Meet our grantees
Wild Animal Initiative funds academic research on high-priority questions in wild animal welfare.
The goal of our grants program is to fund research that deepens scientific knowledge of the welfare of wild animals in order to better understand how to improve the welfare of as many wild animals as possible, regardless of what causes the threats to their well-being.
We showcase our grantees and their projects here and continuously update this page as new projects are added.
Towards welfare-centered prescribed fire
Grantee: Dale Nimmo
Institution: Charles Sturt University, Australia
Project summary
This project will investigate how prescribed burning affects wild animal welfare, and whether welfare state helps determine survival and harm suffered. The team will study bush rats (Rattus fuscipes) in forest blocks scheduled for prescribed burns and in control sites, combining behavioral, body-condition, injury, and non-invasive physiological measures to infer welfare changes. Lightweight telemetry will enable tracking of survival and severe harm, while fire-severity mapping, refuge measurements, and camera traps will help identify the mechanisms involved. The goal is to generate evidence to help burns be planned and implemented in ways that reduce avoidable suffering.
Assessing anthropogenic fear in a wild decapod crustacean
Grantee: Mark Briffa
Institution: University of Plymouth, United Kingdom
Project summary
This project will investigate whether urban coastal environments alter fear-related behavioral and physiological responses in wild hermit crabs, using two species that differ in their history of exposure to urbanization. Researchers will compare crabs of both species collected from urban and rural shorelines and measure indicators that may be linked to affective state, including startle responses, shell investigation behavior, and resting metabolic rate under field conditions. By repeatedly measuring behavior within individuals, they will also test whether urban disturbance influences behavioral predictability, which may provide additional insight into fear and stress-related processes.
Harnessing machine learning for the non-invasive assessment of wild fish welfare
Grantee: Thomas Pike
Institution: University of Lincoln
Project summary
This project aims to develop an open-source aquatic camera system capable of automatically and non-invasively quantifying a suite of behavioural and cognitive welfare indicators in wild fish. Behavioural indicators include the degree of physical closeness between individuals within a group, the spatial arrangement of subgroups, movement behaviour, and agonistic interactions. The project will examine variation in these indicators along an urban–rural gradient to a range of putative stressors, before validating them using “challenge” tests involving ecologically relevant experimental interventions (e.g., acoustic disturbances such as boat noise). The overarching objective is to develop an open-source toolkit that integrates commercially available hardware with custom-designed software, allowing the methods to be readily used by others to assess wild fish welfare.
Integrating individual-level juvenile welfare in dynamic habitats across time and space
Grantee: Tom Luhring
Institution: Wichita State University, Texas State University, and Stephen F. Austin University
Project summary
The project will track four populations of juvenile lesser sirens in Eastern Texas within and across years. Sirens’ health is directly affected by their environment through the impacts of resource availability on body condition and growth rates. Furthermore, sirens show strong size-dependent and seasonal shifts in antagonistic behaviors, which lead to acute injuries. This project will use water-borne corticosterone release rates to investigate changes in stress physiology as a function of changes in the environment experienced by the individual (population density, drought severity index, water temperature, pH, conductivity) across time and space to understand coping capacity. This data will also be used to investigate the welfare impact of an established marking technique compared to a novel machine-learning approach.