Environ indicator sensitivity to flux uncertainty in a phosphorus model of Lake Sidney Lanier, USA

Effective environmental impact assessment and management requires improved understanding of the organization and transformation of ecosystems in which independent agents are linked through an intricate network of energy, matter, and informational interactions. While advances have been made, we still lack a complete understanding of the processes that create, constrain, and sustain ecosystems. Network environ analysis (NEA) provides one approach for building novel ecosystem insights, but it is model dependent. As ecological modeling is an imprecise art, often complicated by inadequate empirical data, the utility of NEA may be limited by model uncertainty. Here, we investigate the sensitivity of NEA indicators of ecosystem growth and development to flow and storage uncertainty in a phosphorus model of Lake Sidney Lanier, USA. The indicators are total system throughflow (TST), total system storage (TSS), total boundary input (Boundary), Finn cycling index (FCI), ratio of indirect-to-direct flows (Indirect/Direct), indirect flow index (IFI), network aggradation (AGG), network homogenization (HMG), and network amplification (AMP). Our results make two primary contributions. First, they demonstrate that five of the indicators – FCI, Indirect/Direct, IFI, AGG and HMG – are relatively robust to the flow and storage uncertainty in the Lake Lanier model. This stability lets us draw robust conclusions about the Lake Lanier ecosystem organization (e.g., phosphorus flux in the lake is dominated by internal processes) in spite of uncertainties in the model. Second, we show that the majority of the indicators co-vary and that most of their common variation could be mapped onto two latent factors, which we interpret as (1) system integration and (2) boundary influences.     

Dynamic environ analysis of compartmental systems: A computational approach

Ecosystems are often modeled as stocks of matter or energy connected by flows. Network environ analysis (NEA) is a set of mathematical methods for using powers of matrices to trace energy and material flows through such models. NEA has revealed several interesting properties of flow–storage networks, including dominance of indirect effects and the tendency for networks to create mutually positive interactions between species. However, the applicability of NEA is greatly limited by the fact that it can only be applied to models at constant steady states. In this paper, we present a new, computationally oriented approach to environ analysis called dynamic environ approximation (DEA). As a test of DEA, we use it to compute compartment throughflow in two implementations of a model of energy flow through an oyster reef ecosystem. We use a newly derived equation to compute model throughflow and compare its output to that of DEA. We find that DEA approximates the exact results given by this equation quite closely – in this particular case, with a mean Euclidean error ranging between 0.0008 and 0.21 – which gives a sense of how closely it reproduces other NEA-related quantities that cannot be exactly computed and discuss how to reduce this error. An application to calculating indirect flows in ecosystems is also discussed and dominance of indirect effects in a nonlinear model is demonstrated.     

Evidence for the dominance of indirect effects in 50 trophic ecosystem networks

Indirect effects are powerful influences in ecosystems that may maintain species diversity and alter apparent relationships between species in surprising ways. Here, we applied network environ analysis to 50 empirically-based trophic ecosystem models to test the hypothesis that indirect flows dominate direct flows in ecosystem networks. Further, we used Monte Carlo based perturbations to investigate the robustness of these results to potential error in the underlying data. To explain our findings, we further investigated the importance of the microbial food web in recycling energy-matter using components of the Finn Cycling Index and analysis of environ centrality. We found that indirect flows dominate direct flows in 37/50 (74.0%) models. This increases to 31/35 (88.5%) models when we consider only models that have cycling structure and a representation of the microbial food web. The uncertainty analysis reveals that there is less error in the I/D values than the ±5% error introduced into the models, suggesting the results are robust to uncertainty. Our results show that the microbial food web mediates a substantial percentage of cycling in some systems (median = 30.2%), but its role is highly variable in these models, in agreement with the literature. Our results, combined with previous work, strongly suggest that indirect effects are dominant components of activity in ecosystems.     

