Abstract
Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.
Highlights
Predicting species current and potential distributions is critical to the effective management of invasive species [1], conservation of threatened and endangered species [2,3,4], and protection of native species and habitats [5]
Model algorithms are designed to analyze the statistical relationships between the dependent variable and independent variables and create geospatial representations of where a species is likely to occur and where it is likely absent
Most spatial modeling techniques share a common process to create a spatial model and a predicted surface. This process includes: obtaining coordinates for species occurrences, extracting environmental predictor values for each coordinate based on environmental layers, running a model algorithm to produce model results, and creating a map of the predicted potential distribution for the species from model results (Figure 1)
Summary
Predicting species current and potential distributions is critical to the effective management of invasive species [1], conservation of threatened and endangered species [2,3,4], and protection of native species and habitats [5]. Over the past few decades, statistical and technical capabilities have progressed to a level where we have a variety of techniques for predicting the potential distribution of a given species [10,11,12,13] These techniques use field observations of a species (i.e., presence-absence data or presence-only data) as a dependent variable, while independent or predictive variables represent environmental conditions (e.g., elevation, minimum temperature, mean monthly precipitation). These tools and methods have only been available to a small audience of researchers with the time and resources to bring together the data, the software, and acquire the knowledge to run the software required to create models
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
