Multiscale codependence analysis: an integrated approach to analyze relationships across scales

The spatial and temporal organization of ecological processes and features and the scales at which they occur are central topics to landscape ecology and metapopulation dynamics, and increasingly regarded as a cornerstone paradigm for understanding ecological processes. Hence, there is need for computational approaches which allow the identification of the proper spatial or temporal scales of ecological processes and the explicit integration of that information in models. For that purpose, we propose a new method (multiscale codependence analysis, MCA) to test the statistical significance of the correlations between two variables at particular spatial or temporal scales. Validation of the method (using Monte Carlo simulations) included the study of type I error rate, under five statistical significance thresholds, and of type II error rate and statistical power. The method was found to be valid, in terms of type I error rate, and to have sufficient statistical power to be useful in practice. MCA has assumptions that are met in a wide range of circumstances. When applied to model the river habitat of juvenile Atlantic salmon, MCA revealed that variables describing substrate composition of the river bed were the most influential predictors of parr abundance at 0.4-4.1 km scales whereas mean channel depth was more influential at 200-300 m scales. When properly assessed, the spatial structuring observed in nature may be used purposefully to refine our understanding of natural processes and enhance model representativeness.     

Bioreduction of nitrate in groundwater using a pilot-scale hydrogen-based membrane biofilm reactor

A long-term pilot-scale H₂-based membrane biofilm reactor (MBfR) was tested for removal of nitrate from actual groundwater. A key feature of this second-generation pilot MBfR is that it employed lower cost polyester hollow fibers and still achieved high loading rate. The steady-state maximum nitrate surface loading at which the effluent nitrate and nitrite concentrations were below the Maximum Contaminant Level (MCL) was at least 5.9 g·N·(m²·d)^?1, which corresponds to a maximum volumetric loading of at least 7.7 kg·N·(m³·d) ^?1. The steady-state maximum nitrate surface area loading was higher than the highest nitrate surface loading reported in the first-generation MBfRs using composite fibers (2.6 g·N·(m²·d)^?1). This work also evaluated the H₂-utilization efficiency in MBfR. The measured H₂ supply rate was only slightly higher than the stoichiometric H₂-utilization rate. Thus, H₂ utilization was controlled by diffusion and was close to 100% efficiency, as long as biofilm accumulated on the polyester-fiber surface and the fibers had no leaks.     

Sampling animal association networks with the gambit of the group

Ecologists increasingly use network theory to examine animal association patterns. The gambit of the group (GoG) is a simple and useful assumption for accumulating the data necessary for a network analysis. The gambit of the group implies that each animal in a group is associating with every other individual in that group. Sampling is an important issue for networks in wild populations collected assuming GoG. Due to time, effort, and resource constraints and the difficulty of tracking animals, sampled data are usually a subset of the actual network. Ecologists often use association indexes to calculate the frequency of associations between individuals. These indexes are often transformed by applying a filter to produce a binary network. We explore GoG sampling using model networks. We examine assortment at the level of the group by a single dichotomous trait, along with many other network measures, to examine the effect of different sampling regimes, and choice of filter on the accuracy and precision with which measures are estimated. We find strong support for the use of weighted, rather than filtered, network measures and show that different filters have different effects depending on the nature of the sampling. We make several practical recommendations for ecologists planning GoG sampling.     

A case for quantile regression in behavioral ecology: getting more out of flight initiation distance data

A central goal of behavioral ecology is to quantify and explain variation in behavior. While much previous work has focused on the differences in mean behavior across groups or treatments, we present a complementary approach studying changes in the distribution of the response variable. This is important because changes in the edges of a distribution may be more informative than changes in the mean if behavior at the edges of a distribution better reflects behavioral constraints. Quantile regression estimates the rate of change of conditional quantiles of a response variable and thus allows the study of changes in any part of its distribution. Although quantile regression is gaining popularity in the ecological literature, it is strikingly unused in behavioral ecology. Here, we demonstrate the usefulness of this method by analyzing the relationship between the starting distance (SD) at which an observer approach a focal animal and its flight initiation distance (FID, the distance between the observer and the animal when it decides to flee). In particular, we used a simple model of flight initiation distance to show that in most situations ordinary least-square regression cannot be used to analyse the SD–FID relationship. Quantile regression conducted on the lowest quantiles appears more robust and we applied this approach to data from four bird species. Overall, changes in the lowest FID values appeared to be the most informative to determine if a species displays a “flush early” strategy, a strategy which has been hypothesized to be a general rule. We hope this example will bring quantile regression to the attention of behavioral ecologists as a valuable tool to add to their statistical toolbox.     

