Complex Ecosystem Model Research Articles

Collaborative commerce model for MSMEs and creative industries in Parepare to face industry 4.0 towards industry 5.0

Collaborative commerce model for MSMEs and creative industries in Parepare to face industry 4.0 towards industry 5.0

Modeling sugarcane development and growth within ECOSMOS biophysical model

Sugarcane plays an important role in electricity and sugar production and is a viable biofuel. Developing and optimizing a mechanism that can predict crop growth and yield at different spatiotemporal scales can promote the understanding of the effects of cultivation on the ecosystem, while providing options for optimizing management measures and improving the operational procedures of sugarcane growers. The main objective of this study is to integrate the sugarcane module into the ECOSystem MOdel Simulator (ECOSMOS) model and calibrate a parameter set for sugarcane genotypes groups (using different datasets); the model supports datasets that vary in complexity (from flux tower experiments to operational plots), while accounting for high genotype-by-environment-by-management (GxExM) variability. First, we calibrated the ECOSMOS biophysical and physiological parameters for the sugarcane module using two micrometeorological experimental sites, based on eddy-covariance and biomass measurements. Second, sugarcane genotypes located in different regions of contrasting climate conditions were split into two groups based on their period of harvest, i.e., early or mid-to-late harvest season, and two parameter sets were proposed. The sugarcane module was used to estimate the yield of numerous plots, using two different parameter sets, namely, the general and regionally-specific parameter sets. The model could successfully simulate the biophysical and physiological processes of the biomass of stalks and leaves, energy and carbon fluxes, and soil-water dynamics; for Experimental Site 2, the Nash-Sutcliffe efficiency (NSE) was 0.14–0.86 and the relative root mean square error (RRMSE) was 13–112. However, the generic parameter set did not perform well in all production environments, and the difference between the observed and simulated yields ranged from 0.9 to 14.5 (Mg ha-1). Hence, a novel calibration approach adopted in this study improved the module’s accuracy, while improving the performances for all five production environments, with the difference between the observed and simulate yields being 0.3–2.2 (Mg ha-1). Although the two parameter sets can be used as a reference for sugarcane plantations in Brazil, we recommend recalibrating the model (for ensuring higher accuracy) before operational applications. Notably, the ECOSMOS-sugarcane model is emerging as a complex ecosystem model that can support the quantifications and evaluations of the effects of sugarcane plantations on the carbon and water balances in different environmental conditions, particularly in tropical regions.

Ensemble optimal interpolation for adjoint-free biogeochemical data assimilation.

Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable covariance localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is computationally suitable for advanced ecosystem models. We test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables, coupled to a physical circulation model for the California Current System. In these tests, our 4dEnOI reference implementation performs similarly well to the 4dVar benchmark in lowering the model observation misfit. We show that the 4dEnOI results depend heavily on covariance localization generally, and benefit from variable localization in particular, when it is applied to reduce the coupling strength between the physical and biogeochemical model and the biogeochemical variables. The 4dEnOI results can be further improved by small modifications to the algorithm, such as multiple 4dEnOI iterations, albeit at additional computational cost.

EcoEnsemble: A general framework for combining ecosystem models in R

Abstract Often there are several complex ecosystem models available to address a specific question. However, structural differences, systematic discrepancies and uncertainties mean that they typically produce different outputs. Rather than selecting a single ‘best’ model, it is desirable to combine them to give a coherent answer to the question at hand. Many methods of combining ecosystem models assume that one of the models is exactly correct, which is unlikely to be the case. Furthermore, models may not be fitted to the same data, have the same outputs, nor be run for the same time period, making many common methods difficult to implement. In this paper, we use a statistical model to describe the relationship between the ecosystem models, prior beliefs and observations to make coherent predictions of the true state of the ecosystem with robust quantification of uncertainty. We introduce EcoEnsemble, an R package that takes advantage of the statistical model's structure to efficiently fit the ensemble model, either sampling from the posterior distribution or maximising the posterior density. We demonstrate EcoEnsemble by investigating what would happen to four fish species in the North Sea under future management scenarios. Although developed for applications in ecology, EcoEnsemble can be used to combine any group of mechanistic models, for example in climate modelling, epidemiology or biology.

