Relative Importance Of Individual Factors Research Articles

Study on the genesis and development trend of tropical depressions under different large-scale backgrounds

Based on self-organizing maps (SOM), large-scale backgrounds associated with tropical depression (TD) genesis over the western North Pacific (WNP) in 1949–2021 are classified into four circulation patterns, monsoon gyre (MG) pattern, monsoon confluence (MC) pattern, monsoon trough (MT) pattern and easterly wave (EW) pattern. TDs generated in the MC pattern has the southernmost average genesis location and the highest development probability, while TDs occurred in the EW pattern are averagely located northernmost and their probability of development is the lowest. TDs formed in the MG, MT and EW patterns are most active in August, whereas in the MC pattern, TD genesis number peaks in October. Advantageous conditions for TD genesis vary in different circulation patterns. The vigorous vorticity "embryo" provides stronger initial disturbances for MG pattern; The strong upper-level divergence and the weak deep-layer VWS provide sufficient dynamic conditions for the MC pattern; The MT pattern possess the highest SST, which supplies an ample supply of heat and moisture; The EW pattern has less beneficial conditions compared with other three patterns. The Extreme Gradient Boosting (XGBoost) method is applied to quantify the relative importance of individual factors to TD development trend. 500-hPa vorticity, 200-hPa divergence and SST are major dynamic and thermal affecting factors for TD development, the importance of which all ranked at top four in the four patterns; VWS plays an indispensable role in TD development for the MC and EW patterns; Comparely,850-hPa vorticity and vertically integrated water vapor flux are not as important as above environmental factors in deciding whether a TD develops.

O45 INDIVIDUAL VS. HEALTH SYSTEM FACTORS IN HYPERTENSION CONTROL PERFORMANCE: DISTRICT-LEVEL CARE CASCADE ANALYSIS

Background: High-quality health systems are crucial for achieving optimal health outcomes, particularly in managing chronic conditions like hypertension. Effective hypertension control reduces the risk of cardiovascular diseases (CVD). This study examines the relative importance of individual factors (social determinants of health, SDOH) versus health system factors in influencing the stages of the hypertension care cascade in Indonesia, focusing on the controlled rate as the endpoint. This study aims to analyze which factors impact the hypertension care cascade. Methods: The performance of 514 districts in Indonesia was analyzed using multivariate regression. Dependent variables included the percentages of individuals ever tested, diagnosed, treated, and controlled for hypertension. Independent variables encompassed healthcare resource availability, socioeconomic factors, and demographic factors. Results: The regression analysis revealed several key findings: The physician ratio positively and significantly impacted all stages of the care cascade, with coefficients increasing from 0.087 in testing to control 0.356 (p<0.01 for all). The midwife ratio positively influenced testing (0.072), diagnosis (0.135), and treatment (0.160) but not control (0.016, p>0.1). Conversely, Average years of schooling positively correlated with testing (0.111, p<0.05). GNI PPP per capita was positively associated with testing (0.235, p<0.01), diagnosis (0.189, p<0.05), and treatment (0.228, p<0.05), but not control (-0.253, p>0.1). Median age showed a strong positive association across all stages, with coefficients of 1.212 for testing, 1.443 for diagnosis, 1.509 for treatment, and 1.274 for control (p<0.01 for all). Regression shows a pattern that while individual SDOH significantly influenced the early stages of the care cascade (testing and diagnosis), these factors had minimal impact on the control rate. Instead, health system performance was particularly critical for achieving control. Conclusion: Individual SDOHs are more influential in the early stages of the care cascade, while health system factors are crucial for controlling hypertension. Efforts to improve hypertension management should focus on enhancing health system resources to achieve better control rates and reduce CVD risks.

