The impact of truncating data on the predictive ability for single‐step genomic best linear unbiased prediction

Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single‐step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively removed 1 year at a time based on the year an animal was born. For the swine data sets, correlations between corrected phenotypes (Cp) and EBV were used to evaluate the predictive ability on young animals born in 2016–2017. In the simulated data set, keeping data two generations back or greater resulted in no statistical difference (p‐value > 0.05) in the reduction in the true breeding value at generation 15 compared to utilizing all available data. Across swine data sets, removing phenotypes from animals born prior to 2011 resulted in a negligible or a slight numerical increase in the correlation between Cp and EBV. Truncating data is a method to alleviate computational issues without negatively impacting the predictive ability of selection candidate EBV.     

Factors associated with practice decisions of Nebraska veterinarians regarding type of practice and community size

Nebraska veterinary practitioners were surveyed to collect data about background characteristics and other factors related to veterinarians' decision to include or not include food animals in their practices and to practice in rural versus urban communities. Background characteristics that were significantly (p < or = 0.05) associated with choosing food-animal practice included growing up on a working farm or ranch; having parents who owned livestock; growing up in a town with a population of less than 10,000; majoring in animal science at university; being male; and having a primary interest, at the time of entering veterinary college, in food animal-exclusive or mixed-animal veterinary practice. The primary factor for choosing the community in which to practice was rural/urban lifestyle for rural veterinarians, while this factor was second for urban veterinarians. For all groups of veterinarians, the primary consideration in selecting their current practice was the species orientation of the practice. The primary reason for not choosing food-animal practice was better working conditions and lifestyle in companion-animal practice, followed by greater interest elsewhere.     

基于稳健统计学确定高潜在污染土壤Cu、Pb基线值

土壤重金属基线值的确定对于评价重金属积累现状具有重要意义。在存在人为污染的情况下,土壤重金属数据经常强烈地偏离正态分布和对数正态分布,影响统计分析结果的可信度,而稳健统计方法可以降低离群值对统计分析结果的影响。本研究以张家港为研究区域,采用稳健回归方法,建立表层和底层土壤Cu、Pb的回归关系,通过底层土壤Cu、Pb含量计算表层土壤Cu、Pb基线值,并对土壤Cu、Pb积累状况进行评价。结果表明,稳健回归方法确定的表层土壤Cu、Pb基线值可以反映出不同样点间,土壤Cu、Pb背景含量的自然变异。与稳健统计学方法相比,使用国家土壤环境质量标准值,或大区域(江苏省)的土壤背景值,评价自然背景本身存在变异区域的土壤重金属积累状况时,可能会产生较大的误差。     

基于稳健统计学确定高潜在污染土壤Cu、Pb基线值

土壤重金属基线值的确定对于评价重金属积累现状具有重要意义。在存在人为污染的情况下,土壤重金属数据经常强烈地偏离正态分布和对数正态分布,影响统计分析结果的可信度,而稳健统计方法可以降低离群值对统计分析结果的影响。本研究以张家港为研究区域,采用稳健回归方法,建立表层和底层土壤Cu、Pb的回归关系,通过底层土壤Cu、Pb含量计算表层土壤Cu、Pb基线值,并对土壤Cu、Pb积累状况进行评价。结果表明,稳健回归方法确定的表层土壤Cu、Pb基线值可以反映出不同样点间,土壤Cu、Pb背景含量的自然变异。与稳健统计学方法相比,使用国家土壤环境质量标准值,或大区域（江苏省）的土壤背景值,评价自然背景本身存在变异区域的土壤重金属积累状况时,可能会产生较大的误差。     

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.

     

Evidence from tag recapture experiments that fish learn to avoid fishing gear

We found evidence that tagging induced trap shyness in snapper (Pagrus auratus), i.e., tagged fish had a reduced probability of recapture by the method by which they had originally been caught. Tagging experiments in 1985 and 1994 involving single release over a short period and single recapture were conducted on a closed population (SNA 1: East Northland-Hauraki Gulf-Bay of Plenty). Initial capture was by trawling and by line fishing, while recapture over an extended period included other methods. A test for trap shyness that removed the possible effects of spatial and fish size heterogeneity gave a significant result for both years. The data suggested that the trap shyness effect might have been smaller for trawl-released fish than for line-released fish. However, we estimated a single trap shyness factor (0.71). There was also some evidence for attenuation of trap shyness over time.     

Artificial neural networks for estimating soil hydraulic parameters from dynamic flow experiments

Inverse methods are often used for estimating soil hydraulic parameters from experiments on flow of water through soil. We propose here an alternative method using neural networks. We teach a problem‐adapted network of radial basis functions (RBF) the relationship between soil parameters and transient flow patterns using a numerical flow model. The trained RBF network accurately identifies soil parameters from flow patterns not contained in the training scenarios. A comparison with the inverse method (Annealing‐Simplex) reveals a similarly good prediction by both approaches for randomly perturbed data and data from the real world. Nonetheless, the inverse method showed dependency on initial parameter estimates not required by the RBF network. Training demands moderately more computation and manpower than the inverse technique, but the absolutely stable and simple network application requires negligible resources. Thus, for individual applications, the network approach is slightly surpassed by the Annealing‐Simplex method. However, the RBF network has to be trained only once and, subsequently, it can be applied easily and without effort upon any number of laboratory experiments with standardized experimental setups.     

Spatiotemporal Distribution of Two Euschistus spp. Stink Bugs (Hemiptera: Pentatomidae) in Southeastern Farmscapes

Stink bugs (Hemiptera: Pentatomidae) are ubiquitous, cryptic, phytophagous pests that are found in many crops. In agroecosystems, individuals disperse from adjacent noncrop hosts and tend to aggregate or cluster within fields. In this study, we characterized the distribution of Euschistus servus (Say) and Euschistus tristigmus (Say) (Hemiptera: Pentatomidae) over 2 yr at three southeastern United States farmscapes. Stink bugs were captured in pheromone-baited traps, and Spatial Analysis by Distance Indices (SADIE) used to identify the location of significant aggregations by habitat type and season. Euschistus servus adults were more likely to be captured in pecan orchards, cotton, other crops, or unmanaged habitats than in woodland habitats. Significant aggregations of E. servus were detected in a variety of habitats including pecan, corn, cotton, peanut, and tobacco, as well as fallow and hay fields, pastures, and hedgerows. Fewer adult E. tristigmus were captured than E. servus adults, and E. tristigmus adults were typically trapped and aggregated in woodland habitats. The resulting data provide an important understanding regarding the seasonal movement and relative abundance levels of stink bug populations, which are critical to the development of integrated pest management strategies.