Evaluation of information indices as indicators of environmental stress in terrestrial soils

Information indices from Ecosystem Network Analysis (ENA) can be used to quantify the development of an ecosystem in terms of its size and organization. There are two types of indices, i.e. absolute indices that describe both the size and organization of ecosystem (Total System Throughput (TST)—system size, Ascendancy (A)—size of organized flows and Development Capacity (C)—upper limit for A, Overhead (L)—size of unorganized flows) and relative indices that describe only the organization (Average Mutual Information (AMI = A:TST), Flow Diversity (H = C:TST), Relative Overhead (RL = L:TST)).It is theorized that environmental stress impair the ecosystem development and that the effect of stress can be quantified with the ENA information indices. Here we applied ENA on a case of environmental stress in a terrestrial ecosystem, i.e. soils that have endured long-term exposure to elevated copper concentration and altered pH.The absolute indices showed an unexpected pattern of response to pollution, suggesting that ecosystems in polluted soils are more active and better organized than these in unpolluted soils. The relative indices, alternatively, responded to pollution as predicted by theory, i.e. with decrease of stress (pollution level) the level of specialization increased (increase of AMI) and losses of energy, e.g. due to respiration, decreased (decrease of Overhead). The diversity and evenness of flows showed hump-backed relationship with stress. Less polluted soils appeared to be less vulnerable to external disturbances and more efficient in processing energy (higher Relative Ascendancy (RA = A:C)) than polluted soils. The relative information indices were rigid to changes in values of assumed parameters. The relative indices, opposite to absolute indices, appeared to be useful as indicators of environmental stress on the ecosystem level.     

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.     

Ecological network analysis for water use systems—A case study of the Yellow River Basin

Ecological network analysis (ENA) is introduced in this paper as a promising approach to study water use systems. Information indices from ENA involving total system throughput (TST), ascendency and overhead are calculated here. Two related aspects including organization inherent in system structures and synthesized water use intensity related with sustainable development of water use systems are analyzed. The indices of ascendency and overhead are applied for analyzing and characterizing water use network organization. For comparison of sustainability of water use systems from integrated aspects of environment, society and economy and based on TST, a new indicator termed as total system throughput intensity (TSTI) is constructed incorporating parameters of land, precipitation, population, GDP and environmental flow, which can be used as a measure of sustainability in terms of synthesized water use intensity. The Yellow River Basin in China during 1998–2006 is chosen as the case study and divided into subsystems according to the six river sections as from source to Lanzhou (S1-L1), Lanzhou to Toudaoguai (L1-T), Toudaoguai to Longmen (T-L2), Longmen to Sanmenxia (L2-S2), Sanmenxia to Huayuankou (S2-H) and Huayuankou to the mouth of Bo Sea (H-B). The results show that (i) the organization levels of L1-T and H-B are better than those of S1-L1 and T-L2, with those of L2-S2 and S2-H the worst; (ii) the synthesized water use intensity has been improving, of which T-L2, L2-S2 and S2-H are at the highest levels while H-B the lowest. In addition, the comparison between TSTI and other metrics and the relationship between ascendency and TSTI are discussed, from which the importance of TSTI is reflected and the optimization criterions for sustainable development of six subsystems are derived. It can be concluded that the application of ENA in water use systems can provide new angles for water resource management to address the challenges of assessing and optimizing options to obtain more sustainable water use.     

Invasive species impacts on ecosystem structure and function: A comparison of Oneida Lake, New York, USA, before and after zebra mussel invasion

Exotic species invasion is widely considered to affect ecosystem structure and function. Yet, few contemporary approaches can assess the effects of exotic species invasion at such an inclusive level. Our research presents one of the first attempts to examine the effects of an exotic species at the ecosystem level in a quantifiable manner. We used ecological network analysis (ENA) and a social network analysis (SNA) method called cohesion analysis to examine the effect of zebra mussel (Dreissena polymorpha) invasion on the Oneida Lake, New York, USA, food web. We used ENA to quantify ecosystem function through an analysis of food web carbon transfer that explicitly incorporated flow over all food web paths (direct and indirect). The cohesion analysis assessed ecosystem structure through an organization of food web members into subgroups of strongly interacting predators and prey. Our analysis detected effects of zebra mussel invasion throughout the entire Oneida Lake food web, including changes in trophic flow efficiency (i.e., carbon flow among trophic levels) and alterations of food web organization (i.e., paths of carbon flow) and ecosystem activity (i.e., total carbon flow). ENA indicated that zebra mussels altered food web function by shunting carbon from pelagic to benthic pathways, increasing dissipative flow loss, and decreasing ecosystem activity. SNA revealed the strength of zebra mussel perturbation as evidenced by a reorganization of food web subgroup structure, with a decrease in importance of pelagic pathways, a concomitant rise of benthic pathways, and a reorganization of interactions between top predator fish. Together, these analyses allowed for a holistic understanding of the effects of zebra mussel invasion on the Oneida Lake food web.     

