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Habitat Modeling

Resource Category: 
Improving Flycatcher Habitat
Habitat Modeling
 
U.S. Geological Survey (James Hatten), 2021
An ArcGIS Online (AGOL) page containing historical and predictive maps develop by the USGS for the southwestern willow flycatcher habitat across the southwestern United States. The model outputs a range of probabilities for suitable and less suitable habitat in 20% probability classes.
 
U.S. Geological Survey (James Hatten), 2021
A guide that walks the user through the use of the AGOL-based habitat viewer. User is provided with instructions for changing base map layers, toggling through data layers, utilizing tools to compare different datasets, and locating the metadata for the provided layers. Manual uses screen shots of the AGOL platform to aid in seamless navigation.
 

Final Report: Southwestern Willow Flycatcher (Empidonax traillii extimus) and Western Yellow-billed Cuckoo (Coccyzus americanus occidentalis) Surveys and Habitat Availability Modeling on the Santa Clara River, California, 26 March 2020

               Hall et al., 2020

               Report of a project to conduct population surveys for SWFL and YBCU in 2018 and 2019, apply existing habitat models to illustrate and predict past, current, and future habitat suitabilities for these two species, and update and standardize classification and mapping of riparian vegetation to reflect recent conditions along the lower 50 miles of the Santa Clara River.

 
U.S. Geological Survey (James Hatten), 2016
Detailed report of the development of a satellite model utilizing flycatcher breeding territory data from six states as well as five years of tamarisk beetle defoliation data from the Lower Virgin River. Change detection showed a large shift in predicted habitat due to drought. A spatially explicit analysis showed a 94% decrease in predicted flycatcher habitat due to beetle defoliation on the Lower Virgin River. However, the model predicts that after beetle defoliation 64% and 45% of habitat will remain in the Lower Colorado and Gila River systems respectively.
 

Characterization and Prediction of Future Habitat Suitability for Three Bird Species Inhabiting the Rio Grande Bosque, NM (poster)

               Friggens and Finch, 2013

               Poster presentation of a maximum entropy (maxent) model to predict future habitat along the Rio Grande for SWFL, yellow-billed cuckoo, and Lucy’ warbler.

 
York et al., 2011
A maximum entropy model used to determine the extent of dense tamarisk cover as well as the potential habitat available for SWFL within the project area. 2008 Landsat Thematic Mapper images and a digital elevation model were used as the input variables within the five USGS watershed units flowing into the northern arm of Lake Mead (encompassing the Virgin River). The study determined that tamarisk beetles would like impact available SWFL habitat within the study region in the short term, but could increase available habitat long-term if managed appropriately.
 
Hatten, et al., 2010
A model developed utilizing 10 years of flycatcher territory data from Roosevelt Lake, AZ. The GIS model explained 79% of the variability in the flycatcher breeding population at Roosevelt Lake. The model determined a high correlation between reservoir levels and predicted habitat, suggesting that habitat can be created and managed for conservation purposes.

               Hatten et al., 2007

               The first large-scale predictive model of its kind for SWFL habitat, this expands on the modeling techniques Hatten developed in 2004 to map potential habitat along a large section of the Upper-Middle Rio Grande.

 
Dockens and Paradzick (eds.), 2004
A detailed discussion of the development and testing of a model to predict southwestern willow flycatcher breeding habitat in Arizona, with discussion of the GIS-based model and a case study analysis using the model in Roosevelt Lake.
 
Hatten and Paradzick, 2003
A GIS model of SWFL breeding habitat developed from riparian habitat data along portions of the San Pedro, Gila, and Salt Rivers, and Tonto Creek. The best model explained 54% of the variability in breeding-site occurrence and was used to map habitat in Arizona and examine changes in habitat abundance and quality over time.

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