Biodiversity protection funding preference: a case study of hotspot geoinformatics and digital governance for the Map of Italian Nature in the presence of multiple indicators of ecological value,ecological sensitivity and anthropic pressure for the Oltrepò Pavese and Ligurian-Emilian Apennine study area in Italy

The environmental decision-maker is aware of the increasing difficulties in finding sufficient financial resources for nature conservation. So he must focus his attention on ecological situations that more than the others merit considering and defending because of elevated value but also because of risk for their intrinsic characteristics and for human pressure acting on them. Usually an ecological scientist focuses his attention on the natural patches of the landscape, analyzing their peculiar ecological traits forgetting that, even if we want to protect some environmental critical situations, this can be done only moving to the administrative partition of the territory since the central and local environmental stakeholders have primary interest in providing funds to those involved in those critical situations. The present work shows a methodological approach, consisting of a set of statistical and geoinformational tools, considering both ecological and socio-demographical indicators. The goal is not simply to give some general guidelines for environmental policies to the involved stakeholders but focuses more on finding out which administrative local partitions in a study area are more worthy to receive urgently the priority funds for biodiversity protection to face critical environmental situations often due to a combination of intrinsic ecological parameters and external human pressure ones. Obtaining results that cover 5% of the Communes involved in the area seems to be a realistic result that a decision-maker can support and fund. Methodologically and geospatial data analytically, the investigation offers interesting challenges for surveillance geoinformatics of hotspot detection and prioritization, because of the presence of multiple hotspots and multiple sets of multiple indicators.