The study evaluated NOAA’s Seamless System for Prediction and Earth System Research, known as SPEAR, and the National Centers for Environmental Prediction’s Climate Forecast System version 2, or CFSv2, across the contiguous United States from 1991 to 2022. SPEAR is the newer of the two, developed at NOAA’s Geophysical Fluid Dynamics Laboratory with an atmospheric resolution of roughly 50 kilometers, and it runs a 15-member ensemble initialized from an ensemble coupled data assimilation system that blends in subsurface ocean temperature and salinity observations. CFSv2, operational since 2011, uses a coarser atmospheric grid of about 0.94 degrees and a 24-member lagged ensemble built from forecasts launched at four different times of day. Both systems were verified against the PRISM observational dataset, a high-resolution 4-kilometer gridded precipitation product built from quality-controlled weather stations, with sea surface temperatures checked against NOAA’s OISST and circulation fields against the ERA5 reanalysis.

The researchers deployed a battery of verification metrics designed to probe different aspects of forecast quality. Percentage bias quantified how much the models systematically over- or under-predicted rainfall, while the anomaly correlation coefficient measured whether forecasts tracked the year-to-year ups and downs of real precipitation. For the first time in this context, they also applied quantile-based categorical metrics, the critical success index, probability of detection, and false alarm ratio, to assess how well the models captured wet extremes of seasonal-mean precipitation, defined as seasons exceeding the 80th and 90th percentile thresholds of the climatological distribution. Statistical significance of model differences was tested with bootstrap resampling over 10,000 iterations, ensuring that the reported advantages of one model over the other were not artifacts of a small sample of hindcast years.

The headline finding is a stark seasonal asymmetry. During spring, both models exhibit modest but regionally coherent skill, with positive anomaly correlations across the West Coast, the north-central states, and parts of the Southwest. SPEAR’s CONUS-average spring correlations ranged from about 0.25 to 0.30, compared with roughly 0.18 to 0.28 for CFSv2, and the bootstrap test showed that SPEAR’s advantage was statistically significant only at the shortest one-month lead time. By summer, skill collapsed in both systems. SPEAR’s correlations fell to between 0.2 and 0.3 while CFSv2 hovered around 0.15, and over the South-Central United States, a case-study region encompassing Kansas, Oklahoma, and Texas, summer correlations dropped to nearly zero, exposing a profound inability of either system to anticipate summer rainfall anomalies in a region where drought and deluge both carry enormous economic stakes.

The bias analysis added further texture to the picture. In spring, both models were too wet over much of the central and eastern United States, but the problem was far worse in CFSv2, whose area-averaged gridded spring bias over the full continent reached 62 to 78 percent, compared with 31 to 33 percent for SPEAR. Over the South-Central region specifically, CFSv2’s spring wet bias climbed as high as 73 percent while SPEAR remained nearly neutral. Summer flipped the sign: SPEAR showed a robust dry bias over the continent and a severe 38 to 43 percent underestimation over the South-Central region, while CFSv2’s local wet and dry errors partially cancelled in the regional mean. The authors emphasize that how bias is summarized matters enormously, because compensating local errors can hide behind a seemingly benign regional average.

Perhaps the most practically troubling results concern wet extremes. When the researchers asked whether the models could identify seasons whose precipitation landed in the top 20 or top 10 percent of the historical distribution, the answer was largely no, especially in summer. Probability of detection declined sharply from the 80th to the 90th percentile threshold, while false alarm ratios climbed toward very high values across much of the country during June through August. Crucially, the spatial patterns of detection and false alarms mirrored the bias maps: in wet-biased regions the models trigger too many threshold exceedances, inflating both hits and false alarms simultaneously, whereas dry-biased regions suppress detections altogether. High detection rates, in other words, do not guarantee reliable forecasts when they come bundled with a flood of false alarms.

