Estimating Bycatch From Non-representative Samples (II): A Case Study of Pair Trawlers and Common Dolphins in the Bay of Biscay

Marine megafauna plays an important functional role in marine ecosystems as top predators but are threatened by a wide range of anthropogenic activities. Bycatch, the incidental capture of non-targeted species in commercial and recreational fisheries, is of particular concern for small cetacean species, such as dolphins and porpoises. In the North-East Atlantic, common dolphin (Delphinus delphis, Linné 1758) bycatch has been increasing and associated with large numbers of animals stranding during winter on the French Atlantic seashore since at least 2017. However, uncertainties around the true magnitude of common dolphin bycatch and the fisheries involved have led to delays in the implementation of mitigation measures. Current data collection on dolphin bycatch in France is with non-dedicated observers deployed on vessels for the purpose of national fisheries sampling programmes. These data cannot be assumed representative of the whole fisheries' bycatch events. This feature makes it difficult to use classic ratio estimators since they require a truly randomised sample of the fishery by dedicated observers. We applied a newly developed approach, regularised multilevel regression with post-stratification, to estimate total bycatch from unrepresentative samples and total fishing effort. The latter is needed for post-stratification and the former is analysed in a Bayesian framework with multilevel regression to regularise and better predict bycatch risk. We estimated the number of bycaught dolphins for each week and 10 International Council for the Exploration of the Sea (ICES) divisions from 2004 to 2020 by estimating jointly bycatch risk, haul duration, and the number of hauls per days at sea (DaS). Bycatch risk in pair trawlers flying the French flag was the highest in winter 2017 and 2019 and was associated with the longest haul durations. ICES divisions 8.a and 8.b (shelf part of the Bay of Biscay) were estimated to have the highest common dolphin bycatch. Our results were consistent with independent estimates of common dolphin bycatch from strandings. Our method show cases how non-representative observer data can nevertheless be analysed to estimate fishing duration, bycatch risk and, ultimately, the number of bycaught dolphins. These weekly-estimates improve upon current knowledge of the nature of common dolphin bycatch and can be used to inform management and policy decisions at a finer spatio-temporal scale than has been possible to date. Our results suggest that limiting haul duration, especially in winter, could serve as an effective mitigation strategy.

Polling India via regression and post-stratification of non-probability online samples

Recent technological advances have facilitated the collection of large-scale administrative data and the online surveying of the Indian population. Building on these we propose a strategy for more robust, frequent and transparent projections of the Indian vote during the campaign. We execute a modified MrP model of Indian vote preferences that proposes innovations to each of its three core components: stratification frame, training data, and a learner. For the post-stratification frame we propose a novel Data Integration approach that allows the simultaneous estimation of counts from multiple complementary sources, such as census tables and auxiliary surveys. For the training data we assemble panels of respondents from two unorthodox online populations: Amazon Mechanical Turks workers and Facebook users. And as a modeling tool, we replace the Bayesian multilevel regression learner with Random Forests. Our 2019 pre-election forecasts for the two largest Lok Sahba coalitions were very close to actual outcomes: we predicted 41.8% for the NDA, against an observed value of 45.0% and 30.8% for the UPA against an observed vote share of just under 31.3%. Our uniform-swing seat projection outperforms other pollsters—we had the lowest absolute error of 89 seats (along with a poll from ‘Jan Ki Baat’); the lowest error on the NDA-UPA lead (a mere 8 seats), and we are the only pollster that can capture real-time preference shifts due to salient campaign events.

MODIFICATION OF SEPARATE RATIO TYPE EXPONENTIAL ESTIMATOR: A POST-STRATIFICATION APPROACH

In this research, modification of separate ratio type exponential estimator introduced in an earlier study is proposed. Expressions for the bias and mean square error (MSE) of the proposed estimator up to first degree of approximation are derived. The optimum value of the constant which minimize the MSE of the suggested estimator is also obtained. In the same vein, efficiency comparisons between the proposed estimator and some related existing ones under the case of post-stratification is conducted. Empirical studies have been conducted to demonstrate the efficiencies of the suggested estimators over other considered estimators. The proposed MSE and Percentage Relative Efficiency (PRE) were used to evaluate the achievement of the modified estimator.

Transposition of cardiovascular outcome trial effects to the real-world population of patients with type 2 diabetes

