Contrasting approaches to statistical regression in ecology and economics

Summary 1 Conservation and natural resource management challenges are as much social problems as biological ones. In recognition of this fact, ecologists and economists work increasingly closely together. We discuss one barrier to effective integration of the two disciplines: put simply, many ecologists and economists approach statistical regression differently. 2 Regression techniques provide the most commonly used approach for empirical analyses of land management decisions. Researchers from each discipline attribute differing importance to a range of possibly conflicting design criteria when formulating regression analyses. 3 Ecologists commonly attribute greater importance to spatial autocorrelation and parsimony than do economists when designing regressions. Economists often attribute greater importance than ecologists to concerns about endogeneity and conformance with a priori theoretical expectations. 4 Synthesis and applications. The differing importance attributed to different design characteristics may reflect a process of cultural drift within each discipline. Greater interdisciplinary collaboration can counteract this process by stimulating the flow of ideas and techniques across disciplinary boundaries.

[1]  Jai Ranganathan,et al.  When Agendas Collide: Human Welfare and Biological Conservation , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[2]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[3]  B. Kendall,et al.  An introduction to biodiversity concepts for environmental economists , 2004 .

[4]  Raymond B. Palmquist Property Value Models , 2005 .

[5]  J. Roughgarden,et al.  An invitation to ecological economics. , 2001, Trends in ecology & evolution.

[6]  James B. Grace,et al.  Structural Equation Modeling and Natural Systems , 2006 .

[7]  J. Keeley,et al.  Fire severity and ecosytem responses following crown fires in California shrublands. , 2008, Ecological applications : a publication of the Ecological Society of America.

[8]  P. Legendre Spatial Autocorrelation: Trouble or New Paradigm? , 1993 .

[9]  S. Hurlbert Pseudoreplication and the Design of Ecological Field Experiments , 1984 .

[10]  S. Ghosh,et al.  Spatio‐Temporal Modeling of Agricultural Yield Data with an Application to Pricing Crop Insurance Contracts , 2008, American journal of agricultural economics.

[11]  Jennifer M. Moslemi,et al.  Quantitative threat analysis for management of an imperiled species: Chinook salmon (Oncorhynchus tshawytscha). , 2007, Ecological applications : a publication of the Ecological Society of America.

[12]  David F. Hendry,et al.  Predictive failure and econometric modelling in macroeconomics: the transactions demand for money , 2005 .

[13]  Jeffrey M. Wooldridge,et al.  Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data , 2003 .

[14]  David Tilman,et al.  A World of Wounds: Ecologists and the Human Dilemma , 1998 .

[15]  R. Hilborn,et al.  The Ecological Detective: Confronting Models with Data , 1997 .

[16]  Robert P Freckleton,et al.  Why do we still use stepwise modelling in ecology and behaviour? , 2006, The Journal of animal ecology.

[17]  K. Gaston,et al.  Protected areas and regional avian species richness in South Africa , 2006, Biology Letters.

[18]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[19]  M. Machina Choice under Uncertainty: Problems Solved and Unsolved , 1987 .

[20]  C. Starmer Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk , 2000 .

[21]  N. Bockstael,et al.  EVIDENCE OF THE EFFECTS OF WATER QUALITY ON RESIDENTIAL LAND PRICES , 2000 .

[22]  N. Hanley,et al.  Economic determinants of biodiversity change over a 400-year period in the Scottish uplands , 2008 .