for predicting species ranges: a metamodelling framework

Aim Current interest in forecasting changes to species ranges has resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods.

[1]  Wilfried Thuiller,et al.  Anticipating the spatio-temporal response of plant diversity and vegetation structure to climate and land use change in a protected area. , 2014, Ecography.

[2]  Damaris Zurell,et al.  Does probability of occurrence relate to population dynamics? , 2014, Ecography.

[3]  Wilfried Thuiller,et al.  The European functional tree of bird life in the face of global change , 2014, Nature Communications.

[4]  C. Eckert,et al.  Are Species’ Range Limits Simply Niche Limits Writ Large? A Review of Transplant Experiments beyond the Range , 2013, The American Naturalist.

[5]  J. Engler,et al.  Species distribution models contribute to determine the effect of climate and interspecific interactions in moving hybrid zones , 2013, Journal of evolutionary biology.

[6]  Brendan A. Wintle,et al.  Predicting species distributions for conservation decisions , 2013, Ecology letters.

[7]  S. Finnegan,et al.  Climate Change and the Past, Present, and Future of Biotic Interactions , 2013, Science.

[8]  Prue F. E. Addison,et al.  Practical solutions for making models indispensable in conservation decision‐making , 2013 .

[9]  Wilfried Thuiller,et al.  A road map for integrating eco-evolutionary processes into biodiversity models. , 2013, Ecology letters.

[10]  J. Tobias,et al.  Species interactions constrain geographic range expansion over evolutionary time. , 2013, Ecology letters.

[11]  S. Higgins,et al.  Connecting dynamic vegetation models to data – an inverse perspective , 2012 .

[12]  Boris Schröder,et al.  How to understand species’ niches and range dynamics: a demographic research agenda for biogeography , 2012 .

[13]  Wilfried Thuiller,et al.  Invasive species distribution models – how violating the equilibrium assumption can create new insights , 2012 .

[14]  Glenn Marion,et al.  Accounting for uncertainty in colonisation times: a novel approach to modelling the spatio‐temporal dynamics of alien invasions using distribution data , 2012 .

[15]  Wilfried Thuiller,et al.  Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty. , 2012, Ecology letters.

[16]  F. Schurr,et al.  Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics , 2012 .

[17]  N Thompson Hobbs,et al.  Introducing data-model assimilation to students of ecology. , 2011, Ecological applications : a publication of the Ecological Society of America.

[18]  J. Kattge,et al.  Improving assessment and modelling of climate change impacts on global terrestrial biodiversity. , 2011, Trends in ecology & evolution.

[19]  Craig R Allen,et al.  Multimodel inference and adaptive management. , 2011, Journal of environmental management.

[20]  Eve McDonald-Madden,et al.  Uncertainty and adaptive management for biodiversity conservation , 2011 .

[21]  G. Mace,et al.  Beyond Predictions: Biodiversity Conservation in a Changing Climate , 2011, Science.

[22]  Wilfried Thuiller,et al.  Effects of competition on tree radial‐growth vary in importance but not in intensity along climatic gradients , 2011 .

[23]  Wilfried Thuiller,et al.  Predicting potential distributions of invasive species: where to go from here? , 2010 .

[24]  L. Stadler,et al.  Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant , 2010 .

[25]  R. Holt.  Bringing the Hutchinsonian niche into the 21st century: Ecological and evolutionary perspectives , 2009, Proceedings of the National Academy of Sciences.

[26]  W. Thuiller,et al.  Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. , 2009, Ecology.

[27]  Catherine A Calder,et al.  Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. , 2009, Ecological applications : a publication of the Ecological Society of America.

[28]  Richard J. Hobbs,et al.  Frontiers inEcology and the Environment Management of novel ecosystems : are novel approaches required ? , 2008 .

[29]  C. Dormann.  Promising the future? Global change projections of species distributions , 2007 .

[30]  M. Austin.  Species distribution models and ecological theory: A critical assessment and some possible new approaches , 2007 .

[31]  Mark New,et al.  Ensemble forecasting of species distributions. , 2007, Trends in ecology & evolution.

[32]  William A Link,et al.  Model weights and the foundations of multimodel inference. , 2006, Ecology.

[33]  M. Araújo,et al.  Five (or so) challenges for species distribution modelling , 2006 .

[34]  James S. Clark,et al.  A future for models and data in environmental science. , 2006, Trends in ecology & evolution.

[35]  Shanshan Wu,et al.  Building statistical models to analyze species distributions. , 2006, Ecological applications : a publication of the Ecological Society of America.

[36]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[37]  Ron Smith,et al.  Bayesian calibration of process-based forest models: bridging the gap between models and data. , 2005, Tree physiology.

[38]  Brandon T Bestelmeyer,et al.  Cross-scale interactions, nonlinearities, and forecasting catastrophic events. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[39]  S. Ferrari,et al.  Beta Regression for Modelling Rates and Proportions , 2004 .

[40]  Michael A. McCarthy,et al.  A Monte Carlo experiment for species mapping problems , 2002 .

[41]  Isabelle Chuine,et al.  Phenology is a major determinant of tree species range , 2001 .

[42]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[43]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

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

[45]  O. Loucks,et al.  From Balance of Nature to Hierarchical Patch Dynamics: A Paradigm Shift in Ecology , 1995, The Quarterly Review of Biology.

[46]  S. Levin.  The problem of pattern and scale in ecology , 1992 .

[47]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[48]  W. Reese,et al.  Atlas of United States Trees. Volume 1. Conifers and Important Hardwoods , 1972 .