Stephen P. Boyte

Ecological Niche of the 2003 West Nile Virus Epidemic in the Northern Great Plains of the United States

Background The incidence of West Nile virus (WNv) has remained high in the northern Great Plains compared to the rest of the United States. However, the reasons for the sustained high risk of WNv transmission in this region have not been determined. To assess the environmental drivers of WNv in the northern Great Plains, we analyzed the county-level spatial pattern of human cases during the 2003 epidemic across a seven-state region. Methodology/Principal Findings County-level data on WNv cases were examined using spatial cluster analysis, and were used to fit statistical models with weather, climate, and land use variables as predictors. In 2003 there was a single large cluster of elevated WNv risk encompassing North Dakota, South Dakota, and Nebraska along with portions of eastern Montana and Wyoming. The relative risk of WNv remained high within the boundaries of this cluster from 2004–2007. WNv incidence during the 2003 epidemic was found to have a stronger relationship with long-term climate patterns than with annual weather in either 2002 or 2003. WNv incidence increased with mean May–July temperature and had a unimodal relationship with total May–July precipitation. WNv incidence also increased with the percentage of irrigated cropland and with the percentage of the human population living in rural areas. Conclusions/Significance The spatial pattern of WNv cases during the 2003 epidemic in the northern Great Plains was associated with both climatic gradients and land use patterns. These results were interpreted as evidence that environmental conditions across much of the northern Great Plains create a favorable ecological niche for Culex tarsalis, a particularly efficient vector of WNv. Further research is needed to determine the proximal causes of sustained WNv transmission and to enhance strategies for disease prevention.

Ecosystem Performance Monitoring of Rangelands by Integrating Modeling and Remote Sensing

Abstract Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystems climate signal. This study separates seasonal weather influences from influences caused by disturbances and management decisions, making interannual time-series analysis more consistent and interpretable. We compared the actual ecosystem performance (AEP) of five rangeland vegetation types in the Owyhee Uplands for 9 yr to their expected ecosystem performance (EEP). Integrated growing season Normalized Difference Vegetation Index data for each of the nine growing seasons served as a proxy for annual AEP. Regression-tree models used long-term site potential, seasonal weather, and land cover data sets to generate annual EEP, an estimate of ecosystem performance incorporating annual weather variations. The difference between AEP and EEP provided a performance measure for each pixel in the study area. Ecosystem performance anomalies occurred when the ecosystem performed significantly better or worse than the model predicted. About 14% of the Owyhee Uplands showed a trend of significant underperformance or overperformance (P < 0.10). Land managers can use results from weather-based rangeland ecosystem performance models to help support adaptive management strategies.

Identifying grasslands suitable for cellulosic feedstock crops in the Greater Platte River Basin: dynamic modeling of ecosystem performance with 250 m eMODIS

This study dynamically monitors ecosystem performance (EP) to identify grasslands potentially suitable for cellulosic feedstock crops (e.g., switchgrass) within the Greater Platte River Basin (GPRB). We computed grassland site potential and EP anomalies using 9‐year (2000–2008) time series of 250 m expedited moderate resolution imaging spectroradiometer Normalized Difference Vegetation Index data, geophysical and biophysical data, weather and climate data, and EP models. We hypothesize that areas with fairly consistent high grassland productivity (i.e., high grassland site potential) in fair to good range condition (i.e., persistent ecosystem overperformance or normal performance, indicating a lack of severe ecological disturbance) are potentially suitable for cellulosic feedstock crop development. Unproductive (i.e., low grassland site potential) or degraded grasslands (i.e., persistent ecosystem underperformance with poor range condition) are not appropriate for cellulosic feedstock development. Grassland pixels with high or moderate ecosystem site potential and with more than 7 years ecosystem normal performance or overperformance during 2000–2008 are identified as possible regions for future cellulosic feedstock crop development (ca. 68 000 km2 within the GPRB, mostly in the eastern areas). Long‐term climate conditions, elevation, soil organic carbon, and yearly seasonal precipitation and temperature are important performance variables to determine the suitable areas in this study. The final map delineating the suitable areas within the GPRB provides a new monitoring and modeling approach that can contribute to decision support tools to help land managers and decision makers make optimal land use decisions regarding cellulosic feedstock crop development and sustainability.

Cheatgrass Percent Cover Change: Comparing Recent Estimates to Climate Change − Driven Predictions in the Northern Great Basin

