Empirical Prediction of Fish Biomass and Yield

1982 ◽  
Vol 39 (2) ◽  
pp. 257-263 ◽  
Author(s):  
John Mark Hanson ◽  
William C. Leggett
Keyword(s):  
Biomass Yield ◽  
Phosphorus Concentration ◽  
Fish Biomass ◽  
Data Sets ◽  
Dissolved Solids ◽  
Total Phosphorus Concentration ◽  
Data Set ◽  
Fish Yield ◽  
Empirical Prediction ◽  
Morphoedaphic Index

Data taken from the literature were used to develop and compare predictors of fish biomass and yield in lakes. Two new indices, total phosphorus concentration and macrobenthos biomass/mean depth, were the best univariate predictors offish yield (r2 = 0.84 and r2 = 0.48, respectively) and biomass (r2 = 0.75 and r2 = 0.83, respectively) for four different data sets. Both new indices were stronger predictors of fish yield when compared to the morphoedaphic index, total dissolved solids, or mean depth for the same data set. The relatively constant relationship between fish biomass and macrobenthos biomass/mean depth implies a near-constant energy transfer from the benthos to the fish regardless of the number of fish species present.Key words: biomass, yield, fish, macrobenthos, phosphorus, depth, dissolved solids, morphoedaphic index, lakes

Fish Production Correlated with Primary Productivity, not the Morphoedaphic Index

1990 ◽  
Vol 47 (10) ◽  
pp. 1929-1936 ◽  
Author(s):  
John A. Downing ◽  
Céline Plante ◽  
Sophie Lalonde
Keyword(s):  
Standing Stock ◽  
Phytoplankton Production ◽  
Phosphorus Concentration ◽  
Regression Equations ◽  
Fish Production ◽  
Oligotrophic Lakes ◽  
Yield Data ◽  
Total Phosphorus Concentration ◽  
Wide Range ◽  
Morphoedaphic Index

Estimates of the biological production of entire lake fish communities were collected from the published literature on lakes covering a wide range of geographic areas and trophic status. Correlation analysis shows that fish production is uncorrected with the morphoedaphic index (p > 0.05) but closely correlated with annual phytoplankton production (r2 = 0.79), mean total phosphorus concentration (r2 = 0.67), and annual average fish standing stock (r2 = 0.67). Empirically derived regression equations are presented and compared with previous models based on catch and yield data. Analysis of these equations suggests that conversion of phytoplankton into fish production is 100 times more efficient in oligotrophic lakes than hyper-eutrophic ones, but that a much lower fraction of fish production can be channeled to sustainable yield in oligotrophic lakes. Sustained yields were frequently as little as 10% of the annual community fish production.

A Statistically Valid Model of the Morphoedaphic Index

1991 ◽  
Vol 48 (10) ◽  
pp. 1937-1943 ◽  
Author(s):  
Robert S. Rempel ◽  
Peter J. Colby
Keyword(s):  
Data Sets ◽  
Annual Fish ◽  
Fish Yield ◽  
Lake Volume ◽  
Morphoedaphic Index ◽  
Partitioned Data ◽  
Continued Use ◽  
Widespread Access ◽  
Ratio Variables

The morphoedaphic index (MEI) has been criticized because of the use of ratio variables in linear regression. Computationally simple, the continued use of the index is questionable given the widespread access fisheries biologists now have to computerized statistical packages. We present a statistically valid analogue to the MEI, the morphoedaphic model (MEM), that utilizes multiple regression to characterize the morphometric and fertility properties of lakes to predict annual fish yield. Surface area, lake volume, and total dissolved solids (TDS) are used to predict annual fish yield for the lake and to derive associated confidence limits. Predicted yield of the newly derived model was compared with predictions from the original MEI Comparisons were also made based on models derived from Ontario sport and commercial fisheries data sets. The MEM derived from these partitioned data sets more accurately modelled the observed long-term yields for these lakes. Analysis of the remaining outliers suggests that several additional variables and stratification may be required to further develop the precision of the statistical model.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Lake Paranoá, Brasília, Brazil: Integrated Management Plan for its Restoration