A dynamic systems analysis of factors affecting success of identity-preserved products: the case of high-oleic soybeans

The factors contributing to success of identity-preserved (IP) products are understood conceptually, but are rarely quantified. A combination of system dynamics modeling and group modeling building with value chain stakeholders provides a generalizable approach to quantify the relative importance of individual factors and identify information needed to assess market potential. The limited growth of IP high-oleic soybean oil (HOSO) provides a case example for application of these methods. The specific assumptions and results for HOSO will differ from other IP products, but some outcomes appear generalizable. First, the values of key drivers need to be documented over time, including the relative cost of the IP and the proportion of potential end users that are aware of the potential benefits. Because sustained cost advantages are a key driver of end-user switching behavior for HOSO, understanding the patterns for relative costs over time would be important to develop realistic estimates of sales growth. Similarly, our analyses suggest that assessment of end-user awareness is important, which may require use of alternative communication channels and monitoring efforts by key decision makers throughout the value chain, including product formulators and procurement staff.

A versatile active learning workflow for optimization of genetic and metabolic networks

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO2-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.

Thriving in a pandemic: Determinants of excellent wellbeing among New Zealanders during the 2020 COVID-19 lockdown; a cross-sectional survey.

The COVID-19 pandemic and associated restrictions are associated with adverse psychological impacts but an assessment of positive wellbeing is required to understand the overall impacts of the pandemic. The NZ Lockdown Psychological Distress Survey is an on-line cross-sectional survey of 3487 New Zealanders undertaken during a strict lockdown for COVID-19. The lockdown extended from 25 March 2020 to 28 April 2020 and the survey was undertaken between 15 April 2020 and 27 April 2020. The survey measured excellent wellbeing categorised by a WHO-Five Well-being Index (WHO-5) score ≥22. The survey also contained demographic and pre-lockdown questions, subjective and objective lockdown experiences, and questions on alcohol use. The proportion of participants with excellent wellbeing is reported with multivariate analysis examining the relative importance of individual factors associated with excellent wellbeing. Approximately 9% of the overall sample (303 participants) reported excellent wellbeing during the New Zealand lockdown. In the multivariable analysis, excellent wellbeing status was positively associated with increasing age (p<0.001), male gender (p = 0.044), Māori and Asian ethnicity (p = 0.008), and lower levels of education (certificate/diploma level qualification or less) (p<0.001). Excellent wellbeing was negatively associated with smoking (p = 0.001), poor physical (p<0.001) and mental health (p = 0.002), and previous trauma (p = 0.033). Nine percent of New Zealanders reported excellent wellbeing during severe COVID-19 pandemic restrictions. Demographic and broader health factors predicted excellent wellbeing status. An understanding of these factors may help to enhance wellbeing during any future lockdowns.

A Bayesian Spatio-Network Model for Multiple Adolescent Adverse Health Behaviours

Abstract The use of alcohol, cigarettes and marijuana among adolescents are major public health concerns, and a number of epidemiological studies have been conducted to understand the drivers of these individual health behaviours. However, there is no literature that jointly models these health behaviours with the aim of understanding the relative importance of individual factors, friendship effects and spatial effects in determining the prevalence of alcohol, cigarette and marijuana use among adolescents. To address this gap in the literature, we propose a novel multivariate spatio-network model for jointly modelling all three of these behaviours, with inference conducted in a Bayesian setting using Markov chain Monte Carlo simulation. The model is motivated by survey data from five schools in Los Angeles, California, and the results indicate the important roles that individual factors and friendship networks play in driving the uptake of these health behaviours.

