The biodiversity of our planet is under threat, with approximately one
million species expected to become extinct within decades. The reason; negative
human actions, which include hunting, overfishing, pollution, and the
conversion of land for urbanisation and agricultural purposes. Despite
significant investment from charities and governments for activities that
benefit nature, global wildlife populations continue to decline. Local wildlife
guardians have historically played a critical role in global conservation
efforts and have shown their ability to achieve sustainability at various
levels. In 2021, COP26 recognised their contributions and pledged US$1.7
billion per year; however, this is a fraction of the global biodiversity budget
available (between US$124 billion and US$143 billion annually) given they
protect 80% of the planets biodiversity. This paper proposes a radical new
solution based on "Interspecies Money," where animals own their own money.
Creating a digital twin for each species allows animals to dispense funds to
their guardians for the services they provide. For example, a rhinoceros may
release a payment to its guardian each time it is detected in a camera trap as
long as it remains alive and well. To test the efficacy of this approach 27
camera traps were deployed over a 400km2 area in Welgevonden Game Reserve in
Limpopo Province in South Africa. The motion-triggered camera traps were
operational for ten months and, using deep learning, we managed to capture
images of 12 distinct animal species. For each species, a makeshift bank
account was set up and credited with {\pounds}100. Each time an animal was
captured in a camera and successfully classified, 1 penny (an arbitrary amount
- mechanisms still need to be developed to determine the real value of species)
was transferred from the animal account to its associated guardian.

本文提出了一种基于 “物种货币” 的彻底新解决方案，让动物拥有自己的货币，通过建立每种动物的数字孪生允许动物向其监护人提供服务，并测试了在南非林波波省 Welgevonden 野生动物保护区使用 27 个相机陷阱的有效性。

野生动物守护者的赋权计划：基于深度学习和 3/4G 相机陷阱的公平数字监管和奖励系统

Empowering Wildlife Guardians: An Equitable Digital Stewardship and  Reward System for Biodiversity Conservation using Deep Learning and 3/4G  Camera Traps