The dolphin FRESH protocol: visual Freshwater-Related Evaluation of Skin Health in free-ranging bottlenose dolphins (Tursiops spp.) - Kristi L. Fazioli; Christina N. Toms; Ryan Takeshita; Alyssa Quackenbush; Denise Greig; Teresa Rowles; Lori Schwacke; Kathleen Colegrove; Ruth Y. Ewing; Deborah Fauquier; Denise Boyd; Aaron Bouwkamp; et al. (Frontiers in Marine Science, 2026)
Exposure to freshwater is a pressing health issue for coastal bottlenose dolphins ( Tursiops spp.). Environmental changes, including increased precipitation events and coastal infrastructure projects, are altering salinity within estuarine systems. Consequently, understanding effects of freshwater exposure on dolphins and developing tools to evaluate related health conditions is urgent. To address this need, a group of veterinarians, pathologists, epidemiologists, natural resource managers, and field biologists convened to create a protocol to visually assess freshwater-related skin lesions in free-ranging bottlenose dolphins. The Dolphin FRESH (Freshwater-Related Evaluation of Skin Health) Protocol guides users without medical backgrounds to screen and evaluate photographs by focusing on the visual identification of three primary indicators of freshwater skin disease: Overgrowth, Target-like Lesions, and Light Discoloration. By determining presence of the primary indicators and scoring associated characteristics, FRESH provides users with a relative assessment of the severity of these skin anomalies, and metrics to track progressive changes. The Scoring Rubric performed well during systematic testing, with evaluators correctly identifying freshwater cases through recognition of primary indicators and with no significant differences in total severity scores between field biologists and medical experts. FRESH is an important step in advancing knowledge on the effects of salinity fluctuations on dolphin health. When applied to photo datasets over time, this tool will enable researchers and managers to evaluate progression and regression of freshwater skin disease, occurrence and effects of multiple exposures, and the relationship between freshwater exposure skin indicators and health and survival outcomes.

Novel Method for Collecting Skin Microbiome Samples From Free‐Ranging Bottlenose Dolphin - Joseph J. Hansen; Jennifer C. G. Bloodgood; Brandi Kiel Reese; Thomas B. Waltzek; Kevin Barry; Thibaut Bouveroux; Carrie Sinclair; Kuttichantran Subramaniam; Keith D. Mullin; Ruth H. Carmichael (Marine Mammal Science, 2026)
Cetacean skin microbiome samples are traditionally collected by swabbing the skin of captured animals or using skin tissue from remote biopsies. The effects of different sampling methods are not well understood, and factors such as swab type and storage methods may affect the microbiome. The objective of this study was to determine if samples collected by swabbing biopsied skin from bottlenose dolphins yield sufficient microbial DNA for gene sequencing, while testing if commonly used sampling and storage methods affect the skin microbiome. Skin swab samples were collected during remote biopsy surveys with either a nylon flocked swab and flash frozen with liquid nitrogen or with a cotton swab and stored in RNAlater. Sequencing of the V3–V4 region of the 16S rRNA gene revealed swabs from dolphin skin biopsies yielded sufficient DNA to characterize the microbiome. We found no significant difference between the two sampling and storage methods tested, suggesting that studies using different techniques may be comparable. This work also demonstrates a novel technique to collect samples for interpretation of the skin microbiome of free‐ranging animals. Benefits of this method are that it can be done without capture and preserves valuable and typically scarce biopsy skin samples for other analyses.

Vegetation management and benthic macroinvertebrate communities in urban stormwater ponds: implications for regional biodiversity - James S. Sinclair; Lindsey S. Reisinger; Carrie R. Adams; Eban Bean; Alexander J. Reisinger; Basil V. Iannone (Urban Ecosystems, 2021)

A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts - David Gomez-Cabrero; Stefan Walter; Imad Abugessaisa; Rebeca Miñambres-Herraiz; Lucia Bernad Palomares; Lee Butcher; Jorge D. Erusalimsky; Francisco Jose Garcia-Garcia; José Carnicero; Timothy C. Hardman; Harald Mischak; Petra Zürbig; et al. (GeroScience, 2021)