Compiled by Alan Carpenter
Volume 22, Number 3, Fall 2011
Smith, L. M., D. A. Haukos, S. T. McMurry, T. LaGrange, and D. Willis.
2011. Ecosystem services provided by playas in the High Plains: potential influences of USDA conservation programs. Ecological Applications 21: S116-127.
Playas are shallow depressional wetlands and the dominant wetland type in the non-glaciated High Plains of the United States. This region is one of the most intensively cultivated regions in the Western Hemisphere, and playas are profoundly impacted by a variety of agricultural activities. Conservation practices promoted through Farm Bills by the U.S. Department of Agriculture (USDA) that influence playas and surrounding catchments impact ecosystem functions and related services provided by wetlands in this region. As part of a national assessment, we review effects of agricultural cultivation and effectiveness of USDA conservation programs and practices on ecosystem functions and associated services of playas. Services provided by playas are influenced by hydrological function, and unlike other wetland types in the United States, hydrological function of playas is impacted more by accumulated sediments than drainage. Most playas with cultivated catchments have lost greater than 100% of their volume from sedimentation causing reduced hydroperiods. The Conservation Reserve Program (CRP) has the largest influence on playa catchments (the High Plains has >2.8 million ha), and associated sedimentation, of any USDA program. Unfortunately, most practices applied under CRP did not consider restoration of playa ecosystem function as a primary benefit, but rather established dense exotic grass in the watersheds to reduce soil erosion. Although this has reduced soil erosion, few studies have investigated its effects on playa hydrological function and services. Our review demonstrates that the Wetlands Reserve Program (WRP) has seldom been applied in the High Plains outside of south-central Nebraska. However, this is the primary program that exists within the USDA allowing conservation practices that restore wetland hydrology such as sediment removal. In addition to sediment removal, this practice has the greatest potential effect on improving hydrologic function by reducing sedimentation in vegetative buffer strips. We estimate that a 50-m native-grass buffer strip could improve individual playa hydroperiods by up to 90 days annually, enhancing delivery of most natural playa services. The potential for restoration of playa services using USDA programs is extensive, but only if WRP and associated practices are promoted and playas are considered an integral part of CRP contract.
Gutzwiller, K. J., and C. H. Flather.
2011. Wetland features and landscape context predict the risk of wetland habitat loss. Ecological Applications 21:968-982.
Wetlands generally provide significant ecosystem services and function as important harbors of biodiversity. To ensure that these habitats are conserved, an efficient means of identifying wetlands at risk of conversion is needed, especially in the southern United States where the rate of wetland loss has been highest in recent decades. We used multivariate adaptive regression splines to develop a model to predict the risk of wetland habitat loss as a function of wetland features and landscape context. Fates of wetland habitats from 1992 to 1997 were obtained from the National Resources Inventory for the U.S. Forest Service’s Southern Region, and land-cover data were obtained from the National Land Cover Data. We randomly selected 70% of our 40?617 observations to build the model (n = 28?432), and randomly divided the remaining 30% of the data into five Test data sets (n = 2437 each). The wetland and landscape variables that were important in the model, and their relative contributions to the model’s predictive ability (100 = largest, 0 = smallest), were land-cover/land-use of the surrounding landscape (100.0), size and proximity of development patches within 570 m (39.5), land ownership (39.1), road density within 570 m (37.5), percent woody and herbaceous wetland cover within 570 m (27.8), size and proximity of development patches within 5130 m (25.7), percent grasslands/herbaceous plants and pasture/hay cover within 5130 m (21.7), wetland type (21.2), and percent woody and herbaceous wetland cover within 1710 m (16.6). For the five Test data sets, Kappa statistics (0.40, 0.50, 0.52, 0.55, 0.56; P < 0.0001), area-under-the-receiver-operating-curve (AUC) statistics (0.78, 0.82, 0.83, 0.83, 0.84; P < 0.0001), and percent correct prediction of wetland habitat loss (69.1, 80.4, 81.7, 82.3, 83.1) indicated the model generally had substantial predictive ability across the South. Policy analysts and land-use planners can use the model and associated maps to prioritize at-risk wetlands for protection, evaluate wetland habitat connectivity, predict future conversion of wetland habitat based on projected land-use trends, and assess the effectiveness of wetland conservation programs.
Harper, E. B., J. C. Stella, and A. K. Fremier.
2011. Global sensitivity analysis for complex ecological models: a case study of riparian cottonwood population dynamics. Ecological Applications 21:1225-1240.
Mechanism-based ecological models are a valuable tool for understanding the drivers of complex ecological systems and for making informed resource-management decisions. However, inaccurate conclusions can be drawn from models with a large degree of uncertainty around multiple parameter estimates if uncertainty is ignored. This is especially true in nonlinear systems with multiple interacting variables. We addressed these issues for a mechanism-based, demographic model of Populus fremontii (Fremont cottonwood), the dominant riparian tree species along southwestern U.S. rivers. Many cottonwood populations have declined following widespread floodplain conversion and flow regulation. As a result, accurate predictive models are needed to analyze effects of future climate change and water management decisions. To quantify effects of parameter uncertainty, we developed an analytical approach that combines global sensitivity analysis (GSA) with classification and regression trees (CART) and Random Forest, a bootstrapping CART method. We used GSA to quantify the interacting effects of the full range of uncertainty around all parameter estimates, Random Forest to rank parameters according to their total effect on model predictions, and CART to identify higher-order interactions. GSA simulations yielded a wide range of predictions, including annual germination frequency of 10-100%, annual first-year survival frequency of 0-50%, and patch occupancy of 0-100%. This variance was explained primarily by complex interactions among abiotic parameters including capillary fringe height, stage-discharge relationship, and floodplain accretion rate, which interacted with biotic factors to affect survival. Model precision was primarily influenced by well-studied parameter estimates with minimal associated uncertainty and was virtually unaffected by parameter estimates for which there are no available empirical data and thus a large degree of uncertainty. Therefore, research to improve model predictions should not always focus on the least-studied parameters, but rather those to which model predictions are most sensitive. We advocate the combined use of global sensitivity analysis, CART, and Random Forest to: (1) prioritize research efforts by ranking variable importance; (2) efficiently improve models by focusing on the most important parameters; and (3) illuminate complex model properties including nonlinear interactions. We present an analytical framework that can be applied to any model with multiple uncertain parameter estimates.