Russia’s novel weapons systems: military innovation in the post-Soviet period

ABSTRACT This article identifies the principal drivers of Russian military innovation involving five novel nuclear, conventional, or dual-capable delivery systems—Avangard, Burevestnik, Poseidon, Kinzhal, and Tsirkon—and analyzes the interplay between these drivers over the course of the innovation process. It does so by means of a structured, focused comparison of the five systems and their progression to date, distinguishing “innovation” from concepts like “invention” and “diffusion,” and defining the stages of an innovation life cycle. The article also distills prior research on Soviet weapons innovation and investigates its continued validity. The analysis finds external factors to be central in driving innovation, specifically Russian threat perceptions around (1) US missile-defense development and (2) the development of Western conventional warfighting capabilities. It also discusses the roles of a range of internal factors, including industry and high-level political support for specific systems, the availability of Soviet-legacy research and engineering initiatives, and the appeal of anticipated industrial and ancillary benefits from the development of specific systems. Cooperation between design bureaus and other industry players is also examined, as is the role of status considerations in driving innovation. Finally, the relative importance of individual factors in explaining innovation is shown to differ across the systems. The structured comparison identifies the continued validity of certain aspects of past studies on Soviet military innovation, while also bringing to light new insights about contemporary Russian weapons innovation.

Risk Factors for Development of Acute Kidney Injury in COVID-19 Patients: A Retrospective Observational Cohort Study

Introduction: Acute kidney injury (AKI) in coronavirus infection disease (COVID-19) is associated with disease severity. We aimed to evaluate risk factors associated with AKI beyond COVID-19 severity. Methods: A retrospective observational study of COVID-19 patients admitted to a tertiary hospital in Singapore. Logistic regression was used to evaluate associations between risk factors and AKI (based on Kidney Disease Improving Global Outcomes criteria). Dominance analysis was performed to evaluate the relative importance of individual factors. Results: Seven hundred seven patients were included. Median age was 46 years (interquartile range [IQR]: 29–57) and 57% were male with few comorbidities (93%, Charlson Comorbidity Index [CCI] <1). AKI occurred in 57 patients (8.1%); 39 were in AKI stage 1 (68%), 9 in stage 2 (16%), and 9 in stage 3 (16%). Older age (adjusted odds ratio [aOR] 1.04; 95% confidence interval [CI]: 1.01–1.07), baseline use of angiotensin-converting enzyme inhibitor (ACE-I) or angiotensin receptor blocker (ARB) (aOR 2.86; 95% CI: 1.20–6.83), exposure to vancomycin (aOR 5.84; 95% CI: 2.10–16.19), use of nonsteroidal anti-inflammatory drugs (NSAIDs) (aOR 3.04; 95% CI: 1.15–8.05), and severe COVID-19 with hypoxia (aOR 13.94; 95% CI: 6.07–31.98) were associated with AKI in the multivariable logistic regression model. The 3 highest ranked predictors were severe COVID-19 with hypoxia, vancomycin exposure, and age, accounting for 79.6% of the predicted variance (41.6, 23.1, and 14.9%, respectively) on dominance analysis. Conclusion: Severe COVID-19 is independently associated with increased risk of AKI beyond premorbid conditions and age. Appropriate avoidance of vancomycin and NSAIDs are potentially modifiable means to prevent AKI in patients with COVID-19.

Practical Applications of Divergent ESG Ratings

In Divergent ESG Ratings, from the November 2020 issue of The Journal of Portfolio Management, authors Elroy Dimson of the University of Cambridge Judge Business School and Paul Marsh and Mike Staunton of London Business School address inconsistencies in how environmental, social, and governance (ESG) ratings are evaluated by raters and used by asset managers and investors. The authors carried out an extensive review of previous literature on ESG fund and index performance, along with an analysis of variations in ESG ratings given by different rating bodies. Their analysis found that many popular claims that ESG investing outperforms conventional indexes are unfounded. Furthermore, there is no clear agreement among raters on how individual companies score, or on the relative importance of individual factors of ESG ratings. The divergent ESG ratings provide little to no clear direction for asset managers and investors intending to implement ESG investment strategies.

