Modelled photosynthesis predicts woody plant richness at three geographic scales across the north-western United States
Article first published online: 24 JUL 2006
Global Ecology and Biogeography
Volume 15, Issue 5, pages 470–485, September 2006
How to Cite
Swenson, J. J. and Waring, R. H. (2006), Modelled photosynthesis predicts woody plant richness at three geographic scales across the north-western United States. Global Ecology and Biogeography, 15: 470–485. doi: 10.1111/j.1466-822X.2006.00242.x
- Issue published online: 24 JUL 2006
- Article first published online: 24 JUL 2006
- Species richness;
- woody plants;
- temperate forest;
- multiple scales;
Aim We analyse regional patterns of woody plant species richness collected from field data in relation to modelled gross photosynthesis, Pg, compare the performance of Pg in relation to other productivity surrogates, and examine the effect of increasing scale on the productivity–richness relationship.
Location The forested areas in the north-western states of Oregon, Washington, Idaho, and Montana, USA.
Methods Data on shrub and tree species richness were assembled from federal vegetation surveys and compared with modelled growing season gross photosynthesis, Pg (the sum of above- and below-ground production plus autotrophic respiration) and two measures of spatial heterogeneity. We analysed the productivity–richness relationship at different scales by changing the focus size through spatial aggregation of field plots using 100 and 1000 km2 windows covering the study area. Regression residuals were plotted spatially. Using the best available tree data set (Continuous Vegetation Survey: CVS), we compared different productivity indices, such as actual evapotranspiration and average temperature, in their ability to predict patterns of tree species richness.
Results The highest species richness (species/unit area) occurred at intermediate levels of productivity. After accounting for variable sampling intensity, the richness–productivity relationship improved as more field plots were aggregated. At coarser levels of aggregation, modelled productivity accounted for 57–71% of the variation in richness patterns for shrubs and trees (CVS data set). Measures of spatial heterogeneity accounted for more variation in richness patterns aggregated by 100 km2 windows than aggregation by 1000 km2 windows. Pg was a better predictor of tree richness in Oregon and Washington (CVS data set) than any surrogate productivity index.
Main conclusions Pg was observed to be a strong unimodal predictor of both tree (CVS) and shrub (FIA) richness when field data were aggregated. For the tree data set examined, seasonally integrated estimates of photosynthesis (Pg) predicted tree richness patterns better than climatic indices did.