Regularity analysis of an individual-based ecosystem simulation

Golestani, Abbas; Gras, Régis

doi:10.1063/1.3514011

Cited by 18 publications

(7 citation statements)

References 44 publications

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“…Compared with traditional mechanistic approaches, machine learning avoids the over-simplification during modelling. Machine learning models are as complex as real ecosystems, therefore, in most cases the results that come from machine learning are more valid for drawing any conclusions for real situations [54,55]. Moreover, machine learning has been widely used for species assessment and prediction [56][57][58].…”

Section: Introductionmentioning

confidence: 99%

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Liang

2018

Remote Sensing

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Many remote sensing metrics have been applied in large-scale animal species monitoring and conservation. However, the capabilities of these metrics have not been well compared and assessed. In this study, we investigated the correlation of 21 remote sensing metrics in three categories with the global species richness of three different animal classes using several statistical methods. As a result, we developed a new index by integrating several highly correlated metrics. Of the 21 remote sensing metrics analyzed, evapotranspiration (ET) had the greatest impact on species richness on a global scale (explained variance: 52%). The metrics with a high explained variance on the global scale were mainly in the energy/productivity category. The metrics in the texture category exhibited higher correlation with species richness at regional scales. We found that radiance and temperature had a larger impact on the distribution of bird richness, compared to their impacts on the distributions of both amphibians and mammals. Three machine learning models (i.e., support vector machine, random forests, and neural networks) were evaluated for metric integration, and the random forest model showed the best performance. Our newly developed index exhibited a 0.7 explained variance for the three animal classes' species richness on a global scale, with an explained variance that was 20% higher than any of the univariate metrics.

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Section: Introductionmentioning

confidence: 99%

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Liang

2018

Remote Sensing

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“…Natural populations as well as theoretical populations from ecosystem models are known to exist in a mixture of deterministic (periodical, ordered) and chaotic (irregular) dynamics [102], [103]. In our study it was shown that the ratio between the two deterministic parts of population dynamics (periodic vs. chaotic) increased with increasing disturbance resulting in the hypothesis that the capability for resilience decreased with increasing disturbance.…”

Section: Discussionmentioning

confidence: 57%

Non-Linear Analysis Indicates Chaotic Dynamics and Reduced Resilience in Model-Based Daphnia Populations Exposed to Environmental Stress

et al. 2014

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In this study we present evidence that anthropogenic stressors can reduce the resilience of age-structured populations. Enhancement of disturbance in a model-based Daphnia population lead to a repression of chaotic population dynamics at the same time increasing the degree of synchrony between the population's age classes. Based on the theory of chaos-mediated survival an increased risk of extinction was revealed for this population exposed to high concentrations of a chemical stressor. The Lyapunov coefficient was supposed to be a useful indicator to detect disturbance thresholds leading to alterations in population dynamics. One possible explanation could be a discrete change in attractor orientation due to external disturbance. The statistical analysis of Lyapunov coefficient distribution is proposed as a methodology to test for significant non-linear effects of general disturbance on populations. Although many new questions arose, this study forms a theoretical basis for a dynamical definition of population recovery.

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“…There have been several other studies conducted using EcoSim. EcoSim has been shown to have realistic species abundance patterns (Devaurs et al, 2010) and chaotic behavior with multi-fractal properties which has been observed in biological systems (Golestani and Gras, 2010). Another study observed disease diffusion patterns and disease control regimes in EcoSim (Farahani et al).…”

Section: The Ecosystem Simulation Ecosimmentioning

confidence: 93%

Comparing Distance-Based Phylogenetic Tree Construction Methods Using An Individual-Based Ecosystem Simulation, EcoSim

Scott¹,

Gras²

2012

Artificial Life 13

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Phylogenetic trees are constructed frequently in biological research to provide an understanding of the evolutionary history of the organisms being studied. Often, the actual phylogenetic tree is unknown and the phylogenetic tree constructed is an estimate. There are many methods of phylogenetic tree construction which fall into two main categories: distance-based methods and character-based methods. To test the accuracy of these methods, it is necessary that the system being studied is one for which the actual phylogenetic tree is known. EcoSim is an ecosystem simulation in which predator and prey agents possessing a complex behavioral model can interact, evolve and speciate. In this experiment, we used EcoSim to test the accuracy of the three main distance-based phylogenetic tree construction methods, when constructing a single tree and when performing phylogenetic bootstrapping. Since EcoSim provides data regarding speciation events, we were able to construct the actual phylogenetic trees from this data. We then performed the UPGMA, Neighbor-Joining, and Fitch-Margoliash methods at various time-steps and used symmetric distance as a metric to compare the topologies of the actual and estimated trees. On average, trees contained nearly 30 taxa. We found that the Fitch-Margoliash method with bootstrapping performed slightly better than the other methods, however no method constructed trees in which more than 50% of the partitions were correct.

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Regularity analysis of an individual-based ecosystem simulation

Cited by 18 publications

References 44 publications

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Developing an Integrated Remote Sensing Based Biodiversity Index for Predicting Animal Species Richness

Non-Linear Analysis Indicates Chaotic Dynamics and Reduced Resilience in Model-Based Daphnia Populations Exposed to Environmental Stress

Comparing Distance-Based Phylogenetic Tree Construction Methods Using An Individual-Based Ecosystem Simulation, EcoSim

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