Genotypic stability and physiological tolerance in the dog-whelk (Nucella lapillus)

Populations of dog-whelks living on fully marine shores are in genotypic equilibrium: variation is produced during zygote formation in proportion to the exposure of the shore to wave action, and this variation is eliminated during life. However, populations collected on the Somerset coast where the salinity of the Bristol Channel begins to be reduced by land-water from the River Severn show no such regular pattern. Out of 15 samples (a total of 4711 individuals) 13 showed signs of temporary extinction or of disruptions of growth apparently caused by periodic food shortages. It is concluded that dog-whelks from the Bristol Channel live in a colonizing situation in which they are struggling to survive rather than extending their range by increasing their physiological tolerance.     

Development of a high-throughput process for detection and screening of genetic polymorphisms

A method is described that uses the ABI PRISM 310 genetic analyzer in conjunction with custom-designed software to identify and classify RAPD products. This methodology will also work well with AFLPs and microsatellite analyses. The methodology uses the ABI PRISM 310's high-throughput (> 500 samples per week) capabilities and in-lane molecular weight standards to efficiently separate and size DNA products. Peak detection, locus classification and export of the data in a form accessible by several genetic analysis programs were accomplished through a custom-written software program (Peaks). Various criteria used by the program to identify and classify loci are described, and their effect on population analyses is examined. Criteria providing an effective, robust determination of population structure are presented.     

A method of deriving subgroups of a population: a study of craniofacial taxonomy

Subgroups within a population are often difficult to discover and describe except by subjective methods. In this study, cluster analysis (numerical taxonomy) methods were used on selected craniofacial measurements obtained from 308 North American White children of both sexes in the age range 6–18 to derive categories of skeletal facial types. Two different cluster analysis approaches were used in conjunction with a separate overall evaluation of facial balance, an independent measure of maxillo-mandibular relationship (AB/FOP), and a traditional classification (Angle). The categories derived rest on corroborative and overlapping evidence from each of those methods. The categories were examined to determine if it is possible to classify a sample by means of cluster analysis, the size and discreteness of each class, how they compare with the Angle classification of the same sample, and the percentage of individuals that may be identified unequivocally by this classification scheme. Five categories were obtained. Labeled Category A — Category E, they show some correspondence to the Angle Classes I, II, and III, but categories A, B, and C appear to be subgroups, heretofore undetected, of Angle Class II. Categories D and E correspond to Angle Classes I and III, respectively. The categories are more realistic and informative than the Angle classes. Each category is reported along with its distinguishing skeletal characteristics.     

Comparison of discriminant function and classification tree analyses for age classification of marmots

We evaluated the predictive power of two classification techniques, one parametric – discriminant function analysis (DFA) and the other non-parametric – classification and regression tree analysis (CART), in order to provide a non-subjective quantitative method of determining age class in Vancouver Island marmots ( Marmota vancouverensis ) and hoary marmots ( Marmota caligata ). For both techniques we used morphological measurements of known-age male and female marmots from two independent population studies to build and test predictive models of age class. Both techniques had high predictive power (69–86%) for both sexes and both species. Overall, the two methods performed identically with 81% correct classification. DFA was marginally better at discriminating among older more challenging age classes compared to CART. However, in our test samples, cases with missing values in any of the discriminant variables were deleted and hence unclassified by DFA, whereas CART used values from closely correlated variables to substitute for the missing values. Therefore, overall, CART performed better (CART 81% vs DFA 76%) because of its ability to classify incomplete cases. Correct classification rates were approximately 10% higher for hoary marmots than for Vancouver Island marmots, a result that could be attributed to different sets of morphological measurements. Zygomatic arch breadth measured in hoary marmots was the most important predictor of age class in both sexes using both classification techniques. We recommend that CART analysis be performed on data-sets with incomplete records and used as a variable screening tool prior to DFA on more complete data-sets.     

松嫩平原全叶马兰种群无性系分株的建成及其年龄结构

全叶马兰（Kalimeris integrifolia Turcz.)为根蘖型多年生草本菊种植物。在生长季后期,新生肉质细根向地表生长形成根蘖芽,根蘖芽成株后形成主根。经过对大量样本观察。创建了按形态特征对根蘖型草本植物种群无性系分株和芽的年龄划分方法,以及对主根生活年限的估计方法。分株年龄是按主根芽再生的次数划分,新根蘖芽成株为1a,1a分株死亡后从主根再萌生的分株为2a,2a分株死亡后从主根再萌生的分株为3a。新根蘖芽为0a芽,其它龄级与分株的龄级相一致,即1a分株主根芽为1a芽,2a分株主根芽为2a芽,在松嫩平原割草草场,全叶马兰种群由3个龄级无性系分株组成,呈稳定型年龄结构。分株主根的生活年限最多为4年,分株的生产力种群水平以2a最高,个体水平以1a最低,并随着龄级的增加呈增长趋势,其中,在生长季初期以3a分株的生产力最高,进入中期以后则1a最低,并随着龄级的增加呈增长趋势。其中,在生长季初期以3a分株的生产力最高,进入中期以后则1a分株的相对生长速率最大,用秋末新生根蘖数量和现时种群数量可以较准确预报翌年种群数量和年龄结构。芽库中潜在种群数量是现时种群数量的2倍以上,但不意味着种群将发生疯长,而是为替补所做的充分准备。     

DeepWILD: Wildlife Identification,Localisation and estimation on camera trap videos using Deep learning

Videos and images from camera traps are more and more used by ecologists to estimate the population of species on a territory. It is a laborious work since experts have to analyse massive data sets manually. This takes also a lot of time to filter these videos when many of them do not contain animals or are with human presence. Fortunately, deep learning algorithms for object detection can help ecologists to identify multiple relevant species on their data and to estimate their population. In this study, we propose to go even further by using object detection model to detect, classify and count species on camera traps videos. To this end, we developed a 3-step process: (i) At the first stage, after splitting videos into images, we annotate images by associating bounding boxes to each label thanks to MegaDetector algorithm; (ii) then, we extend MegaDetector based on Faster R-CNN architecture with backbone Inception-ResNet-v2 in order to not only detect the 13 relevant classes but also to classify them; (iii) finally, we design a method to count individuals based on the maximum number of bounding boxes detected. This final stage of counting is evaluated in two different contexts: first including only detection results (i.e. comparing our predictions against the right number of individuals, no matter their true class), then an evolved version including both detection and classification results (i.e. comparing our predictions against the right number in the right class). The results obtained during the evaluation of our model on the test data set are: (i) 73,92% mAP for classification, (ii) 96,88% mAP for detection with a ratio Intersection-Over-Union (IoU) of 0.5 (overlapping ratio between groundtruth bounding box and the detected one), and (iii) 89,24% mAP for detection at IoU = 0.75. Highly represented classes, like humans, have highest values of mAP around 81% whereas less represented classes in the train data set, such as dogs, have lowest values of mAP around 66%. Regarding the proposed counting method, we predicted a count either exact or $\pm$ 1 unit for 87% with detection results and for 48% with detection and classification results of our test data set. Our model is also able to detect empty videos. To the best of our knowledge, this is the first study in France about the use of object detection model on a French national park to locate, identify and estimate the population of species from camera trap videos.     

Reliable gene signatures for microarray classification: assessment of stability and performance

MOTIVATION: Two important questions for the analysis of gene expression measurements from different sample classes are (1) how to classify samples and (2) how to identify meaningful gene signatures (ranked gene lists) exhibiting the differences between classes and sample subsets. Solutions to both questions have immediate biological and biomedical applications. To achieve optimal classification performance, a suitable combination of classifier and gene selection method needs to be specifically selected for a given dataset. The selected gene signatures can be unstable and the resulting classification accuracy unreliable, particularly when considering different subsets of samples. Both unstable gene signatures and overestimated classification accuracy can impair biological conclusions. METHODS: We address these two issues by repeatedly evaluating the classification performance of all models, i.e. pairwise combinations of various gene selection and classification methods, for random subsets of arrays (sampling). A model score is used to select the most appropriate model for the given dataset. Consensus gene signatures are constructed by extracting those genes frequently selected over many samplings. Sampling additionally permits measurement of the stability of the classification performance for each model, which serves as a measure of model reliability. RESULTS: We analyzed a large gene expression dataset with 78 measurements of four different cartilage sample classes. Classifiers trained on subsets of measurements frequently produce models with highly variable performance. Our approach provides reliable classification performance estimates via sampling. In addition to reliable classification performance, we determined stable consensus signatures (i.e. gene lists) for sample classes. Manual literature screening showed that these genes are highly relevant to our gene expression experiment with osteoarthritic cartilage. We compared our approach to others based on a publicly available dataset on breast cancer. AVAILABILITY: R package at http://www.bio.ifi.lmu.de/~davis/edaprakt     

