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The biodiversity of our planet is under threat, with approximately onemillion species expected to become extinct within decades. The reason; negativehuman actions, which include hunting, overfishing, pollution, and theconversion of land for urbanisation and agricultural purposes. Despitesignificant investment from charities and governments for activities thatbenefit nature, global wildlife populations continue to decline. Local wildlifeguardians have historically played a critical role in global conservationefforts and have shown their ability to achieve sustainability at variouslevels. In 2021, COP26 recognised their contributions and pledged US$1.7billion per year; however, this is a fraction of the global biodiversity budgetavailable (between US$124 billion and US$143 billion annually) given theyprotect 80% of the planets biodiversity. This paper proposes a radical newsolution based on "Interspecies Money," where animals own their own money.Creating a digital twin for each species allows animals to dispense funds totheir guardians for the services they provide. For example, a rhinoceros mayrelease a payment to its guardian each time it is detected in a camera trap aslong as it remains alive and well. To test the efficacy of this approach 27camera traps were deployed over a 400km2 area in Welgevonden Game Reserve inLimpopo Province in South Africa. The motion-triggered camera traps wereoperational for ten months and, using deep learning, we managed to captureimages of 12 distinct animal species. For each species, a makeshift bankaccount was set up and credited with {\pounds}100. Each time an animal wascaptured 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.

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