Agents of Pattern Formation: Biotic Processes

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1. Foundations of the Pattern and Process Paradigm Much of the way we approach pattern in ecology can be traced to seminal papers by Alex Watt (1925, 1947), who in many ways gave us the "pattern-process paradigm" that we use today. Watt based much of this notion on gap dynamics as observed in forests, in which a tree-scale area undergoes a more-or-less predictable trajectory from gap, to old field, to young forest, and so on, until a large tree dies and the cycle repeats. His insight was as follows: if one were to look at a large area of forest, various gap-scale elements would be undergoing similar cycles of gap dynamics, but independently. Averaged over a large area, there would be some elements in all stages of succession represented (somewhere) at any given time, and further, the relative abundance of these stages would reflect the duration of each phase.

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Watt (1947) postulated the notion of a unit pattern: The "unit pattern" is the full representation of the pattern in all its phases (the `community in harmony with itself and its environment'). Moreover, Watt suggested that the phases of the community (seral stages, in his cases) should be present in relative abundances corresponding to the duration of each seral stage: a strict translation of temporal dynamics into pattern on the ground.

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Watt further observed: “departures from this phasic equilibrium either in space or in time could then be measured and correlated with the changed factors of the environment”. This is often heralded as one of the earliest and most lucid translations of temporal dynamics into spatial pattern. This insight into the value of the unit pattern as a benchmark or reference state against which to compare real patterns also represents one of the earlier neutral models in ecology.

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There have been several other variations on this theme: 1. The "climax pattern" (Whittaker 1953) is a steady-state pattern of a variety of community types across a landscape, reconciling Clementsian and Gleasonian notions of vegetation pattern. 2. The "shifting mosaic steady-state" (Borman and Likens 1979) is a stationary distribution of successional stages, wherein fine-scale elements undergo gap dynamics while the aggregate forest appears constant.

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Each of these models shares two main heuristic points 1. First, demographic processes, acting in and of themselves, should generate a pattern with predictable characteristics. 2. Secondly, this expectation might be useful as an interpretive model. That is, the expectation could serve as a reference standard and departures from this, observed for real systems, could be interpreted as interesting deviations.

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2. Demographic Processes as Agents of Pattern Any and all of the basic demographic processes (dispersal, birth or establishment, growth, and mortality) can contribute to vegetation or ecosystem pattern. Some issues that may be relevant in some cases: 1. Establishment.--Small plants see a different environment than big plants. For example, seedlings and herbs experience light as filtered through the canopy, and soil moisture in the topsoil, in contrast to these variables as experienced by a large tree. Thus, while the constraints may have the same names they are not the same variables; moreover, they may vary on quite different spatial and temporal scales. 2. Growth.--Growth is the focus, often only implicitly, of much of gradient analysis and interspecific competition studies (which thus might ignore other demographic processes). Response to direct physical factors (temperature, pH) may differ from that for resource gradients (light, water, nutrients) (Austin and Smith 1989). For direct physical factors, a Shelfordian response model may be appropriate (i.e., ranging from "not enough" through "just right" to "too much"), while for resource gradients a more one-sided response may be the rule (i.e., from "plenty" to "not enough"). Moreover, most (all?) species would be expected to show qualitatively similar responses to resource gradients, which thus invokes competition as the primary mechanism for sorting species along resource gradients (Smith and Huston 1989 and see below). 3. Mortality.--Mortality often reflects multiple factors (the `death spiral'). For example, trees suppressed by drought or shading might be more likely to succumb to insect attack. Mortality patterns may be in response to chronic stress or episodic events, in which case mortality may be explained by the mean environmental conditions or by the variance in environmental conditions. 4. Dispersal.--Dispersal can limit response to physical gradients, by preventing a species from occupying potentially usable resources. Dispersal might also act as a pattern amplifier, reinforcing existing (or initial) patterns through the positive feedback that species already present will contribute to the seed rain in the future. Which effect occurs might depend on the scale of dispersal versus the grain of the physical environment (see Interactions, below). That is, if dispersal distances are long relative to the grain of the physical environment, then dispersal should distribute species across a variety of microsites, allowing chance establishment events and founder effects to mediate the effects of competition. Conversely, if dispersal distances are comparatively short then local seed rain might amplify species response to the environment. (This is somewhat idle speculation, given our rather limited understanding of how dispersal interacts with environmental heterogeneity. In fact, most studies of dispersal have made the simplifying assumption that the environment is homogeneous.)