Estimating the effect of water shortage on olive trees by the combination of meteorological and Sentinel-2 data

ABSTRACT Relative soil water content (RSWC) is widely used to characterize the impact of water stress (WS) on vegetation. In bi-layer ecosystems, such as olive groves, this impact must be primarily estimated for the tree component, which, having greater rooting depth, responds more slowly to WS than understory grass. This complicates the application of methods for RSWC prediction, which must be properly adapted to consider the deeper soil layer influential on olive trees. The current study investigates the modification of a recently proposed RSWC simulation method based on a combination of meteorological and satellite-derived normalized difference vegetation index (NDVI) data. The application of the method to an olive grove in Central Italy requires the estimation of both weather and NDVI contributions affecting solely olive trees, which is carried out through the use of appropriate data processing techniques. The RSWC estimates obtained reasonably reproduce the ground RSWC observations referred to the 1 m soil layer, which are representative of the WS affecting olive trees (r2 = 0.795, RMSE = 0.15 and MBE = −0.09). The limits and prospects of this method are finally discussed with particular reference to the possible integration of the RSWC estimates within more complex ecosystem models.

COVID-19 Lockdown Effects on a Highly Contaminated Coastal Site: The Mar Piccolo Basin of Taranto

The COVID-19 pandemic has had a dramatic socio-economic impact on mankind; however, the COVID-19 lockdown brought a drastic reduction of anthropic impacts on the environment worldwide, including the marine–coastal system. This study is concentrated on the Mar Piccolo basin of Taranto, a complex marine ecosystem model that is important in terms of ecological, social, and economic activities. Although many numerical studies have been conducted to investigate the features of the water fluxes in the Mar Piccolo basin, this is the first study conducted in order to link meteo-oceanographic conditions, water quality, and potential reduction of anthropic inputs. In particular, we used the model results in order to study the response of the Mar Piccolo basin to a drastic reduction in the leakage of heavy metal IPAs from industrial discharges during the two months of the mandated nationwide lockdown. The results show the different behavior of the two sub-basins of Mar Piccolo, showing the different times necessary for a reduction in the concentrations of heavy metals even after a total stop in the leakage of heavy metal IPAs. The results highlight the high sensitivity of the basin to environmental problems and the different times necessary for the renewal of the water in both sub-basins.

Convolutional graph neural networks-based research on estimating heavy metal concentrations in a soil-rice system.

Estimating heavy metal concentrations in soil-rice systems is of great significance to identify the factors controlling heavy metal transfer in soil-crop ecosystems. Recent research utilizes the advantage of convolutional calculations to extract and learn complicated information from 17 environmental covariates in rice and achieve promising results. However, as the complexity and interconnectivity in soil-crop ecosystem, just relying on convolutional calculations and a deep network structure is far from enough. The data processed by traditional deep learning technologies even with convolutional calculations are limited to Euclidean space; these architectures do not have the ability to extract information from the relationships in graph structures, which may contain rich information. Thus, in this paper, we try to integrate graph information into convolutional calculations for heavy metal prediction and propose a model named ConvGNN-HM. ConvGNN-HM combines the advantages of graph learning and convolutional calculations to predict heavy metal concentrations in a soil-rice system with analysis of 17 environmental factors. For comparison, we conduct an experiment to compare ConvGNN-HM with techniques with convolutional neural networks, multilayer perceptron, back-propagation neural networks, support vector machine, random forest, Bayesian ridge regression, and multiple linear regression. The experimental results illustrate that ConvGNN-HM got the best prediction values; the R2 values of ConvGNN-HM for cadmium (Cd), plumbum (Pb), chromium (Cr), arsenic (As), and hydrargyrum (Hg) in rice were 0.84, 0.75, 0.79, 0.49, and 0.83, respectively, and the MAE values were also acceptable. We further conduct sensitivity analysis to demonstrate the stability and robustness of ConvGNN-HM. This study demonstrates the usefulness of combining graph learning and convolutional calculations in the prediction of heavy metal concentrations and provides a new perspective to build multidimensional and multi-scale complex ecosystem models.