BioSTEAM-LCA: An Integrated Modeling Framework for Agile Life Cycle Assessment of Biorefineries under Uncertainty

Biorefineries will play a critical role in sustainable bioeconomies, but projections of their environmental impacts vary widely. A core challenge with life cycle assessments (LCAs) of biorefineries is that they are often disconnected from biorefinery design, simulation, and techno-economic analysis (TEA). This lack of integration is a barrier to early stage technology and process evaluations, reducing consistency and transparency across sustainability indicators while limiting our understanding of the relative importance of individual factors (e.g., design decisions, greenhouse gas emission accounting procedures), how these factors interact, and trade-offs or synergies with process economics. In this study, we propose a new agile LCA framework, BioSTEAM-LCA, which layers onto BioSTEAM (Biorefinery Simulation and Techno-Economic Analysis Modules, which automates biorefinery design, simulation, and TEA) to characterize the environmental impacts of biorefineries across a landscape of designs, technology performance assumptions, and contexts. Inventory databases and impact assessment methods are integrated to enable flexible user defined LCA system models, and the implications of uncertainties throughout the production system are characterized via Monte Carlo simulation. To demonstrate the capabilities of BioSTEAM-LCA, we present a case study for sugarcane ethanol production. Overall, BioSTEAM-LCA enables computationally efficient, agile gate-to-gate LCA to evaluate biorefinery processes, the production of candidate biofuels and bioproducts, and trade-offs among productivity, economics, and environmental impacts under uncertainty.

Difference in Shoppers’ Learning Differentiate Shopping Satisfaction

Gallons of studies have been conducted regarding the customers’ satisfaction and their underlying factors. But, if the same individual is the shopper of both the organized and un-organized retail stores, then how much his/her satisfaction differs from any one retail format to the other retail format is a matter of concern for the strategists or planners. Further, over the time period, what exactly case the above said difference in satisfaction, is unsolved in Indian scenario. In this context, the research problem is stated as “can the shoppers’ learning differentiate their satisfaction with respect to competing retail formats”? Keeping in view this research problem, the objectives of the study are (1) to extract the underlying factors of shoppers’ learning in shopping malls & un-organized family-run stores separately, (2) to examine the impact of shoppers’ learning on their satisfaction towards shopping-mall in comparison to family-run stores, and (3) to examine the relative importance of individual factors of learning for the shoppers’ satisfaction in both the competing retail formats. So far as research design and methodology of this study is concerned, the causative research design is followed and a self administered structured questionnaire is administered to a sample size of 307. The data is analyzed through SPSS 22.0 and AMOS 22.0. The data analysis techniques like explorative factor analysis and path diagram through structural equation modeling (SEM) is adopted. Major findings of this study refers that shoppers’ learning differentiates their satisfaction in the mall more positively in comparison to the satisfaction they get in family-run retail stores (Kirana stores). Originality or major contributions of this study justified with the difference in shoppers’ satisfaction between two competing retail formats and the shoppers’ learning proven as the major driving force of the above said difference in satisfaction.

Drivers of the relative richness of naturalized and invasive plant species on Earth.

Biological invasions are a defining feature of the Anthropocene, but the factors that determine the spatially uneven distribution of alien plant species are still poorly understood. Here, we present the first global analysis of the effects of biogeographic factors, the physical environment and socio-economy on the richness of naturalized and invasive alien plants. We used generalized linear mixed-effects models and variation partitioning to disentangle the relative importance of individual factors, and, more broadly, of biogeography, physical environment and socio-economy. As measures of the magnitude of permanent anthropogenic additions to the regional species pool and of species with negative environmental impacts, we calculated the relative richness of naturalized (= RRN) and invasive (= RRI) alien plant species numbers adjusted for the number of native species in 838 terrestrial regions. Socio-economic factors (per-capita gross domestic product (GDP), population density, proportion of agricultural land) were more important in explaining RRI (~50 % of the explained variation) than RRN (~40 %). Warm-temperate and (sub)tropical regions have higher RRN than tropical or cooler regions. We found that socio-economic pressures are more relevant for invasive than for naturalized species richness. The expectation that the southern hemisphere is more invaded than the northern hemisphere was confirmed only for RRN on islands, but not for mainland regions nor for RRI. On average, islands have ~6-fold RRN, and >3-fold RRI compared to mainland regions. Eighty-two islands (=26 % of all islands) harbour more naturalized alien than native plants. Our findings challenge the widely held expectation that socio-economic pressures are more relevant for plant naturalization than for invasive plants. To meet international biodiversity targets and halt the detrimental consequences of plant invasions, it is essential to disrupt the connection between socio-economic development and plant invasions by improving pathway management, early detection and rapid response.

