Using unsupervised learning techniques to assess interactions among complex traits in soybeans

Soybean yield components and agronomic traits are connected through physiological pathways that impose tradeoffs through genetic and environmental constraints. Our primary aim is to assess the interdependence of soybean traits by using unsupervised machine learning techniques to divide phenotypic associations into environmental and genetic associations. This study was performed on large scale, jointly analyzing 14 quantitative traits in a large multi-parental population designed for genetic studies. We collected phenotypes from 2012 to 2015 from a soybean nested association panel with 40 families of approximately 140 individuals each. Pearson and Spearman correlations measured phenotypic associations. A multivariate mixed linear model provided genotypic and environmental correlations. To evaluate relationships among traits, the study used principal component and undirected graphical models from phenotypic, genotypic, and environmental correlation matrices. Results indicate that high phenotypic correlation occurs when traits display both genetic and environmental correlations. In genetic terms, length of reproductive period, node number, and canopy coverage play important roles in determining yield potential. Optimal grain yield production occurs when the growing environment favors faster canopy closure and extended reproductive length. Environmental associations found among yield components give insight into the nature of yield component compensation. The use of unsupervised learning methods provides a good framework for investigating interactions among various quantitative traits and defining target traits for breeding.     

Assessing the efficacy and impact of management of an invasive species in a protected area: Poa annua on sub‐Antarctic Macquarie Island

Plant eradication is difficult, particularly in remote, protected areas. The Southern Ocean Islands are very isolated and highly protected, but the flora contains many alien plants. Small restricted populations have been eradicated, but eradication of established species has proven difficult. A better understanding of the efficacy of control methods at sub‐Antarctic temperatures and their off‐target impacts may increase eradication success. With interest in controlling non‐native Poa annua in the region, we aimed to determine if physical and chemical methods can control P. annua (the sub‐Antarctic biotype) in sub‐Antarctic conditions and examined their impact on native plants. We quantified the effectiveness of physical control methods on P. annua in situ on sub‐Antarctic Macquarie Island through field‐based experiments and assessed their selectivity on P. annua compared with native grasses. We also quantified the effectiveness of several herbicides on P. annua at sub‐Antarctic temperatures and assessed their selectivity on native grasses. Of the four physical disturbance methods tested, none effectively reduced P. annua cover as one‐off treatments. Of the herbicide treatments, glyphosate and trifloxysulfuron sodium were effective and were less damaging to native grass species, indicating potential selectivity. Physical control was of limited effectiveness, but did not affect native species richness. An integrated weed management programme utilising the strategic use of selective herbicides with follow‐up chemical and physical intervention may balance control and biodiversity outcomes. This research highlights the importance of site‐specific testing of control methods and understanding off‐target impacts of control when managing alien plant species in protected areas.     

The homogenizing influence of agriculture on forest bird communities at landscape scales

Landscape Ecology - Agricultural expansion is a principal driver of biodiversity loss, but the impacts on community assembly in agro-ecosystems are less clear, especially across regional scales at...     

A new framework of spatial targeting for single-species conservation planning

Context

Organisations acting to conserve and protect species across large spatial scales prioritise to optimise use of resources. Spatial conservation prioritization tools typically focus on identifying areas containing species groups of interest, with few tools used to identify the best areas for single-species conservation, in particular, to conserve currently widespread but declining species.

Objective

A single-species prioritization framework, based on temporal and spatial patterns of occupancy and abundance, was developed to spatially prioritize conservation action for widespread species by identifying smaller areas to work within to achieve predefined conservation objectives.

Methods

We demonstrate our approach for 29 widespread bird species in the UK, using breeding bird atlas data from two periods to define distribution, relative abundance and change in relative abundance. We selected occupied 10-km squares with abundance trends that matched species conservation objectives relating to maintaining or increasing population size or range, and then identified spatial clusters of squares for each objective using a Getis-Ord-Gi* or near neighbour analysis.

Results

For each species, the framework identified clusters of 20-km squares that enabled us to identify small areas in which species recovery action could be prioritized.

Conclusions

Our approach identified a proportion of species’ ranges to prioritize for species recovery. This approach is a relatively quick process that can be used to inform single-species conservation for any taxa if sufficiently fine-scale occupancy and abundance information is available for two or more time periods. This is a relatively simple first step for planning single-species focussed conservation to help optimise resource use.

     

Identifying climate refugia for 30 Australian rainforest plant species,from the last glacial maximum to 2070

Landscape Ecology - Climate refugia—areas that remain suitable for species during periods of climate disruption—have played an important role in species persistence over time....     

