Toward an ecosystem approach to ICM: assessing ecological provinces at sea by remote sensing

The ecosystem approach requires that all elements of an ecosystem, and their mutual interactions, be taken into consideration in any management effort. The selection of suitable geographical units, where this approach can be taken, requires the assessment of ecological provinces, characterized by a coherent set of environmental traits. The marine side of coastal zones, where the interaction between atmosphere, land and sea is not bounded by evident geographic markers, represents a critical factor in this assessment. A coastal province can be defined by physical setting, but also by its bio-geo-chemical features, ideally on the basis of synoptic remote sensing data, collected at space/time scales not accessible by other means. Classifications based on indicators such as temperature, wind speed and chlorophyll-like pigments, demonstrate the identification of potential ecological provinces in the Mediterranean Sea. The results suggest remote sensing as the ideal tool to set up the basis for an ecosystem approach to the management of each province.     

Evaluating the environmental flows of China's Wolonghu wetland and land use changes using a hydrological model, a water balance model, and remote sensing

Environmental flows are critical to sustaining a variety of plant and animal communities in wetlands. However, evaluation of environmental flows is hampered by the problem of hydrological and ecological data shortage, especially in many developing countries such as China. Based on a hydrological model, a water balance model and remote sensing data, we assessed the environmental flows of China's Wolonghu wetland with limited data. The hydrological model provides input data for the water balance model of the wetland, and the remote sensing data can be used to assess land use changes. Integration of these two models with the remote sensing data revealed both the environmental flows of the Wolonghu wetland and the relationships between these environmental flows and land use changes. The results demonstrate that environmental flows have direct and indirect influences on the wetland ecosystem and should be linked to sustainable wetland management.     

Lake of flies, or lake of fish? A trophic model of Lake Malawi

Ecosystem-focused models have, for the first time, become available for the combined demersal and pelagic components of a large tropical lake ecosystem, Lake Malawi. These provide the opportunity to explore continuing controversies over the production efficiencies and ecological functioning of large tropical lakes. In Lake Malawi these models can provide important insight to the effect of fishing on fish composition, and the potential competition that the lakefly Chaoborus edulis may have with fisheries production. A mass-balanced trophic model developed for the demersal fish community of the southern and western areas of Lake Malawi was integrated with an existing trophic model developed for the open-water pelagic. Input parameters for the demersal model were obtained from a survey of fish distributions, fish food consumption studies, and from additional published quantitative and qualitative information on the various biotic components of the community. The model was constructed using the Ecopath approach and software. The graphically presented demersal food web spanned four trophic levels and was based primarily on consumption of detritus, zooplankton and sedimented diatoms. Zooplankton was imported into the system at trophic levels three and four through fish predation on carnivorous and herbivorous copepods and Chaoborus larvae. It is proposed that the primary consumption of copepods was by fish migrating into the pelagic zone. Chaoborus larvae in the demersal were probably consumed near the lakebed as they conducted a daily migration from the pelagic to seek refuge in the sediments. This evidence for strong benthic-pelagic coupling provided the opportunity for linking the demersal model to the existing model for the pelagic community so producing the first model for the complete ecosystem. Energy fluxes through the resulting combined model demonstrated that the primary import of biomass to the demersal system was detritus of pelagic origin (72.1%) and pelagic zooplankton (10.6%). Only 15.8% of the biomass consumed within the demersal system was of demersal origin. Lakefly production is efficiently utilised by the lake fish community, and any attempt to improve fishery production through introduction of a non-native plantivorous fish species would have a negative impact on the stability and productivity of the lake ecosystem.     

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain‐referral survey. We then used a workshop‐based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real‐time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing‐derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?     