Weak indirect effects inherent to nitrogen biogeochemical cycling within anthropogenic ecosystems: A network environ analysis

Indirect effects are assumed to be the major causes of the complexity and stability of ecological networks. The complexity of urban-rural complexes (URCs) could also be attributed to the indirect effects associated with human activities. No studies, however, have quantified the strength of indirect effects in relation to urban biogeochemistry. A network environ analysis (NEA) was used for this study to investigate and compare indirect effects in relation to the nitrogen (N) cycling networks of 22 natural ecosystems and five URCs. Results show that indirect effects were proven to be weak for URC N cycling networks (accounting for only ∼2% of the overall effects measured in natural ecosystems). The weak indirect effects found provide a counterexample for the hypothesis that indirect effects are in fact the dominant components of biogeochemical networks. It also implies that human activity in itself does not always raise the complexity of ecological processes as previously suggested. Weak indirect effects also lead to perturbation fragility for URC N cycles (where the decay rate is greater in comparison to natural ecosystems by a factor of 13). In order to improve the robustness and efficiency of URC biogeochemical cycling, a knockout analysis was carried out. By comparing results after removing single interactions between natural ecosystems and URCs it was found that the loss of indirect effects require cooperative strategies to optimize N cycling networks within URCs.     

Cycling in ecosystems: An individual based approach

Cycling index is an important ecological indicator used in ecosystem analysis. The higher the cycling in an ecosystem, the higher the utilization of mass and energy within the system before it is lost due to respiration and other factors. For a stock-flow type ecosystem model at steady state, Finn’s cycling index (FCI) can be computed using simple matrix algebra. However, it is difficult to measure how well this index represents the actual cycling occurring in the system. In this paper, we study cycling in ecological networks using an individual based approach (particle tracking algorithm). This new simulation method provides access to the pathway data of individual particles that flow in the system, therefore one can quantify cycling using this pathway data quite literally. We used particle tracking simulations (PTS) to compute a cycling index using Finn’s idea of flux based cycling. Our simulation based results (using no matrix algebra) agree with Finn’s cycling index, verifying the accuracy of both the PTS, and the original linear algebraic formulation of FCI.     

Throughflow analysis: A stochastic approach

Ecological network analysis (ENA), predicated on systems theory and Leontiev input–output analysis, is a method widely used in ecology to reveal ecosystem properties. An important ecosystem property computed in ENA is throughflows, the amount of matter/energy leaving each compartment of the ecosystem. Throughflows are analyzed via a matrix representing their relationships to the driving input at the boundary. Network particle tracking (NPT) builds on ENA to offer a Lagrangian particle method that describes the activity of the ecosystem at the microscopic level. This paper introduces a Lagrangian throughflow analysis methodology using NPT and shows that the NPT throughflow matrix, , agrees with the conventional ENA throughflow matrix, , for ecosystems at steady-state with donor-controlled flows. The matrix is computed solely from the pathways (particles’ histories) generated by NPT simulations and its average over multiple runs of the algorithm with longer simulation time agrees with the Eulerian matrix (Law of Large Numbers). While the traditional NEA throughflow analysis is mostly used with steady-state ecosystem models, the Lagrangian throughflow analysis that we propose can be used with non-steady-state models and paves the way for the development of dynamic throughflow analysis.     

Do microbial processes regulate the stability of a coral atoll's enclosed pelagic ecosystem?

Complex marine ecosystems contain multiple feedback cycles that can cause unexpected responses to perturbations. To better predict these responses, complicated models are increasingly being developed to enable the study of feedback cycles. However, the sparseness of ecological data often limits the direct empirical parameterization of all model parameters. Here we use a Bayesian inverse analysis approach to synthesize empirical data and ecological theory derived from published studies of a coral atoll's enclosed pelagic ecosystem (Takapoto Atoll, French Polynesia). We then use the estimates of flux magnitudes to parameterize probabilistic compartment models with two forms of heterotrophic consumption: (1) “bottom-up” donor-controlled heterotrophic consumption and (2) “top-down” mass-action heterotrophic consumption. We explore how the flux magnitudes affect the ecosystem's stability properties of resilience, reactivity, and resistance under both assumptions for heterotrophic consumption. The models suggest that the microbial uptake of dissolved organic carbon (DOC) regulates the long term rate of return to steady state following a temporary or pulse perturbation (resilience), and the cycling of carbon between abiotic pools and heterotrophic compartments regulates the short-term response (reactivity). In the bottom-up process model, the sensitivity of steady state masses following a sustained or press perturbation (resistance) is highest for the DOC pool following a sustained change to the microbial uptake rate of DOC. Further, a change in the microbial uptake of DOC propagates through the ecosystem and affects the steady state values of zooplankton. The analysis suggests that the food web is highly dependent on the recycling between the abiotic and biotic carbon pools, particularly as mediated by the microbial consumption of DOC, and this recycling determines how the ecosystem responds to perturbations.     

