RESUMEN
Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns.
Asunto(s)
Biodiversidad , Modelos Biológicos , Redes Neurales de la Computación , Ríos , Agricultura , Sedimentos GeológicosRESUMEN
The usefulness of surrogates to estimate complex variables describing community structure, such as the various components of biodiversity, is long established. Most attention has been given to surrogates of species richness and species diversity and has focused on identifying a subset of taxa as a surrogate of total community richness or diversity. In adopting a surrogate measure, it is assumed that the relationship between the surrogate(s) and total richness or diversity is consistent in both space and time. These assumptions are rarely examined explicitly. We examined the robustness of potential surrogates of familial richness and multivariate community structure for macrofauna communities inhabiting artificial kelp holdfasts by comparing among communities of dissimilar ages and among communities established at different times of the year. This is important because most benthic "landscapes" will be a mosaic of patches reflecting different intensities, frequencies, and timing of disturbances. The total abundance of organisms and familial richness of crustaceans or polychaetes were all good predictors of total familial richness (R2 > 0.68). In contrast, while the familial richness of other groups, such as mollusks and echinoderms, were well correlated with total familial richness for communities at an early stage of development, the strength of these relationships declined with community age. For multivariate community structure, carefully selected subsets of approximately 10% of the total taxa yielded similar patterns to the total suite of taxa, irrespective of the age of the community. Thus, useful surrogates of both familial richness and multivariate community structure can be identified for this type of community. However, the choice of technique for selecting surrogate taxa largely depends on the nature of the pilot data available, and careful selection is required to ensure that surrogates perform consistently across different-aged communities. While the specific taxa selected as surrogates will vary among different communities, and possibly even among similar communities at different sites, the techniques and the concepts we address are applicable to any community type.