Trophic field overlap: A new approach to quantify keystone species

It is a current challenge to better understand the relative importance of species in ecosystems, and the network perspective is able to offer quantitative tools for this. It is plausible to assume, in general, that well-linked species, being key interactors, are also more important for the community. Recently a number of methods have been suggested for quantifying the network position of species in ecological networks (like the topological importance metric, TI). Most of them are based on node centrality indices and it may happen that the two most important species in a food web have very similar interaction structure and they can essentially replace each other if one becomes extinct. For conservation considerations it is a challenge to identify species that are richly connected and, at the same time, have a relatively unique and irreplaceable interaction pattern. We present a new method and illustrate our approach by using the Kuosheng Bay trophic network in Taiwan. The new method is based on the interaction matrix, where the strength of the interaction between nodes i and j depends only on topology. By defining a threshold separating weak and strong interactors, we define the effective range of interactions for each graph node. If the overlaps between pairs of these ranges are quantified, we gain a metric expressing how unique is the interaction pattern of a focal node (TO). The combination of centrality (TI) and uniqueness (TO) is called topological functionality (TF). We compare the nodal importance rank provided by this metric to others based on a variety of centrality measures. The main conclusion is that shrimps seem to have the most unique interaction pattern despite that their structural importance has been underestimated by all conventional centrality indices. Also, our network analysis suggests that fisheries disturb the ecosystem in a more critical network position than the impingement by the local power plant.     

Comparing network analysis methodologies for consumer–resource relations at species and ecosystems scales

This research compares two existing methodologies, mixed trophic impact analysis and utility analysis, which use network analysis to evaluate the direct, pair-wise, and indirect, holistic, ecological relations between ecosystem compartments. The two approaches have many similarities, but differ in some key assumptions which affect both the final results and interpretations. Here, we briefly introduce both methodologies through a series of two simple examples; a 3-compartment competition model and a 3-compartment food chain model, and then apply the methodologies to a 15-compartment ecosystem model of the Chesapeake Bay. This example demonstrates how implementing the various conceptual and methodological assumptions lead to differing results. Notably, the overall number of positive relations is greatly affected by the treatment of the self-interactions and the handling of detritus compartments lead to a distinction between ecological or trophic relations. We recommend slight changes to both methodologies, not necessarily in order to bring them completely together, but because each has some points which are stronger and better defensible.     

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.     

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.     

Comparative network analysis toward characterization of systemic organization for human–environmental sustainability

A preliminary study in comparative ecological network analysis was conducted to identify key assumptions and methodological challenges, test initial hypotheses and explore systemic and network structural characteristics for environmentally sustainable ecosystems. A nitrogen network for the U.S. beef supply chain – a small sub-network of the industrial food system analyzed as a pilot study – was constructed and compared to four non-human carbon and nitrogen trophic networks for the Chesapeake Bay and the Florida Everglades. These non-human food webs served as sustainable reference systems. Contrary to the main original hypothesis, the “window of vitality” and the number of network roles did not clearly differentiate between a human sub-network and the more complete non-human networks. The effective trophic level of humans (a partial estimate of trophic level based on the single food source of beef) was much higher (8.1) than any non-human species (maximum of 4.88). Network connectance, entropy, total dependency coefficients, trophic efficiencies and the ascendency to capacity ratio also indicated differences that serve as hypotheses for future tests on more comprehensive human food webs. The study elucidated important issues related to (1) the steady state assumption, which is more problematic for industrial human systems, (2) the absence or dearth of data on contributions of dead humans and human wastes to feed other species in an integrated food web, (3) the ambiguity of defining some industrial compartments as living versus non-living, and (4) challenges with constructing compartments and trophic transfers in industrial versus non-human food webs. The two main novel results are (1) the progress made toward adapting ecological network analysis (ENA) methodology for analysis of human food networks in industrial cultures and (2) characterizing the critical aspects of comparative ENA for understanding potential causes of the problems, and providing avenues for solutions, for environmental sustainability. Based on this work, construction and comparative network analysis of a more comprehensive industrial human food network seems warranted and likely to provide valuable insights for modifying structures of industrial food networks to be more like natural networks and more sustainable.     

