Prepared for the Bureau of Reclamation, Upper Colorado River Region 

Using a Remote Sensing/GIS Model to Predict 

Southwestern Willow Flycatcher Breeding Habitat 

along the Rio Grande, New Mexico 

By James R. Hatten1 and Mark K. Sogge2 

Open-File Report 2007-1207 

U.S. Department of the Interior 

U.S. Geological Survey 

1 Western Fisheries Research Center, Seattle, Washington 

2

 Southwest Biological Science Center, Flagstaff, AZ 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 2007 

Revised and reprinted: August 22, 2007 

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Suggested citation: 


Hatten, James R., and Sogge, Mark K., 2007, Using a remote sensing/GIS model to predict 


southwestern Willow Flycatcher breeding habitat along the Rio Grande, New Mexico: U.S. 


Geological Survey Open-File Report 2007-1207. [http://pubs.usgs.gov/of/2007/1207/]
 

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Contents 

Introduction .........................................................................................................................................................................1
 

Methods ...............................................................................................................................................................................3
 

Modeling Overview ........................................................................................................................................................3
 

Model Fit and Accuracy Assessment.........................................................................................................................4
 

Results ..................................................................................................................................................................................4
 

Predicted Habitat............................................................................................................................................................4
 

Accuracy Assessment and Model Fit.........................................................................................................................5
 

Discussion ...........................................................................................................................................................................5
 

Model Performance .......................................................................................................................................................5
 

Future Modeling..............................................................................................................................................................6
 

Acknowledgments..............................................................................................................................................................6
 

Literature Cited ...................................................................................................................................................................7
 

Appendix ............................................................................................................................................................................14
 

Figures 

Figure 1. The extent of Landsat Thematic Mapper scenes used for Southwestern Willow Flycatcher 


habitat modeling and mapping, overlaid on NM county boundaries and the Rio Grande. .................................................8
 

Figure 2. The proportion of riparian vegetation found within each probability class, along the Rio Grande 


Figure 4. The density of Southwestern Willow Flycatcher territories within 5 probability classes that were 


under 1,524 m elevation, as determined by the GIS-based model. .....................................................................................9
 

Figure 3. The sensitivity (1 – omission error) of the Southwestern Willow Flycatcher habitat model at 4 


probability thresholds, as modeled along the Rio Grande in 2004.. ..................................................................................10
 

output by the GIS-based habitat model, based on Rio Grande data for 2004. ..................................................................11
 

Tables 

Table 1. Multivariate logistic regression model obtained from Southwestern Willow Flycatcher data .........................12
 

Table 2. The frequency of Southwestern Willow Flycatcher territories, amount of predicted habitat, and 


density of flycatchers within each model probability class for riparian habitat long the Rio Grande in 2004.................13
 

ii 
Using a Remote Sensing/GIS Model to Predict 

Southwestern Willow Flycatcher Breeding Habitat 

along the Rio Grande, New Mexico 

By James R. Hatten1 and Mark K. Sogge2 

U.S. Geological Survey Open-File Report 2007-1207 

1 USGS Western Fisheries, Seattle, Washington 

2 USGS Southwest Biological Science Center, Flagstaff, Arizona 

Introduction 

The Southwestern Willow Flycatcher (Empidonax traillii extimus; hereafter SWFL) is a 

federally endangered bird (USFWS 1995) that breeds in riparian areas in portions of New Mexico, 

Arizona, southwestern Colorado, extreme southern Utah and Nevada, and southern California 

(USFWS 2002). Across this range, it uses a variety of plant species as nesting/breeding habitat, but 

in all cases prefers sites with dense vegetation, high canopy, and proximity to surface water or 

saturated soils (Sogge and Marshall 2000). As of 2005, the known rangewide breeding population 

of SWFLs was roughly 1,214 territories, with approximately 393 territories distributed among 36 

sites in New Mexico (Durst et al. 2006), primarily along the Rio Grande. 

