• Understand and disclose the limitations. Understanding the limitations of
the data is as important as any other aspect of the modeling process and
is critical to the credibility of the project.
• Provide and use metadata. Reveal “where you got the data”; it is critical
to understanding the limitations of the data.
• Determine data access. Determine who gets access to the data and in what
form.
References
Akcakaya, H.R. and J.L. Atwood. 1997. A habitat-based metapopulation model of
the California gnatcatcher. Conservation Biology 11:422–434.
Arnold, C., Jr., D.L. Civco, M.P. Prisloe, Jr., J.D. Hurd, and J.W. Stocker. 2000. Remote
sensing-enhanced outreach education as a decision support system for local
land-use officials. Photogrammetric Engineering and Remote Sensing 66:1251–
1260.
Barber, M.C., editor. 1994. Environmental Monitoring and Assessment Program
Indicator Development Strategy. EPA/620/R-94/022. U.S. Environmental Protection Agency, Office of Research and Development, Environmental Research
Laboratory, Athens, Georgia, USA.
Beard, K., H.N. Hengartner, and D.K. Skelly. 1999. Effectiveness of predicting
breeding bird distributions using probabilistic models. Conservation Biology
13:1108–1116.
Beck, M.B. 1987. Water quality modeling: A review of the analysis of uncertainty.
Water Resources Research 23:1393–1442.
Bedford, T. 1998. Sensitivity indices for (tree-) dependent variables. Pages 17–20 in
K. Chan, S. Tarantola, and F. Campolongo, editors. Proceedings of the Second
International Symposium on Sensitivity Analysis of Model Output (SAMO98).
EUR report 17758 EN. European Commission, Luxembourg.
Brooks, R.P. 1997. Improving habitat suitability index models. Wildlife Society
Bulletin 25:163–167.
Cairns, J. and E.P. Smith. 1993. The statistical validity of biomonitoring data. Pages
49–68 in S.L. Loeb and A. Spacie, editors. Biological Monitoring of Aquatic
Systems. Lewis Publishers, Boca Raton, Florida, USA.
Cochran, W.G. 1977. Sampling Techniques. John Wiley & Sons, New York, New York,
USA.
Conquest, L.L., S.C. Ralph, and R.J. Naiman. 1993. Implementation of large-scale
monitoring efforts: Sampling design and data analysis issues. Pages 69–90 in S.L.
Loeb and A. Spacie, editors. Biological Monitoring of Aquatic Systems. Lewis
Publishers, Boca Raton, Florida, USA.
Chernick, M.R. 1983. Influence functions, outlier detection, and data editing. Pages
167–176 in T. Wright, editor. Statistical Methods and the Improvement of Data
Quality. Academic Press, London, UK.
Church, M.R., K.W. Thornton, P.W. Shaffer, D.L. Stevens, B.P. Rochelle, G.R.
Holdren, M.G. Johnson, J.J. Lee, R.S. Turner, D.L. Cassell, D.A. Lammers,
W.G. Campbell, C.I. Liff, C.C. Brandt, L.H. Liegel, G.D. Bishop, D.C. Mortenson,
S.M. Pierson, and D.D. Schmoyer. 1989. Direct/Delayed Response Project:
Future Effects of Long-Term Sulfur Deposition on Surface Water Chemistry in
10. Effective Ecological Modeling: Data Issues
205
the data is as important as any other aspect of the modeling process and
is critical to the credibility of the project.
• Provide and use metadata. Reveal “where you got the data”; it is critical
to understanding the limitations of the data.
• Determine data access. Determine who gets access to the data and in what
form.
References
Akcakaya, H.R. and J.L. Atwood. 1997. A habitat-based metapopulation model of
the California gnatcatcher. Conservation Biology 11:422–434.
Arnold, C., Jr., D.L. Civco, M.P. Prisloe, Jr., J.D. Hurd, and J.W. Stocker. 2000. Remote
sensing-enhanced outreach education as a decision support system for local
land-use officials. Photogrammetric Engineering and Remote Sensing 66:1251–
1260.
Barber, M.C., editor. 1994. Environmental Monitoring and Assessment Program
Indicator Development Strategy. EPA/620/R-94/022. U.S. Environmental Protection Agency, Office of Research and Development, Environmental Research
Laboratory, Athens, Georgia, USA.
Beard, K., H.N. Hengartner, and D.K. Skelly. 1999. Effectiveness of predicting
breeding bird distributions using probabilistic models. Conservation Biology
13:1108–1116.
Beck, M.B. 1987. Water quality modeling: A review of the analysis of uncertainty.
Water Resources Research 23:1393–1442.
Bedford, T. 1998. Sensitivity indices for (tree-) dependent variables. Pages 17–20 in
K. Chan, S. Tarantola, and F. Campolongo, editors. Proceedings of the Second
International Symposium on Sensitivity Analysis of Model Output (SAMO98).
EUR report 17758 EN. European Commission, Luxembourg.
Brooks, R.P. 1997. Improving habitat suitability index models. Wildlife Society
Bulletin 25:163–167.
Cairns, J. and E.P. Smith. 1993. The statistical validity of biomonitoring data. Pages
49–68 in S.L. Loeb and A. Spacie, editors. Biological Monitoring of Aquatic
Systems. Lewis Publishers, Boca Raton, Florida, USA.
Cochran, W.G. 1977. Sampling Techniques. John Wiley & Sons, New York, New York,
USA.
Conquest, L.L., S.C. Ralph, and R.J. Naiman. 1993. Implementation of large-scale
monitoring efforts: Sampling design and data analysis issues. Pages 69–90 in S.L.
Loeb and A. Spacie, editors. Biological Monitoring of Aquatic Systems. Lewis
Publishers, Boca Raton, Florida, USA.
Chernick, M.R. 1983. Influence functions, outlier detection, and data editing. Pages
167–176 in T. Wright, editor. Statistical Methods and the Improvement of Data
Quality. Academic Press, London, UK.
Church, M.R., K.W. Thornton, P.W. Shaffer, D.L. Stevens, B.P. Rochelle, G.R.
Holdren, M.G. Johnson, J.J. Lee, R.S. Turner, D.L. Cassell, D.A. Lammers,
W.G. Campbell, C.I. Liff, C.C. Brandt, L.H. Liegel, G.D. Bishop, D.C. Mortenson,
S.M. Pierson, and D.D. Schmoyer. 1989. Direct/Delayed Response Project:
Future Effects of Long-Term Sulfur Deposition on Surface Water Chemistry in
10. Effective Ecological Modeling: Data Issues
205