Th e Software for Assisted Habitat Modeling (SAHM) has been created to both expedite habitat modeling and help maintain a record of the various input data, pre-and post-processing steps and modeling options incorporated in the construction of a species distribution model through the established workfl ow management and visualization VisTrails software. Th is paper provides an overview of the VisTrails:SAHM software including a link to the open source code, a table detailing the current SAHM modules, and a simple example modeling an invasive weed species in Rocky Mountain National Park, USA.Understanding where species will thrive is a useful and important consideration for resource managers concerned with either promoting (for threatened or endangered species) or controlling (for invasive or unwanted species). Th e fi eld of species distribution or habitat niche modeling is contributing to this understanding. With an increasing availability of both ecological data (Graham et al. 2004) and software packages to fi t ecological niche models (Phillips et al. 2006, Franklin 2009, Th uiller et al. 2009, Guo and Liu 2010, Peterson et al. 2011 as well as new tools to evaluate model performance (Allouche et al. 2006, Phillips and Elith 2010, Warren et al. 2010), researchers and land managers now have an unprecedented opportunity to explore many parameters and iterations for any given habitat niche modeling exercise. Each niche modeling technique has multiple parameters and options that can be adjusted and choices for input and output data. For habitat models that consider climate change, there are future climate projections from diff erent climate modeling centers and multiple emissions scenarios to consider (IPCC 2007). Land managers might want to evaluate diff erent biological responses; such as diff erent/multiple species or, for a given species, diff erent life cycles (e.g. breeding vs nesting habitat). Furthermore, it may be of interest to modify the spatial extent and spatial resolution of both input/ predictor layers and output/model results. With these options and others not listed here, the potential number of model runs and related results can be overwhelming. Th ere is a need for careful documentation of the precise model confi guration as well as meaningful interpretation of results. Scientifi c workfl ow systems help address this need.

No abstract

Understanding invasive species distributions and potential invasions often requires broad‐scale information on the environmental tolerances of the species. Further, resource managers are often faced with knowing these broad‐scale relationships as well as nuanced environmental factors related to their landscape that influence where an invasive species occurs and potentially could occur. Using invasive buffelgrass (Cenchrus ciliaris), we developed global models and local models for Saguaro National Park, Arizona, USA, based on location records and literature on physiological tolerances to environmental factors to investigate whether environmental relationships of a species at a global scale are also important at local scales. In addition to correlative models with five commonly used algorithms, we also developed a model using a priori user‐defined relationships between occurrence and environmental characteristics based on a literature review. All correlative models at both scales performed well based on statistical evaluations. The user‐defined curves closely matched those produced by the correlative models, indicating that the correlative models may be capturing mechanisms driving the distribution of buffelgrass. Given climate projections for the region, both global and local models indicate that conditions at Saguaro National Park may become more suitable for buffelgrass. Combining global and local data with correlative models and physiological information provided a holistic approach to forecasting invasive species distributions.