Background Transferring results obtained in cardiovascular outcome trials (CVOTs) to the real-world setting is challenging. We herein transposed CVOT results to the population of patients with type 2 diabetes (T2D) seen in routine clinical practice and who may receive the medications tested in CVOTs. Methods We implemented the post-stratification approach based on aggregate data of CVOTs and individual data of a target population of diabetic outpatients. We used stratum-specific estimates available from CVOTs to calculate expected effect size for the target population by weighting the average of the stratum-specific treatment effects according to proportions of a given characteristic in the target population. Data are presented as hazard ratio (HR) and 95% confidence intervals. Results Compared to the target population (n = 139,708), the CVOT population (n = 95,816) was younger and had a two to threefold greater prevalence of cardiovascular disease. EMPA-REG was the CVOT with the largest variety of details on stratum-specific effects, followed by TECOS, whereas DECLARE and PIONEER-6 had more limited stratum-specific information. The post-stratification HR estimate for 3 point major adverse cardiovascular event (MACE) based on EMPA-REG was 0.88 (0.74–1.03) in the target population, compared to 0.86 (0.74–0.99) in the trial. The HR estimate based on LEADER was 0.88 (0.77–0.99) in the target population compared to 0.87 (0.78–0.97) in the trial. Consistent results were obtained for SUSTAIN-6, EXSCEL, PIONEER-6 and DECLARE. The effect of DPP-4 inhibitors observed in CVOTs remained neutral in the target population. Conclusions Based on CVOT stratum-specific effects, cardiovascular protective actions of glucose lowering medications tested in CVOTs are transferrable to a much different real-world population of patients with T2D.

Does the lag time between radicle and epicotyl emergences in acorns of pedunculate oak (Quercus robur L.) depend on the duration of cold stratification and post-stratification temperatures? Modelling with the sigmoidal growth curves approach

The objective of this study was to determine how the current (10–16 weeks) and predicted future (2–8 weeks) length of cold stratification and current and predicted future post-stratification temperatures influence radicle and epicotyl emergence in acorns of Quercus robur. We tested radicle and epicotyl emergence at two temperatures corresponding to the current (15/6°C) and predicted future early autumn and spring temperatures (25/15°C) in Poland. We fitted models to describe and derive parameters for radicle and epicotyl emergences over time. The parameters included maximum percentage, rate of emergences, time to achieve the maximum emergence rate, emergence delay and time to 50% emergence. In most cases, the Gompertz model was the best fit, but in a few cases, the logistic model was the best. Richard's model for most of the cases did not converge. This model, according to both information criteria values, was the best fit for epicotyl emergence at 15/6°C following 8 weeks of cold stratification. Richard's model was also the best fit for epicotyl emergence at 25/15°C following 14 weeks of stratification.. Our results indicate that at temperatures typical for early autumn (15/6°C), the time necessary for radicle emergence from 50% of acorns was longer than that from acorns placed at 25/15°C. Four weeks of cold stratification extended 50% radicle emergence at 15/6°C to 70 d, whereas 12 weeks of stratification shortened the time to 11 d. When the acorns were incubated at 25/15°C, radicle emergence occurred faster than at 15/6°C and the time lag between radicle and epicotyl was shorter.

Investigating the Conflicting Findings for Mobile Device Effects on Cognitive Test Scores: Evidence for Selection Bias in an Operational, Applicant Sample

Past studies have reported inconsistent results regarding the effect of mobile devices on cognitive ability test scores. We investigate selection bias as a potential explanation for cognitive ability test score differences between applicants using mobile or non-mobile devices. The likelihood of using a mobile device was predicted by educational attainment (R = .71) and O*NET codes (R = .84), both of which are also related to cognitive ability. Controlling for selection bias using propensity score weights reduced the standardized mean difference in test scores from d = 0.58 to d = 0.25 in a sample of 76,948 job applicants. The mobile device effect was further minimized when weighting using post-stratification (d = 0.10). This suggest that contradictory findings in past studies on mobile device effects are likely explained by selection bias in non-experimental studies. In practice, applicants with greater educational attainment were less likely to complete pre-hire assessments with a mobile device and tend to score higher on cognitive tests. Mobile use was also more common among applicants for lower complexity jobs which tend to attractapplicant pools with lower cognitive test scores on average. Therefore, it is important to control for demographic and occupational differences between mobile and non-mobile test takers when analyzing operational data. Propensity score weighting and post-stratification are useful for reducing the impact of selection bias in real-world, observational data. We also strongly recommend the use of random assignment in order to prevent selection bias in future research and test development

Realized and potential efficiency for post-stratified estimation in a national forest inventory

Post-stratification is often used to increase the precision of estimates arising from large-area forest inventories with plots established at permanent locations. Remotely sensed data and associated spatial products are often used for developing the post-stratification, which offers a mechanism to increase precision for less cost than increasing the sample size. While important variance reductions have been shown from post-stratification, it remains unknown where observed gains lie along the continuum of possible gains. This information is needed to determine whether efforts to further improve post-stratification outcomes are warranted. In this study, two types of ‘optimal’ post-stratification were compared to typical production-based post-stratifications to estimate the magnitude of remaining gains possible. Although the ‘optimal’ post-stratifications were derived using methods inappropriate for operational usage, the results indicated that substantial further increases in precision for estimates of both forest area and total tree biomass could be obtained with better post-stratifications. The potential gains differed by the attribute being estimated, the population being studied, and the number of strata. Practitioners seeking to optimize post-stratification face challenges such as evaluation of numerous auxiliary data sources, temporal misalignment between plot observations and remotely sensed data acquisition, and spatial misalignment between plot locations and remotely sensed data due to positional errors in both data types.