Over the past 16 years, Mugerwa has dedicated his career to “Africa’s least known, least understood, least studied big cat.” Found in dense tropical forests across Central and Western Africa, the species is so elusive that the last IUCN assessment — over a decade old — has no population estimates, and in all his years of fieldwork, Mugerwa has only managed a fleeting glimpse of the African golden cat three times with his own eyes.

The International Union for Conservation of Nature (IUCN), the world’s largest environmental network of government and society organizations, tracks threatened wildlife.

“They’re really, really difficult to see in the wild,” says Mugerwa, who won the Indianapolis Prize Emerging Conservationist Award earlier this year for his work on the species.

But, realizing that accurately counting the big cats was the first step to protecting them, he set out to conduct the first population census across the species’ range, expected to publish next year.

In 2019, Mugerwa founded the African Golden Cat Conservation Alliance (AGCCA), a network of 46 conservationists across 19 countries. Together, they launched a standardized camera trap survey across the cat’s suspected range, supported by funding from the National Geographic Society.

But manually reviewing thousands of images from 30 sites across 19 countries, the largest camera trap grid for any African wildlife species, was very difficult.

At the same time, U.S.-headquartered nonprofit Panthera, another of Mugerwa’s collaborators, was developing an AI algorithm that could quickly sort the images and identify individual cats based on their unique coat patterns, similar to how tiger stripes are used like fingerprints. Otherwise, distinguishing individual cats would be nearly impossible due to their small size and subtle markings.

Preliminary data suggests the species exists at low densities — even in protected habitats. In Uganda and Gabon, for example, surveys found just 16 individuals per 100 square kilometers.

The surveys have also revealed the impact of poaching: in areas with hunting restrictions, Mugerwa says cat populations were up to 50% higher, with wider distribution. The study has also observed that while the cats are active both day and night, many are strictly nocturnal — likely to avoid human activity during the day.

Early in his research, Mugerwa realized that hunting was the cat’s primary threat.

In East Africa, where Mugerwa is based, the African golden cat is rarely the target of hunters. But bushmeat snares, set for pigs and antelopes, are indiscriminate, often catching other species unintentionally. In 2019, Mugerwa received reports of 80 golden cats caught in snares in three Ugandan forests, 88% of which were accidental.

To combat the cat’s biggest threat, Mugerwa went directly to the residents, creating Embaka: a community-based anti-poaching conservation project focused on the African golden cat. Working with over 8,000 families across the cat’s range, the project engages local communities — many of whom are former poachers — to deploy camera traps and report sightings.

过去16年间，穆格瓦将职业生涯奉献给了“非洲认知度最低、研究最匮乏的大型猫科动物”。非洲金猫栖息于中西部非洲茂密热带雨林，行踪诡秘。上次世界自然保护联盟评估距今已逾十年，而且未能统计其种群数量；穆格瓦在多年野外考察中也仅亲眼瞥见过三次非洲金猫的身影。

作为全球规模最大的政府与社会组织环保网络，世界自然保护联盟持续追踪濒危野生动物动态。穆格瓦因对非洲金猫的研究于今年初荣获印第安纳波利斯保护奖新锐保育学者称号。他坦言：“在

野外极难观测到它们的存在。”

意识到精准统计是保护该物种的第一步，穆格瓦启动了金猫分布区的首次全域种群普查，预计结果将于明年公布。2019年，他创立了非洲金猫保护联盟，汇聚了19个国家的46位保育专家。

在国家地理学会资助下，他们在金猫疑似活动区域布设了标准化相机监测网络。这项覆盖19国30个监测点的项目构成了非洲野生动物研究中规模最大的相机矩阵，但人工处理海量图像困难重重。

彼时，穆格瓦的另一合作方 —— 总部位于美国的非营利组织Panthera正在开发人工智能算法。该技术能快速筛选图像，并依据每只金猫独特的毛皮斑纹识别个体，原理类似于通过虎纹进行个体鉴定。鉴于金猫体型较小且斑纹细微，传统识别方法几乎无法实现个体区分。

初步数据显示该物种分布密度极低 —— 即使在保护区内也如此。例如在乌干达和加蓬的监测中，每100平方公里仅发现16只个体。调查还揭示了盗猎的影响：穆格瓦指出，在禁猎区金猫种群数量高出50%且分布更广。研究还发现，虽然金猫昼夜均活动，但多数夜间活动 —— 可能为了避开白天的人类活动。

研究初期穆格瓦便意识到盗猎是金猫的首要威胁。在他工作的东非地区，非洲金猫虽非主要盗猎目标，但用于捕捉野猪和羚羊的陷阱往往无差别捕获其他动物。2019年，仅乌干达三处森林就上报了80起金猫误入陷阱事件，其中88%属意外捕获。为应对这一最大威胁，穆格瓦直接深入社区创建了“恩巴卡计划”——以非洲金猫为核心的社区反盗猎保护项目。该项目覆盖金猫分布区内8000多个家庭，动员当地社区（包括许多前盗猎者）布设相机并上报观测记录。(Translated by DeepSeek)