A future for models and data in environmental science.

[1]  J. Diamond,et al.  Ecology and Evolution of Communities , 1976, Nature.

[2]  Hal Caswell,et al.  Population Growth Rates and Age Versus Stage-Distribution Models for Teasel (Dipsacus Sylvestris Huds.) , 1977 .

[3]  R. Paine.  Food webs : linkage, interaction strength and community infrastructure , 1980 .

[4]  J. Connell.  Diversity and the coevolution of competitors, or the ghost of competition past , 1980 .

[5]  Jonathan Roughgarden,et al.  Competition and Theory in Community Ecology , 1983, The American Naturalist.

[6]  Donald R. Strong,et al.  Natural Variability and the Manifold Mechanisms of Ecological Communities , 1983, The American Naturalist.

[7]  Joel E. Cohen,et al.  Community food webs have scale-invariant structure , 1984, Nature.

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

[9]  H. Shugart.  A Theory of Forest Dynamics , 1984 .

[10]  W. Parton,et al.  Analysis of factors controlling soil organic matter levels in Great Plains grasslands , 1987 .

[11]  H. Caswell.  THEORY AND MODELS IN ECOLOGY: A DIFFERENT PERSPECTIVE , 1988 .

[12]  R. Paine.  Road Maps of Interactions or Grist for Theoretical Development , 1988 .

[13]  John Skilling,et al.  Maximum Entropy and Bayesian Methods , 1989 .

[14]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[15]  D. Lindley.  The 1988 Wald Memorial Lectures: The Present Position in Bayesian Statistics , 1990 .

[16]  Charles M. Newman,et al.  Community Food Webs , 1990 .

[17]  Craig A. Stow,et al.  Fitting Predator-Prey Models to Time Series with Observation Errors , 1994 .

[18]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[19]  Adrian E. Raftery,et al.  Inference from a Deterministic Population Dynamics Model for Bowhead Whales , 1995 .

[20]  Philip M. Dixon,et al.  Introduction: Ecological Applications of Bayesian Inference , 1996 .

[21]  Peter Kareiva,et al.  Predicting the outcome of competition using experimental data : Maximum likelihood and bayesian approaches , 1996 .

[22]  S. Pacala,et al.  Forest models defined by field measurements : Estimation, error analysis and dynamics , 1996 .

[23]  L. Mark Berliner,et al.  Hierarchical Bayesian Time Series Models , 1996 .

[24]  D. Schluter,et al.  MICROCOSM EXPERIMENTS HAVE LIMITED RELEVANCE FOR COMMUNITY AND ECOSYSTEM ECOLOGY , 1996 .

[25]  S. R. Carpenter,et al.  Microcosm experiments have limited relevance for community and ecosystem ecology : Microcosms , 1996 .

[26]  S. Levin.  Ecosystems and the Biosphere as Complex Adaptive Systems , 1998, Ecosystems.

[27]  Alan E. Gelfand,et al.  Model choice: A minimum posterior predictive loss approach , 1998, AISTATS.

[28]  Stephen R. Carpenter,et al.  MICROCOSM EXPERIMENTS HAVE LIMITED RELEVANCE FOR COMMUNITY AND ECOSYSTEM ECOLOGY: REPLY , 1999 .

[29]  Ray W. Drenner,et al.  MICROCOSM EXPERIMENTS HAVE LIMITED RELEVANCE FOR COMMUNITY AND ECOSYSTEM ECOLOGY: COMMENT , 1999 .

[30]  D. Ludwig.  Is it meaningful to estimate a probability of extinction , 1999 .

[31]  Timothy H. Keitt,et al.  Landscape connectivity: A conservation application of graph theory , 2000 .

[32]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[33]  S. Carpenter,et al.  Ecological forecasts: an emerging imperative. , 2001, Science.

[34]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[35]  J. Carey,et al.  Free journals for developing countries , 2002 .

[36]  William F. Morris,et al.  Quantitative conservation biology , 2002 .

[37]  Alan Hastings,et al.  FITTING POPULATION MODELS INCORPORATING PROCESS NOISE AND OBSERVATION ERROR , 2002 .

[38]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[39]  W. Link,et al.  HIERARCHICAL MODELING OF POPULATION STABILITY AND SPECIES GROUP ATTRIBUTES FROM SURVEY DATA , 2002 .

[40]  Michael C. Dietze,et al.  COEXISTENCE: HOW TO IDENTIFY TROPHIC TRADE-OFFS , 2003 .

[41]  Christopher K. Wikle,et al.  Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes , 2003 .

[42]  Stephen R. Carpenter,et al.  UNCERTAINTY AND THE MANAGEMENT OF MULTISTATE ECOSYSTEMS: AN APPARENTLY RATIONAL ROUTE TO COLLAPSE , 2003 .

[43]  John Sabo,et al.  Morris, W. F., and D. F. Doak. 2003. Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis. Sinauer Associates, Sunderland, Massachusetts, USA , 2003 .

[44]  D. Doak,et al.  Book Review: Quantitative Conservation biology: Theory and Practice of Population Viability analysis , 2004, Landscape Ecology.

[45]  Robert L. Wolpert,et al.  RECONSTRUCTING PLANT ROOT AREA AND WATER UPTAKE PROFILES , 2004 .

[46]  James S. Clark,et al.  Why environmental scientists are becoming Bayesians , 2004 .

[47]  R. Lande,et al.  Demographic models of the northern spotted owl (Strix occidentalis caurina) , 1988, Oecologia.

[48]  Andrew Gonzalez,et al.  Are natural microcosms useful model systems for ecology? , 2004, Trends in ecology & evolution.

[49]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[50]  James S. Clark,et al.  FECUNDITY OF TREES AND THE COLONIZATION–COMPETITION HYPOTHESIS , 2004 .

[51]  Jay M. Ver Hoef,et al.  A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, Alaska , 2003, Environmental and Ecological Statistics.

[52]  Toshinori Okuyama,et al.  COMBINING GENETIC AND ECOLOGICAL DATA TO ESTIMATE SEA TURTLE ORIGINS , 2005 .

[53]  Renate Hagedorn,et al.  Representing model uncertainty in weather and climate prediction , 2005 .

[54]  A. Gelfand,et al.  Explaining Species Distribution Patterns through Hierarchical Modeling , 2006 .

[55]  S. Carpenter,et al.  Ecology for transformation. , 2006, Trends in ecology & evolution.

[56]  William H Schlesinger,et al.  Global change ecology. , 2006, Trends in Ecology & Evolution.

[57]  Kamesh Munagala,et al.  Model-Driven Dynamic Control of Embedded Wireless Sensor Networks , 2006, International Conference on Computational Science.