Use of simulated data as a tool for testing the performance of diversity indices in response to an organic enrichment event

2008 ◽  
Vol 65 (8) ◽  
pp. 1456-1461 ◽  
Author(s):  
Jon Barry ◽  
Hubert L. Rees
Keyword(s):  
Simulated Data ◽  
Diversity Indices ◽  
Organic Enrichment ◽  
Event History ◽  
Ecological Framework ◽  
Marine Science ◽  
Species Numbers ◽  
Stochastic Nature ◽  
Simulation Process

Abstract Barry, J., and Rees, H. L. 2008. Use of simulated data as a tool for testing the performance of diversity indices in response to an organic enrichment event. – ICES Journal of Marine Science, 65: 1456–1461. We demonstrate how data on macrobenthic species numbers and abundance after an organic enrichment event can be simulated using the empirical Pearson–Rosenberg model in combination with further plausible ecological assumptions. The simulations were programmed in the statistical package R, using an ecological framework that included classification of species into opportunistic, tolerant, and sensitive types, together with probabilities for the occurrence of these types at any particular point in the event history. The simulations also included assumptions about the dominance of species types. The exercise was successful in that realistic, simulated datasets could be produced quickly and, because of the stochastic nature of parts of the simulation process, repeat simulations allowed variation of selected diversity indices calculated on the series to be assessed. The approach could provide a useful tool to evaluate both existing and new indicators.

scholarly journals Supervised linear classification of Gaussian spatio-temporal data

2021 ◽  
Vol 62 ◽  
pp. 9-15
Author(s):  
Marta Karaliutė ◽  
Kęstutis Dučinskas
Keyword(s):  
Time Moment ◽  
Gaussian Random Field ◽  
Covariance Structure ◽  
Simulated Data ◽  
Spatial Location ◽  
Error Rates ◽  
Prior Probabilities ◽  
Structure Factors ◽  
Spatio Temporal

In this article we focus on the problem of supervised classifying of the spatio-temporal Gaussian random field observation into one of two classes, specified by different mean parameters. The main distinctive feature of the proposed approach is allowing the class label to depend on spatial location as well as on time moment. It is assumed that the spatio-temporal covariance structure factors into a purely spatial component and a purely temporal component following AR(p) model. In numerical illustrations with simulated data, the influence of the values of spatial and temporal covariance parameters to the derived error rates for several prior probabilities models are studied.

Canada's National Ecological Framework: An asset to reporting on the health of Canadian forests

2001 ◽  
Vol 77 (1) ◽  
pp. 111-115 ◽  
Author(s):  
Harry Hirvonen
Keyword(s):  
Forest Health ◽  
Forest Service ◽  
Ecological Framework ◽  
Single Element ◽  
Ecological Classification ◽  
International Reporting ◽  
Ecological Land Classification ◽  
Reporting Framework ◽  
Ecosystem Classification

The Canadian Forest Service, in cooperation with its partners, has a mandate to report on the health of Canada's forests and determine if, how, and why it is changing. A holistic perspective of forest health is taken whereby the ecosystem rather than a single element is considered. The use of the national ecological classification of Canada as a key reporting framework facilitates this task. Advantages for reporting purposes are several, including the use of ecological over jurisdictional boundaries to discuss ecosystems, wide national acceptance of the framework, and access to a wide array of other environmental databases that use the same framework. Compromises have to be made for forest health reporting as the ecological classification is not a forest ecosystem classification. However, advantages to using the framework for national reporting far outweigh these shortcomings. Key words: ecological land classification, forest health, national and international reporting

scholarly journals Sequence Classification Using Third-Order Moments

2018 ◽  
Vol 30 (1) ◽  
pp. 216-236
Author(s):  
Rasmus Troelsgaard ◽  
Lars Kai Hansen
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Sequence Data ◽  
Hidden Markov ◽  
Score Function ◽  
Simulated Data ◽  
Discrete Observations ◽  
Third Order ◽  
Leibler Divergence

Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent theoretical advances in spectral learning of hidden Markov models, we propose a score function based on third-order moments. In particular, we propose to use the Kullback-Leibler divergence between theoretical and empirical third-order moments for classification of sequence data with discrete observations. The proposed method provides lower computational complexity at classification time than the usual likelihood-based methods. In order to demonstrate the properties of the proposed method, we perform classification of both simulated data and empirical data from a human activity recognition study.