Invasive species impacts on ecosystem structure and function: A comparison of the Bay of Quinte, Canada, and Oneida Lake, USA, before and after zebra mussel invasion

As invasion rates of exotic species increase, an ecosystem level understanding of their impacts is imperative for predicting future spread and consequences. We have previously shown that network analyses are powerful tools for understanding the effects of exotic species perturbation on ecosystems. We now use the network analysis approach to compare how the same perturbation affects another ecosystem of similar trophic status. We compared food web characteristics of the Bay of Quinte, Lake Ontario (Canada), to previous research on Oneida Lake, New York (USA) before and after zebra mussel (Dreissena polymorpha) invasion. We used ecological network analysis (ENA) to rigorously quantify ecosystem function through an analysis of direct and indirect food web transfers. We used a social network analysis method, cohesion analysis (CA), to assess ecosystem structure by organizing food web members into subgroups of strongly interacting predators and prey. Together, ENA and CA allowed us to understand how food web structure and function respond simultaneously to perturbation. In general, zebra mussel effects on the Bay of Quinte, when compared to Oneida Lake, were similar in direction, but greater in magnitude. Both systems underwent functional changes involving focused flow through a small number of taxa and increased use of benthic sources of production; additionally, both systems structurally changed with subgroup membership changing considerably (33% in Oneida Lake) or being disrupted entirely (in the Bay of Quinte). However, the response of total ecosystem activity (as measured by carbon flow) differed between both systems, with increasing activity in the Bay of Quinte, and decreasing activity in Oneida Lake. Thus, these analyses revealed parallel effects of zebra mussel invasion in ecosystems of similar trophic status, yet they also suggested that important differences may exist. As exotic species continue to disrupt the structure and function of our native ecosystems, food web network analyses will be useful for understanding their far-reaching effects.     

Parameter uncertainties in the modelling of vegetation dynamics—Effects on tree community structure and ecosystem functioning in European forest biomes

Dynamic vegetation models are useful tools for analysing terrestrial ecosystem processes and their interactions with climate through variations in carbon and water exchange. Long-term changes in structure and composition (vegetation dynamics) caused by altered competitive strength between plant functional types (PFTs) are attracting increasing attention as controls on ecosystem functioning and potential feedbacks to climate. Imperfect process knowledge and limited observational data restrict the possibility to parameterise these processes adequately and potentially contribute to uncertainty in model results. This study addresses uncertainty among parameters scaling vegetation dynamic processes in a process-based ecosystem model, LPJ-GUESS, designed for regional-scale studies, with the objective to assess the extent to which this uncertainty propagates to additional uncertainty in the tree community structure (in terms of the tree functional types present and their relative abundance) and thus to ecosystem functioning (carbon storage and fluxes). The results clearly indicate that the uncertainties in parameterisation can lead to a shift in competitive balance, most strikingly among deciduous tree PFTs, with dominance of either shade-tolerant or shade-intolerant PFTs being possible, depending on the choice of plausible parameter values. Despite this uncertainty, our results indicate that the resulting effect on ecosystem functioning is low. Since the vegetation dynamics in LPJ-GUESS are representative for the more complex Earth system models now being applied within ecosystem and climate research, we assume that our findings will be of general relevance. We suggest that, in terms of carbon storage and fluxes, the heavier parameterisation requirement of the processes involved does not widen the overall uncertainty in model predictions.     

Evaluation of ecological network analysis: Validation of output

Ecological network analysis (ENA) is a modeling approach increasingly being used to evaluate food webs and provide an ecosystem-based approach to resource management. Unfortunately, validation of ENA output is rarely performed. This study represents part of a larger effort to critically evaluate ENA. Here we validate ENA output using stable isotope analysis (SIA), and where validation is not met, determine the effects of modifying trophic networks to reflect validation.     

