95% CI 14.7-22.3) and 11.1% (95% CI 8.5-13.8) respectively after 10 washes. Landing and biting for resistant and susceptible strains was not significantly different (p80% one hour after wearing treated clothing. CONCLUSION: Whilst partially covering the body with permethrin-treated clothing provided some protection against biting, wearing treated clothing with long sleeves and trousers provided the highest form of protection. Washing treated clothing dramatically reduced protection provided. Permethrin-treated clothing could provide protection to individuals from Ae. aegypti that show permethrin resistance. Additionally, it could continue to provide protection even after the clothing has been worn. Field trials are urgently needed to determine whether clothing can protect against dengue and Zika

FielDHub: A shiny app for design of experiments in life sciences

FielDHub is an R Shiny design of experiments (DOE) app that aids in the creation of traditional, unreplicated, augmented and partially-replicated (Cullis et al., 2006) designs applied to agriculture, plant breeding, forestry, animal and biological sciences. One of the problems that life scientists often face is the lack of freely available and user-friendly interactive tools to create designs that fit their needs. A few open-source DOE R packages options exist including agricolae (de Mendiburu & Yaseen, 2020) and blocksdesign (Edmondson, 2021), but they require users to be familiar with the R programming language and do not have a graphical user interface (GUI)

Mr.Bean: a comprehensive statistical and visualization application for modeling agricultural field trials data

Crop improvement efforts have exploited new methods for modeling spatial trends using the arrangement of the experimental units in the field. These methods have shown improvement in predicting the genetic potential of evaluated genotypes. However, the use of these tools may be limited by the exposure and accessibility to these products. In addition, these new methodologies often require plant scientists to be familiar with the programming environment used to implement them; constraints that limit data analysis efficiency for decision-making. These challenges have led to the development of Mr.Bean, an accessible and user-friendly tool with a comprehensive graphical visualization interface. The application integrates descriptive analysis, measures of dispersion and centralization, linear mixed model fitting, multi-environment trial analysis, factor analytic models, and genomic analysis. All these capabilities are designed to help plant breeders and scientist working with agricultural field trials make informed decisions more quickly. Mr.Bean is available for download at https://github.com/AparicioJohan/MrBeanApp

Phenotypic Description of Theobroma cacao L. for Yield and Vigor Traits From 34 Hybrid Families in Costa Rica Based on the Genetic Basis of the Parental Population

A comprehensive understanding of the genetic basis of target traits in any crop is critical to design breeding strategies for the development and release of new improved varieties. In this study, 34 cacao families were evaluated for vigor and yield related traits over the course of 6 years in Costa Rica. Linear mixed models provided the variance components for the partitioning of additive and non-additive effects. Heritabilities of yield over time ranged from 0.085 to 0.576, from 0.127 to 0.399 for vigor, and 0.141 to 0.146 for disease resistance traits. Significant (p < 0.001) general combining abilities were observed for ICS-43 and LcTeen-37 with negative effect on average yield (−0.674, −0.690), respectively. Specific combining abilities for yield had significant (p < 0.001) positive effect from the cross GU-154-L x UF-273 Type 2 (0.703) and strong negative interaction between ICS-43 and LF-1 (−0.975). A weighted index was used to select the top performers while providing the corresponding genetic gains. At an 1% selection intensity, yield component gains ranged from 17.8 to 331.9%. Agronomic traits such as branch angle, trunk diameter and jorquette height had lower genetic gains and lower heritabilities. In addition, the parents in this study were genotyped with a 96-SNP marker off-typing set and a significant positive correlation of 0.39 (p = 0.019) was found between genetic distance and specific combining abilities for yield. Preliminary comparison of clonal parents vs. seedlings yield in the family with the highest SCA suggest for the first time presence of heterobeltiosis in cacao

Sex Modulates Lactobacillus johnsonii N6.2 and Phytophenol Effectiveness in Reducing High Fat Diet Induced mTOR Activation in Sprague-Dawley Rats