To understand why the models struggle, the team turned to the physics of the El Niño-Southern Oscillation, the dominant source of seasonal predictability for North American rainfall. An empirical orthogonal function analysis confirmed that the leading mode of tropical Pacific sea surface temperature variability during the study period was the canonical eastern-Pacific El Niño pattern, explaining 31.1 percent of SST variance and correlating at 0.95 with the standard Niño-3.4 index. Observations showed that the link between this tropical signal and South-Central U.S. rainfall is strongly seasonal: robust during winter and early spring, weakening through late spring and summer as regional convection, land-atmosphere feedbacks, and the Great Plains Low-Level Jet, which funnels Gulf of Mexico moisture into the continent’s interior, take center stage. Both models broadly reproduced this seasonal ebb and flow, though CFSv2 tended to overstate the SST-precipitation correlation in many seasons.

The centerpiece of the diagnostic work was a singular value decomposition analysis coupling tropical Pacific SST to CONUS precipitation. In spring, the observed leading mode explained 34.2 percent of the squared covariance and traced a coherent pathway: an El Niño-like SST pattern driving a Pacific-North American ridge-trough couplet in the mid-troposphere, with an anomalous ridge over the northeastern Pacific and a trough over the southeastern United States that steers Gulf moisture northward. Both models captured the qualitative structure of this spring teleconnection, but their 500-hectopascal geopotential height anomalies were only 40 to 60 percent of the observed amplitude. Strikingly, the ensemble-mean versions of the models showed squared covariance fractions of 70.5 percent for SPEAR and 87.7 percent for CFSv2, far above observations, yet when the decomposition was repeated for individual ensemble members the values fell back near the observed level. The inflation, the authors show, is a statistical artifact of ensemble averaging, which filters out chaotic internal atmospheric noise and isolates the forced SST signal rather than evidence that the models are genuinely over-dominated by El Niño.

In summer, the teleconnection machinery visibly breaks down in the models. The observed leading coupled mode shifts away from the canonical equatorial El Niño pattern toward western and off-equatorial Pacific anomalies, and the associated circulation features a deep anomalous trough over the central and eastern United States paired with rising motion that feeds continental rainfall. SPEAR retained some spatial fidelity, with a pattern correlation of 0.24 against the observed height regression, but CFSv2’s pattern correlation collapsed to 0.01, indicating a fundamental displacement of the summer circulation response. Vertical-velocity cross sections told the same story: the models produced weaker, smoother, and less vertically coherent ascent and descent anomalies than observations, consistent with known deficiencies in convective parameterizations and the simulation of Rossby wave propagation. An El Niño-like SST signal alone, the study concludes, is simply not enough; the atmospheric bridge that translates ocean warmth into rain must also be simulated faithfully.

The implications reach well beyond model scorecards. Seasonal forecasts underpin drought preparedness, reservoir operations, and agricultural planning, and the South-Central United States, a transition zone between the humid Southeast and the arid Southwest, sits squarely where those decisions hurt most when forecasts fail. The authors argue that future gains will require more than better ocean prediction: improved representation of SST-forced teleconnections, regional atmospheric variability, and additional warm-season drivers such as land-surface memory, soil moisture feedbacks, Gulf moisture transport, and modes of variability beyond ENSO. Until then, the study offers forecast users a candid calibration of what to trust: spring rainfall anomalies over the southern plains deserve guarded confidence, while summer outlooks, particularly for unusually wet seasons, should be treated with the skepticism that three decades of hindcast evidence now firmly justify.

