Dataset Information

Metapopulation Patterns of Iberian Butterflies Revealed by Fuzzy Logic.

ABSTRACT: Metapopulation theory considers that the populations of many species are fragmented into patches connected by the migration of individuals through an interterritorial matrix. We applied fuzzy set theory and environmental favorability (F) functions to reveal the metapopulational structure of the 222 butterfly species in the Iberian Peninsula. We used the sets of contiguous grid cells with high favorability (F ≥ 0.8), to identify the favorable patches for each species. We superimposed the known occurrence data to reveal the occupied and empty favorable patches, as unoccupied patches are functional in a metapopulation dynamics analysis. We analyzed the connectivity between patches of each metapopulation by focusing on the territory of intermediate and low favorability for the species (F < 0.8). The friction that each cell opposes to the passage of individuals was computed as 1-F. We used the r.cost function of QGIS to calculate the cost of reaching each cell from a favorable patch. The inverse of the cost was computed as connectivity. Only 126 species can be considered to have a metapopulation structure. These metapopulation structures are part of the dark biodiversity of butterflies because their identification is not evident from the observation of the occurrence data but was revealed using favorability functions.

SUBMITTER: Pulido-Pastor A

PROVIDER: S-EPMC8145449 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Metapopulation Patterns of Iberian Butterflies Revealed by Fuzzy Logic.

Pulido-Pastor Antonio A Márquez Ana Luz AL Guerrero José Carlos JC García-Barros Enrique E Real Raimundo R

Insects 20210428 5

Metapopulation theory considers that the populations of many species are fragmented into patches connected by the migration of individuals through an interterritorial matrix. We applied fuzzy set theory and environmental favorability (F) functions to reveal the metapopulational structure of the 222 butterfly species in the Iberian Peninsula. We used the sets of contiguous grid cells with high favorability (F ≥ 0.8), to identify the favorable patches for each species. We superimposed the known oc ...[more]

PMID: 33925263

Similar Datasets

Project description:BackgroundTo date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy.ResultsAdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms.ConclusionAdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.

Project description:Background Social networks are large platforms that allow their users to interact with each other on the Internet. Today, the widespread use of social networks has made them vulnerable to malicious use through different methods such as fake accounts and spam. As a result, many social network users are exposed to the harmful effects of spam accounts created by malicious people. Although Twitter, one of the most popular social networking platforms, uses spam filters to protect its users from the harmful effects of spam, these filters are insufficient to detect spam accounts that exhibit new methods and behaviours. That’s why on social networking platforms like Twitter, it has become a necessity to use robust and more dynamic methods to detect spam accounts. Methods Fuzzy logic (FL) based approaches, as they are the models such that generate results by interpreting the data obtained based on heuristics viewpoint according to past experiences, they can provide robust and dynamic solutions in spam detection, as in many application areas. For this purpose, a data set was created by collecting data on the twitter platform for spam detection. In the study, fuzzy logic-based classification approaches are suggested for spam detection. In the first stage of the proposed method, a data set with extracted attributes was obtained by applying normalization and crowdsourcing approaches to the raw data obtained from Twitter. In the next stage, as a process of the data preprocessing step, six attributes in the binary form in the data set were subjected to a rating-based transformation and combined with the other real-valued attribute to create a database to be used in spam detection. Classification process inputs were obtained by applying the fisher-score method, one of the commonly used filter-based methods, to the data set obtained in the second stage. In the last stage, the data were classified based on FL based approaches according to the obtained inputs. As FL approaches, four different Mamdani and Sugeno fuzzy inference systems based on interval type-1 and Interval Type-2 were used. Finally, in the classification phase, four different machine learning (ML) approaches including support vector machine (SVM), Bayesian point machine (BPM), logistic regression (LR) and average perceptron (Avr Prc) methods were used to test the effectiveness of these approaches in detecting spam. Results Experimental results were obtained by applying different FL and ML based approaches on the data set created in the study. As a result of the experiments, the Interval Type-2 Mamdani fuzzy inference system (IT2M-FIS) provided the highest performance with an accuracy of 0.955, a recall of 0.967, an F-score 0.962 and an area under the curve (AUC) of 0.971. However, it has been observed that FL-based spam models have a higher performance than ML-based spam models in terms of metrics including accuracy, recall, F-score and AUC values.

Dataset Information

Metapopulation Patterns of Iberian Butterflies Revealed by Fuzzy Logic.

Publications

Metapopulation Patterns of Iberian Butterflies Revealed by Fuzzy Logic.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets