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- Cover and Acknowledgements
- Table of Contents
- List of Acronyms
- Executive Summary and Report Card
- 1.0 Introduction and Background
- 2.0 Indicator Selection
- 3.0 Indicator Generation, Evaluation, Aggregation
- 3.1 Goal A: Water Quality and Supply
- 3.2 Goal B: Native biota
- 3.3 Goal C: Habitats and ecosystems
- 3.4 Goal D: Fire and Flooding
- 3.5 Goal E: Community, Social and Economic Conditions
- 3.6 Linkages and relationships among objectives, attributes, and indicators
- 4.0 General Methods and Principles
- 4.1. Reporting and analysis subwatershed units
- 4.2 Scoring: Distance to target/reference and scoring transformations
- 4.3 Trend/time series analysis
- 4.4 Confidence in Report Card findings
- 4.5 Spatial scale and aggregation of fine scale data to subwatershed
- 4.6 Temporal scale and aggregation
- 4.7 Cross-indicator score aggregation
- 4.8 Data management and transformation
- 5.0 Interpretation
- 6.0 Conclusions and Recommendations
- Appendix A: Glossary of Terms
- Appendix B: Indicator Selection Criteria
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- Background Primer on Rural Residential Development
- Adaption: Best Management Practices
- A Roadmap to Watershed Management
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3.2.1 Bird Species Diversity
There is an abundance of quantitative data about bird populations, thanks to surveys such as Christmas Bird Counts, the Breeding Bird Survey, and other projects. There are long time series for many of these surveys, allowing for trends to be determined. The Feather River Watershed contains seven Breeding Bird Survey routes and three Christmas Bird Counts locations that can be used for trend analysis.
A number of different metrics can be used to assess bird count data including change in species richness, change in diversity (e.g. Shannon index), or trends in abundance in selected bird species such as those from a particular guild or on a recognized watch list (Magurran 2004, Buckland et al. 2005). Because there are only a limited number of Breeding Bird Survey transects and Christmas Bird Count locations available in this watershed, in this analysis we look at a community-level metric, species richness, rather than single-species trends. Analyses of Breeding Bird Survey data suggest that data from at least 14 routes are needed to determine a robust trend for a population of an individual species (Pardieck and Sauer 2007). Seven routes were available in the watershed forcing a focus on a community-level metric such as species richness.
Detectable change in species richness of birds over time was evaluated for each subwatershed. Because raw counts of species totals underestimate richness due to lack of detection of species, a statistical estimator was used to obtain a value for actual species richness (e.g. Coddington & Colwell 1994, Kery & Royle 2008, Magurran 2004).
This indicator helps assess the bird communities in the region, an important and highly visible component of biodiversity. A change in species richness or dominance may reflect overall shifts in processes in the terrestrial ecosystems of the watershed. (O'Connell et al. 2000). Of the variety of species diversity metrics, richness was chosen instead of dominance-related metrics such as Shannon's index for a number of related reasons (Weber et al. 2004). First, species richness is a metric that is easily communicated to the general public. Second, dominance-related metrics may show confusing results such as decline in counts of abundant species leading to an increase in the value of the metric. Also, species richness may be assessed without the difficulties of measuring population or density. Finally, species richness is a good metric for assessing changes in communities due to declines in species that are widespread but uncommon locally.
The target condition is having a stable or increasing trend in species richness. A target condition based on trend slopes was selected rather than a target condition of a particular species richness value because it would be difficult in practice to establish what a richness value should be for a given landscape. Efforts to measure species richness are always mediated through the biases of a particular counting methodology, and any attempt to compare a particular measured value of richness with a predicted value (modeled for instance by combining knowledge of habitat preferences with broad-scale species range maps) will say just as much about the quality of the predictive model as it does about the quality of the actual richness condition. Steady or increasing species richness was the desired condition and hence represented by an indicator score of 100, and a negative trend in species richness to be represented by an indicator score under 100, the value of the score proportionate to the slope of the trend.
Factors that can influence species richness include broad-scale changes in the landscape such as deforestation, conversion to agriculture, and development. Degradation of habitat in the absence of broad-scale landscape changes can also lead to declining species richness, particularly through the extinction of rare species (Weber et al. 2004). Conversely, good management of reserves where rare species occur may promote species richness stability (Bohning-Gaese and Bauer 1996).
Species richness did not vary appreciably for the nine subwatersheds for which there were data, with all scoring 100. This means that there was no decline in species richness for any subwatershed over the last 10 years.
|B. Protect and enhance native aquatic and terrestrial species, especially sensitive and at-risk species and natural communities||1) Protect and enhance native bird communities.||NFF||n/a|
Values for estimated species richness ranged from 41.2 species to 131.8 species for the Breeding Bird Survey transects and from 95.6 species to 161.8 species for the two Christmas Bird Count datasets (Lower Bear and Lower Yuba watersheds). There appeared to be no significant trends in richness for any subwatershed (Figure 2). Gaps occurring in the graph reflect years in which counts or transects were not conducted.
