Some have argued that in geomorphology and physical geography the term "tipping point" does not describe any concepts or phenomena not long recognized by the fields. The tipping point concept does not (it is argued) have any conceptual or analytical value added. I agree. Here is a previous post on tipping point metaphors.

Blanco River, Texas.

Notwithstanding that, tipping point terminology is au courantin both public discourse and science, particularly as it relates to global and broad scale environmental change. Thus--perhaps analogously to buzzwords such as "sustainability" and "resilience"-- if you want to be a part of broader scientific conversations, it pays to employ the trendy term.

Where forests grow on thin soils over bedrock, the effects of individual trees (as well as the effects of forest cover and litter) may work to deepen or thicken the soil. This occurs due to root penetration of bedrock joints and fractures. This in turn facilitates weathering and funnels moisture into the rock. Uprooting of trees may “mine” bedrock encircled by roots, and leave a locally thicker mound as rootwads deteriorate. If trees do not uproot, as stumps rot away the depressions—often extending deeper than surrounding soil—fill with soil, sediment, and organic matter. This thickening of the soil is a form of direct, positive ecosystem engineering in that it increases habitat suitability for the engineer organisms (the trees). 

Chinquapin Oak growing in limestone, Mercer County, Kentucky.

Eventually, however, the average soil or regolith thickness may increase such that tree roots no longer contact bedrock. Then the biogeomorphic ecosystem engineering effects of the trees on soil thickness ceases. In effect, you have self-limited biogeomorphic ecosystem engineering. 

As sea level rises--and it is rising!--it is causing geomorphological, hydrological, and ecological changes in coastal environments. Though "bathtub" models, which show where the shoreline would be with sea level increased by a certain amount, are useful in showing areas of potential impact, that's not how actual responses to sea level work. Not only does the ocean level change, but also the base level for rivers and terrestrial processes, salinity, ecological habitats, hydroperiods, and any number of other factors. 

Sand and mud flats along the eroding Neuse River estuary shoreline, NC. 

This post continues a series exploring the idea of evolutionary creativity in Earth Surface systems (ESS). Previous posts introduce the evolutionary creativityconcept, explore the possibility of algorithmic evolution modelsof ESS, and discuss the appearance over time of new varieties of landforms. 

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Biologists have put a great deal of effort into identifying the variety of life forms. While there is only one extant species of the genus Homoand 11 of Canis, there are more than 600 species of Quercus (oak trees) and >350,000 of beetles (and counting).  

This continues a discussion that started by considering the possibility of evolutionary creativity in landscapes and continued by exploring the possibility of algorithmic evolution models of Earth surface systems (ESS). 

In biological evolution by natural selection, new varieties arise via genetic mutations. Occasionally one of these turns out to be advantageous--the fitness of the organism is increased, and the variation is "selected" (recall that Darwin used natural selection to contrast with selective breeding of animals and plants by humans). The selected organism can reproduce, and pass the selected trait to its progeny.

This post is a continuation of a thread that starts here.

The algorithmic evolution models pioneered by Gregory Chaitin (see Proving Darwin,2012 for a very readable introduction) are based on a metaphor of an organism as a K-bit program that takes the original organism A and produces a mutated organism A'. The fitness of the organism/algorithm is measured by the largest integer the algorithm can calculate. Thus a mutation that increases the largest calculable number is retained, while others are rejected. This continues until BB(N), defined as the largest integer that can be produced by a <N-bit program that produces a single integer and then halts. 

This is a plausible analog for biological evolution, in which continuous improvement in fitness is possible, up to some maximum limit. In nature the maximum limits are presumably associated with maximum rates of biological processes rather than algorithmic complexity and information (i.e., BB(N), which has the awesome name of the Busy Beaver Number).