Ecosystem network analysis indicators are generally robust to parameter uncertainty in a phosphorus model of Lake Sidney Lanier, USA

Understanding how data uncertainty influences ecosystem analysis is critical as we move toward ecosystem-based management. Here, we investigate how 18 Ecological Network Analysis (ENA) indicators that characterize ecosystem growth, development, and condition are affected by uncertainty in an ecosystem model of Lake Sidney Lanier (USA). We applied ENA to 122 plausible parameterizations of the ecosystem developed by Borrett and Osidele (2007, Ecological Modelling 200, 371-387), and then used the coefficient of variation (CV) to compare system indicator variability. We considered Total System Throughput (TST) as a measure of the underlying model uncertainty and tested three hypotheses. First, we hypothesized that non-ratio indicators whose calculation includes the TST would be at least as variable as TST if not more variable. Second, we postulated that indicators calculated as ratios, with TST in the numerator and denominator would tend to be less variable than TST because its influence will cancel. Last, we expected the Average Mutual Information (AMI) to be less variable than TST because it is a bounded function. Our work shows that the 18 indicators grouped into four categories. The first group has significantly larger CVs than the CV for TST. In this group, model uncertainty is amplified rendering these three indicators less useful. The second group of four indicators shows no significant difference in variability with respect to TST. Finally, there are two groups whose CV values are significantly lower than that for TST. The least variable group includes the ratio-based indicators and Average Mutual Information. Due to their low variability, we conclude that these indicators are the most robust to the parameter uncertainty and most useful for ecosystem assessment and comparative ecosystem analysis. In summary, this work suggests that we can be as certain, or more certain, in most of the selected ENA indicators as we are in the parameters of the model analyzed.     

Korrelation meteorologischer und pflanzenphänologischer Indikatoren des Klimawandels

Goal and Scope

While the rise of the air temperature as part of the global climatic change seems to be widely assured, questions about the effects of this phenomenon in ecological systems get more and more interesting. In this sense this article shows by the example of monitoring sites in Baden-Württemberg the correlation of air temperature measurements with data on the phenology of selected plants.

Methods

To this end the data on air remperature and plant phenology which are gathered from spatial dislocated measurement sites were correlated by prior application of Kriging interpolation. In addition, geostatistics are ssed to analyze and cartographically depict the spatial structure of the phenology in spring and in summer.

Results and Conclusion

The statistical analysis reveals a significant relationship between the rising air temperature and the early beginning phenological phases like blooming or fruit maturation. From 1991 to 1999 spring time as indicated by phenological phases begins up to 14 days earlier than from 1961 to 1990. As proved by geostatistics, this holds true for the whole territory of Baden-Württemberg.

Recommendation and Perspective

The effects of the rise of air temperature should be examined not only by monitoring of biological individuals as for example plants, but on ecosystem level, too. The German ecosystem research and the environmental monitoring should be supplemented by the study of the effects of the climatic change in ecosystems. Because air temperature and humidity have a great influence on the temporal and spatial distribution of pathogen carriers (vectors) and pathogens mapping of the determinants of vector and pathogen distribution in space and time should be done in order to identify hot spots for risk assessment and further detailed epidemiological studies.     

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.     

Using social network analysis tools in ecology: Markov process transition models applied to the seasonal trophic network dynamics of the Chesapeake Bay

