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We describe an AI recommender system (RS) with machine learning to harness past user choices and large volumes of data, yet account for changes in weather and management decisions characteristic of agricultural systems. Our goal is to maximize the use of data relevant to solving agricultural problems and improve the efficiency of the scientific workforce while also improving the accuracy of estimates of the amount of food produced. Our example shows how the RS learns data analysis choices from user behavior for predicting agricultural production responses to rainfall and learns to identify classes of agroecosystem responses to alternative climate scenarios. We account for changes in relationships using spatial and temporal statistics. The RS provides a powerful approach to make use of the large amounts of data and scientific expertise in the agricultural enterprise to predict agroecosystem dynamics under changing environmental conditions.

In drylands, most studies of extreme precipitation events examine effects of individual years or short-term events, yet multiyear periods (&gt, 3 y) are expected to have larger impacts on ecosystem dynamics. Our goal was to take advantage of a sequence of multiple long-term (4-y) periods (dry, wet, average) that occurred naturally within a 26-y time frame to examine responses of plant species richness to extreme rainfall in grasslands and shrublands of the Chihuahuan Desert. Our hypothesis was that richness would be related to rainfall amount, and similar in periods with similar amounts of rainfall. Breakpoint analyses of water-year precipitation showed five sequential periods (1993–2018): AVG1 (mean = 22 cm/y), DRY1 (mean = 18 cm/y), WET (mean = 30 cm/y), DRY2 (mean = 18 cm/y), and AVG2 (mean = 24 cm/y). Detailed analyses revealed changes in daily and seasonal metrics of precipitation over the course of the study: the amount of nongrowing season precipitation decreased since 1993, and summer growing season precipitation increased through time with a corresponding increase in frequency of extreme rainfall events. This increase in summer rainfall could explain the general loss in C3 species after the wet period at most locations through time. Total species richness in the wet period was among the highest in the five periods, with the deepest average storm depth in the summer and the fewest long duration (&gt, 45 day) dry intervals across all seasons. For other species-ecosystem combinations, two richness patterns were observed. Compared to AVG2, AVG1 had lower water-year precipitation yet more C3 species in upland grasslands, creosotebush, and mesquite shrublands, and more C4 perennial grasses in tarbush shrublands. AVG1 also had larger amounts of rainfall and more large storms in fall and spring with higher mean depths of storm and lower mean dry-day interval compared with AVG2. While DRY1 and DRY2 had the same amount of precipitation, DRY2 had more C4 species than DRY1 in creosote bush shrublands, and DRY1 had more C3 species than DRY2 in upland grasslands. Most differences in rainfall between these periods occurred in the summer. Legacy effects were observed for C3 species in upland grasslands where no significant change in richness occurred from DRY1 to WET compared with a 41% loss of species from the WET to DRY2 period. The opposite asymmetry pattern was found for C4 subdominant species in creosote bush and mesquite shrublands, where an increase in richness occurred from DRY1 to WET followed by no change in richness from WET to DRY2. Our results show that understanding plant biodiversity of Chihuahuan Desert landscapes as precipitation continues to change will require daily and seasonal metrics of rainfall within a wet-dry period paradigm, as well as a consideration of species traits (photosynthetic pathways, lifespan, morphologies). Understanding these relationships can provide insights into predicting species-level dynamics in drylands under a changing climate.

Alternative states maintained by feedbacks are notoriously difficult, if not impossible, to reverse. Although positive interactions that modify soil conditions may have the greatest potential to alter self‐reinforcing feedbacks, the conditions leading to these state change reversals have not been resolved. In a 9‐yr study, we modified horizontal connectivity of resources by wind or water on different geomorphic surfaces in an attempt to alter plant–soil feedbacks and shift woody‐plant‐dominated states back toward perennial grass dominance. Modifying connectivity resulted in an increase in litter cover regardless of the vector of transport (wind, water) followed by an increase in perennial grass cover 2 yr later. Modifying connectivity was most effective on sandy soils where wind is the dominant vector, and least effective on gravelly soils on stable surfaces with low sediment movement by water. We found that grass cover was related to precipitation in the first 5 yr of our study, and plant–soil feedbacks developed following 6 yr of modified connectivity to overwhelm effects of precipitation on sandy, wind‐blown soils. These feedbacks persisted through time under variable annual rainfall. On alluvial soils, either plant–soil feedbacks developed after 7 yr that were not persistent (active soils) or did not develop (stable soils). This novel approach has application to drylands globally where desertified lands have suffered losses in ecosystem services, and to other ecosystems where connectivity‐mediated feedbacks modified at fine scales can be expected to impact plant recovery and state change reversals at larger scales, in particular for wind‐impacted sites.

