Thinking like a mountain; landscape ecology

Cartographers are generally big-picture thinkers. Especially since the advent of GIS (Geographic Information Systems), which interfaces between maps and databases, we can ask increasingly sophisticated questions of our maps.

The brown bear’s year 1) Emergence Late-March to May. Most dens in high country. 2) Spring Bears descend for sedges, skunk cabbage, and deer carcasses. Key habitats: south-facing avalanche slopes, fens, and tidal marshes. 3) Early summer Breeding. Until midsummer, dispersed from sea level to alpine ridges. Tidal sedge flats, subalpine meadows, upland forests, and avalanche slopes. 4) Salmon By mid-July, in riparian forests and estuaries for pink & chum. Small, shallow reaches easiest to fish, claimed by alphas. (Some sows with cubs never use streams). 5) Berries By mid-September, into high forest and slide zones for currants and devil’s club. 6) Denning Pregnant females den by mid-October, roots of large trees or natural rock caves. Males last to enter dens.

In 2001, I created this cartoon of a brown bear’s landscape movements in collaboration with Kim Titus of the Department of Fish & Game, based upon his studies of telemetered bears. Since that time, wildlife studies have become ever more sophisticated, following, for example, the hourly movements of collared deer through intimately mapped terrain.

Screen-caps from a movie of deer locations, animated from hourly satellite fix, on terrain coded by habitat type. Dave Gregovich, ADF&G.

The phrase ‘thinking like a mountain’ comes from the title of a famous essay by Aldo Leopold, describing an epiphany as he watched a wolf die, killed just because that’s what young guys did in those days. As he matured, Aldo began to wonder if the mountain didn’t have a different way of ‘thinking’ about wolves than our myopic two-legged assessment. What he was reaching for was landscape ecology.

The way we measure landscapes, increasingly, is through GIS. Technology simultaneously isolates and empowers us in ‘thinking like mountains.’ On the one hand, we spend ever more time at computers. On the other, only through extraordinary tools, like this ‘movie’ of a deer’s winter can we even ask the right questions, let alone muddle toward answers. At Discovery, we hope to nurture a generation of future naturalists, as proficient in bushwacking as in digital measurement.

One of my first immersions in big-picture landscape ecology came in the Landmark Trees Project, 1996 through 2005. One landform that consistently yielded giant spruces was karst—the soluble topography of limestone and marble. About halfway through that big-tree trophy hunt, Bob Christensen introduced me to GIS. Suddenly I was able to make maps like the one below. USFS Regional Geologist Jim Baichtal gave me a shapefile of the extent of karst, and I was well on the way to having a map of strongest Landmark Trees potential.

But all karst is not equally predictive of 10-foot diameter spruces, particularly a one-acre stand of them. In the map below, I separated out the low elevation karst.

Next, of course, we could overlay the USFS layer called activity_polygon.shp, an interesting moniker for clearcuts. This would rule out probably >98% of the mapped high-grade, low elevation karst for landmark trees hunting. This cookie-cutter approach gets us closer and closer to actually hugging one of those miraculous survivors, way back from the coast, where even the intrepid hand loggers never ventured. Oh yeah, we could also apply an exclusionary coastal buffer to deal with that parameter.

You get the idea. . . GIS. Landscape ecology. Aldo would have loved and hated it. Simultaneously.

Landscape ecology connects the biotic and abiotic. In this case, big trees reflect the distribution of carbonate rocks, which in turn are arrayed according to the position of ancient geological terranes.

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