Regional Trend Analysis Form

1966 - 2015 Analysis
See the BBS Summary and Analysis Website for citation, version number, and cautions regarding use.

This part of the Home Page allows you to select a species, a region, and a starting and ending year, and conduct an analysis of population change for that species, region, and period. We identify a few limitations of trend data in our credibility measures discussion. Also note the disclaimer associated with all results.

Analysis methods

Results are based on a hierarchical model for population change, as described in Link and Sauer 2002^a. We use a hierarchical model to produce annual indices of abundance for a region, then estimate trend as the ratio of the annual indices for the first and last year of the interval of interest. The Markov chain Monte-Carlo method used to fit the model is an interative fitting procedure, which produces a series of replicates from which the estimates and their credible intervals can be derived. This summary program uses these replicates, summarized at the level of stratum within states or Provinces, aggregates them into regional estimates for the selected region, and calculates a trend as a ratio of annual indices corresponding to the first and last years of the selected interval.

Output from the program includes:

Trend estimates for the selected interval;
95% and 90% Credible Intervals for the trend estimate;
Long-term estimates of trends with credible intervals for the selected species and region; and
Annual indices and credible intervals from the first year of the survey to the current year of analysis for the selected species and region.

User Notes:

Occasionally, debugging messages will be printed as we evaluate the functioning of the program. These can be ignored.
If a species is not observed in a region, the program will provide headers and missing value ("NaN") indicators instead of estimates.
This analysis is quite new, and we welcome comments regarding the analysis and results.

Please select the species:

Please select the region of interest:

first year: last year: analysis method:

Data Liability Disclaimer

Although these data have been processed successfully on a computer system at the United States Geological Survey (USGS), no warranty expressed or implied is made regarding the accuracy or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data. It is strongly recommended that these data are directly acquired from a USGS server, and not indirectly through other sources which may have changed the data in some way. It is also strongly recommended that careful attention be paid to the contents of the metadata file associated with these data. The USGS shall not be held liable for improper or incorrect use of the data described and/or contained herein.

So, these data are provided "as is" and without any express or implied warranties, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose. Also, use of trade names or commercial products in this home page is solely for the purpose of providing specific information, and does not imply recommendation or endorsement by the U.S. Government.

Regional Credibility Measures

Although the BBS provides a huge amount of information about regional population change for many species, there are a variety of possible problems with estimates of population change from BBS data. Small sample sizes, low relative abundances on survey routes, imprecise trends, and missing data all can compromise BBS results. Often, users do not take these problems into account when viewing BBS results, and use the results inappropriately.

To provide some guidance to interpretation of BBS data, we have implemented a series of checks for some attributes that we view as cause for caution in interpretation of BBS results. We categorize BBS data in 3 credibility categories:

This category reflects data with an important deficiency. In particular:

1. The regional abundance is less than 0.1 birds/route (very low abundance),
2. The sample is based on less than 5 routes for the long term, or is based on less than 3 routes for either subinterval (very small samples), or
3. The results are so imprecise that a 5%/year change would not be detected over the long-term (very imprecise).

This category reflects data with a deficiency. In particular:

1. The regional abundance is less than 1.0 birds/route (low abundance),
2. The sample is based on less than 14 routes for the long term (small sample size)
3. The results are so imprecise that a 3%/year change would not be detected over the long-term (quite imprecise), or
4. The sub-interval trends are significantly different from each other (P less than 0.05, based on a z-test). This suggests inconsistency in trend over time).

This category reflects data with at least 14 samples in the long term, of moderate precision, and of moderate abundance on routes.

Notes:

1. Even data falling in the category may not provide valid results. There are many factors that can influence the validity and use of the information, and any analysis of BBS data should carefully consider the possible problems with the data.
2. We are occasionally asked to identify which deficiency is causing the flag. However, the point of the codes is to provide a quick and simple set of cautions to users, and we are resisting the notion of setting up a complicated series of codes. To determine why the code exists, look at the results. All of these deficiencies (abundances, precisions, etc) will be evident from the results we present.

^aTo download a PDF reader, visit http://get.adobe.com/reader.