scholarly journals gen3sis: the general engine for eco-evolutionary simulations on the origins of biodiversity

2021 ◽  
Author(s):  
Oskar Hagen ◽  
Benjamin Flück ◽  
Fabian Fopp ◽  
Juliano S. Cabral ◽  
Florian Hartig ◽  
...  
Keyword(s):  
Environmental Filtering ◽  
Biotic Interactions ◽  
R Package ◽  
Biological Processes ◽  
Ecological Traits ◽  
Biodiversity Patterns ◽  
Diversity Gradient ◽  
Dynamic Landscapes ◽  
Alternative Hypotheses ◽  
Model Engine

AbstractUnderstanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially-explicit, eco-evolutionary engine with a modular implementation that enables the modelling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatio-temporally dynamic landscapes. Modelled processes can include environmental filtering, biotic interactions, dispersal, speciation and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β and γ diversity, species ranges, ecological traits and phylogenies, emerge as simulations proceed. As a case study, we examined alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth’s Cenozoic era. We found that a carrying capacity linked with energy was the only model variant that could simultaneously produce a realistic LDG, species range size frequencies, and phylogenetic tree balance. The model engine is open source and available as an R-package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a step towards a numeric and mechanistic understanding of the physical and biological processes that shape Earth’s biodiversity.

scholarly journals gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity

PLoS Biology ◽  
2021 ◽  
Vol 19 (7) ◽  
pp. e3001340
Author(s):  
Oskar Hagen ◽  
Benjamin Flück ◽  
Fabian Fopp ◽  
Juliano S. Cabral ◽  
Florian Hartig ◽  
...  
Keyword(s):  
Biotic Interactions ◽  
R Package ◽  
Biological Processes ◽  
Ecological Traits ◽  
Biodiversity Patterns ◽  
Diversity Gradient ◽  
Dynamic Landscapes ◽  
Alternative Hypotheses ◽  
Model Engine ◽  
Evolutionary Simulations

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species’ abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth’s Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth’s biodiversity.

scholarly journals Solutions in microbiome engineering: prioritizing barriers to organism establishment

2021 ◽  
Author(s):  
Michaeline B. N. Albright ◽  
Stilianos Louca ◽  
Daniel E. Winkler ◽  
Kelli L. Feeser ◽  
Sarah-Jane Haig ◽  
...  
Keyword(s):  
Microbial Community ◽  
Ecosystem Function ◽  
Restoration Ecology ◽  
Propagule Pressure ◽  
Environmental Filtering ◽  
Biotic Interactions ◽  
Invasion Biology ◽  
Engineering Research

AbstractMicrobiome engineering is increasingly being employed as a solution to challenges in health, agriculture, and climate. Often manipulation involves inoculation of new microbes designed to improve function into a preexisting microbial community. Despite, increased efforts in microbiome engineering inoculants frequently fail to establish and/or confer long-lasting modifications on ecosystem function. We posit that one underlying cause of these shortfalls is the failure to consider barriers to organism establishment. This is a key challenge and focus of macroecology research, specifically invasion biology and restoration ecology. We adopt a framework from invasion biology that summarizes establishment barriers in three categories: (1) propagule pressure, (2) environmental filtering, and (3) biotic interactions factors. We suggest that biotic interactions is the most neglected factor in microbiome engineering research, and we recommend a number of actions to accelerate engineering solutions.

scholarly journals ACDC: Analysis of Congruent Diversification Classes

2022 ◽  
Author(s):  
Sebastian Hoehna ◽  
Bjoern Tore Kopperud ◽  
Andrew F Magee
Keyword(s):  
Congruence Class ◽  
R Package ◽  
Rate Function ◽  
Diversification Rate ◽  
Extinction Rate ◽  
Diversification Rates ◽  
Common Features ◽  
Alternative Hypotheses ◽  
Rate Functions ◽  
Congruence Classes

