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Keywords:

  • Species richness;
  • woody plants;
  • photosynthesis;
  • productivity;
  • temperate forest;
  • multiple scales;
  • AIC

ABSTRACT

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.