Controls on the relative abundances and rates of nitrifying microorganisms in the ocean

Abstract. Nitrification controls the oxidation state of bioavailable nitrogen. Distinct clades of chemoautotrophic microorganisms – predominantly ammonia-oxidizing archaea (AOA) and nitrite-oxidizing bacteria (NOB) – regulate the two steps of nitrification in the ocean, but explanations for their observed relative abundances and nitrification rates remain incomplete and their contributions to the global marine carbon cycle via carbon fixation remain unresolved. Using a mechanistic microbial ecosystem model with nitrifying functional types, we derive simple expressions for the controls on AOA and NOB in the deep, oxygenated open ocean. The relative biomass yields, loss rates, and cell quotas of AOA and NOB control their relative abundances, though we do not need to invoke a difference in loss rates to explain the observed relative abundances. The supply of ammonium, not the traits of AOA or NOB, controls the relatively equal ammonia and nitrite oxidation rates at steady state. The relative yields of AOA and NOB alone set their relative bulk carbon fixation rates in the water column. The quantitative relationships are consistent with multiple in situ datasets. In a complex global ecosystem model, nitrification emerges dynamically across diverse ocean environments, and ammonia and nitrite oxidation and their associated carbon fixation rates are decoupled due to physical transport and complex ecological interactions in some environments. Nevertheless, the simple expressions capture global patterns to first order. The model provides a mechanistic upper estimate on global chemoautotrophic carbon fixation of 0.2–0.5 Pg C yr−1, which is on the low end of the wide range of previous estimates. Modeled carbon fixation by AOA (0.2–0.3 Pg C yr−1) exceeds that of NOB (about 0.1 Pg C yr−1) because of the higher biomass yield of AOA. The simple expressions derived here can be used to quantify the biogeochemical impacts of additional metabolic pathways (i.e., mixotrophy) of nitrifying clades and to identify alternative metabolisms fueling carbon fixation in the deep ocean.

Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem.

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.

Trophic interactions with heterotrophic bacteria limit the range of Prochlorococcus

Prochlorococcus is both the smallest and numerically most abundant photosynthesizing organism on the planet. While thriving in the warm oligotrophic gyres, Prochlorococcus concentrations drop rapidly in higher-latitude regions. Transect data from the North Pacific show the collapse occurring at a wide range of temperatures and latitudes (temperature is often hypothesized to cause this shift), suggesting an ecological mechanism may be at play. An often used size-based theory of phytoplankton community structure that has been incorporated into computational models correctly predicts the dominance of Prochlorococcus in the gyres, and the relative dominance of larger cells at high latitudes. However, both theory and computational models fail to explain the poleward collapse. When heterotrophic bacteria and predators that prey nonspecifically on both Prochlorococcus and bacteria are included in the theoretical framework, the collapse of Prochlorococcus occurs with increasing nutrient supplies. The poleward collapse of Prochlorococcus populations then naturally emerges when this mechanism of "shared predation" is implemented in a complex global ecosystem model. Additionally, the theory correctly predicts trends in both the abundance and mean size of the heterotrophic bacteria. These results suggest that ecological controls need to be considered to understand the biogeography of Prochlorococcus and predict its changes under future ocean conditions. Indirect interactions within a microbial network can be essential in setting community structure.

Future phytoplankton diversity in a changing climate

The future response of marine ecosystem diversity to continued anthropogenic forcing is poorly constrained. Phytoplankton are a diverse set of organisms that form the base of the marine ecosystem. Currently, ocean biogeochemistry and ecosystem models used for climate change projections typically include only 2−3 phytoplankton types and are, therefore, too simple to adequately assess the potential for changes in plankton community structure. Here, we analyse a complex ecosystem model with 35 phytoplankton types to evaluate the changes in phytoplankton community composition, turnover and size structure over the 21st century. We find that the rate of turnover in the phytoplankton community becomes faster during this century, that is, the community structure becomes increasingly unstable in response to climate change. Combined with alterations to phytoplankton diversity, our results imply a loss of ecological resilience with likely knock-on effects on the productivity and functioning of the marine environment.

