Spatially explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. We present a method for predictive vegetation mapping that applies direct gradient analysis and nearest-neighbor imputation to ascribe detailed ground attributes of vegetation to each pixel in a digital landscape map. The gradient nearest neighbor method integrates vegetation measurements from regional grids of field plots, mapped environmental data, and Landsat Thematic Mapper (TM) imagery. In the Oregon coastal province, species gradients were most strongly associated with regional climate and geographic location, whereas variation in forest structure was best explained by Landsat TM variables. At the regional level, mapped predictions represented the range of variability in the sample data, and predicted area by vegetation type closely matched sample-based estimates. At the site level, mapped predictions maintained the covariance structure among multiple response variables. Prediction accuracy for tree species occurrence and several measures of vegetation structure and composition was good to moderate. Vegetation maps produced with the gradient nearest neighbor method are appropriately used for regional-level planning, policy analysis, and research, not to guide local management decisions.

Vegetation light use efficiency is a key physiological parameter at the canopy scale, and at the daily time step is a component of remote sensing algorithms for scaling gross primary production (GPP) and net primary production (NPP) over regional to global domains. For the purposes of calibrating and validating the light use efficiency (εg) algorithms, the components of εg– absorbed photosynthetically active radiation (APAR) and ecosystem GPP – must be measured in a variety of environments. Micrometeorological and mass flux measurements at eddy covariance flux towers can be used to estimate APAR and GPP, and the emerging network of flux tower sites offers the opportunity to investigate spatial and temporal patterns in εg at the daily time step. In this study, we examined the relationship of daily GPP to APAR, and relationships of εg to climatic variables, at four micrometeorological flux tower sites – an agricultural field, a tallgrass prairie, a deciduous forest, and a boreal forest. The relationship of GPP to APAR was close to linear at the tallgrass prairie site but more nearly hyperbolic at the other sites. The sites differed in the mean and range of daily εg, with higher values associated with the agricultural field than the boreal forest. εg decreased with increasing APAR at all sites, a function of mid‐day saturation of GPP and higher εg under overcast conditions. εg was generally not well correlated with vapor pressure deficit or maximum daily temperature. At the agricultural site, a εg decline towards the end of the growing season was associated with a decrease in foliar nitrogen concentration. At the tallgrass prairie site, a decline in εg in August was associated with soil drought. These results support inclusion of parameters for cloudiness and the phenological status of the vegetation, as well as use of biome‐specific parameterization, in operational εg algorithms.

Across the western US, the two most prevalent native forest insect pests are mountain pine beetle (MPB; Dendroctonus ponderosae; a bark beetle) and western spruce budworm (WSB; Choristoneura freemani; a defoliator). MPB outbreaks have received more forest management attention than WSB outbreaks, but studies to date have not compared their cumulative mortality impacts in an integrated, regional framework. The objectives of this study are to: (1) map tree mortality associated with MPB and WSB outbreaks by integrating forest health aerial detection surveys (ADS; 1970-2012), Landsat time series (1984-2012), and multi-date forest inventory data; (2) compare the timing, extent, and cumulative impacts of recent MPB and WSB outbreaks across forested ecoregions of the US Pacific Northwest Region (PNW; Oregon and Washington). Our Landsat-based insect atlas facilitates comparisons across space, time, and insect agents that have not been possible to date, complementing existing ADS maps in three important ways. The new maps (1) capture variation of insect impacts within ADS polygons at a finer spatial scale (30-m grain), substantially reducing estimated insect extent; (2) provide consistent estimates of change for multiple agents, particularly long-duration changes; (3) quantify change in terms of field-measured tree mortality (dead basal area). Despite high variation across the study region, spatiotemporal patterns are evident in both the ADS-and Landsat-based maps of insect activity. MPB outbreaks occurred in two phases-first during the 1970s and 1980s in eastern and central Oregon and then more synchronously during the 2000s throughout dry interior conifer forests of the PNW. Reflecting differences in habitat susceptibility and epidemiology, WSB outbreaks exhibited early activity in northern Washington and an apparent spread from the eastern to central PNW during the 1980s, returning to northern Washington during the 1990s and 2000s. At ecoregional and regional scales, WSB outbreaks have exceeded MPB outbreaks in extent as well as total tree mortality, suggesting that ongoing studies should account for both bark beetles and defoliators. Given projected increases of insect and fire activity in western forests, the accurate assessment and monitoring of these disturbances will be crucial for sustainable ecosystem management.

No abstract

No 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 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.

Information about how vegetation composition and structure vary quantitatively and spatially with physical environment, disturbance history, and land ownership is fundamental to regional conservation planning. However, current knowledge about patterns of vegetation variability across large regions that is spatially explicit (i.e., mapped) tends to be general and qualitative. We used spatial predictions from gradient models to examine the influence of environment, disturbance, and ownership on patterns of forest vegetation biodiversity across a large forested region, the 3-million-ha Oregon Coast Range (USA). Gradients in tree species composition were strongly associated with environment, especially climate, and insensitive to disturbance, probably because many dominant tree species are long-lived and persist throughout forest succession. In contrast, forest structure was strongly correlated with disturbance and only weakly with environmental gradients. Although forest structure differed among ownerships, differences were blurred by the presence of legacy trees that originated prior to current forest management regimes. Our multi-ownership perspective revealed biodiversity concerns and benefits not readily visible in single-ownership analyses, and all ownerships contributed to regional biodiversity values. Federal lands provided most of the late-successional and old-growth forest. State lands contained a range of forest ages and structures, including diverse young forest, abundant legacy dead wood, and much of the high-elevation true fir forest. Nonindustrial private lands provided diverse young forest and the greatest abundance of hardwood trees, including almost all of the foothill oak woodlands. Forest industry lands encompassed much early-successional forest, most of the mixed hardwood-conifer forest, and large amounts of legacy down wood. The detailed tree- and species-level data in the maps revealed regional trends that would be masked in traditional coarse-filter assessment. Although abundant, most early-successional forests originated after timber harvest and lacked legacy live and dead trees important as habitat and for other ecological functions. Many large-conifer forests that might be classified as old growth using a generalized forest cover map lacked structural features of old growth such as multilayered canopies or dead wood. Our findings suggest that regional conservation planning include all ownerships and land allocations, as well as fine-scale elements of vegetation composition and structure.

No abstract