Discussion Question 2: Regime Shifts

What are key slow and fast variables affecting ecosystem function and regime shifts in urban ecosystems? (You may focus on your team specific ecosystem function).


  1. I think I have an okay grasp on fast and slow variables, but feedback on this response is welcome! Fast variables change relatively quickly, while slow variables react gradually to long-term processes, and influence the way fast variables respond to change (Alberti, Walker). Examples of fast variables include changes in land composition, loss of plant life, and pollution emissions. Slow variables often characterize the ability of natural bodies (such as lakes and soil) to retain pollutants, nutrients, and water (i.e. retention of carbon in forests and woody debris, or retention of water in soil). Slow variables may also be changes in ecosystem dynamics, such as change in competition (Alberti).

    Fast and slow variables respond to each other over time. External drivers may change variable interaction in a way that creates huge shifts in the behavior, composition, and structure of systems. Although slow variables are more difficult to detect and measure, they are more controlling (Walker). For example, marine waters absorb more carbon dioxide (slow variable) due to large inputs of carbon dioxide into the atmosphere (fast variable), mostly from human burning of fossil fuels (driver). Ocean acidification occurs when marine waters have absorbed a certain amount of CO2 from these inputs, which demonstrates that the slow variable (carbon dioxide retention) is the more controlling variable in determining the shift to ocean acidification. Another example of slow-fast variable interaction occurs when human development decreases pervious surfaces (fast variable), which decreases the amount of groundwater that soil and underwater aquifers hold (slow variable). This could lead to a changes in drinking water systems, and an increased risk of flooding.

  2. Slow, or what Walker (2012) refers to as "controlling" variables, tend to be larger-scale systemic variables and, although they may change gradually, have a large influence on the ways in which fast variables will affect a system. Fast variables change relatively more rapidly and might include unpredictable system shocks (such as a storm event) or effects of human processes (such as an increase in impervious surfaces due to urbanization). As slow variables near threshold levels, fluctuation in fast variables increases, resulting in a state of system instability and an increased likelihood of regime shift, or a persistent change in the structure and function of a system (Biggs et al 2011).

    In thinking about hybrid infrastructures and the ecosystem function of urban water quantity control, we might consider examples of the slow variables to be changes in weather patterns that lead to sea level rise and increased coastal flooding, or the ability of urban soils to retain water. Fast variables might include flash flooding caused by a rapid snowmelt or storm event, or the removal of vegetation with development. In this case, changes in the slow variables will impact the ways in which the fast variables affect the urban hydrological system. For example, a decreased capacity of soils in urban areas to retain water will exacerbate the effects of large storm events, leading to an unstable system with the potential to shift into an alternate regime in which urban flooding is much more common and has the potential to occur even in the case of smaller storm events.

  3. Though it is not set in stone, the way I have wrapped my mind around slow and fast variables is based on responsiveness. If we take the example from class, of nitrogen introduction/retention within a lake, the fast variable is the level of nitrogen being introduced into the system. This is controlled by external drivers, such as human development. The slow variable is the lake's ability to process nitrogen, through denitrification or sedimentation. The slow variable is a response to control the fast variable. Slow variables may take longer to show effect and may be more difficult to measure immediately.

    With regard to urban forests, and particularly the function of providing habitat to local species, many slow and fast variables are at play. Global warming (external driver) increases local temperatures (fast variable), which in turn affects the type of tree that can grow in a given region (slow variable). This affects the habitat function as certain animal species prefer certain tree species as a habitat (regime change). Increased human development (external driver) may decrease overall canopy coverage by increasing impervious surface levels (fast variable), decreasing patch size and increasing patch distance (slow variable) and affecting the potential habitat for birds and wildlife (regime change).

  4. I am still getting my head around these variables; this might be more like a "thinking out loud blog post". Thank you for bearing with me!

    I have been naming them variable 1 (fast) and variable 2 (slow) in my head, because while the slow variable reflects the variable in the system responsible for maintaining stability and a systems thresholds, the system's capacity to act as a "system" changes rather quickly in a regime shift. In a regime shift, the slow variable changes quickly, right? I am thinking about the examples above: lake eutrophication, ocean acidification or changes in groundwater recharge. The fast variable is the non "controlling" variable (Walker) and tends to accumulate in a system at rates that are "fast" compared to the system's current state of equilibrium or "regime 1". I think the key to understanding the differences between the two for me lie in the fact that the slow variable is controls the EFFECT the fast variable will have on the system. To some extent, the slow variable is the dependent variable (and internal driver of regime shift) and the fast variable is the independent variable (and external driver of regime shift).

    In relation to the urban forest and the example Travis used above, I would say that the external driver (human development patterns) impact the patterning of impervious surfaces (fast variable), while patch size and patch distance, etc. are measures of the fast variable. The slow variable would be the ability for tree species to thrive in the environment at a density that supports habitat for birds (which I suppose could also be measured in patch size, dispersion & interspersion, etc., which is where the confusion creeps in, but the differentiation seems significant). Perhaps my response to my teammates post reflects my own lack of an understanding of the nuances in these variables, so please, feedback is welcome!

    A simple example for slow and fast variables for the example above might be as follows: the external driver (human development patterns) impact the patterning of impervious surfaces (fast variable) & the slow variable would be the ability of the soil to retain water & nutrients to an extent that the appropriate tree species can thrive at a density that supports habitat for birds (our group's hybrid ecosystem function).

