scholarly journals ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling

2016 ◽  
Author(s):  
Pascal O Title ◽  
Jordan B Bemmels
Keyword(s):  
Species Distribution ◽  
Environmental Variables ◽  
Evolutionary Biology ◽  
Ecological Niche Modeling ◽  
Species Distribution Modeling ◽  
R Package ◽  
Niche Modeling ◽  
Distribution Models ◽  
Distribution Modeling ◽  
Bioclimatic Variables

AbstractSpecies distribution modeling is a valuable tool with many applications across ecology and evolutionary biology. The selection of biologically meaningful environmental variables that determine relative habitat suitability is a crucial aspect of the modeling pipeline. The 19 bioclimatic variables from WorldClim are frequently employed, primarily because they are easily accessible and available globally for past, present and future climate scenarios. Yet, the availability of relatively few other comparable environmental datasets potentially limits our ability to select appropriate variables that will most successfully characterize a species’ distribution. We identified a set of 16 climatic and two topographic variables in the literature, which we call the envirem dataset, many of which are likely to have direct relevance to ecological or physiological processes determining species distributions. We generated this set of variables at the same resolutions as WorldClim, for the present, mid-Holocene, and Last Glacial Maximum (LGM). For 20 North American vertebrate species, we then assessed whether including the envirem variables led to improved species distribution models compared to models using only the existing WorldClim variables. We found that including the ENVIREM dataset in the pool of variables to select from led to substantial improvements in niche modeling performance in 17 out of 20 species. We also show that, when comparing models constructed with different environmental variables, differences in projected distributions were often greater in the LGM than in the present. These variables are worth consideration in species distribution modeling applications, especially as many of the variables have direct links to processes important for species ecology. We provide these variables for download at multiple resolutions and for several time periods at envirem.github.io. Furthermore, we have written the ‘envirem’ R package to facilitate the generation of these variables from other input datasets.

Introduction

2011 ◽  
Author(s):  
A. Townsend Peterson ◽  
Jorge Soberón ◽  
Richard G. Pearson ◽  
Robert P. Anderson ◽  
Enrique Martínez-Meyer ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Species Distribution ◽  
Ecological Niche ◽  
Ecological Niche Modeling ◽  
Species Distribution Modeling ◽  
Niche Modeling ◽  
Ecological Niches ◽  
Distribution Modeling ◽  
Geographic Distributions ◽  
Complex Relationships

This book deals with ecological niche modeling and species distribution modeling, two emerging fields that address the ecological, geographic, and evolutionary dimensions of geographic distributions of species. It provides a conceptual overview of the complex relationships between ecological niches and geographic distributions of species, both across space and (perhaps to a lesser degree) through time. The emphasis is on how that conceptual framework relates to ecological niche modeling and species distribution modeling, which the book argues are complementary and are most broadly applicable to diverse questions regarding the ecology and geography of biodiversity phenomena. Part I of the book introduces the conceptual framework for thinking about and discussing the distributional ecology of species, Part II is concerned with the data and tools that have been used in the early development of the field, and Part III focuses on real-world situations to which these tools have been applied.

The crucial role of the accessible area in ecological niche modeling and species distribution modeling

2011 ◽  
Vol 222 (11) ◽  
pp. 1810-1819 ◽  
Author(s):  
Narayani Barve ◽  
Vijay Barve ◽  
Alberto Jiménez-Valverde ◽  
Andrés Lira-Noriega ◽  
Sean P. Maher ◽  
...  
Keyword(s):  
Species Distribution ◽  
Crucial Role ◽  
Ecological Niche ◽  
Ecological Niche Modeling ◽  
Species Distribution Modeling ◽  
Niche Modeling ◽  
Distribution Modeling ◽  
Accessible Area

Concepts of Niches

2011 ◽  
Author(s):  
A. Townsend Peterson ◽  
Jorge Soberón ◽  
Richard G. Pearson ◽  
Robert P. Anderson ◽  
Enrique Martínez-Meyer ◽  
...  
Keyword(s):  
Species Distribution ◽  
Ecological Niche ◽  
Ecological Niche Modeling ◽  
Species Distribution Modeling ◽  
Operational Definition ◽  
Niche Modeling ◽  
The Other ◽  
Distribution Modeling ◽  
Niche Concept ◽  
Definition Of

This chapter proposes a formal and operational definition of a particular niche concept, introduces approaches for characterizing and measuring it, and uses it as a conceptual and terminological basis for describing and understanding much of the related practices of ecological niche modeling and species distribution modeling. It begins with a discussion of the themes that are most important in understanding niche concepts, focusing on three interrelated points: the meaning of “exist indefinitely”; what kinds of variables constitute the hypervolume; and the nature of feedback loops between a species and the variables composing the hypervolume. The chapter then considers the Grinnellian and Eltonian niches as well as the practicalities of estimating Grinnellian niches. It also considers two important interpretations of the niche concept, one of which is concerned with geographic and environmental spaces, and the other emphasizes the Eltonian niche.