Characterization of the genes involved in nitrogen cycling in wastewater treatment plants using DNA microarray and most probable number-PCR

To improve nitrogen removal performance of wastewater treatment plants (WWTPs), it is essential to understand the behavior of nitrogen cycling communities, which comprise various microorganisms. This study characterized the quantity and diversity of nitrogen cycling genes in various processes of municipal WWTPs by employing two molecular-based methods:most probable number-polymerase chain reaction (MPN-PCR) and DNA microarray. MPN-PCR analysis revealed that gene quantities were not statistically different among processes, suggesting that conventional activated sludge processes (CAS) are similar to nitrogen removal processes in their ability to retain an adequate population of nitrogen cycling microorganisms. Furthermore, most processes in the WWTPs that were researched shared a pattern:the nirS and the bacterial amoA genes were more abundant than the nirK and archaeal amoA genes, respectively. DNA microarray analysis revealed that several kinds of nitrification and denitrification genes were detected in both CAS and anaerobic-oxic processes (AO), whereas limited genes were detected in nitrogen removal processes. Results of this study suggest that CAS maintains a diverse community of nitrogen cycling microorganisms; moreover, the microbial communities in nitrogen removal processes may be specific.

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Equivalence of the realized input and output oriented indirect effects metrics in Ecological Network Analysis

A new understanding of the consequences of how ecosystem elements are interconnected is emerging from the development and application of Ecological Network Analysis. The relative importance of indirect effects is central to this understanding, and the ratio of indirect flow to direct flow (I/D) is one indicator of their importance. Two methods have been proposed for calculating this indicator. The unit approach shows what would happen if each system member had a unit input or output, while the realized technique determines the ratio using the observed system inputs or outputs. When using the unit method, the input oriented and output oriented ratios can be different, potentially leading to conflicting results. However, we show that the input and output oriented I/D ratios are identical using the realized method when the system is at steady state. This work is a step in the maturation of Ecological Network Analysis that will let it be more readily testable empirically and ultimately more useful for environmental assessment and management.     

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude –0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude –0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude –0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude –0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at –0.3769.     

Predicting aquaculture-derived benthic organic enrichment: Model validation

A sediment trap validation study was conducted near the commercial sea bass and sea bream fish farm in order to assess the predictive capability of a particle tracking deposition model. The validation procedure consisted of two distinct phases. First, the deposition of particulate waste (i.e. fecal pellets and excess feed) was measured near a single net pen containing 19 tons of sea bass. Afterwards, the model quality was determined by statistical comparison of predicted and observed values.Goodness of fit analysis indicates that the model successfully accounts for more than 75% of variance in the observed deposition. At 5% significance level, predictions do not underestimate or overestimate observations and there is no bias. Mean absolute relative error of ±48.9% compares favorably to other published deposition models. Obtained results affirm the reliability of particle tracking techniques in modeling the aquaculture-derived benthic organic enrichment.     

On the consequences of aggregation and balancing of networks on system properties derived from ecological network analysis

In the ecological network analysis (ENA) of complex flow food webs the assumption is often made that the models characterizing the flows and stocks of ecosystems occur in a steady state where inflows equals outflows. An assessment of the system indices derived from ENA of six balanced and unbalanced system models, respectively, indicate to differences between indices. The aggregation of highly articulated flow models into models with fewer compartments also has drastic effects on the system metrics, particularly on the information indices.     

A generalized approach to modeling and estimating indirect effects in ecology

The need to model and test hypotheses about complex ecological systems has led to a steady increase in use of path analytical techniques, which allow the modeling of multiple multivariate dependencies reflecting hypothesized causation and mechanisms. The aim is to achieve the estimation of direct, indirect, and total effects of one variable on another and to assess the adequacy of whole models. Path analytical techniques based on maximum likelihood currently used in ecology are rarely adequate for ecological data, which are often sparse, multi-level, and may contain nonlinear relationships as well as nonnormal response data such as counts or proportion data. Here I introduce a more flexible approach in the form of the joint application of hierarchical Bayes, Markov chain Monte Carlo algorithms, Shipley's d-sep test, and the potential outcomes framework to fit path models as well as to decompose and estimate effects. An example based on the direct and indirect interactions between ants, two insect herbivores, and a plant species demonstrates the implementation of these techniques, using freely available software.     

