Abundance estimation of long-diving animals using line transect methods

Line transect sampling is one of the most widely used methods for estimating the size of wild animal populations. An assumption in standard line transect sampling is that all the animals on the trackline are detected without fail. This assumption tends to be violated for marine mammals with surfacing/diving behaviors. The detection probability on the trackline is estimated using duplicate sightings from double-platform line transect methods. The double-platform methods, however, are insufficient to estimate the abundance of long-diving animals because these animals can be completely missed while the observers pass. We developed a more flexible hazard probability model that incorporates information on surfacing/diving patterns obtained from telemetry data. The model is based on a stochastic point process and is statistically tractable. A simulation study showed that the new model provides near-unbiased abundance estimates, whereas the traditional hazard rate and hazard probability models produce considerably biased estimates. As an illustration, we applied the model to data on the Baird's beaked whale (Berardius bairdii) in the western North Pacific.     

Correction for bias in visual transect censuses of coral reef fishes

Transect techniques for censusing reef fishes, and the sources of bias inherent in them are considered. A technique, derived from aeraly survey methods, is demonstrated to correct a bias in density estimates due to the width of the transect being censused. This bias is sufficient on a transect 1 m wide to underestimate density by 11.1–26.7% for five species or species groups examined. The bias is still greater on wider transects. Because this bias varies in degree among species, comparisons among species should not be made using uncorrected transect data. Comments are made on other probable sources of bias in transect data, and on ways of minimising bias when making visual transect censuses.     

Biostratigraphical dating of Cretaceous coral communities using large data sets

Habitats of hermatypic corals are shallow and turbulent marine environments that often lack biostratigraphic index fossils. For that reason many Cretaceous coral faunas are imprecisely dated or dated only on the basis of comparisons with other coral faunas. Using a large database on the taxonomy, stratigraphical and geographical distribution of corals in the Cretaceous, a method is proposed that will make it possible to specify the stratigraphical age of coral associations on the basis of their specific composition. In this process the stratigraphical range of the species (calculated before from well-dated faunas) is summarized and a probable age of the association proposed. The method does not only help to assess the biostratigraphical age of a fauna, but may also indicate whether a fauna represents an original composition or is a mixed association derived from reworked horizons or olistoliths. The method can be applied to any other group of organisms, provided that the essential data for a comparison are available.      

Synthesis of bioprocesses using physical properties data

The aim of this article is to illustrate and evaluate a synthesis procedure which has been extended to tackle bioprocesses. Physical property information is used to screen candidate units thereby reducing the size of the synthesis problem. In this way, only units which exploit large property differences between components in a stream are selected. This is important for bioprocesses because of the large number of components and wide range of unit operations which are available. The screening technique and bioprocess-unit-design methodologies have been incorporated within an implicit enumeration algorithm which was developed for chemical process synthesis and is implemented in Java programming language. An important advantage is the ability of the bioprocess synthesis software to generate a ranked list of flowsheets which may subsequently be analyzed in more detail. Two case studies are used to evaluate the bioprocess-synthesis technique. The first system involves a product which is secreted from the host organism. The second has significantly different characteristics in that the product is intracellular and forms inclusion bodies. The latter case study, in particular, is a large synthesis problem with 12 unit operations and 20 contaminant compounds. The results show that the synthesis methodology identifies a set of economically optimal flowsheets in a reasonable computational time which demonstrates its ability to deal with large synthesis problems. Using the synthesis methodology we can generate bioprocesses which are optimal in a system-wide, rather than unit-by-unit, sense.     

Abundance estimation of diving animals by the double-platform line transect method

In conventional line transect theory, it is assumed that all animals on the line are detected. This article introduces an extended and generalized hazard probability model without the need for such an assumption. The proposed method needs a survey design with independent observers having the same visual region and assumes an explicit distinction of simultaneous and delayed duplicates. It can take account of random heterogeneity caused by surfacing behavior as well as systematic heterogeneity by covariate effects. Furthermore, it can be easily extended to cases in which data from incompletely independent observers are available. The abundance estimate is based on the Horvitz-Thompson estimator in unequal detectability sampling scheme. Simulation studies suggest that the proposed method has good performance. The method is applied to a real data set on Antarctic minke whales in the illustration.     

Determination of physicochemical parameters of liposomes from concentration data

Determination of the physicochemical parameters of unilamellar and multilamellar liposomes from concentration data in light of a recent quantitative biodynamic concept which strictly adheres to the CGS unit system (cm, g, s) and the mass/volume chemical unit, mol.cm-3.     

