Line transect abundance estimation with uncertain detection on the trackline

Bibliography: leaves 225-233.After critically reviewing developments in line transect estimation theory to date, general likelihood functions are derived for the case in which detection probabilities are modelled as functions of any number of explanatory variables and detection of animals on the trackline (i.e. directly in the observer's path) is not certain. Existing models are shown to correspond to special cases of the general models. Maximum likelihood estimators are derived for some special cases of the general model and some existing line transect estimators are shown to correspond to maximum likelihood estimators for other special cases. The likelihoods are shown to be extensions of existing mark-recapture likelihoods as well as being generalizations of existing line transect likelihoods. Two new abundance estimators are developed. The first is a Horvitz-Thompson-like estimator which utilizes the fact that for point estimation of abundance the density of perpendicular distances in the population can be treated as known in appropriately designed line transect surveys. The second is based on modelling the probability density function of detection probabilities in the population. Existing line transect estimators are shown to correspond to special cases of the new Horvitz-Thompson-like estimator, so that this estimator, together with the general likelihoods, provides a unifying framework for estimating abundance from line transect surveys

The Determinants of On-Farm Renewable Energy Adoption

Agribusiness, Resource /Energy Economics and Policy,

Airborne environmental injuries and human health.

The concept that the environment in which we live can have detrimental effects on our health has existed for centuries. Obvious examples of substances that can cause human diseases include infectious agents, poisons, chemicals and other noxious agents, drugs, and physical stimuli such as bright lights and loud sounds. Some less obvious agents can include allergens, nontangible agents such as colorless, odorless gases and aerosolized toxins. In recent decades, humans have developed various new materials and compounds. Additionally, we are now producing known compounds, and even naturally occurring substances, in vastly increased amounts. Many of these substances are generally believed to threaten the health of our environment. However, there is also a considerable amount of hype and exaggeration regarding some of these agents (e.g., mold) that is unsubstantiated. This article extensively reviews the data on a large number of airborne-related illnesses and attempted to place scientific reality in the context of clinical medicine

Nanoscale magnetic structure of ferromagnet/antiferromagnet manganite multilayers

Polarized Neutron Reflectometry and magnetometry measurements have been used to obtain a comprehensive picture of the magnetic structure of a series of La{2/3}Sr{1/3}MnO{3}/Pr{2/3}Ca{1/3}MnO{3} (LSMO/PCMO) superlattices, with varying thickness of the antiferromagnetic (AFM) PCMO layers (0<=t_A<=7.6 nm). While LSMO presents a few magnetically frustrated monolayers at the interfaces with PCMO, in the latter a magnetic contribution due to FM inclusions within the AFM matrix was found to be maximized at t_A~3 nm. This enhancement of the FM moment occurs at the matching between layer thickness and cluster size, where the FM clusters would find the optimal strain conditions to be accommodated within the "non-FM" material. These results have important implications for tuning phase separation via the explicit control of strain.Comment: 4 pages, submitted to PR

How far does the analogy between causal horizon-induced thermalization with the standard heat bath situation go?

After a short presentation of KMS states and modular theory as the unifying description of thermalizing systems we propose the absence of transverse vacuum fluctuations in the holographic projections as the mechanism for an area behavior (the transverse area) of localization entropy as opposed to the volume dependence of ordinary heat bath entropy. Thermalization through causal localization is not a property of QM, but results from the omnipresent vacuum polarization in QFT and does not require a Gibbs type ensemble avaraging (coupling to a heat bath).Comment: 10 pages, based on talk given at the 2002 Londrina Winter Schoo

Automated call detection for acoustic surveys with structured calls of varying length

Funding: Y.W. is partly funded by the China Scholarship Council (CSC) for Ph.D. study at the University of St Andrews, UK.1. When recorders are used to survey acoustically conspicuous species, identification calls of the target species in recordings is essential for estimating density and abundance. We investigate how well deep neural networks identify vocalisations consisting of phrases of varying lengths, each containing a variable number of syllables. We use recordings of Hainan gibbon (Nomascus hainanus) vocalisations to develop and test the methods. 2. We propose two methods for exploiting the two-level structure of such data. The first combines convolutional neural network (CNN) models with a hidden Markov model (HMM) and the second uses a convolutional recurrent neural network (CRNN). Both models learn acoustic features of syllables via a CNN and temporal correlations of syllables into phrases either via an HMM or recurrent network. We compare their performance to commonly used CNNs LeNet and VGGNet, and support vector machine (SVM). We also propose a dynamic programming method to evaluate how well phrases are predicted. This is useful for evaluating performance when vocalisations are labelled by phrases, not syllables. 3. Our methods perform substantially better than the commonly used methods when applied to the gibbon acoustic recordings. The CRNN has an F-score of 90% on phrase prediction, which is 18% higher than the best of the SVM or LeNet and VGGNet methods. HMM post-processing raised the F-score of these last three methods to as much as 87%. The number of phrases is overestimated by CNNs and SVM, leading to error rates between 49% and 54%. With HMM, these error rates can be reduced to 0.4% at the lowest. Similarly, the error rate of CRNN's prediction is no more than 0.5%. 4. CRNNs are better at identifying phrases of varying lengths composed of a varying number of syllables than simpler CNN or SVM models. We find a CRNN model to be best at this task, with a CNN combined with an HMM performing almost as well. We recommend that these kinds of models are used for species whose vocalisations are structured into phrases of varying lengths.Publisher PDFPeer reviewe

Towards Automated Animal Density Estimation with Acoustic Spatial Capture-Recapture

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually. Digital recorders allow surveyors to gather large volumes of data at low cost, but identifying target species vocalisations in these data is non-trivial. Machine learning (ML) methods are often used to do the identification. They can process large volumes of data quickly, but they do not detect all vocalisations and they do generate some false positives (vocalisations that are not from the target species). Existing wildlife abundance survey methods have been designed specifically to deal with the first of these mistakes, but current methods of dealing with false positives are not well-developed. They do not take account of features of individual vocalisations, some of which are more likely to be false positives than others. We propose three methods for acoustic spatial capture-recapture inference that integrate individual-level measures of confidence from ML vocalisation identification into the likelihood and hence integrate ML uncertainty into inference. The methods include a mixture model in which species identity is a latent variable. We test the methods by simulation and find that in a scenario based on acoustic data from Hainan gibbons, in which ignoring false positives results in 17% positive bias, our methods give negligible bias and coverage probabilities that are close to the nominal 95% level.Comment: 35 pages, 5 figure

Exact Likelihoods for N-mixture models with Time-to-Detection Data

This paper is concerned with the formulation of $N$-mixture models for estimating the abundance and probability of detection of a species from binary response, count and time-to-detection data. A modelling framework, which encompasses time-to-first-detection within the context of detection/non-detection and time-to-each-detection and time-to-first-detection within the context of count data, is introduced. Two observation processes which depend on whether or not double counting is assumed to occur are also considered. The main focus of the paper is on the derivation of explicit forms for the likelihoods associated with each of the proposed models. Closed-form expressions for the likelihoods associated with time-to-detection data are new and are developed from the theory of order statistics. A key finding of the study is that, based on the assumption of no double counting, the likelihoods associated with times-to-detection together with count data are the product of the likelihood for the counts alone and a term which depends on the detection probability parameter. This result demonstrates that, in this case, recording times-to-detection could well improve precision in estimation over recording counts alone. In contrast, for the double counting protocol with exponential arrival times, no information was found to be gained by recording times-to-detection in addition to the count data. An R package and an accompanying vignette are also introduced in order to complement the algebraic results and to demonstrate the use of the models in practice.Comment: 21 pages, 1 figur