1992 ◽  
Vol 27 (2) ◽  
pp. 271-286 ◽  
Author(s):  
Sonia Paulino Mattos ◽  
Irene Guimarães Altafin ◽  
Hélio José de Freitas ◽  
Cristine Gobbato Brandão Cavalcanti ◽  
Vera Regina Estuqui Alves
Keyword(s):  
Total Phosphorus ◽  
Drainage Basin ◽  
Integrated Management ◽  
Algal Growth ◽  
Nutrient Content ◽  
Management Plan ◽  
Cylindrospermopsis Raciborskii ◽  
Phosphorus Concentration ◽  
Limiting Factor ◽  
Total Phosphorus Concentration

Abstract Built in 1959, Lake Paranoá, in Brasilia, Brazil, has been undergoing an accelerated process of nutrient enrichment, due to inputs of inadequately treated raw sewage, generated by a population of 600,000 inhabitants. Consequently, it shows high nutrient content (40 µg/L of total phosphorus and 1800 µg/L of total nitrogen), low transparency (0.65 m) and high levels of chlorophyll a (65 µg/L), represented mainly by Cylindrospermopsis raciborskii and sporadic bloom of Microcystis aeruginosa, which is being combatted with copper sulphate. With the absence of seasonality and a vertical distribution which is not very evident, the horizontal pattern assumes great importance in this reservoir, in which five compartments stand out. Based on this segmentation and on the identification of the total phosphorus parameter as the limiting factor for algal growth, mathematical models were developed which demonstrate the need for advanced treatment of all the sewage produced in its drainage basin. With this, it is expected that a process of restoration will be initiated, with a decline in total phosphorus concentration to readings below 25 µg/L. Additional measures are proposed to accelerate this process.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

scholarly journals Phytase-Producing Rahnella aquatilis JZ-GX1 Promotes Seed Germination and Growth in Corn (Zea mays L.)

2021 ◽  
Vol 9 (8) ◽  
pp. 1647
Author(s):  
Gui-E Li ◽  
Wei-Liang Kong ◽  
Xiao-Qin Wu ◽  
Shi-Bo Ma
Keyword(s):  
Seed Germination ◽  
Plant Growth ◽  
Biomass Accumulation ◽  
Root Surface ◽  
Phosphorus Concentration ◽  
Maize Seedlings ◽  
Total Phosphorus Concentration ◽  
Rahnella Aquatilis ◽  
Maize Seeds

Phytase plays an important role in crop seed germination and plant growth. In order to fully understand the plant growth-promoting mechanism by Rahnella aquatilis JZ-GX1，the effect of this strain on germination of maize seeds was determined in vitro, and the colonization of maize root by R. aquatilis JZ-GX1 was observed by scanning electron microscope. Different inoculum concentrations and Phytate-related soil properties were applied to investigate the effect of R. aquatilis JZ-GX1 on the growth of maize seedlings. The results showed that R. aquatilis JZ-GX1 could effectively secrete indole acetic acid and had significantly promoted seed germination and root length of maize. A large number of R. aquatilis JZ-GX1 cells colonized on the root surface, root hair and the root interior of maize. When the inoculation concentration was 107 cfu/mL and the insoluble organophosphorus compound phytate existed in the soil, the net photosynthetic rate, chlorophyll content, phytase activity secreted by roots, total phosphorus concentration and biomass accumulation of maize seedlings were the highest. In contrast, no significant effect of inoculation was found when the total P content was low or when inorganic P was sufficient in the soil. R. aquatilis JZ-GX1 promotes the growth of maize directly by secreting IAA and indirectly by secreting phytase. This work provides beneficial information for the development and application of R. aquatilis JZ-GX1 as a microbial fertilizer in the future.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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