Ecosystem components interact in complex ways and change over time due to a variety of both internal and external influences (climate change, season cycles, human impacts). Such processes need to be modeled dynamically using appropriate statistical methods for assessing change in network structure. Here we use visualizations and statistical models of network dynamics to understand seasonal changes in the trophic network model described by Baird and Ulanowicz [Baird, D., Ulanowicz, R.E., 1989. Seasonal dynamics of the Chesapeake Bay ecosystem. Ecol. Monogr. 501 (59), 329–364] for the Chesapeake Bay (USA). Visualizations of carbon flow networks were created for each season by using a network graphic analysis tool (NETDRAW). The structural relations of the pelagic and benthic compartments (nodes) in each seasonal network were displayed in a two-dimensional space using spring-embedder analyses with nodes color-coded for habitat associations (benthic or pelagic). The most complex network was summer, when pelagic species such as sea nettles, larval fishes, and carnivorous fishes immigrate into Chesapeake Bay and consume prey largely from the plankton and to some extent the benthos. Winter was the simplest of the seasonal networks, and exhibited the highest ascendency, with fewest nodes present and with most of the flows shifting to the benthic bacteria and sediment POC compartments. This shift in system complexity corresponds with a shift from a pelagic- to benthic-dominated system over the seasonal cycle, suggesting that winter is a mostly closed system, relying on internal cycling rather than external input. Network visualization tools are useful in assessing temporal and spatial changes in food web networks, which can be explored for patterns that can be tested using statistical approaches. A simulation-based continuous-time Markov Chain model called SIENA was used to determine the dynamic structural changes in the trophic network across phases of the annual cycle in a statistical as opposed to a visual assessment. There was a significant decrease in outdegree (prey nodes with reduced link density) and an increase in the number of transitive triples (a triad in which i chooses j and h, and j also chooses h, mostly connected via the non-living detritus nodes in position i), suggesting the Chesapeake Bay is a simpler, but structurally more efficient, ecosystem in the winter than in the summer. As in the visual analysis, this shift in system complexity corresponds with a shift from a pelagic to a more benthic-dominated system from summer to winter. Both the SIENA model and the visualization in NETDRAW support the conclusions of Baird and Ulanowicz [Baird, D., Ulanowicz, R.E., 1989. Seasonal dynamics of the Chesapeake Bay ecosystem. Ecol. Monogr. 501 (59), 329–364] that there was an increase in the Chesapeake Bay ecosystem's ascendancy in the winter. We explain such reduced complexity in winter as a system response to lowered temperature and decreased solar energy input, which causes a decline in the production of new carbon, forcing nodes to go extinct; this causes a change in the structure of the system, making it simpler and more efficient than in summer. It appears that the seasonal dynamics of the trophic structure of Chesapeake Bay can be modeled effectively using the SIENA statistical model for network change.     

Prioritizing Conservation Effort through the Use of Biological Soil Crusts as Ecosystem Function Indicators in an Arid Region

Abstract: Conservation prioritization usually focuses on conservation of rare species or biodiversity, rather than ecological processes. This is partially due to a lack of informative indicators of ecosystem function. Biological soil crusts (BSCs) trap and retain soil and water resources in arid ecosystems and function as major carbon and nitrogen fixers; thus, they may be informative indicators of ecosystem function. We created spatial models of multiple indicators of the diversity and function of BSCs (species richness, evenness, functional diversity, functional redundancy, number of rare species, number of habitat specialists, nitrogen and carbon fixation indices, soil stabilization, and surface roughening) for the 800,000‐ha Grand Staircase‐Escalante National Monument (Utah, U.S.A.). We then combined the indicators into a single BSC function map and a single BSC biodiversity map (2 alternative types of conservation value) with an unweighted averaging procedure and a weighted procedure derived from validations performance. We also modeled potential degradation with data from a rangeland assessment survey. To determine which areas on the landscape were the highest conservation priorities, we overlaid the function‐ and diversity‐based conservation‐value layers on the potential degradation layer. Different methods for ascribing conservation‐value and conservation‐priority layers all yielded strikingly similar results (r= 0.89–0.99), which suggests that in this case biodiversity and function can be conserved simultaneously. We believe BSCs can be used as indicators of ecosystem function in concert with other indicators (such as plant‐community properties) and that such information can be used to prioritize conservation effort in drylands.     

Robustness of variance and autocorrelation as indicators of critical slowing down

Ecosystems close to a critical threshold lose resilience, in the sense that perturbations can more easily push them into an alternative state. Recently, it has been proposed that such loss of resilience may be detected from elevated autocorrelation and variance in the fluctuations of the state of an ecosystem due to critical slowing down; the underlying generic phenomenon that occurs at critical thresholds. Here we explore the robustness of autocorrelation and variance as indicators of imminent critical transitions. We show both analytically and in simulations that variance may sometimes decrease close to a transition. This can happen when environmental factors fluctuate stochastically and the ecosystem becomes less sensitive to these factors near the threshold, or when critical slowing down reduces the ecosystem's capacity to follow high-frequency fluctuations in the environment. In addition, when available data is limited, variance can be systematically underestimated due to the prevalence of low frequencies close to a transition. By contrast, autocorrelation always increases toward critical transitions in our analyses. To exemplify this point, we provide cases of rising autocorrelation and increasing or decreasing variance in time series prior to past climate transitions.     

