Dataset Information

Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data.

ABSTRACT: Species distribution modeling often involves high-dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences of the effects of environmental factors on the spatial distribution of species. Few studies have evaluated and compared the performance of multiple machine learning (ML) models in handling multicollinearity. Here, we assessed the effectiveness of removal of correlated covariates and regularization to cope with multicollinearity in ML models for habitat suitability. Three machine learning algorithms maximum entropy (MaxEnt), random forests (RFs), and support vector machines (SVMs) were applied to the original data (OD) of 27 landscape variables, reduced data (RD) with 14 highly correlated covariates being removed, and 15 principal components (PC) of the OD accounting for 90% of the original variability. The performance of the three ML models was measured with the area under the curve and continuous Boyce index. We collected 663 nonduplicated presence locations of Eastern wild turkeys (Meleagris gallopavo silvestris) across the state of Mississippi, United States. Of the total locations, 453 locations separated by a distance of ?2 km were used to train the three ML algorithms on the OD, RD, and PC data, respectively. The remaining 210 locations were used to validate the trained ML models to measure ML performance. Three ML models had excellent performance on the RD and PC data. MaxEnt and SVMs had good performance on the OD data, indicating the adequacy of regularization of the default setting for multicollinearity. Weak learning of RFs through bagging appeared to alleviate multicollinearity and resulted in excellent performance on the OD data. Regularization of ML algorithms may help exploratory studies of the effects of environmental factors on the spatial distribution and habitat suitability of wildlife.

SUBMITTER: Farrell A

PROVIDER: S-EPMC6540709 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data.

Farrell Annie A Wang Guiming G Rush Scott A SA Martin James A JA Belant Jerrold L JL Butler Adam B AB Godwin Dave D

Ecology and evolution 20190424 10

Species distribution modeling often involves high-dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences of the effects of environmental factors on the spatial distribution of species. Few studies have evaluated and compared the performance of multiple machine learning (ML) models in handling multicollinearity. Here, we assessed the effectiveness of removal of correlated co ...[more]

PMID: 31161010

Similar Datasets

Project description:MOTIVATION:Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions. RESULTS:We propose a new rank-flexible machine learning-based compositional approach for taxonomic assignment of metagenomics reads and show that it benefits from increasing the number of fragments sampled from reference genome to tune its parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning the method involves training machine learning models on about 10(8) samples in 10(7) dimensions, which is out of reach of standard softwares but can be done efficiently with modern implementations for large-scale machine learning. The resulting method is competitive in terms of accuracy with well-established alignment and composition-based tools for problems involving a small to moderate number of candidate species and for reasonable amounts of sequencing errors. We show, however, that machine learning-based compositional approaches are still limited in their ability to deal with problems involving a greater number of species and more sensitive to sequencing errors. We finally show that the new method outperforms the state-of-the-art in its ability to classify reads from species of lineage absent from the reference database and confirm that compositional approaches achieve faster prediction times, with a gain of 2-17 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise. AVAILABILITY AND IMPLEMENTATION:Data and codes are available at http://cbio.ensmp.fr/largescalemetagenomics CONTACT:pierre.mahe@biomerieux.com SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Project description:Wild turkeys (Meleagris gallopavo; hereafter turkeys) are an important game animal whose popularity among hunters has increased in recent decades. Yet, the number of hunters pursuing turkeys appears to be in flux, patterns of hunter abundance have primarily been described at broad spatial scales, and the ability of management to impact hunter numbers in the post-restoration era of management through opportunity for quality hunting is unclear. We used county-scale estimates of turkey hunter numbers collected over a 14-year period (2001-2014) and time-series analyses to evaluate the spatial scales at which spring and fall turkey hunter populations fluctuate, and also used generalized linear mixed models to evaluate whether attributes related to quality turkey hunting explain recent patterns in hunter abundance. We found heterogeneity in turkey hunter population growth at finer spatial scales than has been previously described (i.e., counties and management units), and provide evidence for spatial structuring of hunter population dynamics among counties that did not always correspond with existing management units. Specifically, the directionality of hunter population change displays spatial structure along an east-west gradient in southern Michigan. We also found little evidence that factors providing opportunity for quality turkey hunting had meaningful impacts on recent spatial-temporal patterns of hunter numbers. Our results imply that providing quality turkey hunting opportunities alone may be insufficient for sustaining populations of turkey hunters in the future, and that modern determinants of hunter participation extend beyond the availability of abundant turkey populations. Moreover, our results demonstrate that interpretation of harvest data as indices of abundance for turkey populations is difficult in the absence of hunter effort data, as changes to turkey harvest are a function of potentially fine-scaled changes in populations of hunters, not simply changes to turkey populations.

Project description:BackgroundMetagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions.ResultsWe introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability.ConclusionLarge scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).

Dataset Information

Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data.

Publications

Machine learning of large-scale spatial distributions of wild turkeys with high-dimensional environmental data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets