scholarly journals Estimating occupancy dynamics and encounter rates with species misclassification: a semi-supervised individual-level approach

2021 ◽  
Author(s):  
Anna I Spiers ◽  
J Andrew Royle ◽  
Christa L Torrens ◽  
Maxwell B Joseph
Keyword(s):  
Large Scale ◽  
Abundant Species ◽  
Classification Model ◽  
Encounter Rate ◽  
Research Station ◽  
Observation Error ◽  
Biodiversity Monitoring ◽  
Species Identity ◽  
Encounter Rates ◽  
Occupancy Model

1. Large-scale, long-term biodiversity monitoring is essential to meeting conservation and land management goals and identifying threats to biodiversity. However, multispecies surveys are prone to various types of observation error, including false positive/negative detection, and misclassification, where a species is encountered but its species identity is not correctly identified. Previous methods assume an imperfect classifier produces species-level classifications, but in practice, particularly with human observers, we may end up with extraspecific classifications including "unknown", morphospecies designations, and taxonomic identifications coarser than species. Disregarding these types of species misclassification in biodiversity monitoring datasets can bias estimates of ecologically important quantities such as demographic rates, occurrence, and species richness. 2. Here we develop an occupancy model that accounts for species non-detection and misclassification. Our framework accommodates extinction and colonization dynamics, allows for additional uncertain 'morphospecies' designations in the imperfect species classifications, and makes use of individual specimen with known species identities in a semi-supervised setting. We compare the performance of our joint classification-occupancy model to a reduced classification model that discards information about occupancy and encounter rate on a withheld test set. We illustrate our model with an empirical case study of the carabid beetle (Carabidae) community at the National Ecological Observatory Network Niwot Ridge Mountain Research Station, west of Boulder, CO, USA, and quantify taxonomist identification error by accounting for classification probabilities. 3. Species occupancy varied through time and across sites and species. The model yielded high probabilities (30 to 92\% medians) of classification where the imperfect classifier matched the true species. The classification model informed by occupancy and encounter rates outperformed the classification that was not, and these differences were most pronounced for abundant species. 4. Our probabilistic framework can be applied to datasets with imperfect species detection and classification. This model can identify commonly misclassified species, helping biodiversity monitoring organizations systematically prioritize which samples need validation by an expert. Our Bayesian approach propagates classification uncertainty to offer an alternative to making conservation decisions based on point estimates

scholarly journals OCURRENCIA Y TASAS DE ENCUENTRO DE MAMÍFEROS MARINOS EN LAS AGUAS DE LA ISLA MALPELO Y HACIA EL CONTINENTE

2016 ◽  
Vol 40 ◽  
Author(s):  
Julio César Herrera Carmona ◽  
Juan José Capella Alzueta ◽  
Germán Andrés Soler ◽  
Sandra Bessudo ◽  
Carolina García ◽  
...  
Keyword(s):  
Species Richness ◽  
Continental Shelf ◽  
First Trimester ◽  
Abundant Species ◽  
Megaptera Novaeangliae ◽  
Encounter Rate ◽  
Physeter Macrocephalus ◽  
Stenella Coeruleoalba ◽  
Encounter Rates ◽  
Delphinus Delphis

This work compiles a decade (2001-2010) of marine mammal sightings in the Malpelo Fauna and Flora Sanctuary (FFS) and the area between the island and mainland coast. Four separate sources of data were consulted, which used visual searching during cruising efforts while Malpelo’s surrounding waters were surveyed from a vantage point. Seven species were identified in the FFS: October and November were the months with higher species richness. Tursiops truncatus had the highest encounter rates (17.78 groups/100 h), followed by Megaptera novaeangliae (1.62) and Stenella attenuata (0.88). These species were usually within 6 km from the island. Other species seen around the island include Stenella coeruleoalba, Delphinus delphis, Stenella longirostris and Zalophus wollebaeki. On the other hand, thirteen species were identified during cruises, and March and April were the months with the highest species richness. Megaptera novaeangliae had the highest encounter rate (5.94), followed by T. truncatus (3.30), S. attenuata (3.08), D. delphis (3.08) and S. coeruleoalba, Globicephala macrorhynchus and Orcinus orca, each one with 0.66. Other species seen during the cruises include Steno bredanensis, Pseudorca crassidens, Grampus griseus, Peponocephala electra, Physeter macrocephalus and Ziphius cavirostris. Megaptera novaeangliae were associated to the continental shelf, T. trunctaus and D. delphis to oceanic waters, and S. attenuata to both the continental shelf and oceanic waters. Delphinus delphis was more abundant in intermediate waters, during the first trimester (January-March), while T. truncatus was the most abundant species around the Sanctuary during all seasons, suggesting that is the same population. The latter was also the only species found all year round in both zones (around the island and in oceanic waters), and encounter rates did not change across years. Megaptera novaeangliae had a seasonal presence (mostly June to November) with a higher abundance during the third trimester (July-September), both around Malpelo and the transect. The presence of humpback whales calves suggests that Malpelo is used for reproductive purposes. This new information about the marine mammals found in Malpelo FFS and the area between the island and the continent contributes to the understanding of these species in Colombian waters.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Extremes ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