Subject of Research: Seasonal forecast skill of the SPEAR and CFSv2 climate models for warm-season precipitation over the contiguous United States and its relation to ENSO teleconnections

Article Title: Relative skills of SPEAR and CFSv2 in foretelling warm-season precipitation anomalies over the conterminous United States and potential sources of errors

Article References: Nguyen, H., Zhang, Y., Bakhtar, A., Lu, F., & Fernando, N. (2026). Relative skills of SPEAR and CFSv2 in foretelling warm-season precipitation anomalies over the conterminous United States and potential sources of errors. Climate Dynamics, 64(9), Article 406. https://doi.org/10.1007/s00382-026-08357-z

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08357-z

Keywords: seasonal forecasting, SPEAR, CFSv2, precipitation, ENSO, teleconnections, climate models, drought, Great Plains, sea surface temperature, forecast verification, Climate Dynamics

The threats are mounting on multiple fronts. White-nose syndrome, the fungal disease caused by Pseudogymnoascus destructans, has devastated hibernating bat populations across eastern North America and is now pressing into the Plains. Wind energy development is concentrated here at the highest capacity of any land-based region on the continent, and wind turbines are a well-documented source of mortality, particularly for migratory species. Climate change, drought, pesticide bioaccumulation in insect prey, and ongoing conversion of native grassland to intensive agriculture all compound the pressure. Because no comprehensive synthesis of grassland bat research existed until now, conservation managers have been working largely without a map. The new review set out to change that by systematically cataloguing what has been studied, where, how, and, crucially, what has been missed.

The methodology was deliberately rigorous. The team defined the Great Plains using United States Environmental Protection Agency ecoregion boundaries, encompassing the Canadian provinces of Alberta, Saskatchewan, and Manitoba; thirteen US states from Montana and North Dakota down to Texas; and three Mexican provinces. They searched the Web of Science database using combinations of bat and grassland-related terms, added state and province names in a second round, and finally combed the publication records of 23 bat biologists known to work in the region to catch studies in smaller journals. Only primary field research published in English between 1995 and 2024 qualified; grey literature, reviews, and purely museum-based or modeling studies were excluded. After removing duplicates and irrelevant records, the three search rounds yielded the 94 publications that formed the basis of the synthesis.

The topical breakdown of those publications is revealing. Twenty-six reported simple distributional records in a natural-history style, making that the most common category. Roosting biology came second with 24 publications, followed by 20 studies of winter ecology, 12 quantitative habitat-association studies, and just 10 on foraging behavior or diet. Only two studies addressed migration movements, a striking gap given the region’s wind energy boom. Chronologically, winter ecology research was essentially absent until 2006, and habitat-association studies did not appear until 2011. Geographically, publications came from all three Canadian provinces and every US state in the region except Wyoming and Minnesota, while no English-language publications emerged from the Mexican portion of the ecoregion, a gap the authors acknowledge reflects language barriers rather than an absence of research.

One of the clearest findings concerns water. Ten of the habitat studies examined water sources, and all found them to be important components of bat habitat in this semi-arid landscape. Species including the big brown bat (Eptesicus fuscus), the hoary bat (Lasiurus cinereus), the silver-haired bat (Lasionycteris noctivagans), and the little brown bat (Myotis lucifugus) were all positively associated with water. One study even documented temporal niche partitioning, with different species drinking and foraging at different hours of the night. But the picture is complicated by the fact that in the Great Plains, water and trees tend to co-occur in riparian zones, making it difficult to disentangle which feature bats are actually responding to. Notably, two species showed negative associations with herbaceous wetlands, which offer water but lack the vertical structure bats seem to require.

That vertical structure, especially trees, emerged as the most studied biotic habitat feature. Eleven publications included vegetation variables, and most found that bat activity, abundance, or occupancy increased with proximity to trees and forest patches or with greater canopy cover. The northern long-eared myotis (Myotis septentrionalis) consistently showed positive associations with closed canopy and cluttered vegetation. Some results were contradictory, however, likely because studies measured vegetation at different spatial scales and quantified bats in different ways. Intriguingly, one study found acoustic activity of five bat species was significantly higher inside black-tailed prairie dog colonies than outside them, hinting that an umbrella species approach to conservation might benefit bats. Urban studies in Calgary, Alberta, showed that city zones hosted the highest bat abundance but the lowest diversity, dominated by little brown bats in poorer body condition, a possible ecological trap created by urbanization.

