Michael C. Wimberly

Recent land use change in the Western Corn Belt threatens grasslands and wetlands

In the US Corn Belt, a recent doubling in commodity prices has created incentives for landowners to convert grassland to corn and soybean cropping. Here, we use land cover data from the National Agricultural Statistics Service Cropland Data Layer to assess grassland conversion from 2006 to 2011 in the Western Corn Belt (WCB): five states including North Dakota, South Dakota, Nebraska, Minnesota, and Iowa. Our analysis identifies areas with elevated rates of grass-to-corn/soy conversion (1.0–5.4% annually). Across the WCB, we found a net decline in grass-dominated land cover totaling nearly 530,000 ha. With respect to agronomic attributes of lands undergoing grassland conversion, corn/soy production is expanding onto marginal lands characterized by high erosion risk and vulnerability to drought. Grassland conversion is also concentrated in close proximity to wetlands, posing a threat to waterfowl breeding in the Prairie Pothole Region. Longer-term land cover trends from North Dakota and Iowa indicate that recent grassland conversion represents a persistent shift in land use rather than short-term variability in crop rotation patterns. Our results show that the WCB is rapidly moving down a pathway of increased corn and soybean cultivation. As a result, the window of opportunity for realizing the benefits of a biofuel industry based on perennial bioenergy crops, rather than corn ethanol and soy biodiesel, may be closing in the WCB.

Associations of supermarket accessibility with obesity and fruit and vegetable consumption in the conterminous United States

BackgroundLimited access to supermarkets may reduce consumption of healthy foods, resulting in poor nutrition and increased prevalence of obesity. Most studies have focused on accessibility of supermarkets in specific urban settings or localized rural communities. Less is known, however, about how supermarket accessibility is associated with obesity and healthy diet at the national level and how these associations differ in urban versus rural settings. We analyzed data on obesity and fruit and vegetable (F/V) consumption from the Behavioral Risk Factor Surveillance System for 2000-2006 at the county level. We used 2006 Census Zip Code Business Patterns data to compute population-weighted mean distance to supermarket at the county level for different sizes of supermarket. Multilevel logistic regression models were developed to test whether population-weighted mean distance to supermarket was associated with both obesity and F/V consumption and to determine whether these relationships varied for urban (metropolitan) versus rural (nonmetropolitan) areas.ResultsDistance to supermarket was greater in nonmetropolitan than in metropolitan areas. The odds of obesity increased and odds of consuming F/V five times or more per day decreased as distance to supermarket increased in metropolitan areas for most store size categories. In nonmetropolitan areas, however, distance to supermarket had no associations with obesity or F/V consumption for all supermarket size categories.ConclusionsObesity prevalence increased and F/V consumption decreased with increasing distance to supermarket in metropolitan areas, but not in nonmetropolitan areas. These results suggest that there may be a threshold distance in nonmetropolitan areas beyond which distance to supermarket no longer impacts obesity and F/V consumption. In addition, obesity and food environments in nonmetropolitan areas are likely driven by a more complex set of social, cultural, and physical factors than a single measure of supermarket accessibility. Future research should attempt to more precisely quantify the availability and affordability of foods in nonmetropolitan areas and consider alternative sources of healthy foods besides supermarkets.

INFLUENCES OF ENVIRONMENT AND DISTURBANCE ON FOREST PATTERNS IN COASTAL OREGON WATERSHEDS

Modern ecology often emphasizes the distinction between traditional theories of stable, environmentally structured communities and a new paradigm of disturbance-driven, nonequilibrium dynamics. However, multiple hypotheses for observed vegetation patterns have seldom been explicitly tested. We used multivariate statistics and variation partitioning methods to assess the relative influences of environmental factors and disturbance history on riparian and hillslope forests. Our study area was the Cummins Creek Wilderness, located in the Oregon Coast Range. Most of the wilderness burned at least once between the mid-19th and early 20th centuries, creating a mosaic of younger forests with a few old-growth patches. Species composition on hillslopes varied primarily along a climatic gradient from moist maritime environments to drier inland climates but was relatively insensitive to forest age structure. The abundance of Tsuga heterophylla, a fire-sensitive, late-successional tree species, decreased with distance from old-growth patches, suggesting possible seed-source limitations following the historical fires. In contrast to species composition, hillslope forest structure was primarily related to fire history but was largely independent of environmental gradients. Old-growth structure characteristics such as large dominant trees, large snags, high down-wood volumes, and high tree size diversity increased with stand age and with the presence of remnant trees that survived the fires. Riparian forests had high shrub cover, abundant hardwoods, and high down-wood volumes, while the conifer-dominated hillslopes had high overstory density and basal area. Maritime climates and their associated plant species extended further inland in riparian areas than on hillslopes. Advance regeneration densities were higher in riparian forests within 5 km of the coast than in any other portion of the study area. Riparian forest structure and composition were related to both environmental and disturbance variables, with stream gradient and size integrating much of the fine-scale variability in disturbance regimes. No single theoretical framework was sufficient to explain the vegetation patterns observed in these forested watersheds. Our findings suggest a conceptual model of forest landscapes in which the relative influences of environment and disturbance on vegetation patterns are contingent on the facet of vegetation considered (composition vs. structure) and the portion of the landscape examined (hillslope vs. riparian).

