On species richness and rarefaction: size- and coverage-based techniques quantify different characteristics of richness change in biodiversity

Changes in biodiversity today shape the future patterns of biodiversity. This fact underlines the importance of understanding changes in biodiversity through time and space. The number of species, known as species richness, has long been studied as a key indicator that quantifies the state of biodiversity, and standardisation techniques, called rarefaction, have also been used to undertake a fair comparison of the richness observed at different times or locations. The present study asks whether utilising different rarefaction techniques attains comparable results when investigating changes in species richness. The study framework presents the statistical nature of two commonly adopted rarefaction techniques: size-based and coverage-based rarefaction. The key finding is that the rarefied richness results calculated by these two different rarefaction methods reflect different aspects of biodiversity change, the shift in community size and/or composition. This fact illuminates that richness analyses based on different rarefaction techniques can reach different conclusions that may be contradictory. The study also investigates the mechanism creating such divergence. As such, special care is required when evaluating biodiversity change using species richness as an indicator

Detecting multiple spatial disease clusters: information criterion and scan statistic approach

Background: Detecting the geographical tendency for the presence of a disease or incident is, particularly at an early stage, a key challenge for preventing severe consequences. Given recent rapid advancements in information technologies, it is required a comprehensive framework that enables simultaneous detection of multiple spatial clusters, whether disease cases are randomly scattered or clustered around specifc epicenters on a larger scale. We develop a new methodology that detects multiple spatial disease clusters and evaluates its performance compared to existing other methods.Methods: A novel framework for spatial multiple-cluster detection is developed. The framework directly stands on the integrated bases of scan statistics and generalized linear models, adopting a new information criterion that selects the appropriate number of disease clusters. We evaluated the proposed approach using a real dataset, the hospital admission for chronic obstructive pulmonary disease (COPD) in England, and simulated data, whether the approach tends to select the correct number of clusters.Results: A case study and simulation studies conducted both confrmed that the proposed method performed better compared to conventional cluster detection procedures, in terms of higher sensitivity.Conclusions: We proposed a new statistical framework that simultaneously detects and evaluates multiple disease clusters in a large study space, with high detection power compared to conventional approaches.</div

Multiple-cluster detection test for purely temporal disease clustering: integration of scan statistics and generalized linear models

The spatial scan statistic is commonly used to detect spatial and/or temporal disease clusters in epidemiological studies. Although multiple clusters in the study space can be thus identified, current theoretical developments are mainly based on detecting a ‘single’ cluster. The standard scan statistic procedure enables the detection of multiple clusters, recursively identifying additional ‘secondary’ clusters. However, their p-values are calculated one at a time, as if each cluster is a primary one. Therefore, a new procedure that can accurately evaluate multiple clusters as a whole is needed. The present study focuses on purely temporal cases and then proposes a new test procedure that evaluates the p-value for multiple clusters, combining generalized linear models with an information criterion approach. This framework encompasses the conventional, currently widely used detection procedure as a special case. An application study adopting the new framework is presented, analysing the Japanese daily incidence of out-of-hospital cardiac arrest cases. The analysis reveals that the number of the incident increases around New Year’s Day in Japan. Further, simulation studies undertaken confirm that the proposed method possesses a consistency property that tends to select the correct number of clusters when the truth is known

The daily incidence of out-of-hospital cardiac arrest unexpectedly increases around New Year's Day in Japan

Background: Over 100,000 patients are diagnosed every year as out-of-hospital cardiac arrest (OHCA) cases in Japan and their number has continued to rise for the last decade, presenting a challenge for preventive public health research as well as emergency medical care. The purpose of this study was to identify whether there are any temporal patterns in daily OHCA presentations in Japan. Methods: Records of OHCA patients (n=701,651) transported by ambulance over the course of six years (1st January 2005 to 10th March 2011) in Japan were obtained from the All-Japan Utstein registry data of cardiopulmonary arrest patients. Time periods within which the incidence of OHCA significantly increased were identified by a temporal cluster detection test using scan statistics. The risk ratios of OHCA for the detected periods were calculated and adjusted according to a Poisson regression model accounting for effects of other factors. Results: The risk of OHCA significantly rises 1.3-1.6 times around New Year's Day in Japan. Conclusions: Our analysis revealed the increased daily incidence of OHCA around every New Year's Day in Japan

Measuring temporal turnover in ecological communities

Range migrations in response to climate change, invasive species and the emergence of novel ecosystems highlight the importance of temporal turnover in community composition as a fundamental part of global change in the Anthropocene. Temporal turnover is usually quantified using a variety of metrics initially developed to capture spatial change. However, temporal turnover is the consequence of unidirectional community dynamics resulting from processes such as population growth, colonisation and local extinction. Here, we develop a framework based on community dynamics and propose a new temporal turnover measure. A simulation study and an analysis of an estuarine fish community both clearly demonstrate that our proposed turnover measure offers additional insights relative to spatial context-based metrics. Our approach reveals whether community turnover is due to shifts in community composition or in community abundance and identifies the species and/or environmental factors that are responsible for any change