Vesicular stomatitis (VS) outbreaks in the western USA occur cyclically, approximately every 8–10 years. Phylogenetic evidence based on a 450 nt region of the P coding sequences suggests that the initial introduction was a single viral lineage closely related to those circulating in endemic areas of Mexico. In 2004, a VS outbreak was initiated in southern NM and TX, and spread as far north as northern CO. Subsequently, in 2005, VS cases appeared in nine states (AZ, CO, ID, MT, NE, NM, TX, UT and WY), and in 2006, VS reappeared only in WY. Phylogenetic analysis suggested that a single VS virus of New Jersey (VSNJV) lineage caused the 2004 outbreak, and re-emerged in 2005 and 2006. The mechanisms of VS emergence and re-emergence remain unclear. Here, we used near full-length genomic sequences of 60 viral strains isolated from 2004–2006 in the US and Mexico to determine the phylogeographic relationships and environmental variables associated with the outbreak dynamics. The results confirmed that a single VSNJV lineage caused the 2004–2006 US outbreaks, and that its closest ancestor was a virus circulating in Colima, Mexico in 2004. We also present evidence that the virus lineage overwintered in 2005 and 2006. Furthermore, rather than a simple geographic relationship, specific viral sublineages or patristic groups were associated to environmental variables, particularly precipitation and temperature. The results confirm the role of environmental factors in the evolution and spread of VSNJV in the USA.

Climate change is predicted to impact ecosystems through altered precipitation (PPT) regimes. In the Chihuahuan Desert, multiyear wet and dry periods and extreme PPT pulses are the most influential climatic events for vegetation. Vegetation responses are most frequently studied locally, and regional responses are often unclear. We present an approach to quantify correlation of PPT and vegetation responses (as Normalized Difference Vegetation Index [NDVI]) at the Jornada ARS‐LTER site (JRN; 550 km2 area) and the surrounding dryland region (from 0 to 500 km distance; 400,000 km2 study area) as a way to understand regional similarity to locally observed patterns. We focused on fluctuating wet and dry years, multiyear wet or dry periods of 3–4 yr, and multiyear wet periods that contained one or more extreme high PPT pulses or extreme low rainfall. In all but extreme high PPT years, JRN PPT was highly correlated (r > 0.9) to PPT across the regional study area (0–500 km distance; high correlation from 25th to 75th percentiles) and was highly correlated across a greater PPT range subregionally (0–200 km distance; high correlation from 10th to 90th percentiles). In contrast, the statistical distribution of JRN NDVI was less similar to that of regional NDVI. Yet, local‐regional NDVI similarity increased during multiyear periods to a maximum of >90% similarity for 10th–90th percentiles in a number of years. Thus, local‐regional heterogeneity in PPT and vegetation responses is reduced in both multiyear wet and dry periods, with the largest changes in climatic forcing and responses during multiyear wet periods. These wet and dry events support greater similarity between local‐regional PPT and vegetation response patterns. We conclude that site‐based research on multiyear periods can be extended to anticipate larger regional responses, and illustrate the opportunity to enhance understanding of future PPT change through increased focus on multiyear periods.

Dryland vegetation is influenced by biotic and abiotic land surface template (LST) conditions and precipitation (PPT), such that enhanced vegetation responses to periods of high PPT may be shaped by multiple factors. High PPT stochasticity in the Chihuahuan Desert suggests that enhanced responses across broad geographic areas are improbable. Yet, multiyear wet periods may homogenize PPT patterns, interact with favorable LST conditions, and in this way produce enhanced responses. In contrast, periods containing multiple extreme high PPT pulse events could overwhelm LST influences, suggesting a divergence in how climate change could influence vegetation by altering PPT periods. Using a suite of stacked remote sensing and LST datasets from the 1980s to the present, we evaluated PPT-LST-Vegetation relationships across this region and tested the hypothesis that enhanced vegetation responses would be initiated by high PPT, but that LST favorability would underlie response magnitude, producing geographic differences between wet periods. We focused on two multiyear wet periods; one of above average, regionally distributed PPT (1990-1993) and a second with locally distributed PPT that contained two extreme wet pulses (2006-2008). 1990-1993 had regional vegetation responses that were correlated with soil properties. 2006-2008 had higher vegetation responses over a smaller area that were correlated primarily with PPT and secondarily to soil properties. Within the overlapping PPT area of both periods, enhanced vegetation responses occurred in similar locations. Thus, LST favorability underlied the geographic pattern of vegetation responses, whereas PPT initiated the response and controlled response area and maximum response magnitude. Multiyear periods provide foresight on the differing impacts that directional changes in mean climate and changes in extreme PPT pulses could have in drylands. Our study shows that future vegetation responses during wet periods will be tied to LST favorability, yet will be shaped by the pattern and magnitude of multiyear PPT events.