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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.

Tamarisk (Tamarix spp., saltcedar) is a well-known invasive phreatophyte introduced from Asia to North America in the 1800s. This report compares the efficacy of Landsat 5 Thematic Mapper (TM5), QuickBird (QB) and EO-1 Hyperion data in discriminating tamarisk populations near De Beque, Colorado, USA. As a result of highly correlated reflectance among the spectral bands provided by each sensor, relatively standard image analysis methods were employed. Multispectral data at high spatial resolution (QB, 2.5 m Ground Spatial Distance or GSD) proved more effective in tamarisk delineation than either multispectral (TM5) or hyperspectral (Hyperion) data at moderate spatial resolution (30 m GSD).

Aim Species distribution models have often been hampered by poor local species data, reliance on coarse‐scale climate predictors and the assumption that species–environment relationships, even with non‐proximate predictors, are consistent across geographical space. Yet locally accurate maps of invasive species, such as the Africanized honeybee (AHB) in North America, are needed to support conservation efforts. Current AHB range maps are relatively coarse and are inconsistent with observed data. Our aim was to improve distribution maps using more proximate predictors (phenology) and using regional models rather than one across the entire range of interest to explore potential differences in drivers. Location United States of America. Methods We provide a generalized framework for regional and local species distribution modelling with our more nuanced and spatially detailed forecast of potential AHB spread using multiple habitat modelling techniques and newly derived remotely sensed phenology layers. Results Variable importance did differ between the two regions for which we modelled AHB. Phenology metrics were important, especially in the south‐east. Main conclusions Results demonstrate that incorporating a combination of both climate drivers and vegetation phenology information into models can be important for predicting the suitable habitat range of these pollinators. Regional models may provide evidence of differing drivers of distributions geographically. This framework may improve many local and regional species distribution modelling efforts.

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