Trophic state,ecosystem efficiency and biodiversity of transitional aquatic ecosystems: analysis of environmental quality based on different benthic indicators

Estuaries and coastal lagoons are characterized by a strong spatial and temporal variability of physicochemical characteristics and productivity patterns. In these environments, the magnitude and direction of the ecological responses to inorganic nutrient increase (i.e. eutrophication) are difficult to predict. In the framework of the project, New Indicators of Trophic state and environmental quality of marine coastal ecosystems and transitional environments (NITIDA), we analysed benthic indicators of trophic state, ecosystem efficiency, and environmental quality in four different transitional environments. The trophic state of the sediments was assessed in terms of quantity and bioavailability of sediment organic C pools; ecosystem efficiency was determined in terms of the prokaryote efficiency in exploiting enzymatycally degraded organic C; environmental quality was determined in terms of meiofaunal diversity. Here, we provide a synopsis of the results obtained and a meta-analysis of the scores assessments obtained using the different ecological indicators of environmental quality and demonstrate that trophic state, ecosystem efficiency, and biodiversity in transitional ecosystems are closely linked. We conclude that the assessment of the environmental quality of transitional ecosystems should be based upon a battery of trophic state indicators and ‘sensors’ of ecosystem functioning, efficiency, and quality.     

Trophic state, ecosystem efficiency and biodiversity of transitional aquatic ecosystems: analysis of environmental quality based on different benthic indicators

Estuaries and coastal lagoons are characterized by a strong spatial and temporal variability of physicochemical characteristics and productivity patterns. In these environments, the magnitude and direction of the ecological responses to inorganic nutrient increase (i.e. eutrophication) are difficult to predict. In the framework of the project, New Indicators of Trophic state and environmental quality of marine coastal ecosystems and transitional environments (NITIDA), we analysed benthic indicators of trophic state, ecosystem efficiency, and environmental quality in four different transitional environments. The trophic state of the sediments was assessed in terms of quantity and bioavailability of sediment organic C pools; ecosystem efficiency was determined in terms of the prokaryote efficiency in exploiting enzymatycally degraded organic C; environmental quality was determined in terms of meiofaunal diversity. Here, we provide a synopsis of the results obtained and a meta-analysis of the scores assessments obtained using the different ecological indicators of environmental quality and demonstrate that trophic state, ecosystem efficiency, and biodiversity in transitional ecosystems are closely linked. We conclude that the assessment of the environmental quality of transitional ecosystems should be based upon a battery of trophic state indicators and 'sensors' of ecosystem functioning, efficiency, and quality.     

EcoTroph: Modelling marine ecosystem functioning and impact of fishing

EcoTroph (ET) is a model articulated around the idea that the functioning of aquatic ecosystems may be viewed as a biomass flow moving from lower to higher trophic levels, due to predation and ontogenetic processes. Thus, we show that the ecosystem biomass present at a given trophic level may be estimated from two simple equations, one describing biomass flow, the other their kinetics (which quantifies the velocity of biomass transfers towards top predators). The flow kinetic of prey partly depends on the abundance of their predators, and a top-down equation expressing this is included in the model. Based on these relationships, we simulated the impact on a virtual ecosystem of various exploitation patterns. Specifically, we show that the EcoTroph approach is able to mimic the effects of increased fishing effort on ecosystem biomass expected from theory. Particularly, the model exhibits complex patterns observed in field data, notably cascading effects and ‘fishing down the food web’. EcoTroph also provides diagnostic tools for examining the relationships between catch and fishing effort at the ecosystem scale and the effects of strong top-down controls and fast-flow kinetics on ecosystems resilience. Finally, a dynamic version of the model is derived from the steady-state version, thus allowing simulations of time series of ecosystem biomass and catches. Using this dynamic model, we explore the propagation of environmental variability in the food web, and illustrated how exploitation can induce a decrease of ecosystem stability. The potential for applying EcoTroph to specific ecosystems, based on field data, and similarities between EcoTroph and Ecopath with Ecosim (EwE) are finally discussed.     

Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange

Net ecosystem CO₂ exchange (NEE) is typically measured directly by eddy covariance towers or is estimated by ecosystem process models, yet comparisons between the data obtained by these two methods can show poor correspondence. There are three potential explanations for this discrepancy. First, estimates of NEE as measured by the eddy-covariance technique are laden with uncertainty and can potentially provide a poor baseline for models to be tested against. Second, there could be fundamental problems in model structure that prevent an accurate simulation of NEE. Third, ecosystem process models are dependent on ecophysiological parameter sets derived from field measurements in which a single parameter for a given species can vary considerably. The latter problem suggests that with such broad variation among multiple inputs, any ecosystem modeling scheme must account for the possibility that many combinations of apparently feasible parameter values might not allow the model to emulate the observed NEE dynamics of a terrestrial ecosystem, as well as the possibility that there may be many parameter sets within a particular model structure that can successfully reproduce the observed data. We examined the extent to which these three issues influence estimates of NEE in a widely used ecosystem process model, Biome-BGC, by adapting the generalized likelihood uncertainty estimation (GLUE) methodology. This procedure involved 400,000 model runs, each with randomly generated parameter values from a uniform distribution based on published parameter ranges, resulting in estimates of NEE that were compared to daily NEE data from young and mature Ponderosa pine stands at Metolius, Oregon. Of the 400,000 simulations run with different parameter sets for each age class (800,000 total), over 99% of the simulations underestimated the magnitude of net ecosystem CO₂ exchange, with only 4.07% and 0.045% of all simulations providing satisfactory simulations of the field data for the young and mature stands, even when uncertainties in eddy-covariance measurements are accounted for. Results indicate fundamental shortcomings in the ability of this model to produce realistic carbon flux data over the course of forest development, and we suspect that much of the mismatch derives from an inability to realistically model ecosystem respiration. However, difficulties in estimating historic climate data are also a cause for model-data mismatch, particularly in a highly ecotonal region such as central Oregon. This latter difficulty may be less prevalent in other ecosystems, but it nonetheless highlights a challenge in trying to develop a dynamic representation of the terrestrial biosphere.     

Sensitivity of ecological models to their climate drivers: statistical ensembles for forcing.

Global and regional numerical models for terrestrial ecosystem dynamics require fine spatial resolution and temporally complete historical climate fields as input variables. However, because climate observations are unevenly spaced and have incomplete records, such fields need to be estimated. In addition, uncertainty in these fields associated with their estimation are rarely assessed. Ecological models are usually driven with a geostatistical model's mean estimate (kriging) of these fields without accounting for this uncertainty, much less evaluating such errors in terms of their propagation in ecological simulations. We introduce a Bayesian statistical framework to model climate observations to create spatially uniform and temporally complete fields, taking into account correlation in time and space, spatial heterogeneity, lack of normality, and uncertainty about all these factors. A key benefit of the Bayesian model is that it generates uncertainty measures for the generated fields. To demonstrate this method, we reconstruct historical monthly precipitation fields (a driver for ecological models) on a fine resolution grid for a climatically heterogeneous region in the western United States. The main goal of this work is to evaluate the sensitivity of ecological models to the uncertainty associated with prediction of their climate drivers. To assess their numerical sensitivity to predicted input variables, we generate a set of ecological model simulations run using an ensemble of different versions of the reconstructed fields. We construct such an ensemble by sampling from the posterior predictive distribution of the climate field. We demonstrate that the estimated prediction error of the climate field can be very high. We evaluate the importance of such errors in ecological model experiments using an ensemble of historical precipitation time series in simulations of grassland biogeochemical dynamics with an ecological numerical model, Century. We show how uncertainty in predicted precipitation fields is propagated into ecological model results and that this propagation had different modes. Depending on output variable, the response of model dynamics to uncertainty in inputs ranged from uncertainty in outputs that matched that of inputs to those that were muted or that were biased, as well as uncertainty that was persistent in time after input errors dropped.     

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.     

