Identifying Key Processes and Drivers Affecting the Success of Non-indigenous Marine Species in Coastal Waters

Non-indigenous species (NIS) pose a major threat to biodiversity and the functioning and services of ecosystems. Despite their rapid spread in coastal waters worldwide, biotic invasions are widely disregarded in marine conservation planning. To guide conservation actions, a better understanding of the underlying mechanisms determining the success of NIS are therefore needed. Here we develop a joint modelling approach to identify the key drivers and community assembly processes determining the occurrence of invasive benthic invertebrates, using Danish coastal waters as a case study. To reflect factors affecting the introduction, establishment and spread of NIS throughout the area, we compiled long-term monitoring data on NIS, as well as information on commercial shipping, environmental conditions and estimates of larvae settling densities derived from drift model simulations informed by species traits. We then applied a set of species distribution models to identify the key drivers determining the occurrence of NIS. Our results demonstrate a significant positive effect of vessel activity, a negative effect of depth and bottom salinity, as well as a positive effect of the simulated settling densities on the probability of presence. Taken together, our results highlight the role of commercial shipping, habitat characteristics and passive advection of early-life stages on the success of NIS. Our joint modelling approach provide improved process understanding on the key community assembly processes determining the presence of NIS and may serve to guide monitoring, management and conservation planning in order to limit future invasions and their negative consequences on coastal ecosystems.

Building Structure Analysis With and Without Direct Foundation Modelling using Reinforced Concrete Special Moment Resisting Frame

The modelling of placement upon the seismic resistant structure can be carried out separately or directly. Separated modelling refers to the modelling using fixed joint, while direct modelling is defined as the lower structure directly using spring on soil-foundation interaction. Both modelling have differences in the context of behaviour and reaction of structure that must be adjusted based on SNI 1726:2019 and pile needs. This research analysed the calculation of static bearing capacity of pile and manual calculation of k coefficient by Nakazawa method and a supporting program SAP2000 V14.2.5. The analysis results indicated that under the manual calculation, total pile needs on fixed joint modelling of spun pile in diameter 600 mm class B are 185 piles within pile cap modelling at every point of column, meanwhile using SAP2000 V14.2.5, it is obtained the pile needs of spring modelling are 176 piles within integral pile cap modelling. The structural behaviour and the reaction of both modelling demonstrated the values of drift control, period, mass participation, static dynamic shear force, and force output in the fixed joint modelling were less than spring modelling.

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial

Background In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy. Methods We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether–lumefantrine (AL), amodiaquine–artesunate (ASAQ) and dihydroartemisinin–piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment. Results Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p < 0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: − 0.5045, 0.4469; p = 0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: − 0.0889, 0.3194; p = 0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p = 0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p = 0.004) for Poisson model. Conclusion We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes. Trial registration ClinicalTrials.gov: NCT00852423

Plant host shift: The viability and persistence of a polyphagous non-native insect

Drosophila suzukii (Diptera: Drosophilidae) has become a pervasive pest in several countries around the world. Many studies have investigated the preference and attractiveness of potential hosts on this invasive, polyphagous drosophilid. Thus far, no studies have investigated whether a shift of fruit host could affect its ecological viability or spatiotemporal persistence. In this study, we analysed the fecundity and oviposition period jointly with the survival time of D. suzukii subject to a shift in host fruit, using a joint modelling method for longitudinal outcomes and time-until-event outcomes. The number of eggs laid by females was higher in raspberry than in strawberry and when setting adults of F1 generation underwent a first host shift. The joint modelling also suggested that insects reared on raspberry survived longer. We then combined experimental results with a two-patch dispersal model to investigate how host shift in a species that exhibits both passive and density-dependent dispersal may affect its asymptotic dynamics. In line with empirical evidence, we found that a shift in host choice can significantly affect the growth potential and fecundity of a species such as D. suzukii, which ultimately could aid such invasive populations in their ability to persist within a changing environment.

Analysis of multivariate longitudinal immuno-epidemiological data using a pairwise joint modelling approach

Background Immuno-epidemiologists are often faced with multivariate outcomes, measured repeatedly over time. Such data are characterised by complex inter- and intra-outcome relationships which must be accounted for during analysis. Scientific questions of interest might include determining the effect of a treatment on the evolution of all outcomes together, or grouping outcomes that change in the same way. Modelling the different outcomes separately may not be appropriate because it ignores the underlying relationships between outcomes. In such situations, a joint modelling strategy is necessary. This paper describes a pairwise joint modelling approach and discusses its benefits over more simple statistical analysis approaches, with application to data from a study of the response to BCG vaccination in the first year of life, conducted in Entebbe, Uganda. Methods The study aimed to determine the effect of maternal latent Mycobacterium tuberculosis infection (LTBI) on infant immune response (TNF, IFN-γ, IL-13, IL-10, IL-5, IL-17A and IL-2 responses to PPD), following immunisation with BCG. A simple analysis ignoring the correlation structure of multivariate longitudinal data is first shown. Univariate linear mixed models are then used to describe longitudinal profiles of each outcome, and are then combined into a multivariate mixed model, specifying a joint distribution for the random effects to account for correlations between the multiple outcomes. A pairwise joint modelling approach, where all possible pairs of bivariate mixed models are fitted, is then used to obtain parameter estimates. Results Univariate and pairwise longitudinal analysis approaches are consistent in finding that LTBI had no impact on the evolution of cytokine responses to PPD. Estimates from the pairwise joint modelling approach were more precise. Major advantages of the pairwise approach include the opportunity to test for the effect of LTBI on the joint evolution of all, or groups of, outcomes and the ability to estimate association structures of the outcomes. Conclusions The pairwise joint modelling approach reduces the complexity of analysis of high-dimensional multivariate repeated measures, allows for proper accounting for association structures and can improve our understanding and interpretation of longitudinal immuno-epidemiological data.

Accuracy Measure of Separate and Joint Modelling for a Correlated Binary Outcome: The Case Study of Mother Education and Immunization in Bangladesh

Joint modelling is a statistical approach that is used to analyze correlated data when two or more outcome variables are correlated. By joint modeling, we refer to the simultaneous analysis of two or more different response variables from the same individual. But in a separate model, it is unable to measure the effect of covariate simultaneously. This article focuses on separate and joint modelling for correlated discrete data, including logistic regression models for binary outcomes. Since most of the women are illiterate in Bangladesh and most of the people are living in urban areas, as a result, most of the women are not aware of immunization. But an educated mother is always aware of her child's health which is dependent on immunization. Therefore, mother education and immunization are interdependent. We jointly address the effect of maternal education and immunization. Joint modeling of these two outcomes is appropriate because mother education helps raise awareness of the child's health and immunization is the prevention of various diseases for the child's health. We also identified factors influencing maternal education and immunization among women in Bangladesh. By jointly modelling we found the correlation between maternal education and immunization and the most important contributing factor. The joint model removes a less significant impact of covariates as opposed to separate models. These findings further suggested that the simultaneous impact of correlated outcomes can be adequately addressed between different responses, which is overestimated or underestimated when examined separately.