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3. Interactions and Feedbacks 3.1. How do biotic processes themselves interact? Watt's notion of the unit pattern coupled demographic processes to generate spatial pattern. Others have done this more explicitly. For example, Shugart (1984, 1987) categorized tree species in terms of their mode of mortality (does/does not create a gap on dying, a function mostly of size) and regeneration mode (requires/does not require a gap to regenerate). This pair of dichotomies defines four functional roles of trees: Role 1: needs gap and creates gap; Role 2: does not need gap but creates gap; Role 3: needs gap but does not create gap; and Role 4: does not need gap and does not create gap. Shugart noted that the coupling of mortality and regeneration could lead to some interesting feedbacks that would generate characteristic patterns in abundance of the various functional roles: 1. Roles 1 and 4 are self-reinforcing strategies, creating conditions on dying that favor their own regeneration. 2. Role 2 creates conditions that favor other species; i.e., it "gives its advantage away." 3. Role 3 cannot create the conditions it needs to regenerate, and so must depend on other agents for persistence. Shugart suggested that environmental conditions that affected modes of mortality or regeneration would lead to characteristic combinations of roles of trees. For example, frequent disturbance would favor species that require gaps (especially role 3). Conversely, low sun angles at high latitudes would not favor gap-phase regeneration but rather, would favor roles 2 and 4. These are patterns due to interacting biotic processes overlaid onto the physical template. One of the most useful implications of these ideas is the expectation that biotic processes should generate characteristic patterns in species abundance and distribution. Reciprocally, we would like to be able to infer generating processes by observing spatial patterns. But this goal is difficult to achieve.

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3.2. How do biotic processes interact with the physical template? A number of classic efforts in plant ecology have addressed the way that demographic processes interact with the physical template to generate pattern. Among them: 1. Tilman (1982, 1985) posed the resource-ratio hypothesis: Species vary in their relative growth rates in response to levels of resource abundance; The species best adapted to local resource levels "wins" in competition; Spatial heterogeneity in these resources can generate patterns in species diversity.

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How do biotic processes interact with the physical template continued… 2. Grime (1977, 1979) proposed that there are three primary plant strategies: competitor, stress-tolerant, and ruderal (ephemeral), reflecting basic trade-offs in life-history traits. These strategies play out to yield characteristic patterns of abundance: Competitor: continuously abundant, but depressed under low resource levels (in time or space); Stress-tolerant: continuously rare, displaced spatially to resource-poor sites; Ruderal: temporarily abundant, displaced in time by more competitive species.

Douglas fir

W. hemlock

Red alder

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How do biotic processes interact with the physical template continued… 3. Smith and Huston (1989) used a simulation model to explore the interactions between demographic processes and the physical template. Their basic premise: life-history strategies entail compromises that dictate competitive abilities under varying resource levels. Generally, a plant can be competitive under high levels of resource availability or tolerant of low levels (stress), but these are exclusive adaptations (a plant cannot do both at once). Specifically, a plant can: be competitive for light, in which case it will not do well in the shade; be tolerant to drought (or low nutrients), in which case it won't be competitive under more favorable (higher) resource levels; be competitive under high light and high moisture (nutrients), but not the converse (tolerant of drought and shade). So there are three "pure" strategies, which form the corners of a triangle of possible strategies. They used hypothetical species of varying strategies to illustrate the implications of these strategies as they play out along environmental gradients in available water (in space) and light (in successional time). Their simulation results suggested that: The species that grew best under high levels of light and/or moisture "won" on favorable sites. More tolerant species "lost" (were out-competed) on good sites but won on poorer (drier, shady) sites--not because they achieved their own best growth there, but because they persisted there when more competitive species could not. Because life-history traits are correlated, the same explanation that accounts for gradient response also accounts for succession (a gradient in time): species that are shade-intolerant may also be drought-tolerant, and so may be early successional but may persist on dry sites (because shade-tolerant species tend also to be drought-sensitive, and so do not come to dominate dry sites).

