Mapping gradients of community composition with nearest-neighbour imputation: extending plot data for landscape analysis

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Abstract

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 >100 000 km2 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.

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