scholarly journals Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review

2007 ◽  
Vol 64 (11) ◽  
pp. 3766-3784 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Large Scale ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Variational Data Assimilation ◽  
Current Status ◽  
General Question ◽  
Observation Error ◽  
Moist Processes

Abstract This paper first reviews the current status, issues, and limitations of the parameterizations of atmospheric large-scale and convective moist processes that are used in numerical weather prediction and climate general circulation models. Both large-scale (resolved) and convective (subgrid scale) moist processes are dealt with. Then, the general question of the inclusion of diabatic processes in variational data assimilation systems is addressed. The focus is put on linearity and resolution issues, the specification of model and observation error statistics, the formulation of the control vector, and the problems specific to the assimilation of observations directly affected by clouds and precipitation.

scholarly journals Soil Denitrifier Community Size Changes with Land Use Change to Perennial Bioenergy Cropping Systems

2016 ◽  
Author(s):  
Karen A. Thompson ◽  
Bill Deen ◽  
Kari E. Dunfield
Keyword(s):  
Land Use ◽  
16S Rrna ◽  
Land Use Change ◽  
Crop Production ◽  
Large Scale ◽  
Perennial Grasses ◽  
N2o Emissions ◽  
Research Station ◽  
N2o Production ◽  
Nirs Gene

Abstract. Dedicated biomass crops are required for future bioenergy production. However, the effects of large-scale land use change (LUC) from traditional annual crops, such as corn-soybean rotations to the perennial grasses (PGs) switchgrass and miscanthus on soil microbial community functioning is largely unknown. Specifically, ecologically significant denitrifying communities, which regulate N2O production and consumption in soils, may respond differently to LUC due to differences in carbon (C) and nitrogen (N) inputs between crop types and management systems. Our objective was to quantify bacterial denitrifying gene abundances as influenced by corn-soybean crop production compared to PG biomass production. A field trial was established in 2008 at the Elora Research Station in Ontario, Canada (n = 30), with miscanthus and switchgrass grown alongside corn-soybean rotations at different N rates (0 and 160 kg N ha-1) and biomass harvest dates within PG plots. Soil was collected on four dates from 2011–2012 and quantitative PCR was used to enumerate the total bacterial community (16S rRNA), and communities of bacterial denitrifiers by targeting nitrite reductase (nirS) and N2O reductase (nosZ) genes. Miscanthus produced significantly larger yields and supported larger nosZ denitrifying communities than corn-soybean rotations regardless of management, indicating large-scale LUC from corn-soybean to miscanthus may be suitable in variable Ontario conditions while potentially mitigating soil N2O emissions. Harvesting switchgrass in the spring decreased yields in N-fertilized plots, but did not affect gene abundances. Standing miscanthus overwinter resulted in higher 16S rRNA and nirS gene copies than in fall-harvested crops. However, the size of the total (16S rRA) and denitrifying communities changed differently over time and in response to LUC, indicating varying controls on these communities.

scholarly journals Effect of ocean acidification and elevated <i>f</i>CO<sub>2</sub> on trace gas production by a Baltic Sea summer phytoplankton community

2016 ◽  
Vol 13 (15) ◽  
pp. 4595-4613 ◽  
Author(s):  
Alison L. Webb ◽  
Emma Leedham-Elvidge ◽  
Claire Hughes ◽  
Frances E. Hopkins ◽  
Gill Malin ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Large Scale ◽  
Gas Production ◽  
Trace Gas ◽  
Research Station ◽  
The Baltic Sea ◽  
Chl A ◽  
The Baltic ◽  
Upwelling Event ◽  
Ambient Co2