Analysis of the significance of trends in species richness were determined (Table 1). None of the subwatersheds show trends whose slopes are statistically-significantly different from zero. Seven of the subwatersheds show slightly to moderately increasing slopes, and therefore these are assigned an indicator value of 100. Of these subwatersheds with an increasing trend, the East Branch North Fork Feather River shows the highest slope (a value of 2.51), and is also the slope which most approaches statistical significance (a p-value of 0.064). Two of the subwatersheds (Middle Yuba and North Yuba) show slightly decreasing slopes. Based on the lack of statistical significance of any of these trends, it was concluded that bird species richness is relatively stable across all the subwatersheds of the Feather River.
Individual Breeding Bird Survey routes are run annually in late May or early June. In California, there are roughly four routes per latitude-longitude block. Each route is 24.5 miles in length, and consists of 50 stops at 0.5 mile intervals. Since 1997, Breeding Bird Survey count data has been available at the individual stop level. The stops are not precisely geo-referenced; in this analysis they have been assigned to subwatersheds using linear referencing techniques in a GIS along the lines describing each route. The Christmas Bird Counts are likewise held annually, from mid-December to early January. Each count is performed in a circle 15 miles in diameter, and species counts are reported spatially only to the whole circle.
The conclusion that bird species richness is stable across the Feather River Watershed seems robust given examination of the time series data. The main caveat is that the species richness can be steady, but lower than historical or natural conditions. Other studies of bird communities over time have documented little change in species richness despite substantial changes to the landscape (e.g. Bohning-Gaese and Bauer 1996, Parody et al 2001), so this result may not be that surprising. It would be interesting to examine these data for shifts in community composition over time, which would tell more of a story about changes of pattern of bird diversity across this landscape than simply looking at species richness. But it is conceptually difficult to reduce examination of such shifts to a single graded metric.
The two different data sources are not ideal for examining changes in bird diversity at the subwatershed scale. Both the Breeding Bird Survey and the Christmas Bird Count are designed to provide information about changes in bird populations at a regional to continental scale, much coarser than the scale of our analyses. Moreover, since the Breeding Bird Survey and the Chrismas Bird Count take place at different times of the year, they are in fact sampling different bird communities. We amalgamate them here because we assume that trends in each say something about the condition of the subwatersheds, despite the difference in the bird communities that are sampled.
The Breeding Bird Survey data is available from the USGS Patuxent Wildlife Research Center (Sauer et al. 2008). Data from seven routes were used in the this analysis (California route numbers 158, 159, 181, 184, 185, 415, and 436). Christmas Bird Count data is available from the National Audubon Society (National Audubon Society 2010). In this analysis we used data from the Grass Valley and Marysville count circles. We omit data from the Sierra Valley count circle because its subwatershed is already covered by Breeding Bird Survey transects. Because data collection methods for the Breeding Bird Survey are much more standardized than the Christmas Bird Count, we only used Christmas Bird Count data where there was no Breeding Bird Survey data for a subwatershed.
The individual Breeding Bird Survey routes that fell within the boundaries of the Feather River Watershed were examined to see if each was run for enough years to make trend analyses meaningful, routes that were run for five or less times over the period 1997 to 2008 being dropped from the analysis. By georeferencing the start point of each route and measuring distances in a GIS along the trace of each route, we were able to assign individual stops within each route to a subwatershed. A total of seven subwatersheds (Upper Bear, Lower Feather, East Branch North Fork Feather, Middle Fork Feather, North Yuba, Middle Yuba, and South Yuba) were covered by the Breeding Bird Survey route segments. For the subwatersheds that did not have Breeding Bird Survey route coverage, we used Christmas Bird Count coverage where available. This allowed coverage of two more subwatersheds, the Marysville count being assigned to the Lower Yuba subwatershed, and the Grass Valley count being assigned to the Lower Bear subwatershed.
All statistical analyses were performed in the R statistical computing environment (R Development Core Team. 2009). From the raw species counts in the Breeding Bird Survey and Christmas Bird Count datasets, statistical estimators were used that were available in the vegan package (Oksanen et al. 2009) in R to derive measures of actual species richness. For the Breeding Bird Survey data, the specpool function in vegan was used to derive the estimate of richness. The input to this function is a species count by plot matrix; it uses counts of species seen only once or twice as a basis for estimating the richness. The Jackknife 2 estimator in the specpool function was used as literature suggests that it is the least biased estimator of the five available in the function (Coddington and Colwell 1994, Palmer 1991). For the Christmas Bird Count counts, the estimateR function was used with the Chao estimator; this function differs from specpool in that it can provide an estimate based on counts at a single site.