  5. Fast variables are the primary concerns of ecosystems users and can change rapidly. They are shaped by slow variables that change over long periods of time. Walker refers to them as control variables because they are not always slow. Fast and slow variables are relative terms that refer to dynamic relationships between variables that control system functions. The interaction with external drivers shape the combined variables. The relationship between variables is non-linear as the equilibrium levels change and can reach a threshold point where the system is shocked into another regime (Walker, 2012).

    In regards to green infrastructure, our team has identified temperature rise is a dimension of climate change. Temperature rise has resulted in less snow and higher rainfalls which led to more flooding incidents in the Puget Sound. Since there is less snow, there is less snowmelt during the summer months which results in drier landscapes. Extreme weather events such as storms are fast variables have caused catastrophic infrastructure damages. In February 2017, a large storm flooded the county’s sewage facility and a massive spill was release into the Sound. As the economy grows and land development increases green landscapes, slow variables, will be reduced and can cause more flooding if not mitigated. Infrastructure damage can be reduced using green infrastructure to retain and filter water in urban landscapes instead of running off into storm drains. Coastal green infrastructure, such as beaches, dunes, coastal habitat restoration can reduce the velocity of waves, erosion, and flooding.

  6. A fast variable is, to put it simply, fast-acting. Fast variables are relatively sudden and volatile—the input variable can manifest, change, and dissipate relatively quickly, though its effects may be irreversible on a human time scale. They may be relatively small and with local effects (e.g. the ongoing oil spill at Prudhoe Bay) or large and with global effects (e.g. the Chicxulub impact). A slow variable, also known as a controlling variable, tends to act on a slower scale. Slow variables comprise gradual environmental shifts, such as climate change. These variables influence (control) how many fast variables behave, as where heating seas (slow) beget more intense cyclones (fast). Major changes in ecological equilibrium occur as a result of both changes. Usually, it is expected that changing slow variable conditions will influence the occurrence and nature of fast variable events that would overcome the inertia of the previous equilibrium. Often the fast and slow variables are related to each other, as with sea temperature and cyclones, but not always: though climate change (slow) may have threatened many existing species of the late Cretaceous period, the Chicxulub impact (very fast!) was an external input, the asteroid that broke the dinosaurs’ back. (Of course, once introduced, this fast variable decisively reshaped the contours of many slow variables).
    The idea of slow and fast variables is similar to the concept of punctuated equilibrium in evolutionary biology. Species, like the environments they depend on, tend toward equilibrium, but may respond quickly and drastically (by adapting or dying) when slow and fast variables conspire to disrupt that equilibrium.

  7. Slow variables, like gradual loss of wildlife habitat, change slowly and often dictate the responses of fast variables in an underlying way. These slow variables also affect strength of balancing feedback loops and may allow amplifying or reinforcing feedback loops the opportunity to dominate and drive the system into a regime shift. Fast variables are generally more easily defined and measured. Examples include excessive nutrient output, introduction of a new species, or sudden climatic change (like a tornado – high wind speeds - or short period of drought – lack of water input). In the carbon cycle of Puget Sound forests, fast variables may include increased or decreased timber harvesting (how much carbon is the forest still able to sequester after new harvesting intensities are applied and what fluxes are introduced by increases or decrease in the harvesting industry), use of pesticides or fertilizers (direct emissions), species extinction or inc./dec. in species number (impact on vegetation mass available), or change in seasonal rainfall or temperature. These variables can be easily identified, tracked, and measured. For timber harvesting, we might measure stands or acres harvested per quarter in relation to historical data and capture the change in carbon sinks/fluxes. For use of pesticides, we may consider the new use of a pesticide to eliminate a wave of pests introduced to the forest in quantity per acre and quantify direct carbon emissions. For slow variables, we may consider the effect of timber harvesting as a contribution to forest thinning that impacts the forests’ ability to withstand or control natural wildfire which has implications on tree mortality and the forests’ ability to serve as a carbon stock. With the pesticides example, we may consider the long-term effects on soil health and the implications to maintain tree growth rates.

  8. This comment has been removed by the author.

    1. Resilience of ecosystems is determined by the interactions of fast and slow variables. Slow variables refer to variables that have either a slow rate of change or low frequency of change (Walker et al., 2012). Examples of slow variables can be natural factors such as sea level rise and periodic flooding, but also can be human induced factors such as urban sprawl and social inequity (Ernstson et al., 2010). Fast variables, on contrary, are typically variables that change fast and are more controllable, such as crop production and fisheries. They are frequently of primary concern of humans to manage ecosystem service and goods. According to Walker, fast variables are strongly shaped by slow variables, while slow variables are determined by drivers external to the system.

      In relation to carbon cycle, fast variable is carbon emission and slow variable is carbon stock. Variables related to carbon emissions such as transportation volume, family energy consumption, and industrial output can also be classified as fast variables. While carbon stock related variables such as the forest carbon storage, deforestation and organic input can be categorized as slow variables. However this is not an absolute classification between fast and slow and there is no standard answer. Some factors that affect carbon stocks but are related to human activities could also be seen as fast variables, for instance, lumbering and wild land conversion caused by urban sprawl. Therefore to define fast and slow variables, it really depends on which aspect we are looking at, what question we are going to ask, and what factors we identified as relevant and important.


Post a Comment

Popular posts from this blog