One of the key challenges facing the management and conservation of the Southwestern 

Willow Flycatcher is that riparian areas are dynamic, with individual habitat patches subject to 

cycles of creation, growth, and loss due to drought, flooding, fire, and other disturbances. Former 

breeding patches can lose suitability, and new habitat can develop within a matter of only a few 

years, especially in reservoir drawdown zones. Therefore, measuring and predicting flycatcher 

habitat - either to discover areas that might support SWFLs, or to identify areas that may develop 

into appropriate habitat - requires knowledge of recent/current habitat conditions and an 

understanding of the factors that determine flycatcher use of riparian breeding sites. 

In the past, much of the determination of whether a riparian site is likely to support 

breeding flycatchers has been based on qualitative criteria (e.g., “dense vegetation” or “large 

patches”). These determinations often require on-the-ground field evaluations by local or regional 

SWFL experts. While this has proven valuable in locating many of the currently known breeding 

sites, it is difficult or impossible to apply this approach effectively over large geographic areas 

(e.g., the middle Rio Grande). The SWFL Recovery Plan (USFWS 2002) recognizes the 

importance of developing new approaches to habitat identification, and recommends the 

development of drainage-scale, quantitative habitat models. In particular, the plan suggests using 

models based on remote sensing and Geographic Information System (GIS) technology that can 

capture the relatively dynamic habitat changes that occur in southwestern riparian systems. 

1 
In 1999, Arizona Game and Fish Department (AGFD) developed a GIS-based model 

(Hatten and Paradzick 2003) to identify SWFL breeding habitat from Landsat Thematic Mapper 

(TM) imagery and 30-m resolution digital elevation models (DEMs). The model was developed 

with presence/absence survey data acquired along the San Pedro and Gila rivers, and from the Salt 

River and Tonto Creek inlets to Roosevelt Lake in southern Arizona (collectively called the project 

area). The GIS-based model used a logistic regression equation to divide riparian vegetation into 5 

probability classes based upon characteristics of riparian vegetation and floodplain size. This model 

was tested by predicting SWFL breeding habitat at Alamo Lake, Arizona, located 200 km from the 

project area (Hatten and Paradzick 2003). The GIS-based model performed as expected by 

identifying riparian areas with the highest SWFL nest densities, located in the higher probability 

classes. 

In 2002, AGFD applied the GIS-based model throughout Arizona, for riparian areas below 

1,524 m (5,000 ft) elevation and within 1.6 km of perennial or intermittent waters (Dockens et al. 

2004). Overall model accuracy (using probability classes 1-5, with class 5 having the greatest 

probability of nesting activity) for predicting the location of 2001 nest sites was 96.5%; accuracy 

decreased when fewer probability classes were defined as suitable. Map accuracy, determined 

from errors of commission, increased in higher probability classes in a fashion similar to errors of 

omission. Map accuracy, like model accuracy, was dependent on what probability cutpoint was 

selected. A cutpoint, or probability threshold, is a modeler-selected value at which “suitable” (i.e., 

high probability) habitat is dichotomously distinguished from “unsuitable” (i.e., low probability) 

habitat. For the Hatten and Paradzick (2003) model, the lower the cutpoint used, the greater the 

amount of predicted habitat and model sensitivity, but the lower its specificity. Inversely, as the 

cutpoint increased, the model’s specificity increased, but the amount of predicted habitat and the 

sensitivity decreased. To explain the effect of cutpoint choice, Hatten and Paradzick (2003) 

concluded that if all class-5 cells (the 80% cutpoint) had been surveyed in Arizona during 2001, 

approximately 20% would have been unsuitable for SWFLs and roughly 30% of SWFL nests 

would have been missed. If surveyors had searched all class 3-5 cells (representing the 40% 

cutpoint), 32% would not have been breeding habitat, and <5% of nests would have been missed. 

This is an issue inherent with all models of this kind, and there is no perfect cutpoint. The Hatten 

and Paradzick (2003) model was clearly successful at predicting flycatcher breeding sites in 

Arizona based on remote sensing data and GIS-based themes. 

This is the first large-scale predictive model of its kind for SWFL habitat. It focused on low 

to mid-elevation breeding sites because there were relatively few high elevation sites or territories 

in Arizona. Based upon the model’s robust performances in Arizona, we believed that it could be 

applied elsewhere within the species’ range. Because New Mexico has a relatively large SWFL 

breeding population, with intensive survey and nest monitoring of major sites along the Rio Grande 

(Ahlers and Moore 2004), it provided a good opportunity to evaluate the model’s performance 

outside of Arizona. We hypothesized that if the model was applied to the Rio Grande, the 5 

probability classes output by the model would contain either a linear, exponential, or binary 

relationship with SWFL nest or territory density. Because the model outputs georeferenced layers 

that can be queried and viewed with a GIS, the model can be used as a decision support system for 

prioritizing breeding site and nest surveys, or identifying potential restoration or enhancement 

locations. In doing so, users would need to carefully consider the effect of cutpoint choice and the 

amount of predicted habitat. 

2 
 

 

Methods 


Modeling Overview 

We applied the same modeling techniques used in Arizona (Hatten and Paradzick 2003; 

Dockens et al. 2004) to identify and map potential SWFL breeding habitat in 2004 along the Rio 

Grande, from Elephant Butte Dam in southern New Mexico to near the Alamosa National Wildlife 

Refuge in southern Colorado. Our initial focus was the determination of model accuracy, as 

estimated from errors of omission; e.g., nest sites or breeding locations that occurred but were in 

areas not predicted by the model. 

To accomplish our goal it was necessary to complete four steps: (1) create four GIS 

variables derived from Landsat Thematic Mapper (TM) imagery and 30-m resolution DEMs, (2) 

populate a logistic regression model with the four GIS variables to create a probability grid, (3) 

divide riparian vegetation into 5 probability classes, and (4) assess model accuracy within the 5 

probability classes with errors of omission, and model fit with nest or territory density. 

The logistic regression equation we used for this study (Table 1) calculates the probability 

of nest occurrence with the following equation: 

probability = exp(logit) / 1 + exp(logit); where the logit is: 1.483(NDVI) + 

0.098(NDVIBEST) + 0.034(FLOODPL) + 0.648(NDVISTD) – 6.074. 

 The four variables are defined as follows: 

(1) NDVI = dense vegetation (NDVI > 0.33) at the site (0.09 ha); 

(2) NDVIBEST = amount (%) of densest vegetation (NDVI > 0.41) within a 4.5-ha 

neighborhood; 

(3) FLOODPL = amount (%) of floodplain or flat terrain (<2.5 degrees) within a 41-ha 

neighborhood, and 

(4) NDVISTD = the standard deviation in NDVI (12 classes) within a 4.5-ha neighborhood. 

GIS Database 

We created vegetation density grids (0.09-ha cells) for the Rio Grande corridor from 

spectral data that were extracted from five TM images acquired (imaged) on June 13, 2004, during 

a cloud-free period (Figure. 1). To process the imagery and create the four GIS variables (layers) 

necessary to populate the model (see Table 1), we used ERDAS Imagine software (ERDAS Inc., 

Atlanta, Georgia) and ARC/INFO GIS (ESRI Inc., Redlands, CA). After masking agricultural 

fields that were visible on the TM imagery, we created an overall vegetation density grid by 

calculating the Normalized Difference Vegetation Index (NDVI) within 3-5 km of the Rio Grande. 

We selected a wide, variable-width buffer in order to capture riparian vegetation in the lower 

reaches of tributaries that often contain flycatcher habitat. We created riparian-vegetation density 

grids from the overall vegetation grid by using NDVI thresholds and classification, and stored the 

results from each operation in a separate grid (Hatten and Paradzick 2003). 

We created a generalized riparian-vegetation density grid from the overall vegetation 

density grid by extracting all cells with an NDVI value >0.126 (Hatten and Paradzick 2003). We 

3 
also created an intermediately dense (NDVI >0.33) and very dense riparian-density vegetation grid 

(NDVI > 0.41). Next, we created an interval-scaled riparian-density vegetation grid by dividing 

the riparian grid (NDVI > 0.12) into 12 classes, ranging from 0.126 – 0.86). Once the riparianvegetation density grids were created, we calculated the proportion (%) of densest vegetation, and 

heterogeneity in vegetation density (SD), within a 120-m radius (4.5 ha) of each location with 

moving window functions (FOCALSUM and FOCALSTD, respectively). We used the 

FOCALSUM function on the densest riparian vegetation grid (NDVI >0.41), and the FOCALSTD 

function on the interval-scaled riparian grid. Lastly, we identified the proportion (%) of flat area (< 

2.50) off of a 30-m resolution DEM with SLOPE and FOCALSUM functions. 

Model Fit and Accuracy Assessment 

To assess the fit of the model, we overlaid SWFL territory locations upon the 5 probability 

classes that were output by the model. The territory locations were acquired during the 2004 field 

season (June – September) from sites that are intensively monitored between Elephant Butte 

Reservoir and Velarde by the U.S. Bureau of Reclamation (Ahlers and Moore 2004). Nest/territory 

density is obtained by dividing territory numbers within each probability class by the area (ha) 

found with each probability class (Hatten and Paradzick 2003). If the model works as expected, 

there will be an increase in territory density in higher probability classes in either a linear, 

exponential, or binary fashion. We examined model fit for breeding sites under 1,524 m elevation 

because that was the elevational cutoff used in developing the model within Arizona (Hatten and 

Paradzick 2003), and because SWFL nest location data for New Mexico were only available for 

sites below this threshold. However, we did model the NDVI probability classes for riparian 

habitat above 1,524 m, as this might help identify possible breeding habitats in higher elevations 

and thus provide useful information for future surveys or modeling. 

To assess model accuracy, we overlaid the territory locations upon the 5 probability classes, 

generated a frequency histogram, and calculated omission errors. An omission error occurs when a 

territory location falls outside of predicted habitat, thus omission errors change according to what 

probability cutpoint is selected (Hatten and Paradzick 2003). If the model is working correctly, 

omission errors should increase as the cutpoint is raised because less riparian vegetation is 

predicted as suitable for breeding. We also expected an inverse relationship between amount of 

predicted breeding habitat and the 5 probability classes, so higher probability classes should 

contain less riparian vegetation, and lower classes more. 

Results 

Predicted Habitat 

The GIS-based model found 91,104 ha of riparian or upland vegetation that had an NDVI 

value > 0.126 (probability classes 1 – 5) along the Rio Grande (Table 2). Because all probability 

classes were totaled, this number reflects the total amount of riparian vegetation and some nonriparian vegetation in the upper reaches (i.e., >1,524 m) that is probably pinyon/juniper or pine 

(Appendix 1, Figs H – L). When we restricted our analysis to under 1,524 m elevation (i.e., under 

5,000 feet), there were 25,514 ha of riparian vegetation within the NDVI density band of our model 

(classes 1-5). The greatest amount of high probability habitat under 1,524 m was located near 

Elephant Butte Reservoir (Appendix 1, Fig. A); the largest amount above 1,524 m was in the San 

Luis Valley (Appendix 1, Fig. M). The amount of predicted habitat was greatest for probability 

4 
class 1; however, there was not a direct inverse relationship between probability class and predicted 

habitat. Instead, the amount of class 5 habitat was greater than classes 2 – 4. Still, the amount of 

predicted habitat changed dramatically when lower probability classes were excluded. For 

example, probability class 1 contained 46.4% of predicted habitat, classes 2-4 contained 10-12% 

apiece, and class 5 contained 21.2% (Fig. 2). 

Accuracy Assessment and Model Fit 

The results of our binary habitat classification assessment found that the lower the cutpoint, 

the greater the sensitivity of the model. Conversely, the higher the cutpoint, the lower the model’s 

sensitivity (Fig. 3). This reflects the relationship between cutpoint and the proportion of riparian 

vegetation that is considered suitable as SWFL breeding habitat. Specifically, the lower the 

cutpoint, the greater the proportion of riparian vegetation that was classified as suitable for 

breeding, and thus fewer SWFL territories were omitted. At a 20% cutpoint, the model was 96% 

accurate, while at an 80% threshold it was 77% accurate. 

