14 research outputs found
jamiemkass/ENMeval: ENMeval v2.0.0
No full textThis is an archive of the R package ENMeval 2.0.0, which includes the vignette. The package can be found on Github here: https://github.com/jamiemkass/ENMeval. It will also soon be uploaded to CRAN, where it can be found here: https://CRAN.R-project.org/package=ENMeval. The reference for the published paper describing this package, which was just accepted to Methods in Ecology and Evolution, is here:
Kass, J. M., Muscarella, R., Galante, P. J., Bohl, C., Pinilla-Buitrago, G. E., Boria, R. A., Soley‐Guardia, M., & Anderson, R. P. (2021). ENMeval 2.0: redesigned for customizable and reproducible modeling of species’ niches and distributions. Methods in Ecology and Evolution
Data from: A new null model approach to quantify performance and significance for ecological niche models of species distributions
No full textAim: Ecological niche modelling requires robust estimation of model performance and significance, but common evaluation approaches often yield biased estimates. Null models provide a solution but are rarely used in this field. We implemented an important modification to existing null-model tests, evaluating null models with the same withheld records that were used to evaluate the real model. We built and evaluated models across a range of modelling scenarios and for various performance measures using the algorithm Maxent and the monk parakeet (Myiopsitta monachus).
Location: Native range in Southern America and global invasions predominantly in North/Central America and Europe
Methods: We tested the ability of models built under 15 scenarios (five sets of calibration records and three settings that varied the level of model complexity) to predict spatially independent evaluation data in the invaded range (in effect, testing the models under spatial transfer). We quantified performance with measures of discriminatory ability and overfitting based on AUC and the omission error rate. We estimated null distributions of these measures and calculated effect size and significance. We determined how these estimates varied across modelling scenarios, comparing with two tests existing in the literature.
Results: Performance varied starkly across modelling scenarios. As expected, the measures of overfitting agreed with each other and provided different information than that of discriminatory ability. However, high performance per se did not show strong association with high effect size and significance.
Main Conclusions: Ecological niche models should be assessed with measures of effect size and significance based on appropriate null distributions, in contrast to several approaches existing in the literature. The proposed approach using independent evaluation data, implemented with our accompanying code, allows such estimates for either the same or a different region/time period, and it merits use and continued development
Data from: A new null model approach to quantify performance and significance for ecological niche models of species distributions
No full textAim: Ecological niche modelling requires robust estimation of model performance and significance, but common evaluation approaches often yield biased estimates. Null models provide a solution but are rarely used in this field. We implemented an important modification to existing null-model tests, evaluating null models with the same withheld records that were used to evaluate the real model. We built and evaluated models across a range of modelling scenarios and for various performance measures using the algorithm Maxent and the monk parakeet (Myiopsitta monachus).
Location: Native range in Southern America and global invasions predominantly in North/Central America and Europe
Methods: We tested the ability of models built under 15 scenarios (five sets of calibration records and three settings that varied the level of model complexity) to predict spatially independent evaluation data in the invaded range (in effect, testing the models under spatial transfer). We quantified performance with measures of discriminatory ability and overfitting based on AUC and the omission error rate. We estimated null distributions of these measures and calculated effect size and significance. We determined how these estimates varied across modelling scenarios, comparing with two tests existing in the literature.
Results: Performance varied starkly across modelling scenarios. As expected, the measures of overfitting agreed with each other and provided different information than that of discriminatory ability. However, high performance per se did not show strong association with high effect size and significance.
Main Conclusions: Ecological niche models should be assessed with measures of effect size and significance based on appropriate null distributions, in contrast to several approaches existing in the literature. The proposed approach using independent evaluation data, implemented with our accompanying code, allows such estimates for either the same or a different region/time period, and it merits use and continued development
A new null model approach to quantify performance and significance for ecological niche models of species distributions
No full text
The Effects of Subdivision Design on Housing Values: The Case of Alleyways
No full textSubdivision design likely impacts residential housing values. This study examines the sale prices of houses located in subdivisions utilizing rear-entry alleyways in the Greater Dallas-Fort Worth-Denton metroplex. Regression analysis on a sample of 1,672 home sales, some of which are located on alleyways, reveals statistically significant impacts. Consequently, developers, appraisers, New Urbanists and other real estate participants should consider subdivision design when estimating value for residential dwellings.
ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions
No full text１．Quantitative evaluations to optimize complexity have become standard for avoiding overfitting of ecological niche models (ENMs) that estimate species’ potential geographic distributions. ENMeval was the first R package to make such evaluations (often termed model tuning) widely accessible for the Maxent algorithm. It also provided multiple methods for partitioning occurrence data and reported various performance metrics.
２．Requests by users, recent developments in the field, and needs for software compatibility led to a major redesign and expansion. We additionally conducted a literature review to investigate trends in ENMeval use (2015–2019).
３．ENMeval 2.0 has a new object-oriented structure for adding other algorithms, enables customizing algorithmic settings and performance metrics, generates extensive metadata, implements a null-model approach to quantify significance and effect sizes, and includes features to increase the breadth of analyses and visualizations. In our literature review, we found insufficient reporting of model performance and parameterization, heavy reliance on model selection with AICc and low utilization of spatial cross-validation; we explain how ENMeval 2.0 can help address these issues.
４．This redesigned and expanded version can promote progress in the field and improve the information available for decision-making
ENMeval 2.0: Redesigned for customizable and reproducible modeling of species’ niches and distributions
No full text0002
No full text-
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Nuclear RNA sequencing of the mouse erythroid cell transcriptome
No full textCopyright @ 2012 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In addition to protein coding genes a substantial proportion of mammalian genomes are transcribed. However, most transcriptome studies investigate steady-state mRNA levels, ignoring a considerable fraction of the transcribed genome. In addition, steady-state mRNA levels are influenced by both transcriptional and posttranscriptional mechanisms, and thus do not provide a clear picture of transcriptional output. Here, using deep sequencing of nuclear RNAs (nucRNA-Seq) in parallel with chromatin immunoprecipitation sequencing (ChIP-Seq) of active RNA polymerase II, we compared the nuclear transcriptome of mouse anemic spleen erythroid cells with polymerase occupancy on a genome-wide scale. We demonstrate that unspliced transcripts quantified by nucRNA-seq correlate with primary transcript frequencies measured by RNA FISH, but differ from steady-state mRNA levels measured by poly(A)-enriched RNA-seq. Highly expressed protein coding genes showed good correlation between RNAPII occupancy and transcriptional output; however, genome-wide we observed a poor correlation between transcriptional output and RNAPII association. This poor correlation is due to intergenic regions associated with RNAPII which correspond with transcription factor bound regulatory regions and a group of stable, nuclear-retained long non-coding transcripts. In conclusion, sequencing the nuclear transcriptome provides an opportunity to investigate the transcriptional landscape in a given cell type through quantification of unspliced primary transcripts and the identification of nuclear-retained long non-coding RNAs.This work was supported by the Medical Research Council and the Biotechnology and Biological Sciences Research Council, UK (operating grants held by PF) and the Natural Sciences and Engineering Research Council of Canada (Discovery Grant held by JAM). DU was supported by an EMBO fellowship, and CYC was supported in part by an Ontario Graduate Scholarship. The ENCODE project is funded by the National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
