@article {JRN55413, title = {Simulated distribution of Eragrostis lehmanniana (Lehmann lovegrass): Soil{\textendash}climate interactions complicate predictions}, journal = {Ecosphere}, volume = {13}, year = {2022}, month = {3/7/2022}, pages = {e3974}, abstract = {The invasive perennial grass Eragrostis lehmanniana has expanded rapidly throughout the Sonoran Desert (SD) while remaining sparse and patchily distributed in the neighboring Chihuahuan Desert (CD). As temperatures and patterns in precipitation change, identifying the drivers limiting spread in the CD is needed. Our objectives were (1) to identify the climatic and edaphic factors limiting recruitment of E. lehmanniana throughout the CD and (2) to predict the edaphic and climatic locations in the CD where this species is expected to have higher probabilities of recruitment under future climatic conditions. Recruitment was simulated at 57 locations throughout the CD with a daily, multilayer soil water model (SOILWAT) that was parameterized and tested to develop recruitment parameters for E. lehmanniana using long-term data from research stations in the SD and CD. Logistic regression was used to predict recruitment across the CD using climate and soil factors to create a map of simulated recruitment under current and alternative climate scenarios. Simulated recruitment under current climate was low for most of the CD. However, localized areas with high probabilities (>0.8) occurred along the western transition between the CD and the SD, and in the southern extent of the CD. In general, the CD climate is too cool and dry for rapid and widespread invasion. However, increases to temperature and precipitation are likely to increase recruitment success. Interactions among soil, tempera-ture, and precipitation were important to increases in recruitment of E. lehmanniana and are expected to lead to heterogeneous increases in abundance as climate continues to change. This patchy increase in abundance will result in changes in its geographic distribution that will make predictions in the future challenging.}, keywords = {Chihuahuan Desert, climate change, Geographic distribution, numerical model, recruitment, seedling establishment, Special Feature: Dynamic Deserts}, doi = {10.1002/ecs2.3974}, url = {files/bibliography/22-022.pdf}, author = {N. Dylan Burruss and Debra.C.P. Peters and Haitao Huang and Jin Yao} } @article {JRN55337, title = {Predicting the geographic range of an invasive livestock disease across the contiguous USA under current and future climate conditions}, journal = {Climate}, volume = {9}, year = {2021}, month = {10/29/2021}, pages = {159}, abstract = {Vesicular stomatitis (VS) is the most common vesicular livestock disease in North America. Transmitted by direct contact and by several biting insect species, this disease results in quarantines and animal movement restrictions in horses, cattle and swine. As changes in climate drive shifts in geographic distributions of vectors and the viruses they transmit, there is considerable need to improve understanding of relationships among environmental drivers and patterns of disease occurrence. Multidisciplinary approaches integrating pathology, ecology, climatology, and biogeophysics are increasingly relied upon to disentangle complex relationships governing disease. We used a big data model integration approach combined with machine learning to estimate the potential geographic range of VS across the continental United States (CONUS) under long-term mean climate conditions over the past 30 years. The current extent of VS is confined to the western portion of the US and is related to summer and winter precipitation, winter maximum temperature, elevation, fall vegetation biomass, horse density, and proximity to water. Comparison with a climate-only model illustrates the importance of current processes-based parameters and identifies regions where uncertainty is likely to be greatest if mechanistic processes change. We then forecast shifts in the range of VS using climate change projections selected from CMIP5 climate models that most realistically simulate seasonal temperature and precipitation. Climate change scenarios that altered climatic conditions resulted in greater changes to potential range of VS, generally had non-uniform impacts in core areas of the current potential range of VS and expanded the range north and east. We expect that the heterogeneous impacts of climate change across the CONUS will be exacerbated with additional changes in land use and land cover affecting biodiversity and hydrological cycles that are connected to the ecology of insect vectors involved in VS transmission. }, keywords = {big data; machine learning; vesicular stomatitis; land use; macrosystems; climate change; MaxEnt; livestock epidemiology; climate impact assessment}, doi = {10.3390/cli9110159 }, url = {files/bibliography/21-049.pdf}, author = {N. Dylan Burruss and Luis L Rodriguez and Barbara Drolet and Kerrie Geil and Angela Pelzel-McCluskey and Lee W Cohnstaedt and J. D. Derner and Debra C. Peters} } @conference {JRN55246, title = {Harnessing the power of AI technologies for ecology: Grass recovery in shrub dominated landscapes}, booktitle = {Ecological Society of America}, year = {2020}, month = {08/2020}, address = {Virtual Conference}, abstract = {Many drylands of the world have experienced dramatic changes in vegetation from perennial grasslands to woody plant dominance over the past several centuries. These states are thought to be very stable under current climate as a result of feedbacks between woody plants and soil properties such that grass recovery rarely occurs, and restoration is difficult. When grass recovery occurs, spatiotemporal heterogeneity in recovery patterns at multiple scales makes it challenging to identify the drivers and processes governing those dynamics. New technologies that link big data analytics with computer vision and image processing are promising approaches that can move restoration ecology forward. A 15,708 m2 research site in the northern Chihuahuan Desert was cleared in 1996 and kept barren until 2007. Following treatment and a concurrent multi-year (2004-08) period of above-average rainfall, grasses appeared to recolonize the site. Our objectives were to: 1) analyze the patterns of vegetation change through time from over-head photos and 2) use deep learning and UAV fine-scale imagery to estimate above ground biomass and determine the drivers of grass biomass. To better understand the factors governing this process, we explored historic imagery and hyperspectral and thermal imagery paired with systematic vegetation monitoring and in situ automated sensor networks collecting soil moisture, sediment flux, and local climate data. Our objectives were to 1) explore the patterns of vegetation change through time and 2) use deep learning and a structure from motion approach to estimate above ground biomass. We found that the site transited from bare-soil to increased litter and short-lived forbs followed by the establishment of grasses. The perennial grasses Aristida sp. and Dasyochloa pulchella representing 26\% and 4\% of the foliar cover. Mesquite were also identified across the site and represented much less of the foliar cover (<2\%). In addition, 15 additional species of annual and perennial grasses and forbs, each representing <1\% of the foliar cover, were also identified. Automated classification of the grass recovery site with human guided deep learning was able to rapidly classify and provide volumetric estimates of the study area vegetation. Estimates of above-ground biomass were a function of soil moisture, soil water redistribution, and erosion processes. This approach, offered continuous classification of the composition, and once trained, requires much less time and effort to complete than traditional field methodologies (e.g. quadrat, line point intercept). Due to the continuous surface inputs, this approach may also increase the likelihood of identifying relatively rare, cryptic, or newly occurring species that may go undetected. Poster $\#$88102.}, author = {N. Dylan Burruss and Debra C. Peters} } @conference {JRN55139, title = {Using machine learning to model complex landscapes: predicting the geographic range of Vesicular Stomatitis across the western United States}, booktitle = {Ecological Society of America Abstracts}, year = {2019}, abstract = {

