The Sibling Distribution for Multivariate Life Time Data

A flexible class of multivariate distributions for continuous lifetimes is proposed. The distribution is defined in terms of the age-at-death of m siblings. The expression for the joint density is derived using classical results from mathematical demography. The parameters of the distribution are the age-specific birth and death rates, in addition to a vector of relative death times for the m siblings. For the case of constant birth and death rates we are able to derive an explicit expression for the bivariate sibling density, which is proven to be MTP2, and hence has positive dependence. Further, we show that a special case of the sibling distribution belongs to the Block-Basu class of multivariate distribution. In the general case, with age-dependent birth and death rates, evaluation of the density involves numerical integration, but is still feasible.publishedVersio

Consideration of measurement errors for the Norwegian common minke whale (Balaenoptera acutorostrata acutorostrata) surveys

A discrete measurement error model for radial distance and angle to detected objects in line transect surveys is considered. This approach directly quantifies the effect of measurement error on the estimated effective strip half-width. We apply the method to experimental data collected over the period 2008-2013 in North Atlantic both under the assumption of multiplicative and additive measurement errors. Our results indicate that the abundance estimates considering the measurement error are consistently larger than the abundance estimates without any measurement error correction.publishedVersio

Epistemic uncertainty quantification in deep learning classification by the Delta method

The Delta method is a classical procedure for quantifying epistemic uncertainty in statistical models, but its direct application to deep neural networks is prevented by the large number of parameters . We propose a low cost approximation of the Delta method applicable to -regularized deep neural networks based on the top eigenpairs of the Fisher information matrix. We address efficient computation of full-rank approximate eigendecompositions in terms of the exact inverse Hessian, the inverse outer-products of gradients approximation and the so-called Sandwich estimator. Moreover, we provide bounds on the approximation error for the uncertainty of the predictive class probabilities. We show that when the smallest computed eigenvalue of the Fisher information matrix is near the -regularization rate, the approximation error will be close to zero even when . A demonstration of the methodology is presented using a TensorFlow implementation, and we show that meaningful rankings of images based on predictive uncertainty can be obtained for two LeNet and ResNet-based neural networks using the MNIST and CIFAR-10 datasets. Further, we observe that false positives have on average a higher predictive epistemic uncertainty than true positives. This suggests that there is supplementing information in the uncertainty measure not captured by the classification alone.publishedVersio

A comparison of variability and bias when ageing Northeastern Atlantic minke whales (Balaenoptera acutorostrata) by counting growth layer groups in the mandible and bulla tympanica

The age of 43 minke whales (Balaenoptera acutorostrata) was estimated by counting growth layer groups (GLGs) GLGs in 500um thick haemotoxylin stained transverse sections of left and right mandible. The same whales were also aged by counting GLGs in 150um unstained sections of left and right bulla tympanica. The staining and preparation methods were also used to prepare and stain mandible sections of a sperm whale and the GLG count of this was the same as the GLG count of a longitudinal section of a tooth from the same animal. Minke whale mandible age estimates had higher CV (63% on average) than the bulla age estimates (36% on average). Comparing the age estimates with the number of ovulations revealed that both methods underestimated the true age of the whales

Evaluating the suitability of close-kin mark-recapture as a demographic modelling tool for a critically endangered elasmobranch population

Estimating the demographic parameters of contemporary populations is essential to the success of elasmobranch conservation programmes, and to understanding their recent evolutionary history. For benthic elasmobranchs such as skates, traditional fisheries-independent approaches are often unsuitable as the data may be subject to various sources of bias, whilst low recapture rates can render mark-recapture programmes ineffectual. Close-kin mark-recapture (CKMR), a novel demographic modelling approach based on the genetic identification of close relatives within a sample, represents a promising alternative approach as it does not require physical recaptures. We evaluated the suitability of CKMR as a demographic modelling tool for the critically endangered blue skate (Dipturus batis) in the Celtic Sea using samples collected during fisheries-dependent trammel-net surveys that ran from 2011 to 2017. We identified three full-sibling and 16 half-sibling pairs among 662 skates, which were genotyped across 6291 genome-wide single nucleotide polymorphisms, 15 of which were cross-cohort half-sibling pairs that were included in a CKMR model. Despite limitations owing to a lack of validated life-history trait parameters for the species, we produced the first estimates of adult breeding abundance, population growth rate, and annual adult survival rate for D. batis in the Celtic Sea. The results were compared to estimates of genetic diversity, effective population size (Ne), and to catch per unit effort estimates from the trammel-net survey. Although each method was characterized by wide uncertainty bounds, together they suggested a stable population size across the time-series. Recommendations for the implementation of CKMR as a conservation tool for data-limited elasmobranchs are discussed. In addition, the spatio-temporal distribution of the 19 sibling pairs revealed a pattern of site fidelity in D. batis, and supported field observations suggesting an area of critical habitat that could qualify for protection might occur near the Isles of Scilly.publishedVersio

Estimating g(0) from single observer data

In this paper we estimate g(0), or equivalently the effective strip half-width, using data from a line transect survey which has been operated in double observer mode only a small fraction of the time. By letting the proportion of double observer effort approach zero, by increasingly masking data from one of the observers, we find that the estimate of g(0) does not break down. This conclusion rests on the fact that we are using both forward and perpendicular distances, and is perhaps only relevant to northeastern Atlantic minke whale survey

Hva kan et register over vågehvalens DNA fortelle om atferd og biologi?

DNA-registeret for vågehval inneholder data fra alle de ca. 7000 individene som er tatt av norshe hvalfangere siden 1997. De genetishe profilene gjør det mulig å studere bestandsstruktur, vandringsmønster og bestandsstørrelse. Hvordan kan et DNA-register brukes i forvaltningen av viltressurser og gi økt kunnskap om alder og bestander

The Sibling Distribution for Multivariate Life Time Data

A flexible class of multivariate distributions for continuous lifetimes is proposed. The distribution is defined in terms of the age-at-death of m siblings. The expression for the joint density is derived using classical results from mathematical demography. The parameters of the distribution are the age-specific birth and death rates, in addition to a vector of relative death times for the m siblings. For the case of constant birth and death rates we are able to derive an explicit expression for the bivariate sibling density, which is proven to be MTP2, and hence has positive dependence. Further, we show that a special case of the sibling distribution belongs to the Block-Basu class of multivariate distribution. In the general case, with age-dependent birth and death rates, evaluation of the density involves numerical integration, but is still feasible