Distance-dependent and distance-independent models of Douglas-fir and western hemlock basal area growth following silvicultural treatment

Distance-independent and distance-dependent individual-tree basal area growth equations for Douglas-fir and western hemlock growth following thinning and fertilization treatments were developed using regression analysis. Distance-independent models included only non-spatial competition and thinning indices, while distance-dependent models included both spatial and non-spatial indices. The distance-independent models with the highest adjusted multiple coefficient of determination (adjusted R2) for both species included diameter at breast height, crown class, percent basal area removed in thinning, plot basal area greater than the subject tree and stand age as independent variables. The distance-dependent models with the highest adjusted R2 included all of these variables in addition to a variant of the area potentially available index, which is based on the spatial tessellation of the point pattern of trees in the stand. Addition of this spatial index produced only a small (<.01) increase in adjusted R2 for models of both species. The relatively small amount of increase was due to three factors; thinning resulted in an even distribution of growing space among residual trees, tree size explained much of the variation in local competitive stress and the competitive neighborhood of individual trees was large relative to the size of the sample plots. The results suggest that the additional effort and expense required to obtain spatially referenced stand data for developing empirical forest growth models in similar stands is not justified.

Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia

BackgroundMalaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia.MethodsIn this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series.ResultsMalaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates.ConclusionsMalaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions.

Assessing fuel treatment effectiveness using satellite imagery and spatial statistics.

Understanding the influences of forest management practices on wildfire severity is critical in fire-prone ecosystems of the western United States. Newly available geospatial data sets characterizing vegetation, fuels, topography, and burn severity offer new opportunities for studying fuel treatment effectiveness at regional to national scales. In this study, we used ordinary least-squares (OLS) regression and sequential autoregression (SAR) to analyze fuel treatment effects on burn severity for three recent wildfires: the Camp 32 fire in western Montana, the School fire in southeastern Washington, and the Warm fire in northern Arizona. Burn severity was measured using differenced normalized burn ratio (dNBR) maps developed by the Monitoring Trends in Burn Severity project. Geospatial data sets from the LANDFIRE project were used to control for prefire variability in canopy cover, fuels, and topography. Across all three fires, treatments that incorporated prescribed burning were more effective than thinning alone. Treatment effect sizes were lower, and standard errors were higher in the SAR models than in the OLS models. Spatial error terms in the SAR models indirectly controlled for confounding variables not captured in the LANDFIRE data, including spatiotemporal variability in fire weather and landscape-level effects of reduced fire severity outside the treated areas. This research demonstrates the feasibility of carrying out assessments of fuel treatment effectiveness using geospatial data sets and highlights the potential for using spatial autoregression to control for unmeasured confounding factors.

Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods

Land managers need consistent information about the geographic distribution of wildland fuels and forest struc- ture over large areas to evaluate fire risk and plan fuel treatments. We compared spatial predictions for 12 fuel and forest structure variables across three regions in the western United States using gradient nearest neighbor (GNN) imputation, lin- ear models (LMs), classification and regression trees (CART), and geostatistical methods (kriging and universal kriging (UK)). Local-scale map accuracy varied considerably across sites, variables, and methods. GNN performed best for forest structure variables in Oregon, but LMs and UK were better for canopy variables and for forest structure variables in Wash- ington and California. Kriging performed poorly throughout, and kriging did not improve prediction accuracy when added to LMs as UK. GNN outperformed CART in predicting vegetation classes and fuel models, complex variables defined by multiple attributes. Regional distributions of vegetation classes and fuel models were accurately represented by GNN and very poorly by CART and LMs. Despite their often limited accuracy at the local scale, GNN maps are useful when infor- mation on a wide range of forest attributes is needed for analysis and forest management at intermediate, i.e., landscape to ecoregional, scales.

