Abstract
Matschie's tree kangaroo (also known as Huon tree kangaroo Dendrolagus matschiei) is only found in the Huon Peninsula of Papua New Guinea and is poorly understood. Essential metrics like science-based habitat maps or abundance and distribution estimates are missing in a reliable large-scale fashion. While the Matschie's tree kangaroo is an ancient but cryptic species and understandably difficult to investigate due to the difficulty in accessing its habitat, the statuses of many tropical communities worldwide are likewise in equal dire need of urgent, science-based conservation management. Available modern study methods are presented and readily available for use for inference in order to help safeguard this species in a very complex socio-economic environment. Those concepts cover geographic information systems (GIS) – open source and commercial software – and (digital) database techniques, different information sources including available open access data in the Global Biodiversity Information Facility website and the relevance of citizen science. Linked with many publicly available GIS and Remote Sensing layers freely available online ‘in the cloud’, data mining is discussed using ‘latest’ and ‘best’ AI and Machine Learning ensembles, namely stochastic boosting and bagging. A standardized predictive modeling workflow is suggested to show how such methods can help obtain more reliable and updated online status-reports in real-time for this species and its habitats. The evolution of field database delivery, international data sharing online, GIS, software algorithms, cloud computing, workflows, and ISO-compliant metadata is set in context with conservation progress and sustainable landscapes worldwide. This will better serve nations, their people, and ancient livelihoods in an otherwise globally operating and highly complicated telecoupled supply chain that currently marginalizes the environment.
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