The Influence of Nutrients and Physical Habitat in Regulating Algal Biomass in Agricultural Streams

This study examined the relative influence of nutrients (nitrogen and phosphorus) and habitat on algal biomass in five agricultural regions of the United States. Sites were selected to capture a range of nutrient conditions, with 136 sites distributed over five study areas. Samples were collected in either 2003 or 2004, and analyzed for nutrients (nitrogen and phosphorous) and algal biomass (chlorophyll a). Chlorophyll a was measured in three types of samples, fine-grained benthic material (CHL_FG), coarse-grained stable substrate as in rock or wood (CHL_CG), and water column (CHL_S). Stream and riparian habitat were characterized at each site. TP ranged from 0.004–2.69 mg/l and TN from 0.15–21.5 mg/l, with TN concentrations highest in Nebraska and Indiana streams and TP highest in Nebraska. Benthic algal biomass ranged from 0.47–615 mg/m², with higher values generally associated with coarse-grained substrate. Seston chlorophyll ranged from 0.2–73.1 μg/l, with highest concentrations in Nebraska. Regression models were developed to predict algal biomass as a function of TP and/or TN. Seven models were statistically significant, six for TP and one for TN; r ² values ranged from 0.03 to 0.44. No significant regression models could be developed for the two study areas in the Midwest. Model performance increased when stream habitat variables were incorporated, with 12 significant models and an increase in the r ² values (0.16–0.54). Water temperature and percent riparian canopy cover were the most important physical variables in the models. While models that predict algal chlorophyll a as a function of nutrients can be useful, model strength is commonly low due to the overriding influence of stream habitat. Results from our study are presented in context of a nutrient-algal biomass conceptual model.     

Simulating property exchange in estuarine ecosystem models at ecologically appropriate scales

A computational scheme has been developed and tested to simulate property exchange by advection and dispersion in estuaries at time and space scales that are well suited to ecological and management simulations, but are coarse relative to the demands of physical hydrodynamic models. An implementation of the Regional Ocean Model System (ROMS) for the Providence River and Narragansett Bay (RI, USA) was used to determine property exchanges between the spatial elements of an ecological box model. The basis for the method is the statistical tabulation of numerical dye experiments done with the full ROMS physical model. The ROMS model domain was subdivided into fifteen coarse boxes, each with two vertical layers, defining 30 elements that were used for the box model simulations. Dye concentrations were set to arbitrary initial concentrations for all ROMS grids in the large elements, and the ROMS model was run for 24 h. The final distribution of the dye among the elements was used as a tracer for property exchange over that day and was used to develop an exchange matrix. Box model predictions of salinity over 77 days in each element compared favorably with ROMS simulated salinity averaged over the same spatial elements, although the disparity was greater in areas where large river inflows caused strong gradients in ROMS within elements assumed to be homogeneous in the box model. The 77-day simulation included periods of high and low river flow. Despite the large size of the spatial elements, dispersion artifacts were small, much less than the modeled daily exchanges. While others have taken a similar approach, we found a number of theoretical and practical considerations deserved careful attention for this approach to perform satisfactorily. Whereas the full ROMS model takes 9 days on a powerful computing cluster to compute the physics simulation for 77 days, the box model simulates physics and biology for the same interval in 5 s on a personal computer, and a full year in under 1 min. The exchange matrix mixing model is a fast, cost effective, and convenient way to simulate daily variation of complex estuarine physics in ecological modeling at appropriate scales of space and time.     

Decomposition of diverse litter mixtures in streams

In view of growing interest in understanding how biodiversity affects ecosystem functioning, we investigated effects of riparian plant diversity on litter decomposition in forest streams. Leaf litter from 10 deciduous tree species was collected during natural leaf fall at two locations (Massif Central in France and Carpathians in Romania) and exposed in the field in litter bags. There were 35 species combinations, with species richness ranging 1-10. Nonadditive effects on the decomposition of mixed-species litter were minor, although a small synergistic effect was observed in the Massif Central stream where observed litter mass remaining was significantly lower overall than expected from data on single-species litter. In addition, variability in litter mass remaining decreased with litter diversity at both locations. Mean nitrogen concentration of single- and mixed-species litters (0.68-4.47% of litter ash-free dry mass) accounted for a large part of the variation in litter mass loss across species combinations. For a given species or mixture, litter mass loss was also consistently faster in the Massif Central than in the Carpathians, and the similarity in general stream characteristics, other than temperature, suggests that this effect was largely due to differences in thermal regimes. These results support the notion that decomposition of litter mixtures is primarily driven by litter quality and environmental factors, rather than by species richness per se. However, the observed consistent decrease in variability of decomposition rate with increasing plant species richness indicates that conservation of riparian tree diversity is important even when decomposition rates are not greatly influenced by litter mixing.     

Multinomial mixture model with heterogeneous classification probabilities

Royle and Link (Ecology 86(9):2505?C2512, 2005) proposed an analytical method that allowed estimation of multinomial distribution parameters and classification probabilities from categorical data measured with error. While useful, we demonstrate algebraically and by simulations that this method yields biased multinomial parameter estimates when the probabilities of correct category classifications vary among sampling units. We address this shortcoming by treating these probabilities as logit-normal random variables within a Bayesian framework. We use Markov chain Monte Carlo to compute Bayes estimates from a simulated sample from the posterior distribution. Based on simulations, this elaborated Royle-Link model yields nearly unbiased estimates of multinomial and correct classification probability estimates when classification probabilities are allowed to vary according to the normal distribution on the logit scale or according to the Beta distribution. The method is illustrated using categorical submersed aquatic vegetation data.     

The growth-predation risk trade-off under a growing gape-limited predation threat

Growth is a critical ecological trait because it can determine population demography, evolution, and community interactions. Predation risk frequently induces decreased foraging and slow growth in prey. However, such strategies may not always be favored when prey can outgrow a predator's hunting ability. At the same time, a growing gape-limited predator broadens its hunting ability through time by expanding its gape and thereby creates a moving size refuge for susceptible prey. Here, I explore the ramifications of growing gape-limited predators for adaptive prey growth. A discrete demographic model for optimal foraging/growth strategies was derived under the realistic scenario of gape-limited and gape-unconstrained predation threats. Analytic and numerical results demonstrate a novel fitness minimum just above the growth rate of the gape-limited predator. This local fitness minimum separates a slow growth strategy that forages infrequently and accumulates low but constant predation risk from a fast growth strategy that forages frequently and experiences a high early predation risk in return for lower future predation risk and enhanced fecundity. Slow strategies generally were advantageous in communities dominated by gape-unconstrained predators whereas fast strategies were advantageous in gape-limited predator communities. Results were sensitive to the assumed relationships between prey size and fecundity and between prey growth and predation risk. Predator growth increased the parameter space favoring fast prey strategies. The model makes the testable predictions that prey should not grow at the same rate as their gape-limited predator and generally should grow faster than the fastest growing gape-limited predator. By focusing on predator constraints on prey capture, these results integrate the ecological and evolutionary implications of prey growth in diverse predator communities and offer an explanation for empirical growth patterns previously viewed to be anomalies.