Soil Collembola communities along a steep microclimatic gradient in the collapse doline of the Silická ľadnica Cave, Slovak Karst (Slovakia)

Biologia ◽  
2013 ◽  
Vol 68 (3) ◽  
Author(s):  
Natália Raschmanová ◽  
Dana Miklisová ◽  
Ľubomír Kováč
Keyword(s):  
Soil Surface ◽  
Diversity Indices ◽  
Lower Number ◽  
Permafrost Zone ◽  
Middle Section ◽  
Forest Species ◽  
Species Numbers ◽  
Collapse Doline ◽  
Wallis Anova ◽  
Perennial Ice

AbstractIn spring 2005 we investigated the collembolan communities in the 50-m-deep collapse doline of the perennial ice cave Silická ľadnica in the Slovak Karst (Slovakia). Samples were taken at seven sites along a 117.5-m-long transect on the slope from the ice-bearing cave mouth at the bottom of the doline up to the terrain surface at 500 m above sea level (a.s.l.). The temperature above the soil surface (+0.6 to +13.6°C) positively correlated with altitude. Species numbers (ranging from 20–32) and diversity indices were highest at sites in the middle of the slope with rendzina and well developed organic profiles. A Kruskal-Wallis ANOVA revealed significant differences in abundance between the sites. Mean abundance near the permafrost zone at the bottom of the doline was significantly higher than at the sites further upslope. The abundances of some eurytopic and forest species were significantly correlated with soil temperature. Cluster analysis and the IndVal method indicated differences in the structure of Collembolan communities along the transect. The community at the coldest site had the lowest species richness and the highest mean abundance of individuals. A total of ten montane species were recorded, with a lower number near the permafrost zone compared to the micro-climatically more favourable middle section of the gradient.

scholarly journals Fault Classification and Fault Zone Identification in a Thyristor Controlled Series Compensator Based Transmission Line by using Decision Tree

2020 ◽  
Vol 9 (3) ◽  
pp. 1502-1506
Keyword(s):  
Decision Tree ◽  
Simulated Data ◽  
Fault Classification ◽  
Network Systems ◽  
Thyristor Controlled Series Capacitor ◽  
Decision Tree Method ◽  
Thyristor Controlled Series Compensator ◽  
Types Of Faults ◽  
Tree Method

A new method has been introduced for classification of fault and to identify zone of fault in Thyristor Controlled Series Capacitor based line by utilizing Decision Tree method. PSACD/EMTDC software is used in this paper for the simulation of TCSC. Voltage and current samples after fault are used in this method as input against predicted output vectors for zone identification of fault. Decision Tree based classification algorithm also used to classify all ten types of faults in the TCSC based line. This method is being tested on simulated data and the results indicate that this method can classify different types of faults and also identify zone of fault more accurately than any neural network systems in a TCSC based line.

scholarly journals Rapid classification of TESS planet candidates with convolutional neural networks

2020 ◽  
Vol 633 ◽  
pp. A53 ◽  
Author(s):  
H. P. Osborn ◽  
M. Ansdell ◽  
Y. Ioannou ◽  
M. Sasdelli ◽  
D. Angerhausen ◽  
...  
Keyword(s):  
Simulated Data ◽  
Real Data ◽  
Eclipsing Binaries ◽  
Average Precision ◽  
Class Model ◽  
Data Rates ◽  
Threshold Crossing ◽  
Deep Learning Model

Aims. Accurately and rapidly classifying exoplanet candidates from transit surveys is a goal of growing importance as the data rates from space-based survey missions increase. This is especially true for the NASA TESS mission which generates thousands of new candidates each month. Here we created the first deep-learning model capable of classifying TESS planet candidates. Methods. We adapted an existing neural network model and then trained and tested this updated model on four sectors of high-fidelity, pixel-level TESS simulations data created using the Lilith simulator and processed using the full TESS pipeline. With the caveat that direct transfer of the model to real data will not perform as accurately, we also applied this model to four sectors of TESS candidates. Results. We find our model performs very well on our simulated data, with 97% average precision and 92% accuracy on planets in the two-class model. This accuracy is also boosted by another ~4% if planets found at the wrong periods are included. We also performed three-class and four-class classification of planets, blended and target eclipsing binaries, and non-astrophysical false positives, which have slightly lower average precision and planet accuracies but are useful for follow-up decisions. When applied to real TESS data, 61% of threshold crossing events (TCEs) coincident with currently published TESS objects of interest are recovered as planets, 4% more are suggested to be eclipsing binaries, and we propose a further 200 TCEs as planet candidates.

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