Roosting biology, the second most studied topic, highlights how bats cope with a landscape that historically offered few roosting opportunities. Ten species were documented roosting in trees, others in rock crevices and caves, and many in anthropogenic structures such as buildings, bridges, cabins, and culverts. Thermal microclimate was the most intensively studied factor in roost selection, with researchers using dataloggers and temperature-sensitive transmitters to show that stable internal conditions matter most, particularly for reproductive females. Findings on anthropogenic roosts varied with latitude: in central Texas, male Brazilian free-tailed bats (Tadarida brasiliensis) favored buildings, while at higher latitudes female big brown bats preferred buildings and enjoyed greater reproductive success than those in rock crevices. Long-term studies documented interannual fidelity to individual roost trees in several species, and big brown bats were observed relocating entire maternity colonies when roost trees were lost naturally.

Winter ecology may be the most ecologically surprising chapter of the review. Although the southern Plains stay warm enough for year-round bat activity, researchers documented bats overwintering far north in caves, rocky crevices, mines, and even road culverts. In the Badlands of North Dakota, six species were found overwintering, and in Alberta, big brown bats hibernate in rock crevices exposed to temperatures as low as minus 10.4 degrees Celsius. Winter flight activity, bats leaving hibernacula to fly during cold months, was recorded in at least eight species, typically peaking shortly after sunset and correlating with warmer ambient temperatures and calmer winds. Why bats fly in winter remains unresolved: foraging is unlikely because insects are absent, and an artificial water source experiment found no evidence bats used it for drinking. Proposed explanations include dehydration, muscle maintenance, waste elimination, and hibernacula switching, all of which matter for managing white-nose syndrome, which kills bats during the winter season.

The review’s authors are candid about why grassland bat research lags so far behind forest bat research. Logistically, the northern Plains have low human population density, few universities, and resource managers responsible for enormous tracts of land, leaving the North American Bat Monitoring Program (NABat) database conspicuously sparse for the region. Methodologically, mist-netting, the traditional capture tool, performs poorly in open grassland, and most capture-based studies were actually conducted in remnant forest patches. Biologically, Plains bats often sit at the periphery of their ranges, occurring at low densities where ecological processes may differ fundamentally from those in core populations. The authors call for multi-scale research leveraging NABat’s standardized monitoring, adoption of emerging technologies such as automated acoustic detection, radar, LiDAR, Motus tracking, and environmental DNA sampling, and urgent attention to migration, fire effects, and woody encroachment, a genuine conservation dilemma since encroaching trees may provide roosts and corridors even as they degrade native grassland. As wind farms expand and white-nose syndrome advances, the message of this synthesis is clear: the bats of the Great Plains have been flying under the scientific radar for thirty years, and closing that knowledge gap is now a conservation imperative.

Subject of Research: Ecology and conservation of bat populations in the North American Great Plains grassland ecosystem

Article Title: Bat ecology and conservation in the Great Plains: a systematic review of research in the past 30 years and future directions

Article References: Bat ecology and conservation in the Great Plains: a systematic review of research in the past 30 years and future directions. (n.d.). https://doi.org/10.1007/s44353-026-00082-2

Image Credits: AI Generated

DOI: 10.1007/s44353-026-00082-2

Keywords: bats, Great Plains, grasslands, white-nose syndrome, wind energy, roosting ecology, winter ecology, habitat association, foraging behavior, NABat, conservation, systematic review

Septoria nodorum blotch has grown into an economically significant problem over recent decades across the eastern United States, northern Europe, Australia, and parts of North Asia. The fungus attacks both leaves, producing leaf blotch, and spikes, causing glume blotch, and can slash yields by as much as 50 percent on susceptible cultivars. In the Great Plains, where hard winter wheat dominates production, the disease has become increasingly prevalent in recent years. Farming practices such as minimum or no-till agriculture, which leave more infected crop residue on the soil surface, may have contributed to the surge. Compounding the problem, isolates of the fungus collected from hard winter wheat in Oklahoma show unusually high genetic and virulence diversity, suggesting local pathogen populations have adapted to the region’s cultivars.