Predicting radiation pneumonitis in locally advanced stage II–III non-small cell lung cancer using machine learning

Background and purposeRadiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints to limit this toxicity. Using machine learning algorithms, we performed analyses of contributing factors in the development of RP to uncover previously unidentified criteria and elucidate the relative importance of individual factors. Materials and methodsWe evaluated 32 clinical features per patient in a cohort of 203 stage II–III LA-NSCLC patients treated with definitive chemoradiation to a median dose of 66.6 Gy in 1.8 Gy daily fractions at our institution from 2008 to 2016. Of this cohort, 17.7% of patients developed grade ≥2 RP. Univariate analysis was performed using trained decision stumps to individually analyze statistically significant predictors of RP and perform feature selection. Applying Random Forest, we performed multivariate analysis to assess the combined performance of important predictors of RP. ResultsOn univariate analysis, lung V20, lung mean, lung V10 and lung V5 were found to be significant RP predictors with the greatest balance of specificity and sensitivity. On multivariate analysis, Random Forest (AUC = 0.66, p = 0.0005) identified esophagus max (20.5%), lung V20 (16.4%), lung mean (15.7%) and pack-year (14.9%) as the most common primary differentiators of RP. ConclusionsWe highlight Random Forest as an accurate machine learning method to identify known and new predictors of symptomatic RP. Furthermore, this analysis confirms the importance of lung V20, lung mean and pack-year as predictors of RP while also introducing esophagus max as an important RP predictor.

Understanding the mismatch between behaviour and development in a novel host-parasitoid association

Foraging parasitoid females should preferentially oviposit on hosts most suitable for progeny development to maximize their fitness. However, the introduction of a new host species may disrupt the link between the reliability of the cues and the expected adaptive outcome of female choice, leading to an evolutionary trap. This mismatch between behavioural acceptance and lack of development exists for North American and European egg parasitoids (Scelionidae) that encounter invasive Halyomorpha halys in areas where this exotic host has recently established. To explain this mismatch, we utilized an L9 orthogonal array design to assess and rank the influence of several critical factors characterizing host resource (host species, egg age, egg status, and surface wash) on behaviour (acceptance, patch residence and patch exploitation) and development of North American native Trissolcus euschisti egg parasitoid. Our results indicate that the host egg age is most important for behaviour, but is least influential for development of the progeny. This study suggests that the maladaptive decision to oviposit in an unsuitable host is due to a mismatch between the cues that females use, and the subsequent expected outcome of this choice. Therefore, it is the relative importance of individual factors when assessed simultaneously that influences the decision-making of female parasitoids.

(P39) Novel Use of Machine Learning for Predicting Radiation Pneumonitis in Locally Advanced Stage II-III Non-Small Cell Lung Cancer

Radiation pneumonitis (RP) is a common and radiotherapy dose-limiting toxicity for locally-advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints in order to limit this toxicity. We incorporate machine learning techniques in order to perform additional in-depth analyses of contributing factors to the development of RP in order to uncover previously unidentified criteria, establish more discrete and robust dosimetric criteria, and the elucidate the relative importance of individual factors. Utilizing a cohort of 203 consecutive stage II-III LA-NSCLC patients treated with definitive chemoradiation at our institution from 2008-2016, we evaluated 32 continuous and categorical features per patient grouped into risk factors, comorbidities, pretreatment imaging, stage, histology, radiation treatment, chemotherapy, and dosimetry utilizing a novel machine learning algorithm to identify predictive features for the development of RP. Univariate analysis was performed using optimally trained decision stumps to determine statistically significant features and their corresponding RP thresholds. Multivariate analysis was carried out using Mediboost for feature selection. MediBoost is a novel patient stratification tool that builds decision trees with optimal accuracy comparable to less-interpretable ensemble methods commonly used in the literature. Patients were treated to a median dose of 66.6 Gy in 1.8 Gy fractions. 17.7% patients developed grade ≥3 RP. On univariate analysis, lung dosimetric factors of lung mean, lung V10, and lung V20 were found to be the significant RP predictors with the greatest balance of specificity and sensitivity. On multivariate analysis, using the MediBoost algorithm, esophagus max (61.5%) and lung V20 (30.5%) were the two most common primary differentiators of RP, with lung mean and esophagus mean important secondary RP differentiators. We highlight the utility of a novel machine learning algorithm and demonstrate its ability to both identify known and novel predictors for the development of symptomatic RP. We provide interpretable decision trees using MediBoost that can aid clinicians in decision optimization regarding mitigation of RP and demonstrate the potential utility of our machine learning techniques for future studies.