Data collection in the Breeding Bird Survey is standardized through a well-defined protocol, and hence controlled for effort: for instance the observer spends exactly three minutes at each stop counting birds. There is no standard protocol for Christmas Bird Counts. It is common in analyses of Christmas Bird Count data to standardize for levels of effort by dividing raw counts by the number of hours parties spend in the field, but some analyses suggest that this normalization should not be linear (Link & Sauer 1999). In this analysis of the Christmas Bird Count datasets no effort was made to normalize for effort and instead raw counts were used. No literature was found discussing how Christmas Bird Count counts may be normalized for levels of effort if the metric of interest is species richness rather than population levels; moreover, the estimated function does not allow count entries in the species by plot matrix to have fractional values, which would be the case if entries were divided through by number of party-hours. Experience with Christmas Bird Counts indicates that they are biased towards finding as many species as possible, which may mean that differences in level of effort will have relatively minimal impact on species richness totals.
Trends in species richness were evaluated using the Mann-Kendall method described in Section 4.3. Slopes in the trends were derived using Sen's method (Sen 1968). These slopes were converted into an indicator score of 100 because none of the slopes were significantly different from 0.
|Subwatershed||Indicator Value||Data Source||Slope of Trend||Statistical Significance||Confidence Interval for Slope|
|East Branch North Fork Feather||100||Breeding Bird Survey||Increasing – 2.51||Not significant (p = 0.064)||-0.045 – 5.01|
|Lower Bear||100||Christmas Bird Count||Increasing – 0.94||Not significant (p = 0.14)||-0.71 – 2.93|
|Lower Feather||100||Breeding Bird Survey||Increasing – 0.12||Not significant (p = 0.85)||-1.69 – 1.45|
|Lower Yuba||100||Christmas Bird Count||Increasing – 0.49||Not significant (p =0.60)||-4.00 – 4.46|
|Middle Fork Feather||100||Breeding Bird Survey||Increasing – 0.80||Not significant (p = 0.47)||-1.68 – 3.07|
|Middle Yuba||100||Breeding Bird Survey||Decreasing – -0.75||Not significant (p = 0.58)||-2.48 – 1.16|
|North Fork Feather||N/A||N/A||N/A||N/A||N/A|
|North Yuba||100||Breeding Bird Survey||Decreasing – -0.28||Not significant (p = 0.52)||-2.09 – 1.86|
|South Yuba||100||Breeding Bird Survey||Increasing – 0.95||Not significant (p = 0.64)||-1.99 – 2.95|
|Upper Bear||100||Breeding Bird Survey||Increasing – 0.13||Not significant (p = 0.87)||-3.07 – 3.40|
Bohning-Gaese, K., and H. Bauer. 1996. Changes in species abundance, distribution, and diversity in a Central European bird community. Conservation Biology 10:175-187.
Buckland, S. T., A. E. Magurran, R. E. Green, and R. M. Fewster. 2005. Monitoring change in biodiversity through composite indices. Philosophical Transactions of the Royal Society B: Biological Sciences 360:243-254.
Colwell, R. K., and J. A. Coddington. 1994. Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions: Biological Sciences 345:101-118.
Kéry, M. & Royle, J.A., 2008. Hierarchical Bayes estimation of species richness and occupancy in spatially replicated surveys. Journal of Applied Ecology, 45(2), 589-598.
Link, W. A., and J. R. Sauer. 1999. Controlling for varying effort in count surveys: an analysis of Christmas Bird Count data. Journal of Agricultural, Biological, and Environmental Statistics 4:116-125.
Magurran, A.E., 2004. Measuring Biological Diversity, Malden, MA: Blackwell Publishing.
National Audubon Society. 2010. Christmas Bird Count. Retrieved April 5, 2010, from http://www.audubon.org/Bird/cbc/.
O'Connell, T.J., Jackson, L.E. & Brooks, R.P., 2000. Bird guilds as indicators of ecological condition in the Central Appalachians. Ecological Applications, 10(6), 1706-1721.
Oksanen, J., R. Kindt, P. Legendre, B. O'Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens, and H. Wagner. 2009. vegan: Community Ecology Package. Retrieved from http://CRAN.R-project.org/package=vegan.
Palmer, M. W. 1991. Estimating species richness: the second-order jackknife reconsidered. Ecology 72:1512-1513.
Pardieck, K. L., and J. R. Sauer. 2007. The 1999-2003 summary of the North American Breeding Bird Survey. Bird Populations 8:28-45.
Parody, J. M., F. J. Cuthbert, and E. H. Decker. 2001. The effect of 50 years of landscape change on species richness and community composition. Global Ecology and Biogeography 10:305-313.
R Development Core Team. 2009. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org.
Sauer, J. R., J. E. Hines, and J. Fallon. 2008. The North American Breeding Bird Survey, Results and Analysis 1966 - 2007. Version 5.15.2008. USGS Patuxent Wildlife Research Center, Laurel, MD. Retrieved April 5, 2010, from http://www.mbr-pwrc.usgs.gov/bbs/bbs.html.
Sen, P. K. 1968. Estimates of the regression coefficient based on Kendall's Tau. Journal of the American Statistical Association 63:1379-1389.
Weber, D., U. Hintermann, and A. Zangger. 2004. Scale and trends in species richness: considerations for monitoring biological diversity for political purposes. Global Ecology & Biogeography 13:97-104.