The ability to quantify spatial patterns and detect change in terrestrial vegetation across large landscapes depends on linking ground-based measurements of vegetation to remotely sensed data. Unlike non-overlapping categorical vegetation types (i.e., typical vegetation and land cover maps), species-level gradients of foliar cover are consistent with the ecological theories of individualistic response of species and niche space. We collected foliar cover data for vascular plant, bryophyte, and lichen species and 17 environmental variables in the Arctic Coastal Plain and Brooks Foothills of Alaska from 2012 to 2017. We integrated these data into a standardized database with 13 additional vegetation survey and monitoring data sets in northern Alaska collected from 1998 to 2017. To map the patterns of foliar cover for six dominant and widespread vascular plant species in arctic Alaska, we statistically associated ground-based measurements of species distribution and abundance to environmental and multi-season spectral covariates using a Bayesian statistical learning approach. For five of the six modeled species, our models predicted 36% to 65% of the observed species-level variation in foliar cover. Overall, our continuous foliar cover maps predicted more of the observed spatial heterogeneity in species distribution and abundance than an existing categorical vegetation map. Mapping continuous foliar cover at the species level also revealed ecological patterns obscured by aggregation in existing plant functional type approaches. Species-level analysis of vegetation patterns enables quantifying and monitoring landscape-level changes in species, vegetation communities, and wildlife habitat independently of subjective categorical vegetation types and facilitates integrating spatial patterns across multiple ecological scales. The novel species-level foliar cover mapping approach described here provides spatial information about the functional role of plant species in vegetation communities and wildlife habitat that are not available in categorical vegetation maps or quantitative maps of broadly defined vegetation aggregates.
Keywords: Alaska; Arctic; Bayesian statistical learning; North Slope; big data; foliar cover; gradient boosting; proportional abundance; remote sensing; species distribution; vegetation map.
© 2020 The Authors. Ecological Applications published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.