A bioenergetics model for seasonal growth of Indian oil sardine (Sardinella longiceps) in the Indian west coast

The Indian oil sardine (Sardinella longiceps) fishery was observed with wide stock fluctuation. Ecological parameters, mainly temperature and prey density, had a profound influence on the growth of S. longiceps and hence the production. In this study, a fish bioenergetics model, coupled with a lower trophic level model, was developed to reproduce the seasonality in the growth of S. longiceps. For this, we have used an Indian Ocean adaptation of an intermediate complex ecosystem model called North Pacific Ecological Modeling for Understanding Regional Oceanography (NEMURO) for marine productivity simulations. The model has 11-component ecosystem variables such as two types of phytoplankton (small and large including flagellates and diatoms), three types of zoo-planktons (small, large, and predatory, which includes ciliates, copepods, and euphausiids), particulate and dissolved organic matter, opal, cycling of nitrate, ammonia, and silicate. The prey densities derived from the NEMURO were input to the sardine bioenergetics model. The coupled model reproduced the appropriate growth rate and wet-weight of S. longiceps in its seasonal cycle in four major fishery regions such as Kochi, Goa, Ratnagiri, and Mumbai as verifiable from the available observation. In Kochi, the mean wet weight was 72.0 ± 12.8 g (June to September), 65.4 ± 5.3 g (October to November), 82.4 ± 2.7 g (December to February), and 66.7 ± 3.8 g (March to May). Goa and Ratnagiri have moderate weights with mean wet weight as 73.6 ± 10.6 g (June to September), 87.4 ± 3.2 g (October to November), 95.5 ± 4.3 g (December to February), and 76.2 ± 6.8 g (March to May). In the Mumbai region, maximum weight is simulated with mean wet weight as 97.4 ± 13.3 g (June to September), 102.1 ± 1.6 g (October to November), 104.8 ± 1.3 g (December to February), and 101.6 ± 1.2 g (March to May). Sensitivity analysis revealed the importance of temperature and consumption in the growth of sardine. Detailed model validation with available observations is presented.

Meteorological and hydrodynamic data in the Mar Grande and Mar Piccolo, Italy, of the Coastal Engineering Laboratory (LIC) Survey, winter and summer 2015

Abstract. The Coastal Engineering Laboratory (LIC) of DICATECh of the Polytechnic University of Bari (Italy) maintains a place-based research programme in the Mar Grande and Mar Piccolo of Taranto (a coastal system in southern Italy), providing records of hydrodynamic and water-quality measurements. This site is one of the most complex marine ecosystem models in terms of ecological, social and economic activities. It is considered highly vulnerable because of the presence of a naval base, the biggest steel mill of Europe and an oil refinery. Two fixed stations have been installed, one in the Mar Grande (MG station) and another in Mar Piccolo (MP station). In the MG station constituents include wind speed and direction, air temperature and humidity, barometric pressure, net solar radiation, water salinity, water temperature, water pressure, dissolved oxygen, fluorescence, turbidity, CDOM, crude oil and refined fuels, sea currents, and waves. In the MP station constituents include water temperature, sea currents and waves. We provide a summary of how these data have been collected by the research group and how they can be used to deepen understanding of the hydrodynamic structures and characteristics of the basin. These data are available at https://doi.org/10.5281/zenodo.4449641 (Mossa et al., 2020).