Diversification rates inferred from phylogenies are not identifiable. There are infinitely many combinations of speciation and extinction rate functions that have the exact same likelihood score for a given phylogeny, building a congruence class. The specific shape and characteristics of such congruence classes have not yet been studied. Whether speciation and extinction rate functions within a congruence class share common features is also not known. Instead of striving to make the diversification rates identifiable, we can embrace their inherent non-identifiable nature. We use two different approaches to explore a congruence class: (i) testing of specific alternative hypotheses, and (ii) randomly sampling alternative rate function within the congruence class. Our methods are implemented in the open-source R package ACDC (https://github.com/afmagee/ACDC). ACDC provides a flexible approach to explore the congruence class and provides summaries of rate functions within a congruence class. The summaries can highlight common trends, i.e. increasing, flat or decreasing rates. Although there are infinitely many equally likely diversification rate functions, these can share common features. ACDC can be used to assess if diversification rate patterns are robust despite non-identifiability. In our example, we clearly identify three phases of diversification rate changes that are common among all models in the congruence class. Thus, congruence classes are not necessarily a problem for studying historical patterns of biodiversity from phylogenies.

scholarly journals The Coupled Influence of Thermal Physiology and Biotic Interactions on the Distribution and Density of Ant Species along an Elevational Gradient

Diversity ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 456
Author(s):  
Lacy D. Chick ◽  
Jean-Philippe Lessard ◽  
Robert R. Dunn ◽  
Nathan J. Sanders
Keyword(s):  
Observational Data ◽  
Species Interactions ◽  
Phylogenetic Signal ◽  
Environmental Filtering ◽  
Null Model ◽  
Biotic Interactions ◽  
Elevational Gradient ◽  
High Elevation ◽  
Elevational Range ◽  
Stressful Environments

A fundamental tenet of biogeography is that abiotic and biotic factors interact to shape the distributions of species and the organization of communities, with interactions being more important in benign environments, and environmental filtering more important in stressful environments. This pattern is often inferred using large databases or phylogenetic signal, but physiological mechanisms underlying such patterns are rarely examined. We focused on 18 ant species at 29 sites along an extensive elevational gradient, coupling experimental data on critical thermal limits, null model analyses, and observational data of density and abundance to elucidate factors governing species’ elevational range limits. Thermal tolerance data showed that environmental conditions were likely to be more important in colder, more stressful environments, where physiology was the most important constraint on the distribution and density of ant species. Conversely, the evidence for species interactions was strongest in warmer, more benign conditions, as indicated by our observational data and null model analyses. Our results provide a strong test that biotic interactions drive the distributions and density of species in warm climates, but that environmental filtering predominates at colder, high-elevation sites. Such a pattern suggests that the responses of species to climate change are likely to be context-dependent and more specifically, geographically-dependent.

scholarly journals ADAGE signature analysis: differential expression analysis with data-defined gene sets

2017 ◽  
Author(s):  
Jie Tan ◽  
Matthew Huyck ◽  
Dongbo Hu ◽  
René A. Zelaya ◽  
Deborah A. Hogan ◽  
...  
Keyword(s):  
Differential Expression ◽  
Web Server ◽  
R Package ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Signature Analysis ◽  
Biological Processes ◽  
Gene Set ◽  
Gene Sets ◽  
Public Data

AbstractBackgroundGene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data.ResultsHere we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and Δanr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr.ConclusionsWe designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

scholarly journals The latitudinal gradient in rates of evolution for bird beaks, a species interaction trait

2020 ◽  
Author(s):  
Benjamin G Freeman ◽  
Dolph Schluter ◽  
Joseph A Tobias
Keyword(s):  
Meta Analysis ◽  
Biotic Interactions ◽  
Species Interaction ◽  
Temperate Zone ◽  
Ecological Opportunity ◽  
Evolutionary Diversification ◽  
Diversity Gradient ◽  
Rates Of Evolution ◽  
Speciation Rates ◽  
Beak Size

AbstractWhere is evolution fastest? The biotic interactions hypothesis proposes that greater species richness creates more ecological opportunity, driving faster evolution at low latitudes, whereas the “empty niches” hypothesis proposes that ecological opportunity is greater where diversity is low, spurring faster evolution at high latitudes. Here we tested these contrasting predictions by analyzing rates of bird beak evolution for a global dataset of 1141 sister pairs of birds. Beak size evolves at similar rates across latitudes, while beak shape evolves faster in the temperate zone, consistent with the empty niches hypothesis. We show in a meta-analysis that trait evolution and recent speciation rates are faster in the temperate zone, while rates of molecular evolution are slightly faster in the tropics. Our results suggest that drivers of evolutionary diversification are more potent at higher latitudes, thus calling into question multiple hypotheses invoking faster tropical evolution to explain the latitudinal diversity gradient.

scholarly journals Climatic effects on turnover of lowland forest bird communities across a precipitation gradient

