scholarly journals SiMRiv: an R package for mechanistic simulation of individual, spatially-explicit multistate movements in rivers, heterogeneous and homogeneous spaces incorporating landscape bias

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Lorenzo Quaglietta ◽  
Miguel Porto
Keyword(s):  
Homogeneous Spaces ◽  
R Package ◽  
Spatially Explicit ◽  
Mechanistic Simulation

scholarly journals SPARSEMODr: Rapid simulations of spatially explicit and stochastic models of infectious disease

2021 ◽  
Author(s):  
Joseph R Mihaljevic ◽  
Seth Borkovec ◽  
Saikanth Ratnavale ◽  
Toby D Hocking ◽  
Kelsey E Banister ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Stochastic Models ◽  
Disease Modeling ◽  
R Package ◽  
Work Flow ◽  
Spatially Explicit ◽  
Universal Functions ◽  
Spatially Explicit Models ◽  
Complex Models ◽  
The Common

1. Simulating the dynamics of realistically complex models of infectious disease is conceptually challenging and computationally expensive. This results in a heavy reliance on customized software and, correspondingly, lower reproducibility across disease modeling studies. 2. SPARSEMOD stands for SPAtial Resolution-SEnsitive Models of Outbreak Dynamics. The goal of our project, encapsulated by the SPARSEMODr R package, is to offer a framework for rapidly simulating the dynamics of stochastic and spatially-explicit models of infectious disease for use in pedagogical and applied contexts. 3. We outline the universal functions of our package that allow for user-customization while demonstrating the common work flow. 4. SPARSEMODr offers an extendable framework that should allow the open-source community of disease modelers to add new model types and functionalities in future releases.

scholarly journals demoniche - an R-package for simulating spatially-explicit population dynamics

Ecography ◽  
2012 ◽  
Vol 35 (7) ◽  
pp. 577-580 ◽  
Author(s):  
Hedvig K. Nenzén ◽  
Rebecca M. Swab ◽  
David A. Keith ◽  
Miguel B. Araújo
Keyword(s):  
Population Dynamics ◽  
R Package ◽  
Spatially Explicit

scholarly journals Modeling the ecology and evolution of biodiversity: Biogeographical cradles, museums, and graves

Science ◽  
2018 ◽  
Vol 361 (6399) ◽  
pp. eaar5452 ◽  
Author(s):  
Thiago F. Rangel ◽  
Neil R. Edwards ◽  
Philip B. Holden ◽  
José Alexandre F. Diniz-Filho ◽  
William D. Gosling ◽  
...  
Keyword(s):  
Range Shifts ◽  
Analytical Models ◽  
Spatially Explicit ◽  
Target Pattern ◽  
Spatial And Temporal Scales ◽  
Geographical Patterns ◽  
The Past ◽  
Complex Interactions ◽  
The Impact ◽  
Mechanistic Simulation

Individual processes shaping geographical patterns of biodiversity are increasingly understood, but their complex interactions on broad spatial and temporal scales remain beyond the reach of analytical models and traditional experiments. To meet this challenge, we built a spatially explicit, mechanistic simulation model implementing adaptation, range shifts, fragmentation, speciation, dispersal, competition, and extinction, driven by modeled climates of the past 800,000 years in South America. Experimental topographic smoothing confirmed the impact of climate heterogeneity on diversification. The simulations identified regions and episodes of speciation (cradles), persistence (museums), and extinction (graves). Although the simulations had no target pattern and were not parameterized with empirical data, emerging richness maps closely resembled contemporary maps for major taxa, confirming powerful roles for evolution and diversification driven by topography and climate.

scholarly journals An open and extensible framework for spatially explicit land use change modelling: the lulcc R package

2015 ◽  
Vol 8 (10) ◽  
pp. 3215-3229 ◽  
Author(s):  
S. Moulds ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Model Development ◽  
R Package ◽  
Spatially Explicit ◽  
Island Ecosystems ◽  
Data Set ◽  
R Programming Language ◽  
R Programming ◽  
Scientific Results

Abstract. We present the lulcc software package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of alternative models; and (3) additional software is required because existing applications frequently perform only the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a data set included with the package. It is envisaged that lulcc will enable future model development and comparison within an open environment.

scholarly journals Spatial extinction date estimation: a novel method for reconstructing spatiotemporal patterns of extinction and identifying potential zones of rediscovery

2018 ◽  
Author(s):  
Colin J. Carlson ◽  
Kevin R. Burgio ◽  
Tad A. Dallas ◽  
Alexander L. Bond
Keyword(s):  
Historical Ecology ◽  
R Package ◽  
Spatiotemporal Patterns ◽  
Population Persistence ◽  
Spatially Explicit ◽  
List Type ◽  
Explicit Method ◽  
Improve Accuracy ◽  
Novel Method ◽  
Sighting Data

AbstractThe estimation of extinction dates from limited and incomplete sighting records is a key challenge in conservation (when experts are uncertain whether a species has gone extinct) and historical ecology (when the date and mechanism of extinction is controversial).We introduce a spatially-explicit method of interpolating extinction date estimators, allowing users to estimate spatiotemporal surfaces of population persistence from georeferenced sighting data of variable quality.We present the R package spatExtinct, which produces spatially-explicit extinction date surfaces from geolocated sightings, including options for custom randomization schemes to improve accuracy with limited datasets. We use simulations to illustrate the sensitivity of the method to parameterization, and apply the method to identify potential areas where Bachman’s warbler (Vermivora bachmanii) might be rediscovered.Our method, and the spatExtinct package, has the potential to help describe and differentiate different drivers of extinction for historical datasets, and could be used to identify possible regions of population persistence for species with an uncertain extinction status, improving on non-spatial or imprecise methods that are currently in use.

scholarly journals climateStability: An R package to estimate climate stability from time-slice climatologies

2019 ◽  
Vol 14 ◽  
pp. 8-13 ◽  
Author(s):  
Hannah Lois Owens ◽  
Robert Guralnick
Keyword(s):  
Phylogenetic Diversity ◽  
R Package ◽  
Global Scale ◽  
Spatially Explicit ◽  
Stability Estimates ◽  
Climate Variable ◽  
Climate Stability ◽  
The Mean ◽  
Past Climate Change ◽  
The Impact

As continental and global-scale paleoclimate model data become more readily available, biologists can now ask spatially explicit questions about the tempo and mode of past climate change and the impact of those changes on biodiversity patterns. In particular, researchers have focused on climate stability as a key variable that can drive expected patterns of richness, phylogenetic diversity and functional diversity. Yet, climate stability measures are not formalized in the literature and tools for generating stability metrics from existing data are nascent. Here we define “deviation” of a climate variable as the mean standard deviation between time slices over time elapsed; “stability” is defined as the inverse of this deviation. Finally, climate stability is the product of individual climate variable stability estimates. We also present an R package, climateStability, which contains tools for researchers to generate climate stability estimates from their own data.

Marine halogenated compound analysis: from an R package to the isolation of new griseophenone derivatives

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
C Roullier ◽  
Y Guitton ◽  
S Prado ◽  
O Grovel ◽  
YF Pouchus
Keyword(s):  
R Package ◽  
Halogenated Compound

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

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