scholarly journals Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat

Ecosphere ◽  
2017 ◽  
Vol 8 (7) ◽  
pp. e01883 ◽  
Author(s):  
Andrew M. Kramer ◽  
Gust Annis ◽  
Marion E. Wittmann ◽  
William L. Chadderton ◽  
Edward S. Rutherford ◽  
...  
Keyword(s):  
Invasive Species ◽  
Great Lakes ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Laurentian Great Lakes ◽  
Distribution Models ◽  
Local Habitat

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

2011 ◽  
Vol 4 (4) ◽  
pp. 390-401 ◽  
Author(s):  
Gary N. Ervin ◽  
D. Christopher Holly
Keyword(s):  
Invasive Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Imperata Cylindrica ◽  
Environmental Data ◽  
Training Data ◽  
Distribution Data ◽  
Strongly Correlated ◽  
Distribution Models ◽  
Tree Canopy Cover

AbstractSpecies distribution modeling is a tool that is gaining widespread use in the projection of future distributions of invasive species and has important potential as a tool for monitoring invasive species spread. However, the transferability of models from one area to another has been inadequately investigated. This study aimed to determine the degree to which species distribution models (SDMs) for cogongrass, developed with distribution data from Mississippi (USA), could be applied to a similar area in neighboring Alabama. Cogongrass distribution data collected in Mississippi were used to train an SDM that was then tested for accuracy and transferability with cogongrass distribution data collected by a forest management company in Alabama. Analyses indicated the SDM had a relatively high predictive ability within the region of the training data but had poor transferability to the Alabama data. Analysis of the Alabama data, via independent SDM development, indicated that predicted cogongrass distribution in Alabama was more strongly correlated with soil variables than was the case in Mississippi, where the SDM was most strongly correlated with tree canopy cover. Results suggest that model transferability is influenced strongly by (1) data collection methods, (2) landscape context of the survey data, and (3) variations in qualitative aspects of environmental data used in model development.

Invasive species distribution models - how violating the equilibrium assumption can create new insights

2012 ◽  
Vol 21 (11) ◽  
pp. 1126-1136 ◽  
Author(s):  
Laure Gallien ◽  
Rolland Douzet ◽  
Steve Pratte ◽  
Niklaus E. Zimmermann ◽  
Wilfried Thuiller
Keyword(s):  
Invasive Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models

scholarly journals A Lack of “Environmental Earth Data” at the Microhabitat Scale Impacts Efforts to Control Invasive Arthropods That Vector Pathogens

Data ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 133 ◽  
Author(s):  
Emily L. Pascoe ◽  
Sajid Pareeth ◽  
Duccio Rocchini ◽  
Matteo Marcantonio
Keyword(s):  
Invasive Species ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Disease Outbreaks ◽  
Economic Consequences ◽  
Mitigation Strategies ◽  
Distribution Models ◽  
Data Gaps ◽  
Open Access Database ◽  
Temporal And Spatial

We currently live in an era of major global change that has led to the introduction and range expansion of numerous invasive species worldwide. In addition to the ecological and economic consequences associated with most invasive species, invasive arthropods that vector pathogens (IAVPs) to humans and animals pose substantial health risks. Species distribution models that are informed using environmental Earth data are frequently employed to predict the distribution of invasive species, and to advise targeted mitigation strategies. However, there are currently substantial mismatches in the temporal and spatial resolution of these data and the environmental contexts which affect IAVPs. Consequently, targeted actions to control invasive species or to prepare the population for possible disease outbreaks may lack efficacy. Here, we identify and discuss how the currently available environmental Earth data are lacking with respect to their applications in species distribution modeling, particularly when predicting the potential distribution of IAVPs at meaningful space-time scales. For example, we examine the issues related to interpolation of weather station data and the lack of microclimatic data relevant to the environment experienced by IAVPs. In addition, we suggest how these data gaps can be filled, including through the possible development of a dedicated open access database, where data from both remotely- and proximally-sensed sources can be stored, shared, and accessed.

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