Restoring piscivorous fish populations in the Laurentian Great Lakes causes seabird dietary change

Ecosystem change often affects the structure of aquatic communities thereby regulating how much and by what pathways energy and critical nutrients flow through food webs. The availability of energy and essential nutrients to top predators such as seabirds that rely on resources near the water's surface will be affected by changes in pelagic prey abundance. Here, we present results from analysis of a 25-year data set documenting dietary change in a predatory seabird from the Laurentian Great Lakes. We reveal significant declines in trophic position and alterations in energy and nutrient flow over time. Temporal changes in seabird diet tracked decreases in pelagic prey fish abundance. As pelagic prey abundance declined, birds consumed less aquatic prey and more terrestrial food. This pattern was consistent across all five large lake ecosystems. Declines in prey fish abundance may have primarily been the result of predation by stocked piscivorous fishes, but other lake-specific factors were likely also important. Natural resource management activities can have unintended consequences for nontarget ecosystem components. Reductions in pelagic prey abundance have reduced the capacity of the Great Lakes to support the energetic requirements of surface-feeding seabirds. In an environment characterized by increasingly limited pelagic fish resources, they are being offered a Hobsonian choice: switch to less nutritious terrestrial prey or go hungry.     

On maturation and spawning in some penaeid prawns of the Arabian Gulf

A mathematical model is presented of the ecosystem in the upper layers of the marine pelagic zone. The model has been constructed on the basis of presumed connections between biotic and abiotic ecosystem parameters typical of the Black Sea. Details on total model relations as well as on model analysis and construction may be obtained after gaining more insight into the behaviour of models constructed for the upper pelagic ecosystem of different marine aquatoria. The model is characterized by a rather complex behaviour. Realization of the model on the electronic computer is made by employing the method of random trajectories. As a result, a qualitative picture of model behaviour under different conditions is revealed and statistical characteristics of the parameters are obtained.     

Incorporating temporally dynamic baselines in isotopic mixing models

Stable isotopes (particularly C and N) are widely used to make inferences regarding food web structure and the phenology of consumer diet shifts, applications that require accurate isotopic characterization of trophic resources to avoid biased inferences of feeding relationships. For example, most isotope mixing models require that endmembers be adequately represented by a single probability distribution; yet, there is mounting evidence that the isotopic composition of aquatic organisms often used as mixing model endmembers can change over periods of weeks to months. A review of the literature indicated that the delta13C values of five aquatic primary consumer taxa, commonly used as proxies of carbon production sources (i.e., trophic baselines), express seasonally dynamic cycles characterized by an oscillation between summer maxima and winter minima. Based on these results, we built a dynamic baseline mixing model that allows a growing consumer to track temporal gradients in the isotopic baselines of a food web. Simulations showed that the ability of a consumer to maintain or approach isotopic equilibrium with its diet over a realistic growth season was strongly affected by both the rate of change of the isotopic baseline and equilibration rate of the consumer. In an empirical application, mixing models of varying complexity were used to estimate the relative contribution of benthic vs. pelagic carbon sources to nine species of juvenile fish in a fluvial lake of the St. Lawrence River system (Québec, Canada). Estimates of p (proportion of carbon derived from benthic sources) derived from a static mixing model indicated broad interspecific variation in trophic niche, ranging from complete benthivory to > 95% reliance on pelagic food webs. Output from the more realistic dynamic baseline mixing model increased estimated benthivory by an average of 36% among species. Taken together, our results demonstrate that failing to identify dynamic baselines when present, and (or) matching consumers with baseline taxa that possess substantially different equilibration rates can seriously bias interpretation of stable isotope data. Additionally, by providing a formalized framework that allows both resources and consumers to shift their isotopic value through time, our model demonstrates a feasible approach for incorporating temporally dynamic isotope conditions in trophic studies of higher consumers.     

