Causal inference and large-scale expert validation shed light on the drivers of SDM accuracy and variance

Aim

To develop a causal understanding of the drivers of Species distribution model (SDM) performance.

Location

United Kingdom (UK).

Methods

We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among replicate model fits, and taxon experts assessed model accuracy. Using directed acyclic graphs, we developed a causal model depicting plausible effects of explanatory variables (e.g. species' prevalence, sample size) on SDM accuracy and variance and quantified those effects using a multilevel piecewise path model.

Results

According to our model, sample size and niche completeness (proportion of a species' niche covered by sampling) directly affect SDM accuracy and variance. Prevalence and range completeness have indirect effects mediated by sample size. Challenging conventional wisdom, we found that the effect of prevalence on SDM accuracy is positive. This reflects the facts that sample size has a positive effect on accuracy and larger sample sizes are possible for widespread species. It is possible, however, that the omission of an unobserved confounder biased this effect. Previous studies, which reported negative correlations between prevalence and SDM accuracy, conditioned on sample size.

Main conclusions

Our model explicates the causal basis of previously reported correlations between SDM performance and species/data characteristics. It also suggests that niche completeness has similarly large effects on SDM accuracy and variance as sample size. Analysts should consider niche completeness, or proxies thereof, in addition to sample size when deciding whether modelling is worthwhile.     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

Predicting the Risk of Suicide by Analyzing the Text of Clinical Notes

We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.     

Mediterranean fruit fly subplot hot spots prediction by experts' experience

The Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedemann), is a major pest, infesting hundreds of crop types. Since current field monitoring is mostly manual, understanding the spatiotemporal patterns of pest emergence at the fine scale can optimize precise trap placement and site‐specific treatment activities, also within area wide integrated pest management projects. We carried out a three‐year study in deciduous orchards in Israel, testing for the existence of subplot hot spots in which medfly populations display small‐scale aggregations over consecutive seasons, beyond the expected spatial variability resulting from host type and ripening sequence. medfly population increase in these locations often preceded or was parallel to infestations in surrounding orchards, suggesting that hot spots can provide an important tool for effective prediction and control of pest emergence. We also examined the use of expert knowledge to predict hot spot locations and suggest a methodology for verifying them.     

Developing a HACCP-like system for improving animal health and welfare in organic egg production – based on an expert panel analysis

In the process of developing a generic Hazard Analysis and Critical Control Point (HACCP)-like system for securing animal health and welfare in organic egg production, an expert panel analysis was used to perform the initial hazard analysis. Eighteen advisers and researchers in organic egg production were included in the expert panel. In a series of four questionnaires, the expert panel first scored 34 health and welfare problems seen in Danish organic egg production. Based on scorings of severity and occurrence, 10 problems were selected for further analysis. The experts subsequently suggested and scored risk factors for those problems and finally suggested control points, alarm values indicating the need for corrective actions in order to control the risk factors and monitoring frequencies of these. The 10 selected problems were hunger, thirst, piling, crop impaction, blackhead, pasteurellosis, bone fractures, cannibalism, predators and red mites. A total of 154 different risk factors were suggested for these problems. The 41 risk factors which rated highest in a combined scoring of importance and occurrence were selected for further analysis. There was a high degree of consensus between experts when scoring both problems and risk factors. The level of consensus, as defined by an interquartile range ⩽1, was 79% to 100% when scoring the health and welfare problems (scale 1–5) and 77% to 95% when scoring the risk factors (scale 1–4). On average, 5.8 control points were suggested for every risk factor. Alarm values were often not detailed enough to be of practical significance and further analysis is needed in order to define these. The experts were highly diverse in their suggested monitoring frequencies and establishment of monitoring schemes should be part of developing the farm specific systems. An expert panel analysis based on questionnaires was a useful tool during the first steps of developing a HACCP plan, conducting a hazard analysis and suggesting control points. However, care should be taken regarding the panel’s size and fields of expertise in order to assure that the panel fully covers the field of interest throughout the study. A further development of the hazard analysis into a farm-specific HACCP system would include assembling an on-farm HACCP team consisting of farmers and advisors for the practical application of control points, alarm values and monitoring schemes. The results from the fourth questionnaire would be useful as input for this work.     

基于Oracle数据库的水稻病虫综合防治专家系统

应用数据库、计算机网络和空间信息技术,研制出基于Oracle数据库的“水稻病虫综合防治专家系统RIPMES Ⅱ”。系统包括数据采集、数据维护专家决策和GIS应用等子系统,能解决实时数据采集、海量数据存贮与分析、病虫害防治辅助决策、虫害空间分布特征分析等问题,是水稻病虫害综合防治的一个有效工具,同时也可为类似的专家系统的研究提供参考。