A Framework to Investigate the Effects of Observation Error on Neural Network Predictions of Fish Age - Derek W. Chamberlin; Thomas E. Helser; John D. Brogan; Irina M. Benson; Jason Conner; Andrew T. Chin; Christopher M. Gburski; Mary Elizabeth Matta; Julie A. Pearce; Kali R. Stone; Todd T. TenBrink; Morgan B. Arrington (Fish and Fisheries, 2026)
Technological innovations for predicting fish age represent a paradigm shift from conventional age estimation methods used in fisheries science. Recently developed secondary methods rely on models trained on conventional age estimates, derived from subjective interpretation of growth patterns and a biological property of the fish to predict age. Hence, quantifying the error that propagates from these models and the conventional age estimates on which they are trained is critical to fully account for uncertainty in predicted ages used in fisheries stock assessments. We review the development of three secondary age prediction methods: image analysis, epigenetics and Fourier transform near‐infrared spectroscopy (FT‐NIRS); the use of artificial intelligence (AI) within each method; and present an approach to examine the effects of ageing error on AI age prediction models. As a case study, we conducted an empirical study, coupled with simulation, to quantify the effects of ageing error on the performance of a multimodal convolutional neural network (MMCNN) model used to predict eastern Bering Sea walleye pollock ( Gadus chalcogrammus ) ages from otoliths analysed using FT‐NIRS. Results indicated repeatability of predicted ages was high between instrument operators, while adding ageing error resulted in a slight decrease in model performance from R 2 = 0.92 and CV = 7.6% to R 2 = 0.87 and CV = 10.2% on test datasets. Our results also suggest the MMCNN model is robust to noise in calibration age data and model performance may be better than performance metrics indicate when the model is evaluated against data with increased error.

Health Outcomes Following Traumatic Brain Injury: An Umbrella Review - Jason M. Toliao; Junhyuk Lee; Hans Oh; San Lee; Lucas Conner; Stefan Blüml; Peter A. Chiarelli; Pradip P. Chaudhari (Neuropsychology Review, 2026)
An increasing number of systematic reviews and meta-analyses examine health outcomes from traumatic brain injury (TBI). This umbrella review examines the association of TBI with comorbid health outcomes and evaluates the methodological rigor of existing literature. This protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO; Registration No. CRD42024581341). EMBASE, PubMed, MEDLINE, and the Cochrane Library were searched from database inception to November 26, 2025. Two reviewers screened abstracts and full texts using predefined criteria. Meta-analyses of observational studies were included if they assessed health outcomes following TBI and reported effect sizes were convertible to pooled equivalent odds ratios (eOR). PRISMA (Preferred Reporting Items of Systematic Reviews and Meta-Analyses) guidelines were utilized for study formatting. We applied AMSTAR2 (A Measurement Tool to Assess Systematic Reviews 2) criteria to assess the quality of included studies. Health outcomes described in each meta-analysis were reanalyzed and presented through eOR using a random-effects model. 41 meta-analyses were included, covering 671 original studies and 53 comorbid health outcomes, in a cohort comprising 83 million participants across 40 countries. Our findings suggest that traumatic brain injury, acquired at any age, may be associated with impairments across neurological, somnological, cognitive, mental health, and behavioral domains. Despite these associations, 78% of the included studies were themselves classified as having “critically low” methodological quality by AMSTAR2 criteria, which limited the ability to provide causal conclusions. These findings may help inform care guidelines following TBI. Improved research frameworks are important to better understand the lasting effects of TBI.

The Toxicology Investigators Consortium 2024 Annual Report - Kim Aldy; Shao Li; Mari Costantini; Alyssa Falise; Rachel Culbreth; Paul M. Wax; Jeffery Brent; Michael Abesamis; Faiz Ahmed; Salman Ahsan; Peter Akpunonu; Adam Algren; et al. (Journal of Medical Toxicology, 2026)

SNAPSHOT USA 2019–2023: The First Five Years of Data From a Coordinated Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Benjamin R. Goldstein; Christopher Pate; Paula Castiblanco; Maggie E. Abell; Jessie Adley; Briana Agenbroad; Adam A. Ahlers; Peter D. Alexander; et al. (Global Ecology and Biogeography, 2025)
Motivation SNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.

SNAPSHOT USA 2021: A third coordinated national camera trap survey of the U nited S tates - Hila Shamon; Roi Maor; Michael V. Cove; Roland Kays; Jessie Adley; Peter D. Alexander; David N. Allen; Maximilian L. Allen; Cara L. Appel; Evan Barr; Erika L. Barthelmess; Carolina Baruzzi; et al. (Ecology, 2024)
SNAPSHOT USA is a multicontributor, long‐term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application ( https://www.snapshot-usa.org/ ). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights ( https://www.wildlifeinsights.org/ ), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22–5.9 arrays per 100,000 km 2 ), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID ‐19 pandemic - Roland Kays; Michael V. Cove; Jose Diaz; Kimberly Todd; Claire Bresnan; Matt Snider; Thomas E. Lee; Jonathan G. Jasper; Brianna Douglas; Anthony P. Crupi; Katherine C. B. Weiss; Helen Rowe; et al. (Ecology, 2022)
Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap‐nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID‐19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site‐level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.

Are we ready to track climate‐driven shifts in marine species across international boundaries? ‐ A global survey of scientific bottom trawl data - Aurore A. Maureaud; Romain Frelat; Laurène Pécuchet; Nancy Shackell; Bastien Mérigot; Malin L. Pinsky; Kofi Amador; Sean C. Anderson; Alexander Arkhipkin; Arnaud Auber; Iça Barri; Richard J. Bell; et al. (Global Change Biology, 2021)
Marine biota are redistributing at a rapid pace in response to climate change and shifting seascapes. While changes in fish populations and community structure threaten the sustainability of fisheries, our capacity to adapt by tracking and projecting marine species remains a challenge due to data discontinuities in biological observations, lack of data availability, and mismatch between data and real species distributions. To assess the extent of this challenge, we review the global status and accessibility of ongoing scientific bottom trawl surveys. In total, we gathered metadata for 283,925 samples from 95 surveys conducted regularly from 2001 to 2019. We identified that 59% of the metadata collected are not publicly available, highlighting that the availability of data is the most important challenge to assess species redistributions under global climate change. Given that the primary purpose of surveys is to provide independent data to inform stock assessment of commercially important populations, we further highlight that single surveys do not cover the full range of the main commercial demersal fish species. An average of 18 surveys is needed to cover at least 50% of species ranges, demonstrating the importance of combining multiple surveys to evaluate species range shifts. We assess the potential for combining surveys to track transboundary species redistributions and show that differences in sampling schemes and inconsistency in sampling can be overcome with spatio‐temporal modeling to follow species density redistributions. In light of our global assessment, we establish a framework for improving the management and conservation of transboundary and migrating marine demersal species. We provide directions to improve data availability and encourage countries to share survey data, to assess species vulnerabilities, and to support management adaptation in a time of climate‐driven ocean changes.

Adaptive Ankle Resistance from a Wearable Robotic Device to Improve Muscle Recruitment in Cerebral Palsy - Benjamin C. Conner; Jason Luque; Zachary F. Lerner (Annals of Biomedical Engineering, 2020)

Interspecific Differences in Aethia Spp. Auklet Odorants and Evidence for Chemical Defense Against Ectoparasites - Hector D. Douglas; Jason E. Co; Tappey H. Jones; William E. Conner (Journal of Chemical Ecology, 2004)