update: More uncertainty with BIOMOD

ISSN 1948-6596 news and update The pattern-based analysis provided an op- portunity to test a variety of dispersal and refugia scenarios that have been proposed for the Pacific Northwest, because it has three categorical areas: coastal non-glaciated, southern interior non- glaciated, and northern interior glaciated. Species with the highest dispersal capacity had the largest ranges and were more likely to have dispersed to the northern interior glaciated (recently unglaci- ated) zone. The study also found that the north- ern interior zone had been colonized by species from both the coast and from further south in the interior. Less dispersive capable species showed more restricted range, including six endemic spe- cies from Idaho, which had not moved to the north. This last finding led the author to conclude that plant characteristics are likely an important component in the effort to determine what spe- cies may be able to successfully shift range across a fragmented landscape under future climate change. The author points to the importance of including phylogeographies in future work, but has done a remarkable job of identifying vulner- abilities of plant species to climate change using more traditional biogeographic techniques. Gavin, D.G. (2009) The coastal-disjunct mesic flora in the inland Pacific Northwest of USA and Canada: refugia, dispersal and disequilibrium. Diversity and Distributions, 15, 972-982. James H. Thorne Information Center for the Environment, Uni- versity of California at Davis, USA e-mail: jhthorne@ucdavis.edu http://ice.ucdavis.edu/people/jhthorne Edited by Lee Hannah update More uncertainty with BIOMOD Species distribution modeling (SDM) has grown in importance over the last decade to become a powerful tool in conservation planning, global change forecasting, ecological hypothesis testing, and characterization of niche properties in phy- logenetic analyses. Many scientists have contrib- uted to the conceptual, statistical and technical development of this field. While I believe that further development has asymptoted in many do- mains of SDM research, it is clear that BIOMOD is a significant contribution. A decade ago, we faced numerous uncer- tainties and limitations in building SDMs. Few sta- tistical techniques were available and no com- parative studies existed. Generalized Linear Mod- els were a standard method, and key issues in- cluded how best to fit response shapes, how to evaluate competing models, and what statistical methods to use to get “the best model” of a tar- get species. Climate change projections were usu- ally established by simply adding 2-4°C to annual mean temperature maps, and “the best model” was then projected into the future. BIOMOD, in its first version of 2003, was a huge step forward. It included four different statistical methods to model hundreds of species automatically. Further, it used a simple method to identify the model that best fit the general trend among the resulting models. At the same time modeling and forecasting of a range of scenarios, including the assessment of projection uncertainty, became an important aspect of research on climate change impacts. This has dramatically increased the demand for model building, model averaging, ensemble fore- casting, and analysis of complex output. We are no longer interested in identifying “the best model”, but rather the mean and variation of models – currently and when projected to the fu- ture. Ensemble forecasting is so complex that most of us will only include a fraction of the possi- ble uncertainty sources when modeling potential climate change effects upon species distribution patterns. Over the last 6 years BIOMOD has been developed, improved, and extended. It now offers © 2009 the authors; journal compilation © 2009 The International Biogeography Society — frontiers of biogeography 1.2, 2009

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