Metabolic syndrome (MetS) is the underlying cause of some devastating diseases, including type 2 diabetes and cardiovascular disease. These diseases have been associated with over-activation of the mechanistic Target of Rapamycin (mTOR) pathway. This study utilizes a high fat diet (HFD) to induce MetS and to dissect the effects of a beneficial bacterium, L. johnsonii N6.2, and natural phenolics on mTOR complex 1 (mTORC1) expression compared to a reduced energy density diet (REDD). HFD significantly elevated MetS markers in males, as noted through an increase in weight, glucose levels, and triglyceride levels. Treatments were effective in reducing mTORC1-activating phosphorylation of pAKT-T308 and pAKT-S473 (p = 0.0012 and 0.0049, respectively) in HFD-fed females, with the combined treatments of L. johnsonii and phytophenols reducing phosphorylation below REDD-fed control levels, and significantly below HFD-fed control levels. Meanwhile, diet was the significant factor influencing male mTORC1-activating phosphorylation (p < 0.0001), as treatments were only effective in reducing phosphorylation in REDD-fed animals. Downstream analysis of mTORC1 activated genes phosphogluconate dehydrogenase (pgd) and phosphofructose kinase (pfk) followed this similar trend, enforcing the significant effect sex has on a treatments’ ability to modulate diet induced abnormalities. Analyzing mTORC1 stimulators such as insulin, inflammatory cytokines, and tryptophan, revealed no significant differences among groups. These results indicate that the effects observed on mTORC1 are a direct consequence of the treatments, and not exerted indirectly via the modulation of stimuli. This study highlights the potential use of commensal microorganisms and natural compounds in reducing the onset of metabolic diseases through mTORC1

Optimizing sparse testing for genomic prediction of plant breeding crops

While sparse testing methods have been proposed by researchers to improve the efficiency of genomic selection (GS) in breeding programs, there are several factors that can hinder this. In this research, we evaluated four methods (M1–M4) for sparse testing allocation of lines to environments under multi-environmental trails for genomic prediction of unobserved lines. The sparse testing methods described in this study are applied in a two-stage analysis to build the genomic training and testing sets in a strategy that allows each location or environment to evaluate only a subset of all genotypes rather than all of them. To ensure a valid implementation, the sparse testing methods presented here require BLUEs (or BLUPs) of the lines to be computed at the first stage using an appropriate experimental design and statistical analyses in each location (or environment). The evaluation of the four cultivar allocation methods to environments of the second stage was done with four data sets (two large and two small) under a multi-trait and uni-trait framework. We found that the multi-trait model produced better genomic prediction (GP) accuracy than the uni-trait model and that methods M3 and M4 were slightly better than methods M1 and M2 for the allocation of lines to environments. Some of the most important findings, however, were that even under a scenario where we used a training-testing relation of 15–85%, the prediction accuracy of the four methods barely decreased. This indicates that genomic sparse testing methods for data sets under these scenarios can save considerable operational and financial resources with only a small loss in precision, which can be shown in our cost-benefit analysis

Heritability of Attractiveness to Mosquitoes

Female mosquitoes display preferences for certain individuals over others, which is determined by differences in volatile chemicals produced by the human body and detected by mosquitoes. Body odour can be controlled genetically but the existence of a genetic basis for differential attraction to insects has never been formally demonstrated. This study investigated heritability of attractiveness to mosquitoes by evaluating the response of Aedes aegypti (=Stegomyia aegypti) mosquitoes to odours from the hands of identical and non-identical twins in a dual-choice assay. Volatiles from individuals in an identical twin pair showed a high correlation in attractiveness to mosquitoes, while non-identical twin pairs showed a significantly lower correlation. Overall, there was a strong narrow-sense heritability of 0.62 (SE 0.124) for relative attraction and 0.67 (0.354) for flight activity based on the average of ten measurements. The results demonstrate an underlying genetic component detectable by mosquitoes through olfaction. Understanding the genetic basis for attractiveness could create a more informed approach to repellent development

Development of a QTL-environment-based predictive model for node addition rate in common bean

To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day− 1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50–90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions