American Woodcock
Scolopax minor
Patuxent Bird Identification InfoCenter

• Predicted American Woodcock Abundance Map
  
• American Woodcock Model Results
• American Woodcock Internal Validation Graph
• American Woodcock Hotspot Mapping

Predicted Abundance

Predicted American Woodcock Abundance Map
American woodcock abundance (birds/route) predicted for the upper midwestern and northeastern United States, as determined by a hierarchical Bayesian spatial count model of Singing-ground Survey counts.

American Woodcock Model Results

Parameter estimates for the best subset of models fitted to 1981-2001 American Woodcock Singing-ground Survey route counts.

Best subset model	Fixed explanatory variables (Positive [+] or negative [-] association)	Scale (ha)	Parametersa	DICb	DICc	wid	Evidence ratioe
1	Forest (+), Shrub (+), Aspen (+), Human Land Use (-), Aggregation Index (-), Growing Season (-), TCIf (+)	350	2,771.9	33,551.2	0	0.58	1.00
2	Forest (+), Shrub (+), Aspen (+), Grass (-), Human Land Use (-), Aggregation Index (-), Growing Season (-)	4,000	2,789.1	33,552.3	1.1	0.34	1.73
3	Forest (+), Forest2 (-), Shrub (+), Aspen (+), Grass (-), Human Land Use (-), Aggregation Index (-), Growing Season (-)	4,000	2,797.5	33,556.8	5.6	0.04	16.44
4	Forest (+), Shrub (+), Aspen (+), Grass (-), Human Land Use (-), Aggregation Index (-), Growing Season (-), TCI (+)	4,000	2,790.1	33,556.9	5.7	0.03	17.29
5	Forest (+), Shrub (+), Aspen (+), Grass (-), Human Land Use (-), Aggregation Index (-), Growing Season (-)	106,000	2,768.0	33,560.5	9.3	0.01	104.58
6	Forest (+), Shrub (+), Aspen (+), Human Land Use (-), Growing Season (-)	350	2,798.7	33,560.8	9.6	0.005	121.51
Null	Includes only random effects associated with Observer, Year, and Route	NA	2,887.4	33,670.9	119.7	0	9.83 E + 25

^a Parameters is the effective number of parameters (pD) and is given by the posterior mean of the deviance minus the deviance of the posterior means (Spiegelhalter et al. 2003).

^b DIC is Deviance Information Criterion.

^c DIC is the difference between the best model and the model of interest.

^d w_i is the model weight

^e Evidence ratio is the model weight for the best model divided by the weight for the model of interest.

^f Topographic Convergence Index.

American Woodcock Internal Validation Graph

Observed American Woodcock Singing-ground Survey counts compared to expected (predicted) counts (birds/route) from the best model of woodcock abundance. Observed counts were data withheld from model building (collected between 1981 and 2001) and data collected for 2002 and 2003. The bold line is the line of one-to-one correspondence between observed and predicted counts.

American Woodcock Hot Spot Mapping

We modeled patterns in rare bird abundance to identify areas in which to apply management and conservation. We were specifically interested in predicting 'hotspots' of abundance. These areas of concentrated abundance are more readily conserved than areas in which the species are more diffusely distributed, allowing for the efficient expenditure of meager conservation resources.

Another benefit of mapping hotspots is that they allow for assessing the private lands considerations necessary for conserving these species. We related the species-specific hotspots against state- and federal-managed lands and identified areas of concentrated abundance occurring on private lands. These private lands hotspots may require different course of management (e.g., easements, land acquisition, landowner subsidization) than hotspots on governmentally-managed lands. They also present an opportunity to understand how rare birds may co-exist outside of direct governmental protection.

To accomplish this a "smoothed" American Woodcock model was created by applying the ESRI "Focal Statistics" tool to the original American Woodcock predicted abundance model. This tool was used to calculate a mean value for each cell in the model based on a 13 cell radius, circular focal window with a 541.697935 meter cell size.

Next, we calculated the top 5% of values in the smoothed model based upon cell count. The predicted abundance value of "5.591" was used as a cut-off to determine the Hotspots. Values greater than this were highlighted (see figure below).

Next, an ESRI shapefile depicting boundaries around the calculated Hotspots was created and used to calculate Zonal Statistics on the smoothed model using the "Zonal Statistics as Table" tool. This table was then used to calculate the top ten hotspots with a 50% weighting given to maximum MEAN value and a 50% weighting given to maximum SUM value. This was done to take into account not only relative intensity of predicted American Woodcock modeled abundances but also size of the hotspot. These hotspots are shown in the figure below and grouped into 6 frames for mapping purposes.

The hotspots were then mapped in relation to their proximity to Federal and State-management. Shapefiles were collected from various sources to meet this need and are described briefly within the Managed Area Metadata Summary. (.pdf file 159 KB)

Tabular summaries were then created using the "Zonal Statistics as Table" tool. The summaries were done to compare individual states and hotspots according to their state managed/unmanaged status and also for federal managed/unmanaged status (see table below). The percentages of managed and unmanaged lands were calculated using the area totals and also the sum totals for the cells in the model.

* Partners-in-Flight Conservation score for PIF16