Melanoma and Other Melanistic Lesions in Brown Bullhead Ameiurus nebulosus From Waterbodies in the Northeastern United States and Canada: Identification of Risk Factors - Vicki S. Blazer; Peter Emerson; Matthew Bodnar; Thomas Jones; David R. Russell; Matthew Pehrson; Cheyenne R. Smith; Danielle Cleveland; Mark J. Henderson; Patricia M. Mazik (Journal of Fish Diseases, 2026)
Melanistic lesions, including non‐raised black areas due to proliferations of melanocytes and melanomacrophages in the dermis and epidermis, as well as raised black areas consistent with melanoma, are described in brown bullhead (BBH) Ameiurus nebulosus from three water bodies in the northeastern United States and Quebec, Canada. First observed in the Vermont portion of Lake Memphremagog, Vermont, USA and Quebec, Canada, the prevalence of melanistic lesions during 2014–2020 was greater than 30% in BBH 200 mm and longer. In 2023, seven sites throughout the lake were assessed, and prevalence ranged from 18% to 42%. In Hermon Pond, Maine, the prevalence was 29% in 2024, and in Village Pond, New Hampshire, lesions occurred in 22% of BBH in 2025. Compared to skin from visibly normal BBH, skin with melanistic lesions had significantly higher concentrations of seven metals, including arsenic, a known carcinogen and zinc. Lesions associated with oxidative damage, such as the accumulation of ceroid/lipofuscin, were also observed in the gill, spleen and kidney tissue of both affected and visibly normal BBH. The progression of lesions, observed by histopathology, ranged from inflammation, signs of oxidative damage, proliferation and necrosis of club cells, and the presence of melanomacrophages and melanocytes in the epidermis to invasive melanoma and suggests chronic exposure of BBH to environmental initiators and promoters of carcinogenesis.

Chronic exposure to waterborne nickel significantly reduced growth of juvenile crayfish (Faxonius virilis) - Adrian P. Moore; Mark L. Wildhaber; Zachary D. Beaman; Kendell R. Bennett; Karlie K. Ditter; Danielle Cleveland; Jenna A. Blanton; Tyler J. Grant (Ecotoxicology, 2026)

“I’m like, whatever you want me to be. I’m the flavor of the day”: A mixed-methods study of the food dispositions and behaviors of mixed-race individuals - Mark Cleveland; Chenzi Feng Zhao; Sam Ghebrai (Food Quality and Preference, 2024)

Comparative subchronic toxicity of copper and a tertiary copper mixture to early life stage rainbow trout (Oncorhynchus mykiss): impacts on growth, development, and histopathology - Michael E. McKay; Laura Baseler; Jordan Beblow; Mark Cleveland; Vicki L. Marlatt (Ecotoxicology, 2024)

Wild salmon enumeration and monitoring using deep learning empowered detection and tracking - William I. Atlas; Sami Ma; Yi Ching Chou; Katrina Connors; Daniel Scurfield; Brandon Nam; Xiaoqiang Ma; Mark Cleveland; Janvier Doire; Jonathan W. Moore; Ryan Shea; Jiangchuan Liu (Frontiers in Marine Science, 2023)
Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning salmon abundance is often monitored with in-river video or sonar cameras. However, reviewing video for estimates of salmon abundance from these programs requires thousands of hours of staff time, and data are typically not available until after the fishing season is completed. Computer vision deep learning can enable rapid and reliable processing of data, with potentially transformative applications in salmon population assessment and fishery management. Working with two First Nations fishery programs in British Columbia, Canada, we developed, trained, and tested deep learning models to perform object detection and multi-object tracking for automated video enumeration of salmon passing two First Nation-run weirs. We gathered and annotated more than 500,000 frames of video data encompassing 12 species, including seven species of anadromous salmonids, and trained models for multi-object tracking and species detection. Our top performing model achieved a mean average precision (mAP) of 67.6%, and species-specific mAP scores > 90% for coho and > 80% for sockeye salmon when trained with a combined dataset of Kitwanga and Bear Rivers’ salmon annotations. We also tested and deployed a prototype for a real-time monitoring system that can perform computer vision deep learning analyses on site. Computer vision models and off-grid monitoring systems show promise for automated counting and species identification. A key future priority will be working with stewardship practitioners and fishery managers to apply salmon computer vision, testing and applying edge-capable computing solutions for in-situ analysis at remote sites, and developing tools for independent user-led computer vision analysis by non-computer scientists. These efforts can advance in-season monitoring and decision making to support adaptive management of sustainable wild salmon fisheries.

Exploring the Role of Cryptic Nitrogen Fixers in Terrestrial Ecosystems: A Frontier in Nitrogen Cycling Research - Cory C. Cleveland; Carla R. G. Reis; Steven S. Perakis; Katherine A. Dynarski; Sarah A. Batterman; Timothy E. Crews; Maga Gei; Michael J. Gundale; Duncan N. L. Menge; Mark B. Peoples; Sasha C. Reed; Verity G. Salmon; et al. (Ecosystems, 2022)

Mutations in CENPE define a novel kinetochore-centromeric mechanism for microcephalic primordial dwarfism - Ghayda M. Mirzaa; Benjamin Vitre; Gillian Carpenter; Iga Abramowicz; Joseph G. Gleeson; Alex R. Paciorkowski; Don W. Cleveland; William B. Dobyns; Mark O’Driscoll (Human Genetics, 2014)

Global patterns in belowground communities - Noah Fierer; Michael S. Strickland; Daniel Liptzin; Mark A. Bradford; Cory C. Cleveland (Ecology Letters, 2009)
Although belowground ecosystems have been studied extensively and soil biota play integral roles in biogeochemical processes, surprisingly we have a limited understanding of global patterns in belowground biomass and community structure. To address this critical gap, we conducted a meta‐analysis of published data (> 1300 datapoints) to compare belowground plant, microbial and faunal biomass across seven of the major biomes on Earth. We also assembled data to assess biome‐level patterns in belowground microbial community composition. Our analysis suggests that variation in microbial biomass is predictable across biomes, with microbial biomass carbon representing 0.6–1.1% of soil organic carbon ( r 2 = 0.91) and 1–20% of total plant biomass carbon ( r 2 = 0.42). Approximately 50% of total animal biomass can be found belowground and soil faunal biomass represents