BigFintechs and Their Impacts on Macroeconomic Policies

This paper follows directly from the paper “BigFintechs and their Impact on Sustainable Development”, which examines the positive and negative impacts of BigFintech (BFT) activities across the full spectrum of the Sustainable Development Goals (SDGs), particularly with regard to Least Developed Countries (LDCs). This paper serves as an extension of the analysis, specifically on the findings with regard to SDG 16 (peace, justice and strong institutions) to focus on the macroeconomic impact of BFT actors and activities on LDCs. To accomplish the extended analysis, we first address the limitations in bridging BFT activity, SDG indicators and LDC macroeconomic policy impacts. We draw upon the outline of the complex and opaque supply chains, expanding service offerings across multiple business verticals, and the complex ecosystem models that amplify BFT impacts for LDCs. We discuss the key barriers in advancing the analysis including the limitations of the frameworks, tools, indicators and data, to measure the macroeconomic impacts, particularly within the LDC context. Our findings demonstrate that the current narrative ‘digital economy’ sees digital growth, maturity, and market penetration in LDCs largely as positive developments. However, it fails to address the potential for adverse impacts on LDCs specifically owing to BFTs’ complex models and business activities. We then outline the regulatory challenges related to BFT across multiple factors including the cross-border nature of BFT ecosystems and activities, the narrow scope of those digital services, the limitations of foreign exchange rules and taxation classification. In this paper, we set out the positive impacts of BFTs on reducing inequalities, access to capital, increased employment and entrepreneurship and GDP growth, as well as more complex negative implications including potential tax avoidance, crowding out of local businesses and SMEs, evolving ecosystems of interdependence with single points of failure, the potential for draining liquidity from local financial systems and for currency substitution. We discuss the regulatory capacity to address these macroeconomic impacts as well as the new potential points of failure being introduced. The paper then addresses the limitations of current governance parameters and structures that are struggling to keep up with the power, size and complex business model evolution of BFTs generally. The paper points to a widening gap in terms of primary or targeted regulatory focus for LDCs, which are not generally within the scope or mandate of regulators and legislators in more developed markets where most BFTs are headquartered. A summary of the potential macroeconomic impacts and regulatory challenges is followed by a series of conclusions indicating that rather than expanding their current value proposition of financial inclusion, employment and economic growth, BFTs can actually bypass or exploit segments of communities in LDCs with negative environmental, social and economic impacts. We further extrapolate the potential negative impact of BFTs on financial or currency stability that could bypass engagement with national taxation and regulation, through shadow banking particularly given the fragmented regulatory space. Finally, we offer key recommendations to consider with regard to alternative incentives and tools for BFTs to address and report on activities and impacts in LDCs, as well as innovative regulatory approaches for more effective measurement, analysis and remediation of risks and impacts of BFTs on LDCs.

Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach

Parameter uncertainty is a primary source of uncertainty in ocean ecosystem simulations. The deep chlorophyll maximum (DCM) is a ubiquitous ecological phenomenon in the ocean. Using a theoretical nutrients-phytoplankton model and the conditional nonlinear optimal perturbation approach related to parameters, we investigated the effects of parameter uncertainties on DCM simulations. First, the sensitivity of single parameter was analyzed. The sensitivity ranking of 10 parameters was obtained by analyzing the top four specifically. The most sensitive parameter (background turbidity) affects the light supply for DCM formation, whereas the other three parameters (nutrient content of phytoplankton, nutrient recycling coefficient, and vertical turbulent diffusivity) control nutrient supply. To explore the interactions among different parameters, the sensitivity of multiple parameters was further studied by examining combinations of four parameters. The results show that background turbidity is replaced by the phytoplankton loss rate in the optimal parameter combination. In addition, we found that interactions among these parameters are responsible for such differences. Finally, we found that reducing the uncertainties of sensitive parameters could improve DCM simulations remarkably. Compared with the sensitive parameters identified in the single parameter analysis, reducing parameter uncertainties in the optimal combination produced better model performance. This study shows the importance of nonlinear interactions among various parameters in identifying sensitive parameters. In the future, the conditional nonlinear optimal perturbation approach related to parameters, especially optimal parameter combinations, is expected to greatly improve DCM simulations in complex ecosystem models.

Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1

Abstract. Photosynthesis (gross primary production, GPP) and evapotranspiration (ET) are ecosystem processes with global significance for climate, the global carbon and hydrological cycles and a range of ecosystem services. The mechanisms governing these processes are complex but well understood. There is strong coupling between these processes, mediated directly by stomatal conductance and indirectly by root zone soil moisture content and its accessibility. This coupling must be effectively modelled for robust predictions of earth system responses to global change. Yet, it is highly demanding to model leaf and cellular processes, like stomatal conductance or electron transport, with response times of minutes, over decadal and global domains. Computational demand means models resolving this level of complexity cannot be easily evaluated for their parameter sensitivity nor calibrated using earth observation information through data assimilation approaches requiring large ensembles. To overcome these challenges, here we describe a coupled photosynthesis evapotranspiration model of intermediate complexity. The model reduces computational load and parameter numbers by operating at canopy scale and daily time step. Through the inclusion of simplified representation of key process interactions, it retains sensitivity to variation in climate, leaf traits, soil states and atmospheric CO2. The new model is calibrated to match the biophysical responses of a complex terrestrial ecosystem model (TEM) of GPP and ET through a Bayesian model–data fusion framework. The calibrated ACM-GPP-ET generates unbiased estimates of TEM GPP and ET and captures 80 %–95 % of the sensitivity of carbon and water fluxes by the complex TEM. The ACM-GPP-ET model operates 3 orders faster than the complex TEM. Independent evaluation of ACM-GPP-ET at FLUXNET sites, using a single global parameterisation, shows good agreement, with typical R2∼0.60 for both GPP and ET. This intermediate complexity modelling approach allows full Monte Carlo-based quantification of model parameter and structural uncertainties and global-scale sensitivity analyses for these processes and is fast enough for use within terrestrial ecosystem model–data fusion frameworks requiring large ensembles.

Spatio-temporal variations in hexachlorobenzene partitioning in a near shore Antarctic marine environment from a one-dimensional coupled ecosystem-chemical distribution model

Abstract Hexachlorobenzene (HCB) is an example of a persistent organic pollutant (POP) that is relatively abundant and widespread in near shore Antarctic marine environments. By definition, POPs may distribute with an ecosystem, bioaccumulating and potentially reaching hazardous levels in some organisms. Modelling approaches may provide insight into this behaviour and complement physical sampling that is difficult in such environments. Here, a dynamic, trophically complex ecosystem (biological) model comprising biological groups from plankton to apex predators is coupled to a mass conserving, fugacity based chemical distribution model with vertical resolution of the seawater compartment. The model accommodates seasonal variations in solar irradiance, sea ice coverage, and boundary layer air temperature. The greatest proportion of the HCB is predicted to be in sediment with comparatively little intra-annual variation. For biota, highest lipid based concentrations are in aerial seabirds, baleen whales and seals but considerable seasonal variation is apparent. The POPcline, the water depth where vertical HCB concentration gradient is greatest, is coupled with the base of the mixed layer throughout the Antarctic year. Seasonal partitioning of POPs in Antarctic environments is shown to be subject to oscillating or “flip-flop” dynamics with significantly different controlling factors in summer and winter. In summer, HCB in the surface waters, where most of the biological activity is occurring, is isolated from the sediments that contain most of the POP mass. During winter however, fractional sea ice cover increases turbulent diffusion and the mixed layer and POPcline deepen increasing homogenisation in water column. Surface waters are more likely to come into direct contact with sediments, and biota such as krill migrate to the ocean floor and feed on detritus produced from summer plankton blooms that has since sunk to the ocean floor.

High-resolution characterization of the abiotic environment and disturbance regimes on the Great Barrier Reef, 1985-2017.