Complex relationships among a virus, host, vector, and environment challenge prediction of disease spread through time and space. In addition, changing climate and land use regimes result in novel risk to biota and emphasize the need to improve understanding of mechanisms governing disease. Vesicular Stomatitis (VS) is the most common vesicular livestock disease in North America. Recent investigations have characterized how VS generally spreads from its endemic range in Central America northward through the western United States (US). Despite regional insights into VS, there is still a need to explore the broader environmental relationships limiting the spatial extent of VS to improve our understanding of the potential geographic range. The objective of this study was to model the potential range of VS in the US under current and alternative climate scenarios. The current model of VS occurrence was constructed using a transdisciplinary big data-model integration approach coupled with machine learning to predict the potential maxima of VS occurrence using disparate data sources representing soil, climate, land use, host, and vector properties. An information-theoretic approach was used to construct and select a model which was projected into potential future climate scenarios. The current extent of VS is largely confined to the western portion of the United States. The probability of occurrence was most associated with low summer surface runoff, low winter and summer precipitation, moderate summer and fall vegetation greenness, and increased maximum winter temperatures. Predictions using alternative climate data suggest that reductions in NDVI and increases in temperature can result in predicted occurrences that are spatially explicit and non-uniform in their direction of change. Our results suggest that the result of modifications to the environment can be complex and may either mitigate or increase occurrence risks. We suggest that these results can help define priority areas for research, monitoring, and mitigation efforts. Moreover, this analysis provides an illustration of the robustness of the big data-model integration approach in exploring complex problems at multiple scales.

}, author = {N. Dylan Burruss and Debra C. Peters and Luis L Rodriguez and David McVey and Emile H. Elias and Angela Pelzel-McCluskey and Heather Savoy and J. D. Derner and Steven H. Pauszek} } @conference {JRN54908, title = {Invasive Plant Expansion on Spatially Heterogeneous Landscapes: Hot Spots and Hot Moments}, booktitle = {Ecological Society of America}, year = {2018}, month = {08/2018}, publisher = {Ecological Society of America}, organization = {Ecological Society of America}, address = {New Orleans, LA}, abstract = {

Lehmann lovegrass (Eragrostis lehmanniana) is an invasive non-native perennial grass introduced at multiple locations in the American Southwest in the 1930s to restore ground cover and forage production on overgrazed, eroded rangelands. Since that time it has aggressively expanded throughout the Sonoran Desert where now dominates many landscapes. The Chihuahuan Desert, in contrast, has been resistant to its expansion, with local E. lehmanniana patches being dynamic without a clear trend. Climate and soils are known to influence broad-scale distributions of E. lehmanniana. We hypothesize that at the landscape-scale, expansion will be affected by seed availability. Accordingly, we sought to identify the {\textquotedblleft}hot spots{\textquotedblright} and {\textquotedblleft}hot moments{\textquotedblright} of spread across a landscape with spatially heterogeneous soils, climate, and current patch locations that affect seed availability. Long-term climate and spatially-resolved soils data were used to parameterize and test a numerical ecosystems model at the Jornada LTER-USDA site in the northern Chihuahuan Desert. We simulated E. lehmanniana recruitment and spread either assuming (i) seeds were always available or (ii) seed availability was a function of the distance from an existing patch. When seed availability was not constrained, E. lehmanniana recruitment was largely confined to years with above-average annual temperature and variable precipitation. Soils with higher silt content intensified the climate effects on recruitment. When seed availability was constrained, distance from existing patches was the primary driver of E. lehmanniana expansion and it interacted with climatic and edaphic parameters and generate non-linear rates and patterns of spread. Areas downwind from established patches and areas near large patches had the highest E. lehmanniana establishment probabilities. This approach can be used to strategically (i) identify hot spots and hot moments of potential E. lehmanniana expansion into native Chihuahuan Desert ecosystems under dynamic and changing climatic conditions and (ii) prioritize the location of monitoring sites and the timing of their sampling.

}, author = {N. Dylan Burruss and Debra C. Peters and Steven R. Archer} } @conference {JRN54436, title = {The ecology of catastrophic events: understanding abrupt spatial transitions in susceptibility of grasslands and croplands to multi-year drought}, booktitle = {100th Annual Meeting of the Ecological Society of America}, year = {2015}, month = {08/2015}, address = {Baltimore, MD}, abstract = {