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3.3. Interactions: An Illustration Simple spatial models can be used to illustrate the complex interactions among competition and gradient response as modified by local dispersal. The examples shown are generated with a cellular automaton called MetaFor (Urban; see Green 1989 for a similar model). The model includes a physical template and the biotic processes of competition and dispersal, and is configured so that each of these factors can be switched on or off. The model simulates forest pattern as a raster map of tree-sized cells, in which each cell may be occupied by a single species. The examples shown here are based on forest pattern in the Sierra Nevada, but have been abstracted to emphasize general agents of pattern formation. The physical template.--The abiotic environment is simulated as an elevation gradient in which higher elevations are cooler and wetter and the lower elevations are warmer and drier; alternatively, the gradient can be set to constant intermediate (mid-elevation) conditions.

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METAFOR continued… Competitive relationships.--The model includes three species: White fir (Abies concolor, or ABCO) dominates the middle elevations and is the most competitive of the three species; Lodgepole pine (Pinus contorta, PICO) is less competitive but more cold-tolerant than the other two species; Ponderosa pine (Pinus ponderosa, PIPO) is less competitive than white fir but more drought tolerant, and is characteristic of the warmer and drier lower elevations.

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METAFOR continued… Seed dispersal.--Effects of local dispersal are implemented as a simple neighborhood function, in which the probability of a given species establishing in an empty cell is biased by the abundance of that species in an 8-cell window around the empty cell. Each time step during a simulation, a cell may become empty as a result of mortality (a probability based on species longevity). Empty cells may be occupied by a species, in which event the actual establishment probability for each species is the product of the neighborhood effect and the environmental response of that species. Cells that do not "die" become one time step older. Simulations were conducted for a 2x2 factorial of Gradient/No Gradient crossed with Neighborhood Effects/No Neighborhood Effects. Each simulation was for a 100x100-cell grid, and ran 500 years. Cover maps for the final year illustrate the spatial patterns generated by each combination of agents.

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METAFOR continued… The clumping effect of the neighborhood dispersal is visually obvious in these maps. What is less obvious is the effect on the relative abundance of the three species. The interaction between dispersal and competition along the gradient can be illustrated by ‘sampling’ the grid and graphing the relative abundance of each species in each row of the map. Note that in the ‘no gradient’ simulations, ‘elevation’ is merely map position. In the simplest case of no gradient and no local dispersal, the relative abundances of each species simply reflect their relative competitive abilities, i.e., ABCO > PIPO >> PICO. Competition effectively excludes PICO from the map in the case with no gradient and with local dispersal. The gradient in the absence of neighborhood effects allows some sorting of species along the gradient, but there is considerable overlap in species distributions. Adding neighborhood effects to the gradient allows PICO to not only persist but to dominate the higher elevations as PIPO dominates the lower elevations; the environmental sorting of species is greatly amplified. These simulations illustrate that simple biotic processes, interacting among themselves and with the physical template, have the potential to generate rather complex patterns across landscapes. This rich complexity generated from extremely simple processes should temper inferences made of patterns observed on real landscapes.

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4. Implications for Conservation Practice One implication of Watt's unit pattern is the expectation that a landscape at equilibrium with its environment and with its internal biotic processes should exhibit a stationary distribution of patch area in various types; i.e., if the unit pattern is realized, the relative abundances of patch types should remain constant through time. This stability is very much central to practical issues in conservation biology related to the 'minimum size of ecosystems' that could be self-sustaining. Because disturbances--natural or anthropogenic--are often responsible for perturbing systems away from equilibrium or steady-state, we will return to this notion later in the course. The simulation study of Smith and Huston has some interesting implications for conservation practice. If their results are general, then the most effective conservation strategy might be different on physically homogeneous, mostly mesic landscapes as compared to physically heterogeneous landscapes with some less favorable sites: • On homogeneous, mesic sites the strategy would be to manage in time, maintaining a variety of stand ages, because succession would lead to less diversity through competitive exclusion. • On heterogeneous sites, the strategy would be to manage in space because a variety of physically contrasting sites will support (and maintain) more diversity.

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5. Implications for Climatic Change The results of Tilman, Grime, and Smith and Huston all argue that, because competition displaces species from their optimal sites (fundamental niche) to sites where they can persist under competitive pressures (realized niche), we have a limited ability to infer the actual environmental tolerances of species by observing their realized distributions. This undermines naive efforts to predict species responses to climatic change simply by translating species distributions according to a shifting environmental template: we must also consider competitive relationships and how these might change in a changing environment.