scholarly journals SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4095 ◽  
Author(s):  
Jason L. Brown ◽  
Joseph R. Bennett ◽  
Connor M. French
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Species Distribution Modeling ◽  
Environmental Data ◽  
Distribution Model ◽  
Data Types ◽  
Distribution Models ◽  
Distribution Modeling ◽  
Modeling Software ◽  
Cost Paths

SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user.

scholarly journals Distribution modeling applied to deficient data species assessment: A case study with Pithecopus nordestinus (Anura, Phyllomedusidae)

2020 ◽  
Vol 15 (2) ◽  
pp. 165-175
Author(s):  
Felipe Pessoa Da Silva ◽  
Hugo Fernandes-Ferreira ◽  
Martín Alejandro Montes ◽  
Lucas Gonçalves da Silva
Keyword(s):  
Species Distribution ◽  
Environmental Variables ◽  
Species Distribution Modeling ◽  
Distribution Area ◽  
Potential Range ◽  
Species Occurrence ◽  
Extent Of Occurrence ◽  
Habitat Area ◽  
Distribution Modeling ◽  
Occurrence Records

The arboreal frog Pithecopus nordestinus is geographically present in almost all Brazilian Northeast territory and Minas Gerais State. It is currently classified as deficient data (DD) by IUCN Red List of Endangered Species, requiring further knowledge about its geographic distribution and population status. In this context, the species distribution modeling can be applied, since its basis uses species occurrence records and environmental variables related to bioclimatic and landscape features. This kind of method predicts the species suitability of certain organism in the geographic space. We obtained 159 P. nordestinus occurrence records, covering all the previously known distribution of the species. These records were collected from direct field sampling, scientific literature, museum collections, and available online databases. We used four species distribution modeling algorithms to obtain the potential range (extent of occurrence) and available habitat for this frog through habitat area analysis proposed by IUCN. The generated models can be considered as excellent, with mean AUC value of 0.981. The environmental variables related to temperature and radiation were the most important to the construction of this distribution model. Our results indicate that the forested areas of the Atlantic Forest domain and forest patches inside the Caatinga biome present the highest suitability values for the species occurrence and the major part of available habitats, a fact possibly related to the known arboreal habit of this amphibian. We thus provide a new distribution area for P. nordestinus more broadly than previously known and a new polygon for conservation purposes based on extent of occurrence, and an increase of occupancy based on habitat area analysis. The identification of additional areas where the P. nordestinus occurrence was not yet well known, new habitats for possible dispersal or recolonization; and the selection of conservation hotspots applied to this species are direct applications from our study. In addition, the methodological procedures used here may serve as a baseline tool for new investigations with focus on still deficient data species and its ecological and conservation planning requirements.

scholarly journals Genomics-informed models reveal extensive stretches of coastline under threat by an ecologically dominant invasive species

2021 ◽  
Vol 118 (23) ◽  
pp. e2022169118
Author(s):  
Jamie Hudson ◽  
Juan Carlos Castilla ◽  
Peter R. Teske ◽  
Luciano B. Beheregaray ◽  
Ivan D. Haigh ◽  
...  
Keyword(s):  
Invasive Species ◽  
Species Distribution ◽  
Population Genomics ◽  
Species Distribution Modeling ◽  
Genomic Data ◽  
Genomic Diversity ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Distribution Modeling ◽  
Introduced Range

Explaining why some species are widespread, while others are not, is fundamental to biogeography, ecology, and evolutionary biology. A unique way to study evolutionary and ecological mechanisms that either limit species’ spread or facilitate range expansions is to conduct research on species that have restricted distributions. Nonindigenous species, particularly those that are highly invasive but have not yet spread beyond the introduced site, represent ideal systems to study range size changes. Here, we used species distribution modeling and genomic data to study the restricted range of a highly invasive Australian marine species, the ascidian Pyura praeputialis. This species is an aggressive space occupier in its introduced range (Chile), where it has fundamentally altered the coastal community. We found high genomic diversity in Chile, indicating high adaptive potential. In addition, genomic data clearly showed that a single region from Australia was the only donor of genotypes to the introduced range. We identified over 3,500 km of suitable habitat adjacent to its current introduced range that has so far not been occupied, and importantly species distribution models were only accurate when genomic data were considered. Our results suggest that a slight change in currents, or a change in shipping routes, may lead to an expansion of the species’ introduced range that will encompass a vast portion of the South American coast. Our study shows how the use of population genomics and species distribution modeling in combination can unravel mechanisms shaping range sizes and forecast future range shifts of invasive species.

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