Day-neutral flowering candidate genes identified by selection mapping in an upland cotton (Gossypium hirsutum L.) RIL population - Ganesh Pinnika; Martin J. Wubben; Jack C. McCarty; David D. Fang; Gregory N. Thyssen (Theoretical and Applied Genetics, 2026)

Contrasting temporal drivers of methane flux across land uses in freshwater wetlands - Graham A. Stewart; Michael R. Williams; Gregory W. McCarty; Margaret A. Palmer (Limnology and Oceanography, 2026)
Wetlands are collectively the largest natural source of methane (CH 4 ), which complicates the climate mitigation potential of wetland restoration. Land‐use change alters wetland soils, vegetation, and hydrology, but the impact of this disturbance legacy on CH 4 fluxes from restored wetlands is uncertain. To better understand CH 4 dynamics across a gradient of human disturbance, we examined 5 yr of continuous CH 4 flux data from a drained, restored, and least‐disturbed (“natural”) freshwater mineral‐soil wetland. We quantified temporal relationships between CH 4 flux and three key biophysical drivers: soil temperature, gross primary productivity, and hydrology. Using information theoretic and nonlinear state‐space reconstruction approaches, we assessed how these relationships varied across different time scales and evaluated evidence for causality. All sites had highest diel CH 4 fluxes at night, primarily driven by gross primary productivity in the natural wetland and soil temperature in the restored and drained wetland. The seasonal scale accounted for most variability in CH 4 fluxes at the natural and restored wetland, with fluxes influenced by the hysteretic causal effects of soil temperature and water level. The effects of seasonal drivers were less evident at the drained site, where fluxes varied more on shorter timescales. Overall, CH 4 fluxes in the natural and restored wetland were comparable in magnitude, had similar temporal patterns, and responded similarly to drivers, indicating convergence in underlying processes. Our work furthers understanding of how land‐use history modulates wetland ecosystem processes, which is essential for accurate climate projections and effective management amid global change.

Ecovoltaic wild bee pollinator habitat may be a key to habitat connectivity and pollination services in solar-agricultural landscapes - Audra L. Hinson; Gregory W. McCarty; George W. Ogilvie-Russell; Xuesong Zhang; Rich Iovanna; Gordon Kretser; Cathleen J. Hapeman (Energy, Ecology and Environment, 2026)
Photovoltaics is emerging as the most widespread renewable energy sources globally, yet with increased reliance on alternative energy sources, concerns for land usage have been increased as well. Photovoltaic farms, while typically converted from agricultural farms, have the ability to be key multipurpose lands, incorporating agricultural or conservation initiatives in the same area. While developers advertise increased pollination services from under-panel pollinator habitat to surrounding agricultural lands, many sites plant and manage non-native grass instead of pollinator specific habitat. The purpose of this study was to estimate the potential crop yields from converting the non-native grassland under photovoltaic farms to pollinator habitat. We found that incorporating pollinator habitat into photovoltaic farms has the potential to have a substantial impact on crops in the surrounding landscape. We found yield increases of approximately 9.2% in sunflowers, 5.5% in soybeans, and up to 7.2% in alfalfa. In conjunction with other surrounding habitats as a whole, more connective landscape, wild pollination services can be responsible for yield increases of approximately 16.5% in sunflowers, and 13%, 10.8%, and 10% in alfalfa, soybean, and watermelon crops respectively. Soybean yield increases from photovoltaic pollinator habitat can be estimated on average to be worth 16 million USD, up to 39 million USD when part of the whole landscape from these wild pollination services. These results can be important for optimized management of photovoltaic farms and implementation of multiple purpose lands, as well as for the conservation of native bees, sustainability of agricultural landscapes, and increased food security.

