Most ecological data have a spatial component, e.g., a location where they were collected. In the past, analysis methods for ecological data tended to ignore spatial variability and spatial autocorrelation (similarity of samples taken close together in space). However, as new statistical methods have been developed, we can now take advantage of the ecological information contained in patterns of spatial variability and autocorrelation. One area of research interest in our lab is adapting spatial statistical methods for use with ecological data.
For example, geostatistical methods are generally based on the straight-line (Euclidean) distance between two locations. In environments like estuaries, however, the distance between two points as the crow flies can be much different than the distance between those points as the crab swims. We developed a modified approach to calculating variograms and kriging that is based on a lowest-cost path distance (e.g., the shortest distance entirely through the water) and implemented within a GIS (ArcMap). For more details and to download code for this technique, please see Tom Miller’s website at the Chesapeake Biological Laboratory.
Merlin, Y., Hernández, A., Calderón, M., Jensen, O.P., Zaragoza, R., and L. Zambrano. (in press). Urban expansion into a protected natural area in Mexico City: alternative management scenarios. Journal of Environmental Planning and Management. PDF
Diebel, M.W., J.T. Maxted, O.P. Jensen, and M.J. Vander Zanden. 2010. A spatial autocorrelative model for targeting stream restoration to benefit sensitive non-game fishes. Canadian Journal of Fisheries and Aquatic Sciences 67:165-176. PDF
Jensen, O.P., B.J. Benson, J.J. Magnuson, V.M. Card, M.N. Futter, P.A. Soranno, K.M. Stewart. 2007. Spatial analysis of ice phenology trends across the Laurentian Great Lakes region during a recent warming period. Limnology & Oceanography 52:2013-2026. PDF
Olden, J.D., O.P. Jensen, and M.J. Vander Zanden. 2006. Implications of long-term dynamics of fish and zooplankton communities for among-lake comparisons. Canadian Journal of Fisheries and Aquatic Sciences 63: 1812-1821. PDF
Jensen, O.P., M.C. Christman, and T.J. Miller. 2006. Landscape-based geostatistics: A case study of the distribution of blue crab in Chesapeake Bay. Environmetrics 17: 605-621. PDF
Jensen, O.P. and T.J. Miller. 2005. Geostatistical analysis of blue crab (Callinectes sapidus) abundance and winter distribution patterns in Chesapeake Bay. Transactions of the American Fisheries Society 134: 1582-1598. PDF
Jensen, O.P., R. Seppelt, T. J. Miller, and L. J. Bauer. 2005. Winter distribution of blue crab (Callinectes sapidus) in Chesapeake Bay: Application and cross-validation of a two-stage generalized additive model (GAM). Marine Ecology Progress Series 299:239-255. PDF
Kendall, M.S., O.P. Jensen, M.E. Monaco, D. Field, C. Alexander, G. McFall, and R. Bohne. 2005. Benthic mapping on the Georgia bight: sonar, video transects, and an innovative approach to accuracy assessment. Journal of Coastal Research 21:1154-1165. PDF
Magnuson, J. J., B. J. Benson, O. P. Jensen, T. B. Clark, V. Card, M. N. Futter, P. A. Soranno, K. M. Stewart. 2005. Persistence of coherence of ice-off dates for inland lakes across the Laurentian Great Lakes region. Verh. Internat. Verein. Limnol. 29:521-527. PDF