Determination of human arterial wall parameters from clinical data

This study suggests a method to compute the material parameters for arteries in vivo from clinically registered pressure-radius signals. The artery is modelled as a hyperelastic, incompressible, thin-walled cylinder and the membrane stresses are computed using a strain energy. The material parameters are determined in a minimisation process by tuning the membrane stress to the stress obtained by enforcing global equilibrium. In addition to the mechanical model, the study also suggests a preconditioning of the pressure-radius signal. The preconditioning computes an average pressure-radius cycle from all consecutive cycles in the registration and removes, or reduces, undesirable disturbances. The effect is a robust parameter identification that gives a unique solution. The proposed method is tested on clinical data from three human abdominal aortas and the results show that the material parameters from the proposed method do not differ significantly (p < 0.01) from the corresponding parameters obtained by averaging the result from consecutive cycles.     

Correlation between spatial distributions of pollen data, archaeological records and physical parameters from north-western France: a GIS and numerical analysis approach

New methodologies using Geographical Information Systems (GIS) and statistical tools were developed to provide new elements to the interpretation of fossil pollen records on the large spatial scale of north-western France. The originality of these methods lies in the analysis of the spatial distribution of the archaeobotanical data in order to identify correlations with other spatial parameters such as geological, climatic, pedological, topographical and archaeological characteristics. 218 pollen analyses from north-western France and a series of thematic maps (geological, archaeological, climatic, etc.) were used. The application of numerical analyses makes it possible to describe the spatial distribution of pollen data at a regional scale, and to identify spatial correlations between pollen data and other environmental parameters, and between archaeobotanical groups, archaeological and abiotic parameters simultaneously. Two examples are presented and discussed: (A) The spatial distributions of the predominance of hazel over oak between 6700 and 5700 cal b.p. and of modern precipitation are shown to be positively correlated, i.e. hazel is dominant in the most humid areas of the region. (B) The pollen data from the Bronze Age show associations of (1) pollen groups ascribed to meadows, shrubland, and forests with cooler temperatures, higher altitudes and northern latitudes, and (2) pollen groups ascribed to moor environments and anthropogenic vegetation with warmer temperatures, southern latitudes and lower altitudes. The latter implies that the agricultural landscapes of the Bronze Age were mainly confined to southern latitudes and low altitudes of the region, while the areas characterised by high altitudes and low temperatures were characterised by extensive activities such as grazing by cattle. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Predicting and correcting bias caused by measurement error in line transect sampling using multiplicative error models

Line transect sampling is one of the most widely used methods for animal abundance assessment. Standard estimation methods assume certain detection on the transect, no animal movement, and no measurement errors. Failure of the assumptions can cause substantial bias. In this work, the effect of error measurement on line transect estimators is investigated. Based on considerations of the process generating the errors, a multiplicative error model is presented and a simple way of correcting estimates based on knowledge of the error distribution is proposed. Using beta models for the error distribution, the effect of errors and of the proposed correction is assessed by simulation. Adequate confidence intervals for the corrected estimates are obtained using a bootstrap variance estimate for the correction and the delta method. As noted by Chen (1998, Biometrics 54, 899-908), even unbiased estimators of the distances might lead to biased density estimators, depending on the actual error distribution. In contrast with the findings of Chen, who used an additive model, unbiased estimation of distances, given a multiplicative model, lead to overestimation of density. Some error distributions result in observed distance distributions that make efficient estimation impossible, by removing the shoulder present in the original detection function. This indicates the need to improve field methods to reduce measurement error. An application of the new methods to a real data set is presented.     

A comparison of video and point intercept transect methods for monitoring subtropical coral communities

This study evaluated the use of video transects obtained from SCUBA divers or remote operated vehicle (ROV) and point intercept transect (PIT) method from divers for monitoring subtropical coral communities. Comparisons were made between the datasets obtained by the ROV and SCUBA diver video transect (‘Diver’) and the PIT method on three nearby coral sites with different hydrographies, scleractinian coral composition, dominant species and percentage cover. There was no significant difference between the ROV and ‘Diver’ datasets whereas the PIT method tended to over-estimate percentage cover at sites where corals are not extensive. Power analysis showed that the minimum detectable change in coral percentage cover, δ, had low mean values between 0.39% and 1.65% for the ROV dataset, 0.66% for the “Diver’ dataset, and 12.11% for the PIT dataset. This implied that the ROV and SCUBA survey methods can produce higher precision in terms of detecting temporal changes in coral communities and are thus more suitable for scientific research and management purposes than the PIT method. Other advantages of using video transects by SCUBA divers or ROV include provision of permanent records for subsequent studies and public information, less field time incurred and wider survey areas.     