A multi-integrated approach on toxicity effects of engineered TiO2 nanoparticles

The new properties of engineered nanoparticles drive the need for new knowledge on the safety, fate, behavior and biologic effects of these particles on organisms and ecosystems. Titanium dioxide nanoparticles have been used extensively for a wide range of applications, e.g, self-cleaning surface coatings, solar cells, water treatment agents, topical sunscreens. Within this scenario increased environmental exposure can be expected but data on the ecotoxicological evaluation of nanoparticles are still scarce. The main purpose of this work was the evaluation of effects of TiO₂ nanoparticles in several organisms, covering different trophic levels, using a battery of aquatic assays. Using fish as a vertebrate model organism tissue histological and ultrastructural observations and the stress enzyme activity were also studied. TiO₂ nanoparticles (Aeroxide® P25), two phase composition of anatase (65%) and rutile (35%) with an average particle size value of 27.6±11 nm were used. Results on the EC₅₀ for the tested aquatic organisms showed toxicity for the bacteria, the algae and the crustacean, being the algae the most sensitive tested organism. The aquatic plant Lemna minor showed no effect on growth. The fish Carassius auratus showed no effect on a 21 day survival test, though at a biochemical level the cytosolic Glutathione-S-Transferase total activity, in intestines, showed a general significant decrease (p<0.05) after 14 days of exposure for all tested concentrations. The presence of TiO₂ nanoparticles aggregates were observed in the intestine lumen but their internalization by intestine cells could not be confirmed.     

Predictive vegetation modeling for conservation: impact of error propagation from digital elevation data.

The effect of digital elevation model (DEM) error on environmental variables, and subsequently on predictive habitat models, has not been explored. Based on an error analysis of a DEM, multiple error realizations of the DEM were created and used to develop both direct and indirect environmental variables for input to predictive habitat models. The study explores the effects of DEM error and the resultant uncertainty of results on typical steps in the modeling procedure for prediction of vegetation species presence/absence. Results indicate that all of these steps and results, including the statistical significance of environmental variables, shapes of species response curves in generalized additive models (GAMs), stepwise model selection, coefficients and standard errors for generalized linear models (GLMs), prediction accuracy (Cohen's kappa and AUC), and spatial extent of predictions, were greatly affected by this type of error. Error in the DEM can affect the reliability of interpretations of model results and level of accuracy in predictions, as well as the spatial extent of the predictions. We suggest that the sensitivity of DEM-derived environmental variables to error in the DEM should be considered before including them in the modeling processes.     

General methods for sensitivity analysis of equilibrium dynamics in patch occupancy models

Sensitivity analysis is a useful tool for the study of ecological models that has many potential applications for patch occupancy modeling. Drawing from the rich foundation of existing methods for Markov chain models, I demonstrate new methods for sensitivity analysis of the equilibrium state dynamics of occupancy models. Estimates from three previous studies are used to illustrate the utility of the sensitivity calculations: a joint occupancy model for a prey species, its predators, and habitat used by both; occurrence dynamics from a well-known metapopulation study of three butterfly species; and Golden Eagle occupancy and reproductive dynamics. I show how to deal efficiently with multistate models and how to calculate sensitivities involving derived state variables and lower-level parameters. In addition, I extend methods to incorporate environmental variation by allowing for spatial and temporal variability in transition probabilities. The approach used here is concise and general and can fully account for environmental variability in transition parameters. The methods can be used to improve inferences in occupancy studies by quantifying the effects of underlying parameters, aiding prediction of future system states, and identifying priorities for sampling effort.     