Much of the central grasslands region (CGR) of North America experienced a multi-year extreme drought in the 1930s that combined with land management practices to result in broad-scale plant mortality, massive dust storms, and losses of soil and nutrients. All grassland types in the CGR were affected, but to varying degrees. The southern mixedgrass prairie had the most severe impacts on vegetation and soil erosion; these grasslands were also well-studied by early eminent ecologists (e.g., Weaver, Albertson). In some areas, the transition between severely impacted and not impacted sites occurred over short distances, yet our understanding of the drivers of these abrupt transitions remains limited. For example, native prairie sites in southeastern Nebraska suffered high plant mortality while sites on similar soils located \< 100 km to the east in southwestern Iowa only had reductions in cover.\  Our goal was to synthesize historic data and maps from the 1920s to 1940s along this transition zone in order to determine the climatic, soil, and land use drivers of these abrupt changes in vegetation. We analyzed data through time from the literature for native grasslands and from the USDA Census of Agriculture for cropland to compare land use effects on vegetation change. Our results show that the transition from reductions in native grass cover in Iowa to increase in grass mortality in Nebraska in the 1930s drought was related to small increases in temperature and subtle decreases in precipitation along this spatial gradient. Although soil texture was similar, long-term plant available water decreased from east to west corresponding to an increase in grass mortality and increase in susceptibility to drought. Corn yields in the 1930s also decreased along this east to west gradient in plant available water with lower yields in Nebraska compared with Iowa. These abrupt transitions in vegetation would have resulted in an increase in susceptibility to multi-year drought, and an increase in erosion by wind and water through time in the native grasslands where grass mortality occurred. These cumulative effects of multi-year drought challenge our ability to predict ecosystem responses to catastrophic events without long-term data on these types of events.\  Abstract $\#$ PS-161.

}, author = {N. Dylan Burruss and Debra C. Peters and Jin Yao and Kris Havstad and Stacey L P Scroggs} } @conference {JRN54424, title = {Explaining abrupt spatial transitions in agro-ecosystem responses to periods of extended drought}, booktitle = {2015 LTER All Scientists Meeting}, year = {2015}, month = {08/2015}, abstract = {

During the 1930{\textquoteright}s, the North American central grassland region (CGR) experienced an extreme multi-year drought that resulted in broad scale plant mortality, massive dust storms and losses of soil and nutrients. Southern mixed grasslands were among the worst affected and experienced severe broad scale mortality.However, the severity of drought effects were varied in other grassland types of the CGR and in some instances responses formed spatially abrupt transitions between severe and minor drought impacts over short distances. This is exemplified in native grassland sites in southeastern Nebraska which suffered severe plant mortality while neighboring and ecologically similar sites in southwestern Iowa experienced reductions in cover. Additionally, a similar abrupt spatial transition was observed for crop production for the same area.\  Despite the environmental and economic consequences of the 1930{\textquoteright}s drought our understanding of the drivers of these abrupt spatial transitions remains limited. Our objectives were to use available long term data to: 1) characterize the spatial and temporal patterns in responses of native plants and crops before, during, and after the 1930s drought, and 2) to identify the climatic, soil, and land use drivers for the abrupt spatial transition in plant responses. We used corn production from the USDA Census of Agriculture for cropland as a proxy for grassland response. Published data and maps from the 1920{\textquoteright}s to 1940{\textquoteright}s along this transition zone were used to identify the climatic, soil and land use drivers contributing to abrupt transitions zones. Our results suggest the transition from reduction in grass cover in Iowa to severe grass mortality in Nebraska during the 1930{\textquoteright}s were the result of small increases in temperature and subtle decreases in precipitation along this spatial gradient. Additionally, long-term plant available water decreased from east to west corresponding to increased susceptibility to drought and ultimately increased grass mortality. These abrupt transitions in vegetation response would have resulted in an increase in ecosystem susceptibility to multi-year drought and, in grasslands where grass mortality occurred, increases in the rate of soil erosion by wind and water. Without long term data on these types of events cumulative effects of multiyear drought will continue to challenge our ability to predict ecosystem responses to catastrophic events.\  Paper No. 237.

}, author = {N. Dylan Burruss and Jin Yao and Kris Havstad and Stacey L P Scroggs} }