The central puzzle of the wheat-P. nodorum system lies in its inverted genetics. Unlike classic disease resistance, where a plant gene recognizes a pathogen molecule and triggers defense, susceptibility here follows an inverse gene-for-gene model. The fungus produces proteinaceous molecules called necrotrophic effectors, and when a corresponding sensitivity gene in the wheat host recognizes one of these effectors, it triggers programmed cell death. That cell death, paradoxically, feeds the necrotrophic fungus, which thrives on dead tissue. To date, researchers have cloned seven wheat sensitivity genes, including Tsn1-B1, Snn1-B1, Snn2, Snn3-B1, Snn3-B2, Snn3-D1, and Snn5-B1, along with five fungal effector genes named SnToxA, SnTox1, SnTox3, SnTox5, and SnTox267. Thirteen distinct sensitivity gene-effector interactions have been documented in this pathosystem.

To determine how widespread these susceptibility genes are in contemporary hard winter wheat, the research team, led by scientists at Oklahoma State University in collaboration with the USDA Agricultural Research Service and Kansas State University, assembled a panel of 619 genotypes. The panel included 532 doubled-haploid breeding lines derived from 14 bi-parental crosses, 38 elite breeding lines from the Oklahoma State University program, and 49 cultivars from the Oklahoma State and Kansas State breeding programs. Each genotype was tested at the seedling stage against five P. nodorum isolates originally collected from the winter wheat cultivar Gallagher in Canadian County, Oklahoma in 2016, and against purified preparations of the five known necrotrophic effectors.

The results revealed a sobering picture of genetic vulnerability. When inoculated with the fungal isolates, between 40 and 67 percent of the genotypes proved susceptible, depending on the isolate, while resistant genotypes ranged from only 5 to 21 percent. Effector infiltration assays, in which purified effector proteins were injected directly into leaves using a needleless syringe, showed that 54 percent of genotypes were sensitive to SnToxA, 37 percent to SnTox3, 15 percent to SnTox5, 13 percent to SnTox267, and just 2 percent to SnTox1. These frequencies indicate that Tsn1-B1 and Snn3-B1/B2 are the most common sensitivity genes in this germplasm, while functional Snn1-B1 is rare. Notably, 75 genotypes showed resistant to intermediate reactions against all five isolates, including the Oklahoma cultivars Uncharted, Bentley, Big Country, and OK Corral.

The team genotyped the entire panel using genotyping-by-sequencing, which yielded 34,357 high-quality single nucleotide polymorphism markers after filtering, along with diagnostic kompetitive allele-specific PCR, or KASP, markers for the sensitivity genes Tsn1-B1, Snn1-B1, and Snn3-B1/B2. The diagnostic markers performed remarkably well for some genes: the Tsn1-B1 markers achieved 98 percent prediction accuracy against SnToxA sensitivity, and the Snn3-B1/B2 markers reached 92 percent accuracy against SnTox3. The Snn1-B1 marker, by contrast, showed only 75 percent accuracy, with a 25 percent false positive rate, suggesting that structural variations such as mutations, deletions, or inversions may have disabled the gene in many lines without being detectable by the current marker.

Genome-wide association studies using three statistical models, the mixed linear model, FarmCPU, and BLINK, confirmed the presence of the known sensitivity genes Tsn1-B1 on chromosome 5BL, Snn1-B1 on 1BS, Snn3-B1/B2 on 5BS, Snn2 on 2DS, and Snn5-B1 on 4BL, each associated with responses to its corresponding effector. But the analysis went further, uncovering numerous novel loci scattered across the wheat genome that had never before been linked to septoria nodorum blotch responses. In total, the BLINK model identified 23 significant loci associated with responses to the fungal isolates and 49 loci associated with effector responses, positioned on chromosome arms including 1AS, 1AL, 1BL, 1DS, 2AL, 2BL, 3AS, 3AL, 3BS, 3BL, 4AL, 4BL, 5AS, 5AL, 6AL, 6BS, 6BL, 7BS, 7BL, and 7DL.

