John: So, it was the big one! Anytime you’re ready. So 25 years later, we’re still remediating
for the quarter million barrels of crude spilled into the Gulf of Alaska. And the system will
never fully recover, but ongoing efforts are to identify and protect lands that are going
to optimize the work that we’ve already put into restoration. But how do you look
at data across the scale and over an expanse and over such time period and compare it – kind
of apples to apples, here to there? It’s tough to do. Anyways, where are we going…right!
(laughter) The general concept was to take the extent of the Exxon Valdez oil spill and
consider it in terms of relative habitats based on GIS models extrapolated to 11 of
these, what's projected now, 11 of these simplified, modified, extrapolated kind of layers to represent
the 31 injured species identified by the Exxon Valdez oil spill using geospatial methods,
GIS. It’s a great way to take marine and coastal airs and push them inland which is
much more useful for land conservation purposes. You can also take GIS as a means to consider
non-traditional data for ecological models. In this instance, you might, say, look at
native subsistence fisheries in terms of traditional knowledge or the needs of conservation partners
as they apply to your study area. Or even how far, say, the local yacht club travels
day to day to day in a boat for the potential of human use and other recreation purposes.
And then put it all together in a general prioritization model. As we’re looking at
the Exxon Valdez area here, this is zoomed into the Prince William Sound, and expanding
on significant marine harvest areas, these are significant coastal wetlands as they apply
to areas impacted by the oil spill. These are great, great habitats for conservation
purposes – rich in biodiversity. Taking terrestrial wetlands as they connect to those
systems. Again, bumping it inland to features, in this case, that are coastal shore zones.
Here, looking areas that are traditional subsistence use areas giving them geographical extent.
Again, beyond just kind of the point densities that fish and game collects on where people
are taking fish, and looking back over time at where native communities have traditionally
used it. It’s also important to always tie-in because the little land trust I was working
for can’t cover the whole region which is larger than most states. The needs of the
conservation partners, and you can give geographical extent to those, based in ecological analysis.
Here, this is the extent where, again like I was saying, yacht clubs might use coastal
and marine environments for recreation and the potential therein considering again ongoing
mitigation. It’s also important to consider where your landscape exists relative to existing
protected areas. For habitat efficiency, islands are not ideal. So how the whole prioritization
occurred was taking these individual grid squares and presence or absence of those 11
criteria, and overlaying this scoring grid, this raster grid, onto the whole thing. The
problems of scale and of data distribution across this huge area resolved was by breaking
the landscape up into five distinct geostatistically significant data regions and within that,
comparing scores against each other and also scaling them up to compare and create a whole
significant conservation prioritization landscape. In this way, we leveraged this model to…before
I left Alaska in July, to identify and protect 36,000 acres into a state park on Afognak
Island and created a new marine reserve off of the coast of Kodiak Island. Certainly,
work is ongoing and there is no shortage of money. Is that five minutes? Yeah? Alright, thanks.