Co-occurrence patterns of rodents at multiple spatial scales: competitive release of generalists following habitat loss?

2019 ◽  
Vol 100 (4) ◽  
pp. 1229-1242 ◽  
Author(s):  
Thomas Püttker ◽  
Camila S Barros ◽  
Bruno T Pinotti ◽  
Adriana A Bueno ◽  
Renata Pardini
Keyword(s):  
Habitat Loss ◽  
Spatial Scales ◽  
Niche Partitioning ◽  
Large Data ◽  
Data Set ◽  
Generalist Species ◽  
Competitive Release ◽  
Spatial Niche ◽  
Habitat Generalist ◽  
Occurrence Patterns

AbstractTheory predicts that habitat generalist species are excluded by specialist species in optimal habitat for specialists, and empirical data commonly show a shift from specialist- to generalist-dominated communities following disturbance. We investigated co-occurrence patterns of habitat generalist and specialist terrestrial rodents at two spatial scales in the Atlantic Forest, aiming at evaluating the following hypotheses: 1) within-patch spatial niche partitioning promotes coexistence of generalists and specialists, leading to checkerboard presence-absence patterns at small (within-patch) rather than large (among-patch) scales; and 2) the decrease in abundance of specialists due to habitat loss promotes a competitive release of generalists, leading to negative covariance in abundance between generalists and specialists among patches. Drawing on a large data set including 363 sites within three patches in continuous forest, and 45 patches within three landscapes, we used C-scores based on presence-absence and abundance data to evaluate spatial segregation. We found consistent segregation between specialists and generalists at the within-patch rather than among-patch scale, but no consistent negative covariance in abundance between generalists and specialists among patches (as covarying species pairs varied across landscapes). Our findings suggest that spatial patterns caused by competition are scale-dependent, and coexistence of generalists and specialists is promoted by within-patch spatial niche partitioning. However, the influence of competitive release on the proliferation of generalists may be outweighed by other factors in fragmented landscapes.A teoria ecológica prevê que espécies generalistas de habitat são excluídas por espécies especialistas em hábitats ótimos para as especialistas, e dados empíricos comumente mostram uma mudança de dominância das comunidades - de especialistas para generalistas - após distúrbios. Nós investigamos os padrões de coocorrência de roedores terrestres generalistas e especialistas de habitat em duas escalas espaciais na Mata Atlântica, para testar as seguintes hipóteses: 1) a partição espacial do nicho dento de fragmentos promove a coexistência de generalistas e especialistas, levando a padrões de presença-ausência “tabuleiro de damas” em escalas pequenas (dentro de fragmento) mas não em escalas grandes (entre fragmentos); 2) a diminuição da abundância de especialistas devido à perda de habitat promove uma liberação competitiva de generalistas, levando a covariância negativa da abundância de generalistas e especialistas entre fragmentos. A partir de um grande banco de dados - 363 sítios dentro de três fragmentos de floresta contínua, e 45 fragmentos dentro de três paisagens, usamos C-scores baseados em dados de presença/ausência e abundância para avaliar a segregação espacial. Encontramos segregação consistente entre especialistas e generalistas na escala menor (dentro de fragmentos) e não na maior (entre fragmentos), mas não encontramos covariância negativa na abundância de generalistas e especialistas entre fragmentos (dado que os pares de espécies que covariaram mudou entre as paisagens). Nossos resultados sugerem que padrões espaciais causados por competição são dependentes de escala, e que a coexistência de generalistas e especialistas é promovida pela partição espacial de nicho dentro dos fragmentos. No entanto, a influência da liberação competitiva na proliferação de generalistas pode ser superada por outros fatores em paisagens fragmentadas.

scholarly journals Using wavelets to verify the scale structure of precipitation forecasts

2020 ◽  
Vol 6 (1) ◽  
pp. 13-30 ◽  
Author(s):  
Sebastian Buschow ◽  
Petra Friederichs
Keyword(s):  
Boundary Conditions ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Large Data ◽  
Wavelet Spectrum ◽  
Data Set ◽  
Alternative Approach ◽  
Verification Tools ◽  
First Time ◽  
Uncertain Boundary

Abstract. Recently developed verification tools based on local wavelet spectra can isolate errors in the spatial structure of quantitative precipitation forecasts, thereby answering the question of whether the predicted rainfall variability is distributed correctly across a range of spatial scales. This study applies the wavelet-based structure scores to real numerical weather predictions and radar-derived observations for the first time. After tackling important practical concerns such as uncertain boundary conditions and missing data, the behaviour of the scores under realistic conditions is tested in selected case studies and analysed systematically across a large data set. Among the two tested wavelet scores, the approach based on the so-called map of central scales emerges as a particularly convenient and useful tool: summarizing the local spectrum at each pixel by its centre of mass results in a compact and informative visualization of the entire wavelet analysis. The histogram of these scales leads to a structure score which is straightforward to interpret and insensitive to free parameters like wavelet choice and boundary conditions. Its judgement is largely the same as that of the alternative approach (based on the spatial mean wavelet spectrum) and broadly consistent with other, established structural scores.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals The Complete DNA Sequence of the Mitochondrial Genome of a “Living Fossil,” the Coelacanth (Latimeria chalumnae)