Fuzzy evaluation of water quality classification

Traditional classification methods of the water quality parameters use crisp set, and the concentrations values which are close or far from the limits are considered in same classes. Moreover, usually, several parameters are considered in quality determination; therefore, differences of the classes of the parameters may be vagueness, especially, in public consideration.In this study, an index model for quality evaluation of surface water quality classification using fuzzy logic is proposed. In the method, traditional quality classes are transformed into continuous form and then the concentration values of the different quality parameters are summed using fuzzy rules, finally, defuzzyfication of this summed values develops the index. An application of this proposed index model for physical and inorganic chemical parameters in the Eber Lake water (in Turkey) are studied to demonstrate the practical application and feasibility of this index.     

The effect of fixed‐count subsampling on macroinvertebrate biomonitoring in small streams

• 1 When rigorous standards of collecting and analysing data are maintained, biological monitoring adds valuable information to water resource assessments. Decisions, from study design and field methods to laboratory procedures and data analysis, affect assessment quality. Subsampling ‐ a laboratory procedure in which researchers count and identify a random subset of field samples ‐ is widespread yet controversial. What are the consequences of subsampling?
• 2 To explore this question, random subsamples were computer generated for subsample sizes ranging from 100 to 1000 individuals as compared with the results of counting whole samples. The study was done on benthic invertebrate samples collected from five Puget Sound lowland streams near Seattle, WA, USA. For each replicate subsample, values for 10 biological attributes (e.g. total number of taxa) and for the 10‐metric benthic index of biological integrity (B‐IBI) were computed.
• 3 Variance of each metric and B‐IBI for each subsample size was compared with variance associated with fully counted samples generated using the bootstrap algorithm. From the measures of variance, we computed the maximum number of distinguishable classes of stream condition as a function of sample size for each metric and for B‐IBI.
• 4 Subsampling significantly decreased the maximum number of distinguishable stream classes for B‐IBI, from 8.2 for fully counted samples to 2.8 classes for 100‐organism subsamples. For subsamples containing 100–300 individuals, discriminatory power was low enough to mislead water resource decision makers.

     

Bacterial contamination associated with estuarine shoreline development

AIMS: To examine the relationships among increasing estuarine shellfish closings due to bacterial contamination, adjacent shoreline land uses and environmental variables. METHODS AND RESULTS: A 1 year study of faecal coliform bacterial contamination of a small estuary in central NC, USA was done relative to adjacent land uses. The area has experienced rapid growth in residential shoreline development including the installation of adjacent, separate docking facilities for larger boats, each <11 slips (pseudomarina) that appear to be a single marina (individual facilities of >10 slips). Six near-shore sites were selected [old developed shore (OD), undeveloped shore (UD), two pseudomarinas (P1, P2), newly developed shore (ND) and a real marina (RM)]. Five locations were spaced along the shore near each site. Paired Thursday/Monday samples were collected biweekly (summer) and monthly (other seasons). Results indicate that OD had the highest bacteria counts followed by ND, RM and P1 & P2. Three sites (OD, ND and RM) failed to meet NC shellfishing waters standards at all locations. At the pseudomarina sites 4 of 10 locations failed to meet shellfish standards while two locations at UD failed to meet these standards. There were no significant differences between paired Thursday/Monday samples. At three sites (OD, UD and P2) bacteria counts were positively correlated with increased water level due to wind tides. CONCLUSIONS: Any type of estuarine shoreline development may result in closing of adjacent shellfishing waters. ND had bacterial counts second only to OD in spite of the retention of vegetated shoreline buffers and very new septic systems. As expected, the RM also failed to meet shellfish standards. Unexpectedly, only four of the 10 pseudomarina locations failed to meet the standards. Weekend boat use had no effect on bacterial counts. Surface runoff from rain and shoreline flooding from increased water levels increased bacterial counts, probably as a result of suspension of surface deposited faeces from wildlife and domestic animals. SIGNIFICANCE AND IMPACT OF THE STUDY: Multiple docking facilities do not necessarily result in violations of shellfish water quality standards. However, the elevated bacterial counts observed along the newly developed shore suggest caution in approving the practice of allowing individual 'oyster gardening' off private piers if the oysters are intended for human consumption. The practice of automatic closure of shellfish waters around RMs was supported. Correlations of bacterial counts with time following significant rainfall suggests a sampling strategy to separate local sources of bacteria from more remote sources thus focusing limited remedial resources more effectively.     

