(Figure 4.5). The example is based on four landscape scales, with one filter per each scale, and a source pool of three species. Two values are required to parameterize the model: a species-specific trait (the resistance of each species to being removed by the filter) and a landscape-level trait (the strength of the filter relative to its ability to remove species). Poff (1997) arbitrarily chose three resistance categories: 1-strong resistance (not removed by any filter), 0.5-intermediate resistance (affected only by the strongest filter, which has a 50% chance of removing the species), and 0-weak resistance (100% chance of removal by strong filters and 50% chance of removal by intermediate filters). Similarly, Poff chose three filter strengths: 1-strong, 0.5-intermediate, and 0-weak.
FIGURE 4.5. An example of the landscape filter model, based on four landscape scales and a source pool of three species, and illustrating how species traits interact with a hierarchical series of filters to determine species occurrences in assemblages. R = the resistance of each species to being removed by the filter; S = the strength of the filter relative to its ability to remove species. See text for additional explanation. Based on Poff (1997).
To run the hypothetical model and determine the most likely species assemblage at the microhabitat level, I have provided likely species resistance values and filter strengths based on work done in the Pascagoula River drainage of southeastern Mississippi (Baker and Ross 1981; Ross and Baker 1983; Ross et al. 1987; Ross 2001). For instance, at the level of basin with the filter of nutrient enrichment, Brown Bullhead (Ameiurus nebulosus) would likely have a resistance of 1, allowing it to pass through the nutrient enrichment filter, which, because many species are strongly impacted by eutrophication, is also set at 1. At the basin level then, Brown Bullhead would have a score of 1 and be classed as abundant. At the reach level, the filter of flood interval has a strength of 0 (given the generally nonerosive floods of this region) and Brown Bullhead are assigned a resistance strength of 0.5. Therefore, at the reach level, Brown Bullhead would also have a score of 1 and be classed as abundant. At the channel-unit level, the filter of water velocity has a strength of 0.5 and Brown Bullhead have a resistance value of 0, given that they tend to occur in slowvelocity habitats, resulting in a score of 0.5 and being ranked as common. Finally, at the microhabitat level the filter of coarse substrate size has a strength of 1 (since bottom-inhabiting species generally respond strongly to particle size) and Brown Bullhead, which usually occur over fine substrata, have a resistance of 0, resulting in a score of 0 and a local abundance classed as rare. Poff (1997) cautioned that the model should not be used to predict absence because it does not incorporate all factors that might influence species presence or absence. Based on likely resistance levels and filter strengths as cited previously, two more species, Weed Shiner (Notropis texanus) and Blackbanded Darter (Percina nigrofasciata) can also be run through the four filter levels with the end result being a local assemblage most likely comprising Weed Shiner. Brown Bullhead are limited by the microhabitat filter of particle size, and Blackbanded Darter are limited by the basin filter of nutrient enrichment.
To expand the landscape model to real-life situations would require fairly extensive ecological information on the fish species potentially available at the basin-level species pool, along with knowledge of what filters are likely operating at each level of the hierarchy as well as their respective strengths. Although more research is needed to understand species susceptibility to various filters, for many regions of North America there is likely sufficient information to assess the relative resistance of species to various environmental filters as well as filters that are likely important at the different hierarchical levels. The information on composition of local assemblages that is provided by the mechanistic approach of landscape models is qualitative rather than quantitative. In other words, the models suggest relative abundance levels (i.e., abundant, common, or rare), but do not, and likely will not, provide quantitative measures of abundance (i.e., density).
Goldstein and Meador (2004) tested various predictions of the landscape model using their analyses of how fish species traits varied among different stream sizes. For the most part, their work supported the theoretical predictions from Poff’s (1997) landscape model. For instance, their work supported the landscape model prediction that fish morphology would be best predicted by local factors such as hydraulic stress. Some differences between the predictions of the landscape model and the findings of Goldstein and Meador (2004) occurred with how reproductive and substratum-use traits varied within and among streams. Landscape predictions, as interpreted by Goldstein and Meador (2004), were that reproductive traits “will vary with flow and substrate variability” and that “substrate preferences are driven largely by microhabitat scale factors.” In contrast, Goldstein and Meador (2004) found that both reproductive strategies and substratum use varied relative to stream size (i.e., among rather than within streams).
River Continuum Concept
The river continuum model (RCC) (Vannote et al. 1980) emphasizes continuity with gradual changes in species occurrences and functional groups from headwaters to downstream reaches (Figure 4.6). The model was conceived as an extension of the physical, geomorphic changes that occur longitudinally in rivers, with the idea that “over extended river reaches, biological communities should become established which approach equilibrium with the dynamic physical conditions of the channel.” In this sense the RCC is quite similar to the habitat template model.
FIGURE 4.6. The relationship between stream size and the physical and biotic components, as proposed in the river continuum model. CPOM = coarse particulate organic material; FPOM = fine particulate organic material; P = primary production; R = respiration. Based on Vannote et al. (1980) and Paller (1994). See text for further explanation.
Headwater streams, stream orders 1–3 (Box 4.1), are strongly influenced by the presence or absence of riparian vegetation. If riparian vegetation is well developed, or if the stream is otherwise strongly shaded by being in a deep canyon, energy input into the stream is largely derived from the surrounding terrestrial area (i.e., allochthonous input) rather than from within the stream (autochthonous input) so that instream measurements show greater respiration than production (P/R < 1). Such situations are common in many eastern and southeastern streams and montane western streams. As the stream increases in size, there is gradually more light penetration into the water and autochthonous production increases so that P/R > 1. In contrast, in headwater streams without well-developed riparian vegetation that could shade the stream, autochthonous production by submerged vascular plants, periphyton, or algae is well developed so that P/R > 1. Such streams are typical of high elevations and latitudes and arid regions in general.
BOX 4.1 • Stream Order
Streams can be categorized in various ways such as discharge, water depth, gradient, water quality, and branching pattern, to name but a few. The branching pattern of streams, or network analysis, has proven useful as a way to describe streams (Leopold et al. 1964; Leopold 1994). Pioneering work on network analysis of streams involved the concept of stream order, first proposed by Robert Horton (1945) and later modified by Strahler (1952, 1957). Using Strahler’s modification of Horton’s system, the smallest unbranched tributary is classified as first order. The union of two first-order streams results in a second-order stream, and the union of two second-order streams results in a third-order stream. To generalize, when two streams of equal rank join they form a segment of the next highest order. However, ordinal rank is not increased by the entrance of lower-order streams.
The decision as to what constitutes a first-order stream can be somewhat arbitrary and to a certain extent depends on the purpose of the study. For instance, a geomorphologist might be more interested in including small channels even though they are not perennial. Strahler (1952), in fact, suggested that first-order streams were wet-weather streams that were normally dry. Leopold et al. (1964) further refined this by suggesting that first-order streams are the smallest unbranched tributaries shown on a 1:24,000 scale