Estimation of wildfire size and risk changes due to fuels treatments

Human land use practices, altered climates, and shifting forest and fire management policies have increased thefrequencyoflargewildfiresseveral-fold.Mitigationofpotentialfirebehaviourandfireseverityhaveincreasinglybeen attempted through pre-fire alteration of wildland fuels using mechanical treatments and prescribed fires. Despite annual treatment of more than a million hectares of land, quantitative assessments of the effectiveness of existing fuel treatments at reducing the size of actual wildfires or how they might alter the risk of burning across landscapes are currently lacking. Here, we present a method for estimating spatial probabilities of burning as a function of extant fuels treatments for any wildland fire-affected landscape. We examined the landscape effects of more than 72000ha of wildland fuel treatments involved in 14 large wildfires that burned 314000ha of forests in nineUS states between 2002 and 2010. Fuels treatments altered the probability of fire occurrence both positively and negatively across landscapes, effectively redistributing fire risk by changing surface fire spread rates and reducing the likelihood of crowning behaviour. Trade offs are created between formation of large areas with low probabilities of increased burning and smaller, well-defined regions with reduced fire risk. Additional keywords: FARSITE, fire behaviour, fire extent, fire management, fire modelling, fire risk, fire spread.

Spatial Patterns of Obesity and Associated Risk Factors in the Conterminous U.S.

BACKGROUND The obesogenic environment is hypothesized to increase obesity risk by discouraging physical activity and limiting the availability of healthy food. PURPOSE This research reports the prevalence of obesity and risk factors (physical activity and fruit and vegetable consumption) by creating spatially smoothed maps and analyzing local autocorrelation and aims to examine associations of obesity and risk factors at the national level. METHODS Data were obtained in 2008 from the Behavioral Risk Factor Surveillance System for the years 2000-2006 aggregated to the county level. A weighted head-banging smoothing algorithm was used that effectively replaced the proportion of obesity and risk factors for each county with a weighted median that incorporates data from neighboring counties. Significant spatial clusters of obesity and risk factors were identified by a local Morans I analysis. All analyses were performed in 2008-2009. RESULTS A higher prevalence of obesity was generally found in the non-metro counties of the South, whereas lower prevalence was found in the West and the Northeast. A lower prevalence of leisure-time physical activity was generally found in the areas where obesity prevalence was higher and vice versa. A lower prevalence of fruit and vegetable consumption was found mainly in the non-metro counties of the South and the Great Plains. CONCLUSIONS The national patterns of obesity and associated risk factors obtained may reflect a unique set of meso-environmental drivers, including climate, land use, population density, and culture. Future research should address this regional variability and explicitly consider the spatial scales at which such environmental factors operate.

Weather and Land Cover Influences on Mosquito Populations in Sioux Falls, South Dakota

ABSTRACT This study compared the spatial and temporal patterns of Culex tarsalis Coquillett and Aedes vexans Meigen populations and examined their relationships with land cover types and climatic variability in Sioux Falls, SD. Between 24 and 30 CDC CO2-baited light traps were set annually in Sioux Falls from May to September 2005–2008. Land cover data were acquired from the 2001 National Land Cover Dataset and the percentages of selected land cover types were calculated within a 600-m buffer zone around each trap. Meteorological information was summarized from local weather stations. Cx. tarsalis exhibited stronger spatial autocorrelation than Ae. vexans. Land cover analysis indicated that Cx. tarsalis was positively correlated with grass/hay, and Ae. vexans was positively correlated with wetlands. No associations were identified between irrigation and the host-seeking population of each species. Higher temperature in the current week and 2 wk prior and higher precipitation 3–4 wk before collection of host-seeking adult mosquitoes had positive influences on Cx. tarsalis abundance. Temperature in the current week and rainfall 2–3 wk before sampling had positive influences on Ae. vexans abundance. This study revealed the different influences of weather and land cover on important mosquito species in the Northern Great Plains region, which can be used to improve local vector control strategies and West Nile virus prevention efforts.