The density of flycatcher territories (n = 149) increased exponentially within the 5 

probability classes (Fig. 4), with class 1 containing the lowest density (0.0005/ha), and class 5 the 

greatest (0.021/ha). There was an order of magnitude difference between the density of SWFL 

territories found in class 5 compared to class 1. The magnitude of change in density between 

classes varied; nest density increased 120% between classes 1 and 2, 309% between classes 2 and 

3, decreased 2.2% between classes 3 and 4, and increased 382% between classes 4 and 5. 

Discussion 

Model Performance 

Even though the GIS-based model was developed based on SWFL habitat and nest location 

data from Arizona (Hatten and Paradzick 2003), the model performed as expected along the Rio 

Grande, with an exponential relationship between the 5 probability classes and territory density. 

This relationship indicates that NDVI is a robust integrative variable for characterizing riparian 

habitat features that are important in habitat selection by SWFLs. Thus, the GIS-based model can 

be a useful tool to managers in New Mexico for identifying possible SWFL breeding habitats, 

prioritizing survey efforts, identifying potential restoration areas and tracking restoration progress, 

and monitoring riparian habitat changes over time. The densities of flycatcher territories along the 

Rio Grande were roughly 400% smaller than those observed in 2000 along the Gila and San Pedro 

Rivers (Paxton et al. 2007), but the pattern of distribution (i.e., exponential) within the 5 probability 

classes was very similar. This indicates that while SWFL densities can change by location, year, or 

spatial extent of the project area, the response of the flycatchers to the habitat appears similar, with 

SWFL preferring dense, vigorous riparian vegetation as reflected in high NDVI values. 

Based upon our observations in Arizona (Hatten and Paradzick 2003), we expected to see 

an inverse relationship between the five model probability classes and the amount of riparian 

vegetation (NDVI > 0.126), but the middle classes were smaller than the last class (Fig. 2). We 

suspect that the extensive amount of agriculture along the Rio Grande Valley has altered this 

relationship, but more research is necessary to understand the influence of land use and riparianvegetation growth patterns throughout the southwestern U.S, especially anthropogenic factors such 

as groundwater usage and urban development. 

5 
Future Modeling 

There are several potential ways to further test and refine this SWFL habitat model for 

broader application in New Mexico, and potentially elsewhere in the range of the flycatcher. One 

option would be to apply this model to other river systems within New Mexico, particularly the 

Gila River. The Gila supports several known large SFWL populations (Durst et al. 2005), and it 

would be instructive to see if the model correctly predicts the location of these breeding sites, or if 

it identifies new potential breeding habitats that may be worth surveying. 

Another possibility would be to build new “customized” GIS-based models based on Rio 

Grande-specific habitat characteristics and nest/territory data. These models could be based on 

currently available nest location data, or include gathering additional nest data from more breeding 

sites. One could assess whether Rio Grande-specific models improve model sensitivity and 

specificity by looking at territory densities within the 5 probability classes. If a custom model 

improves the sensitivity and specificity of the model, flycatcher territory densities should increase 

in the upper probability classes. Creating river-specific predictive models has recently been shown 

to improve model sensitivity and specificity along the Gila and San Pedro Rivers in Arizona 

(Paxton et al. 2007), thus we could potentially expect such an outcome for the Rio Grande as well. 

By applying the GIS-based model in two or more years, which requires populating the 

model with annual-specific vegetation characteristics derived from the appropriate TM scene, one 

can quantify in a spatially explicit manner changes in SWFL breeding habitat (Paradzick and 

Hatten 2004). Detecting changes in SWFL breeding habitat can be useful for determining whether 

climate, hydrology, or anthropogenic factors are reducing or increasing their breeding habitat 

between one or more time intervals. By incorporating habitat changes into a SWFL breeding 

habitat model, which requires running the model at two different time intervals and creating a 

change detection grid, one can create a spatial-temporal model of SWFL breeding habitat (Paxton 

et al. 2007). Spatial-temporal models have been shown to be the best overall predictors of SWFL 

breeding habitat because they capture some of the underlying stability and dynamics in riparian 

vegetation that are important components in SWFL breeding habitat. 

Lastly, applying the model to other large river systems in the Southwest would provide very 

useful insights on the applicability of the model over a broader geographic range. If the model 

proves robust on a range-wide scale, it would allow for riparian habitat prediction, quantification, 

and change detection over unprecedented spatial and temporal scales. This would help facilitate 

the landscape and drainage level conservation and management recommended in the SWFL 

recovery plan (USWFS 2002). 

Acknowledgments 

This study was supported by funding from the U.S. Bureau of Reclamation (Upper 

Colorado River Region) and the USGS Southwest Biological Science Center . We are indebted to 

Darrell Ahlers and Franciose Leonard for providing the SWFL nest location data that were crucial 

to the testing of this model. Lisa Ellis and Terence Arundel provided insightful peer-review of this 

report, and their comments greatly improved the final product. The use of trade names in this 

paper does not constitute a product endorsement by USGS. 

6 
Literature Cited 

Ahlers, D. and D. Moore. 2004. Southwestern Willow Flycatcher study results: selected sites 

along the Rio Grande from Velarde, New Mexico to the headwaters of Elephant Butte 

Reservoir. Bureau of Reclamation, Technical Service Center, Ecological Planning and 

Assessment. Denver, CO. 

Dockens, P.E.T., C.E. Paradzick, and J.R. Hatten. 2004. Application of a Southwestern Willow 

Flycatcher GIS-based habitat model: an estimate of breeding habitat in Arizona, 2001, In P. 

Dockens and C. Paradzick (eds), Mapping and monitoring Southwestern Willow Flycatcher 

breeding habitat in Arizona: a remote sensing approach. Arizona Game and Fish 

Department Nongame and Endangered Wildlife Program Technical Report 223. 

Durst, S.D., M.K. Sogge, H. English, S.O. Williams, B.E. Kus and S.J. Sferra. 2006. Southwestern 

Willow Flycatcher breeding site and territory summary – 2005. USGS Southwest 

Biological Science Center report to the U.S. Bureau of Reclamation. October 2006. 

Hatten, J.R., and C.E. Paradzick. 2003. A multiscaled model of southwestern willow flycatcher 

breeding habitat. Journal of Wildlife Management 67: 774-788. 

Paradzick, C.E., and J.R. Hatten. 2004. Southwestern willow flycatcher habitat change detection 

analysis: Roosevelt Lake, Arizona, 1985-2001. Pages 60-82 in P.E.T. Dockens and C.E. 

Paradzick, editors. Mapping and monitoring southwestern willow flycatcher habitat in 

Arizona: a remote sensing approach. Nongame and Endangered Wildlife Program Technical 

Report 223. Arizona Game and Fish Department, Phoenix, Arizona, USA. 

Paxton, E.H., M. Sogge, S.L. Durst, T.C. Theimer, and J.R. Hatten. 2007 (In Prep). The Ecology 

of the Southwestern Willow Flycatcher in central Arizona: A 10-year Synthesis Report. 

Chapter 4, Spatial Analysis. U.S. Geological Survey, Southwest Biological Science Center, 

Colorado Plateau Research Station, Flagstaff, AZ 

Sogge, M.K. and R.M. Marshall. 2000. A Survey of Current Breeding habitats. Pages 43-56 in 

Status, Ecology, and Conservation of the Southwestern Willow Flycatcher. Finch, D.M. 

and S.H. Stoleson (eds). USDA Forest Service Rocky Mountain Research Station General 

Technical Report RMRS-GTR-60. 131 pp 

United States Fish and Wildlife Service (1995) Final rule determining endangered status for the 

southwestern Willow Flycatcher. Federal Register, 60, 10694-10715. 

U.S. Fish and Wildlife Service. 2002. Southwestern Willow Flycatcher Final Recovery Plan. US 

Fish and Wildlife Service, Albuquerque, NM. 

7 
Figure 1. The extent of Landsat Thematic Mapper scenes (path 33, rows 33 - 38) used for 

Southwestern Willow Flycatcher habitat modeling and mapping, overlaid on NM county boundaries 

and the Rio Grande. The extent of Landsat Thematic Mapper scenes (path 33, rows 33 - 38) used for 

Southwestern Willow Flycatcher habitat modeling and mapping, overlaid on NM county boundaries 

and the Rio Grande. The Landsat scenes were captured on June 13, 2004. 

8 
0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

50 

Proportion of riparian vegetation (%) 

1 2 3 4 5 

Probability class 

Figure 2. The proportion of riparian vegetation found within each probability class, along the Rio 

Grande under 1,524 m elevation, as determined by the GIS-based model. Probability class 1, which 

contained cells with model probabilities <20%, accounted for 46.4% of the riparian vegetation, and 

probability class 5 (cell probabilities > 80%) accounted for 21.2%. 

9 
Figure 3. The sensitivity (1 – omission error) of the Southwestern Willow Flycatcher habitat model at 

4 probability thresholds, as modeled along the Rio Grande in 2004. For this analysis, we created 4 

binary habitat grids by classifying all cells with a probability greater than the NDVI threshold as 

suitable breeding habitat, and cells under the NDVI threshold unsuitable. We overlaid 

Southwestern Willow Flycatcher territories on each binary habitat grid and determined the model’s 

sensitivity at each probability threshold. In this example, at a 20% threshold, where cells with a 

model probability >20% were coded suitable, 96% of territories were correctly classified (i.e., fell 

inside the suitable zone), and 4% were omitted. 

96 

94 

86 

77 

50 

60 

70 

80 

90 

100 

Sensitivity 

20 40 60 80 

Probability threshold 

10 
Figure 4. The density of Southwestern Willow Flycatcher territories within 5 probability classes that 

were output by the GIS-based habitat model, based on Rio Grande data for 2004. The density of 

Southwestern Willow Flycatcher territories within 5 probability classes that were output by the GISbased habitat model, based on Rio Grande data for 2004. For this analysis, only areas that were less 

than 1,524-m in elevation were included in the analysis. 

0.0005 0.0011 

0.0045 0.0044 

0.0212 

0.0000 

0.0050 

0.0100 

0.0150 

0.0200 

0.0250 

Territory density (territories/ha) 

1 2 3 4 5 

Probability class 

11 
 

 

 

Table 1. Multivariate logistic regression model obtained from Southwestern Willow Flycatcher data (Hatten and Paradzick 2003). 

The model was created from retrospective survey data (71 breeding sites and 136 nonuse sites) collected during 1999 in southcentral Arizona. Breeding sites contained a SWFL nest and nonuse sites did not. 

Variable Coeff SE G Odds Ratioc P 

NDVIa

 1.483 0.48 9.6 4.4 0.002 

NDVIBESTb

 0.098 0.02 29.6 1.6c <0.001 

FLOODPLb

 0.034 0.01 8.7 NA 0.003 

NDVISTDd

 0.648 0.16 19.5 1.9 <0.001 

Constant -6.074 0.98 64.5 0.0 <0.001 

a

Modeled as a binary variable (NDVI classes 1-9 = 0; 10-12 = 1)
 

b

Modeled as a continuous variable (scaled from 0 – 49) 

c

Odds ratio calculated in 10% increments 

d

Modeled as a continuous variable (scaled from 0 – 6)
 

12 
 

 

 

 

 

Table 2. The frequency of Southwestern Willow Flycatcher territories (n = 149), amount of predicted habitat, and density of 

flycatchers within each model probability class for riparian habitat long the Rio Grande in 2004. The table also shows the amount of 

predicted habitat, and density of flycatchers, with and without a 1524-m elevation mask applied. Six flycatcher territories fell 

outside of all probability classes and they were lumped into the lowest probability class. The proportion (%) of the total amount of 

riparian vegetation by class, as determined by the model, is also presented. 

