An adaptable, user-friendly score sheet to monitor welfare in experimental fish.

Fish are increasingly used as experimental animals across research fields. Currently, around a quarter of all experimental animals used are fish. Less than 20% of these are standard model species. Welfare assessments for experimental fish are in their infancy compared with those for rodents. This can be attributed to the diversity of species used, the relative recency of fish as the go-to model for research, and challenges to assess welfare in non-vocal underwater species. The lack of guidelines and tools presents a challenge for researchers (particularly, for newcomers), for ethics committees and for implementing refinement measures. Here, we present an adaptable, user-friendly score sheet for fish based on MS Excel. The parameters are based on a literature review, have been validated by expert interviews and evaluated by a fish pathologist. The tool allows scoring of individual fish as well as groups, calculates summary scores and visualizes trends. We provide the underlying literature, give use examples and provide instructions on the adaptation and use of the score sheet. We hope that this tool will empower researchers to include welfare assessment in their routines, foster discussions on fish welfare parameters among scientists, facilitate interactions with ethics committees and, most importantly, enable the refinement of fish experiments.

Institutions

Publication Details

Journal
Open Access CRIS of the University of Bern
Published
2025-01-09
DOI
https://doi.org/10.48620/84511
Primary Topic
Zebrafish Biomedical Research Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An adaptable, user-friendly score sheet to monitor welfare in experimental fish.

Open Access CRIS of the University of Bern
Zebrafish Biomedical Research Applications
article

An adaptable, user-friendly score sheet to monitor welfare in experimental fish.

article en

Abstract

Fish are increasingly used as experimental animals across research fields. Currently, around a quarter of all experimental animals used are fish. Less than 20% of these are standard model species. Welfare assessments for experimental fish are in their infancy compared with those for rodents. This can be attributed to the diversity of species used, the relative recency of fish as the go-to model for research, and challenges to assess welfare in non-vocal underwater species. The lack of guidelines and tools presents a challenge for researchers (particularly, for newcomers), for ethics committees and for implementing refinement measures. Here, we present an adaptable, user-friendly score sheet for fish based on MS Excel. The parameters are based on a literature review, have been validated by expert interviews and evaluated by a fish pathologist. The tool allows scoring of individual fish as well as groups, calculates summary scores and visualizes trends. We provide the underlying literature, give use examples and provide instructions on the adaptation and use of the score sheet. We hope that this tool will empower researchers to include welfare assessment in their routines, foster discussions on fish welfare parameters among scientists, facilitate interactions with ethics committees and, most importantly, enable the refinement of fish experiments.

Open Access CRIS of the University of Bern
United States Fish and Wildlife Service (US), Florida Fish and Wildlife Conservation Commission (US), Central Institute for Experimental Animals (JP)
Openalex Percentile: Top 56%
Zebrafish Biomedical Research Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.