Instream and offstream environmental conditions and stream biotic integrity: Importance of scale and site similarities for learning and prediction

Two different methods to predict biotic integrity were tested and compared in the present paper. The first one tries to predict the fish indices of biotic integrity (IBI) at the state or regional scale based on the most similar observations to a specific target site of interest using the simple to implement k-nearest neighbors (or kNN) method. Two different distance functions were considered to find the k-nearest neighbors: the Euclidean and the Mahalanobis. The second method was applied on the same datasets and consisted of a step-wise multiple regression. The two modeling approaches yielded similar results but kNN proved to be more time-efficient and very fast computationally for the given dataset sizes, which allowed applying a leave-one-out cross validation.In an attempt to reveal the importance of scale in the prediction of IBI, regression models were constructed at the state (or regional) scale and at the more refined cluster of sampling sites scale. Clusters of sites were extracted using Kohonen's self-organizing maps (SOM) followed by k-means clustering of the SOM neurons. Cluster-level regression models, constructed after site patterning, performed better in IBI prediction than global regression models constructed without any previous site patterning. The importance of identifying groups of sites with similar environmental characteristics affecting the IBI was revealed. The combined use of site patterning and regression modeling for IBI prediction also helped identifying important variables acting at the local scale which remain latent at the global scale.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

Impact of generated solar radiation on simulated crop growth and yield

The availability of observed daily solar radiation (OSR) is restricted to recent years. Its estimation through different methods is necessary to develop long-term data sets for agricultural and environmental applications. The objective of this study was to analyze the impact of using generated daily solar radiation (GSR) on simulated growth and yield of cotton, maize, and peanut. Nine locations representing Georgia's major crop belt were selected. Daily weather data from the Georgia Automated Environmental Monitoring Network (AEMN), including solar radiation, maximum and minimum temperature, and precipitation, were duplicated. The OSR was removed from one set and then generated using a stochastic procedure. The Cropping System Models (CSM)-CROPGRO-Cotton, CERES-Maize, and CROPGRO-Peanut of the Decision Support System for Agrotechnology Transfer (DSSAT) v4 were used to simulate crop growth and yield at each location with both OSR and GSR and for rainfed and irrigated conditions. The statistical analysis included summary statistics, Pearson's coefficient of correlation, mean squared deviation (MSD) and its components, namely: squared bias (SB), squared difference between standard deviations (SDSD), lack of correlation weighted by the standard deviations (LCS), and regressions. Within locations, for the three crops under rainfed and irrigated conditions, GSR did not significantly affect simulated total evapotranspiration and aboveground biomass and yields. For the three crops, deviations of simulated water use and yields from GSR with respect to simulated water use and yields from OSR were lower for the rainfed than for the irrigated conditions. Yields from the CSM-CROPGRO-Cotton and -Peanut models had lower deviations than yields from the CSM-CERES-Maize model. LCS was the major component of the MSD suggesting that the extent of the difference between standard deviations of GSR and OSRG could affect the outputs of the crop models. Nevertheless, for most locations none of the MSD components of the GSR showed significant correlation with simulated yields and the overall performance of the models was not affected. It can be concluded based on the results of this study that GSR can be used as an input for crop model simulation models when OSR is not available.     

高Cu、高Zn对白星花金龟幼虫的毒性作用

Cu(铜)和Zn(锌)是环境中最常见的重金属污染元素,为探究其对白星花金龟幼虫的毒性,采用滤纸接触法、人工土壤法和菌渣培养法研究了高Cu、高Zn对白星花金龟幼虫的生长及氧化胁迫效应。结果表明,滤纸接触法和人工土壤法中试验浓度Cu、Zn对白星花金龟幼虫死亡率无明显影响。菌渣培养法中虫体的取食抑制率和体重增长抑制率均与Cu、Zn浓度呈显著性正相关关系;Cu处理诱导了虫体可溶性蛋白含量的增加,抑制了SOD(超氧化物歧化酶)、GST(谷胱甘肽转移酶)的活性和MDA(丙二醛)的产生,对CAT(过氧化物酶)活性没有显著影响;Zn浓度为3 500和5 000 mg·kg-1的处理组可溶性蛋白含量被显著诱导、MDA含量被显著抑制,浓度为2 000和6 500 mg·kg-1处理组的SOD活性被激活(P0.05),Zn胁迫未对CAT、GST活性造成明显影响。表明Cu、Zn污染对白星花金龟幼虫生长具有毒性效应,在评价高浓度的重金属污染对虫体的毒性作用时,不能仅采用SOD、CAT、GST等指标作为标志物,需要综合其他指标进行分析。