Exploring community assembly through an individual-based model for trophic interactions

Traditionally, the dynamics of community assembly has been analyzed by means of deterministic models of differential equations. Despite the theoretical advances provided by such models, they are restricted to questions about community-wide features. The individual-based modeling offers an opportunity to link bionomic features to patterns at the community scale, allowing us to understand how trait-based assembly rules can arise by dynamical processes. The present paper introduces an individual-based model of community assembly, and discusses some of the major advantages and drawbacks of this approach. The model was framed to deal with predation among size-structured populations, incorporating allometric constraints to energetic requirements, movement, life-history features and interaction relationships among individuals. A protocol of assembly procedure is proposed, in which a period of intense species introductions is followed by a period without introductions. The resultant communities did not present any pattern of trait over-dispersion, meaning that the multivariate distances of bionomic features among co-occurring species were neither larger nor more regular than expected in a random collection of species. It suggests a weak influence of interspecific interactions in the model environment and individualistic rules of coexistence, driven mainly by the spatial structure. This highlights that trait over-dispersion and resource partitioning should not be considered a necessary condition for coexistence, even in communities entirely structured by internal processes like predation and competition.     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

Development and application of a comprehensive ecological risk assessment indicator system in Xiamen,China

Urban ecological risk is one of the important factors that may restrict the social and economic development. Therefore, it is of great significance to carry out a comprehensive assessment of ecological risks so that an ecological risk prevention and control plan can be scientifically formulated. In this paper, a comprehensive ecological risk assessment indicator system of Xiamen was established based on local ecological properties and socioeconomic status. This indicator system covers seven indicators including air pollution, soil pollution, water pollution, fresh water consumption, change in land use, occupation of key zones with ecological functions, and road network expansion. Based on this indicator system and in conjunction with the single factor assessment of ecological risks, this study constructed a model of comprehensive ecological risk assessment and forecasted the comprehensive ecological risk of Xiamen in 2020. The results showed that the comprehensive ecological risk level of Xiamen in 2020 is medium and the main stressors are the discharge of air and water pollutants. From the perspective of risk receptors, i.e. the ecosystem services, the risk posed to the ecosystem services associated to the maintenance of air quality and water purification is the highest. Therefore, this study proposed the recommendations on ecological risk prevention and regulation in Xiamen based on the comprehensive assessment of ecological risks, in the hope to provide scientific support for local ecological protection and sustainable development.     

Ecological literacy and psychographics: lifestyle contributors to ecological knowledge and understanding

Knowledge and understanding about how the ecological systems of the Earth support life form the foundation for ecological literacy, which, in turn, is of critical importance for sustainability. There is widespread concern that levels of ecological literacy within many contemporary human societies are too low to enable effective decision-making about how to live sustainably. This paper presents and discusses the findings of an ecological literacy assessment of South Australian adults. The assessment tested for knowledge and understanding of ecological systems and interconnections with human society. Analyses revealed significant relationships between assessment scores and a range of value-based and behavioural characteristics. Such characteristics included the value placed on nature, time spent outdoors and in nature, involvement in nature-based activities, and perceived sources of ecological knowledge and understanding. Higher average scores were correlated with higher values accorded to involvement in outdoor activities, importance of the outdoors to enjoyment of life, importance of nature in the household, volunteer environmental activity and growing food. Higher scores were also correlated with higher education, research activities, outdoor lifestyles, place-based experiences, volunteer activities, mentors and colleagues, and books and magazines. Lower scores were achieved by those who considered media, natural disasters and environmental marketing to be major contributors to their ecological knowledge. These findings contribute to a broader study of the relationships between ecological literacy and a range of both socio-demographic and psychographic characteristics. They invite serious consideration from any society that values the participation of an informed population with the capacity to make sustainable environmental decisions.     

An ecological risk assessment model for a pulsed contaminant emission into a wetland channel flow

As a continuation of the modelling on ecological degradation and hydraulic dispersion of pollutant emission into an idealized two-dimensional free-surface wetland flow (Zeng, L., Chen, G.Q., 2009b. Ecological degradation and hydraulic dispersion of contaminant in wetland. Ecol. Model., doi:10.1016/j.ecolmodel.2009.10.024), an ecological risk assessment model for the typical case of a pulsed contaminant emission into a realistic three-dimensional wetland channel flow is presented in this paper for the fate of cross-sectional mean concentration under environmental dispersion. An environmental dispersion model for the mean concentration is devised as an extension of Taylor’s classical analysis on dispersion in fluid flows. The velocity distribution and the environmental dispersivity in the fully developed steady flow through the wetland is found and illustrated with limiting cases covering various known solutions for the porous media flow between parallel plates, flow in a shallow wetland, sweeping flow in a densely vegetated wetland, and single phase flow in a channel. Obtained by Aris’s method of moments, the environmental dispersivity is shown characterized with multi-scale asymptotic time variations with stem dominated stage, transitional stage, and width-depth-stem dominated stage. Based on the solution for the evolution of contaminant cloud in the wetland channel flow, critical length and duration of the contaminant cloud with concentration beyond given environmental standard level are concretely illustrated for typical pollutant constituents in wastewater emission. Under the same emission intensity and environmental standard, the duration of contaminant cloud in the wetland channel is revealed shorter than that in a free surface wetland, due to the lateral effect.