Leveraging collaborations between researchers, wildlife managers and citizen-volunteers to monitor endangered carnivore populations across space and time

Keeping track of wild animals across vast, rugged landscapes is one of the hardest tasks in conservation. It requires knowing not just how many individuals exist, but where they are, how their numbers change from year to year, and how they move through the forest. For elusive predators that avoid human contact, traditional methods like counting footprints or spotting them from a vehicle often fail to provide a complete picture. To solve this, scientists have turned to a combination of genetics and careful counting. By collecting biological samples left behind in the wild, such as droppings, researchers can extract DNA to identify individual animals, much like a fingerprint. When these individual sightings are mapped across a specific area over time, statistical models can estimate the total population density with greater accuracy than simple observation alone. However, running such a complex program over many years and across huge territories is expensive and difficult to sustain without help. This is where the role of local communities and government agencies becomes critical, turning a solitary scientific effort into a shared mission.

In the Western Ghats of India, a team of researchers, forest officials, and trained volunteers tackled the challenge of monitoring the dhole, a rare and endangered wild dog. These animals are social hunters that roam through dense forests, making them notoriously difficult to count. The team established a collaborative framework that brought together scientists from universities, staff from the state Forest Department, and local citizen-volunteers. Over a period spanning from 2019 to 2023, they surveyed six different sites, which included three protected wildlife sanctuaries and the surrounding forest divisions that border them. Instead of trying to spot the animals directly, the team walked the forest trails to collect fresh fecal samples. These samples were then sent to a laboratory where scientists analyzed a specific set of genetic markers to identify each individual dhole that had passed through. This method allowed them to build a record of which specific dogs were present in each area without ever needing to see them face-to-face.

The data gathered from these genetic profiles was fed into a specialized computer model designed to estimate animal density based on where and when individuals were detected. The results revealed a clear picture of dhole populations across the landscape. In the Wayanad Wildlife Sanctuary, the population appeared stable over the years, with a slight increase from roughly sixteen individuals per hundred square kilometers in 2019 to nineteen in 2023. However, the story was different when looking at the broader region in 2023. Densities varied significantly from one location to another, ranging from just under ten individuals per hundred square kilometers in the Parambikulam area to nearly thirty in the Nemmara Territorial Division. This variation highlights that dhole populations are not uniform across the landscape; some areas support much higher numbers than others, a detail that is crucial for effective management.

The success of this project relied heavily on the people who did the fieldwork. On average, forty-six Forest Department personnel worked alongside the researchers at each site every year, helping to collect the samples and manage the logistics. The project also trained local volunteers, creating a pipeline of skilled individuals. When the researchers asked former volunteers about their careers, they found that eighty-three percent of them had gone on to work in or study wildlife-related fields. This suggests that the project did more than just count dogs; it built a lasting capacity for conservation within the local community. By combining advanced genetic tools with the boots-on-the-ground efforts of government staff and local residents, the team demonstrated a practical way to monitor endangered predators across large, complex landscapes. This approach offers a scalable model for other conservation efforts, showing that when science, management, and local knowledge work together, it is possible to maintain long-term monitoring programs that are both accurate and sustainable.