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

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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Comparison of alternative strategies for invasive species distribution modeling

Ecological Modelling ◽

10.1016/j.ecolmodel.2010.04.018 ◽

2010 ◽

Vol 221 (19) ◽

pp. 2261-2269 ◽

Cited By ~ 28

Author(s):

Todd P. Robinson ◽

Rieks D. van Klinken ◽

Graciela Metternicht

Keyword(s):

Invasive Species ◽

Species Distribution ◽

Species Distribution Modeling ◽

Alternative Strategies ◽

Distribution Modeling

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

PeerJ ◽

10.7717/peerj.4095 ◽

2017 ◽

Vol 5 ◽

pp. e4095 ◽

Cited By ~ 93

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.

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Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

Ecological Modelling ◽

10.1016/j.ecolmodel.2009.08.013 ◽

2009 ◽

Vol 220 (23) ◽

pp. 3248-3258 ◽

Cited By ~ 165

Author(s):

Tomáš Václavík ◽

Ross K. Meentemeyer

Keyword(s):

Invasive Species ◽

Species Distribution ◽

Species Distribution Modeling ◽

Distribution Modeling

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Better providers of habitat for Javan slow loris (Nycticebus javanicus E. Geoffroy 1812): A species distribution modeling approach in Central Java, Indonesia

Biodiversitas Journal of Biological Diversity ◽

10.13057/biodiv/d210515 ◽

2020 ◽

Vol 21 (5) ◽

Author(s):

Mahfut Sodik ◽

Satyawan Pudyatmoko ◽

Pujo Semedi Hargo Yuwono ◽

Muhammad Tafrichan ◽

Muhammad Ali Imron

Keyword(s):

Species Distribution ◽

Species Distribution Modeling ◽

Distribution Model ◽

Suitable Habitat ◽

Conservation Areas ◽

Slow Loris ◽

Modeling Approach ◽

Central Java ◽

Distribution Modeling ◽

Central Java Province

Abstract. Sodik M, Pudyatmoko S, Yuwono PSH, Tafrichan M, Imron MA. 2020. Better providers of habitat for Javan slow loris (Nycticebus javanicus E. Geoffroy 1812): A species distribution modeling approach in Central Java, Indonesia. Biodiversitas 21: 1890-1900. The Javan slow loris is an arboreal and nocturnal primate endemic to Java, which is known to inhabit primary and secondary forest habitats, such as swamps, plantations, and bamboo forest. The population of the Javan slow loris continues to decline significantly due to forest degradation, habitat loss/fragmentation, and illegal trade. Conservation of this small primate in Java has been hampered by a paucity of local data on how conservation areas support this species. This study aims to build a spatial distribution model of the Javan slow loris and analyzing the role of each stakeholder plays on land use type to support the conservation of N. javanicus. By utilizing Species Distribution Modeling (SDM) with Maximum Entropy species distribution modeling approach, the researchers were able to highlight the importance of which conservation areas in Central Java that play crucial role to conserve the N. javanicus population. Data on the presence of the Javan slow loris was obtained from the result of a survey undertaken in 2017 and communication with researchers. Elevation, slope, landcover, rainfall, distance to road, distance to settlement, distance to river (water source), and NDVI were used as environmental variables. Results showed that 0.76% (25,715.4 ha) of the total area of the Central Java Province is suitable for their habitat. In addition, results revealed that 2.2% of suitable habitat is present within conservation areas, 4.6% in protected forest areas, and 93.2% outside of protected areas. The Javan slow loris is predicted to be mostly scattered in the northern part of Central Java Province. The Javan slow loris is widely distributed in plantations, their most dominant habitat. The findings of this study show that the small percentage of suitable habitat presents within protected forest and conservation areas cannot sustainably maintain the extant Javan slow loris population. Thus, it is important for the Indonesian government and other key related stakeholders to work together in combination with educating local communities to preserve the habitat and population of N. javanicus.

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Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

Global Change Biology ◽

10.1111/gcb.13038 ◽

2015 ◽

Vol 21 (12) ◽

pp. 4464-4480 ◽

Cited By ~ 111

Author(s):

Kumar P. Mainali ◽

Dan L. Warren ◽

Kunjithapatham Dhileepan ◽

Andrew McConnachie ◽

Lorraine Strathie ◽

...

Keyword(s):

Invasive Species ◽

Species Distribution ◽

Species Distribution Modeling ◽

Distribution Modeling

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

10.1101/075200 ◽

2016 ◽

Cited By ~ 1

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.

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Multi-Scenario Species Distribution Modeling

Insects ◽

10.3390/insects10030065 ◽

2019 ◽

Vol 10 (3) ◽

pp. 65 ◽

Cited By ~ 2

Author(s):

Senait D. Senay ◽

Susan P. Worner

Keyword(s):

Dimension Reduction ◽

Species Distribution ◽

Characteristic Curve ◽

Confusion Matrix ◽

Species Distribution Modeling ◽

Model Performance ◽

Distribution Models ◽

Prediction Uncertainty ◽

Distribution Modeling ◽

Reduction Methods

Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty.

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