Landscape Ecol (2015) 30:1879–1892 DOI 10.1007/s10980-015-0217-1 RESEARCH ARTICLE Comparison of a species distribution model and a process model from a hierarchical perspective to quantify effects of projected climate change on tree species Jeffrey E. Schneiderman . Hong S. He . Frank R. Thompson III . William D. Dijak . Jacob S. Fraser Received: 29 July 2014 / Accepted: 12 May 2015 / Published online: 29 May 2015 Ó Springer Science+Business Media Dordrecht 2015 Abstract Methods We compared projections of a species Context Tree species distribution and abundance are distribution model, Climate Change Tree Atlas, and affected by forces operating across a hierarchy of a process model, LINKAGES 2.2. We conducted a ecological scales. Process and species distribution correlation analysis between the models at regional, models have been developed emphasizing forces at ecological subsection, and species level hierarchical different scales. Understanding model agreement scales. across hierarchical scales provides perspective on Results Both models had significant positive correla- prediction uncertainty and ultimately enables policy tion (q = 0.53, P \ 0.001) on the regional scale. The makers and managers to make better decisions. majority of the ecological subsections had greater model Objective Our objective was to test the hypothesis correlation than on the regional level when all climate that agreement between process and species distribu- scenarios were pooled. Correlation was poorest for the tion models varies by hierarchical level. Due to the analysis of individual species. Models had the greatest top-down approach of species distribution models and correlation at the regional scalefortheGFDL-A1fi the bottom-up approach of process models, the most scenario (the scenario with the most climate change). agreement will occur at the mid-level of the hierar- Species near their range edge generally had stronger chical analysis, the ecological subsection level, cap- correlation (loblolly pine, northern red oak, black oak). turing the effects of soil variables. Conclusion Our general hypothesis was partly ac- cepted. This suggests that uncertainties are relatively low when interpreting model results at subsection level. Electronic supplementary material The online version of this article (doi:10.1007/s10980-015-0217-1) contains supple- mentary material, which is available to authorized users. Keywords Climate change Á LINKAGES 2.2 Á Climate Change Tree Atlas Á Hierarchical Á Process J. E. Schneiderman (&) Á H. S. He Á J. S. Fraser model Á Species distribution model Department of Forestry, University of Missouri, 203 Anheuser-Busch Natural Resources Building, Columbia, MO 65211, USA e-mail: [email protected] Introduction F. R. Thompson III Á W. D. Dijak Tree species distribution and abundance are affected U.S.D.A. Forest Service, Northern Research Station, University of Missouri, 202 Anheuser-Busch Natural by forces operating across a hierarchy of ecological Resources Building, Columbia, MO 65211, USA scales (Diez and Pulliam 2007). At the regional level, 123 1880 Landscape Ecol (2015) 30:1879–1892 climate variables such as temperature and precipita- predicted outcomes reflect statistical associations tion may have the greatest control. At the intermediate between the occurrence and abundance of species levels, such as the ecological subsection, landform and and predictor environmental variables that may influ- soil may show a dominant effect. At the species level, ence suitability of habitat (Iverson and McKenzie biotic interactions such as inter- and intra-species 2013). A species niche can be described by climate competition may affect the local variations of species tolerance levels or thresholds, expressed in terms of extinction and colonization. Hierarchy theory pro- climate variables (Gallego-Sala et al. 2010). These vides a perspective for organizing the complexity of variables are considered key attributes of a species ecological systems (O’Neill et al. 1989) from which habitat. The degree that climate affects these attributes different types of models are developed. Models used determines how the species will then react (Pearson to study climate change have been created utilizing and Dawson 2003). Species distribution models can fit hierarchy theory, emphasizing forces at different complicated geographic ranges (Iverson and McKen- scales. Process-based simulation models (hereafter zie 2013). Another benefit is they tend to require less process models) and species distribution, niche, or computer capability or processing time than process envelope models (hereafter species distribution mod- models, so they are capable of projecting greater els) are the most common approaches used to assess numbers of species response to