DCMD: Distance-based classification using mixture distributions on microbiome data

Current advances in next-generation sequencing techniques have allowed researchers to conduct comprehensive research on the microbiome and human diseases, with recent studies identifying associations between the human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means classification and k-nearest neighbours framework. We develop two distance metrics that produce optimal results. The performance of the model is assessed using simulated and human microbiome study data, with results compared against a number of existing machine learning and distance-based classification approaches. The proposed method is competitive when compared to the other machine learning approaches, and shows a clear improvement over commonly used distance-based classifiers, underscoring the importance of modelling sparsity for achieving optimal results. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data. The source code is available at https://github.com/kshestop/DCMD for academic use.     

Ecological niche modelling of the distribution of cold-water coral habitat using underwater remote sensing data

Despite a growing appreciation of the need to protect sensitive deep sea ecosystems such as cold-water corals, efforts to map the extent of their distribution are limited by their remoteness. Here we develop ecological niche models to predict the likely distributions of cold-water corals based on occurrence records and data describing environmental parameters (e.g. seafloor terrain attributes and oceanographic conditions). This study has used bathymetric data derived from ship-borne multibeam swath systems, species occurrence data from remotely operated vehicle video surveys and oceanographic parameters from hydrodynamic models to predict coral locations in regions where there is a paucity of direct observations. Predictions of the locations of the scleractinian coral, Lophelia pertusa are based primarily upon ecological niche modelling using a genetic algorithm. Its accuracy has been quantified at local (~ 25 km²) and regional scales (~ 4000 km²) along the Irish continental slope using a variety of error assessment techniques and a comparison with another ecological niche modelling technique. With appropriate choices of parameters and scales of analyses, ecological niche modelling has been effective in predicting the distributions of species at local and regional scales. Refinements of this approach have the potential to be particularly useful for ocean management given the need to manage areas of sensitive habitat where survey data are often limited.     

A comparison of line transect versus linear percentage sampling for evaluating stony coral (Scleractinia andMilleporina) community similarity and area coverage on reefs of the central Bahamas

Three methods of evaluating stony coral communities were used on selected reefs in the Exuma Cays Land and Sea Park (24°22N, 77°30W) in the central Bahamas. Shallow reefs (< 4 meters depth) were selected from aerial surveys based on size, location, and physical setting, and grouped into three community types: (1) channel patch reefs, (2) soft-coral-sponge patch reefs and (3) fringing reefs. Three survey techniques used to evaluate the stony coral communities were a) species presence and absence lists, b) linear percentage and c) line transects using 1 mx1 m grids. Data collected from these survey methods was used to calculate coral colony density, species area coverage, and species diversity based on colony number and based on linear (cm) coral cover. The linear percentage sampling was considered too convervative in determining distribution patterns of a reef community; this technique takes into account the massive reef framework species such asM. annularis. The line transect technique can account for both colony number and area coverage, thus is a better method for characterizing reef communities. Sample size considerations are discussed for future applications of survey techniques for ground-truthing digital images of small, shallow reef communities.     

Estimating the density of kudu (Tragelaphus strepsiceros) in subtropical thicket using line transect surveys of dung and DISTANCE software

Many ecotourism reserves in the Eastern Cape Province of South Africa have introduced large carnivores which prey on ungulates such as kudu. Kudu occupy thick vegetation and helicopter‐based counts are not appropriate. In this study we tested line transect counts of faecal pellets in conjunction with DISTANCE analysis as a method for assessing the abundance of kudu. Transects were undertaken in three regions of two game reserves and the results compared with helicopter based game counts (for two regions) and a known kudu population in the third region (an enclosed breeding centre). Estimates of kudu abundance were two to three times greater than the helicopter counts, but within the known number of kudu in the breeding centre. The increased accuracy in the breeding centre was probably because the transects covered a greater percentage of the available habitat (0.07%) than in the reserves (approximately 0.007%). We suggest that DISTANCE software and line transects counts of pellet groups can be used for kudu and probably for other ungulates in thicket‐type vegetations. Accuracy will depend on the percentage of the available habitat that is surveyed and on the accuracy of the defecation and decay rates.     

Predicting species distributions across the Amazonian and Andean regions using remote sensing data

Aim We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the maxent algorithm at 1‐km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian–Andean elevational gradients. Within remote sensing subsets, microwave‐based data were more important than optical data in the predictions of the two bird species. Main conclusions Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerable utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large‐scale land‐use change.