Intrinsic factors are relatively more important than habitat features in modulating risk perception in a tropical lizard

Anti-predator responses in animals are dynamic and depend on multiple factors. However, most of our understanding about animal escape responses comes from studies which examine only a small set of factors at a time and are done over a short period of animal life spans. This limits our understanding of the dynamic nature of animal escape behaviour and the relative importance of individual factors in determining their escape behaviour. We used a repeated-measures study design to assess the anti-predator response of a wild population of a sexually dimorphic tropical lizard, Psammophilus dorsalis. We followed marked individuals throughout their breeding lifespan, repeatedly assayed their escape response and measured representative intrinsic and extrinsic factors that could modulate their escape response. Our findings suggest that intrinsic factors, such as sex and body size, influenced escape response relatively more than extrinsic factors did, such as distance to refuge and perch height. Although individual variables influenced escape behaviour, in a direction mostly consistent with predictions from optimal escape theory, the interaction between factors led to novel insights into how animals dynamically evaluate multiple and changing costs throughout their lifetime to evade predation. Fleeing from a potential predator is crucial to animals and depends on different factors, as there are various costs to escaping. Although several studies in the past have evaluated the role of various factors in determining escape response, the relative importance of factors and how it changes across the lifetime of individuals are not clear. By assessing individuals repeatedly over a substantial part of their lifespan, we could gain insight into how escape response changes as associated factors, intrinsic or extrinsic, change for an individual over time. This approach provided us a more robust and accurate representation of the dynamic nature of escape decisions. We also demonstrate that when multiple factors are considered simultaneously, their relative importance in determining timing of escape can considerably differ from theoretical predictions.

Pod drop in Brassica napus is linked to weight-adjusted pod-retention resistance

Canola (Brassica napus L.) often experiences high seed losses prior to and during harvest operations. Pod drop and pod shatter are responsible for pre-harvest seed losses in canola although relatively little is known about the significance of pod drop to total pre-harvest seed losses in canola. This research describes a method to measure the predisposition for pod drop among canola genotypes that was developed and tested across different sites from 2013 to 2015. This method, referred to as pod-retention resistance, consists of the use of a force gauge to quantify the force required to break the pod pedicle at the pedicle-rachis junction. Two sets of field experiments were used, the first developed and modeled the factors that influence pod-retention resistance, pod drop and seed shatter. A second set of experiments was used to validate these findings on a broader range of genotypes and determine whether a relationship between pod retention resistance and pod drop could be determined. Results showed that the pod-retention resistance was significantly greater for pods on the proximal half of the rachis compared to the distal half of the rachis. Several factors contributed strongly to variation in pod retention resistance (rachis position, genotype and site) and pod drop (site, rachis position and genotype). The relative importance of individual factors varied between pod retention resistance and pod drop suggesting no relationship between the two. However, weight-adjusted pod retention resistance, i.e., average pod retention resistance corrected for the average weight of dropped pods, was strongly correlated with pod drop, particularly in the validation experiments. This method could be used by canola breeders to select for and develop genotypes with reduced predisposition for seed losses via pod drop.