Matching biodiversity indicators to policy needs

At the global scale, biodiversity indicators are typically used to monitor general trends, but are rarely implemented with specific purpose or linked directly to decision making. Some indicators are better suited to predicting future change, others are more appropriate for evaluating past actions, but this is seldom made explicit. We developed a conceptual model for assigning biodiversity indicators to appropriate functions based on a common approach used in economics. Using the model, indicators can be classified as leading (indicators that change before the subject of interest, informing preventative actions), coincident (indicators that measure the subject of interest), or lagging (indicators that change after the subject of interest has changed and thus can be used to evaluate past actions). We classified indicators based on ecological theory on biodiversity response times and management objectives in 2 case studies: global species extinction and marine ecosystem collapse. For global species extinctions, indicators of abundance (e.g., the Living Planet Index or biodiversity intactness index) were most likely to respond first, as leading indicators that inform preventative action, while extinction indicators were expected to respond slowly, acting as lagging indicators flagging the need for evaluation. For marine ecosystem collapse, indicators of direct responses to fishing were expected to be leading, while those measuring ecosystem collapse could be lagging. Classification defines an active role for indicators within the policy cycle, creates an explicit link to preventative decision-making, and supports preventative action.     

Indicators of ecosystem function identify alternate states in the sagebrush steppe

Models of ecosystem change that incorporate nonlinear dynamics and thresholds, such as state-and-transition models (STMs), are increasingly popular tools for land management decision-making. However, few models are based on systematic collection and documentation of ecological data, and of these, most rely solely on structural indicators (species composition) to identify states and transitions. As STMs are adopted as an assessment framework throughout the United States, finding effective and efficient ways to create data-driven models that integrate ecosystem function and structure is vital. This study aims to (1) evaluate the utility of functional indicators (indicators of rangeland health, IRH) as proxies for more difficult ecosystem function measurements and (2) create a data-driven STM for the sagebrush steppe of Colorado, USA, that incorporates both ecosystem structure and function. We sampled soils, plant communities, and IRH at 41 plots with similar clayey soils but different site histories to identify potential states and infer the effects of management practices and disturbances on transitions. We found that many IRH were correlated with quantitative measures of functional indicators, suggesting that the IRH can be used to approximate ecosystem function. In addition to a reference state that functions as expected for this soil type, we identified four biotically and functionally distinct potential states, consistent with the theoretical concept of alternate states. Three potential states were related to management practices (chemical and mechanical shrub treatments and seeding history) while one was related only to ecosystem processes (erosion). IRH and potential states were also related to environmental variation (slope, soil texture), suggesting that there are environmental factors within areas with similar soils that affect ecosystem dynamics and should be noted within STMs. Our approach generated an objective, data-driven model of ecosystem dynamics for rangeland management. Our findings suggest that the IRH approximate ecosystem processes and can distinguish between alternate states and communities and identify transitions when building data-driven STMs. Functional indicators are a simple, efficient way to create data-driven models that are consistent with alternate state theory. Managers can use them to improve current model-building methods and thus apply state-and-transition models more broadly for land management decision-making.     

Identifying the possibilities and pitfalls of conducting IUCN Red List assessments from remotely sensed habitat information based on insights from poorly known Cuban mammals