A guide to representing variability and uncertainty in biodiversity indicators

Biodiversity indicators are used to inform decisions and measure progress toward global targets, such as the United Nations Sustainable Development Goals. Indicators aggregate and simplify complex information, so underlying information influencing its reliability and interpretation (e.g., variability in data and uncertainty in indicator values) can be lost. Communicating uncertainty is necessary to ensure robust decisions and limit misinterpretations of trends, yet variability and uncertainty are rarely quantified in biodiversity indicators. We developed a guide to representing uncertainty and variability in biodiversity indicators. We considered the key purposes of biodiversity indicators and commonly used methods for representing uncertainty (standard error, bootstrap resampling, and jackknife resampling) and variability (quantiles, standard deviation, median absolute deviation, and mean absolute deviation) with intervals. Using 3 high-profile biodiversity indicators (Red List Index, Living Planet Index, and Ocean Health Index), we tested the use, suitability, and interpretation of each interval method based on the formulation and data types underpinning the indicators. The methods revealed vastly different information; indicator formula and data distribution affected the suitability of each interval method. Because the data underpinning each indicator were not normally distributed, methods relying on normality or symmetrical spread were unsuitable. Quantiles, bootstrapping, and jackknifing provided useful information about the underlying variability and uncertainty. We built a decision tree to inform selection of the appropriate interval method to represent uncertainty or variation in biodiversity indicators, depending on data type and objectives. Our guide supports transparent and effective communication of biodiversity indicator trends to facilitate accurate interpretation by decision makers.     

A Bayesian strategy for combining predictions from empirical and process-based models

We present a strategy for using an empirical forest growth model to reduce uncertainty in predictions made with a physiological process-based forest ecosystem model. The uncertainty reduction is carried out via Bayesian melding, in which information from prior knowledge and a deterministic computer model is conditioned on a likelihood function. We used predictions from an empirical forest growth model G-HAT in place of field observations of aboveground net primary productivity (ANPP) in a deciduous temperate forest ecosystem. Using Bayesian melding, priors for the inputs of the process-based forest ecosystem PnET-II were propagated through the model, and likelihoods for the PnET-II output ANPP were calculated using the G-HAT predictions. Posterior distributions for ANPP and many PnET-II inputs obtained using the G-HAT predictions largely matched posteriors obtained using field data. Since empirical growth models are often more readily available than extensive field data sets, the method represents a potential gain in efficiency for reducing the uncertainty of process-based model predictions when reliable empirical models are available but high-quality data are not.     

Planktic community assembly in flowing water and the ecosystem health of rivers

Some of the modern criteria for assessing ecosystem health are compared with current understanding of ecosystem function in rivers. Owing to the predominance of catchment imports over autochthonous primary production, most rivers are naturally heterotrophic. This does not make them unhealthy but the pristine condition is that much harder to determine. The case is put for an index of ecosystem health and sustainability that takes into account the system's capacity for processing its resources, the species richness and its interdependence and its resilience to external forcing. Although these are not easily quantified, the qualitative indicators of healthy ecosystem function are easily checked. The sensitivity of organisms in suspension to fluvial flow may seem to counter the suitability of plankton as a reliable state indicator of river health. On the other hand, the rules governing the assembly of planktic communities in rivers are often strict and quantifiable: this makes them attractive candidates to act as indicators of the ecological condition of rivers.     

Estimating the critical phosphorus loading of shallow lakes with the ecosystem model PCLake: Sensitivity, calibration and uncertainty

There is a vast body of knowledge that eutrophication of lakes may cause algal blooms. Among lakes, shallow lakes are peculiar systems in that they typically can be in one of two contrasting (equilibrium) states that are self-stabilizing: a ‘clear’ state with submerged macrophytes or a ‘turbid’ state dominated by phytoplankton. Eutrophication may cause a switch from the clear to the turbid state, if the P loading exceeds a critical value. The ecological processes governing this switch are covered by the ecosystem model PCLake, a dynamic model of nutrient cycling and the biota in shallow lakes. Here we present an extensive analysis of the model, using a three-step procedure. (1) A sensitivity analysis revealed the key parameters for the model output. (2) These parameters were calibrated on the combined data on total phosphorus, chlorophyll-a, macrophytes cover and Secchi depth in over 40 lakes. This was done by a Bayesian procedure, giving a weight to each parameter setting based on its likelihood. (3) These weights were used for an uncertainty analysis, applied to the switchpoints (critical phosphorus loading levels) calculated by the model. The model was most sensitive to changes in water depth, P and N loading, retention time and lake size as external input factors, and to zooplankton growth rate, settling rates and maximum growth rates of phytoplankton and macrophytes as process parameters. The results for the ‘best run’ showed an acceptable agreement between model and data and classified nearly all lakes to which the model was applied correctly as either ‘clear’ (macrophyte-dominated) or ‘turbid’ (phytoplankton-dominated). The critical loading levels for a standard lake showed about a factor two uncertainty due to the variation in the posterior parameter distribution. This study calculates in one coherent analysis uncertainties in critical phosphorus loading, a parameter that is of great importance to water quality managers.     

