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.