Species diversity reduces parasite infection through cross-generational effects on host abundance

With growing interest in the effects of biodiversity on disease, there is a critical need for studies that empirically identify the mechanisms underlying the diversity-disease relationship. Here, we combined wetland surveys of host community structure with mechanistic experiments involving a multi-host parasite to evaluate competing explanations for the dilution effect. Sampling of 320 wetlands in California indicated that snail host communities were strongly nested, with competent hosts for the trematode Ribeiroia ondatrae predominating in low-richness assemblages and unsuitable hosts increasingly present in more diverse communities. Moreover, competent host density was negatively associated with increases in snail species richness. These patterns in host community assembly support a key prerequisite underlying the dilution effect. Results of multigenerational mesocosm experiments designed to mimic field-observed community assemblages allowed us to evaluate the relative importance of host density and diversity in influencing parasite infection success. Increases in snail species richness (from one to four species) had sharply negative effects on the density of infected hosts (-90% reduction). However, this effect was indirect; competition associated with non-host species led to a 95% reduction in host density (susceptible host regulation), owing primarily to a reduction in host reproduction. Among susceptible hosts, there were no differences in infection prevalence as a function of community structure, indicating a lack of support for a direct effect of diversity on infection (encounter reduction). In monospecific conditions, higher initial host densities increased infection among adult hosts; however, compensatory reproduction in the low-density treatments equalized the final number of infected hosts by the next generation, underscoring the relevance of multigenerational studies in understanding the dilution effect. These findings highlight the role of interspecific competition in mediating the relationship between species richness and parasite infection and emphasize the importance of field-informed experimental research in understanding mechanisms underlying the diversity-disease relationship.     

Community ecology of invasions: direct and indirect effects of multiple invasive species on aquatic communities

With many ecosystems now supporting multiple nonnative species from different trophic levels, it can be challenging to disentangle the net effects of invaders within a community context. Here, we combined wetland surveys with a mesocosm experiment to examine the individual and combined effects of nonnative fish predators and nonnative bullfrogs on aquatic communities. Among 139 wetlands, nonnative fish (bass, sunfish, and mosquitofish) negatively influenced the probability of occupancy of Pacific treefrogs (Pseudacris regilla), but neither invader correlated strongly with occupancy by California newts (Taricha torosa), western toads (Anaxyrus boreas), or California red-legged frogs (Rana draytonii). In mesocosms, mosquitofish dramatically reduced the abundance of zooplankton and palatable amphibian larvae (P. regilla and T. torosa), leading to increases in nutrient concentrations and phytoplankton (through loss of zooplankton), and rapid growth of unpalatable toad larvae (through competitive release). Bullfrog larvae reduced the growth of native anurans but had no effect on survival. Despite strong effects on natives, invaders did not negatively influence one another, and their combined effects were additive. Our results highlight how the net effects of multiple nonnative species depend on the trophic level of each invader, the form and magnitude of invader interactions, and the traits of native community members.     

Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study

The Mantel correlogram is an elegant way to compute a correlogram for multivariate data. However, recent papers raised concerns about the power of the Mantel test itself. Hence the question: Is the Mantel correlogram powerful enough to be useful? To explore this issue, we compared the performances of the Mantel correlogram to those of other methods, using numerical simulations based on random, normally distributed data. For a single response variable, we compared it to the Moran and Geary correlograms. Type I error rates of the three methods were correct. Power of the Mantel correlogram was nearly as high as that of the univariate methods. For the multivariate case, the test of the multivariate variogram developed in the context of multiscale ordination is in fact a Mantel test, so that the power of the two methods is the same by definition. We devised an alternative permutation test based on the variance, which yielded similar results. Overall, the power of the Mantel test was high, the method successfully detecting spatial correlation at rates similar to the permutation test of the variance statistic in multivariate variograms. We conclude that the Mantel correlogram deserves its place in the ecologist's toolbox.     

Managing water for life

Water is essential for life. In spite of the entire engineering infrastructure devoted to the treatment, regulation and beneficial uses of water, occasionally sufficient quantities and qualities of water become scarce. When this happens, just how do we decide how much less water to allocate to all of us and the activities we engage in to sustain and enhance our quality of life? This paper addresses some of the complexities of answering such a question, especially as society increasingly recognizes the need to provide flow regimes that will maintain healthy aquatic and floodplain ecosystems that also impact the economic, physical and even the spiritual quality of our lives. For we depend on these ecosystems to sustain our wellbeing. We are indeed a part of our ecosystems. We depend upon on aquatic ecosystems to moderate river flow qualities and quantities, reduce the extremes of floods and droughts, reduce erosion, detoxify and decompose waterborne wastes, generate and preserve flood plain soils and renew their fertility, regulate disease carrying organisms, and to enhance recreational benefits of river systems. This question of deciding just how much water to allocate to each water user and for the maintenance of viable aquatic ecosystems, especially when there is not enough, is a complex, and largely political, issue. This issue is likely to become even more complex and political and contentious in the future as populations grow and as water quantities and their qualities become even more variable and uncertain.     

Selection and Calibration of Numerical Modeling in Flow and Water Quality

Numerical modeling is frequently used in coastal engineering research and application. One possible issue associated with using this method however, is that initial outcomes might differ from expectation. Modeling manipulation addresses this issue by changing the initial parameters which in turn affects the final output results. The advancement of modeling techniques in recent years has seen a gradual but steady accruement of knowledge regarding the relationship between input parameters and possible outputs, which has resulted in improved accuracy and efficiency methods. The advancements within this field have predominantly occurred on an individual basis and as such lack standardisation. An expert numerical modeler may use this knowledge subconsciously, yet may not know how to convey it to the model users. This paper has been written to introduce a systematic intelligent coding schema designed to organise current coastal engineering modeling manipulation knowledge into a standardised format. This system is relevant to the development of appropriate strategies for improved accuracy and efficiency as well as model modification to simulate specific real phenomena in prototype application cases.