Class Frequencya Area (ha)b

 Densityb Area(ha)c Densityc Percentd 

1 6 5,3248.68 0.0001 11,840.31 0.0005 46.4 

2 3 7,565.31 0.0004 2,672.82 0.0011 10.5 

3 12 7,200.99 0.0017 2,661.93 0.0045 10.4 

4 13 8,693.64 0.0015 2,921.67 0.0044 11.5 

5 115 14,395.59 0.0080 5,417.55 0.0212 21.2 

Total 149 91,104.21 25,514.28 100 

a Number of flycatchers 

b

Predicted breeding habitat (classes 1 – 5) or SWFL density along entire Rio Grande study area (see Fig. 1) 

c Predicted breeding habitat (classes 1 – 5) or SWFL density along Rio Grande under 1524 m 

d

Proportion of riparian vegetation, as determined from the model, within each probability class 

13 
Appendix 

Key to location of Appendix figures 

14 
Figure A. SWFL habitat model output (the 5 probability classes are shown in the legend) 

and 2004 SWFL territories (red circles), overlain on a 1:250,000 scale USGS topographic 

map. 

15 
Figure B. SWFL habitat model output (the 5 probability classes are shown in the legend) and 2004 

SWFL territories (red circles), overlain on a 1:250,000 scale USGS topographic map. 

16 
Figure C. SWFL habitat model output (the 5 probability classes are shown in the legend) and 2004 

SWFL territories (red circles), overlain on a 1:250,000 scale USGS topographic map. 

17 
Figure D. SWFL habitat model output (the 5 probability classes are shown in the 

legend) and 2004 flycatcher territories, overlain on a 1:250,000 scale USGS 

topographic map. 

18 
Figure E. SWFL habitat model output (the 5 probability classes are shown in the legend), 

overlain on a 1:250,000 scale USGS topographic map. 

19 
Figure F. SWFL habitat model output (the 5 probability classes are shown in the legend), 

overlain on a 1:250,000 scale USGS topographic map. 

20 
Figure G. SWFL habitat model output (the 5 probability classes are shown in the legend), 

overlain on a 1:250,000 scale USGS topographic map. 

21 
Figure H. SWFL habitat model output (the 5 probability classes are shown in the legend), 

overlain on a 1:250,000 scale USGS topographic map. Note that much of the Probability 

Class 1 vegetation shown away from the river corridor is non-riparian upland vegetation, 

possibly pinyon/juniper or pine. 

22 
Figure I. SWFL habitat model output (the 5 probability classes are shown in the legend), overlain 

on a 1:250,000 scale USGS topographic map. 

23 
Figure J. SWFL habitat model output (the 5 probability classes are shown in the legend) and 

2004 flycatcher territories, overlain on a 1:250,000 scale USGS topographic map. Note that 

much of the Probability Class 1 vegetation shown away from the river corridor is nonriparian upland vegetation, possibly pinyon/juniper or pine. 

24 
Figure K. SWFL habitat model output (the 5 probability classes are shown in the legend) and 

2004 flycatcher territories, overlain on a 1:250,000 scale USGS topographic map. Note that 

much of the Probability Class 1 vegetation shown away from the river corridor is non-riparian 

upland vegetation, possibly pinyon/juniper or pine. 

25 
Figure L. SWFL habitat model output (the 5 probability classes are shown in the legend) and 

2004 flycatcher territories, overlain on a 1:250,000 scale USGS topographic map. Note that 

much of the Probability Class 1 vegetation shown away from the river corridor is nonriparian upland vegetation, possibly pinyon/juniper or pine. 

26 
Figure M. SWFL habitat model output (the 5 probability classes are shown in the legend) and 

2004 flycatcher territories, overlain on a 1:250,000 scale USGS topographic map. Note that 

much of the Probability Class 1 vegetation shown away from the river corridor is non-riparian 

upland vegetation, possibly pinyon/juniper or pine. 

27