Interval-valued analysis for discriminative gene selection and tissue sample classification using microarray data

An important application of gene expression data is to classify samples in a variety of diagnostic fields. However, high dimensionality and a small number of noisy samples pose significant challenges to existing classification methods. Focused on the problems of overfitting and sensitivity to noise of the dataset in the classification of microarray data, we propose an interval-valued analysis method based on a rough set technique to select discriminative genes and to use these genes to classify tissue samples of microarray data. We first select a small subset of genes based on interval-valued rough set by considering the preference-ordered domains of the gene expression data, and then classify test samples into certain classes with a term of similar degree. Experiments show that the proposed method is able to reach high prediction accuracies with a small number of selected genes and its performance is robust to noise.     

Intra-population variability in energy parameters of the salamander Plethodon Cinereus

Summary Mean values of energetic parameters are used in ecosystem studies because only crude estimates are possible when dealing with a large scope. However, when the focus is at the population level, finer resolution is possible and thus potential ontogenetic differences should be examined. Analysis of energetic parameters of a population of terrestrial salamander, Plethodon cinereus, was undertaken in order to determine ontogenetic differences.Digestive efficiency does not differ significantly between age classes, nor is it correlated with energy intake. Adult males and non-gravid females have virtually identical mean efficiencies. Thus it is probably meaningful to calculate energy flow through the population using a mean digestive efficiency value. Percent water content decreases significantly with increasing age whereas percent ash content increases significantly. Thus when estimating energy tied up in standing crop, correction must be applied for ontogenetic differences in water and ash content. Once these corrections have been made, the AFDW-specific energy content is not significantly different between age groups (with the exception of gravid females which is higher). Salamanders accumulate energy in their tissues with increasing age, although immatures are not significantly different from non-gravid adults. Since gravid females have significantly more joules/mg wet weight than other groups, separate energy density values must be calculated for these individuals if a meaningful standing crop energy estimate is to be obtained from wet weight biomass.     

Estimating cladoceran birth rate: use of the egg age distribution to estimate mortality of ovigerous females and eggs

The birth rate of natural cladoceran populations can change rapidly (during 2–3 days), reflecting rapid changes in their environment. If the egg ratio is calculated on the basis of egg age distribution, the birth rate can be estimated at short sampling intervals (shorter than egg stage duration) by modified Paloheimo's (1974) formula. When female size structure and age of eggs in clutches at the beginning and the end of a sampling interval are known, death rates of ovigerous females and eggs in separate size classes can be determined and incorporated in birth rate estimates. All these methods have been employed using the data on the population of Diaphanosoma brahyurum from the lake Obsterno (North-Western Belarus) in July–August, 1992. The birth rate values computed by the proposed methods and Poloheimo's formula differed significantly in many cases. The accuracy of birth rate estimations from various calculation methods was tested using a computer simulation. The model contains the essential features of cladoceran life history: distinct egg, juvenile and adult stages, development of eggs and reproduction. The population was divided into 25 age classes, each of 1 day duration. Durations of the egg, juvenile and adult stages were set at 3, 6 and 20 days, respectively. The embryogenesis was divided into three egg stages, each of 1 day duration. Survivorship was set from 0.2 up to 1.0 for each age class. The survivorship and brood size were changed through each of five time intervals (days) that allowed to simulate an increase or reduction of population density. Fecundity, survivorship and egg stage duration remained constant during each of 5 days that assumed stability of an environment (this does not occur in nature). Nevertheless, the egg ratio, proportion of juveniles and birth rates were variable even under these circumstances. Computer simulations showed that Poloheimo's formula evaluates birth rate with the relative error of 62% and usually overestimates its values. We propose methods to decrease errors of birth rate estimations by 3.5–5.5 times.     

