Geology topics

Michael Wimberly

Publications and source records attributed to Michael Wimberly.

3 recordsLinked to original sources

USGS2016

Divergent projections of future land use in the United States arising from different models and scenarios

A variety of land-use and land-cover (LULC) models operating at scales from local to global have been developed in recent years, including a number of models that provide spatially explicit, multi-class LULC projections for the conterminous United States. This diversity of modeling approaches raises the question: how consistent are their projections of future land use? We compared projections from six LULC modeling applications for the United States and assessed quantitative, spatial, and conceptual inconsistencies. Each set of projections provided multiple scenarios covering a period from roughly 2000 to 2050. Given the unique spatial, thematic, and temporal characteristics of each set of projections, individual projections were aggregated to a common set of basic, generalized LULC classes (i.e., cropland, pasture, forest, range, and urban) and summarized at the county level across the conterminous United States. We found very little agreement in projected future LULC trends and patterns among the different models. Variability among scenarios for a given model was generally lower than variability among different models, in terms of both trends in the amounts of basic LULC classes and their projected spatial patterns. Even when different models assessed the same purported scenario, model projections varied substantially. Projections of agricultural trends were often far above the maximum historical amounts, raising concerns about the realism of the projections. Comparisons among models were hindered by major discrepancies in categorical definitions, and suggest a need for standardization of historical LULC data sources. To capture a broader range of uncertainties, ensemble modeling approaches are also recommended. However, the vast inconsistencies among LULC models raise questions about the theoretical and conceptual underpinnings of current modeling approaches. Given the substantial effects that land-use change can have on ecological and societal processes, there is a need for improvement in LULC theory and modeling capabilities to improve acceptance and use of regional- to national-scale LULC projections for the United States and elsewhere.

Ecological Modelling↗

USGS2015

Simulating forest landscape disturbances as coupled human and natural systems

Anthropogenic disturbances resulting from human land use affect forest landscapes over a range of spatial and temporal scales, with diverse influences on vegetation patterns and dynamics. These processes fall within the scope of the coupled human and natural systems (CHANS) concept, which has emerged as an important framework for understanding the reciprocal interactions and feedbacks that connect human activities and ecosystem responses. Spatial simulation modeling of forest landscape change is an important technique for exploring the dynamics of CHANS over large areas and long time periods. Landscape models for simulating interactions between human activities and forest landscape dynamics can be grouped into two main categories. Forest landscape models (FLMs) focus on landscapes where forests are the dominant land cover and simulate succession and natural disturbances along with forest management activities. In contrast, land change models (LCMs) simulate mosaics of different land cover and land use classes that include forests in addition to other land uses such as developed areas and agricultural lands. There are also several examples of coupled models that combine elements of FLMs and LCMs. These integrated models are particularly useful for simulating human–natural interactions in landscapes where human settlement and agriculture are expanding into forested areas. Despite important differences in spatial scale and disciplinary scope, FLMs and LCMs have many commonalities in conceptual design and technical implementation that can facilitate continued integration. The ultimate goal will be to implement forest landscape disturbance modeling in a CHANS framework that recognizes the contextual effects of regional land use and other human activities on the forest ecosystem while capturing the reciprocal influences of forests and their disturbances on the broader land use mosaic.

Book chapter↗

USGS2012

Spatial analysis of Northern Goshawk territories in the Black Hills, South Dakota

The Northern Goshawk ( Accipiter gentilis ) is the largest of the three North American species of Accipiter and is more closely associated with older forests than are the other species. Its reliance on older forests has resulted in concerns about its status, extensive research into its habitat relationships, and litigation. Our objective was to model the spatial patterns of goshawk territories in the Black Hills, South Dakota, to make inferences about the underlying processes. We used a modification of Ripley's K function that accounts for inhomogeneous intensity to determine whether territoriality or habitat determined the spacing of goshawks in the Black Hills, finding that habitat conditions rather than territoriality were the determining factor. A spatial model incorporating basal area of trees in a stand of forest, canopy cover, age of trees >23 cm in diameter, number of trees per hectare, and geographic coordinates provided good fit to the spatial patterns of territories. There was no indication of repulsion at close distances that would imply spacing was determined by territoriality. These findings contrast with those for the Kaibab Plateau, Arizona, where territoriality is an important limiting factor. Forest stands where the goshawk nested historically are now younger and have trees of smaller diameter, probably having been modified by logging, fire, and insects. These results have important implications for the goshawk's ecology in the Black Hills with respect to mortality, competition, forest fragmentation, and nest-territory protection.

South Dakota↗