The International Union for Conservation of Nature's Red List of Threatened Species (RLS) is the key global tool for objective, repeatable assessment of species’ extinction risk status, and plays an essential role in tracking biodiversity loss and guiding conservation action. Satellite remote sensing (SRS) data sets on global ecosystem distributions and functioning show exciting potential for informing range-based RLS assessment, but their incorporation has been restricted by low temporal resolution and coverage of data sets, lack of incorporation of degradation-driven habitat loss, and noninclusion of assumptions related to identification of changing habitat distributions for taxa with varying habitat dependency and ecologies. For poorly known mangrove-associated Cuban hutias (Mesocapromys spp.), we tested the impact of possible assumptions regarding these issues on range-based RLS assessment outcomes. Specifically, we used annual (1985–2018) Landsat data and land-cover classification and habitat degradation analyses across different internal time series slices to simulate range-based RLS assessments for our case study taxa to explore potential assessment uncertainty arising from temporal SRS data set coverage, incorporating proxies of (change in) habitat quality, and assumptions on spatial scaling of habitat extent for RLS parameter generation. We found extensive variation in simulated species-specific range-based RLS assessments, and this variation was mostly associated with the time series over which parameters were estimated. However, results of some species-specific assessments differed by up to 3 categories (near threatened to critically endangered) within the same time series, due to the effects of incorporating habitat quality and the spatial scaling used in RLS parameter estimation. Our results showed that a one-size-fits-all approach to incorporating SRS information in RLS assessment is inappropriate, and we urge caution in conducting range-based assessments with SRS for species for which habitat dependence on specific ecosystem types is incompletely understood. We propose novel revisions to parameter spatial scaling guidelines to improve integration of existing time series data on ecosystem change into the RLS assessment process.     

基于PSR模型的松嫩平原西部湿地生态环境评价

以1986年和2000年遥感影像作为信息源，在GIS技术的支持下建立了松嫩平原西部湿地空间信息数据库。以压力一状态一响应(PSR)模型作为研究方法，建立了一套湿地生态环境评价指标体系。通过选取反映压力、状态及响应等的指标，对该区湿地生态环境的动态变化进行了分析。得出基本结论，(1)15年来该区人为干扰压力不断加强，造成人工湿地增加，天然湿地减少，湿地生态环境服务功能下降。(2)15年来该区湿地内部结构和状态发生显著变化，湿地景观异质性在不断增加，湿地破碎化及退化严重。(3)15年来湿地对人类活动干扰的响应是湿地面积在不断减少，人类对湿地变化的响应是建立湿地保护区，保护区的面积在不断增加。     

Biological responses to environmental forcing: the linear tracking window hypothesis

Determining the relative contributions of intrinsic and extrinsic processes to the regulation of biological populations has been a recurrent ecological issue. Recent discussions concerning ecosystem "regime shifts" again raise the question of whether population fluctuations are mainly controlled by external forcing. Results of nonlinear time series analyses indicate that pelagic populations typically do not passively track stochastic environmental variables. Rather, population dynamics are better described as nonlinear amplification of physical forcing by biological interactions. However, we illustrate that in some cases populations do show linear tracking of the physical environment. To explain why population dynamics can sometimes be linear, we propose the linear tracking window hypothesis: populations are most likely to track the stochastic environmental forcing when their generation time matches the characteristic time scale of the environmental signal. While our observations follow this hypothesis well, our results indicate that the linear tracking window is a necessary but not a sufficient condition.     

An ecosystem model for estimating potential shellfish culture production in sheltered coastal waters

A generic ecosystem model has been developed for estimating the potential production of shellfish culture and the effect of that cultivation on the pelagic ecosystem in sheltered coastal waters. The model describes the dynamics of a simple food web, nutrient cycling and growth of shellfish. The design of the model is closely tied to the temporal and spatial scales that are important in determining the sustainable production level for a particular embayment. The pelagic ecosystem, mussel energetics, population dynamics and hydrodynamics are coupled to allow fully dynamic predictions of the effect of the shellfish density. When applied to Beatrix Bay, an intensive culture embayment in the Pelorus Sound of New Zealand, the model successfully captured main features of the observed system behaviour. The hydrodynamic regime of the bay controls mussel growth and production. Although high fluxes of water into the bay suppress nutrient and carbon cycling signals in the system, the model simulations demonstrated that the mussel cultivation can have considerable effects on the ecosystem of the bay including food depletion and nutrient cycling. One of the most obvious effects is nutrient enhancement through mussel excretion at low cultivation densities, which promotes primary production particularly during the N-limitation period in summer. The sensitivity analysis identified uncertainty in some parameters and indicated areas for which experimental studies could lead to model improvement. The modelling exercise has established a primary predictive tool for managing mussel aquaculture of a coastal embayment to estimate relationships between the stock level and the growth rate of mussels, and the potentially achievable harvest and stocking density.     

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.