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). 

Recently I had the pleasure of visiting the geography department at Texas A&M, and during my trip I was able to revisit some field sites along the Navasota River I had last been to in 2006. The lower Navasota is basically an anastamosing system--there is a single dominant channel, but multiple subchannels, some active at normal and low flows, at any given valley cross-section. One place is particular, Democrat Crossing, is a particularly confusing locus of recent and ongoing channel change. 

2017 Google Earth^TMimage of Democrat Crossing. Sand Creek is actually a perennial or active Navasota River subchannel (semi-active means, in my lingo, that it usually carries flow, but may dry up in low-flow periods). Some of the subchannels have been highlighted with blue lines I added, as you can't really see them if you don't already know they are there. 

More than one scholarly observer has remarked in the contrast between physics, characterized mostly by deterministic, hard-and-fast laws that operate the same way everywhere and always (at least the Newtonian physics that apply to Earth sciences) and biology. Biology is portrayed as being much more fluid and dynamic, in the sense that chance (e.g., mutations) and environmental adaptations play a major role. As Gregory Chaitin (2012; a mathematician, by the way, not a biologist) puts it, biota and evolution are creative, while physics is not. This tension is sometimes portrayed or exemplified as a Newtonian (as in Isaac Newton) vs. Darwinian (i.e., Charles Darwinian) outlook. 

I have much love for both Newton (left) and Darwin, but have chosen to follow Darwin with respect to my hairstyle. 

Historical contingency in fluviokarst landscape evolution was just published in Geomorphology 303: 41-52.  When I first came to central Kentucky in 2000 I began noticing the strong contrasts in landscape and landform development on inner vs. outer Kentucky River incised meander bends. Investigating this and related phenomena has occupied me off and on ever since. Only a few years ago I realized that given the nature of bend development over the past 1.5 Ma or so, the bend interiors represent a chronosequence of landforms. This paper exploits those chronosequences, using graph theory, to explore the role of historical contingency.

Chronosequence of strath terraces (T1, T2, T3) and other geomorphic surfaces at Polly’s Bend, Kentucky. The surfaces nearest the river are youngest; those farther away are oldest.

Below is a picture of raindrop impact craters after a rain last month on a beach along the Neuse River estuary, N.C. The spot pictured has no overhanging trees or anything else, so the craters represent direct raindrop impacts. As you can see, assuming crater size is related to drop size, they represent a large range (the largest craters pictured are roughly 10 cm in diameter; the craters must be at least slightly larger than the drops). Rainsplash is a significant factor in soil erosion--even if not directly important, the process is key for dislodging grains or particles that are then transported by runoff. Drop impact also influences surface crusting and sealing, and thereby hydrological response. So, I got to thinking, what is the potential significance of such a large variation in drop size?

Kinetic energy is given by KE = 0.5 m V^², where m is mass in kg, and V is velocity in m/sec. A 2 mm diameter raindrop has a mass of 4.19 mg and a terminal velocity of about 6.26 m/sec. This gives a kinetic energy of about  0.00008 joules per raindrop.