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Question: How can nearest-neighbour (NN) imputation be used to develop maps of multiple species and plant communities?Location: Western and central Oregon, USA, but methods are applicable anywhere. Methods:We demonstrate NN imputation by mapping woody plant communities for 4 100 000 km 2 of diverse forests and woodlands. Species abundances on $25 000 plots were related to spatial predictors (rasters) describing climate, topography, soil and geographic location using constrained ordination (CCA). Species data from the nearest plot in multi-dimensional CCA space were imputed to each map pixel. Maps of multiple individual species and community types were constructed from the single imputed surface. We computed a variety of diagnostics to characterize different qualities of the imputed (mapped) community data.Results: Community composition gradients were strongly associated with climate and elevation, and less so with topography and soil. Accuracy of the imputation model for presence/absence of 150 species varied widely (kappa 0.00 to 0.80). Omission error rates were higher than commission rates due to low species prevalence, and areal representation of species was only slightly inflated. A map of 78 community types was 41% correct and 78% fuzzy correct. Errors of omission and commission were balanced, and areal representation of both rare and abundant communities was accurate. Map accuracy may be lower for some species than with other methods, but areal representation of species and communities across large landscapes is preserved. Because imputed vegetation surfaces are developed for all species simultaneously, map units contain suites of species known to co-occur in nature. Maps of individual species, and of community types derived from them, will be internally consistent at map locations.Conclusions: NN imputation is a useful modelling approach where maps of multiple species and plant communities are needed, such as in natural resource management and conservation planning or models that project landscape change under alternative disturbance or climate scenarios. More research is needed to evaluate other ordination methods for NN imputation of plant communities.

The fate of live forest biomass is largely controlled by growth and disturbance processes, both natural and anthropogenic. Thus, biomass monitoring strategies must characterize both the biomass of the forests at a given point in time and the dynamic processes that change it. Here, we describe and test an empirical monitoring system designed to meet those needs. Our system uses a mix of field data, statistical modeling, remotely-sensed time-series imagery, and small-footprint lidar data to build and evaluate maps of forest biomass. It ascribes biomass change to specific change agents, and attempts to capture the impact of uncertainty in methodology. We find that: • A common image framework for biomass estimation and for change detection allows for consistent comparison of both state and change processes controlling biomass dynamics. • Regional estimates of total biomass agree well with those from plot data alone.• The system tracks biomass densities up to 450-500 Mg ha −1 with little bias, but begins underestimating true biomass as densities increase further. • Scale considerations are important. Estimates at the 30 m grain size are noisy, but agreement at broad scales is good. Further investigation to determine the appropriate scales is underway. • Uncertainty from methodological choices is evident, but much smaller than uncertainty based on choice of allometric equation used to estimate biomass from tree data. • In this forest-dominated study area, growth and loss processes largely balance in most years, with loss processes dominated by human removal through harvest. In years with substantial fire activity, however, overall biomass loss greatly outpaces growth. Taken together, our methods represent a unique combination of elements foundational to an operational landscape-scale forest biomass monitoring program.

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