Identifying the key biophysical drivers, connectivity outcomes, and metapopulation consequences of larval dispersal in the sea.

BackgroundPopulation connectivity, which is essential for the persistence of benthic marine metapopulations, depends on how life history traits and the environment interact to influence larval production, dispersal and survival. Although we have made significant advances in our understanding of the spatial and temporal dynamics of these individual processes, developing an approach that integrates the entire population connectivity process from reproduction, through dispersal, and to the recruitment of individuals has been difficult.We present a population connectivity modelling framework and diagnostic approach for quantifying the impact of i) life histories, ii) demographics, iii) larval dispersal, and iv) the physical seascape, on the structure of connectivity and metapopulation dynamics. We illustrate this approach using the subtidal rocky reef ecosystem of Port Phillip Bay, were we provide a broadly-applicable framework of population connectivity and quantitative methodology for evaluating the relative importance of individual factors in determining local and system outcomes.ResultsThe spatial characteristics of marine population connectivity are primarily influenced by larval mortality, the duration of the pelagic larval stage, and the settlement competency characteristics, with significant variability imposed by the geographic setting and the timing of larval release. The relative influence and the direction and strength of the main effects were strongly consistent among 10 connectivity-based metrics.ConclusionsThese important intrinsic factors (mortality, length of the pelagic larval stage, and the extent of the precompetency window) and the spatial and temporal variability represent key research priorities for advancing our understanding of the connectivity process and metapopulation outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-015-0045-6) contains supplementary material, which is available to authorized users.

Behavioural responses to visual environment in early stage Atlantic codGadus morhuaL. larvae

Marine fish larvae exhibit species-specific sensitivity to light, which changes during ontogeny. Our aim was to explore how visual environment, characterized by light intensity, microalgae addition and wall colour, affects spatial distribution of Atlantic cod larvae. We applied two different small-scale factorial designs, each of them as a series of independent short-term trials at fixed larval ages from 5 to 35 days post hatch (DPH). Positive phototaxis increased in larvae from 5 to ≈24 DPH, followed by reduced response at later ages. Effects from the experimental factors on larval behaviour changed with age, and significant interactions between factors were present. Algae significantly affected larvae only at 5 DPH, increasing phototactic response. White wall colour induced distribution of larvae close to the tank walls at all ages. The experimental approach demonstrated clear effects from visual environment on larval distribution, and revealed both relative importance of individual factors, interactive effects and temporal changes due to larval ontogeny. Our results suggest that by fine-tuning the visual environment in tanks, the larval distribution may be managed to improve overall rearing conditions through a more even distribution with reduced closeness to tank walls.

Comparative phylogeography of three trematomid fishes reveals contrasting genetic structure patterns in benthic and pelagic species

Population genetics patterns of marine fish in general and of Southern Ocean fish in particular range from virtual panmixia over ocean-wide scale to deeply fragmented populations. However the causes underlying these different patterns are not properly understood. In this paper, we tested the hypotheses that population connectivity is positively related to a combination of life history traits, namely duration of pelagic larval period and the tendency towards pelagic life style in the adulthood. To do so, we analysed the variability of six microsatellite and one mitochondrial marker (cytochrome b) in three Southern Ocean fish species (Trematomus newnesi, Trematomus hansoni and Trematomus bernacchii). They share a recent common ancestor but notably differ in their duration of pelagic larval period as well as pelagic versus benthic lifestyle. We sampled over a range of more than 5000km for all three species and used a number of population genetics tools to investigate past and contemporary levels of connectivity. All species experienced population fluctuations, but coalescent simulations suggested that contemporary populations are in migration-drift equilibrium. Although global FST values were rather low, a significant population structure separated the High-Antarctic from the Peninsular regions in all species. The level of genetic differentiation was much lower in the pelagic versus benthic species. Present data suggest that past and present genetic structuring in the Southern Ocean are indeed related with the ecological traits of Antarctic fish, however the relative importance of individual factors remains unclear.