The most striking discovery was a locus on chromosome arm 2AS, designated Qsnb.osu-2AS, which was associated with responses to all five P. nodorum isolates tested. For three of the isolates, the effect of this locus on disease response actually exceeded that of the Tsn1 gene. The significant markers in this region spanned a physical interval of roughly 17.69 megabases containing 268 high-confidence genes, several of which are annotated as involved in disease resistance and defense-related pathways. Encouragingly, the resistant allele of Qsnb.osu-2AS was already present at frequencies of roughly 45 to 60 percent in the germplasm, and the region shows some overlap with a previously reported resistance quantitative trait locus, Qsnb.cur-2AS2, mapped in an earlier study.

Translating these discoveries into practical breeding tools, the researchers developed new KASP markers linked to Snn5-B1, Snn2, and Qsnb.osu-2AS, sensitivity and resistance loci that previously lacked diagnostic markers. The marker KASP_S4B_643615365, targeting the Snn5-B1 region on chromosome 4BL, achieved 89 percent accuracy in predicting SnTox5 sensitivity across the full panel, while KASP_S2D_16184991, linked to Snn2 on chromosome 2DS, reached 80 percent accuracy for SnTox267 sensitivity. Two additional markers, KASP_S2A_9833162 and KASP_S2A_14367498, were validated for selecting the resistant allele at Qsnb.osu-2AS. Together with the existing diagnostic markers for Tsn1-B1 and Snn3-B1/B2, these tools give breeders the ability to deliberately eliminate susceptibility genes and stack resistance alleles through marker-assisted selection.

The study also revealed evidence of fungal weapons not yet characterized. Several cultivars, including Green Hammer, currently the second most grown wheat variety in Oklahoma, were insensitive to all five known effectors and lacked the corresponding sensitivity genes, yet remained susceptible to particular isolates. This pattern strongly suggests that P. nodorum harbors additional, undiscovered effectors and that wheat carries uncharacterized sensitivity genes targeted by them. The researchers also noted intriguing epistatic effects among effectors, consistent with earlier findings that SnTox1 expression can be suppressed in the presence of SnToxA, which may explain why the Snn1 locus showed no association with responses to the whole isolates even though it responds to purified SnTox1. Because Tsn1 also confers susceptibility to tan spot and spot blotch, two other major fungal diseases, the authors argue that eliminating Tsn1-B1 and Snn3-B1/B2, which are present at high frequencies in contemporary hard winter wheat, should be the top priority for breeding programs. The study focused on seedling-stage responses, and the team emphasizes that future work will need to identify loci governing adult plant resistance under field conditions, where genotype-by-environment interactions and quantitatively inherited resistance play a larger role.

Subject of Research: Identification of known and novel septoria nodorum blotch susceptibility and resistance genes in US hard winter wheat using effector assays, diagnostic markers, and genome-wide association studies.

Article Title: Identification of septoria nodorum blotch susceptibility genes in hard winter wheat

Article References: Ara, A. M., Holmes, D. J., Friesen, T. L., Carver, B. F., Bai, G., Amand, P. S., Bernado, A., Sharma, R., & Aoun, M. (2026). Identification of septoria nodorum blotch susceptibility genes in hard winter wheat. Theoretical and Applied Genetics, 139(10), Article 268. https://doi.org/10.1007/s00122-026-05359-7

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05359-7

Keywords: septoria nodorum blotch, hard winter wheat, Parastagonospora nodorum, necrotrophic effectors, Tsn1, Snn3, genome-wide association study, KASP markers, marker-assisted selection, wheat breeding, disease resistance, Great Plains