Abstract. The Baltic Sea is a unique environment as the largest body of brackish water in the world. Acidification of the surface oceans due to absorption of anthropogenic CO2 emissions is an additional stressor facing the pelagic community of the already challenging Baltic Sea. To investigate its impact on trace gas biogeochemistry, a large-scale mesocosm experiment was performed off Tvärminne Research Station, Finland, in summer 2012. During the second half of the experiment, dimethylsulfide (DMS) concentrations in the highest-fCO2 mesocosms (1075–1333 µatm) were 34 % lower than at ambient CO2 (350 µatm). However, the net production (as measured by concentration change) of seven halocarbons analysed was not significantly affected by even the highest CO2 levels after 5 weeks' exposure. Methyl iodide (CH3I) and diiodomethane (CH2I2) showed 15 and 57 % increases in mean mesocosm concentration (3.8 ± 0.6 increasing to 4.3 ± 0.4 pmol L−1 and 87.4 ± 14.9 increasing to 134.4 ± 24.1 pmol L−1 respectively) during Phase II of the experiment, which were unrelated to CO2 and corresponded to 30 % lower Chl a concentrations compared to Phase I. No other iodocarbons increased or showed a peak, with mean chloroiodomethane (CH2ClI) concentrations measured at 5.3 (±0.9) pmol L−1 and iodoethane (C2H5I) at 0.5 (±0.1) pmol L−1. Of the concentrations of bromoform (CHBr3; mean 88.1 ± 13.2 pmol L−1), dibromomethane (CH2Br2; mean 5.3 ± 0.8 pmol L−1), and dibromochloromethane (CHBr2Cl, mean 3.0 ± 0.5 pmol L−1), only CH2Br2 showed a decrease of 17 % between Phases I and II, with CHBr3 and CHBr2Cl showing similar mean concentrations in both phases. Outside the mesocosms, an upwelling event was responsible for bringing colder, high-CO2, low-pH water to the surface starting on day t16 of the experiment; this variable CO2 system with frequent upwelling events implies that the community of the Baltic Sea is acclimated to regular significant declines in pH caused by up to 800 µatm fCO2. After this upwelling, DMS concentrations declined, but halocarbon concentrations remained similar or increased compared to measurements prior to the change in conditions. Based on our findings, with future acidification of Baltic Sea waters, biogenic halocarbon emissions are likely to remain at similar values to today; however, emissions of biogenic sulfur could significantly decrease in this region.

scholarly journals Adult caddisflies (Trichoptera) attracted to artificial lights in the middle reaches of the Shinano River from 2005 to 2007

Zoosymposia ◽  
2011 ◽  
Vol 5 (1) ◽  
pp. 143-146 ◽  
Author(s):  
KIMIO HIRABAYASHI ◽  
GORO KIMURA ◽  
EISO INOUE
Keyword(s):  
Species Composition ◽  
Air Temperature ◽  
Large Scale ◽  
Abundant Species ◽  
The Other ◽  
Seasonal Abundance ◽  
Other Hand ◽  
The Mean ◽  
Summer Flood

The species composition and abundance of adult caddisflies attracted to the illuminated showcase of a vending machine set along the middle reaches of the Shinano River were investigated every Sunday night from April to November in 2005 to 2007. A total of 1,405 adult caddisflies was collected during the investigation periods. We identified a total of 13 species belonging to 11 genera of 8 families. The most abundant species was Psychomyia acutipennis (Ulmer 1908) each year. Psychomyia acutipennis adults were collected from mid-May to the beginning of October (the range of mean air temperature was 13.8 to 27.7°C), with its seasonal abundance divided into several peaks, i.e., the end of May, the beginning of June, and the end of August to the beginning of September in both 2006 and 2007. On the other hand, in 2005 when there was no large-scale summer flood and there were no marked abundance peaks. The present study suggests that the mean air temperature and summer floods impacted the seasonal abundance of P. acutipennis adults.

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique

2020 ◽  
Author(s):  
Yu Wang ◽  
ZAHEER ULLAH KHAN ◽  
Shaukat Ali ◽  
Maqsood Hayat
Keyword(s):  
Feature Selection ◽  
Bacterial Infections ◽  
Large Scale ◽  
Computational Models ◽  
Genetic Material ◽  
Feature Space ◽  
Classification Model ◽  
Antibacterial Drug ◽  
Accurate Identification ◽  
Multi Level

Abstract BackgroundBacteriophage or phage is a type of virus that replicates itself inside bacteria. It consist of genetic material surrounded by a protein structure. Bacteriophage plays a vital role in the domain of phage therapy and genetic engineering. Phage and hydrolases enzyme proteins have a significant impact on the cure of pathogenic bacterial infections and disease treatment. Accurate identification of bacteriophage proteins is important in the host subcellular localization for further understanding of the interaction between phage, hydrolases, and in designing antibacterial drugs. Looking at the significance of Bacteriophage proteins, besides wet laboratory-based methods several computational models have been developed so far. However, the performance was not considerable due to inefficient feature schemes, redundancy, noise, and lack of an intelligent learning engine. Therefore we have developed an anovative bi-layered model name DeepEnzyPred. A Hybrid feature vector was obtained via a novel Multi-Level Multi-Threshold subset feature selection (MLMT-SFS) algorithm. A two-dimensional convolutional neural network was adopted as a baseline classifier.ResultsA conductive hybrid feature was obtained via a serial combination of CTD and KSAACGP features. The optimum feature was selected via a Novel Multi-Level Multi-Threshold Subset Feature selection algorithm. Over 5-fold jackknife cross-validation, an accuracy of 91.6 %, Sensitivity of 63.39%, Specificity 95.72%, MCC of 0.6049, and ROC value of 0.8772 over Layer-1 were recorded respectively. Similarly, the underline model obtained an Accuracy of 96.05%, Sensitivity of 96.22%, Specificity of 95.91%, MCC of 0.9219, and ROC value of 0.9899 over layer-2 respectivily.ConclusionThis paper presents a robust and effective classification model was developed for bacteriophage and their types. Primitive features were extracted via CTD and KSAACGP. A novel method (MLMT-SFS ) was devised for yielding optimum hybrid feature space out of primitive features. The result drew over hybrid feature space and 2D-CNN shown an excellent classification. Based on the recorded results, we believe that the developed predictor will be a valuable resource for large scale discrimination of unknown Phage and hydrolase enzymes in particular and new antibacterial drug design in pharmaceutical companies in general.

scholarly journals A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons

2020 ◽  
Vol 117 (31) ◽  
pp. 18412-18423 ◽  
Author(s):  
Chia-Chen Hsu ◽  
Jiabao Xu ◽  
Bas Brinkhof ◽  
Hui Wang ◽  
Zhanfeng Cui ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Raman Spectra ◽  
Large Scale ◽  
Developmental Stages ◽  
Single Cells ◽  
Classification Model ◽  
Label Free ◽  
Machine Learning Classification

Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.

scholarly journals Indirect methods of identifying mammals: a case study from Shivapuri National Park, Nepal

1970 ◽  
Vol 12 ◽  
pp. 43-57 ◽  
Author(s):  
B. Shrestha ◽  
K. Basnet
Keyword(s):  
Relative Frequency ◽  
National Park ◽  
Mammalian Species ◽  
Black Bear ◽  
Abundant Species ◽  
Encounter Rate ◽  
Indirect Methods ◽  
Characteristic Features ◽  
Basic Characteristics

The main objective of this study was to explore diversity of mammalian species in Shivapuri national Park (ShNP) through indirect method. Specific objectives were (i) to identify and describe characteristic features of different signs as key to species identification, and (ii) to determine occurrence and abundance of mammalian species based on the signs. Survey was conducted by walking through fixed 11 transect lines of total 229 km long, collecting and recording of footprints, feces, scrapes, scratches, shelters of burrows, calls and quills of mammals. Altogether 344 indirect signs were collected and observed through fixed transect lines and 25 signs through random searching of mammals from Kakani, Panimuhan, Shivapuri Peak, Baghdwar, Sundarijal, Chisapani and Manichur in ShNP. Basic characteristics of identified signs of different mamals as key to their identification have been described. The occurrence of species was confirmed through indirect signs and abundance was estimated on the basis of encounter rate (number/km/day) and relative frequency percentage of the signs. Among 20 species, 18 species belonging to six orderas and 14 families were recorded confirming by different indirect validation techniques. The study also identified large civet, a new record for ShNP. The highest percentage relative frequency (35%) and encounter rate (0.53/km) of signs of wild boar and house rat implied these species were the most abundant mammals in the park. This was followed by barking dear (17% and 0.26), common leopard (17% and 0.25), jungle cat (9.6% and 0.14), Himalayan squirrel and rhesus monkey, which were intermediate in abundance. Himalayan goral (6.7% and 0.10), Indian hare (4.3% and 0.06), yellow throated marten (4% and 0.06), golden jackal (3.5% and 0.05), large civit (2.6% and 0.04), black bear (0.3% and 0.004), Chinese pangolin, hanumal langur, royel's pika, porcupine and small mongoose were the least abundant species. Key words: Mammals; Identification; Footprints; scrapes; Feces; Shelters doi: 10.3126/eco.v12i0.3196 Ecoprint: An International Journal of Ecology 12: 43-58, 2005

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