Enhancement of hydrologic model optimization with single-step reinforcement learning - Byeongwon Lee; Hyemin Jeong; Younghun Lee; Gregory W. McCarty; Xuesong Zhang; Sangchul Lee (Journal of Hydrology, 2026)

Developmental PBDE exposure persistently impairs mast cell function in vivo and drives sex-specific morphological and transcriptional changes in bone marrow–derived mast cells - Jared Franges; Lauren Malinowski; Chathuri De Alwis; Taylor Doolittle; Yixing Li; Hannahlee Dixon; Yang Tang; Helen Watson; Jasmine Peace; Dereje Jima; Leslie Sombers; Gregory McCarty; et al. (Environmental Research, 2026)

Impacts of spatial scales and data distribution patterns on geographically weighted machine learning models for the spatial prediction of soil organic carbon - Younghun LEE; Hyemin JEONG; Ling DU; Xia LI; Xin ZHANG; Byeongwon LEE; Gregory W. MCCARTY; Sangchul LEE (Pedosphere, 2025)

Integrating vegetation constraints to reduce parameter uncertainty in watershed modeling of in-stream organic carbon - Byeongwon Lee; Hyemin Jeong; Younghun Lee; Minchang Kim; Yoonnoh Lee; Shinbeom Park; Min-Gyeong Kim; Moonil Kim; Gregory McCarty; Xuesong Zhang; Sangchul Lee (CATENA, 2025)

Morphometric and soil chemical properties govern spatiotemporal variations of porewater carbon in coastal mangroves - Xia Li; Ke Shi; Le Fang; Ying Peng; Qichun Yang; Guodong Su; Cheng Zhang; Junfeng Wang; Ling Du; Gregory W. McCarty (Journal of Hydrology, 2025)

Wood decomposition in poorly-drained forested wetland soils: How important are termites? - Mary Beth Adams; Chris Miller; Gregory W. McCarty; Megan Lang; Steve Strano; Deborah S. Page-Dumroese; Al Rizzo; Bradford M. Kard; Martin F. Jurgensen (Soil Biology and Biochemistry, 2025)

Relative abundance of atropisomer pairs in metolachlor metabolites, MESA and MOXA, vary with slope and hydric soils in subwatersheds of the Choptank River watershed, Maryland - Clifford P. Rice; Matthew H.H. Fischel; Marla R. Bianca; Bryan T. Vinyard; Gregory W. McCarty; Cathleen J. Hapeman (Science of The Total Environment, 2025)

Evaluation of heritability and genotype effects of resistance to reniform nematode (Rotylenchulus reniformis) in an upland cotton (Gossypium hirsutum) recombinant inbred line population derived from MT2468 Ren1 - Jixiang Wu; Gregory N. Thyssen; Amanda G. Gaudin; Jack C. McCarty; Johnie N. Jenkins; Martin J. Wubben (Euphytica, 2025)

Wetland classification based on depth-adaptive convolutional neural networks using leaf-off SAR imagery - Xin Zhang; Ling Du; Shen Tan; Gregory W. McCarty; Zhenhua Zou (Science of The Total Environment, 2024)

Synergistic water quality and soil organic carbon sequestration benefits of winter cover crops - Xuesong Zhang; Yiming Wang; Sangchul Lee; Kang Liang; Kaiguang Zhao; Gregory W. McCarty; Joseph G. Alfieri; Glenn E. Moglen; W. Dean Hively; Daniel T. Myers; Diana Oviedo-Vargas; Tam V. Nguyen; et al. (Journal of Environmental Management, 2024)

Modeling lateral carbon fluxes for agroecosystems in the Mid-Atlantic region: Control factors and importance for carbon budget - Xi Luo; Avay Risal; Junyu Qi; Sangchul Lee; Xuesong Zhang; Joseph G. Alfieri; Gregory W. McCarty (Science of The Total Environment, 2024)

Developmental framework for a desktop hydrogeomorphic wetland functional assessment derived from field-based data - Peter J. Backhaus; Denice H. Wardrop; Gregory W. McCarty; Robert P. Brooks (Environmental Monitoring and Assessment, 2024)
With loss of wetlands and their associated ecosystem services within landscapes, it is imperative to be able to understand the change in ecological functions underlying these services. Field-based functional assessments can produce a range of specific scores among a robust set of functions but are time and cost prohibitive as the number of wetlands assessed increases. Remote-based functional assessments are an alternative for broad scale assessments, but trade-off cost for limitations in scoring and functional assemblage. To address these concerns, we created a framework for the development of the Hydrogeomorphic Remote Assessment of Wetland Function (HGM-RAWF). Rooted in the hydrogeomorphic approach of an existing field-based functional assessment and its underlying models, this remote functional assessment substitutes field-based assessment methods with remotely assessed proxies. As potential remote proxies were determined through literature review and statistically screened for use in the remote assessment, a field-based reference wetland database of 222 freshwater wetlands in the Mid-Atlantic Region provided a baseline by which remote data could be compared and calibrated. The resulting HGM-RAWF protocol remotely assesses seven hydrology and biogeochemistry functions in the Mid-Atlantic with assessment scores similar to its field-based counterparts. With noted limitations, the HGM-RAWF framework provides the means to create desktop functional assessments across broad geographic scales with the diversity and specificity of field-based assessments at the reduced costs associated with remote assessments. Its basis in the HGM approach and use of public spatial datasets allows the framework to be adopted regionally and can be used as a model for national wetland functional assessment.