Data assimilation in a simple marine ecosystem model based on spatial biological parameterizations

Assuming that a set of constant parameters fits for marine ecosystem modeling and parameter estimation studies on large space scales is questionable since ecosystem types spanning long distances are quite different. In this study, SeaWiFS chlorophyll-a data are assimilated into a simple NPZD model by the adjoint method in a climatological physical environment provided by FOAM. To improve the assimilation results, different spatial parameterization schemes are utilized. The results show that the values of the selected sensitive parameters are spatially variable and the application of spatial parameterizations can improve the assimilation results significantly.     

Information indices as a tool for quantifying development of below-ground terrestrial ecosystems

Information indices from ecosystem network analysis (ENA) describe the size and organization of an ecosystem and are claimed to quantify ecosystem development [Ulanowicz, R.E., 1986, Growth and Development, Springler-Verslag, New York, 203 pp.]. To date, these indices were not used to describe a gradient of ecosystem development for a field situation. Here we used information indices to quantify soil succession with soils of different age on the island Schiermonnikoog, The Netherlands. We evaluated whether information indices describe ecosystem development as predicted by ENA.For the Island of Schiermonnikoog the biomasses of soil organisms and roots were measured on four stages of succession (0, 10, 25 and 100 years old soils). Organisms were grouped based on their feeding characteristics. With these data consumption, respiration, excretion, external input and output flows to, from and between groups were calculated. These flows, in turn, were used to calculate the information indices. Relative information indices describe the organization of an ecosystem; i.e. level of organisation (specialization of flows), diversity and evenness of flows, and disorganisation. Absolute indices describe both size (in terms of energy flow) and organisation of the system. System size is used to scale the absolute indices and will be analysed separately as well.We found that the absolute indices increased when succession processed, as predicted by theory. This pattern could have been due to the build-up of biomass, which apparently did not level off. Because the succession gradient deals mostly with young soils (0, 10 and 25 years old) and only one older field (100 years old), the gradient should include more soils of around 100 years old and older to exclude this possibility. Relative indices, on the other hand, increased initially, but then levelled off. We think that this was due to the strong aggregation of functional groups, especially at lower trophic levels, because information in some functional groups showed (expected) trends.Our results suggest that the absolute indices are able to describe ecological succession of terrestrial below-ground ecosystems. The relative indices, in contrast, appeared to be insensitive to subtle succesional changes.     

Examining the relationship between ecosystem structure and function using structural equation modelling: A case study examining denitrification potential in restored wetland soils

An ongoing debate in ecology is the relationship between community or ecosystem structure and function. This relationship is particularly important in restored ecosystems because it is often assumed that restoring ecosystem structure will restore ecosystem functioning, but this assumption is frequently not tested. In this study, we used a novel application of structural equation modelling (SEM) to examine the relationship between ecosystem structure and function. To exemplify how to apply SEM to explore this relationship, we used a case study examining soil controls on denitrification potential (DNP) in two restored wetlands. Our objectives were to examine (1) whether both restored wetland soil ecosystems had similar relationships among soils variables (i.e. similar soil ecosystem structure) and (2) whether the soil variables driving denitrification potential (DNP) were similar at both sites (i.e. the soil ecosystems were functioning in a similar manner). Using the unique ability of SEM to test model structure, we proposed a SEM to represent the soil ecosystem and tested this structure with field data. We determined that the same model structure was supported by data from both systems suggesting that the two restored wetland systems had similar soil ecosystem structure. To test whether both ecosystems were functioning in a similar way, we examined the parameters of each model. We determined that the drivers of DNP function were not the same at both sites. Higher soil organic matter was the most important predictor of higher DNP at both sites. However, the other significant relationships among soils variables were different at each system indicating that the soils were not functioning in exactly the same way at each site. Overall, these results suggest that the restoration of ecosystem structure may not necessarily ensure the restoration of ecosystem functioning. In this study we capitalize on an inherent feature of SEM, the ability to test model structure, to test a fundamental ecological question. This novel approach is widely applicable to other systems and improves our understanding of the general relationship between ecosystem structure and function.