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ForestERA Data Layer Overview - Avian Species Richness

Description

This is a layer representing avian species richness in ponderosa pine and pine-oak habitats across the assessment area. Avian communities have been well studied in southwestern ponderosa pine forests (Block & Finch 1997; Griffis-Kyle & Beier 2003; Rosenstock 1996) and birds are likely to be good species for monitoring the effects of restoration treatments (Chambers & Germaine 2003). The layer was built using a regression tree methodology and data from point counts taken in the assessment area between 1995 and 2004. The layer has a resolution of 90m (0.8 ha or 2 acres).

Purpose

These data layers were created as part of the ForestERA project to support landscape-scale forest restoration planning efforts by a broad group of stakeholders including federal and state agencies, academic institutions, and non-governmental entities. These data are intended for regional analyses over spatial extents on the order of tens to hundreds of thousands of acres, and were not developed for use at finer spatial scales, although they may be useful for some applications at finer scales.

Development

The avian species richness model predicts the number of species that would be found in areas of pine or pine-oak habitat on the western Mogollon Plateau. The model was built using presence absence data for avain species at 312 point count locations scattered across the plateau. Point count data was obtained from Steve Rosenstock of the Arizona Game and Fish Department, Brett Dickson of Colorado State Univeristy, Bill Block of the USDA Forest Service, Rocky Mountain Research Station, Carol Chambers of Northern Arizona University, and Kerry Griffis-Kyle of Syracuse University.

To build the model we picked a total of 30 species that were regularly found in at point counts, and were representative of the ponderosa pine and pine-oak avian community. We eliminated large and wide-ranging species, species that responded largely to local habitat conditions not related to forest structure (e.g., aquatic habitats), and species not typically found in ponderosa pine or pine-oak. Contact ForestERA for a complete list of species used in the model.

We used a regression tree (CART) methodology (Breiman et al. 1984) and S-Plus software (Insightful Corperation, Seattle, Washington) for model development. CART procedures have been shown to be very useful in ecological contexts because continuous and discrete predictive variables can be used in the models, the models are statistically rigorous, and outputs are easily understood (De’ath & Fabricius 2000). Variables chosen as potential predictor variables in the model included slope, sine and cosine of aspect, basal area, tree stem density, and canopy cover. The minimum number of observations in a node was set at 6 and the minimum number of observations to make a split was set at 12. The final tree was pruned back to a tree of minimum size but having error within 1 standard error of the minimum cross-validation error (Breiman et al. 1984; De’ath & Fabricius 2000). In addition, nodes that made no sense biologically were removed. The final model has 6 terminal nodes, and explains 44% of the variation in species richness at the point count locations where training data were collected. The following rules occur in the model;

1. In pure ponderosa pine, an average of 8.5 species are expected to occur in locations with basal area < 23.5 m2/ha.
2. In pure ponderosa pine, an average of 11.5 species are predicted to occur in locations with basal area > 23.5 m2/ha and slope < 7.5 degrees.
3. In pure ponderosa pine, an average of 6.8 species are predicted to occur in locations with basal area > 23.5 m2/ha and slope > 7.5 degrees.
4. In pine-oak, an average of 10.1 species are predicted to occur in locations with slope > 8.5 degrees.
5. In pine-oak, an average of 10 species are predicted to occur in areas with slope < 8.5 degrees and tree density < 187 stems/ha.
6. In pine-oak, an average of 14.3 species are predicted to occur in areas with slope < 8.5 degrees and tree density > 187 stems/ha.

Accuracy Assessment

An accuracy assessment was undertaken using data from 56 point count locations that were withheld for analysis so they could be used as a test dataset. For each of the terminal nodes, we averaged the species richness value from all of the test point count locations falling within those nodes. We used simple linear regression to assess the relationships between average species richness at those locations and the predicted average species richness from the model. The results suggested the model was doing an excellent job at predicting the pattern of species richness (n = 6; r² = 0.91, slope of regression line = 1.04) across the landscape.

Sources of errors

While we believe this model is giving a good general representation of avian species richness across the landscape, it is coarse and many finer-scale factors undoubtedly affect local richness. Many of the nodes in the model still show high variance, indicating that further splitting may be necessary. As this layer represents average species richness, we recommend that it be used as general guidance for predicting whether areas will have higher or lower number of species. In addition, because only 30 species were used in the analysis, actual species richness will be higher than predicted at many locations.

Recommendations

We recommend that this layer be used at a minimum resolution of 90m (0.8 ha or 2 acres) for purposes of analysis and display. However, ForestERA data layers were not designed for analyses at the level of individual pixels, and uncertainty in the data will generally decline over greater spatial extents. Therefore, we recommend using larger analysis units, with groupings of at least 50 cells (40 ha or 100 acres). Finally, we reiterate that ForestERA data layers were developed for the purpose of regional landscape-level planning, and we suggest that the analyses be applied over spatial extents of tens to hundreds of thousands of acres. We recognize, however, that this layer may be useful for analyses over smaller spatial extents depending on the type and purpose of those analyses.

Literature Cited

Block, W. M., and D. M. Finch. 1997. Songbird ecology in Southwestern Ponderosa Pine forests: a literature review. USDA Forest Service General Technical Report RM-GTR-292.

Breiman, L., J. J. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth and Brooks Publishing, Monterey, California.

Chambers, C.L., and S. S. Germaine, 2003. Vertebrates. Pp. 268-285 in: P. Friederici (Editor), Ecological Restoration of Southwestern Ponderosa Pine Forests. Island Press, Washington D.C.

De’ath, G., and K. E. Fabricius. 2000. Classification and Regression Trees: a powerful, yet simple, technique for ecological data analysis. Ecology 81: 3178-3192.

Griffis-Kyle, K. L., and P. Beier. 2003. Small isolated aspen stands enrich bird communities in Southwestern ponderosa pine forests. Biological Conservation 110: 375-385.

Rosenstock, S. S. 1996. Habitat relationships of breeding birds in northern Arizona ponderosa pine and pine-oak forests. Arizona Game and Fish Department Research Technical Report #23.