I have degrees in computer science, applied mathematics, and population biology, and my hobbies include philately and woodworking. Have worked for over 25 years around topics such as spatial biology, metapopulations, multi-species conservation planning, systematic conservation planning, spatial conservation prioritization, ecologically based land use planning, biodiversity offsets, etc. – ecological decision analysis in general. My approach has been top-down conceptual and methodological, with an interest in operational solutions and software, including the Zonation approach to spatial planning and recent work on biodiversity offsets.
The aim of Sfeeri aligns with a lot that I’ve been interested in over the past decade – much of my background is relevant for various aspects of global biodiversity analysis. Several years ago, I had a comparatively large ERC project titled “GlobalEnvironmental Decision Analysis”, and advising Sfeeri allowed some of those thoughts to be developed further and hopefully made big-scale operational. While my main position is presently at the University of Helsinki[ , but I’m quite pleased about the possibility of discussing global biodiversity data and analysis with Sfeeri. It is a conceptually, methodologically and operationally fascinating task.
One could of course approach this question in different ways, but here is one approach to understanding global biodiversity.
There are four primary components (i) information about pre-identified areas of importance, including the global protected area network. (ii) Information about the distributions of individual “biodiversity features”, including species and habitat types. (iii) Modelled maps for indicators of high aggregate biodiversity; biomass, species richness, etc. (iv)Spatial data about human-caused threats and pressures that reduce naturalness.
Secondary considerations include such as ecological connectivity, ecosystem services, or expected effects of climate change, all of which have complications with respect to the reliability of data and analysis. For example, connectivity is highly species-specific. The flow of ecosystem services depends on human demand, which is changeable. Modeling of consequences of climate change is difficult, because the amount of climate change is unknown and will depend on future political decisions.
All global biodiversity analysis faces three major challenges, which are (i) availability of high-quality, open access, global data (that can be used by businesses); (ii) general conceptual and methodological challenges, including how to combine data, and (iii) analytical and computatingional challenges inherent in high-dimensional, high-resolution global biodiversity analysis.
A huge variety of things are ongoing globally in the broad fields of ecology and conservation biology. Relevant to the present case, it is interesting that the availability of spatial GIS data is improving all the time, making increasingly advanced analyses possible. Another interesting development is improvements in the automated identification of species, e.g. from environmental DNA, which holds promise for major improvements in data sometime in the future. Interesting in a not-so-pleasant manner is the continuing decline of our global climate and environment. There has never been a more urgent need for global biodiversity analysis than now.