甘蓝黑腐病菌rPf基因簇对胞外蛋白酶Ⅰ基因表达的调控

甘蓝黑腐病黄单胞菌（ＸａｎｔｈｏｍｏｎａｓｃａｍｐｅｓｔｒｉｓＰｖ．ｃａｍｐｅｓｔｒｉｓ）产生的胞外蛋白酶Ⅰ在致病的早期阶段起重要作用，该酶以及其它胞外酶和胞外多糖的合成受一致病因子调控基因簇（ｒＰｆ基因簇）的正向调控。本研究利用带有β－半乳糖苷酶报道基因（ｌａｃＺ）的转座子Ｔｎ５－Ｂ２０诱变蛋白酶Ⅰ基因克隆，获得了ｌａｃＺ在蛋白酶Ⅰ基因启动子控制下表达的Ｔｎ５－Ｂ２０插入突变质粒。通过将这种突变质粒导入野生型和各ｒｐｆ基因突变体菌株后，测定ｌａｃＺ基因在细胞生长周期中的表达水平，不仅进一步证实了这些ｒｐｆ基因对蛋白酶Ⅰ基因的正向调控作用，而且明确了它们的调控水平．发现ｒｐｆＡ、ｒｐｆＣ、ｒｐｆＥ、ｒｐｆＧ或ｒｐｆＨ突变后，蛋白酶Ⅰ基因的转录会降低９０％左右，而ｒｐｆＢ突变后，蛋白酶Ⅰ基因的转录只降低４８％。     

Chemical composition of caneberry (Rubus spp.) seeds and oils and their antioxidant potential

Caneberries (Rubus spp. L.) are grown primarily throughout the Pacific Northwestern United States and Canada. Processing of caneberry fruit typically removes the seed, and the development of a value-added use of seeds could expand the market for caneberries and the profit margins for growers. An initial step toward the use of the seeds is a characterization of seed and oil. Our investigation has described compositional characteristics for seeds of five commonly grown caneberry species: red raspberry, black raspberry, boysenberry, Marion blackberry, and evergreen blackberry. Seeds from all five species had 6-7% protein and 11-18% oil. The oils contained 53-63% linoleic acid, 15-31% linolenic acid, and 3-8% saturated fatty acids. The two smaller seeded raspberry species had higher percentages of oil, the lowest amounts of saturated fatty acid, and the highest amounts of linolenic acid. Antioxidant capacities were detected both for whole seeds and for cold-pressed oils but did not correlate to total phenolics or tocopherols. Ellagitannins and free ellagic acid were the main phenolics detected in all five caneberry species and were approximately 3-fold more abundant in the blackberries and the boysenberry than in the raspberries.     

Crop rotation and nitrogen fertilization effect on soil CO2 emissions in central Iowa

Depending upon how soil is managed, it can serve as a source or sink for atmospheric carbon dioxide (CO₂). As the atmospheric CO₂ concentration continues to increase, more attention is being focused on the soil as a possible sink for atmospheric CO₂. This study was conducted to examine the short-term effects of crop rotation and N fertilization on soil CO₂ emissions in Central Iowa. Soil CO₂ emissions were measured during the growing seasons of 2003 and 2004 from plots fertilized with three N rates (0, 135, and 270 kg N ha⁻¹) in continuous corn and a corn–soybean rotation in a split-plot design. Soil samples were collected in the spring of 2004 from the 0–15 cm soil depth to determine soil organic C content. Crop residue input was estimated using a harvest index based on the measured crop yield. The results show that increasing N fertilization generally decreased soil CO₂ emissions and the continuous corn cropping system had higher soil CO₂ emissions than the corn–soybean rotation. Soil CO₂ emission rate at the peak time during the growing season and cumulative CO₂ under continuous corn increased by 24 and 18%, respectively compared to that from corn–soybean rotation. During this period, the soil fertilized with 270 kg N ha⁻¹ emitted, on average, 23% less CO₂ than the soil fertilized with the other two N rates. The greatest difference in CO₂ emission rate was observed in 2004; where plots that received 0 N rate had 31% greater CO₂ emission rate than plots fertilized with 270 kg N ha⁻¹. The findings of this research indicate that changes in cropping systems can have immediate impact on both rate and cumulative soil CO₂ emissions, where continuous corn caused greater soil CO₂ emissions than corn soybean rotation.     

Genetic Advancement of Newly Developed Wheat Populations Under Drought-Stressed and Non-Stressed Conditions

Journal of Crop Science and Biotechnology - Genetic variation and heritability estimates in early generations are important in identifying superior families that can be targeted for genetic...