climate change over climate change impacts on forests at large spatial very large areas (Brandt et al. 2014). scales (Morin and Thuiller 2009). Comparing process and species distribution models Process models simulate the behavior of a system in terms of their assumptions, approaches, and results based on interactions between physiological mechan- provides perspective on prediction uncertainty and isms and functional components and their interaction ultimately enables policy makers and managers to with the environment, generally represented as make better decisions (Beaumont et al. 2007; Marcot mathematical equations (Ma¨kela¨ et al. 2000; Lands- et al. 2012). When different approaches result in berg and Sands 2011). They use a bottom up approach similar predictions, more confidence is attained. When beginning with simulating site-scale (e.g. individual disagreement occurs between models, assumptions plots within ecological subsections) species and can be challenged and new directions for analysis environmental (soil and climate) interactions and studied (Iverson and McKenzie 2013). Research of expanding to regional scales that account for broader this type has shown that when process and species climatic patterns. Process models are able to take into distribution models are compared to observed data, account species response to environmental conditions there is overlap in assumptions, validation and repro- by utilizing biological processes calibrated by obser- ducibility challenges (Dormann et al. 2012). Research vations on individuals in natural environments (Morin has highlighted problems related to estimating species and Thuiller 2009). They are better equipped for distributions as well as uncertainty in making future predicting species responses to novel environment projections (Keenan et al. 2011; Cheaib et al. 2012), conditions than niche models by simulating mechan- and has found agreement in the ability to show large isms affecting species (Gustafson 2013).They may scale range contractions of some species (Cheaib et al. require parameter values that are difficult to obtain 2012). More studies are necessary to improve current (Landsberg and Gower 1997) as well as more com- and future models. Understanding model dynamics on putational power and time than species distribution multiple scales is one area that is beneficial. models, which could result in consideration of fewer We hypothesize that agreement between process species in assessments (Brandt et al. 2014). and species distribution models varies by hierarchical Species distribution models use a species’ observed level. Specifically, because of the top-down approach distribution or biological characteristics to predict of species distribution models and the bottom-up future distribution (Iverson and McKenzie 2013). approach of process models, the most agreement will They use a top-down approach beginning with using occur at the mid-level of the hierarchical analysis, the climatic variables at the regional scale and adding ecological subsection level, capturing the effects of local (ecological subsection) soil information to soil variables. Our objective was to use Climate improve prediction realism. This approach empha- Change Tree Atlas (Landscape Change Research sizes abiotic controls (climate and soil) so that the Group 2014), which utilizes the species distribution 123 Landscape Ecol (2015) 30:1879–1892 1881 model DISTRIB, and the process model LINKAGES Tree species analyzed 2.2 (Wullschleger et al. 2003)toassessclimate change impacts on tree species in Missouri and to We selected nine tree species for analysis: white oak compare results of these two approaches on a (Quercus alba), northern red oak (Quercus rubra), hierarchical level. We hereafter refer to Climate black oak (Quercus velutina), shortleaf pine (Pinus ChangeTreeAtlasasaspecies distribution model echinata), loblolly pine (Pinus taeda), eastern red- because of the DISTRIB portion. We chose LIN- cedar (Juniperus virginiana), sugar maple (Acer KAGES 2.2 and Climate Change Tree Atlas for our saccharum), American elm (Ulmus americana), and analysis because they have been widely used (Pastor flowering dogwood (Cornus florida). We selected and Post 1985;Heetal.1999, 2005; Iverson et al. these species because they met at least one of three 2008;Daleetal.2009; Matthews et al. 2011)to criteria: they were abundant; they had high economic predict effects of climate change. Specifically, we value; or they were at the northern or southern extent compared the change in biomass predicted by of their range (Fig. 2) and were poised to expand or LINKAGES and Importance Values predicted by reduce their range with climate change. Tree Atlas for current climate and future climate projections. We were interested