2018 ◽  
Author(s):  
Juan Pablo Gomez ◽  
José Miguel Ponciano ◽  
Scott K. Robinson
Keyword(s):  
Community Composition ◽  
Species Abundance ◽  
Environmental Filtering ◽  
Bird Species ◽  
Biotic Interactions ◽  
Dry Forest ◽  
Precipitation Gradient ◽  
Climatic Effects ◽  
Lowland Forest ◽  
Abundance And Distribution

AbstractOne of the main goals of community ecology is to understand the influence of the abiotic environment on the abundance and distribution of species. It has been hypothesized that dry forests are harsher environments than wet forests, which leads to the prediction that environmental filtering should be a more important determinant of patterns of species abundance and composition than in wet forest, where biotic interactions or random assembly should be more important. We attempt to understand the influence of rainfall on the abundance and distribution of bird species along a steep precipitation gradient in an inter-Andean valley in Colombia. We gathered data on species distributions, abundance, morphological traits and phylogenetic relationships to determine the influence of rainfall on the taxonomic, functional and phylogenetic turnover of species along the Magdalena Valley. We demonstrate that there is a strong turnover of community composition at the limit of the dry forest. The taxonomic turnover is steeper than the phylogenetic turnover, suggesting that replacement of closely related species accounts for a disproportionate number of changes along the gradient. We found evidence for environmental filtering in dry forest as species tend to be more tolerant of higher temperature ranges, stronger rainfall seasonality and lower minimum rainfall. On the other hand, wet forest species tend to compete actively for nest space but not for the resources associated with the axes we measured. Our results suggest that rainfall is a strong determinant of community composition when comparing localities above and below the 2400 mm rainfall isocline.

scholarly journals Two-stage Linked Component Analysis for Joint Decomposition of Multiple Biologically Related Data Sets

2021 ◽  
Author(s):  
By Huan Chen ◽  
Brian Caffo ◽  
Genevieve Stein-O’Brien ◽  
Jinrui Liu ◽  
Ben Langmead ◽  
...  
Keyword(s):  
R Package ◽  
Component Analysis ◽  
Biological Data ◽  
Joint Analysis ◽  
Data Sets ◽  
Biological Processes ◽  
Two Stage ◽  
Data Set ◽  
Multiple Data ◽  
Multiple Data Sets

SummaryIntegrative analysis of multiple data sets has the potential of fully leveraging the vast amount of high throughput biological data being generated. In particular such analysis will be powerful in making inference from publicly available collections of genetic, transcriptomic and epigenetic data sets which are designed to study shared biological processes, but which vary in their target measurements, biological variation, unwanted noise, and batch variation. Thus, methods that enable the joint analysis of multiple data sets are needed to gain insights into shared biological processes that would otherwise be hidden by unwanted intra-data set variation. Here, we propose a method called two-stage linked component analysis (2s-LCA) to jointly decompose multiple biologically related experimental data sets with biological and technological relationships that can be structured into the decomposition. The consistency of the proposed method is established and its empirical performance is evaluated via simulation studies. We apply 2s-LCA to jointly analyze four data sets focused on human brain development and identify meaningful patterns of gene expression in human neurogenesis that have shared structure across these data sets. The code to conduct 2s-LCA has been complied into an R package “PJD”, which is available at https://github.com/CHuanSite/PJD.

scholarly journals miRetrieve—an R package and web application for miRNA text mining

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Julian Friedrich ◽  
Hans-Peter Hammes ◽  
Guido Krenning
Keyword(s):  
Text Mining ◽  
Web Application ◽  
R Package ◽  
Biological Processes ◽  
Regulate Gene Expression ◽  
Advantages And Disadvantages ◽  
Growing Body ◽  
Health And Disease ◽  
Regulate Gene

Abstract microRNAs (miRNAs) regulate gene expression and thereby influence biological processes in health and disease. As a consequence, miRNAs are intensely studied and literature on miRNAs has been constantly growing. While this growing body of literature reflects the interest in miRNAs, it generates a challenge to maintain an overview, and the comparison of miRNAs that may function across diverse disease fields is complex due to this large number of relevant publications. To address these challenges, we designed miRetrieve, an R package and web application that provides an overview on miRNAs. By text mining, miRetrieve can characterize and compare miRNAs within specific disease fields and across disease areas. This overview provides focus and facilitates the generation of new hypotheses. Here, we explain how miRetrieve works and how it is used. Furthermore, we demonstrate its applicability in an exemplary case study and discuss its advantages and disadvantages.

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