Growth charts of head length and breadth for regional areas? A study in Sardinia (Italy)

There exist few standards of head length and breadth from childhood to adulthood in Europoid populations. Moreover, such standards are based on samples that cannot be used as references for all populations since they were taken from different ethnic groups and from different periods. The aims of this study were: (1) to test whether standards derived from North Americans of European extraction can be used to assess the Sardinian population; and (2) to produce growth charts for head length and breadth for Sardinian males and females from 3 to 22 years of age.The cross sectional sample consisted of 9,721 subjects of Sardinian origin (4,884 males and 4,837 females), aged 3–22 years, measured from 1998 to 2008. Growth percentiles were produced with the LMS method. The mean values for each sex in each age class (3–18 years) are almost always significantly lower for both head length and breadth than the corresponding North American values. The exceptions are the head length of boys of 14 years and girls of 16–18 years where values for Sardinians are lower, but not significantly so. The results show that the North American standards are not appropriate for the assessment of Sardinian children. For the Sardinian population, specific regional growth charts should be used to correctly evaluate the normal range and the cut-off points of the extreme percentiles.     

Age-group estimation in free-ranging African elephants based on acoustic cues of low-frequency rumbles

Animal vocal signals are increasingly used to monitor wildlife populations and to obtain estimates of species occurrence and abundance. In future, acoustic monitoring should function not only to detect animals, but also to extract detailed information about populations by discriminating sexes, age groups, social or kin groups, and potentially individuals. Here we show that it is possible to estimate age groups of African elephants (Loxodonta africana) based on acoustic parameters extracted from rumbles recorded under field conditions in a National Park in South Africa. Statistical models reached up to 70% correct classification to four age groups (infants, calves, juveniles and adults) and 95% correct classification when categorizing into two groups (infants/calves lumped into one group vs. adults). The models revealed that parameters representing absolute frequency values have the most discriminative power. Comparable classification results were obtained by fully automated classification of rumbles by high-dimensional features that represent the entire spectral envelope, such as Mel-frequency cepstral coefficient (75% correct classification) and Greenwood function cepstral coefficient (74% correct classification). The reported results and methods provide the scientific foundation for a future system that could potentially automatically estimate the demography of an acoustically monitored elephant group or population.     

On sparse Fisher discriminant method for microarray data analysis

One of the applications of the discriminant analysis on microarray data is to classify patient and normal samples based on gene expression values. The analysis is especially important in medical trials and diagnosis of cancer subtypes. The main contribution of this paper is to propose a simple Fisher-type discriminant method on gene selection in microarray data. In the new algorithm, we calculate a weight for each gene and use the weight values as an indicator to identify the subsets of relevant genes that categorize patient and normal samples. A l(2) - l(1) norm minimization method is implemented to the discriminant process to automatically compute the weights of all genes in the samples. The experiments on two microarray data sets have shown that the new algorithm can generate classification results as good as other classification methods, and effectively determine relevant genes for classification purpose. In this study, we demonstrate the gene selection's ability and the computational effectiveness of the proposed algorithm. Experimental results are given to illustrate the usefulness of the proposed model.     

Calibrating Recruitment Estimates for Mourning Doves From Harvest Age Ratios

ABSTRACT We examined results from the first national-scale effort to estimate mourning dove (Zenaida macroura) age ratios and developed a simple, efficient, and generalizable methodology for calibrating estimates. Our method predicted age classes of unknown-age wings based on backward projection of molt distributions from fall harvest collections to preseason banding. We estimated 1) the proportion of late-molt individuals in each age class, and 2) the molt rates of juvenile and adult birds. Monte Carlo simulations demonstrated our estimator was minimally biased. We estimated model parameters using 96,811 wings collected from hunters and 42,189 birds banded during preseason from 68 collection blocks in 22 states during the 2005–2007 hunting seasons. We also used estimates to derive a correction factor, based on latitude and longitude of samples, which can be applied to future surveys. We estimated differential vulnerability of age classes to harvest using data from banded birds and applied that to harvest age ratios to estimate population age ratios. Average, uncorrected age ratio of known-age wings for states that allow hunting was 2.25 (SD 0.85) juveniles:adult, and average, corrected ratio was 1.91 (SD 0.68), as determined from harvest age ratios from an independent sample of 41,084 wings collected from random hunters in 2007 and 2008. We used an independent estimate of differential vulnerability to adjust corrected harvest age ratios and estimated the average population age ratio as 1.45 (SD 0.52), a direct measure of recruitment rates. Average annual recruitment rates were highest east of the Mississippi River and in the northwestern United States, with lower rates between. Our results demonstrate a robust methodology for calibrating recruitment estimates for mourning doves and represent the first large-scale estimates of recruitment for the species. Our methods can be used by managers to correct future harvest survey data to generate recruitment estimates for use in formulating harvest management strategies.     

Comparative studies on the biology of nerita atramentosa Reeve, bembicium nanum (Lamarck) and cellana tramoserica (Sowerby) (gastropoda: Prosobranchia) in S.E. Australia

Populations of three coexisting intertidal gastropods, Nerita atramentosa Reeve, Bembicium nanum (Lamarck) and Cellana tramoserica (Sowerby), were sampled from a shore in Botany Bay, New South Wales, from July 1972 to September 1973. The recruitment and growth rates of each species were analysed from size frequency distributions. Mortality of each age cohort, and longevity, were estimated from analyses of the densities of Nerita atramentosa and Bembicium nanum.Nerita atramentosa showed no significant mortality during the first two years on the shore, but high mortality (at an instantaneous rate of 0.084 deaths/individual/month) after reaching the age of reproductive maturity, which was 20 months from settlement and at a mean shell-length of 13.5 mm. No growth could be detected after reproductive maturity was reached. Longevity of N. atramentosa was estimated as 3–5.5 years. Bembicium nanum juveniles showed higher mortality (at an instantaneous rate of 0.233 deaths/individual/month) than reproductively mature animals (0.060 deaths/individual/month). Reproductive maturity was reached at a mean shell-breadth of 11.0mm, i.e., about ten months after settlement on the shore. Longevity was estimated as from 4–8 years. Cellana tramoserica showed different growth patterns depending on the time of settlement.The three species showed different patterns of growth and life history relating to variable recruitment (which was demonstrated in all three species) and different rates of mortality of age cohorts.     

A neural network based pattern recognition system for somatic embryos of Douglas fir

A pattern recognition system was developed to classify Douglas fir somatic embryos by employing an image analysis system and two neural network based classifiers. The contour of embryo images was segmented, digitalized and converted to numerical values after the discrete and fast Fourier transformation. These values, or Fourier features, along with some other shape factors, were used for embryo classification. The pattern recognition system used a hierarchical decision tree to classify Douglas fir embryos into three normal and one abnormal embryo classes. An accuracy of greater than 80% was achieved for normal embryos. This system provides an objective and efficient method of classifying embryos of Douglas fir. It will be a useful tool for kinetic studies and process optimization of conifer somatic embryogenesis.     

Improved Minimum Squared Error Algorithm with Applications to Face Recognition

Minimum squared error based classification (MSEC) method establishes a unique classification model for all the test samples. However, this classification model may be not optimal for each test sample. This paper proposes an improved MSEC (IMSEC) method, which is tailored for each test sample. The proposed method first roughly identifies the possible classes of the test sample, and then establishes a minimum squared error (MSE) model based on the training samples from these possible classes of the test sample. We apply our method to face recognition. The experimental results on several datasets show that IMSEC outperforms MSEC and the other state-of-the-art methods in terms of accuracy.