Genetics ◽  
1997 ◽  
Vol 146 (3) ◽  
pp. 995-1010 ◽  
Author(s):  
Rafael Zardoya ◽  
Axel Meyer
Keyword(s):  
Mitochondrial Genome ◽  
Tandem Repeats ◽  
Phylogenetic Analyses ◽  
Large Data ◽  
Molecular Data ◽  
Phylogenetic Position ◽  
Data Set ◽  
Living Fossil ◽  
Latimeria Chalumnae ◽  
Relationship Of

The complete nucleotide sequence of the 16,407-bp mitochondrial genome of the coelacanth (Latimeria chalumnae) was determined. The coelacanth mitochondrial genome order is identical to the consensus vertebrate gene order which is also found in all ray-finned fishes, the lungfish, and most tetrapods. Base composition and codon usage also conform to typical vertebrate patterns. The entire mitochondrial genome was PCR-amplified with 24 sets of primers that are expected to amplify homologous regions in other related vertebrate species. Analyses of the control region of the coelacanth mitochondrial genome revealed the existence of four 22-bp tandem repeats close to its 3′ end. The phylogenetic analyses of a large data set combining genes coding for rRNAs, tRNA, and proteins (16,140 characters) confirmed the phylogenetic position of the coelacanth as a lobe-finned fish; it is more closely related to tetrapods than to ray-finned fishes. However, different phylogenetic methods applied to this largest available molecular data set were unable to resolve unambiguously the relationship of the coelacanth to the two other groups of extant lobe-finned fishes, the lungfishes and the tetrapods. Maximum parsimony favored a lungfish/coelacanth or a lungfish/tetrapod sistergroup relationship depending on which transversion:transition weighting is assumed. Neighbor-joining and maximum likelihood supported a lungfish/tetrapod sistergroup relationship.

Climatology of nutrient distributions in the South China Sea based on a large data set derived from a new algorithm

2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Large Data ◽  
The South China Sea ◽  
The South ◽  
Data Set ◽  
China Sea ◽  
Large Data Set

scholarly journals Spike detection: Inter-reader agreement and a statistical Turing test on a large data set

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson
Keyword(s):  
Large Data ◽  
Turing Test ◽  
Spike Detection ◽  
Data Set ◽  
Large Data Set

scholarly journals Horizontal interactions in local personal income taxes

2021 ◽  
Author(s):  
Johan Lundberg
Keyword(s):  
Population Size ◽  
Political Representation ◽  
Large Data ◽  
Personal Income ◽  
Set Covering ◽  
Tax Rates ◽  
Data Set ◽  
Relative Population ◽  
Tax Rate ◽  
Local Council

AbstractTheories of inter-jurisdictional tax and yardstick competition assume that the tax decisions of one jurisdiction will influence the tax decisions of other jurisdictions. This paper empirically addresses the issue of horizontal dependence in local personal income tax rates across jurisdictions. Based on a large data set covering Swedish municipalities over a period of 14 years, we test for interactions across municipalities that share a common border, across municipalities within a distance of 100 km of each other, and across municipalities with similar political representation in the local council. We also test the hypothesis that the tax rate of relatively larger municipalities has a greater influence on their neighbors' tax rate compared to the influence of their smaller neighbors. Our results suggest that when lagged tax rates are controlled for, the horizontal correlation across municipalities that share a common border or are within a distance of 100 km from each other becomes insignificant. This result is of importance as it suggests that lagged tax rates should be included or at least tested for when testing for horizontal interactions or mimicking in local tax rates. However, our results support the hypothesis of horizontal interactions across municipalities that share a common border when the influence of neighboring municipalities is also weighted by their relative population size, i.e. relatively larger neighbors tend to have a greater impact on their neighbor's tax rates than their relatively smaller neighbors. This is of importance as it suggests that distance or proximity matters, although only in combination with the relative population size. We also find some evidence of horizontal dependence across municipalities with similar political preferences.

scholarly journals Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning

2021 ◽  
Vol 15 ◽  
pp. 174830262199962
Author(s):  
Patrick O Kano ◽  
Moysey Brio ◽  
Jacob Bailey
Keyword(s):  
Laplace Transform ◽  
Optimal Parameter ◽  
Large Data ◽  
Matrix Exponential ◽  
Resolvent Matrix ◽  
Data Set ◽  
Network Training ◽  
The Matrix ◽  
The Laplace Transform ◽  
Space Function

The Weeks method for the numerical inversion of the Laplace transform utilizes a Möbius transformation which is parameterized by two real quantities, σ and b. Proper selection of these parameters depends highly on the Laplace space function F( s) and is generally a nontrivial task. In this paper, a convolutional neural network is trained to determine optimal values for these parameters for the specific case of the matrix exponential. The matrix exponential eA is estimated by numerically inverting the corresponding resolvent matrix [Formula: see text] via the Weeks method at [Formula: see text] pairs provided by the network. For illustration, classes of square real matrices of size three to six are studied. For these small matrices, the Cayley-Hamilton theorem and rational approximations can be utilized to obtain values to compare with the results from the network derived estimates. The network learned by minimizing the error of the matrix exponentials from the Weeks method over a large data set spanning [Formula: see text] pairs. Network training using the Jacobi identity as a metric was found to yield a self-contained approach that does not require a truth matrix exponential for comparison.

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