This data compilation synthesizes 36 static environmental and spatial variables, and temporally explicit modeled estimates of three major disturbances to coral cover on the Great Barrier Reef (GBR): (1) coral bleaching, (2) tropical cyclones, and (3) outbreaks of the coral-eating crown-of-thorns starfish Acanthaster cf. solaris. Data are provided on a standardized grid (0.01°×0.01° ~1×1 km) for reef locations along the GBR, containing 15,928 pixels and excluding the northernmost sections (<12° S) where empirical data were sparse. This compilation provides a consistent and high-resolution characterization of the abiotic environment and disturbance regimes for GBR reef locations at a fine spatial scale to be used in the development of complex ecosystem models. Static estimates of environmental variables (e.g., depth, bed shear stress, average temperature, temperature variation) originally developed by the Commonwealth of Australia's Environment Research Facility (CERF) Marine Biodiversity Hub were provided by Geoscience Australia. Annual (1985-2017) disturbance estimates were either interpolated from empirical data (A. cf. solaris), predicted from proxy indicators (e.g., degree heating weeks [DHW] as a proxy for bleaching severity), or explicitly modeled (e.g., wave height model for each cyclone). This data set synthesizes some of the most recent advances in remote sensing and modeling of environmental conditions on the GBR; yet it is not exhaustive and we highlight areas that should be expanded through future research. The characterization of abiotic and disturbance regimes presented here represent an essential tool for the development of complex regional scale models of the GBR; preventing redundancy between working groups and promoting collaboration, innovation, and consistency. When using the data set, we kindly request that you cite this article and/or the articles cited in the reference section, recognizing the work that went into compiling the data together and the original authors' willingness to make it publicly available.

Nitrogen fixation does not axiomatically lead to phosphorus limitation in aquatic ecosystems

A long‐standing debate in ecology deals with the role of nitrogen and phosphorus in management and restoration of aquatic ecosystems. It has been argued that nutrient reduction strategies to combat blooms of phytoplankton or floating plants should solely focus on phosphorus (P). The underlying argument is that reducing nitrogen (N) inputs is ineffective because N2‐fixing species will compensate for N deficits, thus perpetuating P limitation of primary production. A mechanistic understanding of this principle is, however, incomplete. Here, we use resource competition theory, a complex dynamic ecosystem model and a 32‐year field data set on eutrophic, floating‐plant dominated ecosystems to show that the growth of non‐N2‐fixing species can become N limited under high P and low N inputs, even in the presence of N2 fixing species. N2‐fixers typically require higher P concentrations than non‐N2‐fixers to persist. Hence, the N2 fixers cannot deplete the P concentration enough for the non‐N2‐fixing community to become P limited because they would be outcompeted. These findings provide a testable mechanistic basis for the need to consider the reduction of both N and P inputs to most effectively restore nutrient over‐enriched aquatic ecosystems.

Modelling, singular perturbation and bifurcation analyses of bitrophic food chains

Two predator-prey model formulations are studied: the classical Rosenzweig–MacArthur (RM) model and the Mass Balance (MB) chemostat model. When the growth and loss rate of the predator is much smaller than that of the prey these models are slow–fast systems leading mathematically to a singular perturbation problem. In contradiction to the RM-model, the resource for the prey is modelled explicitly in the MB-model but this comes with additional parameters. These parameter values are chosen such that the two models become easy to compare. In both models the transcritical bifurcation, a threshold above which invasion of predator into prey-only system occurs, and the Hopf bifurcation where the interior equilibrium becomes unstable leading to a stable limit cycle occur. The slow-fast limit cycles are called relaxation oscillations which for increasing differences in time scales leads to the well known degenerated trajectories being concatenations of slow parts of the trajectory and fast parts of the trajectory. In the fast–slow version of the RM-model a canard explosion of the stable limit cycles occurs in the oscillatory region of the parameter space. To our knowledge this type of dynamics has not been observed for the RM-model and not even for more complex ecosystem models. When a bifurcation parameter crosses the Hopf bifurcation point the amplitude of the emerging stable limit cycles increases. However, depending of the perturbation parameter the shape of this limit cycle changes abruptly from one consisting of two concatenated slow and fast episodes with small amplitude of the limit cycle, to a shape with large amplitude of which the shape is similar to the relaxation oscillation, the well known degenerated phase trajectories consisting of four episodes (concatenation of two slow and two fast). The canard explosion point is accurately predicted by using an extended asymptotic expansion technique in the perturbation and bifurcation parameter simultaneously where the small amplitude stable limit cycles exist. The predicted dynamics of the MB-model is in a large part of the parameter space similar to that of the RM-model. However, the fast–slow version of MB-model does not predict a canard explosion phenomenon.