Drainage ditch network extraction from lidar data using deep convolutional neural networks in a low relief landscape - Ling Du; Gregory W. McCarty; Xia Li; Xin Zhang; Martin C. Rabenhorst; Megan W. Lang; Zhenhua Zou; Xuesong Zhang; Audra L. Hinson (Journal of Hydrology, 2024)

Simulating agroecosystem soil inorganic nitrogen dynamics under long-term management with an improved SWAT-C model - Kang Liang; Xuesong Zhang; Xin-Zhong Liang; Virginia L. Jin; Girma Birru; Marty R. Schmer; G. Philip Robertson; Gregory W. McCarty; Glenn E. Moglen (Science of The Total Environment, 2023)

Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay - Jiye Lee; Ather Abbas; Gregory W. McCarty; Xuesong Zhang; Sangchul Lee; Kyung Hwa Cho (Journal of Hydrology, 2023)

Genomic confirmation of Gossypium barbadense introgression into G. hirsutum and a subsequent MAGIC population - David D. Fang; Gregory N. Thyssen; Maojun Wang; Johnie N. Jenkins; Jack C. McCarty; Don C. Jones (Molecular Genetics and Genomics, 2023)

A GWAS identified a major QTL for resistance to Fusarium wilt (Fusarium oxysporum f. sp. vasinfectum) race 4 in a MAGIC population of Upland cotton and a meta-analysis of QTLs for Fusarium wilt resistance - Yi Zhu; Gregory N. Thyssen; Abdelraheem Abdelraheem; Zonghua Teng; David D. Fang; Johnie N. Jenkins; Jack C. McCarty; Tom Wedegaertner; Kater Hake; Jinfa Zhang (Theoretical and Applied Genetics, 2022)

GWAS reveals consistent QTL for drought and salt tolerance in a MAGIC population of 550 lines derived from intermating of 11 Upland cotton (Gossypium hirsutum) parents - Abdelraheem Abdelraheem; Gregory N. Thyssen; David D. Fang; Johnie N. Jenkins; Jack C. McCarty; Tom Wedegaertner; Jinfa Zhang (Molecular Genetics and Genomics, 2021)

Evaluation and genome-wide association study of Verticillium wilt resistance in a MAGIC population derived from intermating of eleven Upland cotton (Gossypium hirsutum) parents - Jinfa Zhang; Abdelraheem Abdelraheem; Gregory N. Thyssen; David D. Fang; Johnie N. Jenkins; Jack C. McCarty; Tom Wedegaertner (Euphytica, 2020)

A novel variant of Gh_D02G0276 is required for root-knot nematode resistance on chromosome 14 (D02) in Upland cotton - Martin J. Wubben; Gregory N. Thyssen; Franklin E. Callahan; David D. Fang; Dewayne D. Deng; Jack C. McCarty; Ping Li; Md Sariful Islam; Johnie N. Jenkins (Theoretical and Applied Genetics, 2019)

Whole genome sequencing of a MAGIC population identified genomic loci and candidate genes for major fiber quality traits in upland cotton (Gossypium hirsutum L.) - Gregory N. Thyssen; Johnie N. Jenkins; Jack C. McCarty; Linghe Zeng; B. Todd Campbell; Christopher D. Delhom; Md. Sariful Islam; Ping Li; Don C. Jones; Brian D. Condon; David D. Fang (Theoretical and Applied Genetics, 2019)

Analysis of manure and soil nitrogen mineralization during incubation - Francisco J. Calderón; Gregory W. McCarty; James B. Reeves (Biology and Fertility of Soils, 2005)