Imperfect observations in ecological studies

© 2016 The Author(s) Every ecological data set is the result of sampling the biota at sampling locations. Such samples are rarely a census of the biota at the sampling locations and so will inherently contain biases. It is crucial to account for the bias induced by sampling if valid inference on biodiversity quantities is to be drawn from the observed data. The literature on accounting for sampling effects is large, but most are dedicated to the specific type of inference required, the type of analysis performed and the type of survey undertaken. There is no general and systematic approach to sampling. Here, we explore the unification of modelling approaches to account for sampling. We focus on individuals in ecological communities as the fundamental sampling element, and show that methods for accounting for sampling at the species level can be equated to individual sampling effects. Particular emphasis is given to the case where the probability of observing an individual, when it is present at the site sampled, is less than one. We call these situations ‘imperfect observations’. The proposed framework is easily implemented in standard software packages. We highlight some practical benefits of this formal framework: the ability of predicting the true number of individuals using an expectation that conditions on the observed data, and designing appropriate survey plans accounting for uncertainty due to sampling. The principles and methods are illustrated with marine survey data from tropical northern Australia

Spillover effects of a community-managed marine reserve

The value of no-take marine reserves as fisheries-management tools is controversial, particularly in high-poverty areas where human populations depend heavily on fish as a source of protein. Spillover, the net export of adult fish, is one mechanism by which no-take marine reserves may have a positive influence on adjacent fisheries. Spillover can contribute to poverty alleviation, although its effect is modulated by the number of fishermen and fishing intensity. In this study, we quantify the effects of a community-managed marine reserve in a high poverty area of Northern Mozambique. For this purpose, underwater visual censuses of reef fish were undertaken at three different times: 3 years before (2003), at the time of establishment (2006) and 6 years after the marine reserve establishment (2012). The survey locations were chosen inside, outside and on the border of the marine reserve. Benthic cover composition was quantified at the same sites in 2006 and 2012. After the reserve establishment, fish sizes were also estimated. Regression tree models show that the distance from the border and the time after reserve establishment were the variables with the strongest effect on fish abundance. The extent and direction of the spillover depends on trophic group and fish size. Poisson Generalized Linear Models show that, prior to the reserve establishment, the survey sites did not differ but, after 6 years, the abundance of all fish inside the reserve has increased and caused spillover of herbivorous fish. Spillover was detected 1km beyond the limit of the reserve for small herbivorous fishes. Six years after the establishment of a community-managed reserve, the fish assemblages have changed dramatically inside the reserve, and spillover is benefitting fish assemblages outside the reserve

Supplementary information files for Regional occupancy increases for widespread species but decreases for narrowly distributed species in metacommunity time series

Supplementary files for article Regional occupancy increases for widespread species but decreases for narrowly distributed species in metacommunity time series   While human activities are known to elicit rapid turnover in species composition through time, the properties of the species that increase or decrease their spatial occupancy underlying this turnover are less clear. Here, we used an extensive dataset of 238 metacommunity time series of multiple taxa spread across the globe to evaluate whether species that are more widespread (large-ranged species) differed in how they changed their site occupancy over the10-90 years the metacommunities were monitored relative to species that are more narrowly distributed (small-ranged species). We found that on average, large-ranged species tended to increase in occupancy through time, whereas small-ranged species tended to decrease. These relationships were stronger in marine than in terrestrial and freshwater realms. However, in terrestrial regions, the directional changes in occupancy were less extreme in protected areas. Our findings provide evidence for systematic decreases in occupancy of small-ranged species, and that habitat protection could mitigate these losses in the face of environmental change.</p

Estimating allometric energy allocation between somatic and gonadic growth

1. Quantifying growth patterns and energy allocation strategies is essential to comprehend the biological characteristics of organisms and their interactions with broader biological communities in which they reside. Mathematical models, such as mono and di-phasic allometric energy-based growth models, play a pivotal role in delineating such body growth patterns. However, modelling approaches often face some major challenges that stem from both model nonlinearity and data limitation in practice.2. The present study investigates the nature of the challenges and develops a flexible diphasic allometric growth model. The proposed modelling framework offers an effective parameter estimation approach directly built upon statistical smoothing techniques and numerical optimisation methods.3. The simulation study undertaken demonstrates that the proposed approach can provide accurate parameter estimates. The illustrative example analyses the individual body and gonadic weight of subtropical cutlassfish (Trichiurus japonicus) from the cooler northern and warmer southern coasts of Taiwan. The results reveal the linear growth of fish in the south compared with those in the north, which distinctive growth pattern results from the lower dependency to body mass in the somatic and gonadic growth.4. The proposed unified modelling framework offers new advances in growth modelling to shed light upon the intraspecific life-history strategies, quantifying growth patterns and energy allocation strategies.</div

Causality model for text data with a hierarchical topic structure

This study describes a method for constructing a causality model from text data, such as review data. Topic modeling is useful to find these evaluation factors from text data. The method based on hierarchical latent Dirichlet allocation is useful because it automatically constructs relationships among topics. However, the depth of each topic in a hierarchical structure is the same even if the contents differ for each topic. Accordingly, the method can generate less important topics that are not worth analyzing. To solve this problem, we construct a hierarchical topic structure with different depths and more important topics by using Bayesian rose trees. In the experiment, the values of the hyperparameters for constructing a hierarchical topic structure are estimated by using evaluation indexes for causal analysis. In addition, the experiment compares the proposed method with related approaches to demonstrate the usefulness of this model