Structuring Interaction Networks Between Epiphytic Bryophytes and Their Hosts in Yunnan, SW China

Ecological networks are commonly applied to depict general patterns of biotic interactions, which provide tools to understand the mechanism of community assembly. Commensal interactions between epiphytes and their hosts are a major component of species interactions in forest canopies; however, few studies have investigated species assemblage patterns and network structures of epiphyte–host interactions, particularly non-vascular epiphytes in different types of forest. To analyze the characteristics of network structures between epiphytes and their hosts, composition and distribution of epiphytic bryophytes were investigated from 138 host individuals using canopy cranes in a tropical lowland seasonal rain forest (TRF) and a subtropical montane moist evergreen broad-leaved forest (STF), in Southwest China. We structured binary networks between epiphytic bryophytes and their hosts in these two forests, which presented 329 interactions in the TRF and 545 interactions in the STF. Compared to TRF, the bryophyte–host plant networks were more nested but less modular in the STF. However, both forests generally exhibited a significantly nested structure with low levels of specialization and modularity. The relatively high nestedness may stabilize the ecological networks between epiphytic bryophytes and their hosts. Nevertheless, the low modularity in epiphyte–host networks could be attributed to the lack of co-evolutionary processes, and the low degree of specialization suggests that epiphytes are less likely to colonize specific host species. Vertical distribution of the bryophyte species showed structured modules in the tree basal and crown zones, probably attributing to the adaptation to microclimates within a host individual. This study highlights the nested structure of commensal interaction between epiphytic bryophytes and host trees, and provides a scientific basis to identify key host tree species for conservation and management of biodiversity in forest ecosystems.

Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 Dementias Platform UK (DPUK) population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

Research-ready data for multi-cohort analyses: The Dementias Platform UK (DPUK) C-Surv data model

Research-ready data (that curated to a defined standard) increases scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following user consultation, a standard data model (C-Surv), optimised for data discovery, was developed using data from 12 population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes and 137 domains selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. It was concluded that developing and applying a standard data model (C-Surv) for research cohort data is feasible and useful.

Smoothed Nested Testing on Directed Acyclic Graphs

Summary We consider the problem of multiple hypothesis testing when there is a logical nested structure to the hypotheses. When one hypothesis is nested inside another, the outer hypothesis must be false if the inner hypothesis is false. We model the nested structure as a directed acyclic graph, including chain and tree graphs as special cases. Each node in the graph is a hypothesis and rejecting a node requires also rejecting all of its ancestors. We propose a general framework for adjusting node-level test statistics using the known logical constraints. Within this framework, we study a smoothing procedure that combines each node with all of its descendants to form a more powerful statistic. We prove a broad class of smoothing strategies can be used with existing selection procedures to control the familywise error rate, false discovery exceedance rate, or false discovery rate, so long as the original test statistics are independent under the null. When the null statistics are not independent but are derived from positively-correlated normal observations, we prove control for all three error rates when the smoothing method is arithmetic averaging of the observations. Simulations and an application to a real biology dataset demonstrate that smoothing leads to substantial power gains.

Results of the First Herpetological Survey of Israel’s Mediterranean Coastal Islets

Small islets in the Mediterranean Sea are often home to reptiles, typically representing an impoverished sample of the continental fauna, yet with high population densities and signs of rapid morphological and behavioral evolution. In this paper, we present the first herpetofaunal survey of several small islet clusters in close proximity to the Mediterranean coast of Israel, only recently geologically separated from the mainland. We performed surveys of five islets during March of 2017 – 2018 and recorded the presence of five different species of reptiles on four of the surveyed islets. Species richness varied between 1 and 4 species, and appeared to be correlated with island area, with a distinct nested structure. Reptile species may have colonized the islets by natural dispersal from nearby coastal populations, or by hitch-hiking on fishing boats and similar methods of human-assisted dispersal. Alternatively, the recorded reptiles may represent relictual populations from earlier geologic periods, when lower sea-levels supported continuous land-bridges between the islets and the mainland. These insular reptile populations require further study to establish the exact means of colonization and describe if and how they differ from mainland populations. We stress the importance of such small Mediterranean islets such as these as centers of unique biodiversity and encourage future study and conservation action aimed at them and similar islets.