ABSTRACT Cheatgrass (Bromus tectorum L.) is a highly invasive species in the Northern Great Basin that helps decrease fire return intervals. Fire fragments the shrub steppe and reduces its capacity to provide forage for livestock and wildlife and habitat critical to sagebrush obligates. Of particular interest is the greater sage grouse (Centrocercus urophasianus), an obligate whose populations have declined so severely due, in part, to increases in cheatgrass and fires that it was considered for inclusion as an endangered species. Remote sensing technologies and satellite archives help scientists monitor terrestrial vegetation globally, including cheatgrass in the Northern Great Basin. Along with geospatial analysis and advanced spatial modeling, these data and technologies can identify areas susceptible to increased cheatgrass cover and compare these with greater sage grouse priority areas for conservation (PAC). Future climate models forecast a warmer and wetter climate for the Northern Great Basin, which likely will force changing cheatgrass dynamics. Therefore, we examine potential climate-caused changes to cheatgrass. Our results indicate that future cheatgrass percent cover will remain stable over more than 80% of the study area when compared with recent estimates, and higher overall cheatgrass cover will occur with slightly more spatial variability. The land area projected to increase or decrease in cheatgrass cover equals 18% and 1%, respectively, malking an increase in fire disturbances in greater sage grouse habitat likely. Relative susceptibility measures, created by integrating cheatgrass percent cover and temporal standard deviation datasets, show that potential increases in future cheatgrass cover match future projections. This discovery indicates that some greater sage grouse PACs for conservation could be at heightened risk of fire disturbance. Multiple factors will affect future cheatgrass cover including changes in precipitation timing and totals and increases in freeze-thaw cycles. Understanding these effects can help direct land management, guide scientific research, and influence policy.

The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth

Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on remote sensing input data – to estimate cheatgrass beginning of spring growth (BOSG) in the northern Great Basin. The model was then applied to map the location and timing of cheatgrass spring growth for the entire area. The model was strong (R2 = 0.85) and predicted an average cheatgrass BOSG across the study area of 29 March–4 April. Of early cheatgrass BOSG areas, 65% occurred at elevations below 1452 m. The highest proportion of cheatgrass BOSG occurred between mid-April and late May. Predicted cheatgrass BOSG in this study matched well with previous Great Basin cheatgrass green-up studies.

An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-Sensed Data

Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R2 values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.

Near-Real-Time Cheatgrass Percent Cover in the Northern Great Basin, USA, 2015

On the Ground Cheatgrass (Bromus tectorum L.) dramatically changes shrub steppe ecosystems in the Northern Great Basin, United States. Current-season cheatgrass location and percent cover are difficult to estimate rapidly. We explain the development of a near-real-time cheatgrass percent cover dataset and map in the Northern Great Basin for the current year (2015), display the current years map, provide analysis of the map, and provide a website link to download the map (as a PDF) and the associated dataset. The near-real-time cheatgrass percent cover dataset and map were consistent with non-expedited, historical cheatgrass percent cover datasets and maps. Having cheatgrass maps available mid-summer can help land managers, policy makers, and Geographic Information Systems personnel as they work to protect socially relevant areas such as critical wildlife habitats.

Projecting future grassland productivity to assess the sustainability of potential biofuel feedstock areas in the Greater Platte River Basin

This study projects future (e.g., 2050 and 2099) grassland productivities in the Greater Platte River Basin (GPRB) using ecosystem performance (EP, a surrogate for measuring ecosystem productivity) models and future climate projections. The EP models developed from a previous study were based on the satellite vegetation index, site geophysical and biophysical features, and weather and climate drivers. The future climate data used in this study were derived from the National Center for Atmospheric Research Community Climate System Model 3.0 ‘SRES A1B’ (a ‘middle’ emissions path). The main objective of this study is to assess the future sustainability of the potential biofuel feedstock areas identified in a previous study. Results show that the potential biofuel feedstock areas (the more mesic eastern part of the GPRB) will remain productive (i.e., aboveground grassland biomass productivity >2750 kg ha−1 year−1) with a slight increasing trend in the future. The spatially averaged EPs for these areas are 3519, 3432, 3557, 3605, 3752, and 3583 kg ha−1 year−1 for current site potential (2000–2008 average), 2020, 2030, 2040, 2050, and 2099, respectively. Therefore, the identified potential biofuel feedstock areas will likely continue to be sustainable for future biofuel development. On the other hand, grasslands identified as having no biofuel potential in the drier western part of the GPRB would be expected to stay unproductive in the future (spatially averaged EPs are 1822, 1691, 1896, 2306, 1994, and 2169 kg ha−1 year−1 for site potential, 2020, 2030, 2040, 2050, and 2099). These areas should continue to be unsuitable for biofuel feedstock development in the future. These future grassland productivity estimation maps can help land managers to understand and adapt to the expected changes in future EP in the GPRB and to assess the future sustainability and feasibility of potential biofuel feedstock areas.

Understanding Landscapes Through Spatial Modeling

This chapter outlines how landscape simulation models can be used to support forest landscape restoration. In the first type of application, landscape models of disturbance and forest succession were used to estimate historical variability in landscape composition and configuration. An example is given based on a study in the Oregon Coast Range of USA which showed the present day forest patterns are outside the range of historical variability. Problems with this approach lie in deciding the landscape metrics to use and, in this particular case, in assembling reliable data on historical fire regimes. A second common application of landscape simulation models is to project future landscapes under alternative landscape restoration scenarios. These types of simulation experiments with landscape models focus less on making predictions of historical or future landscape conditions but, rather, place more emphasis on exploring general hypotheses about pattern-process relationships. One important insight is that changes in landscape composition and configuration often lag behind shifts in disturbance regimes, and that temporal as well as spatial landscape heterogeneity is important to consider when assessing ecological responses to changing disturbance regimes.