Wells A. F., G. V. Frost, T. Christopherson, M. J. Macander, and E. R. Trainor. 2016. Ecological land survey and soil landscapes map for Aniakchak National Monument and Preserve, Alaska, 2014. National Park Service. Fort Collins, CO. Natural Resource Report No. NPS/ANIA/NRR—2016/1133. 190 pp. <available at https://irma.nps.gov/
This study was conducted to inventory and classify soils and vegetation within the ecosystems of Aniakchak National Monument and Preserve using an ecological land survey (ELS) approach. The classifications identified by the ELS were then mapped across the park, using an archive of Geographic Information System (GIS) and Remote Sensing (RS) datasets pertaining to land cover, topography, and glacial history. The description and mapping of the landform-vegetation-soil relationships identified by the ELS offers tools to support the design and implementation of future field- and RS-based studies; facilitates further analysis and contextualization of existing data; and informs natural resource management decisions.
We collected information on the geomorphic, topographic, hydrologic, pedologic, and vegetation characteristics of ecosystems within a network of 294 field plots, of which 90 were sampled by us in 2014, and 204 were sampled by the Alaska Natural Heritage Program (AKNHP) and NPS in 2009–2010. The plot network encompassed all of the major environmental gradients and landscape histories present in ANIA. Individual state-factors (e.g., soil pH, slope-aspect) and other ecosystem components (e.g., geomorphic unit, vegetation species-cover data) were measured or categorized using standard classification schemes developed for Alaska. We described and analyzed the hierarchical relationships among the ecosystem components to classify 48 ecotypes (local-scale ecosystems) that best partition the variation in soils, vegetation, and disturbance properties observed at field plots. From the 48 ecotypes, we developed classifications of soil landscapes and disturbance landscapes that could be mapped across the park.
Detailed soil descriptions for the 294 field plots pertained to 7 soil orders: Alfisols (<1% of plots), Andisols (49%), Entisols (24%), Histosols (6%), Inceptisols (17%), Mollisols (2%), and Spodosols (2%). Within these 7 soil orders, field plots corresponded to a total of 63 soil subgroups, the most common of which were Typic Vitricryands, Eutric Duricryands, Humic Vitricryands, Vitrandic Cryorthents, Vitrandic Cryofluvents, and Aquandic Cryaquents.
At the landscape scale (hundreds of hectares to thousands of square kilometers), soil formation in ANIA is driven in large measure by proximal distance to the active Aniakchak volcano. The 10 km diameter Aniakchak caldera, and it’s steep tephra-covered slopes, are the result of a massive eruption ca. 3,590 cal yr B.P (Bacon 2014). There is no portion of ANIA that in some way has not been influenced by volcanism. Yet the magnitude of that volcanic influence on soil development decreases with distance, on a generalized continuum from southwest to northeast. The Andisol soil order dominates the landscape in the western portion of ANIA, in areas surrounding the caldera. This is particularly true, in the older, more developed, pyroclastic flow deposits that blanket most river valleys within a 40 km radius of Surprise Lake. To the north and east of the Aniakchak caldera, the steep, rounded peaks of the Aleutian Mountains dominate the skyline. The accumulation rates of tephra in the Aleutian Mountains, are typically insufficient to support the development of Andisols. Soils here are relatively diverse when compared to western portions of ANIA, supporting the development of Entisols, Inceptisols, Spodosols, and Mollisols.
The field data, the classifications of ecotypes and soil landscapes, a pre-existing land-cover map developed by AKNHP, and ancillary GIS and RS data were used to produce a series of ecosystem maps for ANIA. Eight physiographic units capturing broad-scale divisions in landscape position, microclimate, and other state-factors were mapped using a combination rule-based modeling related to topography and surficial geology. The ecotypes classified using field data were aggregated into a reduced set of map ecotypes that could be mapped across the study domain; aggregation was based on similarities in vegetation structure, general soil texture, and successional processes. The map ecotypes were further organized into 26 soil landscape and 15 disturbance landscape classes. The most widespread soil landscapes include Alpine Rocky Barrens and Dwarf Shrub (29.6% of mapping area excluding Marine Water), Upland Ashy-Loamy-Rocky Forb Meadows and Alder Tall Shrub (21.2%), Volcanic Rocky Barrens, Lichen, and Beach Rye (13.8%), Upland Ashy-Rocky Barrens and Dwarf Shrub (11.9%), and Upland Ashy-Sandy-Rocky Willow Low and Tall Shrub (10.7%). The disturbance landscapes were derived from map ecotypes with broadly similar disturbance regimes. The most common disturbance landscape was Mass Wasting and Landslide (42.4% of mapping area excluding Marine Water). This disturbance landscape is associated with steep mountainsides that are common across the park, which are commonly affected by active hillslope processes.
The ELS approach to understanding landscape processes, their influence on ecosystem functions, and the environments in which they operate provides several benefits. First, landscapes are analyzed as ecological systems with functionally-related parts, recognizing the importance of geomorphic and hydrologic processes to disturbance regimes, the flow of energy and material, and ecosystem development. This hierarchical approach, which incorporates numerous ecosystem components into ecotypes with co-varying properties, allows users to partition the variability of a wide range of ecological characteristics. Additionally, the linkage of the land-cover map to climatic, physiographic, topographic, and volcanic history variables to develop ecosystem maps improves our ability to predict the susceptibility and response of ANIA ecosystems to a range of human impacts and natural forcings. It also facilitates the production of a variety of thematic maps for resource management applications and analyses.