HTLinker: A Head-to-Tail Linker for Nested Named Entity Recognition

Named entity recognition (NER) aims to extract entities from unstructured text, and a nested structure often exists between entities. However, most previous studies paid more attention to flair named entity recognition while ignoring nested entities. The importance of words in the text should vary for different entity categories. In this paper, we propose a head-to-tail linker for nested NER. The proposed model exploits the extracted entity head as conditional information to locate the corresponding entity tails under different entity categories. This strategy takes part of the symmetric boundary information of the entity as a condition and effectively leverages the information from the text to improve the entity boundary recognition effectiveness. The proposed model considers the variability in the semantic correlation between tokens for different entity heads under different entity categories. To verify the effectiveness of the model, numerous experiments were implemented on three datasets: ACE2004, ACE2005, and GENIA, with F1-scores of 80.5%, 79.3%, and 76.4%, respectively. The experimental results show that our model is the most effective of all the methods used for comparison.

Long-Term Dynamics of Bird Dispersal Network Structures Associated with the Functions in Bird Migration and Fruit Abundance

In temperate zones, the complex network of seed dispersal by migrant birds is formed and the structure is dynamic on long time scale. Over 12 years, we examined interannual variability of structures of bird dispersal networks and factors affecting them by observing the characteristics of fruit abundance, bird migration and bird dispersal interactions in central Japan. The fruit abundance exhibited a remarkable fluctuation across years, with the number of fruiting trees and matured fruits fluctuating repeatedly every other year, leading to the periodic fluctuations. The abundance of migrants was also fluctuated. According to the abundance of fruits and migrants, the 12 years as study period was classified into three types. The seed transporting frequency and the dispersal networks were investigated by collecting faeces of migrants. Of the 6652 samples collected from 15 bird species, 1671 (25.1%) included seeds from 60 plant species. Main dispersers were composed of Turdus pallidus, T. obscurus and Zosterops japonicus. The structures of bird dispersal networks were highly nested over 12 years, suggesting the networks are stable. Specifically, the nested structure developed in years when fruit abundance was low. GLM analyses showed the abundance of migrants, particularly T. pallidus and T. obscurus, had strong positive effects on construction of nested structure. The development of nested structure may be caused by the fact the two Turdus species were more frequently functioning as generalist dispersers when fruit abundance was lower. Our study revealed one of the mechanisms determining the structure of bird dispersal network on long time scale.

Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships

Our article explores an underused mathematical analytical methodology in the social sciences. In addition to describing the method and its advantages, we extend a previously reported application of mixed models in a well-known database about corruption in 149 countries. The dataset in the mentioned study included a reasonable amount of zeros (13.19%) in the outcome variable, which is typical of this type of research, as well as quite a bit of social sciences research. In our paper, present detailed guidelines regarding the estimation of models where the data for the outcome variable includes an excess number of zeros, and the dataset has a natural nested structure. We believe our research is not likely to reject the hypothesis favoring the adoption of mixed modeling and the inflation of zeros over the original simpler framework. Instead, our results demonstrate the importance of considering random effects at country levels and the zero-inflated nature of the outcome variable.

The Effect of School and Student-Related Factors on PISA 2015 Science Performances in Turkey

The Program for International Student Assessment (PISA) is a research project conducted by the Organization for Economic Co-operation and Development, which evaluates the knowledge and skills gained by 15-year-old students over three-year terms. Within this study’s' scope, the PISA 2015 data were analysed to determine whether school-related factors [including the schools’ economic, social, and cultural status (ESCS)] were related to Turkish students’ science performances. Due to its nested structure, the released PISA 2015 data were analysed using the hierarchical linear model (HLM). Two models were considered to examine how Aggregated ESCS at the school level makes a difference. Thereby in model 1 shortage of educational material, staff shortage, student behaviours, and teacher behaviours were included in the analysis; in addition to these variables listed, aggregated ESCS was also added to the analysis in Model 2. The results of the analysis revealed that school-related factors - in particular, staff shortage, student behaviours, and aggregated ESCS indexes - were statistically related to students’ science performances. When the aggregated ESCS was controlled, it is observed that the school-level variables had a higher effect on students’ science performances.