Effects of riparian deforestation on benthic invertebrate community and leaf processing in Atlantic forest streams

Riparian deforestation may strongly affect stream functioning, with consequences for biodiversity and ecosystem services. These effects can be assessed using bioindicators relating to biotic community structure and ecosystem functioning. We evaluated the effects of riparian deforestation on 1. measures of community structure using aquatic benthic invertebrates, and 2. an aspect of ecosystem functioning, aquatic leaf processing. We selected sites along gradients of riparian land use in four Atlantic rainforest streams and measured physical and chemical properties for their association with riparian deforestation. We sampled benthic invertebrates and calculated metrics of community structure at each site. We measured rates of leaf processing using leaves of a common riparian tree, Guarea guidonia. Riparian deforestation was accompanied by increasing concentration of ammonia, water current and temperature and decreasing nightly oxygen saturation. Invertebrate diversity decreased and community metrics changed with deforestation as expected of negative impacts. Leaf processing decreased with deforestation. Although there were significant differences in physical and chemical measurements among streams, the gradients in community and ecosystem responses were similar, thus suggesting that both types of bioindicators were useful for monitoring changes and relating them to loss of biodiversity and ecosystem function

Generalized Linear Models outperform commonly used canonical analysis n estimating spatial structure of presence/absence data

Background Ecological communities tend to be spatially structured due to environmental gradients and/or spatially contagious processes such as growth, dispersion and species interactions. Data transformation followed by usage of algorithms such as Redundancy Analysis (RDA) is a fairly common approach in studies searching for spatial structure in ecological communities, despite recent suggestions advocating the use of Generalized Linear Models (GLMs). Here, we compared the performance of GLMs and RDA in describing spatial structure in ecological community composition data. We simulated realistic presence/absence data typical of many β-diversity studies. For model selection we used standard methods commonly used in most studies involving RDA and GLMs. Methods We simulated communities with known spatial structure, based on three real spatial community presence/absence datasets (one terrestrial, one marine and one freshwater). We used spatial eigenvectors as explanatory variables. We varied the number of non-zero coefficients of the spatial variables, and the spatial scales with which these coefficients were associated and then compared the performance of GLMs and RDA frameworks to correctly retrieve the spatial patterns contained in the simulated communities. We used two different methods for model selection, Forward Selection (FW) for RDA and the Akaike Information Criterion (AIC) for GLMs. The performance of each method was assessed by scoring overall accuracy as the proportion of variables whose inclusion/exclusion status was correct, and by distinguishing which kind of error was observed for each method. We also assessed whether errors in variable selection could affect the interpretation of spatial structure. Results Overall GLM with AIC-based model selection (GLM/AIC) performed better than RDA/FW in selecting spatial explanatory variables, although under some simulations the methods performed similarly. In general, RDA/FW performed unpredictably, often retaining too many explanatory variables and selecting variables associated with incorrect spatial scales. The spatial scale of the pattern had a negligible effect on GLM/AIC performance but consistently affected RDA’s error rates under almost all scenarios. Conclusion We encourage the use of GLM/AIC for studies searching for spatial drivers of species presence/absence patterns, since this framework outperformed RDA/FW in situations most likely to be found in natural communities. It is likely that such recommendations might extend to other types of explanatory variables.publishedVersio

Disentangling the impacts of multiple stressors in the Upper Clark Fork food webs

The UCFR headwaters suffer from mining legacy impacts and also nutrient enrichment caused by land use and other natural features of the watershed. These impacts historically led to a major decline in riverine integrity. The Montana EPSCOR-CREWS project has as one of its main objectives to determine how heavy metals contamination, coupled with nutrient enrichment alter aquatic ecosystems, acting as subsidies and stressors in the UCFR. The form and size of the metals in question can determine the propensity for uptake by primary producers and transfer to higher trophic levels. Through its influences on primary production, nutrient enrichment can exacerbate contaminant effects via increased resource availability during algal blooms or diminish such effects by distributing contaminants more broadly within autotrophic biomass. The timing and location of algal blooms might affect the quality and availability of basal resources that modulate the transfer of pollutants to higher trophic levels. Lastly, the health of the fish communities in the river might not only be affected by trophic interactions and metal concentrations, but also by habitat availability and other important features of the system also currently impaired by human activities. Here we showcase the ongoing multidisciplinary efforts to disentangle the effects of these multiple stressors and provide information to restoration efforts and stakeholders in the UCFR